This study explores the intricate relationship between public sentiment and Bitcoin market dynamics, leveraging sentiment analysis of Twitter data to uncover patterns in emotional discourse surrounding cryptocurrency. By analyzing sentiment trends from 2013 to 2019, the research reveals a cyclical interplay between positive and negative sentiment, often aligning with Bitcoin’s dramatic price movements. Positive sentiment peaks coincide with periods of market optimism, driven by narratives of technological innovation and mainstream adoption, while negative sentiment troughs reflect moments of fear, uncertainty, and doubt (FUD) during market corrections. Despite the observed alignment, the correlation between sentiment and Bitcoin prices remains weak, underscoring the complexity of market behavior and the influence of external factors such as macroeconomic trends and regulatory developments. The findings highlight the potential of sentiment analysis as a complementary tool for market prediction, offering valuable insights into the emotional undercurrents that shape cryptocurrency markets. This study contributes to a deeper understanding of the socio-economic and psychological dimensions of Bitcoin, providing a foundation for future research in sentiment-driven market analysis.
This article examines the macroeconomic implications of central bank digital currencies (CBDCs) and private cryptocurrencies using a simple real business cycle model. The analysis explores how agents allocate their portfolios among fiat money, CBDCs and private cryptocurrencies in response to inflation shocks and technological advancements in cryptocurrency production. The model predicts that rising consumer confidence can gen-erate inflationary pressures, prompting a shift towards private cryptocurrencies, which are insulated from inflation tax. Additionally, positive shocks in cryptocurrency production can lead to capital reallocation, reducing final goods production and causing a brief spell of recession. A central bank can remarkably counteract this recessionary effect of a crypto boom by lowering the policy rate. These findings highlight the complex interplay between digital currencies and monetary policy, emphasizing the need for strategic interventions using policy rate as a tool to balance economic stability and crypto innovation. JEL Classification: E50, E52, E58
Andrea Bongini, Marco Sparacino, Luca Marzi, Carlo Biagini
In recent years, Facility Management has undergone significant technological and methodological advancements, primarily driven by Building Information Modelling (BIM), Computer-Aided Facility Management (CAFM), and Computerized Maintenance Management Systems (CMMS). These innovations have improved process efficiency and risk management. However, challenges remain in asset management, maintenance, traceability, and transparency. This study investigates the potential of blockchain technology and non-fungible tokens (NFTs) to address these challenges. By referencing international (ISO, BOMA) and European (EN) standards, the research develops an asset management process model incorporating blockchain and NFTs. The methodology includes evaluating the technical and practical aspects of this model and strategies for metadata utilization. The model ensures an immutable record of transactions and maintenance activities, reducing errors and fraud. Smart contracts automate sub-phases like progress validation and milestone-based payments, increasing operational efficiency. The study’s practical implications are significant, offering advanced solutions for transparent, efficient, and secure Facility Management. It lays the groundwork for future research, emphasizing practical implementations and real-world case studies. Additionally, integrating blockchain with emerging technologies like artificial intelligence and machine learning could further enhance Facility Management processes.
Decentralized finance is often perceived as an alternative to the securities market, which does not require the participation of intermediaries; however, their participation can significantly facilitate the functioning of the crypto-asset market, among other things. This is especially relevant for the Russian digital financial assets market, which is built following a model very similar to the traditional securities market. At the same time, there are currently a significant number of legal obstacles to the functioning of intermediaries in the digital financial assets market. The paper examines some ways to build the infrastructure of the digital financial assets market and proposes changes to the regulatory framework that will help achieve this goal. Legislative barriers to the functioning of intermediaries in the digital financial assets market have been identified. A conclusion is made about the possibility of building an infrastructure of intermediaries in the digital financial assets market by bringing together the regulation and legal regime of digital financial assets and uncertificated securities.
