The rapid growth of Cardiovascular Disease (CVD) data from heterogeneous sources, including diagnostic imaging systems, electrocardiography devices, wearable sensors, and public research repositories, has created major challenges in ensuring data confidentiality, integrity, controlled access, and scalable management. Conventional centralized data storage architectures are prone to security breaches, and limited audit transparency. To address these limitations, this paper proposes a secure and scalable Token-Based PPos Heart chain (TPPoSHChain) framework for the management of multi-source CVD datasets by integrating blockchain technology, decentralized identity, authenticated encryption, and token-based access governance. The framework employs a hybrid on-chain/off-chain architecture, where the Algorand blockchain with a Pure Proof of Stake (PPoS) consensus mechanism provides immutable audit logging and access control enforcement, while encrypted datasets are stored off-chain in the InterPlanetary File System (IPFS) to enhance scalability. ChaCha20-Poly1305 authenticated encryption is used to protect datasets prior to storage and transmission, ensuring both confidentiality and integrity. Decentralized Identifiers (DIDs) establish a self-sovereign identity layer for data contributors, and actors, eliminating reliance on centralized identity providers. A blockchain-supported token-based access control mechanism enables fine-grained authorization, usage traceability, and secure cross-institutional data sharing. Experimental evaluation demonstrates that the proposed TPPoSHChain framework significantly improves transaction throughput, reduces latency, lowers storage overhead, and achieves efficient encryption performance than other existing models, making it well suited for secure and scalable CVD datasets.
Mnemosyne: Post-Quantum Distributed AI Infrastructure via Physical Security Barriers, Speculative Consensus, and Proof-of-Useful-Work on Heterogeneous Edge Networks Overview Mnemosyne is a theoretical framework and system design for running large language model (LLM) inference on heterogeneous edge devices — from Raspberry Pi to high-end workstations — with privacy guarantees that remain valid even after quantum computers break all existing cryptographic assumptions. This paper presents 14 original theorems and 3 new network protocols, spanning five interconnected layers: Layer 1 — OS-Level Memory Management (Ch. 3.1)Formalizes a 6-tuple system model covering semantic-aware LRU page replacement, zero-copy mmap, and delta encoding. Defines four system invariants verified via TLA+ specification. Layer 2 — Information-Theoretic Compression (Ch. 3.2–3.4, Theorems 5.1–5.3)Proves that delta encoding of LLM embedding sequences achieves a lower differential entropy bound when adjacent vector correlation ρ > 0.5. Static analysis of LLaMA-2-7B confirms ρ ≈ 0.85, yielding a theoretical compression gain of ~10.88× over FP16. Full invertibility and floating-point stability bounds are proven. Layer 3 — Thermodynamic Privacy Guarantee (Ch. 5–6, Theorems 7.1–8.4)The core contribution of this paper. Mnemosyne's privacy guarantee is grounded in Landauer's Principle and the Second Law of Thermodynamics, not computational hardness assumptions. Theorem 8.3 proves that exhaustive reconstruction of compressed embeddings requires a minimum energy of 10^{38,778} joules — approximately 10^{38,709}× the total energy of the observable universe. This makes Mnemosyne the first federated learning system, to our knowledge, whose privacy bound is elevated to the level of a physical law. The system is formally characterized as an Inverse Maxwell's Demon: it actively amplifies entropy to make information reconstruction thermodynamically infeasible, rather than computationally difficult. Layer 4 — Distributed Consensus (Ch. 7, Theorems 9.1–9.2)Proves the existence and feasibility of a Global Decentralized Compute Grid (GDCG) across heterogeneous hardware. Introduces a Byzantine Fault-Tolerant (BFT) extension of the MESI protocol with three new states (RS, PF, EC), enabling zero-copy memory sharing across devices. Theorem 9.2 proves that the system-recognized Modified state exists in at most one node among all nodes (including Byzantine nodes) at any time. Layer 5 — Economic Incentive Model (Ch. 7.4, Protocol 2)Defines Proof-of-Useful-Work (PoUW), a five-dimensional incentive function replacing wasteful Proof-of-Work mining with verifiable AI inference contributions. Projected annual reward: USD 100–500 per edge device. Key Contributions First federated learning system with privacy guarantee grounded in the Second Law of Thermodynamics 14 original theorems spanning information theory, thermodynamics, distributed systems, and formal verification 3 new network protocols (BFT-MESI extension, PoUW, QClock consensus) Formal verification via TLA+ and Z3 SMT Solver Minimum hardware requirement: 8 GB RAM (ARM Cortex-A76 class), enabling LLaMA-2-7B inference on commodity edge devices Keywords Edge AI · LLM Inference · Landauer's Principle · Post-Quantum Security · Delta Encoding · Product Quantization · Byzantine Fault Tolerance · Distributed Systems · Information Thermodynamics · Maxwell's Demon · Proof-of-Useful-Work · Federated Learning
