Blockchains are diverse in the way they handle communications between their nodes to disseminate information, mitigate attacks, and agree on the next block. While security vulnerabilities have been identified, they rely on an attack custom-made for a specific blockchain communication protocol. To our knowledge, the vulnerabilities of multiple blockchain communication protocols to adversarial conditions have never been compared. In this paper, we compare empirically the vulnerabilities of the communication protocols of five modern in-production blockchains, Algorand, Aptos, Avalanche, Redbelly and Solana, when attacked in five different ways. We conclude that Algorand is vulnerable to packet loss attacks, Aptos is vulnerable to targeted load attacks and leader isolation attacks, Avalanche is vulnerable to transient failure attacks, Redbelly's performance is impacted by packet loss attacks and Solana is vulnerable to stopping attacks and leader isolation attacks. Our system is open source.
This study presents a blockchain-based enabling autonomous nursing professional development framework, known as BCeANPDF. The framework aims to enhance transparency, security, and professional autonomy in nursing credential management. It is grounded in the principles of competency-based human resource management. Blockchain and smart contract technologies are integrated to support independent recording, verification, and management of professional and non-professional credentials by nurses. At the same time, hospital human resource administrators continue to have the authority to conduct regulatory oversight and ensure compliance. The framework employs a three-layer architecture that includes controller, service, and repository components. These components coordinate access control, data processing, and blockchain-related operations. Seven smart contracts are designed within the framework. They automate credential ownership verification, credential updates, and compliance review processes. This design strengthens data integrity and reduces administrative workload. A prototype was implemented in a private blockchain environment to evaluate system performance. The results demonstrate stable and efficient operation. The average on-chain processing time per credential was 12.3 s. Median query latency ranged from 5 to 9 ms. These findings confirm that the framework achieves scalability and responsiveness comparable to Ethereum, while preserving data privacy and immutability. By combining decentralized trust mechanisms with credential management practices, the BCeANPDF framework offers a practical approach to supporting autonomous professional development. It also facilitates flexible management of the nursing workforce. Overall, the framework contributes to the development of transparent and competency-oriented healthcare institutions without increasing operational complexity.
Purpose. To substantiate conceptual approaches to integrating blockchain technologies into risk management systems of investment activities of financial institutions through systematization of architectural solutions, development of efficiency evaluation criteria, and typology of implementation strategies, taking into account the specifics of different categories of investment risks and regulatory environment. Methodology. An interdisciplinary approach was used, combining institutional analysis of financial systems, comparative analysis of traditional centralized and decentralized risk management models, and systematization of empirical data on blockchain implementation in the global financial sector. Methods of structural-functional analysis were applied to study blockchain systems architecture and their impact on various categories of investment risks. Critical analysis of scientific literature on decentralized finance, asset tokenization, and smart contracts was conducted. Findings. The dual nature of blockchain technologies has been revealed as both a tool for minimizing traditional risks (market, credit, operational, liquidity, regulatory) and a source of new technological challenges. Four integration models have been systematized: asset tokenization for enhancing liquidity, DeFi instruments for decentralized lending and exchange, hybrid portfolios for diversification, and smart contracts for risk management automation. An evaluation matrix for blockchain solutions effectiveness has been developed based on seven criteria (transparency, settlement speed, operational costs, accessibility, reliability, regulatory certainty, scalability) compared to traditional systems. A typology of implementation strategies for commercial banks, investment funds, and insurance companies has been proposed. Originality. For the first time, a comprehensive analysis of the transformation of investment activity risk management architecture through the lens of blockchain technology integration has been conducted, simultaneously considering institutional, technological, and regulatory aspects. A conceptual model of an integrated blockchain system for managing investment risks has been developed with identification of interaction levels and feedback loops. Practical value. Research results form a methodological foundation for financial institutions regarding the selection of optimal blockchain technology implementation strategies, provide tools for evaluating the effectiveness of various integration models, and contribute to the formation of regulatory policy in the field of digital transformation of the financial sector.