— In an age of increasing digital threats and data complexity, organizations need to adopt integrated security frameworks that leverage the analytical capabilities of Artificial Intelligence (AI) alongside the immutability of Blockchain. This study examines how AIdriven cybersecurity enhances anomaly detection, predictive threat modeling, and automated response mechanisms to address advanced persistent threats and internal vulnerabilities. Machine learning techniques surpass traditional systems by enabling real-time monitoring, behavioral analytics, and rapid risk assessments across enterprise networks. At the same time, blockchain's decentralized architecture and cryptographic strength provide tamper-proof data storage, secure access management, and regulatory compliance, especially in privacy-sensitive sectors like healthcare. The paper highlights the practical applications of blockchain in securing electronic health records, facilitating traceable supply chain operations, and supporting reliable digital forensic investigations. Additionally, it proposes a synergistic AI-blockchain ecosystem capable of delivering autonomous, transparent, and resilient cybersecurity solutions. Through detailed use cases and architectural insights, this work showcases the transformative potential of merging AI and blockchain to redefine cybersecurity protocols, enhance privacy, and maintain data integrity across digital infrastructures.
The proliferation of FinTech platforms has transformed global financial systems by offering innovative, real-time services.However, this evolution has also expanded the surface area for cyber-enabled financial fraud, especially across multi-layered infrastructures comprising mobile banking apps, decentralized finance (DeFi) platforms, digital wallets, and cloud-based services.Traditional machine learning and rule-based systems have demonstrated limited adaptability in detecting increasingly sophisticated attack vectors that span multiple digital layers.This paper presents a comprehensive exploration of explainable deep learning (XDL) models tailored to detect complex cyber-enabled fraud schemes across interconnected FinTech ecosystems.The study begins with an overview of the structural and technological evolution of FinTech infrastructure, followed by an examination of the most prevalent and emerging fraud typologies including synthetic identity fraud, account takeover, transaction laundering, and insider collusion.Emphasis is placed on the limitations of black-box AI models in high-stakes financial environments where interpretability is critical for regulatory compliance, stakeholder trust, and legal recourse.We introduce an explainable deep learning framework incorporating convolutional neural networks (CNNs) for behavioral biometrics, graph neural networks (GNNs) for multi-entity relationship mapping, and attention-based mechanisms for anomaly prioritization.The model integrates SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to improve transparency without compromising predictive performance.Evaluation is conducted using real-world transaction data from anonymized FinTech institutions, with metrics highlighting accuracy, false positive reduction, and interpretability scores.The paper concludes by discussing policy implications, ethical considerations, and future research directions in explainable AI for secure financial innovation.
Blockchain technology has undergone transformative advancements since its introduction as the underlying framework for Bitcoin.Initially a decentralized ledger for cryptocurrencies, its applications now span finance, healthcare, supply chain management, and governance.This study investigates contemporary trends such as decentralized finance (DeFi), non-fungible tokens (NFTs), cross-chain interoperability, and evolving regulatory landscapes.Challenges including scalability limitations, energy inefficiencies, and security vulnerabilities are critically evaluated.The analysis indicates that blockchain is transitioning from experimental use cases to institutional adoption, driven by technological innovation and growing enterprise demand.
As the U.S. healthcare system shifts toward value-based care, there is a growing need for patient-centered technologies that ensure data ownership, interoperability, and trust.This paper proposes a novel framework integrating neuro-symbolic artificial intelligence (AI) and zero-knowledge (ZK) blockchain to enable a secure, scalable, and ethically grounded digital-twin marketplace for patient data.The goal is to empower individuals to own and control their health digital twins-comprehensive, dynamic, AI-driven models representing real-time physiological, behavioral, and clinical states-while facilitating precision care and research collaborations.At a macro level, the framework leverages neuro-symbolic AI to enhance digital twin reasoning, enabling explainable predictions and treatment simulations across diverse datasets.This is paired with ZK-proof blockchain infrastructure to ensure privacy-preserving authentication, decentralized governance, and monetization of patient data without revealing sensitive health information.The integration addresses key challenges in trust, transparency, and consent in patient-provider and patient-researcher relationships.Zooming into operational layers, the paper outlines a decentralized application (dApp) architecture that supports smart contracts for patient-informed data sharing, automated payer-provider interactions, and regulatory compliance tracking.It also highlights how incentives within the marketplace can align with care quality metrics, promote social determinants of health inclusion, and advance equitable data access in underrepresented populations.Case scenarios in chronic disease management and clinical trials illustrate the feasibility of this patient-owned digital-twin ecosystem.Ethical considerations, including algorithmic fairness, data sovereignty, and digital consent protocols, are also critically examined.By combining symbolic logic, neural learning, and cryptographic assurance, this framework sets the foundation for a secure and equitable next-generation health economy.
