Umar Al Faruq, Dwi Fitrizal Salim, Farida Titik Kristanti
This study conducted a large-scale analysis to evaluate the performance of traditional and Markov-Switching GARCH (MS-GARCH) models to estimate the volatility of the top 10 cryptocurrencies by market capitalization. The study compared the performance of the models using goodness-of-fit measures, specifically the Deviance Information Criterion (DIC) and the Bayesian Predictive Information Criterion (BPC). Secondly, we assess the forecasting accuracy for one-day-ahead conditional volatility and Value-at-Risk (VaR). The results obtained show that, in a manner consistent with the findings for the broader cryptocurrency market, the time-varying regime-switching model exhibits superior performance in capturing the complex volatility patterns observed in cryptocurrencies when compared to the traditional GARCH model.
Blockchain technology has emerged as a secure and decentralized ledger, fundamentally transforming the verification and storage of transactions across various industries. At its core, smart contracts enable automated and trustless execution of agreements, offering immense potential for efficiency and transparency. However, these contracts face significant challenges in verification, security, and standardization, which hinder their adoption in critical applications.This paper investigates these challenges and highlights the role of Formal Methods in enhancing the security, correctness, and reliability of smart contracts. By synthesizing insights from existing research, tools, and approaches, the study provides a comprehensive analysis of the domain. The integration of Formal Methods is proposed as a robust solution for addressing vulnerabilities, ensuring functional correctness, and establishing standardization practices, thereby advancing the practical and secure implementation of smart contracts in blockchain systems.
To address the challenges of inefficiency, vulnerability to attacks, and the lack of transparency and fairness in distributed power trading, a distributed power trading mechanism based on blockchain smart contracts is proposed. Firstly, a strategy for matching distributed power trading orders is designed, and a model for managing user credit is constructed using the entropy weight method and deviation cost. Secondly, a smart contract for distributed power trading is compiled using the Solidity language. Finally, the designed smart contract is deployed, tested, and a distributed power trading network is simulated. The security analysis indicates that the Merkle Tree structure and PoW consensus mechanism effectively ensure the data and system security of distributed power transactions. Simulation test results demonstrate that the transaction mechanism can reliably complete transaction settlements, safeguard the interests of trustworthy users, and enhance the economic benefits of both parties involved in the transaction.
Anton Krivonogov, K. Starodubov, Alexander Prokofyev, Yuri Gromov
The article is devoted to the issue of sustainability of blockchain systems and their impact on the functioning of smart contracts that automate complex processes. An approach to determining the initial stability of a blockchain system is proposed, which includes the assessment of operational and technical parameters of the blockchain system using the method of direct expert evaluation. The proposed approach is tested on the example of the blockchain Ethereum.
Open access
Economic and Technological Systems Analysis
Digitalization and Economic Development in Agriculture
The evolution of regulatory compliance in the financial sector has transformed enterprise data systems from basic record-keeping tools into critical strategic assets. Financial institutions face mounting regulatory requirements across jurisdictions, necessitating sophisticated technological solutions to ensure compliance while maintaining operational efficiency. This article explores how integrated regulatory reporting systems consolidate disparate data sources, real-time monitoring capabilities enable proactive compliance management, and data lake architectures provide comprehensive audit trails. It examines blockchain and distributed ledger technology's role in enhancing transparency and traceability across various compliance domains, including KYC/AML processes, securities settlement, trade finance, and cross-border payments. The article also addresses integration challenges through API-first architectures, data governance frameworks, regulatory change management, and cloud-based platforms. Finally, it explores emerging innovations such as AI-powered regulatory intelligence, predictive analytics, regulatory-as-a-service models, and cross-institutional compliance networks that represent the future of enterprise data systems in regulatory
