The rapid growth of the digital economy has brought unprecedented advantages, enabling seamless transactions, real-time data exchange and global connectivity for the businesses. However, this digital expansion has also exposed businesses, governments and individuals to an evolving landscape of cyber threats. Traditional cybersecurity frameworks which rely heavily on centralized models are increasingly proving inadequate in the face of sophisticated cyber intrusions. Blockchain technology is a decentralized, cryptographically secure and immutable ledger system that introduces an innovative approach to cybersecurity. This research article examines the role of blockchain technology in enhancing cybersecurity, discussing its capabilities in securing online transactions, ensuring data integrity, preventing cyber threats and facilitating a proactive security mechanism against cyberattacks for businesses by integrating the CHIPS framework (ConnectāHarnessāInnovateāProtectāSustain). This framework emphasizes the ability of blockchain to Connect stakeholders via trust less networks, Harness distributed ledgers to ensure data integrity, Innovate mechanisms for secure transactions, Protect digital assets with tamper resistant architectures and Sustain long term cyber resilience through scalable and adaptable systems. This study also highlights the effectiveness of this integration in securing digital transactions, thwarting cyberattacks and facilitating proactive cybersecurity strategies in business operations.
The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functionsāincluding data management, security, device management, group coordination, and automated billingāto enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchainās potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (ā86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (ā0.586ā1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead.
This paper develops computational methods for optimizing revenue recognition in machine learning platforms operating on cloud computing infrastructure. We analyze how Artificial Intelligence as a Service (AIaaS) platforms leverage distributed computing architectures, containerization technologies (Docker, Kubernetes), and microservices patterns to deliver AI capabilities, creating complex revenue recognition challenges under IFRS 15. Our research employs algorithmic analysis to examine five critical technical challenges: (1) computational resource allocation tracking across multi-tenant cloud environments, (2) real-time transaction price determination using usage metering APIs and consumption-based billing algorithms, (3) automated revenue allocation across platform components using distributed ledger technologies, (4) temporal revenue recognition optimization through event-driven architectures and streaming data processing, and (5) network effect quantification using graph algorithms and data analytics.
En el caso de los smart contracts, nos encontramos ante un contrato que maĢs bien concebimos como una nueva forma de realizarlo. Son escasos los marcos legales existentes y por sus caracteriĢsticas propias poseen una gran incidencia en el derecho internacional privado. Actualmente se estaĢn haciendo marcos normativos y referenciales, como por ejemplo en la CNUDMI. El resto queda librado a las legislaciones comunitarias, como en el caso de Europa, o interna de los paiĢses, con dificultades a la hora de aplicar las normas tradicionales de conflicto. La lex mercatoria en la materia llamada Lex Cryptographia nos parece uĢtil. Estamos ante nuevas soluciones de resolucioĢn de conflictos no jurisdiccionales. Las relaciones de consumo plantean mayores desafiĢos.
This paper proposes a hybrid IoT-blockchain architecture designed to ensure the security and value enhancement of flue gas desulfurization (FGD) gypsum throughout its entire lifecycle. At the edge, sensor data is encrypted using AES-256, with RSA-2048 handling key exchange, achieving a hybrid encryption overhead of 1.84 milliseconds per kilobyte. A permissioned Proof-of-Authority consensus mechanism delivers$\text{1, 7 0 0}$transactions per second with a confirmation time of just 0.59 seconds. An immutable ledger records purity, moisture, volume, and origin data; smart contracts automatically execute compliance checks and quality balance reconciliations. During a$\text{1 2}$-month field deployment at a 1.2-million-ton coal-fired power plant, the system reduced unauthorized access attempts by 94.7%, lowered transportation quality disputes by 80%, and improved downstream price stability by 18%. Scalable to 145,000 daily records, the system supports sub-second queries for$\text{8 5 {\%}}$of calls and achieves post-quantum security through zero-knowledge proof integration. This framework transforms industrial byproduct tracking into a verifiable, real-time asset valuation tool.
To address centralized trust risks, inadequate privacy protection, and quantum vulnerability of traditional crossdomain authentication systems, this paper proposes a quantumresistant self-sovereign identity (SSI) scheme based on lattice cryptography. Centered on Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), it constructs a decentralized ecosystem with two key innovations: a āsingle trapdoor - multi-attribute public keyā structure (reducing storage and simplifying key management) and a lattice-based linkable ring signature (balancing anonymity and traceability). Implemented via TrapGen, SamplePre, and rejection sampling, the scheme's security relies on the Short Integer Solution (SIS) problem, with unconditional anonymity and unforgeability proven in the random oracle model. Efficiency comparisons confirm advantages in signature/verification time and storage overhead. This work provides a secure, privacy-preserving post-quantum solution for crossdomain collaboration in smart cities and supply chains. Future work will focus on lattice optimization and zero-knowledge proof integration.