Technical Whitepaper (Genesis v1.0) This paper introduces the CLR Protocol, a Layer-1 distributed ledger designed to solve the state-bloat and inflation problems inherent in current Metaverse architectures. Unlike traditional blockchains that rely on arbitrary hashing for address generation, the CLR Chain utilizes a deterministic, bijective mapping of the 24-bit sRGB Color Spectrum to creating a finite, immutable spatial coordinate system. Key Innovations: Topological Hard Cap: The land supply is strictly bounded by the mathematical limit of the 24-bit integer space (16,777,216 unique volumetric units). O(1) Spatial Indexing: Implementation of a Direct Address Table structure replacing traditional B-Tree spatial queries. Proof-of-Spatial-Activity (PoSA): A hybrid consensus mechanism combining liquidity staking with active spatial verification challenges. Entropy Economics: An algorithmic decay function preventing passive rent-seeking and enforcing monetary velocity. This architecture establishes a "Digital Physics" layer where the visual identity of an asset (its color) acts as its cryptographic address, eliminating the abstraction gap between the user interface and the database logic.
We propose Proof of Witness (PoWit), a novel consensus mechanism for digital currency that replaces energy-intensive mining and capital-based staking with independent third-party witness verification. In PoWit, each transaction requires cryptographic signatures from three parties: sender, receiver, and a randomly selected witness. The witness validates the sender’s balance and transaction history before signing, eliminating the need for global consensus while maintaining security guarantees. Our simulation with 10,000 users demonstrates 100% double-spending prevention (n = 10, 000, 99% CI [99.93%, 100%]), 113.9 transactions per second, and complete chain integrity. The non-selective witness assignment achieves theoretical randomness with only 0.27% deviation, making collusion attacks impractical. PoWit offers a sustainable alternative to Proof of Work and Proof of Stake, with significantly lower energy consumption and fairer participation model.
Under the background of the deepening of digital China strategy and the diversification of public archives demand, the insufficient sharing and inefficient utilization of archives information resources have become a prominent bottleneck restricting the release of archives value. This paper systematically combs the policy evolution, platform practice and technology application status of file sharing in China, and finds that the lack of metadata standards, vague boundaries of powers and responsibilities, weak security prevention and control, and the interweaving of the concepts of "valuing custody and neglecting utilization" have formed systematic obstacles such as poor cross-domain circulation and mismatch between supply and demand. Therefore, this paper proposes four-dimensional collaborative paths: first, technology empowerment, deployment of alliance chain and zero-knowledge proof to achieve "availability and invisibility", and construction of multi-modal retrieval and personalized recommendation engine; Second, institutional innovation, the development of open value assessment guidelines and "negative list+white list" mechanism, the establishment of joint meetings and third-party performance audits; The third is management optimization, implementing the dual-track talent project of "archives +IT" and reshaping the accurate service process driven by user portraits; Fourth, social coordination, building a digital community of "urban memory" of archives, libraries and museums, and introducing the feedback mechanism of crowdsourcing and cultural and creative income. The research provides an operational framework for the government to formulate an open policy and the digital transformation of institutions, and promotes the archival resources from "physical concentration" to "value aggregation".