This article explores the impacts of digital transformations and new technologies in industrial sector (particularly through the Fourth Industrial Revolution) on optimizing production processes. Characterized by key technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), blockchains, and advanced robotics, Industry 4.0 has significantly shaped modern manufacturing management. IoT enables autonomous communications between machines and equipment, providing real-time insights into production parameters and enabling predictive maintenance, and big data plays a vital role by analyzing the large volumes of data that are generated by these devices, thus supporting informed management decisions. AI and machine learning help automate complex tasks, optimize production schedules, and improve product quality through real-time adjustments. Blockchain enables decentralized and secure data recording, which is particularly useful in supply-chain management. Advanced robotics increases production speed and accuracy, thus reducing labor costs and mitigating any risks that are associated with hazardous tasks. Integrating these technologies requires strategic planning, including identifying key challenges, conducting pilot projects, integrating with existing IT and OT systems, and managing organizational change. Measuring the effectiveness of Industry 4.0 implementation should involve well-defined key performance indicators (KPIs) and return-on-investment (ROI) analysis. The primary challenges that are associated with adopting Industry 4.0 include the alignment of technology with specific business needs, employee resistance to change, and hidden costs of implementation. In summary, industrial transformation offers opportunities for companies to optimize production processes, reduce costs, and increase competitiveness in the global marketplace. However, a careful approach is necessary to maximize efficiency, foster innovation, and secure long-term success in an increasingly digitalized world.
The article explores the integration of digital payment systems into e-commerce as a key factor in strengthening enterprise economic security. It emphasises the strategic role that digital payments play in ensuring financial stability, reducing operational risks, and enhancing customer trust, especially for small and medium-sized enterprises (SMEs). The study synthesises current academic discourse and industry reports, focusing on the advantages of real-time transaction processing, transparency, compliance with regulatory standards, and operational efficiency. It highlights the risks associated with cybersecurity threats, platform interoperability, and legal non-compliance. Empirical insights are drawn from the integration experiences of Eastern European SMEs using platforms such as PayPal, LiqPay, and Fondy. In addition, the article examines the transformative potential of blockchain-based systems, artificial intelligence, and decentralized finance technologies in reshaping payment infrastructures. It concludes that the integration of secure and innovative digital payment systems is not merely a technological upgrade, but a strategic necessity that directly supports economic resilience and long-term competitiveness in the digital economy.
Cryptocurrency is a novel exploration of a form of currency that proposes a decentralized electronic payment scheme based on blockchain technology and cryptographic theory. While cryptocurrency has the security characteristics of being distributed and tamper-proof, increasing market demand has led to a rise in malicious transactions and attacks, thereby exposing cryptocurrency to vulnerabilities, privacy issues, and security threats. Particularly concerning are the emerging types of attacks and threats, which have made securing cryptocurrency increasingly urgent. Therefore, this paper classifies existing cryptocurrency security threats and attacks into five fundamental categories based on the blockchain infrastructure and analyzes in detail the vulnerability principles exploited by each type of threat and attack. Additionally, the paper examines the attackers' logic and methods and successfully reproduces the vulnerabilities. Furthermore, the author summarizes the existing detection and defense solutions and evaluates them, all of which provide important references for ensuring the security of cryptocurrency. Finally, the paper discusses the future development trends of cryptocurrency, as well as the public challenges it may face.
The article is dedicated to analyzing the potential for the implementation of blockchain solutions in the activities of large agro-industrial holdings. Based on a review of current research and the generalization of pilot project experiences, key areas for applying distributed ledger technology in the agro-industrial complex have been identified: supply chain management, product quality monitoring, logistics optimization, and automation of financial transactions. An assessment of the economic impact of integrating blockchain into the business processes of agro-holdings has been conducted. The results obtained indicate significant potential for increasing companies’ efficiency and sustainability through enhanced transparency, security, and speed of operations. Barriers hindering the widespread adoption of blockchain in the agro-industrial complex have been highlighted, and measures to overcome them have been proposed. The conclusions drawn are valuable for strategic planning of digital transformation in the agricultural sector. (127 words).