This paper examines spillover dynamics, hedging effectiveness, and portfolio optimisation across tourism, cryptocurrency, and Fintech markets within a time-varying connectedness framework that incorporates traditional financial markets. We document pronounced time-varying spillovers, peaking during the COVID-19 pandemic, with traditional finance emerging as the dominant shock transmitter and the tourism sector as a key net receiver. Transmission-channel evidence suggests that total connectedness increases with credit stress and is positively correlated with market uncertainty and tourism mobility, with these effects intensifying during the COVID-19 pandemic. Cryptocurrencies offer the least costly but weakest hedges, while tourism assets hedge crypto exposure more effectively, albeit with greater downside risk. Dynamic portfolio weight strategies outperform hedge-ratio strategies, and the minimum connectedness portfolio (MCoP) delivers the highest risk-adjusted returns. DieboldāMariano tests indicate no significant differences in return predictability, whereas JobsonāKorkie results show that minimum correlation portfolio (MCP) and MCoP significantly outperform the minimum-variance portfolio (MVP). Downside risk measures highlight the superior performance of MCoP at the cost of deeper drawdowns. These findings underscore the value of connectedness-based strategies for portfolio design in increasingly integrated markets.
Darlington Chizema, Ramos E. Mabugu, Christelle Meniago
This study examines the effect of renewable energy consumption on energy poverty across 43 Sub-Saharan African countries from 2002 to 2021. Using a dynamic panel data approach and a two-step System GMM estimator, it addresses endogeneity concerns in energy poverty analysis. Results show energy poverty is persistent, reflecting deep institutional and infrastructural challenges. While renewable energy consumption is positively associated with energy poverty, the modest impact suggests current investments are concentrated in urban or grid-connected areas, with limited benefits for rural populations. This highlights the need for inclusive, decentralized energy strategies. Human capital emerges as a key factor in alleviating energy poverty, emphasizing the importance of integrating energy access with education and health initiatives. Conversely, GDP per capita, institutional quality, and population density show no significant effects, challenging assumptions that economic growth alone can resolve energy deprivation. The lack of a declining trend in energy poverty underscores the urgency for targeted, long-term interventions. The study advocates pro-poor energy policies, innovative financing, and multi-sectoral approaches linking energy access to broader development goals to advance Sustainable Development Goal 7 (SDG 7). Future research should explore subnational disparities and the varied impacts of renewable technologies to inform context-specific solutions.
Federated Learning (FL) enables distributed model training while preserving data privacy; however, it remains vulnerable to poisoning attacks and lacks computational integrity. Recent solutions integrating Zero-Knowledge Proofs (ZKPs) and blockchain have successfully established process-level verifiability but suffer from prohibitive computational overhead due to the requirement of generating cryptographic proofs for every local update. To address this efficiency bottleneck, this paper proposes Pro-ZkFL, a reputation-aware probabilistic verification framework. Unlike deterministic approaches that verify every transaction, Pro-ZkFL utilizes Verifiable Random Functions (VRF) on-chain to dynamically select a subset of clients for auditing based on their historical reputation scores. We design a dual-commitment scheme where clients submit lightweight cryptographic commitments for every round but generate heavy ZKPs only when challenged. Experimental results on FashionMNIST and CIFAR-10 demonstrate that Pro-ZkFL reduces computational overhead by approximately 82 % and gas costs by 73 % compared to full-verification baselines while maintaining a 99 % detection rate against persistent adversaries, offering a scalable trade-off between security and efficiency.
Developments and changes in technology play a significant role in addressing climate change, one of which is decentralized finance, which is currently expanding, and it is still unclear whether it has a dynamic relationship with climate change. This study employs the TVP-VAR Connectedness model with the aim of analyzing the dynamic relationship between the decentralized finance operations and CO2 emissions, the impact of shocks from DeFi operations (Total Value Locked, Volume, Returns, Fees, and Revenues) dynamically increasing CO2 emissions, as well as to assess the role of DeFi returns in strengthening the transmissions of DeFi activity to CO2 emissions. The results show that DeFi operations have a dynamic relationship with CO2 emissions at a moderate level through shocks transmitted by DeFi operational indicators. It was also found that TVL acts more as a net receiver than a net transmitter, unlike Volume, Fees and Revenues. Returns do not significantly transmit shocks to CO2 emissions and are more exogenous in nature, while both TVL and Returns are predominantly influenced by internal idiosyncratic shocks. These findings emphasize the importance of integrating Green FinTech policies to ensure sustainable DeFi growth. The findings also provide important implications for regulators, industry practitioners and academics in their efforts to balance the advancement of DeFi with environmental sustainability.
