A Software Bill of Materials (SBOM) is a key component for the transparency of software supply chain; it is a structured inventory of the components, dependencies, and associated metadata of a software artifact. However, an SBOM often contain sensitive information that organizations are unwilling to disclose in full to anyone, for two main concerns: technological risks deriving from exposing proprietary dependencies or unpatched vulnerabilities, and business risks, deriving from exposing architectural strategies. Therefore, delivering a plaintext SBOM may result in the disruption of the intellectual property of a company. To address this, we present VeriSBOM, a trustless, selectively disclosed SBOM framework that provides cryptographic verifiability of SBOMs using zero-knowledge proofs. Within VeriSBOM, third parties can validate specific statements about a delivered software. Respectively, VeriSBOM allows independent third parties to verify if a software contains authentic dependencies distributed by official package managers and that the same dependencies satisfy rigorous policy constraints such as the absence of vulnerable dependencies or the adherence with specific licenses models. VeriSBOM leverages a scalable vector commitment scheme together with folding-based proof aggregation to produce succinct zero-knowledge proofs that attest to security and compliance properties while preserving confidentiality. Crucially, the verification process requires no trust in the SBOM publisher beyond the soundness of the underlying primitives, and third parties can independently check proofs against the public cryptographic commitments. We implement VeriSBOM, analyze its security, and evaluate its performance on real-world package registries. The results show that our method enables scalable, privacy-preserving, and verifiable SBOM sharing and validation.
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Physical Unclonable Functions (PUFs) and Hardware Security
Komang Irvan Tri Permadi, Si Ngurah Ardhya, Ratna Artha Windari
Skripsi ini membahas mengenai analisis yuridis perlindungan hak cipta atas gambar Non Fungible Token (NFT) ditinjau dari Undang-Undang Hak Cipta. Penelitian ini bertujuan untuk memahami pengaturan, dan perlindungan hak cipta atas gambar yang diperuntukkan sebagai Non-Fungible Token yang berada di indonesia dengan menggunakan perbandingan negara amerika dan UniEropa. Jenis penelitian hukum normatif penelitian ini terfokus kepada doktrin ataupun peraturan perundang-undangan (law in books) dipandang dari hukum positif atau das sollen. Bahan hukum primer, sekunder, dan tersier adalah sumber bahan hukum yang akan digunakan sebagai acuan dalam merancang penelitian normatif ini. penelitian normatif terutama berkaitan dengan data sekunder, yang mencakup undang-undang, putusan pengadilan, teori hukum, konsep hukum, dan hasil penelitian ilmiah akademisi (doktrin). Hasil dari penelitian ini menunjukan bahwa Pengaturan Hak Cipta di Indonesia seperti Undang-Undang Nomor 28 Tahun 2014 Tentang Hak Cipta. Belum secara spesifik mengatur keberadaan hak cipta melalui NFT. Selain itu, bentuk sistem pengawasan hak cipta belum sepenuhnya adaptif terhadap teknologi baru seperti blockchain. Di Amerika Serikat, undang-undang hak cipta, terutama Digital Millennium Copyright Act (DMCA) dan Undang-Undang Hak Cipta 1976, mengatur perlindungan karya yang dicetak sebagai NFT. Uni Eropa belum memiliki UU khusus yang mengatur hak cipta NFT secara spesifik, Pengaturan Directive on Copyright In The Digital Single Market (EU 2019/790) dalam pasal 17 yang memberikan hak eksklusif kepada pemegang hak cipta atas karya digital.
This paper presents a framework that integrates blockchain-enabled Federated Learning (FL) with consensus mechanisms to mitigate poisoning attacks in healthcare environments. The framework incorporates blockchain consensus mechanisms, with Proof-of-Work (PoW) used as a baseline and Proof-of-Stake (PoS) adopted as the proposed approach; both are evaluated independently within the same Secure Multiparty Computation (SMPC)-enabled federated learning architecture for privacy preservation. The proposed system is evaluated on the OCTMNIST and TissueMNIST datasets under both centralized and federated settings, including poisoning scenarios with 10% and 50% malicious clients. Results show that consensus-aware aggregation reduces the influence of unreliable client updates and improves the robustness of the global model under poisoning conditions. In addition, the framework prioritizes trustworthy client contributions during aggregation, supporting reliable model sharing in collaborative healthcare learning environments. Unlike prior blockchain-based federated learning defenses that introduce heavy cryptographic overhead, the proposed PoS-based aggregation explicitly balances robustness and computational efficiency, enabling practical deployment under high poisoning ratios.
