In the context of the digital transformation of public administration, one of the key tasks is to create a unified information space that ensures the consolidation of information about the activities of core domestic enterprises in the economy. However, the existing approaches to the disclosure of data from strategic and systemically important budgetary (autonomous) institutions are fragmented, which makes it difficult to use them for analysis. The purpose of the study, based on the methods of systematic and comparative analysis, is to substantiate the choice of data architecture when designing the digital profile of strategic and systemically important budgetary (autonomous) institutions. As a result of the work, a three-level architecture of a digital profile with open and closed access contours is proposed, within which information is collected, processed and published. The choice of technological solutions is justified supporting automated upload with blockchain fixation, multidimensional grouping, extraction of parameters from unstructured documents and visualization in dashboards. The proposed architecture corresponds to the priorities of the national project «Data Economics and Digital Transformation of the State» regarding the implementation of platform solutions and artificial intelligence technologies in public administration. The results of the study are of practical value for economic entities, specialists in the corresponding field, as well as for a wide range of users of financial and non-financial reporting.
Цифровая трансформация страховой отрасли и внедрение продуктов параметрического страхования требуют пересмотра подходов к обеспечению доверия между участниками сделки. Ключевым вызовом становится выбор архитектуры доверенной третьей стороны, которая могла бы гарантировать не только юридическую значимость транзакций, но и их защищенность в условиях растущих угроз. Существующие централизованные модели, доказавшие эффективность в массовом сегменте, часто не отвечают требованиям прозрачности и безопасности, критически важным для крупных сделок перестрахования и рынков связанного со страхованием капитала. Целью работы является сравнительный анализ трех архитектурных подходов к построению доверенной третьей стороны: традиционной централизованной на базе инфраструктуры открытых ключей, децентрализованной на базе блокчейна и гибридной. Для объективизации выбора применяется двухэтапная методика системного анализа: метод главных компонент для снижения размерности и визуализации компромиссов, а также многокритериальный анализ решений для ранжирования архитектур. Особое внимание уделено теоретическим ограничениям распределенных систем («трилемма блокчейна», «проблема оракула»), моделированию векторов атак и стратегиям их минимизации. Источником данных послужили экспертные оценки, полученные методом «Дельфи». Исследование показало, что архитектура на базе блокчейна является оптимальной для задач перестрахования благодаря высокой скорости мобилизации ликвидности. В то же время для массового розничного сегмента экономически наиболее эффективной остается централизованная модель. Обоснована необходимость перехода к гибридным архитектурам, использующим криптографические доказательства с нулевым разглашением и доверенные среды исполнения. Такой подход позволяет сочетать высокую пропускную способность с проверяемой безопасностью, что подтверждается опытом реализации национальных цифровых валют, в частности, цифрового The digital transformation of the insurance industry and the introduction of parametric insurance products require a rethinking of approaches to ensuring trust between transaction parties. A key challenge is choosing a trusted third party architecture that can guarantee not only the legal validity of transactions but also their security in the face of growing threats. Existing centralized models, while proven effective in the mass market, often fail to meet the transparency and security requirements critical for large-scale reinsurance transactions and insurance-linked capital markets. This paper aims to comparatively analyze three architectural approaches to building trusted third parties: a traditional centralized approach based on a public key infrastructure, a decentralized approach based on a blockchain, and a hybrid approach. To objectively evaluate the choice, a two-stage system analysis method is applied: principal component analysis for dimensionality reduction and tradeoff visualization, and multicriteria decision analysis for ranking architectures. Particular attention is paid to the theoretical limitations of distributed systems (the "blockchain trilemma" and "oracle problem"), attack vector modeling, and mitigation strategies. The data source was expert assessments obtained using the Delphi method. The study showed that a blockchain-based architecture is optimal for reinsurance applications due to its high liquidity mobilization speed. However, for the mass retail segment, a centralized model remains the most cost-effective. The study substantiates the need to transition to hybrid architectures using zero-knowledge cryptographic proofs and trusted execution environments. This approach combines high throughput with verifiable security, as evidenced by the experience of implementing national digital currencies, in particular, the Bank of Russia's digital ruble.
