Search Results (201)

To address the limitations of Retrieval-Augmented Generation (RAG) systems in handling long policy documents, mitigating information dilution, and reducing hallucinations in engineering-oriented applications, this paper proposes SPR-RAG, a retrieval-augmented framework designed for knowledge-intensive vertical domains such as electric power policy analysis. With practicality and interpretability as core design goals, SPR-RAG introduces a Semantic Parsing Retriever (SPR), which integrates community detection–based entity disambiguation and transforms natural language queries into logical forms for structured querying over a domain knowledge graph, thereby retrieving verifiable triple-based evidence. To further resolve retrieval bias arising from diverse policy writing styles and inconsistencies between user queries and policy text expressions, a question-repository–based indirect retrieval mechanism is developed. By generating and matching latent questions, this module enables more robust retrieval of non-structured contextual evidence. The system then fuses structured and unstructured evidence into a unified dual-source context, providing the generator with an interpretable and reliable grounding signal. Experiments conducted on real electric power policy corpora demonstrate that SPR-RAG achieves 90.01% faithfulness—representing a 5.26% improvement over traditional RAG—and 76.77% context relevance, with a 5.96% gain. These results show that SPR-RAG effectively mitigates hallucinations caused by ambiguous entity names, textual redundancy, and irrelevant retrieved content, thereby improving the verifiability and factual grounding of generated answers. Overall, SPR-RAG demonstrates strong deployability and cross-domain transfer potential through its “Text → Knowledge Graph → RAG” engineering paradigm. The framework provides a practical and generalizable technical blueprint for building high-trust, industry-grade question–answering systems, offering substantial engineering value and real-world applicability. Full article

The main objective of this article is to conduct a literature review on the use of open innovation (OI) for green transition to identify tools and methods that can make green transition more effective, efficient, and socially acceptable. This review is accompanied by an attempt to answer the following research questions: R1. How can open innovation be used in the economy and by individual entities to achieve the goals of the green transition? R2. How can individual stakeholders be activated and motivated to participate in the process of creating open innovation for the green transition? and R3. What are the real effects of using open innovation on a macroeconomic, social, and individual scale? The results allow concluding that OI is used by enterprises, cities, regions, and entire economies. Among the methods of activating and motivating individual stakeholders to engage in the process of creating OI for green transition, the following can be selected: (1) internal resources and competencies (knowledge management, internal programs, open leadership, trust, complementarity of resources); (2) partnership characteristics (modern business models, involvement of partnership intermediaries, strengthening relationships with suppliers and customers, involvement of prosumers, cooperation with universities and research institutions); (3) external legal and regulatory conditions (protection of intellectual property rights, pro-innovation and pro-environmental education systems, creation of a legal framework for cooperation between science and business); and (4) external technical and organizational solutions (online platforms, social media, Living Labs, external sources of knowledge). The most frequently mentioned individual effects of open innovation in the energy sector include: improved efficiency, effectiveness and competitiveness in environmental management and the implementation of sustainable development, as well as the use of modern technologies. At the economic level, OI supports investment and economic growth. It can also have a positive impact on reducing energy poverty and developing renewable energy sources, including in emerging economies. This form of innovation also promotes social integration and the creation of social values. The findings of this review can be utilized by scholars to identify current and future research directions. They may also prove valuable for practitioners as both an incentive to engage in open innovation and guidance for its design and implementation. Furthermore, the results can contribute to disseminating knowledge about open innovation and its role in the green transformation. Full article

Corruption in Colombia remains a critical barrier to development, institutional trust, and equitable access to public services, despite legislative efforts such as the Anti-Corruption Statute. This article explores the intersection between fiscal management and artificial intelligence (AI) as integrated strategies for enhancing transparency, accountability, and risk assessment in public administration. Drawing on theoretical frameworks and empirical data from 2020 to 2022, this study analyzes the scale and impact of corruption and the effectiveness of oversight mechanisms led by the Comptroller General of the Republic (CGR). A key innovation examined is the implementation of a GPT-based scoring model that automates the evaluation of internal accounting controls in 219 public entities. By leveraging AI to support fiscal audits, Colombia demonstrates a scalable approach to modernizing anti-corruption practices. The study concludes with policy recommendations that emphasize digital transformation, institutional strengthening, citizen engagement, and capacity building to improve fiscal governance and reduce corruption. Full article