Sihao Hu, Selim Furkan Tekin, Yichang Xu, Ling Liu
Launchpads have become the dominant mechanism for issuing memecoins, exposing investors to a new class of high-risk launches that existing rug-pull detection methods cannot capture. We argue that detecting these threats requires structured behavioral traces that underlie raw heterogeneous blockchain data, i.e., how insiders accumulate, coordinate, and unwind positions. To enable such analysis, we introduce MELT (MEmecoin Launch Trace, the first behavioral trace dataset for analyzing and detecting high-risk memecoin launches on Solana. MELT covers 41k+ memecoin launches with 200M+ transactions parsed into typed behavioral records that distinguish swaps, wash trades, transfers, and mints. Beyond per-account behaviors, MELT contributes bundle-trace data that links accounts controlled by the same entity, revealing that, on average, 36.5% of token supply is held by coordinated accounts, a concealment strategy that disguises the true ownership concentration from unsuspecting buyers. On top of these traces, MELT provides 122 behavioral features and risk-level annotations, enabling supervised learning at a population scale. We benchmark representative ML models on the high-risk launch detection task. Integrating their predictions into a simple memecoin selection strategy reduces investment loss significantly, demonstrating that behavioral traces can be translated into risk mitigation. Our dataset and code is available at https://github.com/git-disl/MELT.
This article introduces the co-encoding/decoding (co-en/de) model to theorize the stakes of human–Generative AI (GenAI) communication. Extending Stuart Hall’s encoding/decoding framework, the model conceptualizes human–GenAI interactions as recursive circuits of meaning-making. Through a typology of co-encoding and decoding modes, this article identifies how these interactions feed back into three core conditions of subjectivity—knowledge, agency, and power infrastructures—ultimately shaping the relational ontology of human actors and GenAI systems. We argue that 2 emergent hybrid subjectivities are formed on a fluid spectrum, from the Android, a diminished human subject shaped by machinic hegemony, to the Cyborg, an augmented subjectivity that fosters resistance and democratic agency. By moving beyond techno-determinist and institutionalist accounts, this study offers a relational, critical framework for understanding how GenAI systems mediate discourse and subject formation. It concludes by outlining the conditions under which counter-hegemonic human–GenAI interactions may emerge.
This study develops a blockchain-enabled framework to improve educational administration data sharing by addressing persistent challenges related to data silos, security risks, trust deficits, and inefficient approval workflows that hinder cross-institutional collaboration. The approach examines the alignment between blockchain capabilities and data-sharing requirements and designs a consortium-chain implementation pathway that integrates distributed ledgers, smart contracts, encryption, and dynamic authorization across both standardized and emerging scenarios. A multi-province pilot involving 40 institutions was conducted to evaluate impacts on efficiency, security, and user trust. The results demonstrate an 89% reduction in processing time, near-zero internal tampering, substantial decreases in error rates, and significant improvements in perceived data credibility and clarity of rights and responsibilities. These findings indicate that blockchain can function as a foundational trust infrastructure, supporting the development of secure, accountable, and scalable educational data ecosystems.
The paper investigates tax risks arising in the taxation of cryptocurrency transactions in Ukraine and in the broader international context. It substantiates that the absence of a unified legal qualification of cryptocurrencies significantly complicates the identification of the taxable object, the determination of the tax base, and the establishment of the moment at which tax liabilities arise. The paper highlights key challenges associated with the high volatility of digital assets, the insufficient transparency of transaction recording mechanisms, the complexity of verifying the origin of crypto assets, and the increased risks of tax evasion. Particular attention is devoted to the transnational nature of cryptocurrency circulation, which creates favorable conditions for tax arbitrage, regulatory fragmentation, and manipulation of tax residency. These phenomena weaken the effectiveness of national tax systems and generate additional threats to fiscal stability. It is argued that existing regulatory approaches in many jurisdictions remain fragmented and inadequately adapted to the specific features of decentralized digital technologies. The paper identifies priority directions for mitigating tax risks, including the harmonization of national legislation with international standards, the development of a coherent and unified model for the taxation of digital assets, the improvement of financial monitoring mechanisms, and the enhancement of transparency in cryptocurrency-related transactions. The paper concludes that only a systematic, balanced, and coordinated approach to the legal regulation of the cryptocurrency market is capable of ensuring tax certainty, strengthening compliance, and reducing risks both for the state and for market participants.
