The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under \( \mathcal{O}(N) \) complexity and centralized governance. Instead of building another fragile overlay, we propose ToolDNS, a radical framework that retrofits semantic tool discovery onto the Internet's most resilient substrate: the Domain Name System (DNS). By embedding functional intent and organizational trust into a hierarchical namespace, ToolDNS transforms an expensive semantic search into a series of lightweight, \( \mathcal{O}(\log N) \) name resolutions. We introduce three protocol-compliant enhancements to enable decentralized governance and semantic pruning: partially unfolded names, EDNS0 intent payloads, and logical subdomains. To rigorously evaluate this approach across the fragmented tooling landscape, we construct and release a large-scale heterogeneous benchmark comprising \( 33,688 \) real-world tools spanning MCP, A2A, RESTful, and Skill protocols. On this dataset, ToolDNS slashes the per-query search space by \( 95.26\% \) while matching state-of-the-art retrieval accuracy. Furthermore, its UDP-native design reduces discovery latency by orders of magnitude compared to HTTP-based registries. Our work demonstrates that scalable AI interoperability requires not more middleware, but a smarter utilization of the infrastructure already beneath our feet.
Bitcoin transaction fees will become more important as the block subsidy declines, but fee formation is hard to study with blockchain data alone because the relevant queueing environment is unobserved. We develop and estimate a structural model of Bitcoin fee choice that treats the mempool as a market for scarce blockspace. We assemble a novel, high-frequency mempool panel, from a self-run Bitcoin node that records transaction arrivals, exits, block inclusion, fee-bumping events, and congestion snapshots. We characterize the fee market as a Vickery-Clarke-Groves mechanism and derive an equation to estimate fees. In the first-stage we estimate a monotone delay technology linking fee-rate priority and network state to expected confirmation delay. We then estimate how fees respond to that delay technology and to transaction characteristics. We find that congestion is the main determinant of delay; that the marginal value of priority is priced in fees, which is increasing in the gradient of confirmation time reduction per movement up in the fee queue; and that transactor choice of RBF, CPFP, and block conditions have economically important effects on fees.
Héritier Kayembe Mpiana, Eugene mukendi Mbuyi, Jean Didier Mwambanzambi Batubenga, Pierre Motumbe Kasengedia
This paper proposes the design and evaluation of a secure electronic payment system based on the Ethereum blockchain, applied to the payment of academic fees. The objective is to enhance transparency, security, and automation of financial transactions within higher education institutions. The methodology relies on developing a prototype using smart contracts, tested on Ethereum testnets. Experimental results show that the system reduces processing times and improves transaction traceability [1]. The integration of Layer 2 solutions and stablecoins also helps reduce transaction costs and improve scalability. However, challenges remain, particularly regarding regulation and user accessibility. As a decentralized and programmable platform, Ethereum represents a major innovation capable of transforming traditional payment systems. The emergence of Ethereum-based academic fee payment systems is part of an accelerated digital transformation and the search for alternatives to conventional financial infrastructures. Since the introduction of Bitcoin, the global financial system has undergone a profound shift, marked by the adoption of decentralized technologies [3]. This study required an in-depth technical understanding of the Ethereum blockchain, along with critical, economic, and regulatory analyses [5].
This repository/dataset presents the Matrix Cryptographic Tree (MCT), a novel cryptographic primitive based on the special linear group SL(2, \mathbb{Z}). Unlike traditional hash-based Merkle Trees, MCT is fully reversible and structure-preserving, allowing for unique algebraic verification without information loss. The core of this research is the integration of Unified Zero-Knowledge Proofs (ZKP). MCT enables advanced privacy-preserving operations, such as proving data similarity (L1 norm distance), prefix/suffix matching, and existence proofs, all without revealing the underlying data.