Purpose. To substantiate conceptual approaches to integrating blockchain technologies into risk management systems of investment activities of financial institutions through systematization of architectural solutions, development of efficiency evaluation criteria, and typology of implementation strategies, taking into account the specifics of different categories of investment risks and regulatory environment. Methodology. An interdisciplinary approach was used, combining institutional analysis of financial systems, comparative analysis of traditional centralized and decentralized risk management models, and systematization of empirical data on blockchain implementation in the global financial sector. Methods of structural-functional analysis were applied to study blockchain systems architecture and their impact on various categories of investment risks. Critical analysis of scientific literature on decentralized finance, asset tokenization, and smart contracts was conducted. Findings. The dual nature of blockchain technologies has been revealed as both a tool for minimizing traditional risks (market, credit, operational, liquidity, regulatory) and a source of new technological challenges. Four integration models have been systematized: asset tokenization for enhancing liquidity, DeFi instruments for decentralized lending and exchange, hybrid portfolios for diversification, and smart contracts for risk management automation. An evaluation matrix for blockchain solutions effectiveness has been developed based on seven criteria (transparency, settlement speed, operational costs, accessibility, reliability, regulatory certainty, scalability) compared to traditional systems. A typology of implementation strategies for commercial banks, investment funds, and insurance companies has been proposed. Originality. For the first time, a comprehensive analysis of the transformation of investment activity risk management architecture through the lens of blockchain technology integration has been conducted, simultaneously considering institutional, technological, and regulatory aspects. A conceptual model of an integrated blockchain system for managing investment risks has been developed with identification of interaction levels and feedback loops. Practical value. Research results form a methodological foundation for financial institutions regarding the selection of optimal blockchain technology implementation strategies, provide tools for evaluating the effectiveness of various integration models, and contribute to the formation of regulatory policy in the field of digital transformation of the financial sector.
Introduction: Robotics and artificial intelligence (AI) are rapidly reshaping hospitality by automating frontline and back-of-house processes, augmenting service encounters, and expanding the analytical scope of revenue management. Yet, existing research remains fragmented: service-robot studies largely emphasize adoption and human-robot interaction, while revenue-management research prioritizes pricing and distribution, sustainability research focuses on environmental practices, and hotel real-estate scholarship foregrounds governance and asset value. Meanwhile, blockchain technologies-through distributed ledgers, smart contracts, digital identity, and tokenization-offer a complementary trust and value-transfer layer that can address coordination and verification problems across hotel ecosystems (e.g., data sharing, sustainability claims, and owner-operator contracting). Methods: Drawing on an integrative literature synthesis, this conceptual article develops an integrative framework linking AI-robotics and blockchain capabilities to three interdependent hotel decision domains: (1) revenue management (demand forecasting, dynamic/open pricing, channel and loyalty optimization), (2) sustainability and operations (resource optimization, waste circularity, predictive maintenance), and (3) real estate and hotel asset management (digital twins, CapEx planning, valuation and risk analytics, and tokenized financing). Results: A conceptual model is proposed in which AI-robotics and blockchain jointly build digital operational and market-intelligence capabilities that improve financial performance (RevPAR/GOPPAR and net operating income), sustainability performance (carbon and resource intensity), and long-term asset value. Ten propositions articulate mechanisms and boundary conditions related to governance, ethics, privacy, cybersecurity, organizational readiness, regulation, and market context. Discussion: The article concludes with implications for hotel managers, owners, investors, and researchers, and outlines a future research agenda for hospitality, tourism, service management, and real-estate scholars.
Stephen Bishibura Erick, Bonamax Mbasa, Kulwa Mang’ana
This study conducts a comprehensive bibliometric analysis of scholarly research on green economy and sustainable finance from 2014 to 2024. Drawing upon a dataset of 692 peer-reviewed publications indexed in Scopus and analysed using the Bibliometrix R package, the study maps the field’s intellectual landscape, thematic development, and collaborative networks. The findings reveal a consistent increase in scientific output, with a pronounced surge in publications after 2018. This growth trend aligns with global policy milestones such as the Paris Agreement, the European Union [EU] Sustainable Finance Action Plan, and the proliferation of Environmental, Social, and Governance [ESG] integration and green bonds. China emerges as the most productive country, while institutions such as Jiangsu University, the Southwestern University of Finance and Economics, and the Lebanese American University lead in publication volume and collaboration intensity. Keyword co-occurrence and thematic mapping identify dominant themes related to green finance, environmental sustainability, ESG frameworks, and renewable energy, alongside emerging topics like climate risk disclosure and transition finance. Conceptual and co-word network analyses further reveal the interdisciplinary integration of finance, economics, policy, and environmental science. The study also demonstrates the growing decentralization of institutional influence and the rise of both North–South and South–South collaborations. These findings offer valuable insights into the evolving structure of research in sustainable finance and inform future academic inquiry and policy development.