The paper proposes an extended quality assessment model for Distributed Ledger Technology platforms, referred to as DLT-QM, developed on the basis of the ISO/IEC 25010 standard while considering the architectural and operational specifics of decentralized and blockchain-based systems. The relevance of the study is determined by the rapid development of digital technologies and the growing adoption of DLT platforms in finance, e-government, logistics, IoT ecosystems, and enterprise information systems, alongside the absence of a unified formalized approach for comprehensive quality assessment of such platforms. The study analyzes the applicability of ISO/IEC 25010 charac-teristics to DLT-oriented software systems and identifies a set of DLT-specific quality attributes reflecting the unique properties of distributed ledger environments, including decentralization level, consensus reliability, transaction finality, auditability, trust model, interoperability, and on-chain/off-chain balance. For each characteristic, mathematical metrics are formalized to support multicriteria quality assessment and optimization of architectural decisions in software engineering tasks. The integral quality indicator QDLT is defined as a weighted combination of the traditional ISO/IEC 25010 component and a DLT-specific component, enabling the adaptation of the model to various application scenarios. The proposed model is validated using four representative DLT platforms: Hyperledger Fabric, Ethereum, Corda, and Polygon. The obtained results confirm the existence of structural trade-offs between decentralization, performance, security, and interoperability in modern distributed systems. Furthermore, a scenario-oriented application methodology is developed, including a procedure for determining weighting coefficients depending on the application domain, such as financial consortium systems, e-government infrastructures, and IoT supply chain environments. The practical significance of the research lies in the development of a formalized decision-support instrument for selecting DLT platforms in the design and implementation of modern software systems and digital services. Keywords: blockchain, distributed ledger technology, DLT platforms, decentralized systems, distributed systems, information technologies, digital technologies, software engineering.
Yuliia Husieva, Igor Chumachenkо, I. B. Nekrasov, Illia Khudiakov · 5 authors
The subject of this study is the processes of ensuring transparency, accountability, and data integrity in project portfolio management systems based on distributed ledger technologies. The objective of this work is to develop a conceptual model, the Blockchain Portfolio Governance Model (BPGM), to enhance the transparency, integrity, and manageability of strategic portfolio management processes. Objectives: to develop a multi-level model architecture that combines traditional management cycles with cryptographic event logging mechanisms; to formalize management decisions as distributed ledger objects using asymmetric cryptography; to propose a comprehensive management quality assessment metric that accounts for both technical integrity and procedural discipline; to validate the model through simulation modeling of business processes. Research methods: systems analysis, methods of mathematical and simulation modeling in the Bizagi Modeler environment, asymmetric encryption, and hashing algorithms to ensure data integrity in distributed networks. Results. This paper proposes and justifies the architecture of the Blockchain Portfolio Governance Model, comprising five levels: governance, data aggregation, decision formalization, cryptographic integrity, and audit. A mathematical framework for event logging has been developed, where each decision is signed using the ECDSA digital signature algorithm. A new comprehensive metric has been introduced – the Portfolio Governance Compliance Index – which enables the detection of "shadow" management actions by comparing the number of requests initiated in external systems with the number of validated transactions on the blockchain. A series of simulation experiments demonstrated that implementing Proof-of-Authority consensus algorithms in a corporate network introduces negligible time delays (less than 1% of the total cycle), while the majority of the process time is spent on expert analysis. Conclusions: The application of the BPGM model enables transforming subjective portfolio management into a transparent, algorithmic process. The proposed solution ensures the creation of a «single source of truth» for all stakeholders, significantly simplifies audit procedures, and enhances the organization’s institutional reliability without compromising its operational efficiency.
