This thematic issue examines how artificial intelligence, metaverse imaginaries, and decentralized Web3 systems have become arenas for states to build infrastructures, set technical standards, and project geopolitical power. It reconceptualizes technology not merely as an object of regulation but as a medium of statecraft through which sovereignty, security, and leadership are contested and remade in a multipolar digital order. This issue analyzes three interconnected dimensions: (a) the impact of global AI competition on state-making processes, enhancing coercive, extractive, delivery, and informational capacities similar to earlier state formation phases; (b) the nature of technological leadership as a relational and dynamic process influenced by interactions between leading and following states; and (c) the role of security logics in transforming external rivalry and internal governance through securitization. Through comparative analysis of the US, China, the EU, and emerging economies, this issue explores how diverse political systems encode openness, sovereignty, and accountability into their technological regimes, demonstrating that technological governance is inseparable from state-making. The contributions map competing logicsâsovereign, liberal, entrepreneurialâshowing that digital governance emerges not as convergence toward a singular model but as recursive entanglements of imagination and infrastructure.
Decentralized Finance (DeFi) has emerged as a transformative force in global finance, offering trustless, blockchain-based alternatives to traditional intermediated systems. This paper examines how DeFi innovations â such as tokenized assets, decentralized exchanges (DEXs), and automated smart contracts â are reshaping corporate fundraising. It analyzes the efficiency, accessibility, and regulatory implications of using decentralized protocols for capital raising, comparing DeFi mechanisms (e.g., IDOs, security token offerings, DAOs) with traditional equity and debt issuance models. Using case studies and data from leading DeFi ecosystems (Ethereum, Polygon, Solana) and corporate blockchain pilots, we evaluate DeFiâs impact on fundraising costs, investor reach, and transparency. The findings suggest that while DeFi offers reduced friction and democratized access to capital, challenges in regulation, governance, and investor protection must be resolved before large-scale corporate adoption.
Luqman Hakim Abdul Majid, Yudi Fernando, Ming K. Lim, MingâLang Tseng
Achieving carbon neutrality in supply chains is a complex challenge, given the urgency of climate change mitigation. This paper explores how non-fungible tokens, carbon transparency, and blockchain carbon credits can support reaching carbon neutrality. We surveyed 140 Malaysian semiconductor firms involved in carbon-neutrality initiatives and conducted necessary condition analysis using the bottleneck technique. Our results show that non-fungible tokens enhance carbon transparency by providing traceable, verifiable carbon data. This transparency positively influences the issuance of blockchain carbon credits and carbon neutrality, though its effect is limited. Carbon transparency serves as a mediator between non-fungible tokens and carbon neutrality, underscoring its role in leveraging digital tools for effective carbon management. However, regulatory compliance and scalability challenges hinder both carbon transparency and digital transformation. This paper provides a foundation for integrating non-fungible tokens and blockchain technology into supply chains, offering policymakers pursuing transparent decarbonisation strategies valuable insights.
This paper investigates how Web3 technologies, such as blockchain, NFTs, and the metaverse, can drive Business Model Innovation (BMI) by enabling new forms of value creation, delivery, and capture. While the strategic potential of Web3 has been widely discussed, there remains a lack of operational tools to guide its implementation in real-world business contexts. To address this gap, we introduce the Web3 Value Exploitation De sign Model (Web3 VEDM), a step-by-step framework grounded in the GUEST methodology. The model is designed to support engineering managers in assessing Web3 readiness, aligning stakeholders, and developing decentralized business models. The framework is empirically validated through a real-world case study in the agri-food sector, offering actionable insights into how organizations can leverage Web3 to transition from centralized to decentralized, participatory ecosystems. The study contributes both theoretically and practically by bridging the gap between conceptual exploration and structured application of Web3 in business transformation.
