Self-collecting Internet of Things (IoT) gadgets have transformed healthcare systems. Centralising IoT healthcare data processing and storage introduces scalability, speed, security, and privacy issues. On the other hand, Blockchain technology attracts interest in the IoT healthcare industries because of its decentralisation, data protection, transparency, and security aspects. Single public blockchain ledgers are inefficient for healthcare IoT security and efficiency due to high transaction fees, limited scalability, and high patient traffic. Specifically, this article focuses on the concerns around privacy, security, performance, scalability, and energy consumption in healthcare blockchain-IoT systems. In this paper, we propose CareChain, an IPFS storage system with two blockchains, one for patients and the other for healthcare providers, to manage healthcare IoT data. The proposed model uses IPFS distributed storage to improve system throughput, reducing transaction latency and blockchain storage overhead. It improves storage requirements, energy efficiency, transaction speed, privacy, and security. It envisions a system-wide data and information security architecture that uses the Elliptic Curve Digital Signature Algorithm (ECDSA) and a device proxy to keep tabs on low-cost devices. The prototype model was tested to investigate its security, efficiency, and energy use. The results show that this system is more robust than the existing healthcare models.
K. Swathi, Putta Durga, K. Venkata Prasad, A Krishna Chaitanya · 7 authors
An enormous demand for a secure, scalable, intelligent edge computing framework has emerged for the exponentially increasing number of Internet of Things (IoT) devices for any substrate of modern digital infrastructure. These edge nodes distributed across heterogeneous environments serve as primary interfaces for sensing, computation, and actuations. Their physical deployment in unattended scenarios puts them at risk of being targets for resource manipulation. One widely accepted IoT architecture with traditional notions of edge may consider a threat to its centralized knowledge with an unbounded attack surface that includes anything that can remotely connect to the edge from the cloud-like domain. Existing strategies either forget the dynamic risk context of edge nodes or do not achieve a reasonable trade-off between security and resource constraints, essentially degrading the robustness and trustworthiness of solutions intended for real-life scenarios. To address the existing gaps, the work presents a novel Blockchain Integrated Deep Learning Framework for secure IoT edge computing, introducing a hybrid architecture where the transparency of blockchain meets deep learning flexibility. The proposed system incorporates five specialized components: Blockchain-Orchestrated Federated Curriculum Learning (BOFCL), which ensures risk-prioritized training using threat indices derived from blockchain logs; this adaptive sequencing enhances responsiveness to high-risk edge scenarios. Zero-Knowledge Proof Enabled Secure Inference Engine (ZK-SIE) provides verifiable privacy-preserving inference, ensuring model integrity without exposing input data or model internals in process. Blockchain Indexed Adversarial Attack Simulator (BI-AAS) focuses on testing the models in edge environments against attack scenarios drawn from common adversarial profiles and thereby facilitates a model defensive retraining. Energy-Aware Lightweight Consensus with Adaptive Synchronization (ELCAS) avoids overhead by seeking energy-efficient participants for global model synchronization in constrained environments. Trust Indexed Model Provenance and Deployment Ledger (TIMPDL) ensures model lineage tracking and deploy ability in a transparent manner by providing composite trust scores computed from data quality, node reputation, and validation metrics. Altogether, the framework combines the data integrity, adversarial robustness, and trust-aware deployment, shortening training latency, synchronization energy, and privacy leakage. It is a foundational advancement supporting secure decentralized edge intelligence for next-generation IoT ecosystems.
Neither fiscal federalism nor austerity theory adequately explain shifts in US local government expenditure after the Great Recession. We assess spatial differences in local government expenditure composition using finance data for all local governments in the USA from 2007 to 2017. Overall, there was considerable stability in local expenditure patterns, despite pressures generated by the Great Recession. State decentralization, state aid, politics, local capacity, and need all affect local expenditure patterns. Panel regressions of education, social, and allocational expenditures show decentralization is associated with more redistributive expenditure at the local level—not less, as fiscal federalism and austerity theory claim. However, decentralization of educational expenditure is associated with less local educational expenditure. State aid, by contrast, has a complementary effect on local education expenditure. Education spending dropped and then recovered, but state educational aid was found to privilege suburbs. Social expenditure grew during the Great Recession as most cities and counties maintained social welfare expenditures even in the face of fiscal constraints. Overall state aid for social welfare relieved local social expenditure, but it had a complementary effect on social welfare spending in states with historically high social welfare commitments (NY, CA, the Midwest). Localities in the Midwest faced greater fiscal stress, as state aid for social welfare plummeted in these states during the Great Recession. Despite the austerity faced by Midwestern counties, they maintained redistributive expenditure levels. US local governments are not austerity machines. They practice pragmatic municipalism—meeting needs despite limited fiscal resources.