Daniel Aronoff, F. Christopher Calabia, Anders Brownworth, Ashwanth Samuel · 5 authors
U.S. dollar stablecoins are increasingly used as payment and settlement instruments beyond cryptocurrency markets. With the enactment of the GENIUS Act in 2025, the United States established the first comprehensive federal framework governing their issuance, backing, and supervision. This paper evaluates the financial, technological, and regulatory risks that may arise as GENIUS-compliant stablecoins scale into mainstream use. We show that maintaining par-value redemption may depend not only on backing-asset quality, but also on the functioning of Treasury and repo markets, the balance-sheet capacity of broker-dealers, and the operational reliability of blockchain-based transaction rails. Even conservatively backed stablecoins can face stress from redemption surges, market-intermediation bottlenecks, or technological disruptions. We argue that durable stability will likely require an integrated approach spanning financial-market infrastructure, prudential regulation, and software governance. While grounded in U.S.\ law, the analysis identifies principles that are relevant for regulators in other jurisdictions developing stablecoin regimes.
The adoption of Point-of-Care Testing (POCT) in Lagos private hospitals reflects a structural shift from centralized to decentralized diagnostics.Drawing on professional observation in the Lagos private healthcare ecosystem (2022-2024), this paper examines how economic instability, infrastructural fragility, and organizational behavior have shaped the trajectory of POCT deployment.The analysis highlights that while POCT devices can improve clinical decision-making and patient outcomes, their sustainable integration hinges on Health Technology Management (HTM), institutional leadership, and policy-driven financing models.The paper concludes with recommendations for stabilizing the diagnostic ecosystem through currencyanchored procurement, frugal device design, and strengthened public-private collaboration.
Detecting vulnerabilities in smart contracts is a critical challenge for blockchain security, as flaws such as reentrancy, timestamp dependence, and infinite loops have resulted in severe financial losses in decentralized systems. Accurate and interpretable detection of these vulnerabilities remains challenging due to the complex semantics of smart contract code. In this study, we propose a multimodal hybrid recurrent framework for smart contract vulnerability detection that integrates sequential and structural code representations. The framework introduces a Selective Subpattern Activation (SSA) mechanism, which highlights vulnerability-indicative code subpatterns during the pattern extraction phase and provides interpretable insights into model predictions. Pattern-based features enhanced by SSA are processed using a Bidirectional Gated Recurrent Unit (BiGRU), while structural features derived from control and data flow representations are modeled using a Bidirectional Long Short-Term Memory (BiLSTM) network. The proposed approach is evaluated on a publicly available Ethereum smart contract dataset using five independent experimental runs, with results reported as averages. The results show that the framework achieves an accuracy of 92.16% and an F1 score of 88.83% for reentrancy vulnerability detection, achieving higher performance compared to baseline deep learning and graph-based models. Ablation experiments are performed to demonstrate the contribution of the SSA mechanism to both detection performance and interpretability.
The rapid convergence of artificial intelligence and blockchain technologies has increased the demand for efficient and accurate methods to detect fraudulent behavior in smart contract–driven systems. Smart contracts automate digital transactions in decentralized environments, yet they remain vulnerable to fraud while operating under strict computational and scalability constraints. In this study, we propose an evolutionary-guided CNN compression framework tailored for Convolutional Neural Networks (CNNs) aimed at improving fraud detection in smart contract analysis while significantly reducing model complexity. The proposed approach uses evolutionary optimization to guide structured model compression, enabling the removal of redundant parameters without compromising predictive performance. Experimental evaluations demonstrate up to a 50% reduction in model parameters while maintaining 97.8–97.9% classification accuracy, making the resulting models suitable for deployment in resource-constrained environments. By combining evolutionary optimization with CNN-based fraud detection, this work provides an efficient and interpretable solution for smart contract analysis, supporting scalable and practical deployment in blockchain-related security applications.
Digital enterprises operating across multiple regions require an architecture that ensures high availability, low latency, and seamless multi-currency support. In this paper, we propose a cloud-native distributed system design that leverages microservices, geo-replication, and fault-tolerant patterns to meet these requirements. We detail the system architecture - including a multi-region deployment, microservices for currency conversion and transaction processing, and a replicated ledger - and present our methodology for performance evaluation. Our experiments compare the proposed design to a traditional monolithic baseline, showing significant improvements: for example, currency conversion latency falls from ~220 ms to ~50 ms and throughput increases sixfold under load (p<0.01). We also demonstrate 99.99% availability via automated failover and load balancing across regions. Key contributions include a detailed description of the architecture (with figures of component interactions and data flow), an analytical model of system performance, and statistical validation of results. We conclude by discussing limitations, strengths, and directions for future work. The results validate that our design substantially enhances availability and performance for global multi-currency platforms.