We test price efficiency, which shows the fairness of trading for retail investors using the runs tests and variance ratio tests. We reject the hypothesis that Bitcoin prices are price efficient on most markets, but efficient on the Bitstamp BTC/USD. Coinbase departs from efficiency, indicating that fraud, later found by regulators, has significantly harmed retail investors. We also document barriers to trading of Bitcoin, which result in difficulties in arbitrage despite global price differences. My results predict the hack of the Bitfinex exchange, which caused it to close and harmed many people.
This study explores the key determinants influencing cryptocurrency in Indonesia, focusing on macroeconomic variables including inflation, money supply, gold prices, and crude oil prices over the period from 2013 to 2023. It investigates the dynamic relationships between these variables and Bitcoin, the most widely recognized cryptocurrency globally. The research offers a novel contribution by integrating both domestic economic indicators and external commodity prices into a comprehensive framework for cryptocurrency pricing tailored specifically to the Indonesian market context. This innovative and comprehensive approach significantly enhances the understanding of how macroeconomic factors interact with cryptocurrency behavior, which is crucial for various stakeholders and policymakers alike. The findings aim to provide valuable insights to support the formulation of effective monetary policies in an evolving, increasingly complex financial landscape. Future studies are encouraged to build upon this framework by examining the connections between cryptocurrency and other components of the broader financial system.
Digitization of healthcare has provided opportunities for improving patient care but also has brought with it major security vulnerabilities that could compromise the confidentiality, availability, and integrity of protected health information. This article reviews the proposed distributed system constructs for providing health data security between heterogeneous systems, organizations, and multiple institutions. It categorizes and reviews three approaches to distributed healthcare security: (1) Advanced Encryption Algorithms, including symmetric, asymmetric and homomorphic algorithms for encrypting health information-at-rest and in-transit, and key management mechanisms for secure access to cryptographic material across multiple nodes that may not be trusted; (2) Distributed Storage Systems, including distributed-ledger technology (DLT), distributed file systems, and fragmentation approaches for immutable patient consent and audit trail logging, redundancy to tolerate physical node compromise, and avoiding total infrastructure data loss due to localized security attacks; and (3) Access Control Mechanisms, including multi-factor authentication, role-based access control, attribute-based access control, federated identity management for distributed healthcare organizations, and patient access control and monitoring for distributed threat detection. The distributed model is now more attractive in modern health systems. Perimeter security models do not adequately protect health data. The health data moves through networks connecting hospitals, outpatient clinics, clinical research organizations, insurance companies, and third-party organizations. The proposed framework satisfies regulations according to HIPAA, the General Data Protection Regulation (GDPR), and the Health Information Technology for Economic and Clinical Health (HITECH) Act. The system can be performance optimized to balance between cryptographic strength and system responsiveness. The combination of encryption, decentralized storage, and access control provides defense-in-depth protection against cyberattacks. Future developments, such as artificial intelligence-enabled threat detection, quantum-resistant cryptographic algorithms and models for patient data control will shape how to create secure healthcare systems in our growing digital health networks.
The accelerating adoption of electric vehicles (EVs) is intensifying pressure on urban power grids, particularly during evening peak hours. Existing smart-charging frameworks remain constrained by centralized control, static pricing, and limited integration of predictive intelligence. This study presents SMARGE, a hybrid AI–Blockchain smart charging platform that combines load forecasting, dynamic pricing, and cryptocurrency-based incentives to enhance decentralized EV energy management in Gaziantep Province. An ensemble of forecasting models (SARIMA, LightGBM, N-BEATS, and TFT) predicts 2026 hourly electricity demand, while an adaptive inverse-sigmoid pricing mechanism generates real-time incentives and disincentives for EV charging behavior. A fuzzy logic-based behavioral model simulates both unmanaged and managed charging across three scenarios. Results show that managed charging reduces peak load by 22.43%, shifts 67.45% of energy demand to off-peak periods, and achieves 94.86% charging fulfillment under constrained grid conditions. The blockchain layer—implemented through a custom ERC-20 token (SMARGE) on the Ethereum Sepolia testnet—enables secure, transparent, and low-cost microtransactions with an average confirmation time of 0.63 s. These findings demonstrate that tightly coupling AI forecasting with tokenized blockchain incentives can improve grid stability, lower operational costs, and enhance user autonomy in a scalable and decentralized manner. While promising, the study is limited by assumptions of synthetic user behavior and ideal communication conditions; future work will validate the platform in real-world pilot deployments and across different urban regions.