Elif Nur Kucur, Tolga BĆ¼yĆ¼ktanır, Muharrem Ugurelli, Kazım Yıldız
Privacy-preserving machine learning (PPML) constitutes a core element of responsible AI by supporting model training and inference without exposing sensitive information. This survey presents a comprehensive examination of the major cryptographic PPML techniques and introduces a unified taxonomy covering technical models, verification criteria, and evaluation dimensions. The study consolidates findings from both survey and experimental works using structured comparison tables and emphasizes that recent research increasingly adopts hybrid and verifiable PPML designs. In addition, we map PPML applications across domains such as healthcare, finance, Internet of Things (IoT), and edge systems, indicating that cryptographic approaches are progressively transitioning from theoretical constructs to deployable solutions. Finally, the survey outlines emerging trendsāincluding the growth of zero-knowledge proofs (ZKPs)-based verification and domain-specific hybrid architecturesāand identifies practical considerations that shape PPML adoption in real systems.
Consider a social-choice function (SCF) is chosen to decide votes in a formal system, including votes to replace the voting method itself. Agents vote according to their ex-ante belief over what decisions are considered, and whether they prefer them to be decided by the incumbent SCF or the suggested replacement. The existing SCF then aggregates the agents' votes and arrives at a decision of whether it should itself be replaced. An SCF is self-maintaining if it can not be replaced in such fashion by any other SCF. Our focus is on the implications of self-maintenance for centralization. For this purpose, unlike [Barbera and Jackson, 2004], we do not generally restrict attention to anonymous SCFs. We also do not restrict attention to neutral SCFs, unlike [Koray, 2000]. We present results considering optimistic, pessimistic and i.i.d. approaches with respect to agent beliefs, different tie-breaking rules, and different SCF domains. To highlight two of the results, (i) for the i.i.d. unbiased case with arbitrary tie-breaking and general Boolean functions, we prove an Arrow-Style Theorem for Dynamics: We show that only a dictatorship is self-maintaining, and any other SCF has a path of changes that arrives at a dictatorship. (ii) With a pessimistic approach, tie-breaking that prefers the status quo, and WMGs, we provide a tight characterization of the self-maintaining rules, which are exactly all games with minimal winning coalitions of size at most 2. We then consider two extensions, (i) forward-looking voters, (ii) Where the voter utility depends on wisdom of the crowd effects. In both cases, less centralized SCFs become self-maintaining. All in all we provide a basic framework and body of results for centralization dynamics and stability, applicable for institution design, especially in formal De-Jure systems, such as Blockchain Decentralized Autonomous Organizations (DAOs).
Aso Mohammad Darwesh, Atefeh Nekouie, Mohammad Hossein Moattar, Parisa Khoshvaght Ā· 7 authors
Abstract Electronic Health Record (EHR) management is one of the challenging problems in digital healthcare and is related to several issues such as data security, privacy, scalability, interoperability, and ownership which are very crucial for reliable exchange of information. This review discusses the recent trend and technological solutions for the mentioned challenges. These solutions mainly focus on cloud-based infrastructures, attribute-based encryption (ABE), blockchain frameworks, and Non-Fungible Token (NFT)-based data ownership. This study highlights the strengths and limitations of each approach using comparative analysis and evaluations. Also, this review introduces a conceptual integration framework that combines graph neural networks (GNNs), multi-reference attribute-based encryption (MA-ABE), blockchain, and NFTs. The proposed model integrates predictive artificial intelligence, decentralized mechanism, immutable auditing, and verifiable ownership in a multi-layered architecture to address the issues and challenges of HER systems. Quantitative analysis of the reviewed literature reveals a clear upward trend in research activity, with more than 80 peer-reviewed studies published between 2017 and 2024, representing an approximate 250% growth in blockchain-, ABE-, and NFT-based EHR solutions. Among these, 41% focus on security and privacy, 27% on scalability, and 19% on interoperability, underscoring the fieldās growing emphasis on decentralized and intelligent healthcare systems. This article not only contributes to a comprehensive review of the previous researches, but also provides a perspective on how the future of healthcare systems will be reshaped by intelligent and decentralized technologies.