Open access
Blockchain Technology Applications and Security
Digitalization and Economic Development in Agriculture
It is not uncommon for customers who intend to buy a used product in the secondary market to end up with a counterfeit because they have imperfect information about product authenticity . Blockchain is being piloted as a cutting-edge solution to this challenge. We use a two-period game to study the impact of utilizing blockchain to combat counterfeit products in the secondary market. We show that, even when the cost of implementing blockchain is negligible, the manufacturer can be better off incurring reputation damage than adopting blockchain. Further, the used goods reseller can be worse off from blockchain, even though that seller is not responsible for the implementation cost and benefits from blockchain’s signaling capability. We also demonstrate that the counterfeiter can benefit as a result of blockchain. When the quality of a fake product is sufficiently low, blockchain lowers consumer surplus . The winning situation of blockchain between the manufacturer, reseller, and customers is achieved only when the fake product is of intermediate quality. Blockchain can be powerful in situations when used products have a low perceived quality; otherwise, blockchain may not be ideal.
Abhishek Rawat, Rajat Verma, Raghuraj Singh Suryavanshi
The advent of Cryptography, Blockchain, and Artificial Intelligence (AI) has resulted in tremendous improvements in a variety of fields. As the science of secure communication, cryptography protects private data through encryption, decryption, and key management. It lays the groundwork for safe operations, interaction, and data storage. Blockchain technology, known for its decentralized and irreversible ledger and transparency and tamper-resistant qualities, has revolutionized a few industries, including financial services, management of supply chains, and healthcare. It makes transactions transparent and safe, does away with the need for middlemen, and boosts accountability and confidence. AI has been able to examine large datasets, identify patterns, and generate predictions that have altered how people make decisions by using machine learning. Many industries, including healthcare, finance, and retail, have benefited from automation, optimization, and tailored experiences. Multidisciplinary research and collaboration are necessary for the efficient implementation of these technologies and integrating them has the potential to fundamentally alter industries. An exciting era of societal change and innovation is promised by the combination of the blockchain, machine learning (AI), and cryptography. It might result in a digital environment that is safer, more transparent, and more effective, which would hasten advancements in sectors like banking, healthcare, and cyber security, among others. As these technologies advance, issues like privacy, scalability, seamless integration, and ethics must be considered to facilitate the appropriate and advantageous implementation of these technologies.
Methods. The application of the abstraction method allowed for the isolation of volatility characteristics, simplifying the analysis of complex financial data of the cryptocurrency market. Analysis with synthesis facilitated the identification of patterns and the integration of traditional and modern forecasting approaches, providing a comprehensive assessment of methods. Logical and historical approaches enabled evolutionary analysis, while classification methods based on general and specific analysis principles, combined with comparative and abstract-logical analysis, allowed for an objective evaluation of the developed models’ effectiveness and justified the feasibility of developing innovative solutions for optimizing trading strategies and minimizing risks. Results. The study conducted a comparative analysis of cryptocurrency market volatility prediction methods using traditional statistical approaches and modern machine learning algorithms. The results confirm the advantages of integrating classical methods with machine learning algorithms, which allow for more accurate risk assessment and optimization of trading strategies in the highly volatile cryptocurrency markets. The determined volatility can be used in conjunction with Reinforcement Learning (RL) to optimize trading strategies, allowing an agent to learn to make decisions in an environment to maximize cumulative reward. The use of RL in cryptocurrency trading is a promising direction but requires a cautious approach and thorough testing of strategies before their application in real trading.Novelty. The scientific novelty lies in a comprehensive approach to forecasting cryptocurrency market volatility, combining classical statistical methods with modern machine learning algorithms. The advantages of ensemble machine learning methods for analyzing cryptocurrency volatility have been established. The integration of Reinforcement Learning (RL) for optimizing trading strategies based on predicted volatility is proposed, representing a new approach to cryptocurrency trading automation. Practical value. The research results have practical significance for cryptocurrency market participants, including investors, traders, and financial analysts. The integration of machine learning methods with traditional statistical approaches opens new opportunities for developing effective trading strategies, contributing to increased profitability and stability in the cryptocurrency market. The research is also useful for developers of trading platforms and analytical tools, as it provides empirical data for improving prediction algorithms and market data analysis.