Foundational results in machine learning establish that all human labor may in principle be automatable. Without deliberate intervention, this trajectory risks concentrating productive capacity in a handful of corporations, resulting in techno-feudalism: mass economic redundancy, surveillance-based control and dependence on corporate benevolence for survival. To avert this outcome, this paper introduces anarchist automation, a rigorously defined sociotechnical framework grounded in the 200-year anarchist tradition from Godwin through Kropotkin to Bookchin for ensuring that full automation is decentralized and oriented toward universal care. Specifically, I state five formal hypotheses and six research objectives, present a formal definition through analytical categories of interdependent spheres, and propose the Liberation Stack as a layered technical architecture with explicit preconditions and gate conditions for each layer, incorporating crypto-economic coordination tools appropriated from the crypto-anarchist tradition for commons financing and governance. Furthermore, I introduce Universal Desired Resources as a post-monetary design principle that eliminates the material basis of intersectional oppression, and address the Mises-Hayek economic calculation problem by arguing that AI-based distributed optimization and federated preference elicitation can substitute for market price signals under conditions of material abundance. I develop a framework for progressive state dissolution through incremental, reversible commons-building compatible with existing democratic institutions. Empirical evidence from Linux, Mondragon and contemporary commons initiatives confirms that commons-based systems already operate at scale. Finally, I conclude with a phased roadmap specifying explicit assumptions, hard constraints, gate conditions between phases, and detailed limitations.
KRILL — Bio-Inspired Architecture for IoT Consensus Decentralized IoT consensus without blockchain — inspired by ant colonies, immune systems & chemical diffusion. What is KRILL? The problem: Blockchain doesn't work for IoT. It's too heavy, too slow, and too expensive for devices running on batteries with 32KB of RAM. IoT needs to answer "What is the physical state of the world?" — not "Who has how much money?" The solution: KRILL replaces blockchain with 9 mechanisms borrowed from biology: Mechanism Biological inspiration What it does Stigmergic Consensus Ant pheromone trails Nodes "deposit" readings like ants deposit pheromones. Truth emerges from convergence, not voting. Pentastratic Immune System Human immune layers 5-layer anomaly detection: skin (format check) → innate (statistical) → adaptive (learned) → NK audit → autoimmune suppression. Metabolic State Cell metabolism Data has a "half-life" — old readings decay and die automatically. No infinite ledger. Entropic Data Valuation Thermodynamic entropy Network autonomously decides which data is worth storing based on information theory. Quorum Sensing Bacterial quorum sensing Nodes detect local density and switch modes (solo → quorum → swarm) without any coordinator. Horizontal Gene Transfer Bacterial gene sharing Firmware updates spread node-to-node like genes between bacteria. No update server needed. Morphogenetic Topology Embryonic development Network self-organizes its topology using reaction-diffusion (Turing patterns). Thymic Tolerance T-cell training in thymus System learns what "normal" looks like to avoid false alarms. Immunological Memory Vaccine/antibody memory Once the network detects an attack pattern, it "vaccinates" all nodes. The result: 1000x less energy than blockchain consensus Runs on a $2 ESP32 microcontroller (240KB RAM) Works with intermittent connectivity (mesh, BLE, LoRa, WiFi) No miners, no staking, no tokens — consensus is grounded in physical reality Scales to millions of nodes without coordinator Status: Research paper + engineering specification. No working implementation yet. Documents Document Description Research Paper (HTML) Full academic paper — mathematical formalizations, energy analysis, novelty assessment, risk analysis. 