Decentralized Autonomous Organizations (DAOs), powered by blockchain technology and smart contracts, have opened new avenues for addressing corporate governance challenges, such as effectively reducing contract risks and mitigating other organizational management issues. However, as a typical complex system that integrates both social and engineering complexities, DAOs still face problems in governance practice, including insufficient decentralization and low member participation. In particular, the liquid democracy design in their voting mechanisms-intended to allow members to flexibly switch between direct voting and delegated proxy-often leads to the phenomenon of ādelegation equals abstention,ā which is particularly acute in Product and Service DAOs, resulting in declining overall participation rates and excessive concentration of governance power among a few individuals. To address these challenges, this paper employs the ACP method (Artificial Systems + Computational Experiments + Parallel Execution) and proposes a dual-token governance mechanism that couples governance rights with an incentive layer. This mechanism moderately decouples governance rights from utility rights, encouraging ordinary members to shift from passive delegation to active participation in governance. At the same time, we introduce an SBT-based reputation system grounded in cumulative contributions, which dynamically constrains the upper limit of delegated receipts, thereby institutionally curbing the unchecked expansion of power by super delegates. Through case analysis combined with computational experiments, the effectiveness of this mechanism in enhancing the degree of governance decentralization and member participation is validated, providing both technical pathways and theoretical references for DAO governance optimization.
Conventional educational accreditation systems face significant challenges, including issues of data integrity, procedural inefficiencies, and insufficient privacy protection. This study introduces a lifelong learning accreditation framework based on a Decentralized Autonomous Organization (DAO). Our approach leverages blockchain technology and smart contracts to create a decentralized and immutable credentialing infrastructure. Our work addresses critical vulnerabilities such as single-point-of-failure trust risks, inefficiencies in manual verification, and data privacy breaches. To counter these issues, we propose three key innovations: A hybrid IPFS-blockchain architecture for efficient and permanent data storage. A threshold signature scheme to enable multi-party governance and prevent centralized control. An NFT-based protocol for the automated issuance of credentials. The proposed framework provides a practical solution for enhancing transparency and security in lifelong learning. Future work will focus on improving the efficiency of the cryptographic mechanisms and enabling cross-chain interoperability.
In modern conditions of functioning, healthcare institutions in Ukraine are undergoing a period of profound transformations, which are associated with the activation of the European vector of state development, military aggression from Russia, the challenges of the pandemic, migration mines and changes in state policy priorities. Based on this, it can be argued that the functioning of the healthcare sector takes place in a period of dynamic changes and variability of the external and internal environment. A feature of the organizational support of the functioning of healthcare institutions is the change in the management model - from centralized and inertial to autonomous, which is aimed at results. In accordance with the implemented medical reforms, some healthcare institutions have acquired the status of municipal non-profit enterprises, which has expanded the scope of independence in making management decisions in matters of organization and financing of institutions [1, 2]. However, the implementation of such changes requires time and appropriate organizational support, which in the conditions of the security and economic situation is a difficult task. From the point of view of financial support, the changes made according to the principle of "money follows the patient" should stimulate the competitiveness of healthcare institutions within the framework of cooperation with the National Health Service of Ukraine, but led to uneven conditions for the functioning of institutions in regional distribution, especially in rural areas. The reform of decentralization of power, which aimed to optimize the healthcare sector, which in practice was implemented in the reduction of healthcare institutions in rural areas and impaired the ability to receive medical services among the population of such territories, also made its corresponding adjustments. Therefore, the issue of organizational and economic support for the effective functioning of healthcare institutions in the context of transformational challenges is an urgent task for the authorities today. The greatest destruction and losses for the sphere of functioning of healthcare institutions were caused by the full-scale invasion, which led to significant destruction of facilities throughout the country, especially in border areas and zones of active hostilities. At the same time, part of the health facilities was destroyed by the war, part suffered significant damage and losses, which affected the ability of thousands of citizens to receive the necessary medical care. In addition to the physical losses of facilities, the challenge was the relocation of part of the health facilities from the territories of temporary occupation to safer regions, which affected the preservation of the material and technical base.
