In this paper, we generalize the work of P.T.Landsberg\cite{web1,web2} and S.S.Sidhu\cite{web3} by providing an inequality that has its main motivation from the laws of thermodynamics, in the form of a theorem which is quite useful in generating different inequalities such as the weighted AM-GM-HM inequality, the p-th power inequality , Jensen's inequality and many other inequalities.In this paper, we have not only given the thermodynamic motivation behind the inequality but we have given the required mathematical justification in the form of a straightforward rigorous proof using basic real analysis , which was not present in the works of Landsberg and Sidhu. In fact, the first statement of the theorem mathematically proves the uniqueness of the equilibrium temperature that is attained when n different bodies at different temperatures are brought in contact. The second statement of the theorem gives a mathematical proof of the fact that the process in which n bodies at different temperatures when brought in contact equilibriate to a common temperature is spontaneous,i.e., entropically favourable. Thus, this article motivates the students to come up with different mathematical results by observing the phenomena already existing in nature and also helps them to appreciate the conventional inequalities taught to them at the secondary school and undergraduate level by associating relevant physical phenomena with those inequalities.
Smart cities use advanced infrastructure and technology to improve the quality of life for their citizens. Collaborative services in smart cities are making the smart city ecosystem more reliable. These services are required to enhance the operation of interoperable systems, such as smart transportation services that share their data with smart safety services to execute emergency response, surveillance, and criminal prevention measures. However, an important issue in this ecosystem is data security, which involves the protection of sensitive data exchange during the interoperability of heterogeneous smart services. Researchers have addressed these issues through blockchain integration and the implementation of smart contracts, where collaborative applications can enhance both the efficiency and security of the smart city ecosystem. Despite these facts, complexity is an issue in smart contracts since complex coding associated with their deployment might influence the performance and scalability of collaborative applications in interconnected systems. These challenges underscore the need to optimize smart contract code to ensure efficient and scalable solutions in the smart city ecosystem. In this article, we propose a new framework that integrates generative AI with blockchain in order to eliminate the limitations of smart contracts. We make use of models such as GPT-2, GPT-3, and GPT4, which natively can write and optimize code in an efficient manner and support multiple programming languages, including Python 3.12.x and Solidity. To validate our proposed framework, we integrate these models with already existing frameworks for collaborative smart services to optimize smart contract code, reducing resource-intensive processes while maintaining security and efficiency. Our findings demonstrate that GPT-4-based optimized smart contracts outperform other optimized and non-optimized approaches. This integration reduces smart contract execution overhead, enhances security, and improves scalability, paving the way for a more robust and efficient smart contract ecosystem in smart city applications.
This article presents research on emerging global techno-libertarian networks for the establishment of venture-capital, crypto, and Web3-based jurisdictions and the new territorial and state projects they produce, including free private cities, charter cities, seasteads, and network states. Rooted in a self-professed anarcho-capitalist ideology, many of these projects paradoxically claim to eliminate “the state” in favor of decentralized and self-organized societies, while simultaneously proposing or producing different forms of centralized power. The article provides a critical analysis of techno-libertarian statecraft by examining the visual and discursive representations used to convey and obscure ideas of state, governance, and power. To do so, I look primarily at the use of metaphor (Semino 2008) and spectacle (Tsing, 2005) in techno-libertarian representations of territory. Finally, the article uses the Próspera Zone for Economic Development and Employment (ZEDE) located on the Honduran island of Roatán and in the Satuyé Port, La Ceiba as a case study in private statecraft. In addition to analyzing the structures created by Honduras Próspera Inc to govern the highly autonomous jurisdiction and the longevity biotech “network state” that it hosts, the article explores the visual representations that accompany actual structures of governance and state power.