Chanuka Wijayakoon, Hai Dong, H. M. N. Dilum Bandara, Zahir Tari · 5 authors
Smart contracts can implement and automate parts of legal contracts, but ensuring their legal compliance remains challenging. Existing approaches such as formal specification, verification, and model-based development require expertise in both legal and software development domains, as well as extensive manual effort. Given the recent advances of Large Language Models (LLMs) in code generation, we investigate their ability to generate legally compliant smart contracts directly from natural language legal contracts, addressing these challenges. We propose a novel suite of metrics to quantify legal compliance based on modeling both legal and smart contracts as processes and comparing their behaviors. We select four LLMs, generate 20 smart contracts based on five legal contracts, and analyze their legal compliance. We find that while all LLMs generate syntactically correct code, there is significant variance in their legal compliance with larger models generally showing higher levels of compliance. We also evaluate the proposed metrics against properties of software metrics, showing they provide fine-grained distinctions, enable nuanced comparisons, and are applicable across domains for code from any source, LLM or developer. Our results suggest that LLMs can assist in generating starter code for legally compliant smart contracts with strict reviews, and the proposed metrics provide a foundation for automated and self-improving development workflows.
The integration of Non-Fungible Tokens (NFTs) into the gaming industry has introduced a novel economic model, reshaping monetization strategies and player engagement. This paper analyzes the market potential of NFT integration in video games through a comprehensive approach combining market segmentation, trend analysis, and predictive modeling. Using historical sales data from various genres and platforms, the research identified key segments that show high potential for NFT adoption, particularly action, role-playing, and sports games on mainstream platforms such as PlayStation and Xbox. The market segmentation, achieved through K-Means clustering, revealed distinct groups of video games based on genre, platform, and regional sales performance. Trend analysis using time series models like ARIMA and Prophet highlighted emerging and declining popularity across different genres and platforms. The study also applied predictive modeling techniques, including Random Forest and Gradient Boosting, to forecast the potential success of NFTs in specific game genres. The models demonstrated strong performance, with low mean absolute error (MAE) and root mean squared error (RMSE), confirming that high-engagement genres are likely to benefit most from NFT integration. The findings suggest that NFTs can enhance player experiences by offering unique, tradable in-game assets, thus creating new revenue streams for developers. The paper concludes by recommending strategies for NFT implementation, targeting high-potential genres and platforms, and addressing regional market preferences. Limitations related to data constraints and emerging trends are discussed, and future research directions are proposed, focusing on consumer sentiment analysis and real-world case studies of NFT integration in video games.
Energy prosumer communities offer a mechanism where prosumers can share, and trade locally produced renewable generation directly with consumers within the same energy community. Accordingly, there is need for a decentralized approaches that enables prosumers to locally balance generation and consumption. The deployment of emerging technologies such as Distributed Ledger Technologies (DLT), Internet of Things (IoT), and Artificial Intelligence (AI) can accelerate the integration of Renewable Energy Sources (RES) and advance the development of energy internet. Therefore, this article develops an energy system architecture that shows how DLT, IoT, and AI can be deployed to support the design and actualization of energy internet in Distributed Prosumer Energy Communities (DPEC). The system architecture support energy sharing and trading from energy prosumers to consumer. Findings from this study presents how DLT based smart contracts can be employed to securely manage energy transactions within the energy internet. The system architecture provides energy consumers and prosumers with a decentralized approach for sharing and trading local energy generation without requiring any central intermediary. More importantly, this study presents a use case on the applicability of DLT and AI to support micro grid operations in DPEC.