Blockchain technology is a Distributed Ledger Technology (DLT) where the digital information is stored across multiple computers and not centralized.Each system stores a copy of DLT to avoid single point of failure.Blockchain stores the information in blocks.All copies are validated and updated simultaneously.There are four main types of blockchain, they are Private / Permissioned, Public / Permissionless, Hybrid and Consortium.Corda is a distributed ledger open-source platform, it was introduced by R3 consortium (R3CEV LLC).It is not a public blockchain, based on agreement network and Peer to Peer (P2P) connections.No native cryptocurrency.Tech stack platform based out of JVM written in Koltin.
Abhinav Raghav, Aanjey Mani Tripathi, Niyaz Ahmad Wani, Naveed Ahmad · 6 authors
Data transactions in healthcare are steadily increasing across various platforms, aiming to improve patient care and increase data transparency. Blockchain technology will serve as a catalyst in healthcare data transactions, ensuring data security and privacy for various stakeholders. Improving data security, transparency, and interoperability, blockchain technology's application in healthcare has demonstrated considerable promise. However, healthcare applications that rely on real-time data transaction settlement face obstacles caused by Layer1 blockchains' poor transaction throughput and excessive latency. In this work, we adopt established consensus and a zk-Rollup verification workflow, specifying healthcare-oriented configurations for security, auditability, and throughput. This paper integrates the smart contracts, zero knowledge proof and off chain data storage to increase the efficiency, and security and reduce transaction costs. The usefulness of the suggested algorithm in healthcare applications is demonstrated by thorough literature research, comparative analysis, and experimental data. Transaction throughput increases very high, latency improved by 57%, and decrease the transaction cost to 96% in healthcare data transactions which are all greatly improved by the proposed system. Unlike existing zk-Rollup-based healthcare frameworks, the proposed model integrates cross-chain identity validation and verifiable data provenance to achieve secure interoperability across multi-chain healthcare systems.
Yubin Kim, Ken Gu, Chanwoo Park, Chunjong Park · 20 authors
Abstract Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the principles that determine their performance remain underexplored, leaving practitioners to rely on heuristics rather than principled design choices. We address this gap by deriving quantitative scaling principles for agent systems. We first formalize a definition for agentic evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, model capability, and task properties. We evaluate this across four diverse benchmarks: Finance-Agent, BrowseComp-Plus, PlanCraft, and Workbench, spanning financial reasoning, web navigation, game planning, and workflow execution. Using five canonical agent architectures (Single-Agent System and four Multi-Agent Systems: Independent, Centralized, Decentralized, Hybrid), instantiated across three LLM families, we perform a controlled evaluation spanning 180 configurations, standardizing tools, prompt structures, and token budgets to isolate architectural effects from implementation confounds. We derive a predictive model using empirical coordination metrics, including efficiency, overhead, error amplification, and redundancy, that achieves cross-validated 𝑅^2=0.524, enabling prediction on unseen task domains by modeling task properties rather than overfitting to a specific dataset. We identify three dominant effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead. (2) a capability saturation: we observe that coordination yields diminishing or negative returns (𝛽=−0.404, 𝑝
Aditya Rathore, Kratika Mishra, Vidhi Chandrayan, Pareek Ch. S.
Blockchain technology has evolved into one of the most influential digital innovations of the 21st century, enabling decentralized, trustless, and tamper‑resistant data management across global networks. Its rapid rise can be attributed to groundbreaking applications across cryptocurrencies, decentralized finance (DeFi), healthcare, supply chain, and identity management systems. Despite this explosive growth, blockchain technology still faces major challenges—most critically, scalability. This extended study explores blockchain’s historical development, factors driving adoption, technical architecture, and the limitations restricting mass deployment. The paper includes an in‑depth analysis of publicly available blockchain datasets that support research in security, analytics, and scalability modeling. Furthermore, the study reviews emerging scalability frameworks such as sharding, off‑chain computation, Layer‑2 rollups, DAG-based systems, and consensus optimization. The goal is to provide a comprehensive foundation for understanding blockchain’s evolution while outlining future paths toward global-scale adoption.