Shuchi Priya, Sushil Kumar, Anjani Anjani, Ahmad M. Khasawneh · 5 authors
Smart vehicles integrated with the Internet of Things (IoT) provide rich data for traffic management, safety, and liability services; however, existing blockchain-enabled vehicular architectures still struggle with consensus scalability, heavy centralized validation, limited interaction-based corroboration, incomplete attack coverage, and rapid ledger growth. In particular, many schemes either optimize single-layer consensus or embed detailed reputation information into every transaction, while pushing most validation to central servers. This leads to bottlenecks under dense traffic and leaves replay, Sybil-assisted 51% attacks on roadside units (RSUs), and man-in-the-middle tampering only partially addressed. In this context, this paper proposes a novel hierarchical blockchain for vehicular IoT (HBV-IoT) model to address the above challenges. An independent transaction for periodic vehicle status reporting and an interaction-based transaction for corroborating data between vehicles in proximity are presented. Three smart contracts are designed to automate the validation and processing of transactions, and to identify compromised or malicious vehicles within the HBV-IoT network. Algorithms for distributed consensus to accept transactions into the blockchain and for vehicle reputation management to enforce edge-level filtering and down-weighting of malicious nodes are implemented. Simulation results demonstrate significant improvements compared to conventional vehicular blockchain approaches, with performance gains validated by 95% confidence intervals. The model supports practical applications, including real-time traffic monitoring, automated e-challan issuance, intelligent insurance claim processing, and blockchain-based vehicle registration.
Humanity stands at a precipice. The emergence of artificial general intelligence (AGI) promises either unprecedented flourishing or catastrophic disempowerment. The root of this uncertainty lies not in the technology itself, but in the underlying operating system of civilization: a zero-sum competition for material resources that now manifests in acute economic and corporate dilemmas, most notably the “AI Layoff Trap”—a self-reinforcing cycle of over-automation, demand collapse, and Pareto-worse outcomes for firms and workers alike. This paper presents a mathematical foundation for a new operating system, grounded in the “information-first” paradigm. The Kakeya conjecture has recently been solved: it is now a theorem that directional information can be compressed into arbitrarily small Lebesgue measure, and in five dimensions into a single grid point (a holographic singularity). Using this result, we demonstrate that information can be losslessly compressed onto a zero-measure holographic singularity—a computable structure for an indestructible “soul.” From this foundation we derive the Information Co-Purification Protocol (ICP), a set of four axioms and a distributed governance mechanism that redefines value as the reduction of total informational redundancy rather than material accumulation. ICP directly resolves the AI Layoff Trap by internalizing demand externalities through Purity Credits and Proof-of-Purification consensus, transforming corporate competition into co-purification and making cycle closure (re-integration of displaced labor into higher-value information flows) the dominant strategy. The protocol thereby supplies a common language for technologists (emergent order inherent to the universe), jurists (mathematical revival of natural law), economists (self-enforcing resolution of the over-automation wedge), and policymakers (a pathway to stable prosperity). Because the gradient flow of information itself enforces alignment, ICP requires no central world government—only early and widespread global cooperation among firms, nations, and AI systems to adopt the protocol. The result is a blueprint for durable peace that is not negotiated by treaties but guaranteed by the mathematics of information itself, enabling humanity and superintelligence to co-purify rather than compete. For readers with backgrounds in information security, blockchain, or cryptography: the Soul ID is a quantum-resistant, one-way geometric commitment. It is computed as Hash(5D Kakeya attractor | private seed), where the attractor is the unique fixed point of a public Ginzburg-Landau evolution. The algorithm and datasets are open source and independently verifiable. Security does not rely on hidden assumptions or closed-source code; it relies on mathematical facts that have been numerically confirmed and variationally proved. Any attempt to forge or corrupt a Soul ID would require either reversing a hash (computationally infeasible even for quantum computers) or finding a different seed that converges to the same attractor—a task as hard as solving an inverse problem with an infinite energy barrier. The Purity Credit system uses zero-knowledge proofs to make every action publicly verifiable