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561 results · page 5 of 24
Zihao Liu, Huaping Wu
No abstract is available for this record.
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Implementing multiple reporting sites is another trend that is logical to increase in the financial industry due to the need for instant access to financial information across multiple locations.Yet, there is one crucial issue that needs to be addressed: guaranteeing data consistency across distributed systems is very burdensome because of specific problems related to distributed databases and communication protocols.The topic of this paper is the comparison of the contemporary approaches to storage, with a specific emphasis on the methods that would enhance data integrity and coherency in distributed systems utilized at companies for financial reporting.The discussed techniques include the conventional and modern forms of databases such as relational, NoSQL, Distributed Ledger Technology (DLT), and cloud storage solutions.In this paper, the author reviews the literature and compares and contrasts the benefits and shortcomings of each storage method.Other factors include Transaction Management, Latency, Availability, and Fault tolerances, which are also assessed.In addition, the paper describes the method by which these storage techniques can be deployed in a distributed financial reporting environment.Moreover, the findings of this study point toward the fact that the proposal of the integration of both basic elements of relational databases, as well as of DLT, offers the most durable solution to applied issues of distributed financial reporting.Last, of all, the recommendations of this paper are presented as useful for those financial institutions that use the distributed reporting system and intend to achieve maximum efficiency in further practices, such as choosing the right type of storage depending on certain parameters of operation.
Tahmid Noor Rahman, Muhammad Siddique Ahmed Khan, Labib Bin Shahed, Abdulla Hil Kafi
Blockchain technology, first developed for Bitcoin, offers transformative potential for project management. We present ChainManager, a conceptual blockchain-based project management platform developed with Flutter. Built on Ethereum, the blockchain network is deployed using OCI (Oracle Cloud Infrastructure), offering adaptability to varying project needs. This platform for managing projects makes use of smart contracts and immutable activity tracking in order to ensure the safety of activities such as monitoring work hours and controlling access. According to our findings, blockchain technology has the capability to solve long-standing problems, such as maintaining the integrity of data and gaining the trust of stakeholders. On the other hand, limitations such as scalability and high energy consumption continue to exist, which calls for additional study and innovation. Although there are obstacles to overcome, the purpose of this study is to investigate how blockchain technology can revolutionize project management by securing project data, automating processes, enhancing overall operational efficiency, and guaranteeing responsibility to stakeholders.
Iveren M. Leghemo, Chima Azubuike, Osinachi Deborah Segun-Falade, Chinekwu Somtochukwu Odionu
As emerging technologies such as Blockchain, the Internet of Things (IoT), and Artificial Intelligence (AI) continue to reshape industries, the need for robust data governance frameworks has become increasingly critical. These technologies introduce unique challenges, including data privacy concerns, security vulnerabilities, and the complexity of managing vast, decentralized data sets. This paper proposes a conceptual framework for data governance tailored to the specific requirements of Blockchain, IoT, and AI technologies. The framework emphasizes a holistic approach, integrating key governance principles such as transparency, accountability, and compliance with regulatory standards. It also highlights the importance of fostering collaboration between stakeholders, including technologists, legal experts, and policymakers, to create a cohesive governance structure that can adapt to the rapid evolution of these technologies. The proposed framework addresses three core areas: data integrity and quality, security and privacy, and ethical considerations. For Blockchain, the focus is on ensuring the immutability and transparency of records while safeguarding against potential misuse of decentralized data. In the context of IoT, the framework prioritizes the management of data from diverse sources, ensuring interoperability and protecting sensitive information from unauthorized access. For AI, the emphasis is on developing ethical guidelines for data usage, preventing bias in algorithmic decision-making, and maintaining transparency in AI-driven processes. The framework also advocates for the integration of advanced data analytics and machine learning techniques to enhance data governance capabilities, enabling real-time monitoring and predictive insights. Additionally, it underscores the need for continuous training and education for all stakeholders to keep pace with the dynamic nature of emerging technologies. By adopting this comprehensive data governance framework, organizations can mitigate risks, ensure compliance, and harness the full potential of Blockchain, IoT, and AI while maintaining public trust.
Peng Liu, Xinglong Wu, Yanjun Peng, Hangguan Shan · 7 authors
To facilitate flexible manufacturing, modern industries have incorporated numerous modular operations such as multi-robot services which can be expediently arranged or offloaded to other production resources. However, complex manufacturing projects often consist of multiple tasks with fixed sequences, posing a significant challenge for smart factories in efficiently scheduling limited robot resources to complete specific tasks. Additionally, when projects span across factories, ensuring faithful execution of contracts becomes another challenge. In this paper, we propose a modified combinatorial auction method combined with blockchain and edge computing technologies to organize project scheduling. Firstly, we transform efficient resource scheduling into a resource-constrained multi-project scheduling problem (RCPSP). Subsequently, the solution integrates combinatorial auction with random sampling (CA-RS) into smart contracts. Alongside security analysis, simulations are conducted using real data sets. The results indicate that the suggested CA-RS approach significantly enhances efficiency and security in resource arrangement within the industrial Internet of Things compared to baseline algorithms.