Henry Segun Uwabor, Igba Emmanuel, Onuh Matthew Ijiga
The emergence of decentralized finance (DeFi) has transformed global financial ecosystems by enabling transparent, permissionless, and automated investment systems. However, the inherent volatility, regulatory uncertainty, and data complexity within DeFi ecosystems pose significant challenges for risk modeling and compliance assurance. This review explores the integration of AI-powered predictive frameworks to enhance risk assessment, fraud detection, and regulatory compliance in decentralized finance investment systems. By leveraging machine learning (ML), deep learning (DL), and natural language processing (NLP) models, the study examines how predictive analytics can proactively identify anomalous transactions, assess smart contract vulnerabilities, and optimize portfolio risk exposure. The paper also evaluates how AI-driven systems can align DeFi operations with emerging regulatory frameworks, including KYC/AML protocols, data protection standards, and algorithmic auditing requirements. Additionally, the review highlights the role of explainable AI (XAI) in promoting transparency, interpretability, and trust among regulators and investors. Through a synthesis of existing literature and real-world applications, this paper presents a comprehensive framework illustrating how predictive AI technologies can bridge the gap between financial innovation and regulatory governance in DeFi. The findings underscore the potential of intelligent, adaptive, and compliant DeFi systems capable of ensuring sustainable growth, investor protection, and systemic stability in the evolving digital financial landscape.
In an age where sustainability is of paramount importance, the significance of both high-performance computing and intelligent algorithms cannot be understated. Yet, these domains often demand hefty computational power, translating to substantial energy usage and potentially sidelining less robust computing systems. It's evident that we need an approach that is more encompassing, scalable, and eco-friendly for intelligent algorithm development and implementation. The strategy we present in this paper offers a compelling answer to these issues. We unveil a fresh framework that seamlessly melds high-performance cluster computing with intelligent algorithms, all within a blockchain infrastructure. This promotes both efficiency and a broad-based participation. At its core, our design integrates an evolved proof-of-work consensus process, which links computational efforts directly to rewards for producing blocks. This ensures both optimal resource use and participation from a wide spectrum of computational capacities. Additionally, our approach incorporates a dynamic 'trust rating' that evolves based on a track record of accurate block validations. This rating determines the likelihood of a node being chosen for block generation, creating a merit-based system that recognizes and rewards genuine and precise contributions. To level the playing field further, we suggest a statistical 'draw' system, allowing even less powerful nodes a chance to be part of the block creation process.
Soufiane El Amine El Alami, Abderazzak Mouiha, Abdelatif Hafid, Ahmed El Hilali Alaoui
This systematic review examines how machine learning (ML) and deep learning (DL) have transformed forecasting, decision-making, and financial modelling, promoting innovation and efficiency in financial systems. Following PRISMA 2020 guidelines, we analyze 22 peer-reviewed and open-access articles (2024 to 2026) indexed in Scopus, applying ML and DL models across credit risk prediction, cryptocurrency, asset pricing, and macroeconomic policy modeling. The most used models include Random Forest, XG-Boost, Support Vector Machine, Long Short-Term Memory (LSTM), Bidirectional LSTM, Convolutional Neural Network (CNN), and hybrid or ensemble approaches combining statistical and AI methods. ML and DL techniques outperform traditional models by capturing nonlinear dependencies and enhancing predictive accuracy, while explainable AI methods (e.g., SHAP and feature importance analysis) improve transparency and interpretability. Emerging trends include cross-domain applications and the integration of responsible AI in finance. Despite notable progress, challenges remain in interpretability, generalizability, and data quality. Overall, this review provides a comprehensive overview of AI-driven computational finance and outlines future research directions.