With the advent of blockchain, Decentralised Finance (DeFi) has become an accessible and decentralised way to create financial services. Under the umbrella of DeFi services, we find Decentralised Exchanges (DEXs) i.e., smart contracts that allow one to exchange tokens. Over time, new malicious activities have begun to spread, particularly those related to DEXs. Maximal Extractable Value (MEV), a practice in which block creators can add, remove, or change the order of transactions to increase their gains at the expense of other users, is becoming pervasive.In this study, we collected a 2-year dataset and analysed the MEV activity on the Ethereum blockchain. With our analyses, we show that despite the countermeasures, the problem is still actual and that the value extracted could hinder the adoption of Ethereum in future projects. Furthermore, we provide an in-depth analysis of DEX platforms and tokens that are more susceptible to MEV attacks, showing that major markets are still far from solving the problem. Finally, we show that MEV attackers are becoming more sophisticated as they tend to chain different types of attacks (i.e., sandwich and arbitrage). Linked attacks are more profitable, as attackers extract more than 5 billion USD, while traditional attacks net 382 million USD.
Sanctioning blockchain addresses has become a common regulatory response to malicious activities. However, enforcement on permissionless blockchains remains challenging due to complex transaction flows and sophisticated fund-obfuscation techniques. Using cryptocurrency mixing tool Tornado Cash as a case study, we quantitatively assess the effectiveness of U.S. Office of Foreign Assets Control (OFAC) sanctions over a 957-day period, covering 6.79 million Ethereum blocks and 1.07 billion transactions. Our analysis reveals that while OFAC sanctions reduced overall Tornado Cash deposit volume by 71.03% to approximately 2 billion USD, attackers still relied on Tornado Cash in 78.33% of Ethereum-related security incidents, underscoring persistent evasion strategies. In this paper, we identify three significant, structural limitations in current sanction enforcement practices: (i) fragmented censorship in blockchain consensus and application layer; (ii) the complexity of obfuscation virtual asset services exploited by users; and (iii) the susceptibility of naive binary sanction classifications to dusting attacks. Our analysis and findings contribute to ongoing discussions around regulatory effectiveness in Decentralized Finance by providing empirical evidence, clarifying enforcement challenges, and informing future compliance strategies in response to sanctions and blockchain-based security risks.
Federated Learning (FL) has undergone significant development since its inception in 2016, advancing from basic algorithms to complex methodologies tailored to address diverse challenges and use cases. However, research and benchmarking of novel FL techniques against a plethora of established state-of-the-art solutions remain challenging. To streamline this process, we introduce FLsim, a comprehensive FL simulation framework designed to meet the diverse requirements of FL workflows in the literature. FLsim is characterized by its modularity, scalability, resource efficiency, and controlled reproducibility of experimental outcomes. Its easy to use interface allows users to specify customized FL requirements through job configuration, which supports: (a) customized data distributions, ranging from non-independent and identically distributed (non-iid) data to independent and identically distributed (iid) data, (b) selection of local learning algorithms according to user preferences, with complete agnosticism to ML libraries, (c) choice of network topology illustrating communication patterns among nodes, (d) definition of model aggregation and consensus algorithms, and (e) pluggable blockchain support for enhanced robustness. Through a series of experimental evaluations, we demonstrate the effectiveness and versatility of FLsim in simulating a diverse range of state-of-the-art FL experiments. We envisage that FLsim would mark a significant advancement in FL simulation frameworks, offering unprecedented flexibility and functionality for researchers and practitioners alike.
Ailiya Borjigin, Cong He, Charles CC Lee, Wei Zhou
Decentralized trading of real-world alternative assets (e.g., gold) requires bridging physical asset custody with blockchain systems while meeting strict requirements for compliance, liquidity, and risk management. We present GoldMine OS, a research oriented architecture that employs multiple specialized AI agents to automate and secure the tokenization and exchange of physical gold into a blockchain based stablecoin ("OZ"). Our approach combines on chain smart contracts for critical risk controls with off chain AI agents for decision making, blending the transparency and reliability of blockchains with the flexibility of AI driven automation. We describe four cooperative agents (Compliance, Token Issuance, Market Making, and Risk Control) and a coordinating core, and evaluate the system through simulation and a controlled pilot deployment. In experiments the prototype delivers on demand token issuance in under 1.2 s, more than 100 times faster than manual workflows. The Market Making agent maintains tight liquidity with spreads often below 0.5 percent even under volatile conditions. Fault injection tests show resilience: an oracle price spoofing attack is detected and mitigated within 10 s, and a simulated vault mis reporting halts issuance immediately with minimal user impact. The architecture scales to 5000 transactions per second with 10000 concurrent users in benchmarks. These results indicate that an AI agent based decentralized exchange for alternative assets can satisfy rigorous performance and safety requirements. We discuss broader implications for democratizing access to traditionally illiquid assets and explain how our governance model -- multi signature agent updates and on chain community voting on risk parameters -- provides ongoing transparency, adaptability, and formal assurance of system integrity.
