Muhammad Sannan Khaliq, Love Allen Chijioke Ahakonye, Jae Min Lee, Dong-Seong Kim
With the rapid proliferation of blockchain technology and smart contracts in consumer IoT systems, ensuring digital trust and security remains a persistent challenge due to vulnerability diversity, data scarcity, and limited on-chain auditability. To address these issues, this paper presents PureChain, a scalable trust framework that integrates semi-supervised deep learning for smart contract vulnerability detection with blockchain-based audit logging for transparent security assurance. The framework employs a lightweight Conv1D neural architecture trained via iterative pseudo-labeling and a Mean Teacher strategy, effectively leveraging tens of thousands of unlabeled contracts to minimize annotation requirements. Experimental evaluation shows that PureChain achieves macro F1-scores exceeding 99% and significant improvements in recall for rare vulnerabilities, outperforming both supervised and state-of-the-art baselines. The integration of blockchain ensures all detection events are immutably recorded, enabling accountable and verifiable device operation. These results demonstrate that adaptive semi-supervised learning, combined with on-chain transparency, provides a robust and efficient foundation for secure smart contract monitoring across IoT and edge environments, with future work targeting more adaptive pseudo-labeling, semi supervised learning (SSL) techniques and cross-chain generalization.
Mohamed Seifelnasr, Mohamed Mobarak, Riham AlTawy, Amr Youssef
The metaverse transforms how individuals interact, work, and engage in virtual environments, creating new opportunities in gaming, education, e-commerce, and social networking. At the core of this experience are avatars (i.e., digital representations of users that serve as their proxies in virtual spaces). Ensuring secure and privacy-preserving communication between avatars presents critical challenges, particularly in protecting metaverse user privacy by preventing the linking of avatars to users' social and professional lives. Adopting anonymous schemes such as ring signature schemes requires access to all public keys within the anonymity set, which is impractical in the metaverse. In this paper, we proposeSPARTA(Secure andPrivacy-preserving protocol withRole separation andTrustworthiness forAvatars in the metaverse), a protocol that enables avatar authentication and maintains avatar unlinkability. By leveraging mercurial signatures, our approach allows metaverse users to generate multiple unlinkable avatars without requiring repeated registration with the metaverse service provider, thereby enabling seamless role separation. Additionally, by using a time-based hash chain, only avatars in possession of a reputation token from the time-based hash chain can submit their feedback on a smart contract based on their interactions. Given the soundness property of zero-knowledge proof and the origin-hiding property of mercurial signatures, we formally prove that${\sf SPARTA}$achieves mutual authentication, avatar unlinkability, and penalization enforcement. Additionally, we analyze the performance overheads introduced by its cryptographic primitives and compare${\sf SPARTA}$with existing metaverse authentication protocols. Furthermore, we implement the protocol using socket programming. This implementation simulates real-time message exchanges between protocol entities, resulting in an end-to-end latency of 105 ms. Compared to existing metaverse authentication frameworks,${\sf SPARTA}$provides unlinkable avatar authentication that achieves mutual authentication, role separation, and data sovereignty without reliance on an online trusted third party. The concurrent implementation between two Raspberry Pi devices demonstrates the scalability of${\sf SPARTA}$, achieving a total completion time of 17.338 seconds for 1000 concurrent authentications, corresponding to a throughput of approximately 57.7 authentications per second, confirming its practicality for large-scale metaverse environments.
Although cloud storage offers the much-needed scalability, flexibility, and economic efficacy, it also presents significant security and privacy issues, which are mostly caused by the fact that, since data are encrypted, the traditional plaintext search approaches are compromised. To overcome this shortcoming, we introduce the Enhanced Aware Record Maintenance (EARM) architecture, a compound, privacy-aware architecture that supports ranked, multi-keyword encrypted data retrieval in the cloud. The fundamental retrieval process of EARM is novel, incorporating ontology-based keyword mapping, enhanced association rule mining, and inner-product similarities to produce highly accurate and relevant search results. The framework deploys a strong multi-layer defense model, which integrates quantum-resistant cryptography (Zero-Knowledge Proofs/Homomorphic Encryption), high-grade cipher suites (AES/RSA), and granular Attribute-Based Encryption (ABE) access controls (such as geofencing and decentralized identity management). Moreover, EARM also brings intelligent defensive layers of AI-based orchestration, which include ransomware, intrusion, and anomaly detection, behavioral biometrics, and a tamper-proof blockchain audit trail. Secure file versioning, rollback, and an encrypted trash bin are other ancillary features that improve the system's resilience. Empirical analyses performed on actual data sets indicate that EARM maintains high privacy and high retrieval rates and suffers only a few system overheads, hence making it an effective and highly secure encrypted cloud storage solution for modern challenges.
