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INTRODUCTION: This study introduces a groundbreaking approach that integrates Artificial Intelligence (AI) with macroeconomic analysis to address a critical gap in existing economic forecasting methodologies. By leveraging diverse economic data sources, the study aims to transcend traditional analytical boundaries and provide a more comprehensive understanding of macroeconomic trends. OBJECTIVE: The primary objective is to pioneer a scalable framework for economic data analysis by combining AI with macroeconomic analysis. The study aims to utilize advanced machine learning algorithms to analyze and synthesize macroeconomic indicators, offering enhanced accuracy and predictive power. A key focus is on dynamically incorporating real-time data to adapt to evolving economic landscapes. METHODS: The research employs advanced machine learning algorithms to analyze and synthesize macroeconomic indicators. The integration of AI allows for a more nuanced understanding of complex economic dynamics. The methodology uniquely adapts to real-time data, providing a scalable framework for economic data analysis. RESULTS: The findings demonstrate the model's efficacy in predicting economic trends, surpassing conventional models in both precision and reliability. The study showcases the potential of AI-driven economic analysis to offer insights into economic dynamics with unprecedented accuracy. CONCLUSION: This study significantly contributes to the fields of AI and economics by proposing a transformative approach to macroeconomic analysis. The integration of technology and economics sets a new precedent, paving the way for future innovations in economic forecasting. The research also explores the implications of AI-driven economic analysis for policy-making, emphasizing its potential to inform more effective economic strategies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Quantum computing technology has become a center of attention in various scientific disciplines, including economic analysis. The adoption of quantum computing in economic analysis offers tremendous potential to improve the processing of complex economic data and provide deep insights. However, the use of quantum technology in the context of distributed information systems also raises several challenges, including data security and the limitations of quantum technology. OBJECTIVE: This research aims to investigate the implications of adopting quantum computing in economic analysis, with a focus on distributed information systems. METHODS: This research was carried out using a descriptive qualitative approach, with data derived from the results of relevant research and previous studies. The collected data will be processed and analyzed to gain a deeper understanding of the adoption of quantum computing in economic analysis in distributed information systems. RESULTS: This research then finds that the adoption of quantum computing in economic analysis has the potential to increase efficiency, accuracy, and depth of economic insight. However, limitations of current quantum technologies, including quantum errors, limited scale of operations, and data security issues, limit their applications. In the long term, research and development will be key to overcoming these obstacles and maximizing the potential of this technology in economic analysis. CONCLUSION: The long-term implications include increased economic competitiveness and significant changes in the way economic decision-making is carried out, assuming that ethical and regulatory issues are also carefully considered. [ABSTRACT FROM AUTHOR]

The blockchain paradigm and its application of Distributed Ledger Technology (DLT) are now enabling technologies for the worldwide development of trustworthy and decentralized solutions. DLT offers enormous potential to accommodate the intricate architectural patterns of individual Distributed Information Systems (DIS). Despite its immense potential, the current research effort is not focused on deploying DLT inside the DIS area. We identified sixteen essential DIS Architectural Properties (APs) that offer issues for DIS system architects using Grounded Theory. Then, we assessed the capabilities of the major DLT platform and highlighted its prospects and limits with regard to supporting DIS APs. Using the meta-analysis research technique, we demonstrated that DLT might serve as a development platform for a blockchain-native DIS (BN-DIS) that enables design and development processes. A BN-DIS utilizes DLT infrastructure and blockchain design principles to expand internationally, support thousands of distributed nodes, and employ DIS APs unique to the Blockchain. Lastly, we give a case study illustrating how DLT handles DIS APs in a real-world context, giving empirical proof of the DLT’s efficacy in servicing DIS APs.