Jie Zhang, Xiaohong Li, Man Zheng, Ruitao Feng · 7 authors
Enabling search over encrypted cloud data is essential for privacy-preserving data outsourcing. While searchable encryption has evolved to support individual requirements like fuzzy matching, dynamic updates, and result verification, designing a service that supports dynamic, verifiable fuzzy search (DVFS) over encrypted cloud data remains a fundamental challenge due to inherent conflicts between underlying technologies. Existing approaches struggle with simultaneously achieving efficiency, functionality, and security, often forcing impractical trade-offs. This paper presents \textbf{VeriFuzzy}, a novel DVFS service framework that cohesively integrates three innovations: an \textit{Enhanced Virtual Binary Tree (EVBTree)} that decouples fuzzy semantics from index logic to support $O(\log n)$ search/updates; a \textit{blockchain-reconstructed verification} mechanism that ensures result integrity with logarithmic complexity; and a \textit{dual-repository state management} scheme that achieves IND-CKA2 security by neutralizing branch leakage. Extensive evaluation on 3,500+ documents shows VeriFuzzy achieves 41\% faster search, $5\times$ more efficient verification, and constant-time index updates compared to state-of-the-art alternatives. Our code and dataset are now open source, hoping to inspire future DVFS research.
Alisa Kalacheva, Pavel Kuznetsov, Igor Vodolazov, Yury Yanovich
The rise in cryptoasset valuations and the ease of creating new tokens have spurred an increase in illicit activities within the market. Decentralized exchanges (DEX) facilitate the trading of a vast array of tokens, including those with minimal liquidity, amplifying the risk of fraudulent schemes. Fraudulent practices take various forms, including counterfeit tokens, rug pulls, and pump-and-dump schemes, all lacking functional innovation and relying heavily on aggressive social media marketing. This study contributes to the identification and profiling of deceitful tokens on DEX platforms. Our approach involved compiling on new tokens with an active trading start and attracted competition to buy them in first blocks spanning multiple years from the Ethereum blockchain, tracking all associated purchase and sale transactions. Our analysis revealed that Uniswap V2 predominantly hosts the trading of new tokens, with an alarming discovery that over 98% of tokens minted daily exhibit fraudulent characteristics. Subsequently, a machine learning model was developed to predict the likelihood of a rug pull occurring shortly after trading commencement. Although the dataset labeling methodology and detection problem statement are exploratory, we demonstrate the economic significance of the proposed approach within trading pipeline. The findings highlight the importance of identifying fraudulent activities and emphasize the need for collaboration between decentralized exchanges and regulatory bodies to mitigate financial losses for investors.
The Smart Mobility vision calls for dynamic resource and service discovery to cope with the intrinsic topology volatility of Internet of Things (IoT) platforms without sacrificing the required business continuity and service flexibility. For an extended automation of collaboration within and across enterprise boundaries, trust management is equally important, granting security, reliability and scalability at the same time. To tackle the above challenges, this paper proposes the integration of a semantic-based service management layer in an IoT infrastructure grounded on the Hyperledger Sawtooth blockchain. Every service in the outlined framework is annotated with reference to a domain ontology, so that smart contracts can exploit knowledge representation and non-standard reasoning for service registration, discovery, outcomes explanation and service selection. A case study on power management of Plug-in Electric Vehicles (PEVs) is proposed to clarify the benefits of the proposal. Early performance evaluation results support the feasibility and sustainability of the approach.