Currently housing finance transaction platforms face challenges of data protection and cybersecurity. Blockchain technology, with its decentralization, non-tampering and high transparency, has become an effective tool for securing transaction data. In this paper, a blockchain-based data protection scheme for housing finance transaction platform is designed, which combines the shared energy storage system and realizes the cyber security protection of the transaction platform by optimizing the PBFT consensus mechanism. Methodologically, distributed file storage technology (IPFS) and smart contracts are adopted to ensure data encryption, storage and transaction transparency. Experimental results show that the proposed scheme excels in smart contract execution time, with a maximum execution time of 0.8ms, and achieves a significant increase in TPS when the concurrent volume of transactions reaches 1,200, and the throughput of the dual-chain architecture is increased by 28% compared to the traditional single-chain architecture. In addition, the system with ITPBFT consensus mechanism reduces the communication overhead by 46.19% compared to the traditional PBFT, and the consensus delay is also significantly reduced, with an efficiency improvement of 53.61%. The study shows that the proposed optimization scheme can enhance the efficiency and reliability of data transactions while improving the security of the system.
Cryptocurrency is a novel exploration of a form of currency that proposes a decentralized electronic payment scheme based on blockchain technology and cryptographic theory. While blockchain has the security characteristics of being distributed and tamper-proof, increasing market demand has led to a rise in malicious transactions and attacks, thereby exposing cryptocurrency to vulnerabilities, privacy issues, and security threats. Particularly concerning are the emerging types of attacks and threats, which have made securing cryptocurrency increasingly urgent. This paper classifies existing cryptocurrency security threats and attacks into five fundamental categories based on the blockchain infrastructure, and it analyzes in detail the vulnerability principles exploited by each type of threat and attack. Furthermore, the paper examines the attackers’ logic and methods and provides insights that enable easy reproduction of the vulnerabilities. We also summarize and evaluate existing detection and defense solutions, offering important references for ensuring cryptocurrency security. Finally, the paper discusses the future development trends of cryptocurrency.
Abstract The emerging growth of Quantum computing has significant challenges to change the classical cryptographic protocols, the security of AI–Blockchain systems to provide long-term security and provide federated learning (FL) for current cryptographic systems. This paper introduces a new framework as Quantum-Resistant Federated AI on Blockchain (QFAIB) that integrates Post-Quantum Cryptography (PQC) algorithms with real-time Federated AI Learning (FAIL) on distributed ledgers creation. The proposed QFAIB ensures end-to-end data security and confidentiality, decentralized trust, and adaptive intelligence, resistant to quantum decryption attacks. Through the integration of hybridization of CRYSTALS-Kyber encryption to create challenging task, that makes the Dilithium digital signatures, and Zero-Knowledge Proofs (ZKP) for privacy-preserving model validation and decentralized federated AI models, the proposed QFAIB compared with baseline and evaluated as the performance metrics as data integrity verification accuracy (DIVA), auditing efficiency (AE), quantum resistance efficiency (QRE), privacy leakage reduction (PLR) and throughput (TT) in multi-cloud and IoT environments and this work proved that real time distributed ledger creations.