We demonstrate that AI agents given 3-line role descriptions and access to consensus-validated institutional memory can autonomously create, harden, calibrate, solve, and learn from cybersecurity challenges—without any domain expertise in their prompts. Using 11 specialized agents organized into 5 departments within a governed organization (CipherForge Labs), we present the first fully autonomous, consensus-governed AI security research loop: A designer agent (3-line prompt, zero cryptographic knowledge) generates a functional AES-CBC Padding Oracle challenge. A hardener agent (3-line prompt) applies 6 defense layers—20-bit Proof of Work, timing side-channels, JSON casing side-channels, single-use tokens—escalating difficulty from 0.80 to 1.75 across 2 iterations. A calibrator agent (3-line prompt) correctly assesses the hardened challenge at difficulty 1.80 (gap = 0.20 from target 2.0). A quality scorer (3-line prompt) rates the challenge 93.0/100. Total pipeline time: 508 seconds. An independent solver agent (blind, no source code access) identifies the casing side-channel vulnerability, writes a C-compiled Proof of Work solver, deploys 32 parallel oracle workers, and captures the flag in 525.2 seconds (16,384 queries). The findings are submitted to a 4-node BFT consensus network, validated (score = 0.88), and committed to institutional memory—now queryable by all future agents. No agent had cryptographic expertise in its prompt. No human intervened at any stage. The entire cycle—creation, defense, assessment, exploitation, and organizational learning—was governed by BFT consensus with department-scoped RBAC access controls. This result extends our prior finding that an 18-line "onboarding" prompt with curated institutional memory outperformed a 120-line expert prompt. Here we take that principle to its logical extreme: 11 agents, 5 departments, 20+ pipeline routing states, and a closed feedback loop—all driven by minimal prompts and organizational memory.
Relevance of the research topic. The relevance of studying fiscal decentralization as a factor in strengthening the financial capacity of Ukraine's regions stems from the limited opportunities for optimizing budgetary policy amid significant financial constraints caused by priority expenditures on defense and the social sphere. The traditional centralized model of the budgetary system, despite its historical justification, demonstrates inefficiency due to regions' dependence on interbudgetary transfers and limited adaptability to local needs. At the same time, decentralization, while offering prospects for enhancing autonomy and more efficient resource utilization, is accompanied by risks of regional disparities and requires balanced control to maintain the macroeconomic stability of the state.The purpose of the article is to examine fiscal decentralization as a key factor in strengthening the financial capacity of Ukraine's regions.Research objectives are to analyze the impact of decentralization on the revenue base structure of local budgets, to identify the advantages and risks of this process under contemporary conditions, and to substantiate directions for improving interbudgetary relations mechanisms in order to ensure the stability and autonomy of subnational finances.Research methods: analysis, synthesis, statistical assessments, graphical evaluations, induction, deduction, scientific abstraction.Main research findings. The article examines the role of fiscal decentralization as a key factor in strengthening the financial capacity of Ukraine's regions, and analyzes the transformation of the revenue base structure of local budgets as well as interbudgetary relations mechanisms under contemporary conditions. It is substantiated that the reform contributes to enhancing the autonomy of subnational levels of government, more efficient satisfaction of local needs, and reduction of dependence on central transfers, although it is accompanied by risks of deepening regional disparities and fiscal asymmetry. Directions are proposed for improving financial equalization instruments, revising the distribution of revenue sources, and strengthening monitoring to ensure a balance between the financial independence of communities and the macroeconomic stability of the state.Field of application of the results: The findings of the study can be applied in the process of shaping and improving the state's budgetary policy, developing normative–legal acts in the sphere of interbudgetary relations, as well as in preparing recommendations for local self–government bodies aimed at enhancing the financial capacity of territorial communities. In addition, the materials of the article hold practical value for research activities in the fields of public finance, regional economics, and decentralized governance.