The scientific novelty of the article is the technology of using a medical blockchain based on a smart contract to support patient therapy after IT diagnosis of Alzheimer’s disease. In combination with the subsystem of IT diagnostics of a patient, the exchange of recognition data for the degree of Alzheimer’s disease is implemented. The smart contract performs automated management of therapy support for patients with diagnosed Alzheimer’s disease . The functional structure of the therapy support subsystem (smart contract in the Etherium blockchain) has been developed, including initialization of patient data, treatment plans, registration of medical staff, record management, dynamic switching of therapy plans based on diagnostic data, monitoring of medical staff warnings, as well as management of payments and their distribution. The proposed smart contract enables a new approach to interaction and collaboration in the field of therapy management and can be used for neurological patients with other diseases.
Proposes are offered Decentralized Ledger Journalism (DLJ) as a distinct and timely subfield within data journalism, emerging at the intersection of technological innovation and investigative practice. Drawing on the unique affordances of blockchain and other distributed ledger technologies (DLT), this approach positions public, immutable records not merely as supplementary datasets, but as primary sources for journalistic inquiry. From financial transactions and smart contract events to decentralized governance and identity systems, distributed ledgers offer a new evidentiary terrain - structured, transparent, and resistant to alteration. Beyond their utility as data sources, these systems provide native mechanisms for content authentication, including cryptographic timestamping, verifiable provenance, and censorship-resistant publication infrastructures. Such tools enable new methods of verification and preservation, allowing journalists to secure both the integrity of their sources and the durability of their outputs. By exploring the methodological and epistemological implications of blockchain-based journalism, this study outlines how decentralized ledgers can serve both as subject and substrate of inquiry. DLJ, we argue, offers a novel framework for enhancing journalistic integrity in a digital environment increasingly shaped by opacity, manipulation, and central control.
This paper presents the design, development, and thorough evaluation of a novel network security prototype that integrates Artificial Intelligence (AI) and blockchain technology to significantly enhance cyber security. As AI becomes increasingly embedded in cybersecurity solutions, ensuring the provenance, accountability, and integrity of AI-generated decisions has emerged as a critical challenge. Without reliable logging mechanisms, AI models remain vulnerable to adversarial manipulation and pose significant risks to critical security infrastructure. To address this, our research combines a state-of-the-art Convolutional Neural Network (CNN)-based threat detection module with a permissioned Ethereum-compatible blockchain. A custom-designed Solidity smart contract ensures secure, structured storage of comprehensive AI model metadata, while interactions with the blockchain are seamlessly managed through a lightweight Flask-based REST API. Each recorded transaction generates a unique cryptographic fingerprint, providing robust evidence for audits and forensic analyses. We evaluated the system's effectiveness through rigorous experimentation on a controlled test network, confirming immutability, traceability, and verifiable integrity of all logged metadata entries. Results demonstrated significant improvements in anomaly detection accuracy, reduced false-positive rates, and ensured real-time responsiveness essential for effective intrusion prevention. Despite controlled-environment limitations, such as transaction latency and blockchain-related operational costs, our prototype successfully establishes proof-of-concept for leveraging blockchain as an immutable audit trail for AI-driven cybersecurity systems. Future research directions include integrating advanced scaling techniques, such as layer 2 solutions, and extending the blockchain logging capabilities to cover the entire AI model lifecycle, including detailed training logs and comprehensive version histories. This work provides foundational contributions towards building trusted, auditable, and transparent AI solutions in regulated cyber security domains.
Существующие типовые модели аутентификации с использованием цифровых удостоверений носят абстрактный характер. Для конкретизации модели аутентификации с использованием цифровых удостоверений предлагаются: алгоритм эмиссии цифровых удостоверений; алгоритм аутентификации на основе доказательства с нулевым разглашением. Производится количественная оценка раскрытых данных удостоверений в результате: предложенного алгоритма аутентификации на основе доказательства с нулевым разглашением; аутентификации с полным раскрытием атрибутов; аутентификации с частичным раскрытием атрибутов. Полученные результаты оценок анализируются и делаются соответствующие выводы. Existing standard authentication models using digital credentials tend to be abstract. To refine the authentication model using digital credentials, the following are proposed: a digital credential issuance algorithm and an authentication algorithm based on zero-knowledge proof. A quantitative assessment is conducted on the amount of disclosed credential data resulting from the proposed zero-knowledge proof-based authentication algorithm, authentication with full attribute disclosure, and authentication with partial attribute disclosure. The assessment results are analyzed, and relevant conclusions are drawn.