Online voting with blockchain technology is a decentralised and secure live-voting system which can be implemented using an Ethereum blockchain. Our proposed blockchain-based voting system (BVote) uses the Ethereum blockchain technology to overcome the issues of the existing voting system, such as vote tampering, double voting, and hacking. Our system offers several benefits including increased security, transparency, and immutability. The immutability of blockchain ensures that once a vote is recorded, it cannot be altered or deleted, thereby enhancing the integrity of the election process. Additionally, voting and counting occur simultaneously, thereby providing partial results. Double voting is addressed using a unique digital identity for each voter that is verified in the blockchain network. To authenticate the user identity, each transaction should be digitally signed using a private key. This ensures that only voters can access and use their digital identity to cast a vote. To ensure the privacy of the voter, the data are hashed using a Secure Hash Algorithm. To ensure the anonymity of the voter, the vote is encrypted to break the link between voter and their vote. This helps to protect votersā privacy and prevents attempts to trace their votes. By utilising the Elliptic Curve Digital Signature Algorithm, a digital signature is generated for each transaction that occurs during the voting process. This ensures the authenticity of the data stored on the blockchain and prevents any unauthorised modifications or tampering of the data, which further improves security.
Guilin Guan, Zhou Wg, Hongtao Xie, Yang Cao Ā· 6 authors
The rapid advancement of big data and cloud computing technologies has elevated the importance of data transmission consistency verification in scenarios such as distributed storage, data backup, and content delivery networks. Traditional verification methods, including hash-based checks and digital signatures, inherently require access to raw data for computation and comparison. This dependency introduces risks of original data leakage and imposes substantial computational overhead in high-concurrency or large-scale data environments. Zero-knowledge proof (ZKP) technology offers a promising alternative by enabling a prover to demonstrate the validity of a statement to a verifier without disclosing any supplementary information. However, conventional ZKP schemes, particularly interactive ones, often suffer from complex communication rounds and significant computational burdens, rendering them unsuitable for data transmission scenarios demanding high real-time performance. This paper proposes a Lightweight Non-interactive Zero-Knowledge Proof (L-NIZK) protocol specifically designed for secure and efficient data transmission consistency verification. The protocol employs a data-blocking strategy combined with a Merkle tree structure and integrates an enhanced Pedersen commitment scheme with elliptic curve cryptography to achieve non-interactive and computationally efficient consistency proofs. A formal security analysis demonstrates that the proposed scheme satisfies completeness, soundness, and zero-knowledge properties under the random oracle model. Comprehensive performance evaluations indicate that the L-NIZK protocol surpasses existing mainstream solutions in proof generation time, verification time, and communication overhead, establishing its suitability for large-scale, high-concurrency data transmission environments.
Introduction: The study examined how Distributed Ledger Technology (DLT) can play a role in business ethics and how the ethical conduct of business can help consumers have more confidence in the global supply chain. It further explored how the adoption of Ethical Sourcing Practices (ESP) mediates the relationship between DLT adoption and consumer confidence in the Saudi Arabian context. Methods: A purposive sampling approach was followed in accordance with a positivist approach. In order to gather the information among 355 respondents, an online survey was distributed, and the data have been analysed with the help of partial least squares structural equation modelling (PLS-SEM) in SmartPLS 4.0. Results: The findings of the PLS-SEM established that DLT significantly predicted Ethical Sourcing Practices (0.641, p < 0.001) whereas ethical sourcing practices also had significant and positive impact on perceived consumer trust (0.518, p < 0.001). The direct effect of DLT on perceived consumer trust was significantly positive (Ī² = 0.325, p < 0.001). The model explains 41% of ESP and 59% of trust. Additionally, a significant indirect effect of DLT on perceived consumer trust via ethical sourcing practices was confirmed, indicating partial mediation (Ī² = 0.331, p < 0.001). Conclusion: This research combined model that correlates DLT adoption, ethical sourcing practices, and perceived consumer trust using the TOE and signalling theories. It uses data on multi-industry supply chains from multi-industry surveys (Saudi Vision 2030) to illustrate the capacity of blockchain-enabled sourcing capabilities and turn it into a trust gain. The research involves the cross-sectional survey data, which would allow finding statistical correlations but would not allow to establish the causality. Additionally, the results may not apply to all individuals in the industry.