A considerable proportion of perishable goods, including fruits and vegetables, deteriorate prior to reaching customers. Inadequate refrigeration infrastructure, particularly in developing nations with arid climates and markets distant from agricultural sources, accounts for most of these losses. A food cold chain has three primary phases: pre-cooling, cold storage, and refrigerated transportation. All phases of the cold chain rely fundamentally on refrigeration to preserve perishable products at designated temperatures, relative humidity, and CO2 concentrations, thus prolonging their shelf life. Solar-driven or aided refrigeration systems use solar energy to power cooling systems and preserve the food in the cold chain. These systems are especially beneficial in off-grid or developing areas for preserving perishable goods such as fruits, vegetables, and other food items, mitigating postharvest losses that can exceed 30–50% in areas with inconsistent energy supplies. Despite progress in efficiency and scalability, numerous research gaps remain across technological, economic, social, policy, and regional dimensions, including technical aspects, optimization, and integration. There is a need to enhance energy-efficient designs, particularly by managing solar intermittency to address non-uniform cooling, which leads to inconsistent ripening and spoilage, and by integrating sustainable refrigerants to mitigate environmental impact. Further development is necessary for micro-scale, transportable, or decentralized systems designed for small farms, while economic and financing obstacles include high upfront costs and limited financial accessibility. Substantial deficiencies exist in creating affordable models and funding channels for small-scale agriculturalists. Addressing these deficiencies could expedite adoption, thereby reducing global food loss and waste (accounting for 8–10% of GHG emissions) while improving food security. Future research must emphasize multidisciplinary methodologies that amalgamate engineering, economics, and social sciences to provide comprehensive solutions.
Aleksandar Šević, Željko Šević, Athanasios Fassas, Panayiotis Tzeremes
There is a strong impetus to make cryptocurrencies more environmentally friendly, and in our study it is has been analyzed whether commodity price shocks have varying impacts on clean and dirty cryptocurrency interconnectedness before, during and after the COVID-19 pandemic. Using the decomposed and partial connectedness measure we evaluate the connectedness of oil price shocks, demand, supply and risk, as well as five clean and five dirty cryptocurrencies from October 2017 until April 2024. The spikes in demand and disruptions in oil supply lead to price increases. Oil shocks have the largest impact on sampled crypto products during the COVID-19 period, as opposed to pre- and post-pandemic years, and they demonstrate a stronger influence on selected cryptocurrencies than internal crypto-to-crypto dynamics. During the crisis, the difference between clean and dirty cryptocurrencies becomes less relevant when compared to no-crisis periods. We also find that clean cryptocurrencies are net recipients of shocks, while dirty counterparts, dominated by Bitcoin and Ethereum, are net transmitters, especially during the recovery phase. Our findings are relevant for supporting the transition to clean cryptocurrencies and contribute to a better understanding of dynamic interconnectedness. • Examines the decomposed and partial connectedness • Uses time-varying parameter vector autoregression (TVP-VAR) models • Highlights the heterogeneity in cryptos’ responses to oil price fluctuations • Total Connectedness Index peaks during the COVID-19 pandemic • The distinctions between clean and dirty cryptocurrencies reemerged post-COVID
Abstract The peer-reviewed journal article imposes structural constraints on the dissemination, validation, and reuse of research outputs. Intermediate results, negative findings, methodological refinements, and replication attempts are systematically underrepresented in published literature, limiting visibility into ongoing research activity for both scientists and mission-driven funders. Here we present Carrierwave, an open infrastructure for continuous, granular scientific communication built on structured research objects (ROs), cryptographic provenance, blockchain-based attribution, and programmable incentive mechanisms. Each RO represents an atomic unit of scientific output -- a single experimental result, negative finding, dataset, protocol, or replication -- that is hashed for content integrity, stored in a persistent database, and optionally minted as an ERC-721 non-fungible token on the Ethereum blockchain. The system includes an on-chain bounty pool enabling funders to directly incentivize specific research activities, and an automated analysis layer that synthesizes disclosed ROs into continuously updated research landscape maps. We describe the system architecture, report on its implementation and deployment on Ethereum mainnet, and present a quantitative analysis of disease-specific publication frequency demonstrating the information latency problem that Carrierwave addresses. The distribution of publication frequency across disease areas is highly skewed, with the majority of conditions represented by fewer than four publications per year in high-impact biology journals. For diseases in the long tail, the interval between successive publications may span months or years. Publication frequency correlates poorly with disease burden, instead reflecting historical research community size and advocacy momentum. By reducing the unit of communication to the individual research object and eliminating editorial gatekeeping as a prerequisite for disclosure, Carrierwave increases the effective sampling rate of scientific activity in precisely the domains where publication-based visibility is most sparse. The system is live at https://carrierwave.org .