The paper investigates the problem of ensuring confidentiality in authentication processes within enterprise information-intelligent systems under increasing cybersecurity threats and growing requirements for data protection. The introduction substantiates the relevance of modern cryptographic approaches that minimize the transmission of sensitive information during user authentication. The literature review analyzes approaches to constructing zero-knowledge proofs, which enable verification of a statement without revealing secret data, including succinct non-interactive arguments of knowledge, transparent scalable arguments of knowledge, and compact proof systems without trusted setup. Their cryptographic properties, trust assumptions, scalability, and computational characteristics are examined. In the methodology section, an adaptive authentication model is proposed, based on the integration of cryptographic proofs with risk assessment mechanisms and contextual access analysis. A formal decision-making model for access control is developed, taking into account user parameters, environmental characteristics, and threat levels, enabling dynamic selection of the proof type depending on the current risk level. An authentication algorithm is designed, including stages of identification, context evaluation, proof generation, and verification. In the results section, a comparative analysis of different types of zero-knowledge proofs in enterprise systems is conducted, evaluating their impact on performance, security level, and resistance to attacks. It is shown that the adaptive approach ensures a balance between cryptographic strength and computational efficiency. The conclusions justify the feasibility of implementing the proposed model as part of modern continuous access verification concepts and as a means of improving enterprise information security.
In the context of rapid and widespread digitalization of society, active implementation of new innovative technologies, and the growth of cyber threats, the issue of organizing effective cybersecurity for enterprises is becoming particularly important. To protect today's modern digital enterprise, you need a comprehensive strategy for secure access to your corporate resources anytime, anywhere, regardless of where they are located. By following Zero Trust Architecture (ZTA) principles, which call for least privilege access and continuous verification, businesses can effectively minimize their attack surface and limit potential losses from compromised accounts. However, existing access control and authentication mechanisms alone are not always sufficient to ensure complete protection of critical data, especially in scenarios where proof of access rights or actions is required without revealing content. In such cases, an effective addition to ZTA can be the use of Zero-Knowledge (ZK) concept, which allows confirming access rights or ownership (knowledge) of certain information without the need to disclose it, which significantly reduces the risks of leaks and unauthorized access. At the same time, representatives of businesses interested in the security of their systems are not yet fully aware of the advantages of this concept. The practical application of already known Zero-Knowledge Proof (ZKP) capabilities in various relevant areas that ensure security is being hampered, among other things, by a lack of awareness and insufficient theoretical training in this area among specialists responsible for security and communicating these capabilities (their potential) to the managers of relevant IT companies. In other words, there is currently a problem related to a lack of awareness about the zero-knowledge concept (its theoretical and practical significance) for making the right decision when building a security system for a corporate information system in modern conditions. This article is exactly aimed at solving this problem. The purpose of this work is to systematize the theoretical foundations and practical application of the zero-knowledge concept using simple and obvious examples in order to understand the potential of ZKP in solving problems of confidentiality/privacy and data verification. To this end, it outlined the main aspects of the zero-knowledge concept, including an analysis of the applicability of interactive and non-interactive approaches, an assessment of existing ZKP systems, and a conceptual representation of zk-SNARK technology based on the popular Groth16 scheme with mathematical justification. This contributes to a better understanding and future use of this dynamically developing and complex concept as one of the key mechanisms of modern cryptography, providing the ability to prove the correctness of calculations without disclosing the computational process or the initial data.
The article examines the problem of formalizing investment cash flow in a distributed ledger environment. Within the framework of the digital transformation of financial relations, the cash flow of an investment project can be represented as a digital twin, recorded in the distributed ledger infrastructure and implemented through smart contracts. The aim of the study is to develop a mathematical model of the digital twin of investment cash flow and an algorithm for its forecasting using neural networks. Theoretical approaches to the interpretation of digital twins are systematized, and the limitations of the classical discounted cash flow model in relation to the digital environment are analyzed. A formalized model of digital cash flow is proposed, taking into account transaction fees of the distributed ledger, algorithmically accrued income, and an extended discount rate structure including technological and regulatory risk premiums. An algorithm for neural network forecasting of the digital twin is developed based on a feature vector integrating financial and infrastructure parameters. A comparative analysis of the digital and classical models is performed, which allowed establishing the structural modification of the investment process in the digital environment. The obtained results can be used in the valuation of digital financial assets and the construction of adaptive systems for forecasting their cash flows.