Smart manufacturing demands adaptive, scalable, and human-centric solutions for predictive maintenance. This paper introduces the concept of Agentic AI, a paradigm that extends beyond traditional multi-agent systems and collaborative AI by emphasizing agency: the ability of AI entities to act autonomously, coordinate proactively, and remain accountable under human oversight. Through federated learning, edge computing, and distributed intelligence, the proposed framework enables intentional, goal-oriented monitoring agents to form self-organizing predictive maintenance ecosystems. Validated in a ceramic manufacturing facility, the system achieved 94% predictive accuracy, a 67% reduction in false positives, and a 43% decrease in unplanned downtime. Economic analysis confirmed financial viability with a 1.6-year payback period and a €447,300 NPV over five years. The framework also embeds explainable AI and trust calibration mechanisms, ensuring transparency and safe human–machine collaboration. These results demonstrate that Agentic AI provides both conceptual and practical pathways for transitioning from reactive monitoring to resilient, autonomous, and human-centered industrial intelligence. Full article

Industrial trademark examination enables users to apply for and manage their trademarks efficiently, promoting industrial and commercial economic development. However, there still exist many challenges, e.g., how to customize a blockchain-based crowdsourcing method for interactive trust evaluation, how to decentralize the functionalities of a centralized entity to nodes in a blockchain network instead of removing the entity directly, how to design a protocol for the method and prove its security, etc. In order to overcome these challenges, in this paper, we propose the Blockchain-based Crowdsourcing Interactive Trust Evaluation (Block-CITE for short) method to improve the efficiency and security of the current industrial trademark management schemes. Specifically, Block-CITE adopts a dual-blockchain structure and a crowdsourcing technique to record operations and store relevant data in a decentralized way. Furthermore, Block-CITE customizes a protocol for blockchain-based crowdsourced industrial trademark examination and algorithms of smart contracts to run the protocol automatically. In addition, Block-CITE analyzes the threat model and proves the security of the protocol. Security analysis shows that Block-CITE is able to defend against the malicious entities and attacks in the blockchain network. Experimental analysis shows that Block-CITE has a higher transaction throughput and lower network latency and storage overhead than the baseline methods. Full article

Purpose: This article evaluated the decline of registered auditors (RAs) and its impact on the future of the assurance industry in South Africa. Auditors play a critical role in ensuring the transparency, trust, and credibility of financial statements. The decrease in the number of registered auditors has become a pressing issue, raising concerns about the assurance industry’s ability to maintain a sufficient number of registered auditors and continue providing assurance services to public and private entities. Methodology: A qualitative Delphi methodology was employed, involving interviews with RAs who are registered with the Independent Regulatory Board for Auditors (IRBA). Eight RAs participated in structured interviews. This approach enabled the researcher to gather expert opinions, identify emerging trends, and explore challenges and opportunities within the audit profession related to the decline of RAs. Main findings: The decline of RAs is straining client demands, increasing workloads, and leading to a shortage of audit firms, which in turn affects audit quality and methodologies. Audit firms struggle to attract and retain talent due to regulatory burdens, economic pressures, and concerns about work–life balance. These pressures have resulted in higher audit fees, increased compliance costs, and more extensive training requirements. Smaller audit firms are especially impacted, with some downscaling their assurance services or exiting the market entirely. Practical implications: This study underscores the pressing need for regulatory bodies, such as the IRBA, to address the challenges faced by audit firms, particularly in terms of compliance and workforce retention. Proactive strategies are required to preserve the quality and accessibility of assurance services. Contribution: This study contributes to the ongoing discourse on the future of the audit profession by offering grounded insights into how the industry might sustain itself amid a declining number of RAs and changing professional dynamics. Full article

The growing importance of social networks has led to increased research into trust estimation and interpretation among network entities. It is important to predict the trust score between users in order to minimize the risks in user interactions. This article enables the identification of the most reliable and least reliable entities in a network by expressing trust scores numerically. In this paper, the social network is modeled as a graph, and trust scores are calculated by taking the powers of the ratio matrix between entities and summing them. Taking the power of the proportion matrix based on the number of entities in the network requires a lot of arithmetic load. After taking the powers of the eigenvalues of the ratio matrix, these are multiplied by the eigenvector matrix to obtain the power of the ratio matrix. In this way, the arithmetic cost required for calculating trust between entities is reduced. This paper calculates the trust score between entities using linear algebra techniques to reduce the arithmetic load. Trust detection algorithms use shortest paths and similar methods to eliminate paths that are deemed unimportant, which makes the result questionable because of the loss of data. The novelty of this method is that it calculates the trust score without the need for explicit path numbering and without any data loss. Full article

