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.
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.
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.
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.
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.
<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.
Nghia Dinh, Vinh Truong Hoang, Bay Nguyen Van, Thien Ho Huong · 7 authors
Smart contracts are central to decentralized applications but are often vulnerable to security flaws, both during development and in deployment. Due to their immutable nature, any vulnerabilities introduced are permanent and exploitable, highlighting the need for a secure, structured development lifecycle. This study proposes an adaptive DevSecOps approach tailored to blockchain applications, drawing on established practices and gray literature. By embedding security into Continuous Integration and Continuous Delivery (CI/CD), the model ensures continuous protection from development through deployment. At its core is an adaptive cognitive security framework that automates vulnerability detection using techniques like static analysis, fuzzing, and symbolic execution. It also streamlines secure provisioning across various environments. To further enhance security, Agentic AI is integrated into the pipeline. These autonomous agents monitor code, infrastructure, and behavior in real-time, learning from past incidents, detecting threats, applying patches, and adapting CI/CD workflows dynamically. Experimental results validate the model’s effectiveness, showing significant improvements in the security and reliability of smart contracts. This approach offers a scalable, proactive solution for securing blockchain systems in a rapidly evolving threat landscape.
The article addresses the scientific problem of forming an efficient approach to assessing the market value of business projects in the rapidly evolving area of decentralized finance (DeFi) within the digital economy. The article analyzes the limitations of applying traditional financial evaluation methods, specifically discounted cash flow (DCF) models and economic value added (EVA), which lose their relevance in the DeFi context due to the instability of cash flows, absence of centralized reporting, and the specific profitability structure of tokenized assets. In order to overcome the mentioned limitations in the study, a new multifactor model has been proposed, which combines classical financial indicators with key tokenomic metrics: total value locked (TVL), the utility function of the token in governance, liquidity mining incentives, as well as the distribution of tokens over time (vesting schedules). An empirical validation of the model’s efficiency was conducted through the construction of a multiple linear regression based on data from 20 leading DeFi projects for the years 2023–2024. The results obtained demonstrated a high statistical significance of the included tokenomic variables (p < 0.01) and a high explanatory power of the model (R? = 0.92), confirming its efficiency for predicting the market capitalization of digital assets. It is demonstrated that tokenomic characteristics have a decisive impact on the value of DeFi projects, while traditional indicators (DCF, EVA) are secondary or insignificant due to the changing nature of value in the Web3 economy. The proposed model enables the development of a sound methodology for the strategic analysis of investment attractiveness of decentralized platforms, particularly from the perspective of DAO organizations, venture funds, and analytical agencies.
Enterprise Information Systems have a long-established and crucial role for modern organizations, as they enable seamless integration and management of critical business processes, ensuring efficiency in operations, data accuracy, and enhanced decision-making capabilities. One of their most interesting emerging technologies refer to the use of Artificial Intelligence as they may seamlessly automate routine tasks, offer predictive analytics, and provide deep insights, ultimately leading to intelligent data-driven decisions and improved operational efficiency. Of course, this direction of work is accompanied by some important challenges that come from the opacity of certain AI models and their potential biases due to low-quality training data used. In this paper, we argue that such challenges can be mitigated by a novel framework able to integrate, in a transparent manner, quality-related metadata on datasets used for training the AI-enabled emerging technologies in the field of EIS systems. These metadata are minted as Non-Fungible Tokens (NFTs) over the blockchain.