The decentralized finance (DeFi) ecosystem is a complex and ever-evolving system composed of various protocols. One of these protocols is lending, which has seen significant growth in recent times. However, the motivations behind investors’ interest in this area remain largely unknown. Lending protocols operate on predefined algorithms that automatically provide loans to users, allowing them to actively participate in DeFi lending platforms on public blockchain networks. The adaptation of these algorithms to a blockchain network within the framework of state legislation has not been explored in depth. This determines the importance of the study. The object of the study is to compare lending in a blockchain network with traditional forms; the subject is to identify the factors that influence decentralized lending and its relationship with traditional finance. The aim of this study is to develop a model architecture that can be used to create decentralized credit applications within a consortium blockchain network that uses a native currency, such as a central bank digital currency (CBDC). The main objectives of this study are:1) using data on transactions from the Aave lending protocol, one of the leading decentralized finance (DeFi) ecosystems in terms of market capitalization, to identify the motivations that drive participants to engage in DeFi lending activities; 2) based on research into the DeFi token ecosystem and its market, as well as analogues of traditional financial lending models, to develop a mathematical model and an architectural diagram for a decentralized lending system built on a consortium blockchain with a Central Bank Digital Currency (CBDC) as the native currency. The results of the study are presented in the form of a mathematical model and a diagram of the architecture for a decentralized lending system based on a consortium blockchain network using a consortium with a native cryptocurrency, known as CBDC.
Smart grids are a modern model for developing electric power infrastructure based on the integration of information and communication technologies and intelligent control systems. These networks enable the creation of a highly efficient, reliable, and adaptive energy environment capable of quickly responding to changes in electricity generation and consumption patterns. Key principles of a smart grid include adaptive load management, two-way data exchange between power system elements, the integration of distributed energy resources, and the use of modern digital technologies, including the Internet of Things, artificial intelligence, and distributed ledger technologies. The implementation of smart grids optimizes the generation, transmission, and distribution of electricity, improves the reliability and sustainability of the power system, and develops effective consumer interaction mechanisms based on intelligent energy management and dynamic pricing.
This study presents a comprehensive analysis of the cryptocurrency market through the lens of classical and modern economic schools, focusing on key regulatory mechanisms: staking, halving, token burning, and asset locking. The relevance of the research stems from the need to develop a theoretical framework for managing the stability and liquidity of decentralized financial systems amid high volatility and technological transformation. The hypothesis posits that integrating principles from economic schools (classical, Keynesian, monetarist, Austrian, institutional) with algorithmic cryptocurrency mechanisms can create a hybrid model of market resilience. Using an interdisciplinary approach, including mathematical modeling, regression, and correlation analysis of data on Bitcoin, Ethereum, XRP, and BNB, the study confirmed Bitcoin’s dominant role as a systemic asset through token burning and vesting. The practical implications include recommendations for optimizing regulatory mechanisms, diversifying investment portfolios, and designing stress tests to mitigate systemic risks.
Several works in the literature have focused on the analysis of key stylized facts of financial and cryptocurrency returns linked to fundamental problems of efficiency and predictability of financial and cryptocurrency markets, including heavy tails, absence of linear autocorrelations and volatility clustering. This paper provides a study of the above properties of Bitcoin and Ethereum markets using recently proposed robust, valid and statistically justified definitions of and methods for inference on market (in)efficiency, volatility clustering, and nonlinear dependence in return time series. In contrast to existing approaches, the inference methods used in the analysis are robust to heavy-tailedness, dependence and nonlinear dynamics of returns. The results of the study indicate that Bitcoin and Ethereum returns exhibit heavy tails, uncorrelatedness over time and volatility clustering largely similar to those in developed financial markets. The analysis has important implications for cryptocurrency pricing, market efficiency, econometric modeling, risk management, market participants and regulators.
The article deals with the development and theoretical justification of a set of economic and mathematical models that ensure the risk management of decentralised research projects in the pharmaceutical industry using crypto-economic tools. The necessity of this development stems not only from the challenges posed by geopolitical instability and the obsolescence of the traditional “blockbuster” funding model in pharmaceutical corporations, but also from the development of highly specialised markets of medications for the treatment of rare diseases, research into longevity therapies, and the advancement of “long-tail science”, as well as new ways of organising research and development within the paradigm of decentralised science based on Web3 technologies. The study presents models that are unified by an endto-end risk management logic: from the assessment of management structure and human resource capacity, through fundamental valuation, to revenue distribution and protection against biomedical risks. The results obtained make it possible to establish threshold criteria for the management structure in scientific decentralised autonomous organisations (DAOs) and to formulate targeted recommendations for public authorities on improving the regulation of decentralised organisations.