The built environment is a critical frontier for climate change mitigation and adaptation, with residential buildings accounting for a substantial portion of global energy consumption and greenhouse gas emissions. This paper presents a critical review of contemporary literature (2020-2025) synthesizing advancements in climate-resilient housing through integrated architectural and renewable energy solutions. A systematic analysis of 51 studies examines three core areas: passive and active architectural design for thermal resilience; the role of decentralized renewable energy in enhancing autonomy; and the socio-technical, policy, and governance dimensions of implementation. The present review identifies a paradigm shift from static efficiency toward dynamic, adaptive building systems, highlighting the efficacy of bioclimatic design, smart materials, and AI-driven management. Decentralized solar energy is underscored as fundamental for decarbonization and energy security, though its success depends on supportive policies, community engagement, and equitable finance. Persistent gaps are noted, including the need for holistic lifecycle assessments, scalable models for low-income contexts, and stronger integration of technical and social equity approaches. The review concludes by advocating for a transformative shift toward adaptive, regenerative, and just residential environments.
Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption combined with parallel processing architecture to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 500,000 transactions per second with parallel processing and 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 1000× over existing privacy-focused systems while maintaining equivalent security properties through formally proven cryptographic guarantees.
Hari Purnama, I Putu Bakta Hari Sudewa, Tazkia Nizami, Bagas Sambega Rosyada · 6 authors
Electronic Medical Records (EMRs) are mandatory in Indonesia following the Ministry of Health regulation, which raises significant challenges in data security and patient-centric access control. Current implementations rely on centralized healthcare systems or third-party vendors, creating risks of unauthorized access, data leakage, and uncertain data integrity. To address these issues, this study proposes DecMed, a decentralized EMR management framework built on IOTA Distributed Ledger Technology (DLT). DecMed integrates Capability-Based Access Control (CapBAC), Proxy Re-Encryption (PRE), and the InterPlanetary File System (IPFS) to enforce patient ownership of medical data. Patients actively grant or revoke access, define access duration, and selectively share data with healthcare personnel. The system is implemented using smart contracts in the Move programming language on the IOTA ledger, while encrypted clinical data is stored on IPFS. Evaluation through unit testing of various unauthorized access scenarios demonstrates that DecMed effectively enforces fine-grained access rules, preserves data confidentiality and integrity, and ensures compliance with national healthcare requirements.
Hamid Najafi Bouyaghchi, Ameneh Farahani, Ismail A Mageed
The cryptocurrency market is volatile, which makes it very difficult to accurately predict. The Long Short-Term Memory (LSTM) is an approach to Predict Price Cryptocurrency (PPC) that uses price time series data. However, in this method, the prediction accuracy is dependent on the tuning of meta-parameters. Therefore, to tune these meta-parameters, an improved version of the optimization algorithms is needed that provides the task of selecting the optimal values of these parameters for price predictions. Therefore, in this study, the LSTM is combined with the classic version of the Differential Evolution (DE) algorithm, and the real data against the prediction results of the model presented in this study showed the appropriate accuracy of this model. Then, the classic version of the DE algorithm was modified to reduce its errors compared to previous algorithms. In this regard, coding was done in MATLAB version 2023b software, and the improved version was compared in terms of error rate with the Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the new Bald Eagle Search (BES) algorithm, which showed an accuracy of 86.94% for the improved model in this study.
The convergence of social networking and electronic commerce has given rise to the social e-commerce paradigm, where content creators serve as the primary drivers of consumer engagement and purchase decisions. However, this ecosystem faces a critical tension between the need for high-precision ad targeting to sustain monetization and the increasingly stringent requirements for user privacy preservation. Traditional centralized recommendation systems require the aggregation of massive user behavioral datasets, creating significant risks of data leakage and violating emerging regulatory frameworks. To address this challenge, we propose a novel framework titled Fed-ZKC (Federated Zero-Knowledge Creator). This architecture synergizes Federated Learning (FL) with Zero-Knowledge Proofs (ZKP) to enable privacy-preserving ad targeting while ensuring verifiable monetization attribution for creators. In our system, user preference models are trained locally on edge devices to prevent raw data transmission, while a cryptographic verification layer ensures that ad interactions are genuine without revealing user identities to the platform or the creators. Extensive experiments conducted on large-scale real-world datasets demonstrate that Fed-ZKC achieves recommendation accuracy comparable to centralized baselines while reducing privacy leakage risks by orders of magnitude. Furthermore, the implementation of succinct non-interactive arguments of knowledge (zk-SNARKs) introduces minimal computational overhead, making the protocol feasible for deployment on modern mobile processors.