This study investigates the development of AI-generated art styles within the growing non-fungible token (NFT) market. Using time series analysis, the research identifies key trends and shifts in art styles from 2022 to 2024, revealing how various art forms, algorithms, and mediums evolved in response to technological advancements and market forces. Data was collected from a sample of 10,000 NFT artworks, categorized by creation date, style, and algorithm usage. Exploratory Data Analysis (EDA) techniques, including line graphs and heatmaps, were employed to visualize and interpret trends across different art styles and AI tools. Results indicate a significant increase in the popularity of styles like surrealism and realism, with deepdream and GANpaint algorithms being frequently associated with these styles. Stacked area charts further highlighted the proportional growth of art styles over time, providing insights into both short-term popularity spikes and long-term trends. The findings suggest that the integration of AI algorithms significantly influenced the rise of specific art genres, with certain algorithms correlating strongly with particular styles. Practical implications for artists and collectors include the potential for data-driven insights to guide creative choices and investment strategies. The study's limitations, such as the lack of broader market data, provide a foundation for future research to explore the intersection of AI-generated art, NFT marketplaces, and cultural influences. The paper concludes that AI and NFTs are reshaping the traditional art market, presenting new opportunities for creativity, ownership, and artistic value in a digital age.
ABSTRACT The rise of mining pools in Blockchain networks has improved reward distribution but introduced critical challenges related to centralization and malicious miner activity, which threaten the integrity of decentralized consensus. Addressing this gap, this paper proposes the Reputation‐based Consensus Protocol (RCP), a novel framework designed to enhance trust and security in mining pools by incorporating a transparent and dynamic reputation system. Unlike traditional consensus algorithms like Proof of Work (PoW) and Proof of Stake (PoS), which do not differentiate between trustworthy and malicious participants, RCP evaluates miners based on a multi‐dimensional scoring mechanism, including historical reputation, willingness reputation, and indirect feedback reputation. This targeted approach allows the network to prioritize reputable miners for block creation, thereby mitigating attacks and improving consensus reliability. By integrating RCP with modular Blockchain frameworks such as Hyperledger, this protocol not only strengthens miner accountability but also sets the foundation for more secure and trustworthy decentralized networks. The proposed model has the potential to redefine mining pool operations and significantly contribute to the evolution of secure Blockchain consensus protocols.
G. Sugitha, R. Vasanthi, A. Solairaj, A. V. Kalpana
Secure and efficient data transmission is crucial for maintaining seamless system operations and user trust in the rapidly evolving Internet of Things (IoT) environments.However, IoT networks consistently suffer from data integrity breaches, security vulnerabilities at various network layers, and a high computational cost.Bridging the gap between IoT applications and network infrastructure is essential to addressing these issues.This paper introduces SeCo2, a secure cognitive semantic communication framework for 6G-IoT networks.The framework incorporates a blockchain-based system to provide a secure and privacypreserving data transmission mechanism.Data preprocessing is conducted using the IoT-Sense dataset, and then encryption is done through a hybrid combination of Key-Policy Attribute-Based Encryption (KP-ABE) and Elliptic Curve Cryptography (ECC).Access control and data permissions are implemented via smart contracts to ensure secure transmission.Additionally, a blockchain security layer utilizing Proof of Stake with Fixed Staking Amounts (PoS-FSA) enhances network security and energy efficiency.For further protection of data integrity, tamper-proof provenance logging prevents unauthorized tampering.Experimental results demonstrate ultra-low latency data transmission (in the microsecond range), with a transmission delay as low as 0.003001 s for data sizes ranging from 1 GB to 50 GB, and a network security rate of 98%, ensuring more reliable and privacy-preserving IoT ecosystems.
Megha Dabas, Thakur Mohit Singh, G. Sai Gautham, Pradeepthi kaniki · 5 authors
The Know Your consumer (KYC) process, which ensures the security and legality of consumer identity, is a requirement for financial institutions to comply with regulatory standards.With its immutability, security, and transparency, blockchain technology presents a ground-breaking approach to enhancing the KYC process.By using decentralized platforms like Ethereum, blockchain technology enables more efficient and cost-effective customer data management.This significantly reduces the amount of time and money required for compliance.Blockchain technology can help banks overcome the challenges they have when conducting KYC and customer onboarding.To ensure that large payments are accurately recorded and authenticated, it also puts in place a system that demands KYC identity for clients who make significant transactions that exceed a predefined threshold.A central regulatory body oversees the thorough registration of financial firms and rigorously enforces KYC regulations in the proposed architecture.In addition to improving security, reducing fraud, and ensuring compliance, this solution provides a quick and efficient process for safely handling both routine and complex transactions.