20 sections. Open in browser → Print → Save as PDF. Engineering Specification (HTML) Implementation reference — byte-level wire formats, state machines, pseudocode, test vectors, transport layers. Ready to code from. Source files (Markdown): krill-bioinspired-architecture.md — Research paper krill-bia-engineering-spec.md — Engineering spec Architecture at a Glance ┌─────────────────────────────────────────────────────────┐ │ KRILL Node (ESP32) │ ├──────────┬──────────┬──────────┬──────────┬─────────────┤ │ Stigmer- │ Immune │ Metabolic│ Quorum │ Morpho- │ │ gic │ System │ State │ Sensing │ genetic │ │ Consensus│ (5-layer)│ (decay) │ (modes) │ Topology │ ├──────────┴──────────┴──────────┴──────────┴─────────────┤ │ Transport: BLE mesh / WiFi / LoRa │ ├─────────────────────────────────────────────────────────┤ │ PUF Identity + Ed25519 Enrollment │ └─────────────────────────────────────────────────────────┘ MVP — Where to Start If you want to implement KRILL, start with these 4 subsystems (the rest can be added later): ES-13 — Cryptographic enrollment (PUF + Ed25519 identity) ES-12 — Transport layer (BLE mesh for local, WiFi for bridging) ES-1 — Core data types and wire formats ES-3 — Stigmergic Consensus (the core algorithm) ES-10 — Main event loop and message dispatch Target hardware: ESP32 (Nano node) + nRF52840 (Dust node, optional) Why Not Blockchain? Blockchain (e.g. Ethereum) KRILL-BIA Consensus energy ~50 Wh/tx (PoW) or ~0.01 Wh/tx (PoS) ~0.00001 Wh/tx Minimum RAM 512MB+ 32KB (Dust), 240KB (Nano) State growth Infinite (append-only) Bounded (data decays) Offline tolerance Minutes before fork Days (pheromone half-life) Hardware cost $50+ SBC $2 ESP32 Finality Probabilistic (blocks) Convergent (pheromone field) Key Innovation: Physical-World Consensus Grounding Unlike blockchain where consensus is purely computational, KRILL grounds consensus in physical reality: Sensor readings must be physically plausible (a thermometer can't jump 50C in 1 second) Nodes that are physically closer have more weight (radio signal strength = distance proxy) The laws of physics constrain what values are possible — this is a defense layer that doesn't exist in financial systems This means an attacker must not only compromise the software but also defeat physics — a fundamentally harder problem. Contributing See CONTRIBUTING.md for how to get involved. Areas where help is most needed: Rust/C firmware for ESP32 (core protocol implementation) Simulation — model pheromone convergence with 100-10,000 virtual nodes Hardware testing — BLE mesh range, LoRa timing, PUF enrollment on real chips Security review — formal verification of immune system thresholds Documentation — diagrams, tutorials, translations License This project is licensed under the MIT License. Supporting This Work If KRILL is useful to your research or organization, consider supporting further development: ETH / ERC-20 / Base / Arbitrum / Polygon: 0x0BC290355c0B16B5B247701B7BC9AB2E1e61ffa7 Funds go toward: Reference firmware for ESP32 + nRF52840 Hardware test beds (100-node BLE mesh) Independent security audits Bug bounty program for protocol vulnerabilities Code contributions are equally welcome — see CONTRIBUTING.md.
The rapid growth of decentralized systems in theWeb3 ecosystem has introduced numerous challenges, particularly in ensuring data security, privacy, and scalability [3, 8]. These systems rely heavily on distributed architectures, requiring robust mechanisms to manage data and interactions among participants securely. One critical aspect of decentralized systems is key management, which is essential for encrypting files, securing database segments, and enabling private transactions. However, securely managing cryptographic keys in a distributed environment poses significant risks, especially when nodes in the network can be compromised [9]. This research proposes a decentralized database scheme specifically designed for secure and private key management. Our approach ensures that cryptographic keys are not stored explicitly at any location, preventing their discovery even if an attacker gains control of multiple nodes. Instead of traditional storage, keys are encoded and distributed using the BFLUT (Bloom Filter for Private Look-Up Tables) algorithm [7], which enables secure retrieval without direct exposure. The system leverages OrbitDB [4], IPFS [1], and IPNS [10] for decentralized data management, providing robust support for consistency, scalability, and simultaneous updates. By combining these technologies, our scheme enhances both security and privacy while maintaining high performance and reliability. Our findings demonstrate the system's capability to securely manage keys, prevent unauthorized access, and ensure privacy, making it a foundational solution for Web3 applications requiring decentralized security.