Blockchain security is threatened by selfish mining, where a miner (operator) deviates from the protocol to increase their revenue. Selfish mining is exacerbated by adverse conditions: rushing (network propagation advantage for the selfish miner), varying block rewards due to block contents, called miner extractable value (MEV), and petty-compliant miners who accept bribes from the selfish miner. The state-of-the-art selfish-mining-resistant blockchain protocol, Colordag, does not treat these adverse conditions and was proven secure only when its latency is impractically high. We present MAD-DAG, Mutually-Assured-Destruction Directed-Acyclic-Graph, the first practical protocol to counter selfish mining under adverse conditions. MAD-DAG achieves this thanks to its novel ledger function, which discards the contents of equal-length chains competing to be the longest. We analyze selfish mining in both Colordag and MAD-DAG by modeling a rational miner using a Markov Decision Process (MDP). We obtain a tractable model for both by developing conservative reward rules that favor the selfish miner to yield an upper bound on selfish mining revenue. To the best of our knowledge, this is the first tractable model of selfish mining in a practical DAG-based blockchain. This enables us to obtain a lower bound on the security threshold, the minimum fraction of computational power a miner needs in order to profit from selfish mining. MAD-DAG withstands adverse conditions under which Colordag and Bitcoin fail, while otherwise maintaining comparable security. For example, with petty-compliant miners and high levels of block reward variability, MAD-DAG's security threshold ranges from 11% to 31%, whereas both Colordag and Bitcoin achieve 0% for all levels.
South Korea faces the dual challenge of managing growing distributed solar energy surpluses and the high energy demand of industries like Bitcoin mining. Leveraging mining operations as a flexible load to monetize this `net-metering surplus' presents a viable synergy, but requires a robust site selection methodology. Traditional GIS-based Multi-Criteria Decision Analysis (MCDA) struggles with subjective weighting and integrating heterogeneous spatial data (areal-level and lattice-level). This thesis develops and implements a Two-Stage Hierarchical Optimization framework to overcome these limitations. Stage 1 (Areal-Level) employs a cost-benefit optimization to determine the optimal number ($K^*$) and combination of regions, maximizing a final adjusted net profit by balancing surplus power revenue against detailed land and non-linear infrastructure costs. Stage 2 (Point-Level) then uses a GIS-based sliding window search within these selected regions, applying topographic (slope $< 6.0^\circ$) and land-use constraints at a 30m resolution to identify physically constructible `unit sites'. The model identified an optimal configuration of $K^*=3$ regions (Yongin, Damyang, Miryang) yielding a maximum potential net profit of approximately \$307 million. Crucially, the Stage 2 screening revealed that Yongin, the most profitable region, was also the most physically constrained, 87\% of sites filtered out. This research contributes a scalable, objective framework for energy infrastructure siting that effectively integrates multi-scale spatial data. It provides a data-driven strategy for policymakers and grid operators (like Korea Electric Power Corporation) to monetize curtailed renewables and enhance grid stability.
The Byzantine Generals Problem, introduced by Lamport, Shostak, and Pease, fundamentally addresses how a distributed system can achieve consensus even when some of its components are unreliable or malicious. This paper delves into the mathematical bounds that govern the solvability and efficiency of Byzantine Agreement (BA) protocols, specifically exploring the role of network topology in these limits. We examine classical impossibility results, such as the n $>$ 3f requirement for unauthenticated synchronous systems and the Fischer-Lynch-Paterson impossibility for deterministic asynchronous systems. Furthermore, we introduce "treasonous topologies" as a conceptual framework to systematically analyze how graph-theoretic properties like connectivity and diameter influence the minimum number of honest nodes required, message complexity, and time complexity. Special attention is paid to authenticated protocols which can relax certain bounds by employing digital signatures. This study elucidates the intricate relationship between adversarial capabilities, network structure, and the inherent mathematical constraints on achieving robust agreement in the presence of malicious nodes. We also touch upon modern applications in blockchain and distributed ledger technologies, where these theoretical bounds translate into practical considerations for security, scalability, and decentralization. A core contribution of this work is the synthesis of these established bounds with a detailed examination of how varying network structures fundamentally dictate protocol design and performance, offering a clearer lens through which to understand the vulnerabilities and strengths of real-world distributed systems.
As the world grapples with climate change and energy insecurity, renewable energy has emerged as a central pillar of sustainable development. However, the transition to renewables faces persistent technological, economic, policy, and social challenges. This paper explores the dual nature of renewable energyâits immense promise and its complex barriersâthrough global trends and India-focused case studies. By analyzing large-scale and decentralized renewable projects, including Bhadla, Pavagada, Rewa, and Kurnool solar parks, as well as microgrid initiatives in Dharnai and Indira Nagar, this study identifies strategic pathways for inclusive and resilient energy futures. The analysis reveals that integrated policies, innovative financing, community participation, and technological innovation are key to maximizing renewable energyâs transformative potential. Key words: climate change, energy, renewable.