Non-fungible tokens (NFTs) are not only blockchain-based digital assets; they are theatrical practices taking shape between social bodies. Understanding the theatricality of NFTs provides a way to account for the criticism directed at them. NFT critiques often purport to focus on underlying politico-economic ideologies but, in actuality, reveal deep-seated antitheatrical anxieties recontextualized for the digital realm.
With the emergence of the metaverse, some problems relating to trader responsibility, which had previously long been addressed, have now resurfaced and come back to life. One of these problems is the question of who should be held accountable for harm inflicted by defective or counterfeit products sold by third-party vendors in metaverse marketplaces. Under the common law, liability for defective or counterfeit products rests with the immediate seller of the product. But, unique aspects of the metaverse may make holding sellers liable unwise, difficult, or even impossible. The law confronted a similar question after online platforms emerged. Currently, common law principles of negligence and product liability still assume liability rests with the seller. But, in some cases, courts have modified the law to impose contributory liability on online platforms in addition, as these platforms are viewed as the cheapest cost avoiders and are in the best position to distribute the damage. As the metaverse, an augmented reality platform, gains momentum, it poses new problems for products liability. Imposing liability on these augmented reality platforms does not necessarily follow the same rationales as imposing liability on e-commerce platforms. This is because, unlike traditional e-commerce platforms, metaverse platforms are operated on the blockchain and are governed by decentralized autonomous organizations (DAOs) enabled by algorithms. Metaverse platforms do not reside on a single server. Instead, content is distributed across an infinite number of servers in a peer-to-peer network. This means metaverses have no single point of authority making it essentially impossible to assign liability to the platforms. Even if it were possible to assign liability to individual DAO members, there would be tenuous economic justification for assigning such liability, as members on the metaverse lack the ability to monitor transactions on the platform. As such, unlike typical online platforms such as Amazon, metaverse members are likely not the cheapest cost avoiders. Applying the law for e-commerce platforms to metaverse platforms risks generating an accountability gap resulting from diffusion of responsibility where many entities are involved in a transaction and none of them act to prevent harm. This also risks leaving victims of defective products or fraudulent transactions without recourse. For these reasons, holding metaverse platforms responsible for the merchandise sold on them may be undesirable as a policy matter. In this Article, we propose a “know your trader” rule for marketplaces. Under this new approach to the long-standing financial trading rule of “know your customer,” traditional online marketplaces and innovative metaverse marketplaces would have to verify the identity of their traders before the traders could enter the system. The marketplace would confidentially maintain traders’ identities to protect the anonymity that draws many to the metaverse in the first place. However, a plaintiff could pierce the veil of anonymity when they present prima facie evidence that their case could survive a motion to dismiss. This idea builds on several statutory proposals and laws in the European Union and the United States that require online marketplaces to identify and verify traders. The Article explains why this rule would be more effective and more efficient than the current application of the rule. Finally, the Article addresses potential free speech objections based on trader anonymity, concluding that the proposed framework is permissible under the First Amendment.
Muhammad Iqbal, Kunal Raj, K.V. Narasimha Reddy, Mohd. Mudaseer Mazharuddin
In today's digital age, student academic data is still largely controlled by educational institutions, which creates major risks and limitations.Centralized systems are vulnerable to data loss due to natural disasters, political instability, or system failures.They also make it difficult for students to access or share their records when participating in exchange programs or pursuing lifelong learning across different platforms.To solve these issues, this paper introduces a decentralized approach where students have full control over their educational data.Using blockchain technology-specifically the Ethereum public network-and Web3 tools, we present DecentralEduChain, a framework that allows students to securely store and manage their academic records through smart contracts.Educational institutions can interact with these contracts via integrated Learning Management Systems (LMS), enabling both the reading and updating of student records without relying on centralized databases.This system not only enhances security and transparency but also empowers students with ownership of their data, making it easier to share academic credentials across institutions.The paper also outlines the practical steps for implementing the system, including smart contract creation and integration with LMS platforms, making it a promising solution for the future of educational data management.