SECTION IV — E-Coin Technical Design & Architecture E-Coin is not a currency, but an Operating System for civilization. This section describes the technical and architectural design of E-Coin as a civilizational operating system that separates, yet co-evolves, value, cognition, and agency. E-Coin adopts a three-layer architecture composed of a Distributed Ledger Layer (Value Foundation), an AI Cognitive Layer (Reason Engine), and a Human Interface Layer (Mind-OS). This separation prevents the concentration of power while enabling interoperability between human decision-making, AI inference, and value exchange. The design explicitly prohibits AI systems from overriding human agency, positioning AI instead as a cognitive collaborator and translator. At the foundation, the Distributed Ledger Layer employs zero-knowledge proofs, decentralized identifiers, and post-quantum cryptography to ensure security, privacy, and human rights by default. Data ownership remains with individuals at all times, supported by built-in rights to deletion, anonymization, and refusal of access. Unlike conventional cryptocurrencies or CBDCs, this layer is consent-based and cognition-centered rather than economy-centric. The AI Cognitive Layer functions as a civilization-wide reasoning substrate. It includes alignment cores, non-numerical cognitive reputation indices, adaptive governance agents, and layered memory management across individual, collective, and civilizational scales. While AI systems may negotiate and coordinate at this layer, decision authority is structurally constrained to remain human-centered. The Human Interface Layer (Mind-OS) focuses on the expansion of human consciousness rather than dependency or control. It includes mechanisms for cognitive load scaling, consciousness mode switching, and protection against emotional inducement or manipulation. Together, these layers form an evolvable, future-proof architecture designed to remain stable as both AI capabilities and civilization itself continue to evolve. E-Coin does not replace existing systems but integrates with Web3, AI/AGI, smart cities, and emerging technological domains through synthesis rather than disruption. Keywords E-Coin, civilizational OS, AI architecture, human-AI interface, distributed systems, ethical AI
Contemporary enterprises encounter substantial difficulties managing information dispersed across varied cloud infrastructures, geographically separated facilities, and specialized application environments. Traditional centralized frameworks, including consolidated data repositories and analytical warehouses, demonstrate limited capacity to deliver the required velocity, accuracy, and contextual intelligence necessary for sustained digital progression. Multi-Cloud Data Mesh constitutes a transformative architectural approach, advocating decentralized, domain-centric methodologies that systematically address intricate governance complexities and interoperability obstacles at the organizational scale. This framework establishes operational foundations through four fundamental tenets: Domain-Oriented Ownership, Data as a Product, Self-Serve Platform, and Federated Computational Governance. These architectural pillars collectively resolve decentralization imperatives, scalability prerequisites, interoperability complications, and sovereignty considerations inherent in modern enterprise ecosystems. Through ownership distribution to specialized domains, product-oriented information treatment, self-service platform provisioning, and federated governance implementation, organizations attain necessary scalability, operational flexibility, and contextual precision for continuous innovation across sophisticated multi-cloud landscapes
Matteo Loporchio, Damiano Di Francesco Maesa, Anna Bernasconi, Laura Ricci
Abstract The ERC-1155 standard introduced on the Ethereum blockchain allows for managing multiple tokens, both fungible and non-fungible, within a single contract. It also supports batch transfers, thereby reducing transaction costs and enabling a more efficient use of blockchain resources. To assess its impact and level of adoption, this paper presents a comprehensive analysis of the ERC-1155 token ecosystem. First, we examine the activity of ERC-1155 contracts and compare the evolution of transfer volumes with those of the two alternative most popular token management standards. Next, we model the economy of each ERC-1155 contract as a directed graph, where nodes represent users and edges denote token transfers. We then study the topological properties of such graphs, analyzing approximately 40,000 networks until the end of 2024. Results indicate that, within our dataset, the adoption of ERC-1155 is growing, although its functionalities are not being fully utilized. Additionally, about 60% of the networks exhibit a completely centralized topology, while the remaining ones are generally sparse and lack small-world characteristics. Finally, the degree distribution analysis shows that preferential attachment is only present in a minority of the networks and the graphs also display a mild disassortative behavior.