Abstract Blockchain technology has become a transformative solution for secure and transparent digital ecosystems. This paper explores how decentralization, cryptographic hashing, distributed consensus, and immutable ledger architecture contribute to advanced data protection in the IT industry. The study integrates findings from existing literature, evaluates blockchainâs practical applications in sectors including finance, healthcare, supply chain, and governance, and examines a proposed multi-layer blockchain framework. The research highlights blockchainâs advantages in enhancing confidentiality, integrity, availability, and auditability, while identifying its limitations such as scalability, regulatory constraints, and environmental impact. Future scope emphasizes integration with AI, IoT, Web 3.0, quantum-resistant models, and cross-chain interoperability. Overall, the study concludes that blockchain is a critical technology for advancing trust-driven IT infrastructures. Keywords Blockchain, Data Security, Transparency, Decentralization, Smart Contracts, IT Industry
La investigaciĂłn analiza la compatibilidad entre los contratos inteligentes basados en tecnologĂa blockchain y el derecho al retracto reconocido en la legislaciĂłn colombiana. Los contratos inteligentes permiten la ejecuciĂłn automĂĄtica de obligaciones mediante cĂłdigos informĂĄticos almacenados en registros descentralizados, lo que garantiza seguridad, transparencia y eliminaciĂłn de intermediarios. Sin embargo, una de sus principales caracterĂsticas es la inmutabilidad de la informaciĂłn registrada, lo que dificulta la modificaciĂłn, suspensiĂłn o reversiĂłn de los acuerdos una vez ejecutados. Esta situaciĂłn genera tensiones con la naturaleza dinĂĄmica de las relaciones contractuales y con mecanismos jurĂdicos de protecciĂłn al consumidor, como el derecho al retracto previsto en el Estatuto del Consumidor colombiano. Dicho derecho permite al consumidor desistir de ciertos contratos dentro de un plazo determinado, especialmente en modalidades de contrataciĂłn donde no existe contacto directo con el producto o servicio. El estudio plantea que la rigidez inherente de la tecnologĂa blockchain puede entrar en conflicto con principios fundamentales del derecho contractual, en particular con las facultades de modificaciĂłn, terminaciĂłn o arrepentimiento reconocidas por la ley. En consecuencia, se reflexiona sobre la necesidad de desarrollar contratos inteligentes que incorporen mecanismos jurĂdicos y tecnolĂłgicos capaces de armonizar la automatizaciĂłn de las obligaciones con la protecciĂłn de los derechos de las partes y la flexibilidad requerida por las relaciones comerciales contemporĂĄneas.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, DongâSeong Kim, JaeâMin Lee
In cyber-physical multi-agent systems, ensuring secure and decentralized cooperation among autonomous agents is crucial for maintaining system integrity and operational reliability. However, the presence of malicious or selfish agents in these systems often disrupts message delivery and trust. This paper presents a blockchain-based reputation management framework that integrates adaptive trust scoring, decentralized relay selection, and non-transferable soulbound tokens to incentivize cooperation and build reputation. By combining off-chain trust evaluation with on-chain transparency through the PureChain permissioned blockchain, the framework achieves secure, tamper-proof reputation management. Experimental results show average trust score above 80% under varying proportions of malicious behavior (20% to 80%). The proposed PureChain blockchain also achieved approximately 7.7 times lower transaction latency and 1.4 times higher throughput compared to Ethereum Sepolia, making it suitable for resilient communication in cyber-physical multi-agent systems.