Open access
Economic and Technological Systems Analysis
Digitalization and Economic Development in Agriculture
The subject of the stud y is a digital token in a cross–border payment infrastructure (hereinafter referred to as CBPI) based on distributed ledger technology (hereinafter referred to as DLT). The purpose of the work is to analyze and scientifically evaluate methodological approaches to the formation of CBPI. The relevance of the work is due to the atmosphere of uncertainty and growing risks of external impact on the cross-border payment infrastructure that the Russian Federation has faced in recent years, as well as the need to address the challenge of ensuring accessibility, continuity, sustainability and security of its operation. As a result of the research, using heterodox, systemic, structural-functional, cybernetic, pragmatic and institutional approaches, the economic characteristics of the payment token have been developed and presented, including the most significant ones for the smooth implementation of cross-border payment transactions. It is concluded that the existing approaches make it possible to determine the main economic characteristics of a digital token in a cross-border payment infrastructure based on DLT, including security, cost stability, liquidity, volatility, as well as auxiliary ones — interoperability, scalability, transactional neutrality, economic isolation.
J. D. C. Vergara, D. E. Burdin, R.H. Davletbaev, Д. К. Д. Вергара · 6 authors
In the context of the digitalization of the economy, the problem of organizing effective document management in the non-profit sector has become particularly pressing. Traditional methods of managing information flows struggle to fully adapt to the requirements of transparency, accountability, and the legal significance of data. This article proposes a methodological approach to solving the document management problem based on the integration of distributed ledger technologies and smart contracts. A conceptual model of digital document management has been developed, in which each business event is represented as a smart document with legal verification in a blockchain environment. The paper describes in detail the stages of architecture development, the algorithms for interaction between participants, and the mechanisms for ensuring the immutability of records. The obtained results make it possible to increase transparency and trust between participants in non-profit organizations, ensure the automation of legally significant transactions, and minimize the risk of data falsification. The practical significance lies in the possibility of implementing the proposed approach into existing management systems of non-profit structures, which creates the basis for the formation of digital ecosystems of trusted document management.
Radovan Vladisavljević, Aleksandra Zlatić-Tešić, Svetlana Marković
The aim of the work is to present a model of tax control automation using smart contracts, this is a relatively new application of blockchain technologies. The use of new technologies can greatly improve the operations of modern organizations that have digitized their operations. New technologies not only provide a high degree of automation but also provide a high degree of transparency. This leads to faster business with an increase in the level of trust of all participants in the business venture.
This report examines smart contracts as a key element in the development of decentralized systems and as a factor for a profound transformation of traditional contract law.The analysis focuses on the essence of smart contracts, their technological mechanism of action and the role of cryptography in ensuring trust and security without the need for a central intermediary.Particular attention is paid to the way in which program code begins to perform functions traditionally inherent in legal norms and institutions.Smart contracts are not just a technical tool, but a new socioeconomic mechanism for regulating relations between entities in a digital environment.The report also examines the concept of "Code is Law" as a philosophical and practical framework that questions the classical legal principles of interpretation, flexibility and judicial review.Both the potential benefits of this paradigm and the risks arising from full automation are analyzed.Additionally, the main vulnerabilities of smart contracts that arise as a result of human errors when writing the code and the irreversibility of actions in a blockchain environment are examined.These risks show that technological security does not always mean legal justice.Finally, the legal status of smart contracts in Bulgaria and the European Union is examined.