Cyber-Physical-Social Systems (CPSS), as emerging paradigms, are evolving to address the growing need for intelligent, adaptive, and transparent decision-making in complex environments such as smart cities and industrial systems. However, the advancements enabled by Digital Twins (DTs), centralized governance models, and opaque analytics limit scalability and resilience. In this study, we propose a decentralized governance framework that integrates Decentralized Autonomous Organizations (DAOs) and blockchain-based predictive analytics to enhance trust, interoperability, and ethical decision-making for CPSS. The proposed framework utilizes immutable ledgers, automated smart contracts, and community-driven governance to improve real-time collaboration, thereby ensuring system resilience and facilitating adaptive optimization. The remarkable innovation of the CPSS framework lies in combining digital tokens (DTs) within a blockchain application. We believe that our approach provides a scalable mechanism for autonomous decision-making and secure data sharing across multi-stakeholder ecosystems. In this regard, using theoretical analysis and comparative evaluations, we have demonstrated how our framework mitigates conventional challenges in CPSS governance, including security vulnerabilities, algorithmic fairness, and data integrity. Moreover, this research makes a significant contribution to the advancement of decentralized digital twin (DT) infrastructures, paving the way for more robust, ethically aligned, and resilient cyber-physical systems.
As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms&mdash;blockchain, decentralized autonomous organizations (DAOs), and data cooperatives&mdash;as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU&rsquo;s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe lighthouse project called Enfield: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) Zero-Knowledge Proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) Privacy-Preserving Machine Learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization.
Abstract This study explores the interplay between blockchain-based smart contracts and big data analytics for the supply chain value creation of micro, small, and medium enterprises (MSMEs). We implement our Ethereum Virtual Machine (EVM) procedure with the ganache blockchain, and addresses generated by the Metamask wallet. Each supply chain player in the blockchain is assigned a wallet address to observe the hashes created when data is added to the blockchain. Our findings unfold that supply chain value creation emphasises traceability, transparency, security, and profit maximisation interlocked with how effectively companies utilise big data collected through blockchain-based smart contracts. This subsequentially assists managers in using data types and a variety of analytics, spanning from descriptive, diagnostic, predictive, and prescriptive to cognitive analytics. This synergy between the blockchain and the types of analytics provides opportunities to identify new interactions and directions for future research.
Siti Hajar Mohd Yusof, R. Zahilah, Siti Hajar Othman
This research explores the development of a hybrid consensus algorithm that combines the benefits of Proof of Authority (PoA), Delegated Proof of Stake (DPoS), and threshold cryptography to create a secure, efficient, and scalable consensus mechanism for resource-constrained devices. The proposed algorithm addresses traditional consensus algorithms' limitations in resource-constrained environments, where energy efficiency, security, and decentralisation are crucial. By leveraging the strengths of PoA, DPoS, and threshold cryptography, this hybrid approach is anticipated to provide a robust and adaptable consensus mechanism to support many applications in IoT, edge computing, and other resource-constrained domains. The research aims to investigate the feasibility, performance, and security of this hybrid consensus algorithm and its potential to enable secure, decentralised, and scalable blockchain-based systems for resource-constrained devices.
Ajibola Oluwafemi Oyeleye, Onyeka Franca Asuzu, Adaobi Vivian Ibeh
This paper presents a conceptual model for raising Accounts Payable (AP) accuracy in research institutions by embedding process intelligence across the procure-to-pay lifecycle. The model integrates process mining, rule-based controls, and machine-learning anomaly detection with grant compliance logic to reduce mismatches, duplicate payments, and breaches. It addresses the context of universities and research hospitals, where varied funding sources, sponsor terms, and decentralized purchasing create transaction patterns and compliance risk. The model positions AP as a data-driven assurance hub connecting principal investigators, central finance, and suppliers. The architecture has four layers: first, data acquisition that unifies ERP, e-procurement, and grant management logs via standardized event schemas; second, conformance engines encoding sponsor allowability, period of performance, three-way match, and delegation rules; third, analytics and prediction that combine process discovery, first-pass-yield forecasting, vendor normalization, and exception clustering; and fourth, workflow orchestration that returns prescriptive alerts to case managers and routes exceptions to approvers for timely resolution. Methodologically, the model adopts a design-science and DMAIC hybrid. Teams baseline cycle time, touchpoints, and first-pass accuracy; mine event logs to map as-is variants; prioritize failure modes through FMEA; implement targeted controls; and measure effects with interrupted time series and segmented regression. Data quality is elevated through master-data maintenance, vendor deduplication, and invoice OCR confidence thresholds with human-in-the-loop review. Expected outcomes include higher first-pass yield, fewer late-payment penalties, improved sponsor billing, and cleaner audit trails. Leading indicators exception rate, conformance score, and rework loops feed a control chart to sustain gains, while lagging indicators write-offs, questioned costs, and audit findings confirm risk reduction. The model also incorporates equity and accessibility by simplifying small-supplier onboarding and enabling transparent status notifications to reduce inquiry volume and payment anxiety. A change-management plan aligns incentives across finance, research administration, and procurement, with skills uplift delivered through training and playbooks. This conceptualization offers a scalable blueprint aligning AP accuracy with research integrity, stewardship of public funds, and overall operational resilience, enabling institutions to realize predictable, compliant payables operations and stronger supplier relationships.