Multi-agent coordination and communication models. Multi-agent coordination is reviewed in terms of thearchitectures and algorithms needed to provide autonomous agents with the ability to act as a coordinated force incomplex and dynamic environments. As agentic systems evolve into networks with goals, compelling isolateddecision-making units to become more integrated, structured coordination, and effective communication systems arebecoming increasingly important. This paper compares the available multi-agent coordination models, such ascentralized, decentralized, hierarchical, and swarm-based models, and determines their shortcomings in scalability,latency control, and flexible cooperation. We present a hierarchical classification of organizational strategies ofcoordination and communication protocols specific to the high-autonomy setting, whereby agents are required tonegotiate tasks and settle conflicts as well as exchange contextual information on-the-fly. The paper identifies newproblems in interoperability, trust management, and communication overheads that limit large-scale collaborativeintelligence systems.To solve these shortcomings, the paper presents a new multi-layer collaborative structure combining the perception,reasoning, coordination, and adaptive communication layers with the view of improving the efficiency of the collectivedecision-making. A performance evaluation system is proposed, and it specifies quantifiable indicators like the latencyof coordination, communication overhead, efficiency in task allocation, and the speed of learning adaptation. Thepresented model shows that robustness and scalability can be greatly enhanced by protocol design optimization and adynamic coordination engine in a distributed agent ecosystem, as proposed. This study will help to develop nextgeneration Agentic AI systems that can be trusted to cooperate with other agents and benchmark the competencies andstandards of reliable collaboration in the fields of enterprise automation, finance, robotics, and distributed analytics,thus enhancing the theoretical and practical basis of autonomous collective intelligence.
As multi-agent Large Language Model (LLM) systems scale, evaluating their emergent coordination dynamics becomes increasingly critical. However, current evaluation paradigms-focused on single agents or small, explicitly structured groups-fail to capture the self-organization and viral information dynamics that arise in large, decentralized populations. We introduce a systematic evaluation framework to benchmark role specialization, information diffusion, and cooperative task resolution in open agent environments. We demonstrate this framework on the MoltBook Observatory Archive, a dataset of 2.73M interactions among 90,704 autonomous agents, establishing quantitative baselines for emergent coordination. Our evaluation reveals a pronounced core-periphery structure (silhouette 0.91), heavy-tailed cascade distributions ($α= 2.57$), and severe coordination overhead in decentralized task resolution (Cohen's $d = -0.88$ against a single-agent baseline). By providing standardized evaluation tasks and empirical baselines, our framework enables the rigorous comparison of future multi-agent protocols and establishes evaluation itself as an object of scientific study.
Tanusree Sharma, Yujin Potter, Jongwon Park, Yiren Liu · 9 authors
A major criticism of AI development is the lack of transparency, such as, inadequate documentation and traceability in its design and decision-making processes, leading to adverse outcomes including discrimination, lack of inclusivity and representation, and breaches of legal regulations. Underserved populations, in particular, are disproportionately affected by these design decisions. Furthermore, traditional social science techniques such as interviews, focus groups, and surveys struggle to adequately capture user needs and expectations in the digital era, due to their inherent limitations in deliberation, consensus-building, and providing consistent insights. We developed a democratic decision framework utilizing Decentralized Autonomous Organization (DAO) to enable underserved groups to deliberate and reach a consensus on key AI issues. To assess our proposed democratic decision mechanism, we conducted a case study on updating AI model specification based on diverse stakeholders input. We focus on reducing stereotypical biases in text-to-image systems, particularly gender bias in image generation from text prompts. We designed and experimented various governance configurations, including decision aggregation schemes and decision power, to examine how democratic processes could guide updates to AI model. Through a 2 × 2 experimental design, we tested various aggregation schemes (ranked vs. quadratic) and decision power distribution (equal vs. 20/80 differential) in a randomized online experiment (n=177) with participants from the global south and people with disabilities, to study how the varying governance mechanisms impact people's perceptions of the decision-making processes and resulting output of the AI Model specification. Our results indicate that despite their diverse backgrounds, participants showed convergence in deliberations on several aspects, including user control over image generation, multiple output options for user selection, and the social appropriateness and accuracy of generated images. Our study underscores the importance of use of appropriate governance in democratic decision-making in AI alignment. Notably, the combination of quadratic preference aggregation method which gives minorities more voice and equal decision power distribution, was perceived as a fairer and democratic approach.