Open access
Cryptography and Data Security
Advanced Research in Systems and Signal Processing
The article examines the theoretical foundations for selecting algorithms and data structures to ensure secure storage and processing of metadata in IoT systems using the Ethereum blockchain. A classification of metadata types specific to heterogeneous IoT environments is presented, taking into account semantic significance, update frequency, and data criticality. Formal requirements for algorithms are formulated, covering resistance to forgery, computational complexity, scalability under high-intensity request loads, and resource efficiency in terms of gas costs and network throughput. A comparative analysis of data structures employed in the Ethereum infrastructure, including Merkle Tree, Merkle-Patricia Trie (MPT), Multi-State MPT, and GPU-accelerated modifications, is performed according to criteria such as asymptotic complexity, memory efficiency, and suitability for incremental updates. A conceptual model for organizing metadata exchange between IoT nodes and smart contracts is proposed, incorporating modules for encoding, verification, gas cost optimization, and standardized interaction interfaces. The presented results provide a theoretical basis for developing formally verified and energy-efficient solutions in the field of secure Ethereum blockchain integration with the Internet of Things.
The study's relevance is determined by the critical dependence of cryptocurrency market stability on thetechnical reliability of smart contracts and the increasing risks of financial losses due to their defects. Aim:The aim of the study is to formalize the ranking of technical vulnerabilities of smart contracts by theirimpact on the economic stability of domestic capital markets through systematization, simulationmodelling, and quantitative assessment of financial indicators. Methods: The research used the followingtechniques: vulnerability typing, simulation modelling, financial analytics, and comparative analysis.Obtained results: The study confirmed the critical impact of smart contract technical vulnerabilities on thefinancial stability of the markets, with peak VaR of up to -68.5% and liquidity deterioration of over -80%for reentrancy attack, delegatecall injection, and oracle manipulation. The risks were reduced by more thanhalf after implementing multi-level optimisations, demonstrating the effectiveness of comprehensivemitigation to stabilise key financial indicators. Academic novelty of the study: The academic novelty of thestudy is the formalized classification of technical vulnerabilities of smart contracts and the first empiricalassessment of their impact on the economic stability of capital markets based on comprehensive financialand economic metrics, which extends the theory of DeFi structural risks. Prospects for future research:Prospects for further research include the development of a pilot project for technical optimization ofsmart contracts with a focus on increasing resilience to logical and synchronization defects.
Reliable asset price data are critical for the functioning of decentralized finance (DeFi) protocols, particularly those involving collateralized lending. The accuracy of blockchain-based price oracles directly affects key processes such as collateral valuation, liquidation, and risk management. This paper presents a comprehensive empirical analysis of Chainlink Price Feeds (CPFs), the dominant oracle infrastructure in DeFi. We compile a novel dataset of over 150 million observations from 40 CPFs on Ethereum over an 18-month period, matched to benchmark prices from a centralized exchange. To identify the determinants of oracle inaccuracy, we estimate pooled OLS and fixed effects regressions, relating price deviations to design parameters, reporter dynamics, and market conditions. We then introduce a Markov-like state transition framework to model the resolution of target corridor violations, using multinomial logistic regression to estimate transition probabilities. Finally, we exploit position-level data from one of the largest decentralized lending markets and apply entity fixed effects regressions to examine how users adjust collateralization in response to oracle design. Our findings highlight economically significant deviations that are systematically related to oracle accuracy configurations and market stress, and show that users internalize these risks in their financial decisions. The results offer new insights for the design of resilient oracle systems and the management of risk in decentralized financial markets.
The subject of the research is methods for detecting attacks in networks with the Proof-of-Stake (PoS) consensus mechanism. The purpose of this experimental investigation and analysis is to evaluate the effectiveness of classical machine learning algorithms for detecting malicious nodes in blockchain systems. The tasks include the analysis of blockchain technology vulnerabilities, the creation and use of a specialized dataset for PoS networks, as well as the construction and testing of machine learning models. The main focus is placed on comparing three algorithms – Random Forest, Support Vector Machine, and k-Nearest Neighbors – in order to determine their suitability for monitoring node activity and detecting anomalies. To solve the tasks set, the following methods were implemented: modeling, empirical, and mathematical approaches were applied. Modeling consisted of software implementation of the selected algorithms and subsequent analysis of their performance using accuracy, recall, F1-score metrics, and confusion matrices. Empirical methods were realized through testing the models on a partially synthetic dataset containing more than 10,000 records of blockchain nodes and transactions. Mathematical methods involved the calculation of statistical performance indicators and the analysis of feature importance that characterizes node behavior. The achieved results include the validation of a dataset for PoS blockchains that incorporates key operational parameters of transactions and nodes, the development of recommendations for further use of machine learning models, and the testing of selected models. Conclusions. The study demonstrated that machine learning is an effective tool for identifying anomalies and malicious activity in blockchain systems. The obtained results lay the foundation for further research, which may focus on expanding the feature space, integrating deep neural networks, developing ensemble approaches, and adapting methods to different types of blockchains.