In modern decentralized environments, ensuring robust security, privacy, and trust remains a critical challenge, particularly in private blockchain ecosystems. This study proposes a Blockchain-Enabled Optimized Crypto Table-Based Key Generation Framework that integrates an anonymous reputation system and smart contract-driven security within a private Ethereum network. Existing key generation mechanisms are vulnerable to brute-force attacks, frequency analysis, and centralized trust failures, while existing blockchain-based solutions often suffer from computational overhead and lack of anonymity. To address these issues, the proposed approach employs an optimized crypto table-based key generation algorithm enhanced by metaheuristic optimization for faster and stronger cryptographic key creation. A reputation-based trust model preserves participant anonymity while deterring malicious behaviour, and smart contracts enforce secure, automated access control and transaction validation. The framework is deployed on a private Ethereum testbed, ensuring high throughput, low latency, and tamper-proof auditability. Experimental results demonstrate a 38% improvement in key generation speed, a 29% increase in resistance to brute-force attacks, and enhanced trust metrics compared to baseline models, with minimal performance degradation. This work establishes a secure, privacy-preserving, and performance-optimized blockchain architecture for sensitive and enterprise-grade applications.
ABSTRACT This paper employs deep learning and machine learning‐based NLP models to investigate the impact of the news sentiment on the Bitcoin price. The lagged Bitcoin variables, news indicators, macroeconomic, and financial factors were taken into account to explain the importance of news sentiment on the Bitcoin price. Moreover, FinBERT‐based sentiment scores and semantic features extracted from over 650,000 financial news headlines were integrated with financial and macroeconomic variables. The importance scores of the investigation showed that Bitcoin was largely explained by its lagged price movements, which suggests the speculative nature of the cryptocurrency. However, the investigation also revealed that Bitcoin was significantly influenced by the news sentiment score. In other words, the paper indicates that the movements in the Bitcoin price can be predominantly explained by the news sentiment. Advanced hybrid models (all ML and DL models with the addition of variables obtained with the FinBERT model) were optimized using Optuna and RandomizedSearchCV. The FinBERT‐LSTM model achieved the best prediction accuracy. Nevertheless, the main findings indicated that the response of the Bitcoin price to negative news was much stronger than to positive and neutral news. This finding suggests that the asymmetric relationship between the Bitcoin price and news sentiment was evident. GARCH‐based volatility and what‐if scenario analyses further demonstrated that negative sentiment leads to sharper fluctuations in the Bitcoin price. The paper provides important implications for policymakers, portfolio managers, investors, and academics.
Haydeer MohamadAbbas, Pokhraj Sahu, A. S. Kannan, Mahmudov Kahramon Shuhratjon Ugli · 8 authors
Although the global art industry is adjusting to digital trends, it is still troubled by forgery, losing information about a work’s past, and the difficulty in tracking all artworks at any time. This research proposes a new framework, NFTraX-ART, that leverages dynamic Non-Fungible Tokens (dNFTs), IoT smart tags, and blockchain to enable live tracking, smooth ownership transfers, and a clear asset history for art. Unlike static NFTs, dNFTs keep changing as metadata that shows where they are now, who owns them, their exhibition history, and their official appraisal information is updated. As for physical artworks, Internet of Things technology with location and movement sensors continuously sends this data to the blockchain to confirm their authenticity and provide clear traceability. Using smart contracts, royalties are automatically applied, all transactions are validated, and alerts are sent when any movements are made outside the programmed areas. Thanks to this mode, the system connects digital and physical worlds, making it safe and straightforward to track essential art pieces. It outlines the important steps for the methodology, the smart contract code, and the simulation process, and checks how the model stacks up against today’s static NFT platforms. According to the results, blockchain systems are more accurate, safer, and track transaction history. Thanks to NFTraX-ART, everyone involved has a secure, up-to-date record of every artwork. Using this approach, NFTs take on an active role in the tracking system, helping change how art is possessed, transferred, and appreciated in the art trading world.