Accurate forecasting of Bitcoin (BTC) has always been a challenge because decentralized markets are non-linear, highly volatile, and have temporal irregularities. Existing deep learning models often struggle with interpretability and generalization across diverse market conditions. This research presents a hybrid stacked-generalization framework, TFT-ACB-XML, for BTC closing price prediction. The framework integrates two parallel base learners: a customized Temporal Fusion Transformer (TFT) and an Attention-Customized Bidirectional Long Short-Term Memory network (ACB), followed by an XGBoost regressor as the meta-learner. The customized TFT model handles long-range dependencies and global temporal dynamics via variable selection networks and interpretable single-head attention. The ACB module uses a new attention mechanism alongside the customized BiLSTM to capture short-term sequential dependencies. Predictions from both customized TFT and ACB are weighted through an error-reciprocal weighting strategy. These weights are derived from validation performance, where a model showing lower prediction error receives a higher weight. Finally, the framework concatenates these weighted outputs into a feature vector and feeds the vector to an XGBoost regressor, which captures non-linear residuals and produces the final BTC closing price prediction. Empirical validation using BTC data from October 1, 2014, to January 5, 2026, shows improved performance of the proposed framework compared to recent Deep Learning and Transformer baseline models. The results show a MAPE of 0.65%, an MAE of 198.15, and an RMSE of 258.30 for one-step-ahead out-of-sample under a walk-forward evaluation on the test block. The evaluation period spans the 2024 BTC halving and the spot ETFs (exchange-traded funds) period, which coincide with major liquidity and volatility shifts.
With the proliferation of intelligent healthcare systems, patients' Personal Health Records (PHR) generated by the Internet of Medical Things (IoMT) in real-time play a vital role in disease diagnosis. The integration of emerging blockchain technologies signiffcantly enhanced the data security inside intelligent medical systems. However, data sharing across different systems based on varied blockchain architectures is still constrained by the unsolved performance and security challenges. This paper constructs a cross-chain data sharing scheme, termed MedExChain, which aims to securely share PHR across heterogeneous blockchain systems. The MedExChain scheme ensures that PHR can be shared across chains even under the performance limitations of IoMT devices. Additionally, the scheme incorporates Cryptographic Reverse Firewall (CRF) and a blockchain audit mechanism to defend against both internal and external security threats. The robustness of our scheme is validated through BAN logic, Scyther tool, Chosen Plaintext Attack (CPA) and Algorithm Substitution Attack (ASA) security analysis veriffcation. Extensive evaluations demonstrate that MedExChain signiffcantly minimizes computation and communication overhead, making it suitable for IoMT devices and fostering the efffcient circulation of PHR across diverse blockchain systems.
Tingxuan Su, Haoxiang Luo, Ruichen Zhang, Yinqiu Liu · 6 authors
Next-generation communication networks are characterized by integrated ultra-high reliability, ultra-low latency, massive connectivity, and ubiquitous coverage. However, this paradigm faces significant structural challenges of liquidity and security. Liquidity issues arise from prohibitive upfront costs of network resources, which strain the limited capital and financial flexibility. This also limits the deployment of the resource- and investment-intensive security solutions, bringing security issues. Security vulnerabilities arise from the decentralized architecture as well, particularly threats posed by Byzantine nodes. To address these dual challenges, we propose a novel framework utilizing Real-World Asset (RWA) tokenization for tokenizing network resources. RWA tokenization uses blockchain to convert ownership rights of real-world assets into digital tokens that can be programmed, divided, and traded. We then analyze the criteria for identifying suitable assets. Through a case study on dynamic spectrum allocation, we demonstrate the superior performance of this RWA approach. Particularly under conditions of resource scarcity, it can exhibit strong resilience against collusion and default attacks. Finally, we delineate fruitful avenues for future research in this nascent field.
Cryptocurrency markets exhibit pronounced momentum effects and regime-dependent volatility, presenting both opportunities and challenges for systematic trading strategies. We propose AdaptiveTrend, a multi-component algorithmic trading framework that integrates high-frequency trend-following on 6-hour intervals with monthly adaptive portfolio construction and asymmetric long-short capital allocation. Our framework introduces three key innovations: (1) a dynamic trailing stop mechanism calibrated to intra-day volatility regimes, (2) a rolling Sharpe-ratio-based asset selection procedure with market-capitalization-aware filtering, and (3) a theoretically motivated asymmetric 70/30 long-short allocation scheme grounded in the empirical positive drift of crypto markets. Through extensive out-of-sample backtesting across 150+ cryptocurrency pairs over a 36-month evaluation window (2022-2024), AdaptiveTrend achieves an annualized Sharpe ratio of 2.41, a maximum drawdown of -12.7%, and a Calmar ratio of 3.18, significantly outperforming benchmark trend-following strategies (TSMOM, time-series momentum) and equal-weighted buy-and-hold portfolios. We further conduct rigorous robustness analyses including parameter sensitivity, transaction cost modeling, and regime-conditional performance decomposition, demonstrating the strategy's resilience across bull, bear, and sideways market conditions.