Current decentralized architectures, while powerful, operate in silos.Intent-centric protocols solve for user expression but create trust assumptions for off-chain solvers. 1 Verifiable computation markets address this trust but lack a native framework for complex, goal-oriented coordination. 2Formal verification methods provide security for individual components but struggle with the emergent complexity of their composition. 3 This paper introduces Chimera, a novel protocol architecture that unifies these disparate paradigms.Chimera integrates generalized intent-centricity, a decentralized market for verifiable AI agents (zk-Agents), and autonomic governance inspired by closed-loop control theory, all within a framework of provable composability.We argue that by treating all network actions-from swaps to governance-as intents fulfilled by verifiable AI agents, and by governing the system with an autonomic management layer that monitors and adapts its own parameters, Chimera enables a new class of self-organizing, intelligent, and provably secure decentralized applications.We present the full architectural design and demonstrate its power through case studies in verifiable AI-driven finance and autonomic public goods funding.
Bertalan Zoltán Péter, Zsófia Ádám, Zoltán Micskei, Imre Kocsis
Due to their decentralized and trustless nature, blockchain and distributed ledger technologies are increasingly used in several domains, including critical applications. The behavior of such blockchain-integrated systems is typically driven by smart contracts. However, smart contracts are application-specific software and may contain faults with severe system-level impacts. This is especially true in the case of the extensively used Hyperledger Fabric (HLF) platform, where smart contracts are written in general-purpose languages (Java, among others), and applications can go far beyond handling virtual-currency-like assets. In this work, we present a novel formal-verification-based approach to smart contract verification and a high-level empirical model of the HLF platform. Our Smart Contract in the Loop (SCIL) method uses a model checker (Java Pathfinder) to check whether specific error properties hold for a given smart contract, while a predefined combination of platform-level fault modes is active. We facilitate the checking of HLF smart contracts without modification and enable the propagation or non-propagation of platform faults through the smart contracts to the system failure level.
Sayed Mahbub Hasan Amiri, Sayed Mahbub Hasan Amiri, Md. Mainul Islam, Md. Shahadat Hossen · 8 authors
Global supply chains suffer from fragmented data silos, limited transparency, and vulnerability to fraud/counterfeiting. Traditional centralized systems fail to provide real-time, immutable traceability, leading to inefficiencies in recalls, compliance, and stakeholder trust. We propose blockchain-based architecture leveraging distributed ledger technology (DLT) and smart contracts to create an end-to-end transparent, tamper-proof traceability system. This work designs and validates an enterprise-ready blockchain framework (Hyperledger Fabric) integrated with IoT sensors, uniquely addressing scalability and interoperability gaps in prior solutions. It quantifies performance-security trade-offs and stakeholder adoption barriers. A modular architecture was implemented, combining RFID/GPS sensors for data acquisition, PBFT consensus, and automated smart contracts. A real-world agri-food supply chain case study (organic coffee) evaluated performance, security, and usability across 5 stakeholder tiers. The system achieved 350 TPS throughput with <2-second latency, reducing paperwork by 85% and dispute resolution time by 30%. Security audits confirmed zero tampering incidents. Stakeholder surveys (N=42) showed 89% trust improvement but highlighted cost (72%) and technical literacy (58%) as adoption hurdles. Comparative analysis demonstrated 40% lower operational redundancy versus hybrid systems. Blockchain significantly enhances supply chain transparency and traceability with measurable efficiency gains. Future work will integrate AI-driven predictive analytics and cross-chain protocols.