This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach. [ABSTRACT FROM AUTHOR]

The distribution of information through web protocols is today based on the client-server model. Recently, decentralized protocols with greater availability appear as well as blockchain-based attestation methods, allowing for proving the existence of information. In combination, these methods promise a secure, decentralized and long-term storage. However, there exist two major problems: (1) the scalability of blockchains limits their storage capacity and (2) various (de)centralized web protocols are in use and could alleviate this problem, but they do not support blockchain-based attestations. In this paper, we extend an approach for blockchain-based attestation with compatibility for multi-protocol storage. Instead of specific protocols or blockchains, the extended approach aims to contribute novel concepts to the discussion on blockchain scalability. It augments the capabilities of existing protocols for applications such as certification or timestamping of digital artifacts. With the use of decentralized protocols such as IPFS, further availability and inherent resilience properties are gained, allowing for applications such as open research repositories and digital registries. We discuss the architecture of the extended approach, a possible implementation in a smart contract on the Ethereum blockchain with IPFS and Git, and evaluate the time and cost of attestations.

This paper presents robust positioning methods that use range measurements to estimate location parameters. The existing maximum correntropy criterion-based localization algorithm uses only the $l_{2}$ norm minimization. Therefore, the localization performance may not be satisfying because the $l_{2}$ norm minimization is vulnerable to the large error. Therefore, we propose the convex combination of $l_{1}$ and $l_{2}$ norm because the $l_{1}$ norm minimization is effective in the large noise condition. The mixed-norm maximum Versoria criterion-based unscented Kalman filter, mixed-norm least lncosh unscented Kalman filter, mixed-norm maximum Versoria criterion iterative reweighted least-squares, mixed-norm least lncosh iterative reweighted least squares and closed-form localization approaches are proposed for mixed line-of-sight/ non-line-of-sight environments. The proposed mixed-norm unscented Kalman filter-based algorithms are more superior to the other methods as the line-of-sight noise level increases by the use of the convex combination of $l_{1}$ norm and $l_{2}$ norm. The iterative reweighted least sqaures-based methods employ a weight matrix. The closed-form weighted least squares algorithm has an advantage that its computational complexity is lower than that of other methods. Simulation and experiments illustrate the localization accuracies of the proposed unscented Kalman filter-based methods are found to be superior to those of the other algorithms under large noise level conditions.

Distributed Ledger Technology (DLT) is making the first steps toward becoming a solution for the growing number of various decentralized systems worldwide. Unlike pure Blockchain, DLT finds many uses across different industries, including eHealth, finance, supply chain monitoring, and the Internet of Things (IoT). One of the vital DLT features is the ability to provide an immutable and commonly verifiable ledger for larger-scale and highly complex systems. Today's centralized systems can no longer guarantee the required level of availability and reliability due to the growing number of the involved nodes, complicated heterogeneous architectures, and task load, while the publicly available distributed systems are still in their infancy. This paper aims to provide an exhaustive topical review of the state-of-theart of Distributed Ledger Technology applicability in various sectors. It outlines the importance of the practical integration of technology-related challenges, as well as potential solutions. [ABSTRACT FROM AUTHOR]

This paper deals with problems of the development and security of distributed information systems. It explores the challenges of risk modeling in such systems and suggests a risk-modeling approach that is responsive to the requirements of complex, distributed, and large-scale systems. This article provides aggregate information on various risk assessment methodologies; such as quantitative, qualitative, and hybrid methods; a comparison of their advantages and disadvantages; as well as an analysis of the possibility of application in distributed information systems. It also presents research on a comprehensive, dynamic, and multilevel approach to cyber risk assessment and modeling in distributed information systems based on security metrics and techniques for their calculation, which provides sufficient accuracy and reliability of risk assessment and demonstrates an ability to solve problems of intelligent classification and risk assessment modeling for large arrays of distributed data. The paper considers the main issues and recommendations for using risk assessment techniques based on the suggested approach.

The article detail with the application of the probabilistic approach to the calculation of the expected performance indicators of the digitalization strategy is substantiated. The process of formation of intellectual assets in the course of implementing this strategy has been analysed. The influence of distributed information systems on increasing the share of intellectual assets of an enterprise is proved.