As geopolitical, organizational, and technological fragmentation deepens, resilient digital collaboration becomes imperative. This paper develops a spectrum framework of polycentric digital ecosystems-nested socio-technical systems spanning personal, organizational, inter-organizational, and global layers. Integration across these layers is enabled by four technology clusters: AI and automation, blockchain trust, federated data spaces, and immersive technologies. By redefining digital ecosystems as distributed, adaptive networks of loosely coupled actors, this study outlines new pathways for crossborder coordination and innovation. The framework extends platform theory by introducing a multi-layer conceptualization of polycentric digital ecosystems and demonstrates how AI-enabled infrastructures can be orchestrated to achieve digital integration in a fragmented, multipolar world.
Fei Xu, Cheng Ye, Jie OuYang, Ziqiang Wu · 12 authors
The security foundation of blockchain system relies primarily on classical cryptographic methods and consensus algorithms. However, the advent of quantum computing poses a significant threat to conventional public-key cryptosystems based on computational hardness assumptions. In particular, Shor's algorithm can efficiently solve discrete logarithm and integer factorization problems in polynomial time, thereby undermining the immutability and security guarantees of existing systems. Moreover, current Practical Byzantine Fault Tolerance (PBFT) protocols, widely adopted in consortium blockchains, suffer from high communication overhead and limited efficiency when coping with dynamic node reconfigurations, while offering no intrinsic protection against quantum adversaries. To address these challenges, we propose QDBFT, a quantum-secured dynamic consensus algorithm, with two main contributions: first,we design a primary node automatic rotation mechanism based on a consistent hash ring to enable consensus under dynamic membership changes, ensuring equitable authority distribution; second, we integrate Quantum Key Distribution (QKD) networks to provide message authentication for inter-node communication, thereby achieving information-theoretic security in the consensus process. Experimental evaluations demonstrate that QDBFT achieves performance comparable to traditional PBFT while delivering strong resilience against quantum attacks, making it a promising solution for future quantum-secure decentralized infrastructures.
Quantum Byzantine agreement (QBA), a cornerstone of quantum blockchain, offers inherent advantages in security and fault tolerance over classical protocols, guaranteed by the laws of quantum mechanics. However, existing multiparty QBA protocols face challenges for large-scale deployment due to exponential communication complexity or reliance on complex multi-particle entanglement. To address this, we propose a multiparty circular QBA protocol that adopts a semi-decentralized architecture, leveraging circular message gathering and quantum digital signatures to achieve quadratic communication complexity and enhanced fault tolerance. Our protocol is experimentally feasible, requiring only weak coherent states, and is compatible with existing star-shaped quantum networks. Simulations conducted on a global satellite-to-ground network demonstrate that the protocol sustains high consensus rates among multiple users, even when employing different key generation protocols under realistic conditions. This work presents a scalable framework for large-scale QBA networks, establishing the foundation for a practical quantum blockchain that enables secure and fault-tolerant decentralized services.
This paper examines the growing institutional adoption of tokenization. Drawing on examples such as BlackRock’s BUIDL fund, we show how traditional financial institutions are experimenting with blockchain-based issuance, settlement, and custody models. We also outline the lifecycle of a hypothetical tokenized bond and demonstrate how it employs smart contracts and data oracles at each stage of the process. The paper contributes to current discussions by explaining how tokenization interacts with existing financial infrastructure and by identifying areas in which tokenization may influence market structure and regulation.
Blockchain technology, characterized by its immutable, distributed ledger, has evolved significantly beyond its cryptocurrency origins, finding application in healthcare and organ donation systems. Specifically, Hyperledger Fabric emerges as a secure, enterprise grade solution for healthcare data management, with a primary focus on patient medical records. Traditional centralized storage of medical records poses challenges for patients, prompting the development of a Hyperledger Fabric-based system driven by smart contracts to enhance accessibility and security. In the realm of organ donation systems, blockchain is proposed as a remedy for the shortcomings of centralized models, offering heightened transparency and security. Notably, while previous solutions often leaned on Ethereum-based blockchains, this research pioneers the use of Hyperledger Fabric. Beyond organ donation, blockchain's attributes, including decentralization, transparency, and privacy, offer transformative potential in healthcare.