In recent years, various digital Business-to-Business (B2B) platforms have been accelerating the promotion of digital transformation in manufacturing. Consider a supply chain setting where an online B2B platform offers Internet of Things (IoT) service and selling channels to a manufacturer, this paper examines digital innovation investments and service pricing decisions under two common contracts: sell-on and sell-to contracts. Firstly, we have identified the impact of demand spillover from IoT platform services and IoT technology on manufacturer innovation and product line decisions. We found that under significant demand spillover , the manufacturer will exclusively produce smart products and discontinue the production of traditional products. Secondly, we find that under sell-on contracts, the innovation investments of the platform and manufacturer are always substitutable as the platform commission rate increases. Also, both the manufacturer and the IoT platform tend to increase innovation provision when using sell-on contracts compared to sell-to contracts. Finally, our results illustrate that the profitability of the IoT platform and the manufacturer is contingent upon the type of contract in place, with the IoT platform being more profitable under sell-to contracts when demand spillovers are small. Our research findings offer a valuable reference point for developing IoT platforms and insights into innovation and pricing decisions for smart device manufacturers in the digital transformation of manufacturing.
Tiago Guimarães, Ricardo Duarte, Francini Hak, Manuel Filipe Santos
Hospital inpatient care relies on constant monitoring and reliable real-time data. Continuous improvement, adaptability, and state-of-the-art technologies are critical for ongoing efficiency, productivity, and readiness growth. When appropriately used, technologies, such as blockchain and IoT-enabled devices, can change the practice of medicine and ensure that it is performed based on correct assumptions and reliable data. The proposed electronic health record (EHR) can obtain context information from beacons, change the user interface of medical devices according to their location, and provide a more user-friendly interface for medical devices. The data generated, which are associated with the location of the beacons and devices, were stored in Hyperledger Fabric, a permissioned distributed ledger technology. Overall, by prompting and adjusting the user interface to context- and location-specific information while ensuring the immutability and value of the data, this solution targets a decrease in medical errors and an increase in the efficiency in healthcare inpatient care by improving user experience and ease of access to data for health professionals. Moreover, given auditing, accountability, and governance needs, it must ensure when, if, and by whom the data are accessed.
Distributed DevOps is a software development methodology that aims to integrate the work of development and operations teams without being bound by geographical constraints. This methodology excels in enhancing collaboration and speeding software development. However, it does suffer from a lack of security, transparency, and traceability, which can result in project delays, a lack of trust between stakeholders, and even project failure. This paper addresses these issues of Distributed DevOps by implementing Blockchain technology. In this paper, we propose a novel framework that leverages blockchain technology to address the challenges faced by Distributed DevOps. Through performance analysis, we demonstrate the effectiveness of our framework in a real-world scenario, highlighting its ability to improve transparency, traceability, and the security of the DevOps pipeline. Our findings underscore the potential of blockchain-empowered solutions in revolutionizing DevOps practices. Furthermore, this research offers a practical framework for organizations seeking to optimize their development processes by integrating blockchain technology.