The integration of blockchain technology into healthcare offers a robust solution to challenges in secure data sharing, privacy protection, and operational efficiency. Effective exchange of sensitive patient information among hospitals, clinics, insurers, and researchers is essential for better outcomes and medical advancements. Traditional centralized systems often suffer from data breaches, inefficiency, and poor interoperability. This paper presents a blockchain-based secure data-sharing framework tailored for healthcare, addressing these limitations. The framework employs a hybrid blockchain model, combining private and public blockchains: the private chain ensures fast transactions and controlled access, while the public chain fosters transparency and trust. Advanced cryptographic methods—such as asymmetric encryption, hashing, and digital signatures—safeguard patient data and maintain integrity throughout the datalifecycle. Smart contracts automate processes like consent management, access control, and auditing, ensuring dynamic permission enforcement without intermediaries. Role-based access control (RBAC) further limits access to authorized entities, enhancing privacy. To tackle interoperability, standardized data formats and protocols enable smooth communication across diverse healthcare systems. Large files, such as medical images, are stored off-chain, with only essential metadata and logs on the blockchain. This approach optimizes performance, scalability, and suitability for large-scale healthcare deployments. Full article

Phishing is a form of cyber-attack that aims to steal sensitive information by impersonating a trusted entity. To overcome this threat, various artificial intelligence (AI) methods have been developed to improve the effectiveness of phishing detection. This study evaluates three machine learning models, namely Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM), using an open dataset containing phishing and non-phishing URLs. The research process includes data preprocessing stages such as cleaning, normalization, categorical feature encoding, feature selection, and dividing the dataset into training and test data. The trained models are then evaluated using accuracy, precision, recall, F1-score, and comparison score metrics to determine the best model in phishing classification. The evaluation results show that the Random Forest model has the best performance with higher accuracy and generalization of 98.64% compared to Decision Tree which is only 98.37% and SVM 92.67%. Decision Tree has advantages in speed and interpretability but is susceptible to overfitting. SVM shows good performance on high-dimensional datasets but is less efficient in computing time. Based on the research results, Random Forest is recommended as the most optimal model for machine learning-based phishing detection. Full article

Digital Twins (DTs) have become essential tools for improving efficiency, security, and decision-making across various industries. DTs enable deeper insight and more informed decision-making through the creation of virtual replicas of physical entities. However, they face privacy and security risks due to their real-time connectivity, making them vulnerable to cyber attacks. These attacks can lead to data breaches, disrupt operations, and cause communication delays, undermining system reliability. To address these risks, integrating advanced security frameworks such as blockchain technology offers a promising solution. Blockchains’ decentralized, tamper-resistant architecture enhances data integrity, transparency, and trust in DT environments. This paper examines security vulnerabilities associated with DTs and explores blockchain-based solutions to mitigate these challenges. A case study is presented involving how blockchain-based DTs can facilitate secure, decentralized data sharing between autonomous connected vehicles and traffic infrastructure. This integration supports real-time vehicle tracking, collision avoidance, and optimized traffic flow through secure data exchange between the DTs of vehicles and traffic lights. The study also reviews performance metrics for evaluating blockchain and DT systems and outlines future research directions. By highlighting the collaboration between blockchain and DTs, the paper proposes a pathway towards building more resilient, secure, and intelligent digital ecosystems for critical applications. Full article

Software-Defined Wide-Area Networks (SD-WAN) efficiently manage and route traffic across multiple WAN connections, enhancing the reliability of modern enterprise networks. However, the performance of SD-WANs is largely affected due to malicious activities of unauthorized and faulty nodes. To solve these issues, many machine-learning-based malicious-node-detection techniques have been proposed. However, these techniques are vulnerable to various issues such as low classification accuracy and privacy leakage of network entities. Furthermore, most operations of traditional SD-WANs are dependent on a third-party or a centralized party, which leads to issues such single point of failure, large computational overheads, and performance bottlenecks. To solve the aforementioned issues, we propose a Blockchain Federated-Learning-Enabled Trust Framework for Secure East–West Communication in Multi-Controller SD-WANs (BFL-SDWANTrust). The proposed model ensures local model learning at the edge nodes while utilizing the capabilities of federated learning. In the proposed model, we ensure distributed training without requiring central data aggregation, which preserves the privacy of network entities while simultaneously improving generalization across heterogeneous SD-WAN environments. We also propose a blockchain-based network that validates all network communication and malicious node-detection transactions without the involvement of any third party. We evaluate the performance of our proposed BFL-SDWANTrust on the InSDN dataset and compare its performance with various benchmark malicious-node-detection models. The simulation results show that BFL-SDWANTrust outperforms all benchmark models across various metrics and achieves the highest accuracy (98.8%), precision (98.0%), recall (97.0%), and F1-score (97.7%). Furthermore, our proposed model has the shortest training and testing times of 12 s and 3.1 s, respectively. Full article