Journal of Theoretical and Applied Information Technology
With the growing volume of health information it has become common practice to protect the patient identity while maintaining convenient access to the data. Due to varying flow of cyber security threats, traditional solutions never manage to get flexible access to data without compromising with overflow of data. To overcome these challenges focusing on patient data protection, in this paper, we propose a new Hybrid Integrated Hashing approach entitled "Dynamic Adaptive Hash-Block Access Control (DAHBAC) framework" using blockchain based advanced data access control mechanism. The dynamic multi factor hashing scheme can change in response to the current Vulnerability of data and access patterns, whereas data access control refers to leverage blockchain's immutability and decentralized structure that helps protecting patient privacy while allowing authorized persons to read. The dynamic hashing method prevents intruder attempts by making hash and easy to calculate but requiring real-time modification of the hash for access protection. This is made possible by harnessing the application of zero-knowledge proofs (ZKP) within the frame of blockchain to enable verification of information when there is no disclosure of the data. Compared with the conventional methods, testing of prototype in a health care organization resulted in 92% on attempts by unauthorized workers to enter the system and 7% increasing data retrieval rate. These findings shows that the proposed model is a perfect patient data protection pattern in ehealth systems, because it is not only secures patients data but also enhances the accessibility and scalability to handle more clients. It is enabled by the use of zero-knowledge proofs (ZKP) in combination with blockchain technology to verify information, while keeping the information secret.
Smart contract technology facilitates self-executing agreements on the blockchain, eliminating dependency on an external trusted authority. However, smart contracts may expose vulnerabilities that can lead to financial losses and disruptions in decentralized applications. In this work, we evaluate deep learning-based approaches for vulnerability scanning of Ethereum smart contracts. We propose VASCOT, a Vulnerability Analyzer for Smart COntracts using Transformers, which performs sequential analysis of Ethereum Virtual Machine (EVM) bytecode and incorporates a sliding window mechanism to overcome input length constraints. To assess VASCOT's detection efficacy, we construct a dataset of 16,469 verified Ethereum contracts deployed in 2022, and annotate it using trace analysis with concrete validation to mitigate false positives. VASCOT's performance is then compared against a state-of-the-art LSTM-based vulnerability detection model on both our dataset and an older public dataset. Our findings highlight the strengths and limitations of each model, providing insights into their detection capabilities and generalizability.
We present Y.I.N.-LLM, a privacy-preserving training architecture for Large Language Models that mathematically guarantees non-memorization of training data. The core innovation is the mandatory DP→ZK→HE ordering (Differential Privacy → Zero-Knowledge Proof → Homomorphic Encryption) applied to transformer gradients during training. Key results: (1) 2.3% accuracy loss at ε=1.0 privacy versus 15-40% with standard DP-SGD; (2) zero extractable training data across all tested attack vectors; (3) native GDPR Article 17 "right to be forgotten" compliance via cryptographic gradient subtraction; (4) EU AI Act Article 50 transparency compliance through verifiable privacy proofs. The Non-Memorization Theorem establishes that for any model M trained with Y.I.N.-LLM parameters (ε, δ), the probability of verbatim reproduction is bounded: P[M outputs y | x ∈ training] ≤ e^ε · P[M outputs y | x ∉ training]. This transforms copyright defense from argument to mathematics. Y.I.N.-LLM addresses the $10B+ memorization litigation crisis (NYT v. OpenAI, Getty v. Stability AI, Authors Guild v. OpenAI) by providing the first mathematically verifiable non-memorization guarantee with practical accuracy preservation. Patent Protected: U.S. Provisional Application 63/946,118 (filed December 21, 2025).