Karlyga Kutybayeva, Abdul Razaque, Hari Mohan
The pharmaceutical industry faces critical challenges like counterfeiting and supply chain inefficiencies, jeopardizing public health and the sector’s integrity. This paper introduces the efficient blockchain-enhanced transparent pharmaceutical supply chain management (EBETPSCM) model, which innovatively integrates blockchain and big data analytics to enhance traceability, security, and operational efficiency. At the heart of this model is the strategic use of Hyperledger fabric, renowned for its decentralized consensus mechanism and robust cryptographic methods. This ensures the security and reliability of the supply chain, with its decentralized nature bolstering data immutability, a key factor in maintaining the integrity of supply chain information. Concurrently, big data analytics provide real-time insights, enhancing stakeholder visibility across the chain. Our study critically appraises prevailing challenges, highlighting blockchain’s potential to achieve data immutability and transparency. Empirical evidence from existing studies affirms blockchain’s role in safeguarding pharmaceutical data and refining supply chain operations. The proposed EBETPSCM model integrates a comprehensive framework, addressing technical, methodological, and regulatory aspects. Theoretical outcomes include a well-defined conceptual model, technical insights into blockchain, and big data analytics methodologies. Practically, the study endeavors to implement a prototype system to demonstrate significant improvements in efficiency, transparency, and security. To overcome extant challenges, we advocate for resolving technological issues, enhancing collaborative efforts, and developing new legislative frameworks. The anticipated outcomes promise substantial advancements in safety, efficiency, and transparency within pharmaceutical supply chains. Conclusively, our study emphasizes the necessity of continuous research, collaborative engagement, and regulatory support for the successful adoption of these technologies in the pharmaceutical sector.
Vincent de Wit, Liuwen Yu, Amro Najjar, Réka Markovich
No abstract is available for this record.
Abiodun Okunola, Beloved Joy
No abstract is available for this record.
Sumanth Kadulla
Advanced cloud automation architectures establish the foundation for transformative humanitarian aid operations through resilient, scalable infrastructure deployments. This technical transformation utilizes containerized microservices, distributed edge computing nodes, and event-driven patterns that maintain operational integrity despite connectivity challenges characteristic of disaster environments. Distributed ledger integration with standardized API frameworks facilitates secure tracking across heterogeneous systems, while multi-cloud analytics platforms with machine learning capabilities enable proactive disaster management. These systems demonstrate exceptional performance metrics in real-world implementations, including significant reductions in processing latency, dramatic improvements in forecast accuracy, and unprecedented resource tracking transparency. The widespread adoption of these technologies represents a fundamental shift from traditional emergency response toward sophisticated, automated solutions that dynamically adapt to changing conditions. As edge computing, quantum optimization, and adaptive machine learning frameworks continue to mature, the humanitarian sector finds itself at the leading edge of distributed systems innovation, deploying complex cloud architectures that establish new benchmarks for performance and reliability under extreme conditions.
Meghna Chaudhary, M. Afshar Alam, Sherin Zafar
No abstract is available for this record.
J. Thimmia Raja, Ashish Ashish, Sindhu Boianapalli, Soma Sabitha M · 6 authors
Blockchain technology, as a growing innovation, offers a viable solution to enhance transparency, traceability, and efficiency in global supply chains. While blockchain has great potential, several challenges remain, including scalability, integration with legacy systems, standardization, energy consumption, privacy concerns, and regulatory uncertainty. This enrollment of data is hoped to tackle these problems of dilemmas, whislt subsequent improved effective equates being more reasonable in terms of deploying blockchain applications. With the goal of offering a bridge to most projects still unsure whether to adopt Blockchain technology or to continue under what we call "Blockchain in the cloud" approach, this study introduces effective solutions on how to integrate Blockchain with current systems through innovative hybrid systems, standardized user protocols, and new consensus protocols that optimize cost of implementation and environmental impact. The other aspect of the research undertakes to design clear regulatory frameworks and change management systems that can aid in the adoption of blockchain technology for traditional supply chain stakeholders. By utilizing real-world case studies and the simplification of smart contract deployment, this work illustrates the practical benefits blockchain can provide in enhancing supply chain operations. In conclusion, the goal of this study is to pioneer a sustainable, secure, and efficient blockchain ecosystem that promotes trust, transparency, and collaboration among supply chain partners, ensuring the future viability of the technology.