This study investigates how state-owned enterprises (SOEs) contribute to rural revitalization in China through systemic interventions that enable the transfer of social capital. Addressing the gap between external resource inputs and internal development needs, the study adopts a systems thinking framework to conceptualize social capital as comprising structural, relational, and cognitive components. Drawing on multi-case evidence from assistance projects led by China Southern Power Grid, this study selects 11 assistance projects from a broader pool of 199 cases, to demonstrate how SOEs act as institutional nodes to reshape rural governance systems. They rebuild local organizational networks (structural capital), establish long-term trust through “strong commitment–weak contract” mechanisms (relational capital), and localize technical knowledge to align with rural contexts (cognitive capital). These interlinked processes form an integrated system that enhances rural governance capacity and promotes sustainable development. The findings highlight that SOEs are not merely resource providers but systemic catalysts that support cross-scalar collaboration and social infrastructure building. The study contributes a novel perspective by integrating social capital theory with a systemic governance lens and offer a actionable insights into the institutional design of assistance models for the future interventions by SOEs and similar entities in underdeveloped areas. Full article

This paper presents a novel digital identity ecosystem built upon a hierarchical structure of Blockchain tokens, where both government-certified and uncertified tokens can coexist to represent various attributes of an individual’s identity. At the core of this system is the government, which functions as a trusted authority capable of creating entities and issuing a unique, non-replicable digital identity token for each one. Entities are the exclusive owners of their identity tokens and can attach additional tokens—such as those issued by the government, educational institutions, or financial entities—to form a verifiable, token-based digital identity tree. This model accommodates a flexible identity framework that enables decentralized yet accountable identity construction. Our contributions include the design of a digital identity system (supported by smart contracts) that enforces uniqueness through state-issued identity tokens while supporting user-driven identity formation. The model differentiates between user types and certifies tokens according to their source, enabling a scalable and extensible structure. We also analyze the economic, technical, and social feasibility of deploying this system, including a breakdown of transaction costs for key stakeholders such as governments, end-users, and institutions like universities. Considering the benefits of blockchain, implementing a digital identity ecosystem in this technology is economically viable for all involved stakeholders. Full article

Environmental crime has been increasingly recognised as transnational organised crime, but efforts to build a coherent and effective international response are still in development and under threat from shifts in the funding landscape. This mixed methods study addresses the role of one significant group of actors in environmental crime enforcement, which are non-governmental organisations (NGOs) who gather intelligence that can be shared with law enforcement and regulatory agencies. The study compares their intelligence practices to findings from traditional intelligence sectors, with a focus upon criminal justice and policing. The research generated quantitative and qualitative data from NGO practitioners, which is integrated to discern three overarching themes inherent in these NGOs’ intelligence practices: the implementation of formal intelligence practices is still underway in the sector; there remains a need to improve cooperation to break down silos between agencies and NGOs, which requires an improvement in trust between these entities; the operating environment provides both opportunities and challenges to the abilities of the NGOs to deliver impact. The study concludes by positing that the characteristics of NGOs mean that this situation constitutes ‘green intelligence’, contextualising intelligence theory and highlighting areas in which agencies can further combat environmental crime. Full article

Cybersecurity represents a critical challenge for data-sharing platforms involving multiple stakeholders, particularly within complex and decentralized systems such as livestock supply chain networks. These systems demand novel approaches, robust security protocols, and advanced data management strategies to address key challenges such as data consistency, transparency, ownership, controlled access or exposure, and privacy-preserving analytics for value-added services. In this paper, we introduced the Framework for Livestock Empowerment and Decentralized Secure Data eXchange (FLEX), as a comprehensive solution grounded on five core design principles: (i) enhanced security and privacy, (ii) human-centric approach, (iii) decentralized and trusted infrastructure, (iv) system resilience, and (v) seamless collaboration across the supply chain. FLEX integrates interdisciplinary innovations, leveraging decentralized infrastructure-based protocols to ensure trust, traceability, and integrity. It employs secure data-sharing protocols and cryptographic techniques to enable controlled information exchange with authorized entities. Additionally, the use of data anonymization techniques ensures privacy. FLEX is designed and implemented using a microservices architecture and edge computing to support modularity and scalable deployment. These components collectively serve as a foundational pillar of the development of a digital product passport. The FLEX architecture adopts a layered design and incorporates robust security controls to mitigate threats identified using the STRIDE threat modeling framework. The evaluation results demonstrate the framework’s effectiveness in countering well-known cyberattacks while fulfilling its intended objectives. The performance evaluation of the implementation further validates its feasibility and stability, particularly as the volume of evidence associated with animal identities increases. All the infrastructure components, along with detailed deployment instructions, are publicly available as open-source libraries on GitHub, promoting transparency and community-driven development for wider public benefit. Full article