Account-based anonymous blockchain systems can provide robust privacy protection for users. However, they become highly inefficient when handling high-frequency micro-payment scenarios. This paper presents systematic optimizations for batch processing and micro-payment transactions in account-based anonymous blockchain systems to enhance both privacy and efficiency. Building on BlockMaze, the first account-based anonymous blockchain system fully protecting transaction privacy, we propose innovations in batch transfers, batch receipts, and micro-payment handling. By reducing redundant data, improving circuit design, and optimizing zk-SNARK proof generation, we achieve up to 55.90% and 23.02% reductions in overall time consumption for batch transfers and receipts, respectively, significantly cutting computational cost and memory use. For micro-payments, a solution encapsulating the payment deadline reduces transaction delays and fund freezing. Experimental results show only slight increases in proof generation time—1.41 seconds for transfers and 1.02 seconds for payments—while maintaining privacy protection. This research lays a foundation for practical applications of account-based anonymous blockchain systems, enhancing privacy, processing efficiency, and transferability to other systems. • Optimized batch processing and improve transaction efficiency in account-based anonymous blockchain systems. • Optimized circuit design reduces redundant data and shortens zero-knowledge proof times. • Time consumption decreased by up to 55.90% in batch transfer function and 23.02% in batch receipt function. • Highly transferable to other account-based anonymous blockchain systems, offering strong flexibility and application potential. • Offers future research directions to improve blockchain efficiency and privacy protection.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The current hike in electricity demand, deterioration of electrical grids, and climatic conditions have necessitated the push for technology to enhance energy efficiency, optimize energy usage, and minimize greenhouse gas emissions. The Transactive Energy System (TES) is a highly favoured technology designed to provide solutions for optimizing energy usage since it incorporates economic and dynamic control mechanisms to balance the amount of energy generated and supplied. Cost savings present a clear advantage of TES for consumers, translating to reduced bills, and the platform enables customers with Distributed Energy Resources to trade their excess energy, transforming consumers into prosumers. However, energy trading in TES comes with challenges such as maintaining a dynamic balance between supply and demand, as well as issues of privacy, trust, and resilience. Blockchain Technology (BT)-based TES can address these challenges due to its reliability, transaction transparency, and robust encryption methods. However, BT has its shortcomings that need to be addressed. Therefore, this research analyzes the opportunities, limitations, challenges, and complexities of implementing blockchain-based energy trading platforms within a decentralized TES. This review adopted a systematic approach, known as the Preferred Reporting Items for Systematic reviews and Meta-Analyses, to provide in-depth insights into the review purpose, methodology, findings, recommendations, and future research directions. It was observed from the review that certain challenges underscore the necessity for standardization in BT-based TES implementation. Moreover, it was discovered that decentralizing the TES energy trading infrastructure promotes energy democracy and that adopting fast computing techniques will facilitate digital and intelligent operations in TES. It was also found that the Directed Acyclic Graph-based distributed ledger may soon replace generic blockchain, as it can simultaneously process large micro-transactions in P2P networks. It is observed that implementing a peer rating mechanism in the energy trading network will enhance participants' commitment to their reputational standing in the market, while adapting analytical modelling for performance evaluation of this energy solution could equally be encouraged.
The rapid advancement of 6G communication networks presents both considerable problems and opportunities in network management, necessitating sophisticated solutions that extend beyond conventional methods. This study seeks to investigate and evaluate autonomous network management solutions designed for 6G communication networks, highlighting their technical advantages and potential implications. We examine the role of Artificial Intelligence (AI), Machine Learning (ML), and network automation in facilitating self-organization, optimization, and decision-making within critical network domains, including spectrum management, traffic load balancing, fault detection, and security and privacy. We examine the integration of edge computing and Distributed Ledger Technologies (DLT), specifically blockchain, to improve trust, transparency, and security in autonomous networks. This study provides a comprehensive understanding of the technological developments driving fully autonomous, efficient, and resilient 6G network infrastructures by methodically analyzing existing methodologies, identifying significant research gaps, and exploring potential prospects. The results offer significant insights for researchers, engineers, and industry experts involved in the development and deployment of advanced autonomous network management systems.