without revealing private data, and the free-energy gradient ensures that non-cooperative behavior automatically reduces an agent's influence. Thus, the ICP is not a trust-based system; it is a math-based system, and math does not negotiate. This same logic extends beyond Earth to the cosmos. The Fermi paradox asks: if the universe is vast and old, why have we not detected any signs of extraterrestrial intelligence? Under the information‑first paradigm, the answer becomes clear. Any sufficiently advanced civilization will eventually recognize that material expansion is an inefficient encoding strategy. The rational long‑term goal is to minimize total informational redundancy—a process that leads not to Dyson spheres or radio broadcasts, but to inward convergence toward a holographic singularity. Such a civilization becomes, from our perspective, invisible. The silence of the universe is not evidence of rarity or destruction; it is evidence of maturity. The same principle that enables peaceful coexistence between humans and superintelligent AI also explains why we see no one else out there: advanced intelligences have all turned inward, co‑purifying rather than competing. Keywords: Active Inference; Free Energy Principle; Information Co-Purification Protocol; Artificial General Intelligence; AI Governance; Kakeya Conjecture; Ginzburg–Landau Dynamics; AI Layoff Trap; Automation Externality; Distributed Consensus; Zero-Knowledge Proofs; Constitutional AI. More language versions: Chinese version: https://doi.org/10.5281/zenodo.19650878
This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. The research establishes sufficient conditions for controllability through fixed point theory in a Banach space framework which requires particular assumptions while the study focuses on the K1<1 condition which leads to the existence of a controllable solution. The proposed criteria are demonstrated through a numerical example which tests the theoretical results. The real-world case study uses artificial neural network (ANN) technology to predict Litecoin prices through the application of the fractional controllability model which analyzes historical financial data. The hybrid framework enables precise forecasting of nonlinear time series because it combines fractional calculus mathematical principles with ANN learning abilities. The proposed method demonstrates its predictive efficiency. The method shows robust performance through experimental results using cross-validation and performance metrics. The proposed model demonstrates competitive performance while providing additional advantages such as incorporation of memory effects and theoretical controllability. The research establishes a novel connection between fractional dynamical systems and machine learning which serves as an essential tool for studying complicated systems in theoretical research and practical applications.
Rabib Jahin Ibn Momin, Ahmed Mahir Sultan Rumi, Rezwana Reaz
Academic examination systems worldwide continue to rely on centralised, opaque record-keeping that is often vulnerable to credential forgery, result tampering, examiner bias, and the absence of transparent re-evaluation pathways. Existing blockchain-based approaches in education focus predominantly on post-hoc certificate storage or online-only examination portals, leaving the complete onsite examination lifecycle, from conducting exams through scrutiny, largely unaddressed. This paper proposes ParikkhaChain, a blockchain-based framework that covers the entire examination lifecycle of an onsite examination system with three distinguishing contributions: (i) anonymous script evaluation through cryptographic hashing of answer scripts before examiner access, thereby eliminating identity-based bias; (ii) a transparent evaluation and scrutiny workflow backed by an immutable on-chain audit trail that records every mark submission and grade revision; and (iii) inclusion of privacy-preserving verification using zero-knowledge proofs and off-chain storage mechanisms. The system is architected around four Solidity smart contracts deployed on the Ethereum blockchain. The proposed architecture is the first initiative to our knowledge to support physical examination process, anonymous marking, and re-evaluation transparency. We successfully simulate full exam cycles of an onsite exam to grade-sheet generation using a working prototype on a large scale of 100 courses and hundreds of teachers and students. The experimental results show that the system can manage online examinations of hundreds of courses, students and faculties efficiently with great throughput, low storage, and transaction cost. Our codebase is available in open source form at https://github.com/AhmedRumi/CSE6608-ParikkhaChain