Distributed Energy Resources (DER) such as solar PV, wind micro-turbines, smart inverters, electric vehicles (EVs), and home energy storage systems are rapidly increasing in modern power systems. However, their decentralized nature introduces complexities in coordination, demand–supply balancing, and resilience. Existing blockchain-based DER frameworks primarily focus on peer-to-peer (P2P) trading, security, and certificate validation, but lack mechanisms for coordinated swarm-like behaviour among DER units. This paper introduces a novel concept—Blockchain-Based Swarm Coordination of DER Clusters, inspired by swarm intelligence principles such as self-organization, collaboration, local decision-making, and emergent global behaviour. The proposed system integrates blockchain, multi-agent coordination, and decentralized smart contracts to enable secure, autonomous, and scalable coordination of DER clusters. A layered architecture, cluster formation mechanism, consensus-driven decisioning, and energy-sharing algorithms are presented. The framework significantly enhances grid flexibility, improves energy distribution efficiency, reduces central-dependency, and enables real-time proactive response during grid fluctuations. Simulation-driven conceptual outcomes demonstrate improved DER responsiveness, fault tolerance, trust, and transparency. This work establishes a new research direction by merging blockchain with swarm intelligence for next-generation decentralized energy systems.
D. Sravanthi, O. Shanmukha Hari Prasad, J. Srinath Reddy, A. Chiru Vardhan Reddy
The uncontrolled propagation of fake news on the digital frontiers continues to undermine the confidence of the populace, disorganizing supply chains and misled decision making on high stake regions. The existing fake news detectors are all largely text-based and are driven by machine learning or deep learning and do not pay much focus to integrity, provenance, and post-hoc verifiability of the model predictions. Moreover, the current procedures are usually trained and tested in controlled settings and do not facilitate adversarial manipulation of content and metadata, and are rarely provided to produce audit trails which cannot be modified by auditors of identified artifacts. It is in this respect that this paper introduces DeepTrustChain, a blockchain anchored hybrid deep learning framework in the detection of high-integrity fake news. The proposed system uses an encoder of text a transformer encoder with a Bidirectional recurrent module and an ensemble stacking layer to improve the robustness and generalization owing to the recent discoveries on stacking and ensemble methods of identifying fake news, and reliability-based methods of evaluation. At the same time, DeepTrustChain computes a trust score that represents a combination of model confidence and source level and propagation level features and anchors the prediction and proof on an authorized blockchain. Such a design provides verifiable, immutable records of the histories of the detection results and enables the decentralized checking of the news items by time. The conceptual evaluation of the framework in respect to multilingual and low resource states and can also leverage advancements in sequence modeling and representation learning in other applications such as music and affective computing. The key feature of DeepTrustChain is that it is an architecture that integrates integrity conscious hybrid deep learning with blockchain based anchoring, so that simultaneously achieves the high degree of detection accuracy, resistance to decision manipulation, and ultimate traceability of decision making regarding fake news.
The smart cities that collaborate with AI-driven autonomous robots are attracting attention for supporting various social activities in the real world. In facilities that provide such services, various systems managing the facility and robots may coexist in the common area. By enabling the systems to interoperate and share information about the status of the facility and robots, it becomes possible to realize a variety of services that support safety and security within the facility. However, while the operators of the facility want to monitor the detailed conditions of the robots, the operators of the robots are cautious about providing the information about the status of robots such as the moving trajectory and various sensor data. To resolve this dilemma, we propose a new system that enables estimation of the operational conditions of the robots by verifying the positions and trajectories at landmarks in the facility without disclosing their internal information. In the proposed system, we focus on the observation of environmental information that accurately reflects the real-world situation for estimating the proximity between the robot and each landmark. As the environmental information, both systems on robots and a facility measure CSI (Channel State Information) and acoustic information. In addition, by utilizing zero-knowledge proof (ZKP) technology, the system for the facility confirms the reliability of the process for estimating the proximity of the robots to the landmark without exchanging detailed internal information. Through the proof-of-concept experiment, applying the proposed system achieved high-accuracy proximity detection with both methods (CSI and acoustic information) yielding precision and recall rates exceeding 0.90.