In the following paper, the possibilities of the latest industry 4.0 and digitization technologies, such as the use of Blockchain (BC) in Model-Based Systems Engineering with the associated product data (PDM) and product life cycle (PLM) management information, will be considered holistically. A Blockchain Engineering approach called EngineeringBlock will be researched in Systems engineering for the management of software data and engineering data within product lifecycle management. At this point, data input refers, for example, to the uploading of software versions to a control unit in the Engineering Data like Requirements and Functions. The two most important directions of the research paper will show the use of Blockchain technology with the related PLM System. This paper will present a Framework which enables the presentation of all product-relevant requirements, functions and logical aspects of the product up to the usage phase in a holistic and consistent way.
Aaryan Patel, Ahmed Hamzah, Manasvi More, Mohit Panchariya
The Land Registration system is a very crucial and valuable system for Indian citizens. The existing system is having a lot of issues be it technical or non-technical. These issues concern the Indian citizens. It could cause harm to the Indian economy as well due to the issues like fraud, corruption, etc. A new look of the system has to come to light to provide better features such as safety, reliability, efficiency to the people. Blockchain technology is a blessing in disguise. Its principles help in safeguarding the property rights of Indian citizens. Our decentralized application will act as a replacement to the current paper system to avoid involvement of brokers and other intermediaries.
In today's global landscape marked by environmental degradation and limited resources, the pursuit of sustainable development has evolved as a critical necessity. Leveraging digital technologies represents a promising pathway to attain this goal by potentially decoupling economic growth from its environmental impact. Nonetheless, it is paramount to ensure that the design and governance of these technologies align closely with environmental priorities. This paper delves into the exploration of how blockchain and artificial intelligence (AI) can play pivotal roles in increasing supply chain coordination and mitigating environmental impacts. These technologies also have the potential to incentivize recycling, promote circular business models, facilitate carbon accounting, and support offsetting initiatives. To fully harness these benefits, it is imperative to implement these technologies within inclusive collaborative frameworks that consider social and ecological factors.
Andreia de Castro Costa Xavier, Cláudio Gottschalg Duque, Tomás Roberto Cotta Orlandi
This study proposes an archival management method for Electronic Health Records (EHRs) based on architectural techniques and Blockchain and Smart Contracts technologies to ensure governance, security, and privacy in Health 4.0 contexts. Given the increasing relevance of EHRs as sources of information, evidence, and research in digital healthcare ecosystems, the research highlights challenges related to data governance, interoperability, and cybersecurity. Through a qualitative, exploratory approach, the authors present a method structured in seven macro-processes, covering the EHR lifecycle from capture to permanent archival or disposal. The implementation of private Blockchain networks and Smart Contracts automates processes, guarantees data integrity, and strengthens patients' control over their personal data, aligned with legal frameworks for data protection. The findings reinforce the need for innovative archival practices and technological strategies to enhance efficiency, security, and transparency in healthcare information management.
This research aims at examining how blockchain and smart contract technologies can enhance the circular economy in construction. In this quantitative research, data was collected from 134 construction industry professionals from different countries with majority from the UK and Australia by an online questionnaire. The research targeted respondents who possessed certain levels of experience in the field of engineering, construction, project management, and consultation and, therefore, used purposive sampling. An analysis of the survey data indicated that there was a level of support for the application of blockchain (BC) and smart contract (SC) technologies in enhancing circular economy practises. Industry professionals provided higher consensus regarding the benefits of these technologies for promoting circular economy in construction, with mean scores in all the statements above 4.6 on a 5-point scale. The pre to posttest findings were statistically significant t (58.00) p < .000 indicating that the participants’ views shifted from being neutral about the technologies’ advantages. From these findings, the research suggests awareness creation and training, implementation partnership models, policy and incentives support, and more research on the application of BC and SC technologies in the construction sector circular economy.