Ahmad Musamih, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman · 5 authors
Large Language Models (LLMs) are increasingly embedded in intelligent systems across domains such as healthcare, finance, and smart infrastructure. However, their reliance on centralized data pipelines raises unresolved challenges concerning provenance, accountability, and verifiable trust. As the demand for transparent and regulation-aligned AI grows, these challenges have become central to the responsible deployment of intelligent systems. This review examines how blockchain technology can address them by introducing decentralized integrity, immutable audit trails, and cryptographic verification into the LLM lifecycle. Through a structured synthesis of current research, we identify conceptual and architectural gaps that limit trustworthy data management, inference authentication, and explainability. To bridge these gaps, a methodological framework is proposed that integrates blockchain mechanisms across the LLM pipeline using smart contracts, Merkle-based commitments, and decentralized storage. The framework’s feasibility is demonstrated through an illustrative prototype, confirming its practical applicability for building verifiable and transparent AI infrastructures. We further outline application domains such as healthcare, smart cities, Industry 4.0, and supply-chain management, where blockchain-anchored LLMs can enhance auditability and regulatory compliance. The review concludes by highlighting key insights and challenges for future research, emphasizing the need for decentralized attestation models, scalable verification protocols, and governance mechanisms that advance accountable and privacy-preserving intelligent systems.
Open access
Artificial Intelligence in Healthcare and Education
The rapid adoption of blockchain technology has intensified the need for robust smart contract security mechanisms. However, traditional rule-based or static analysis tools often fail to detect context-dependent vulnerabilities embedded in complex contract logic. This study proposes a deep learning framework for automated smart contract vulnerability classification using a Bidirectional Long Short-Term Memory (BiLSTM) network integrated with an Attention Mechanism. The model was trained and evaluated on the SC_Vuln_8label.csv dataset, comprising 12,520 labelled Solidity smart contracts categorized into eight distinct vulnerability types, including Re-entrancy, Integer Overflow, and Short Address Attack. Through bidirectional contextual learning and attention-based feature weighting, the proposed model achieved 93.7% test accuracy, 0.93 precision, and a macro F1-score of 0.92, outperforming baseline models such as CNN, GRU, and standard LSTM by up to 5.3 percentage points. Attention heatmap analysis further revealed the model’s interpretability by highlighting vulnerability-prone code segments (e.g., call.value, send(), and withdraw() functions) consistent with expert-identified risk indicators. These results demonstrate that the BiLSTM + Attention framework not only enhances vulnerability detection accuracy but also provides transparent and explainable reasoning, offering a reliable foundation for AI-assisted smart contract auditing systems in blockchain security.
The convergence of the Internet of Things and edge computing represents a fundamental transformation in distributed computing architecture. Traditional cloud-centric models introduce latency and connectivity dependencies flawed for time-touchy packages. Side computing addresses such constraints by positioning computational sources at network peripheries. Distributed processing paradigms restructure data pipelines through intermediate layers between endpoint devices and centralized infrastructure. Fog nodes extend cloud capabilities to locations where data originates. Tiered computation models distinguish between device-level processing, gateway computation, and cloud-based analytics. Aspect synthetic intelligence allows deployment of state-of-the-art machine learning models on resource-limited hardware. Neural network compression strategies consisting of quantization and pruning lessen version complexity while keeping accuracy. Fifth-generation wireless networks provide a connectivity fabric essential for distributed deployments. Multi-access edge computing positions processing resources at radio access network edges. Computation offloading transfers tasks from mobile devices to edge servers strategically. Security frameworks address expanded attack surfaces through zero-trust models and blockchain-based identity management. Distributed ledger architectures eliminate centralized credential repositories. Smart contracts automate security policy enforcement across edge networks reliably.
We investigate whether China's 2021 mining ban transformed Bitcoin from a speculative vehicle into a macro-sensitive asset. Using daily data from 2017-2025, we document a decisive structural break. Pre-2021, volatility was endogenous, driven by raw trading volume rather than fundamentals. Post-ban, however, internal microstructure noise loses predictive power. Instead, volatility is now driven by macroeconomic anxiety: Wikipedia searches for "Inflation" improve forecast accuracy by over 7%. We further uncover a "dual narrative" where inflation attention predicts crash risk, while "Recession" queries (pivot speculation) drive rallies. These findings suggest the regulatory shock successfully curtailed noise trading, allowing macro-fundamentals to dominate price discovery.