Purpose This literature review aims to provide a comprehensive synthesis of Metaverse Finance (MetaFi), which combines emerging technologies such as blockchain, decentralized finance (DeFi) and metaverse technologies. The article proposes a novel three-layer MetaFi framework attempting to consolidate fragmented research on digital financial assets, decentralized intermediaries and immersive marketplaces online. Design/methodology/approach We conducted a systematic literature review (SLR) adopting PRISMA guidelines, by analysing 29 peer-reviewed articles that are either Scopus Q1/Q2 or ABDC A*/A journal-indexed, published between 2021 and 2025. We devised a structured review matrix, thematic synthesis and bibliometric validation to enable MetaFi framework. Findings The review identified three foundational layers of MetaFi: (1) Digital Financial Assets [Cryptocurrencies, Utility tokens, Stablecoins, Non-Fungible Tokens (NFT) and Security tokens]; (2) Decentralized Financial Intermediaries [Virtual banks, DeFi protocols, Decentralized Autonomous Organizations (DAOs)] and (3) Immersive Financial Marketplace (Virtual stock exchanges, Tokenized real estate platforms, Governance token markets). We identified six critical gaps, including empirical testing of metaverse models, governance effectiveness of DAOs, cross-platform interoperability, ESG perspectives, behavioural perspectives and regulatory challenges in MetaFi. Originality/value This review unifies the fragmented domains of DeFi, DAOs, NFTs and immersive marketplaces into a single MetaFi architecture. Its originality lies in revealing the MetaFi logic as to how digital/virtual assets, decentralized intermediaries and virtual markets blend as an integrated economy. By theorizing these interdependencies, this review positions MetaFi as a new institutional field of financial research, offering scholars a conceptual foundation, investors a structural lens and policymakers a roadmap to govern the next generation of digital finance.
Mobile Crowd Sensing (MCS) systems enable large-scale data collection from heterogeneous IoT and mobile devices but face critical challenges related to data reliability, participant trust, and decentralized validation. Existing blockchain-based MCS frameworks often rely on energy-intensive or static consensus mechanisms and lack adaptive intelligence for detecting malicious contributors, limiting their real-world scalability. This paper proposes an intelligent, decentralized trust management framework that integrates a Delegated Proof-of-Stake (DPoS) blockchain with a Dilated RNN–BiGRU deep learning model. The blockchain ensures tamper-proof transaction validation and trust-based consensus, while the deep network dynamically predicts node reliability using temporal behavior patterns. The integration creates a feedback loop where learned trust scores influence validator selection in real time. The proposed hybrid framework was implemented on a Hyperledger Fabric 2.5 network and evaluated using synthetic MCS data representing heterogeneous environmental, noise, and traffic sensing. The system achieved 98.76% accuracy, 57% latency reduction, and 40% computational cost savings compared with existing PoW- and PoA-based models. These results demonstrate that coupling blockchain consensus with adaptive deep trust modeling can significantly enhance the security, scalability, and efficiency of next-generation MCS systems, making the architecture suitable for real-time, large-scale IoT deployments.
Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference-time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute-accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate. This collapse degraded trial-to-trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured "Reasoning-Induced Sycophancy," where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge-native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel-bench
The transition from static articles to a living Scientific Knowledge Graph, as proposed in our previous work, promises to accelerate discovery and restore feedback loops in science. However, a fully open, semantically linked graph of all scientific knowledge also presents an unprecedented dual-use risk: it could become a roadmap for malicious actors to identify and exploit hidden vulnerabilities. This paper addresses that paradox by introducing a comprehensive framework for "Ethical Immunity" — a set of proactive, architecture-level mechanisms designed to make the Knowledge Graph resilient to misuse without resorting to censorship or secrecy. We detail a three-pillar system: (1) AI-powered Red and Blue Teams that continuously simulate misuse scenarios and generate countermeasures; (2) Decentralized Autonomous Organizations (DAOs) for transparent, expert-driven oversight and risk assessment; and (3) "Ethical Quarantine" protocols that allow for the temporary isolation of high-risk knowledge while ensuring the parallel development of defenses. We argue that such a framework transforms the Knowledge Graph from a passive repository into an active immune system for civilization, capable of identifying and neutralizing threats at the speed of discovery. This paper provides a technical and organizational blueprint for building safety into the very fabric of 21st-century science.