We propose Trustless Agent Swarms, a framework enabling privacy-preserving coordination among autonomous AI agents on EVM-compatible blockchains. Our system integrates four cryptographic primitives: (1) Groth16 zero-knowledge proofs for proving reputation thresholds without revealing scores; (2) EIP-5564 stealth addresses for unlinkable fund transfers; (3) ERC-4337 account abstraction for gasless autonomous execution; and (4) Semaphore for anonymous group signaling. We implement a 586-constraint reputation proof circuit and deploy five smart contracts on Base Sepolia. Proof generation: 580ms. On-chain verification: 407,576 gas.
Materi ini membahas aspek praktis investasi dan trading aset kripto dengan pendekatan literasi risiko dan kehati-hatian, khususnya untuk membantu peserta memahami bahwa kripto pada dasarnya merupakan aset, sehingga interaksinya harus dianalisis sebagaimana interaksi pada ekosistem aset pada umumnya. Pembahasan dimulai dari kerangka besar ekosistem kripto yang menempatkan pengguna (aktor), platform (venue), dan aset (goods) sebagai tiga elemen utama pembentuk risiko, kemudian dilanjutkan dengan pengenalan spektrum aset kripto (coin, token, dan NFT), mekanisme kustodi (custodial vs non-custodial), serta pentingnya pengamanan private key dan seed phrase. Materi juga menguraikan mekanisme transaksi pada pasar spot di CEX (order book) dan DEX (AMM), termasuk peran liquidity provider, arbitrage, slippage, dan risiko likuiditas. Selain itu, dijelaskan berbagai aktivitas dalam ekosistem Web3 seperti staking, lending-borrowing DeFi, strategi long/short berbasis jaminan, hingga bahaya derivatif dan leverage yang dapat memicu likuidasi cepat. Selanjutnya, materi menekankan pentingnya analisis fundamental (tokenomics, aktivitas developer, metrik finansial, dan data on-chain), serta memberikan pengantar mengenai aset dunia nyata yang ditokenisasi (RWA), metaverse, dan NFT beserta parameter evaluasinya (provenance, kolektibilitas, utilitas, finansial, roadmap, dan komunitas). Pada sisi mitigasi risiko, materi memetakan bahaya utama di ekosistem kripto—mulai dari risiko CEX, token, DeFi, hingga interaksi sosial (phishing, social engineering, pig butchering, dan FOMO)—serta menawarkan kerangka due diligence 6D (Define, Document, Diversify, Detect, Defend, Discipline) sebagai panduan pengambilan keputusan yang lebih rasional. Kesimpulan utama materi ini menegaskan bahwa risiko utama dalam kripto bukan hanya terletak pada instrumennya, tetapi juga pada kualitas riset, pengendalian diri, dan kemampuan menjaga diri pengguna saat berinteraksi dengan ekosistem digital.
ABSTRACT AI‐driven personalization now structures search, recommendation, pricing, and service across the consumer journey, heightening a core dilemma: maximizing relevance and efficiency without compromising autonomy and trust. This article advances a capability‐based account of responsible personalization. I theorize that technology sense‐breaking (challenging legacy assumptions) and sense‐giving (constructing shared meanings) foster strategic flexibility, which, in turn, enables two outcomes: (a) product/process innovation performance and (b) consumer‐facing safeguards that calibrate trust—transparent AI disclosure, adjustable recommendation intensity, and human‐override/redress mechanisms. I further argue that transformational leadership amplifies the translation of sensemaking into flexibility, steering reconfiguration toward “engagement without coercion.” A firm‐level, multi‐respondent survey of Taiwan‐based organizations adopting AI/Web3 in marketing and service contexts is used to test a moderated‐mediation model with validated multi‐item measures and PLS‐SEM, alongside power checks, CMV diagnostics, and robustness analyses. By endogenizing UX governance within organizational capabilities and leadership, the study links internal reconfiguration to external consumer dignity, specifying when firms are most likely to implement autonomy‐preserving designs. The contribution is a precise, operational blueprint for aligning market performance with ethical experience through capability formation and trust calibration
Abstract As artificial intelligence (AI) systems become increasingly integrated into critical applications, ensuring trust in their outputs has emerged as a central challenge. Verifiable machine learning (ML) is one approach to addressing this challenge, providing guarantees that results are both correct and reproducible. Existing paradigms, however, provide only partial solutions: zero-knowledge ML (ZKML) achieves strong cryptographic assurances but suffers from limited scalability and high resource costs, while optimistic ML (OPML) supports a wider range of models but relies on economic incentives and long dispute periods. In this work, we propose zk-OPML, a novel hybrid framework that integrates optimistic verification with zero-knowledge proofs (ZKPs). The approach decomposes ML inference into operator-level computations, selectively generating ZKPs for isolated ONNX operators, while retaining the scalability of the optimistic paradigm. We present a prototype implementation and evaluate its performance by benchmarking it against ZKML and OPML. Our results show that zk-OPML achieves faster verification for more complex inference tasks and scales more effectively to larger models, while avoiding the excessive costs of end-to-end ZKML. The modular design of zk-OPML further enables future extensions with the latest advances in the field of ZK.