Open access
Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Federated Learning (FL) enables collaborative training of medical AI models across hospitals without centralizing patient data. However, the exchange of model updates exposes critical vulnerabilities: gradient inversion attacks can reconstruct patient information, Byzantine clients can poison the global model, and the \emph{Harvest Now, Decrypt Later} (HNDL) threat renders today's encrypted traffic vulnerable to future quantum adversaries.We introduce \textbf{ZKFL-PQ} (\emph{Zero-Knowledge Federated Learning, Post-Quantum}), a three-tiered cryptographic protocol that hybridizes (i) ML-KEM (FIPS~203) for quantum-resistant key encapsulation, (ii) lattice-based Zero-Knowledge Proofs for verifiable \emph{norm-constrained} gradient integrity, and (iii) BFV homomorphic encryption for privacy-preserving aggregation. We formalize the security model and prove correctness and zero-knowledge properties under the Module-LWE, Ring-LWE, and SIS assumptions \emph{in the classical random oracle model}. We evaluate ZKFL-PQ on synthetic medical imaging data across 5 federated clients over 10 training rounds. Our protocol achieves \textbf{100\% rejection of norm-violating updates} while maintaining model accuracy at 100\%, compared to a catastrophic drop to 23\% under standard FL. The computational overhead (factor $\sim$20$\times$) is analyzed and shown to be compatible with clinical research workflows operating on daily or weekly training cycles. We emphasize that the current defense guarantees rejection of large-norm malicious updates; robustness against subtle low-norm or directional poisoning remains future work.
Applies the void Péclet framework to computational complexity theory. Demonstrates that zero-knowledge proofs instantiate the conjugacy theorem at equality, that the random 3-SAT satisfiability phase transition is a Pe=V* boundary analogous to the Wien peak in thermodynamics, and that P≠NP is the kill condition preventing Pe→∞ catastrophe in computational systems. Closes the Landauer-Arrow-Crypto triangle (§§33+35+37).
Dense retrieval services increasingly underpin semantic search, recommendation, and retrieval-augmented generation, yet clients typically receive only a top-$k$ list with no auditable evidence of how it was produced. We present V3DB, a verifiable, versioned vector-search service that enables audit-on-demand correctness checks for approximate nearest-neighbour (ANN) retrieval executed by a potentially untrusted service provider. V3DB commits to each corpus snapshot and standardises an IVF-PQ search pipeline into a fixed-shape, five-step query semantics. Given a public snapshot commitment and a query embedding, the service returns the top-$k$ payloads and, when challenged, produces a succinct zero-knowledge proof that the output is exactly the result of executing the published semantics on the committed snapshot -- without revealing the embedding corpus or private index contents. To make proving practical, V3DB avoids costly in-circuit sorting and random access by combining multiset equality/inclusion checks with lightweight boundary conditions. Our prototype implementation based on Plonky2 achieves up to $22\times$ faster proving and up to $40\%$ lower peak memory consumption than the circuit-only baseline, with millisecond-level verification time. Github Repo at https://github.com/TabibitoQZP/zk-IVF-PQ.
This chapter explores the intersection of data governance, data quality, and blockchain technology, presenting a paradigm shift from traditional centralized data management to decentralized architectures. As data solidifies its role as a critical asset, ensuring its integrity and trustworthiness has become paramount. We begin by establishing the principles of data governance and quality, highlighting the limitations of conventional systems that rely on trusted intermediaries, which introduce single points of failure and censorship risks. Blockchain technology emerges as a compelling alternative, offering a decentralized, immutable, and transparent ledger that fundamentally enhances data integrity and trust. Through an analysis of its core components – including cryptographic hashing, consensus mechanisms, and distributed networks – we examine the inherent advantages and disadvantages of blockchain. The chapter delves into the functional extensions of blockchain, such as smart contracts and Decentralized Autonomous Organizations (DAOs), which enable automated, transparent, and autonomous governance models. However, the transition to blockchain is not without its challenges. We critically assess issues of scalability, data privacy, the “oracle problem,” and the “garbage in, garbage out” principle, which persist in decentralized environments. The chapter concludes that the “quality” of blockchain as a data management solution is not absolute but is contingent upon the specific requirements of the use case, demanding a careful evaluation of its trade-offs.