In Ethereum, DevP2P is the fundamental network-layer protocol set that supports consensus mechanisms, transaction propagation and smart contract execution. Due to the importance of DevP2P, its bugs can be exploited by the attacker to cause security problems like denial of service, leading to property loss on Ethereum. However, existing blockchain testing approaches focus on the bug detection of consensus and application layers, causing many serious DevP2P bugs to be missed. In fact, detecting DevP2P bugs has some key challenges, including how to generate effective inputs and how to detect complex bugs. This paper designs D2PFuzz, the first network-layer differential fuzzing approach of bug detection for Ethereum. It consists of two key techniques: (1) aquery-based fuzzing strategythat dynamically generates valid DevP2P messages according to network, chain and node state changes; and (2) amulti-node differential checking methodthat identifies important differences of DevP2P response messages from multiple nodes in the same blockchain to detect semantic bugs. We have evaluated D2PFuzz on five open-source and popular Ethereum node implementations, including Geth, Erigon, Reth, Besu and Nethermind. D2PFuzz in total finds 15 unique bugs, 12 of which are previously unknown. Compared to two state-of-the-art blockchain testing approaches including LOKI and Hive, D2PFuzz improves testing coverage by 3.7x and 21.6x, respectively, and finds 13 bugs missed by these approaches.
Khang Wen Goh, Burhan Ul Islam Khan, Abdul Raouf Khan, Dwi Sudarno Putra Ā· 6 authors
Blockchain systems built on classical cryptography face immediate risks from large-scale quantum computers, while purely quantum-based blockchains often rely on a single Private Key Generator (PKG) and incur heavy resource overheads. To overcome these issues, this paper proposes a hybrid quantum and post-quantum blockchain approach that removes single points of trust by using Distributed Key Generation and a dual-layer signature mechanism. This method integrates quantum digital signatures, rooted in the Fully Flipped Permutation problem, with classical post-quantum (lattice-based) cryptography, enabling users to switch between quantum and classical signatures according to security requirements and channel conditions. Delegated Proof-of-Stake with node behavior and Borda count has been incorporated to manage consensus, ensuring that witness nodes are regularly re-elected and malicious actors are penalized by distributing secret shares among multiple rotating witnesses. We eliminate the central vulnerability of a sole PKG while maintaining rigorous resistance to collusions. Our analytical model indicates that a fraction of transactions can use quantum signatures without system-wide bottlenecks, while the remaining transactions follow classical PQC paths with throughput approaching classical baselines under our modeling assumptions. Consequently, this hybrid method offers higher scalability, robust collusion resistance, and long-term security even under quantum-capable adversaries. This paper presents extensive theoretical analyses, probability models, and algorithmic complexities, demonstrating that our design provides resilient infrastructure that meets the key performance and security requirements of next-generation blockchain systems.
The Decentralized Finance (DeFi) platforms are proving to be a calling card of the financial system in the global front. The de-Fi platforms provide peer-to-peer financial services without relying on any type of significant centralized intermediaries. This paper evaluates the success of DeFi platforms using a two-frame analysis, the Decentralized Infrastructure Sustainability and Security (DISS) Model, and the System Usability Scale (SUS). This study assesses the role of different elements of DISS and SUS models in affecting the use of DeFi platforms. Such findings in their turn assist us in comprehending the significance of encouraging mechanisms and data privacy in constructing and increasing the user trust and making the platform more successful.
Md Motaleb Hossen Manik, Md Zabirul Islam, Ge Wang
Modern vision--language models (VLMs) are increasingly used to interpret and generate educational content, yet their semantic outputs remain challenging to verify, reproduce, and audit over time. Inconsistencies across model families, inference settings, and computing environments undermine the reliability of AI-generated instructional material, particularly in high-stakes and quantitative STEM domains. This work introduces SlideChain, a blockchain-backed provenance framework designed to provide verifiable integrity for multimodal semantic extraction at scale. Using the SlideChain Slides Dataset-a curated corpus of 1,117 medical imaging lecture slides from a university course-we extract concepts and relational triples from four state-of-the-art VLMs and construct structured provenance records for every slide. SlideChain anchors cryptographic hashes of these records on a local EVM (Ethereum Virtual Machine)-compatible blockchain, providing tamper-evident auditability and persistent semantic baselines. Through the first systematic analysis of semantic disagreement, cross-model similarity, and lecture-level variability in multimodal educational content, we reveal pronounced cross-model discrepancies, including low concept overlap and near-zero agreement in relational triples on many slides. We further evaluate gas usage, throughput, and scalability under simulated deployment conditions, and demonstrate perfect tamper detection along with deterministic reproducibility across independent extraction runs. Together, these results show that SlideChain provides a practical and scalable step toward trustworthy, verifiable multimodal educational pipelines, supporting long-term auditability, reproducibility, and integrity for AI-assisted instructional systems.