The environmental impact of cryptocurrencies has attracted increasing scrutiny, largely due to the high energy consumption of blockchain networks. However, empirical research on the causal relationship between cryptocurrency trading activity and carbon emissions remains scarce. This study addresses this gap by analysing the dynamic interplay between cryptocurrency trading and CO₂ emissions for Bitcoin, Ethereum, and Binance Coin, using monthly data from January 2015 to September 2024. Employing the Toda-Yamamoto augmented Granger causality approach, we apply logarithmic transformations to ensure data stationarity and address integration and endogeneity concerns. Our results reveal a bidirectional Granger causality between Bitcoin trading and CO₂ emissions, suggesting a feedback loop between market activity and environmental impact. For Ethereum, we find a similar albeit weaker bidirectional causality from trading to emissions, while no significant causal link is detected for Binance Coin, likely reflecting its more energy-efficient consensus mechanism. These findings highlight the disproportionate environmental burden of proof-of-work cryptocurrencies and underscore the need for targeted regulatory responses. We recommend the adoption of carbon-sensitive crypto policies, such as mandatory energy usage disclosures and incentives for transitioning to sustainable consensus mechanisms. This study advances the environmental finance literature by providing robust empirical evidence on the links between digital asset markets and carbon emissions.
This study examines the nexus between Google Trends’ collective interest in specific keywords related to technological advancements utilized in design and the stock performance of major companies in design-related sectors. Specifically, the paper examines causality patterns between Google Trends keywords and stock prices of design companies, also employing multi-fractal detrended cross-correlation analysis, to test for long-term relationships. According to the results, varying impacts across keywords on stock prices are identified, with non-fungible token (NFT) exhibiting the greatest influence, followed by three-dimensional (3D) printing and computer-aided design, virtual reality (VR) displays a noteworthy impact, while artificial intelligence (AI) design and generative design indicate the least impact. The results also reveal persistent long-term relationships between the examined variables, with rich multifractal behavior indicating complex relationships, mostly balanced. The findings are important for policymakers and managers, necessitating close monitoring, especially of NFT, and for design companies to align strategies for market movements. • Examine how online interest in design technologies influences stock performance in design-focused industries. • Identify NFTs and 3D printing as major drivers of stock movements in the design market. • Reveal multifractal patterns indicating persistent, complex links between trend data and stock values. • Recommend tracking emerging design technologies for timely decision-making in investment and policy. • Provide data-driven insights for anticipating stock behavior in response to evolving digital interest.
This study investigates the heterogeneous responses of Bitcoin (BTC), gold (GOLD), and green bonds (GBOND) to geopolitical risk (GPR) shocks across different market regimes and investment horizons. Using a triadic empirical framework that encompasses wavelet quantile-on-quantile regression (QQR), wavelet cross-quantilogram (WCQ), and advanced portfolio optimization strategies, our analysis captures asymmetric dependence, tail risks, and time-frequency dynamics from January 2015 to December 2024. Our results show that BTC consistently has strong hedging potential at lower quantiles, particularly during short-term stress, whereas GOLD and GBOND offer greater stability over medium- and long-term horizons. Conditional expected shortfall (CES) and extreme downside correlation (EDC) analyses highlight BTC’s resilience to extreme downside risks, whereas GOLD and GBOND serve primarily as long-term defensive assets. Portfolio optimization confirms BTC’s critical role in diversification under minimum correlation and connectedness strategies, and GBOND dominates variance-minimizing portfolios. These findings offer practical guidance for constructing robust, adaptive portfolios under geopolitical uncertainty.
Daniel Commey, Benjamin Appiah, Griffith Selorm Klogo, Garth V. Crosby
Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information through shared performance metrics. In this paper, we propose a novel protocol that incorporates Zero-Knowledge Proofs (ZKPs) to enable privacy-preserving and verifiable evaluation for FL. Instead of revealing raw loss values, clients generate a succinct proof asserting that their local loss is below a predefined threshold. Our approach is implemented without reliance on external APIs, using self-contained modules for federated learning simulation, ZKP circuit design, and experimental evaluation on both the MNIST and Human Activity Recognition (HAR) datasets. We focus on a threshold-based proof for a simple Convolutional Neural Network (CNN) model (for MNIST) and a multi-layer perceptron (MLP) model (for HAR), and evaluate the approach in terms of computational overhead, communication cost, and verifiability.