In today’s fast-paced digital world, NFT have become mainstream, reaching a market value of $50 billion. They act as digital certificates of ownership of online resources, reshaping how we perceive ourselves to be on the digital realm. Our plan is to have a BidCraft NFT Hub, a marketplace where people can easily buy, sell and trade NFT. We simplify the process by using blockchain technology. For the user interface, we use web3.js for a smooth experience. In the background, Node.js and Express.js ensure smooth operation. We integrate MetaMask, a trusted digital wallet for account management and secure transactions. To ensure security and transparency in transactions, the platform relies on contract written in Solidity. Testing is done on the Hardhat network, which is planned to run on the Polygon blockchain in the testing environment. In summary, the BidCraft NFT Hub aims to make blockchain technology and NFTs accessible to everyone by leveraging the Polygon blockchain and prioritizing user friendliness while maintaining safety and security
In an era where the intersection of artificial intelligence (AI) and energy finance drives critical infrastructure decision-making, designing resilient AI architectures has become imperative.Predictive energy finance systems-spanning investment forecasting, carbon pricing, and grid demand-supply modeling-face mounting complexity due to shifting policy landscapes, data sovereignty regulations, and the escalating risk of adversarial threats.This paper presents a multidisciplinary framework for constructing AI architectures that maintain operational integrity, adaptability, and security in volatile environments.At a macro level, the study outlines the integration of federated learning, edge analytics, and privacy-preserving AI techniques to ensure compliance with crossborder data governance regimes while enabling decentralized energy financial modeling.It further examines adversarial machine learning risks-such as data poisoning and model inversion-that compromise predictive validity in high-stakes financial applications.Through threat modeling and robust training paradigms, the architecture includes defense-in-depth strategies like adversarial regularization, ensemble resilience, and real-time anomaly detection.The paper also analyzes the effects of dynamic policy shiftssuch as carbon credit revaluation and renewable energy subsidies-on model reliability and system adaptation.A scenario-based approach illustrates how the proposed architecture adjusts to policy-induced discontinuities through modular retraining, real-time policy rule parsing, and simulation-informed decision loops.Case studies from green energy bonds, smart grid investment portfolios, and climate-linked derivatives are used to validate the architectural robustness under varying policy, regulatory, and cyber conditions.Ultimately, this work provides a systems-engineered blueprint for resilient AI in predictive energy finance, enabling trustworthy, secure, and sovereign-compliant deployment.
Equitable education systems contribute to fostering thriving societies. However, decentralization reforms in school finance pose challenges to equity and social justice. Using longitudinal multilevel models, we examined the trends in equity of local education funding distribution in 250 Israeli local authorities from 2014 to 2020. Our findings revealed a consistently inequitable allocation: high-SES and majority-populated areas allocated double the resources compared with low-SES and minority-populated areas, with funding disparities increasing over time. These findings suggest the need for regulations governing local funding, particularly in diverse societies, to promote equity in education finance.
The advance of Information Technology is closely related to and has a direct impact on the development of people’s lives. One of the real technological advancements that plays a major role in creating evolution in the community life order is Internet progress. As time passes, the internet world continues to experience rapid development, such as Metaverse, Non-Fungible Tokens (NFTs), and Cryptocurrency. Meanwhile, the change of regulations and legal products that are not as fast as the advance of the internet and the business world raises their abuse potential as means of Money Laundering Crime. The research method used was normative juridical with analytical descriptive research specifications. Metaverse, NFTs, and Cryptocurrency are relatively new phenomena in this globalization era. The lack of regulation and the high volatility of price characteristics that are strongly influenced by public interest make them potential as means to hide or disguise the origin of assets from criminal acts. So, this research was conducted to analyse the potential use of Metaverse and Non-Fungible Tokens as means of money laundering.