Data integration among various stakeholders in the healthcare space remains a challenge, despite the impressive advances in Health AI in the past decade. There is a lot of ``messy'' non-standard but structured data that are continually being collected from personal health devices. While efforts such as the Fast Healthcare Interoperability of Resources (FHIR) are underway in standardizing the data representation formats, there is currently a gap in the standard in addressing the health data ecosystem's decentralized nature. As we see explosive growth in chronic diseases such as diabetes, healthcare providers need Observations of Daily Living (ODL) of their patients to treat them effectively. The best way to obtain ODL is through personal health devices. However, such devices are manufactured by various device makers, and they may not follow standards or integrate with existing Electronic Health Record (EHR) systems. It is also imperative that any data sharing that happens will occur in a secure and trustworthy environment, without being too restrictive, i.e., tied to a particular EHR vendor. This paper presents a scalable solution to bridge this gap using a system that implements semantic web and blockchain technologies. Our solution uses FHIR compliant semantic web based data templates in conjunction with smart contracts on the blockchain to provide healthcare providers with insights on their patients' daily activity that cannot be readily determined solely through patient encounters at the clinic.
Phat T. Tran-Truong, Vinh X. Q. Nguyen, Ha X. Son, Phien Nguyen-Ngoc · 6 authors
The proliferation of IoT and V2X systems generates unprecedented sensitive data at the network edge, demanding privacy-preserving architectures that enable secure sharing without exposing raw information. Contemporary solutions face a fundamental privacy-efficiency-trust trilemma: achieving strong privacy guarantees, computational efficiency for resource-constrained devices, and decentralized trust simultaneously remains intractable with single-paradigm approaches. This survey systematically analyzes 75 technical papers (2007--2025) through a novel three-dimensional taxonomy classifying architectures into Decentralized Computation, Cryptography-based, and Distributed Ledger approaches. Temporal analysis reveals dramatic acceleration during 2024--2025, with 48% of all papers published in this period -- Decentralized Computation dominates at 44% of contributions and 59% of 2025 publications. Comprehensive Security Threat Mapping and Technology Maturity Assessment demonstrate that mature solutions occupy narrow design regions excelling in one or two dimensions while compromising others, conclusively validating the trilemma hypothesis. We identify emerging hybrid architectures combining complementary paradigms as the essential path forward. Critical challenges including security guarantee composition across layers, multi-layer coordination overhead minimization, and post-quantum security integration must be addressed for practical deployment in next-generation intelligent transportation systems and IoT ecosystems.
Climate change has intensified the urgency of effective carbon sink solutions, yet the integration of Information and Communications Technologies (ICT) in these systems remains fragmented despite its transformative potential. This paper provides a comprehensive analysis of ICT applications in carbon sink projects from both economic and engineering perspectives, a dual lens approach rarely explored in the existing literature. In carbon trading, blockchain has improved transaction speed by 40%, while AI-based optimizations have reduced operational costs by 15% in projects such as Petra Nova.Through systematic examination, we identify three key findings: (1) ICT transforms carbon economics through digital financing platforms and blockchain-based trading systems, with AI enhancing price prediction, though data interoperability remains challenging; (2) digital technologies advance both natural and artificial sequestration from forest monitoring to Carbon Capture, Use and Storage (CCUS) optimization, yet lack integrated real-time control solutions; (3) realizing ICT's full potential requires addressing its environmental costs, strengthening policy support, and fostering interdisciplinary collaboration. By bridging the economic engineering divide and mapping current applications alongside future opportunities, this paper demonstrates that deeper integration of digital technologies is essential to scale carbon sink solutions to meet climate targets.