Open access
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
This paper explores the integration of Blockchain technology into Structural Health Monitoring (SHM) to enhance data traceability, integrity, and automation in infrastructure asset management. Traditional SHM approaches, including Digital Twin-based systems, often face limitations related to data tampering, sensor unreliability, and the lack of transparent and verifiable data workflows. To address these challenges, the SHERPA framework is proposed. SHERPA leverages decentralized storage via the InterPlanetary File System and three Smart Contracts dedicated to data validation, anomaly flagging, and automated workflow execution. Rather than focusing on the structural interpretation of data, SHERPA establishes a secure and auditable backbone for SHM data governance. A prototype implementation on the Canalone Viaduct in Italy demonstrated the feasibility of the system, showcasing automated response to threshold violations and immutable data registration. The framework proved effective in enhancing transparency, traceability, and stakeholder confidence, positioning SHERPA as a promising enabler of more trustworthy and accountable SHM systems.
Stanislav I. Trofimov, Leonid Voskov, Mikhail Komarov
In the face of growing competition in the transportation market, companies are looking for new ways to improve operational efficiency and reduce fleet maintenance costs. This article presents an innovative vehicle technical condition management model that describes a mechanism for assessing the condition of vehicles using distributed ledger technology (DLT) and smart contracts. An information system for automating maintenance is proposed that can perform monitoring functions and initiate vehicle maintenance without human intervention by automatically registering operation and maintenance events, as well as using smart contracts to launch predefined actions. This level of automation allows timely prevention of unplanned breakdowns, which directly contributes to an increase in the service life of vehicles. The proposed solution allows transport companies to automate decision-making processes on maintenance, reduce transport downtime and optimize operating costs. The model ensures transparency of vehicle operation data, increases trust in information and shortens the decision-making chain. The solution is of particular value for public transport companies, where uninterrupted transportation and passenger safety are critically important.
Open access
Transportation Systems and Logistics
Advanced Research in Systems and Signal Processing
Purpose. The aim of the study is to develop a detailed role model for the implementation of smart contracts in the logistics processes of freight transportation, which will enable the automation of interaction between participants and increase the transparency of operations. Methodology. To achieve the stated goal, a systemic approach using context-role analysis was applied. The study involves a detailed decomposition of the stages of the logistics chain when applying smart contracts, identification of key participants, and definition of their functions, rights, and responsibilities. This approach makes it possible to clearly delineate areas of responsibility, reduce the risk of conflicts, and ensure the transparency of each participant’s actions. The developed UML diagram demonstrates the sequence of interactions between subjects, and the integration of smart contracts ensures the automation and immutability of operations. Findings. A comprehensive analysis of logistics processes using smart contracts was carried out, which made it possible to define the rights and responsibilities for seven basic roles of logistics operation participants. This approach provides a holistic view of the system and makes it possible to describe the logic of interactions between subjects. The developed model demonstrates the automation of contract conclusion and execution, which contributes to the reduction of document processing time, optimization of operations, and ensuring a high level of data security in the distributed ledger. Originality. An approach is proposed that enables the integration of formalized roles of freight transportation participants with smart contract technology. The detailed structuring of the functional responsibilities of each role makes it possible to implement the program logic of a decentralized system, which significantly expands the possibilities of automated logistics process management. The approach is universal and can be adapted to different types of logistics scenarios. Practical value. The developed role model creates favorable conditions for the implementation of blockchain solutions in the field of freight transportation, which makes it possible to digitalize logistics processes, increase trust between supply chain participants, and reduce operational costs. The obtained results have practical application for logistics operators, software developers, and consulting companies that seek to modernize existing transportation management systems. The model can also be useful for educational purposes in the fields of logistics, computer science, and management.