The significant overflow of the cup, symbolizing the total capacity of the world’s real assets, with a superior flow of world financial assets leads to the ongoing spread of excess financial assets, inflating another financial bubble since 2008, in various directions. The ongoing unrestrained emission of money by the «golden antelope» represented by the Federal Reserve System leads to numerous market transformations that distort the picture of the equilibrium market, turning the world economy into a kingdom of crooked mirrors. Many countries could not decide for a long time on recognizing the legitimacy of cryptocurrency transactions at the state level. However, under the pressure of increasing volumes of financial flows generated at the instigation of the states themselves, more and more countries began to officially recognize cryptocurrency transactions. In 2024, Russia joined this list, which makes it relevant to analyze the likely impact of the global cryptocurrency market on the development of the national economy. The purpose of the presented studies is to analyze the expected impact of the development processes of global cryptocurrency markets on the domestic market. The scientific novelty of the obtained results lies in the analysis of the current state of affairs on cryptocurrency exchanges and their comparison with traditional exchanges, the speculative nature of crypto transactions, trading volumes that determine the size of cryptocurrency exchanges, indicators of manipulation in the cryptocurrency market, key results of trading on cryptocurrency exchanges, etc. The practical significance of the obtained results lies in the development of proposals to reduce the risks associated with cryptocurrency transactions that affect both the financial system of Russia and the economy of the country as a whole.
As a result of the conducted research, it has been revealed that the scientific and practical literature lacks sufficient studying of the functioning and systematisation of existing data on new financial technologies, on the basis of which modern financial services and products are developed. There-fore, the features of distributed ledger technology (hereinafter referred to as DLT) are chosen as the subject of research in this work . The purpose of the study is to analyse the application of the DLT in modern conditions based on exploring theoretical and practical aspects of the problem under study. In accordance with the set purpose, the objectives are to identify the features of the DLT, to develop a classification of this technology according to appropriate criteria as well as to identify the directions of regulating the use of the DLT in modern conditions. The sources of information are informational and analytical materials and empirical data from open sources of both public authorities and commercial organisations. In the course of the study, the features of the DLT are considered, the criteria for its classification are presented as well. Based on the results of the conducted study, it has been concluded that the features of the DLT described by us are fragmentary reflected in regulatory legal acts, which once again emphasises the need to develop common guidelines for public policy in relation to the legal and technical regulation of the DLT in Russia.
In today's financial landscape, individuals face challenges when it comes to determining the most effective investment strategies. Cryptocurrencies have emerged as a recent and enticing option for investment. This paper focuses on forecasting the price of Ethereum using two distinct methods: artificial intelligence (AI)-based methods like Genetic Algorithms (GA), and econometric models such as regression analysis and time series models. The study incorporates economic indicators such as Crude Oil Prices and the Federal Funds Effective Rate, as well as global indices like the Dow Jones Industrial Average and Standard and Poor's 500, as input variables for prediction. To achieve accurate predictions for Ethereum's price one day ahead, we develop a hybrid algorithm combining Genetic Algorithms (GA) and Artificial Neural Networks (ANN). Furthermore, regression analysis serves as an additional prediction tool. Additionally, we employ the Autoregressive Moving Average (ARMA) model to assess the relationships between variables (dependent and independent variables). To evaluate the performance of our chosen methods, we utilize daily historical data encompassing economic and global indices from the beginning of 2019 until the end of 2021. The results demonstrate the superiority of AI-based approaches over econometric methods in terms of predictability, as evidenced by lower loss functions and increased accuracy. Moreover, our findings suggest that the AI approach enhances computational speed while maintaining accuracy and minimizing errors.