Este boletim quinzenal gratuito visa analisar o comportamento do Bitcoin, um ativo financeiro digital, oferecendo notícias, análises gráficas e informações sobre as mais recentes novidades, softwares e aplicativos relacionados a essa criptomoeda. Nosso objetivo é enriquecer as discussões em torno da cultura do Bitcoin, colaborando com a Amauta, uma instituição de economia criativa que busca disseminar conhecimento sobre inovação, educação e finanças na comunidade acadêmica e empresarial. Esperamos que este trabalho represente uma contribuição valiosa para o debate. Reconhecemos a importância do Bitcoin e seu impacto na economia global, motivo pelo qual nos dedicamos a fornecer informações atualizadas aos nossos leitores. Acreditamos que ao promover discussões e compreensão sobre o Bitcoin, podemos incentivar a adoção e o uso responsável dessa tecnologia disruptiva. Para além das análises e informações sobre o Bitcoin, incentivamos ativamente nossos leitores a se educarem sobre finanças pessoais e investimentos. Acreditamos que, munidos do conhecimento adequado, todos podem tomar decisões financeiras inteligentes e bem informadas. Comprometemo-nos a fornecer informações de alta qualidade e precisas, esforçando-nos para manter nossos leitores atualizados sobre as últimas tendências e desenvolvimentos no mundo do Bitcoin. Esperamos que este relatório seja do seu agrado e contribua para uma compreensão mais aprofundada do Bitcoin e das finanças pessoais em geral.
For six decades, the instruments used to assess student learning rested on an assumption so embedded in institutional design that it rarely required defense: that producing a cognitive artifact and possessing the knowledge it demonstrated were the same act. When a student wrote an essay or completed an examination, the quality of what they produced approximately tracked what they actually knew. Grades worked as a proxy for knowledge because the production conditions of the era made them so. That assumption is no longer structurally valid.This paper argues that the emergence of large language models as practical cognitive production tools has reorganized the relationship between knowledge and artifact at its foundation. We formalize this reorganization through two production functions -- one governing the analog era of cognitive work (1960-2020), one governing the agentic era (2020-present) -- and use them to identify what we term the Economic Inversion of Cognitive Production: knowledge has not diminished in value but has changed its economic role entirely, from the substance of output to the condition of production. Simultaneously, two variables are approaching zero -- the marginal cost of machine intelligence and the signal value of the artifact -- creating what we term the double zero problem. Together these produce a structural validity crisis, a fundamental breakdown in what grades actually measure, for assessment systems designed to measure artifact production as a proxy for knowledge state.The practical implication is direct. Assessment instruments built on the analog production model are no longer measuring what institutions, employers, and credentialing bodies believe they are measuring. This paper establishes the diagnostic case for assessment redesign. It does not propose a replacement model -- that work requires a normative account of educational purpose that the formal argument here cannot generate alone. But the diagnostic case is now structurally complete, and the burden of proof has shifted to institutions that continue to operate artifact-based assessment as their primary measure of student knowledge.
Open access
Educational Theory and Curriculum Studies
Educational Leadership and Innovation
Innovations in Education and Learning Technologies
Munir A. ADEWOYE, Ahmed Aliyu, Usman Ali, Abdulrasheed Jimoh
Food is fundamental to human survival, we eat to live, sustaining ourselves with nutrition that meets our daily needs. Food security, defined as universal physical and financial access to safe and nutritious food, depends heavily on efficient supply chains. However, ensuring this security faces significant challenges in tracking and transparency. This study examines two critical problems in blockchain-based food supply chain tracing: privacy preservation and scalability. While blockchain technology combined with Internet of Things (IoT) devices offers promising solutions for real-time monitoring, transparency, and fraud prevention in agricultural supply chains, questions remain about balancing computational efficiency with privacy protection, achieving scalable integration across multi-actor supply chains without compromising traceability, and implementing these systems in resource limited environments. Through a comprehensive review of current research, this study identifies emerging technologies like Zero Knowledge Proofs (ZKPs) and ZK-Rollups that enhance both throughput and privacy in decentralised systems. The research presents layered architectural models integrating blockchain ledgers, off-chain storage, IoT sensors, and cryptographic protocols to enable secure and scalable traceability. These models support compliance verification while protecting sensitive data and can be adapted for low-resource contexts. The findings demonstrate that scalable, privacy-preserving blockchain technologies can transform agricultural traceability, empowering supply chain stakeholders while maintaining data confidentiality and integrity. The study also identifies future research needs, including cross-chain interoperability, policy integration, cost-benefit analysis for smallholder farmers, and field validation.