Hessah A. Alsalamah, Saeed Alqahtani, Ghazlan Al-Arifi, Jana Al-Sadhan · 8 authors
Assisted Reproductive Technology (ART), particularly In Vitro Fertilization (IVF), generates highly sensitive medical data classified as Protected Health Information (PHI) under international privacy and data protection laws. Ensuring the secure, transparent, and ethically governed management of this data is both essential and legally mandated. However, conventional Electronic Medical Record (EMR) systems often present significant challenges, including data-integrity risks, unauthorized access, and limited patient controlâissues that become especially critical in contexts such as fertility preservation for cancer patients. EmbryoTrust introduces a blockchain-based framework designed to ensure the confidentiality, integrity, and availability of IVF-related information through a private, permissioned network integrated with role-based access control (RBAC). Smart contracts, implemented in Solidity on the Ethereum platform, verify spousal identities and enforce data immutability in compliance with religious legislation and ethical regulations. Off-chain data are stored in MongoDB for scalable, privacy-preserving management, while on-chain summaries provide tamper-evident traceability and verifiable auditability. The system was deployed and validated on the Ethereum HoleĆĄky testnet using Solidity 0.8.21 and Node.js 18.17, achieving an average transaction-confirmation time of 2.8 s, 99.9% uptime and a 95% user-satisfaction rate. Functional, integration, and usability testing confirmed secure and efficient data handling with minimal computational overhead. Comparative analysis demonstrated that the hybrid on-/off-chain architecture reduces latency and gas costs while maintaining automated compliance enforcement. The modular design enables adaptation to other jurisdictions by reconfiguring ethical and regulatory parameters within the smart-contract layer, ensuring flexibility for global deployment. Overall, the EmbryoTrust framework illustrates how blockchain logic can technically enforce medical and ethical rules in real time, providing a reproducible model for secure, culturally compliant, and privacy-preserving digital-health information management. Its alignment with Saudi Vision 2030 and the Wold Health Organization (WHO) Global Strategy on Digital Health 2020â2025 highlights its potential as a scalable solution for next-generation ART information systems.
Message spoofing and denial-of-service (DoS) attacks threaten vehicular network security by disrupting communication channels and falsifying safety-critical data. Traditional intrusion detection systems (IDS) exhibit high computational overhead and limited adaptability to evolving attack patterns. This paper presents a hybrid security framework integrating Language Agent Models (LAM) with a dual-layer blockchain architecture for real-time threat detection in Internet of Vehicles (IoV) networks. The LAM operates on edge devices to analyze heterogeneous data streams from CAN bus, V2X, and GPS sources. It identifies spoofing and DoS anomalies through transformer-based attention mechanisms with fewer than 1 billion parameters. The dual-layer blockchain combines Proof-of-Authority-and-Association (PoA2) consensus at layer 1 with zero-knowledge rollup (zk-Rollup) at layer 2. This architecture ensures tamper-proof alert logging while reducing on-chain storage overhead. The PoA2mechanism employs pre-authenticated validators to achieve microsecond-scale transaction finality. The zk-Rollup layer aggregates alert transactions into cryptographic validity proofs, minimizing blockchain storage requirements. Performance evaluation demonstrates the frameworkâs effectiveness across multiple metrics. Detection accuracy reaches 95.7% on the CICIoV2024 dataset and 96.9% on the Car-Hacking dataset. Precision exceeds 97% with F1-scores above 95% on both benchmarks. The system maintains false positive rates below 5.2%. End-to-end response latency remains under 5 milliseconds (ms), meeting real-time safety requirements. The blockchain layer processes over 2,187 transactions per second with 25 validator nodes. Storage optimization achieves a 92% reduction in on-chain data volume. Energy consumption decreases by 4.3 times compared to cloud-hosted language models. The proposed architecture provides deterministic threat detection with cryptographic auditability for large-scale IoV deployments.