Open access
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
This paper presents a novel architecture for developing Decentralized Applications (DApps) on Blockchain that integrate Artificial Intelligence (AI) and decentralized storage for the age of Web3 applications. As these emerging technologies continue to evolve, the synergy among them offers not only unprecedented opportunities for innovation and advancement but also raises confusions, incompatibilities and unreliability. The proposed architecture aims to harness the strengths of Blockchain's distributed computation and ledger technology for transparency and security, AI's capabilities for fraud detection and penalization, and Social Media's network effects for user engagement and decentralized storage's reliability for trust. Through the integration of these technologies, DApps can offer enhanced privacy, autonomy, and trust while fostering inclusive and participatory ecosystems. The paper discusses the design principles, components, and potential use cases of such a hybrid architecture, highlighting its potential to revolutionize various domains, including content creation, social networking, land registration, and property market. To verify and validate the architecture, we have developed two DApps- one for social media and another for land registration and property market. Our developed DApps provided upto 100 fold gains in speed, 10 folds gains in cost, more reliably and automation than existing similar centralized applications.
In the Web3 ecosystem, "dead assets" such as micro-token fractions (dust) and abandoned low-value NFTs continue to accumulate on networks, losing liquidity due to transaction fee constraints. This paper proposes the concept of the "Scavenger Protocol," which leverages the object-oriented architecture and parallel processing capabilities of the Sui blockchain to automatically detect, melt down, and recirculate these dormant assets back into the market as reusable resources (Scrap tokens). By implementing a smart contract-based mechanism where "abandoned value is automatically shuffled" without the need for central administrators, we aim to achieve the autonomous revitalization of the on-chain economy. Charitable Pledge: In the event that the implementation of this protocol generates any personal revenue or financial returns for the author, 60% of those proceeds will be donated to charitable causes. This commitment aligns with the Web3 ethos of supporting public goods and giving back to society. Disclaimer: This paper is for informational, academic, and conceptual purposes only and does not constitute financial, investment, legal, or regulatory advice. The "Scavenger Protocol" presented herein is a theoretical framework and thought experiment. The authors do not guarantee the realization of this protocol, its technical feasibility, or any financial returns. Any future implementation of this protocol by third parties must be executed strictly in compliance with applicable local and international laws. The authors assume no liability or responsibility for any damages, legal disputes, or financial losses arising from the use, interpretation, or implementation of the concepts described in this document. Open Design and Protocol Revenue Model (Tokenomics) This protocol is designed as a universal foundational primitive, unconstrained by specific applications or use cases. Other developers and projects are encouraged to freely fork, adapt, and build upon this mechanism to construct systems with their own customized rules. To ensure the system's long-term sustainability, we define a minimal baseline rule: the system automatically collects a flat 1% protocol fee during operations—specifically at the time of melting down dormant assets and generating new ones. The revenue generated from this 1% fee is directly applied to the "Charitable Pledge" outlined above. This creates the financial engine where 60% of the proceeds are directed toward social givebacks and donations, while the remaining 40% is allocated to protocol maintenance and the founder.