Wenzhong Fan
No abstract is available for this record.
Imran Hussain Shah
Purpose:This study provides a user-prioritized, data-driven framework for evaluating DeFi risk management platforms and offers actionable insights for developers, investors, and regulators seeking to enhance the transparency, security, and sustainability of the DeFi ecosystem.Design/Methodology/Approach: This study investigates the effectiveness of six leading DeFi tracking platforms-Chainalysis, Elliptic, Nansen, Dune Analytics, DeBank, and Etherscan-in mitigating these risks.Employing a mixed-methods approach, the research integrates survey data (n = 138), expert interviews, and platform metrics, analyzed through advanced statistical techniques such as T-Test, MANOVA, Logistic Regression, Kruskal-Wallis H Test, Cohen's D Effect Size, Survival Analysis, Cluster Analysis, and a Utility-Based Scoring Model.Findings: Results reveal significant differences in platform performance, with Chainalysis and Etherscan emerging as top performers in compliance and usability, respectively.Practical Implications: The rapid expansion of Decentralized Finance (DeFi) has revolutionized financial services by eliminating intermediaries and enabling peer-to-peer interactions through blockchain-based smart contracts.However, this innovation introduces significant risk management challenges, including smart contract vulnerabilities, transaction opacity, and compliance limitations.Originality value: The utility model highlights the importance of real-time alerts, trust, and compliance tools in platform adoption.
Zoey WEN, Kani Chen
No abstract is available for this record.
Muhammadamin Dilmurodov
This capstone project explores the integration of decentralized finance (DeFi) functionalities—Token Swapping, Staking/Yield Tracking, and Liquidity Provision—in blockchain explorers within the Ethereum ecosystem. Through a qualitative analysis of 12 explorers, it assesses their support for these functions, revealing a divide between general-purpose platforms focused on data transparency and DeFi-focused platforms offering advanced financial tools. The study highlights varying levels of functionality availability and usability, providing insights into enhancing accessibility for DeFi users.
Giovanni Farina, Gianluca Brunori, Stefano Chessa, Alexander Kocian · 16 authors
No abstract is available for this record.
Kharzan P. Matasheva, Yunus M.-G. Gadamurov
The article examines cryptographic methods of zero-knowledge proof (ZKP) as a tool for overcoming information asymmetry in data markets. The theoretical foundations of ZKP are considered, including their formal properties and classification, as well as practical application scenarios in digital identification systems, confidential auctions, machine learning and data management. The impact of ZKP on the behavior of economic agents, transaction costs and market structure is analyzed. Both potential benefits of the technology — increased trust and automated verification — and risks associated with computational complexity and possible participant segmentation are identified. The study concludes that further development of hybrid privacy architectures and regulatory mechanisms is necessary to support open data markets.
Omar Isaac Asensio, Catherine E. Moore, NÃcola UlibarrÃ, Mecit Can Emre Simsekler · 6 authors
Abstract Data for Policy ( dataforpolicy.org ), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.
Xue Ping Gu, Tian Xia, Jingwen Yang, Min Liu
<title>Abstract</title> Proposes Reg-Twin—a digitalization of the end-to-end process from transaction → alert → case → SAR/SEC reporting. It incorporates a built-in policy rule engine, reconciliation and drift monitoring, and strategy A/B sandboxing. Through hash-chain data inheritance + selective zero-knowledge proofs (ZK), it demonstrates key compliance points to regulators without data leakage. In simulations and replays across 3,500+ funds and 70+ institutions: - Consistency defects reduced by 41% - Cross-report discrepancies decreased by 36% - Closing cycles shortened by 22% - Estimated alert volume/personnel efficiency/SLA error for strategy changes ≤ ±5% ZK proofs minimize sensitive field disclosure while enabling auditable verification. Reg-Twin demonstrates a technical pathway where enhanced transparency coexists with reduced compliance costs.
Arthur G. Bubolz, Giancarlo Lucca, Lizandro de Souza Oliveira, Thiago Teixeira · 7 authors
No abstract is available for this record.
A. S. Mamatha, M. Vaneeta, Uzma Sulthana, J. Ranjitha · 5 authors
No abstract is available for this record.
Tuan-Dung Tran, Ngoc-Hau Tran, Nguyen Tan Cam, Van-Hau Pham
No abstract is available for this record.
Soraya González-Mendes, Carlos J. Costa
No abstract is available for this record.
S. Udhayakumar, Kotte Mohan Krisha, Silviya Nancy John Mari Vienni, D. Uma Nandhini
No abstract is available for this record.