Although distributed ledger technologies (DLTs) have transformed financial sectors, their manufacturing applications lack systematic maturity assessment frameworks. Previous reviews identified DLT benefits but show critical gaps, including a lack of quantitative maturity metrics, insufficient categorization of use cases, and limited platform-specific comparative analysis. This systematic literature review addresses these gaps through three key contributions: (i) a novel Distributed Ledger Technology Maturity Level (DLTML) framework, (ii) a four-category manufacturing taxonomy, and (iii) platform-specific implementation analysis. Analyzing 60 primary studies (2018â2025) using Kitchenhamâs guidelines, Wohlinâs snowballing, and Treiblmaierâs assessment framework, we answer the following: (i) What are the categories of use of DLT in manufacturing? (ii) What features of DLT are key enablers and what specific challenges have been addressed in current manufacturing solutions? (iii) What is the level of technological maturity of DLT applications in manufacturing according to the existing literature? The category Process Execution Tracking dominates (80 % of the studies), followed by Provenance (65 %), Ownership Management (30 %) and Payment Management (25 %). Smart contracts are the main enablers (81. 67 %), followed by decentralization (48.33 %). Governance mechanisms remain unaddressed, and interoperability progress is limited. Hyperledger Fabric leads privacy-sensitive scenarios (55 %), while Ethereum dominates transparency-focused applications (38 %). DLTML assessment shows that 61.66 % achieve intermediate maturity (DLTML-3), 20 % achieve high-fidelity prototypes (DLTML-4), but none achieves verified operational deployment (DLTML-5). This study provides evidence-based guidance for researchers and decision makers pursuing the adoption of DLT in manufacturing.
The paper explores the possibility of expanding the use of end-to-end encryption protocols based on the Double Ratchet algorithm in applications with low trust in the server, particularly in turn-based games and strategic interactions. The relevance of the research is due to the growing need for secure communication in cyberattacks, especially during military operations. The field of end-to-end encryption requires the study of additional applications beyond the usual ones, such as encrypted communication in text messengers. The developed implementation of the protocol can be safely used in any applications that aim to implement end-to-end encryption and satisfy the criterion of session ephemerality (in cases where secrets are stored outside a secure environment). The implemented server supports ephemeral sessions, which guarantee minimal risks of information compromise, and uses digital signatures (EdDSA) for user authentication. Logical routing of requests ensures efficient message transmission in secure scenarios. The choice of the classic game of checkers as an example allowed the authors to effectively demonstrate the advantages of end-to-end encryption and the capabilities of the implemented protocol. All cryptographic operations, including key generation, encryption and decryption of messages, are successfully performed on client devices. It is important to improve error handling mechanisms and optimize the operation of WebAssembly. An interesting area of further research is the creation of zero-knowledge proof mechanisms to prevent Man-In-The-Middle attacks during the creation of a shared secret, optimizing integration with cryptographic hardware security modules (HSM), and exploring the scalability of the solution. The proposed approach can be used to solve real-world information security problems where trust in the data transmission channel is critically important. Thus, the work has created a comprehensive solution that includes a cryptographic protocol, a backend, and a web client, which demonstrates the viability of end-to-end encryption in browser environments and multiplayer games. The work can be used as a basis for further research and development in the field of security of communication systems and privacy in multiplayer games.
Abstract We present a spatial analysis of Bitcoin-accepting merchants using BTC Map, a global crowdsourced dataset built on OpenStreetMap, to provide ground-level evidence on Bitcoinâs payment ecosystem. While prior research emphasizes macroeconomic drivers, our analysis of approximately 11,000 merchants shows that local adoption is more strongly shaped by community dynamics and sectoral niches. Acknowledging quality variance in crowdsourced data, we focus on verified regional clusters. We find a global concentration of adoption in the hospitality sector, localised clusters driven by grassroots initiatives rather than national policy and significant presence in alternative healthcare and IT services. These findings highlight the limits of top-down interventions such as El Salvadorâs legal tender law and underscore the role of social networks in sustaining adoption. By contrasting spatial micro-level evidence with national studies, this work positions merchant data as a key lens for understanding Bitcoinâs evolving role as a medium of exchange.