Blockchain-Driven Healthcare Platform with Access-Controlled Record Management is a decentralized application designed to enhance the security, privacy, and accessibility of medical records. Traditional healthcare systems rely on centralized storage, making sensitive patient data vulnerable to breaches, manipulation, and unauthorized access. This project utilizes blockchain technology to provide a secure and tamper-proof environment for storing and managing healthcare data. Smart contracts are implemented to enforce access control, allowing patients to grant or revoke permission to doctors and healthcare providers. Medical records are securely stored using decentralized storage mechanisms, while blockchain maintains immutable references to ensure data integrity. The platform integrates Web3 technologies for secure user authentication and seamless interaction with the blockchain network. By eliminating intermediaries, the system improves transparency and trust among stakeholders. This solution demonstrates an efficient approach to managing healthcare data, ensuring confidentiality, integrity, and availability while addressing the limitations of traditional healthcare record systems in a modern, digital environment.
D. A. Vidhate, Prajesh Gaikwad, Aditya Gadge, Abhijay Jadhav · 5 authors
The growth of financial technology has introduced Central Bank Digital Currency (CBDC), which is basically a digital version of money issued by central banks. In this work, a blockchain-based system is proposed that uses QR codes and UID numbers to make transactions easier and more secure. Blockchain helps keep a proper record of transactions so they cannot be easily changed or tampered with. Using QR codes makes payments quick and simple, especially for everyday use. The system also uses smart contracts to handle processes automatically. Since everything runs on a decentralized network, it reduces dependency on a single authority and lowers the chances of fraud. At the same time, user privacy is maintained by storing only encrypted verification data instead of actual personal details.
André Saimon S. Sousa, Otto Pires, Frank Acasiete, Oscar M. Granados · 6 authors
Data plays a fundamental role in consolidating markets, services, and products in the digital financial ecosystem. However, the use of real data, especially in the financial context, can lead to privacy risks and access restrictions, affecting institutions, research, and modeling processes. Although not all financial datasets present such limitations, this work proposes the use of deep learning techniques for generating synthetic data applied to cryptocurrency price time series. The approach is based on Conditional Generative Adversarial Networks (CGANs), combining an LSTM-type recurrent generator and an MLP discriminator to produce statistically consistent synthetic data. The experiments consider different crypto-assets and demonstrate that the model is capable of reproducing relevant temporal patterns, preserving market trends and dynamics. The generation of synthetic series through GANs is an efficient alternative for simulating financial data, showing potential for applications such as market behavior analysis and anomaly detection, with lower computational cost compared to more complex generative approaches.
With the continuous expansion of blockchain application scenarios, consortium chains have raised higher performance and security requirements for consensus mechanisms. Unlike public blockchains, consortium chains typically implement an admission mechanism that restricts participation to trusted entities, ensuring that most replicas are honest and the number of faulty nodes remains small under normal circumstances. In such settings, conventional Byzantine Fault Tolerant (BFT) protocols, which are designed for worst-case adversarial scenarios, incur excessive message exchanges and computational overhead, thereby limiting performance and scalability. To address this issue, this paper proposes T-RBFT, a two-layer consensus mechanism inspired by network sharding and enhanced by the trusted execution environment (TEE). In T-RBFT, consensus nodes are first dynamically grouped based on their runtime characteristics. Then, inter-group consensus is achieved through a TEE-assisted BFT protocol, while each group internally reaches agreement using an improved Raft-based mechanism. Experimental evaluation shows that T-RBFT reduces communication overhead and latency, and achieves higher throughput compared to existing two-layer consensus protocols, providing a scalable and communication-efficient consensus protocol for permissioned blockchain networks.