Financial assets are central to economic stability, yet the macroeconomic sensitivity and predictability of emerging digital assets, particularly non-fungible tokens (NFTs), remain unclear. This study evaluates the responsiveness of NFTs, cryptocurrencies, and traditional assets to interest rate and inflation fluctuations using time series forecasting and sensitivity analysis. ARIMAX, Partial Least Squares, Ridge Regression, and Long Short-Term Memory (LSTM) models are employed to capture linear and nonlinear dynamics across asset classes. Using daily data from July 2017 to November 2024, results indicate that LSTM achieves superior predictive accuracy for highly volatile and nonlinear assets, although forecast reliability is limited by structural breaks and thin trading. Traditional assets such as bonds and gold display stable sensitivities to macroeconomic variables, reinforcing their hedging role. In contrast, digital assets exhibit higher volatility and weaker, less stable macroeconomic linkages. NFTs show low correlations with traditional assets, suggesting diversification potential, but low forecast error variance does not imply low risk. Cryptocurrencies demonstrate stronger macroeconomic sensitivity alongside greater instability. Overall, the findings reveal a trade-off between diversification benefits and forecast reliability when integrating digital assets into portfolios.
Byzantine Fault Tolerant (BFT) consensus forms the foundation of many modern blockchains striving for both high throughput and low latency. A growing bottleneck is transaction execution and validation on the critical path of consensus, which has led to modular decoupled designs that separate ordering from execution: Consensus orders only metadata, while transactions are executed and validated concurrently. While this approach improves performance, it can leave invalid transactions in the ledger, increasing storage costs and enabling new forms of strategic behavior. We present the first systematic study of this setting, providing a formal framework to reason about the interaction between consensus and execution. Using this framework, we show that the decoupled design enables a previously unidentified attack, which we term gaslighting. We prove a fundamental trade-off between resilience to this attack and resource capacity utilization, where both are impossible to achieve deterministically in the decoupled model. To address this trade-off, we discuss an intermediate model for leader-based protocols that is robust to gaslighting attacks while achieving high throughput and low latency.
Coroutines, as an abstract programming construct, are a generalization of functions that can suspend execution part- way for later resumption. Coroutine Types are behavioral types to model interactions of coroutines with a single receiving operation followed by a single yielding operation. Coroutine Types have been applied to model-driven engineering, smart contracts, and test case generation. We contribute a Flow extension to Coroutine Types, so that coroutines with more than one receiving and yielding operation can be modeled. We accordingly revise the reduction rules of Coroutine Types. To show the usefulness of the Flow extension, we contribute a type system that maps expressions of the Go programming language to Coroutine Types. If the reduction result is 0, the two channel operations are paired properly and the program has no deadlocks. We choose Go because it is a popular programming language for distributed systems, but a frequent kind of bugs in Go is deadlocks due to the wrong use of concurrency features. We concentrate on the most commonly used semantics in Go: unbuffered channels with the keywords go and defer. Our Flow extension and the type system recognize 17 patterns of channels and goroutine interactions, including mismatched receivers and senders, nested goroutines, etc. We also integrate the Z3 SMT solver to take account of conditional execution and type inheritance. Other static or dynamic deadlock detectors crashed or gave wrong predictions in some patterns. Therefore, our type-based deadlock analyzer not only fills the gap in the landscape of value-based detection, but also complements existing detectors.
The emergence of Bitcoin as a major alternative investment asset has driven the development of financial instruments like Bitcoin Spot Exchange-Traded Funds (ETFs), offering broader market access and deeper integration into global trading ecosystems. This study analyses the impact of the introduction of Bitcoin exchange-traded funds (ETFs) on spot Bitcoin prices by analysing how the introduction of ETFs and their trading volumes influence price dynamics. Employing high-frequency trading data and advanced econometric methods, the research highlights the short-term and long-term interplay between ETF inflows and Bitcoin market behaviour. The findings provide insights for investors, policymakers, and market participants navigating the cryptocurrency landscape, emphasising the feedback mechanisms between traditional derivative instruments and native digital trading. This study reveals that Bitcoin ETF inflows influence spot market price dynamics in the short term, driven by investor sentiment and market momentum. However, Bitcoin prices exhibit independence from ETF inflows over longer horizons, highlighting the dominant role of underlying market mechanisms. Higher-frequency data analysis underscores the rapid adjustments in Bitcoin trading, while advanced econometric models confirm a stable long-term equilibrium relationship between ETF inflows and Bitcoin prices. These insights offer critical implications for observers navigating the evolving cryptocurrency ecosystem.