Graph-structured data has become central to modern analytics, enabling institutions to model relationships in domains such as healthcare, finance, cyber security, and education. However, privacy regulations and institutional policies restrict the sharing of sensitive nodes, edges, or interaction logs, preventing the discovery of global graph patterns. This paper introduces a novel framework for Federated Graph Pattern Mining Across Institutions (FGPM-AI), enabling multiple organizations to collaboratively extract global sub graphs, motifs, and temporal patterns without sharing raw graph data. The framework proposes six novel contributions: (1) Privacy-Preserving Pattern Signatures (PPPS) for anonymized sub graph encoding, (2) Federated Temporal Graph Pattern Mining (FT-GPM) to learn evolving patterns across distributed graphs, (3) Zero-Exchange Federated Sub graph Matching (ZE-FSM) using zero-knowledge proofs, (4) Heterogeneity-Aware Graph Pattern Consensus (HGPC) for semantic alignment between distinct graph schemas, (5) Communication-Adaptive Pattern Sharing (CA-FGM) for bandwidth-efficient collaboration, and (6) Multi-Party Graph Pattern Distillation (MGPD) for merging patterns into a unified knowledge model. Experimental design considerations demonstrate the feasibility and robustness of the framework. The results highlight FGPM-AI as a promising direction for secure, scalable, and intelligent cross-institution graph analytics.
Data protection in smart agricultural markets remains a critical area of inquiry amid escalating digital transformations. This chapter examines the challenges and solutions surrounding privacy in agricultural data ecosystems, with particular attention to encryption, blockchain, and federated learning technologies. It also evaluates global legal, ethical, and regulatory frameworks such as the GDPR and CCPA that influence data governance in digital agriculture. As of 2023, over 75% of large-scale farms in developed nations employ IoT and AI-driven tools for data collection, yet only 38% have robust data protection measures in place, exposing vulnerabilities in ownership, consent, and access control. The integration of privacy-preserving mechanisms such as homomorphic encryption, zero-knowledge proofs, and secure multi-party computation is discussed as a pathway to sustainable data security. The findings emphasize the urgent need for interoperable standards and policy-driven frameworks to protect stakeholders, particularly smallholder farmers, within smart agricultural marketplaces.
Byzantine Fault Tolerance (BFT) protocols are fundamental to achieving consensus in distributed systems where some nodes may behave maliciously. However, traditional BFT mechanisms often rely on strong trust assumptions in a majority of honest participants or incur significant communication overhead for extensive verification, thereby limiting scalability and introducing explicit points of trust. This paper proposes a novel approach to verifiable Byzantine agreement that leverages the power of Zero-Knowledge Proofs (ZKPs) to enhance trustlessness and verifiability. By integrating ZKPs into the consensus process, participants can cryptographically prove the correctness of their protocol actions and proposed states without revealing the underlying sensitive information or requiring every other node to re-execute complex computations. This paradigm shift enables a new class of BFT protocols where agreement is not merely reached but is {em verifiably} correct by any observer, reducing implicit trust and increasing transparency. We outline a conceptual framework for such a ZKP-enhanced BFT protocol, discussing the key integration points for zero-knowledge proofs, the expected benefits in terms of security and scalability, and the challenges associated with its implementation. Our approach aims to pave the way for more robust, scalable, and genuinely trustless decentralized systems.
Shrutika Singh, Anton Alyakin, Daniel Alexander Alber, Jaden Stryker · 12 authors
The performance of Large Language Models (LLMs) on multiple-choice question (MCQ) benchmarks is frequently cited as proof of their medical capabilities. We hypothesized that LLM performance on medical MCQs may in part be illusory and driven by factors beyond medical content knowledge and reasoning capabilities. To assess this, we created a novel benchmark of free-response questions with paired MCQs (FreeMedQA). Using this benchmark, we evaluated three state-of-the-art LLMs (GPT-4o, GPT-3.5, and LLama-3-70B-instruct) and found an average absolute deterioration of 39.43% in performance on free-response questions relative to multiple-choice (p = 1.3 * 10 -5 ) which was greater than the human performance decline of 22.29%. To isolate the role of the MCQ format on performance, we performed a masking study, iteratively masking out parts of the question stem. At 100% masking, the average LLM multiple-choice performance was 6.70% greater than random chance (p = 0.002) with one LLM (GPT-4o) obtaining an accuracy of 37.34%. Notably, for all LLMs the free-response performance was near zero. Our results highlight the shortcomings in medical MCQ benchmarks for overestimating the capabilities of LLMs in medicine, and, broadly, the potential for improving both human and machine assessments using LLM-evaluated free-response questions.
Open access
Artificial Intelligence in Healthcare and Education