‘गोंड चित्रकला’ ही भारताच्या आदिवासी सांस्कृतिक वारशातील एक महत्त्वपूर्ण आणि वैशिष्ट्यपूर्ण कला आहे. मध्य भारतातील गोंड समाजाशी निगडित असलेली ही चित्रशैली निसर्गाशी असलेल्या त्यांच्या नात्याचं, श्रद्धेचं आणि जीवनदृष्टीचं दृश्य रूप मानली जाते. या कलेतील ठिपक्यांची रचना, लयबद्ध रेषा आणि निसर्गाशी जोडलेले विषय हे तिचे मुख्य वैशिष्ट्य आहे. पूर्वी घरांच्या भिंतींवर साजऱ्या होणाऱ्या या चित्रकलेचा आधुनिक कॅनव्हास, कापड, कागद आणि डिजिटल माध्यमांपर्यंतचा प्रवास अत्यंत वैशिष्ट्यपूर्ण आहे. जनगढ सिंह श्याम यांच्या कार्यामुळे गोंड चित्रकलेला नवीन दिशा मिळाली, तर त्यांच्या पाठोपाठ शर्मन श्याम, दुर्गाबाई व्याम, भज्जू श्याम यांसारख्या समकालीन कलाकारांनी या परंपरेला नव्या सामाजिक आणि जागतिक संदर्भांतून समृद्ध केलं. गोंड चित्रकलेला एप्रिल २०२३ मध्ये GI टॅग प्राप्त झाला, ज्यामुळे या पारंपरिक कलेला अधिक मान्यता, संरक्षण आणि जागतिक बाजारपेठेत स्थान मिळालं. NFT (Non-Fungible Token), डिजिटल गॅलरी, ऑनलाईन एक्झिबिशन्स आणि ग्राफिक पुस्तकांमधून ही चित्रशैली नव्या पिढीशी संवाद साधत आहे. एकूणच, गोंड चित्रकला ही केवळ एक पारंपरिक चित्रशैली नसून, गोंड समाजाच्या सांस्कृतिक अस्मितेचं आणि निसर्गाभिमुख जीवनदृष्टीचं प्रभावी माध्यम आहे, जी काळानुरूप नव्या रूपांतरणातून अधिक व्यापक आणि सजीव होत आहे.
Blockchain technology has emerged as the backbone of cryptocurrencies and decentralized finance, yet its long-term resilience is increasingly threatened by advances in quantum computing. Quantum algorithms, such as Shor’s algorithm, can undermine public-key cryptography, while Grover’s algorithm accelerates brute-force search, weakening proof-of-work schemes. In this paper, we propose a Quantum Blockchain Framework that integrates quantum communication protocols, quantum consensus mechanisms, and quantum-resistant cryptography. We construct a theoretical model of quantum-secured distributed ledgers, where qubits, entanglement, and quantum key distribution (QKD) enhance security and efficiency. Applications to cryptocurrency are explored, highlighting how quantum blockchain can mitigate security risks, improve consensus speed, and enable quantum-native digital assets.
The growing impact of climate change has made carbon credit trading an essential strategy for controlling industrial emissions. Traditional systems face issues such as fraud, inefficiency, and limited transparency. This paper presents a blockchain-based platform for carbon credit trading that supports the minting, exchange, and burning of digital credits. Organizations can retire (burn) credits to offset emissions and obtain a non-fungible token (NFT) certificate as verified proof of compliance. Through decentralization, immutability, and transparency, the framework removes intermediaries and builds trust. The proposed system offers a secure, scalable, and tamper-resistant solution for verifiable carbon offsetting, advancing sustainable blockchain innovation.
Francesco Salzano, Simone Scalabrino, Rocco Oliveto, Remo Pareschi
Smart Contracts are critical components of blockchain ecosystems, with Solidity as the dominant programming language. While LLMs excel at general-purpose code generation, the unique constraints of Smart Contracts, such as gas consumption, security, and determinism, raise open questions about the reliability of LLM-generated Solidity code. Existing studies lack a comprehensive evaluation of these critical functional and non-functional properties. We benchmark four state-of-the-art models under zero-shot and retrieval-augmented generation settings across 500 real-world functions. Our multi-faceted assessment employs code similarity metrics, semantic embeddings, automated test execution, gas profiling, and cognitive and cyclomatic complexity analysis. Results show that while LLMs produce code with high semantic similarity to real contracts, their functional correctness is low: only 20% to 26% of zero-shot generations behave identically to ground-truth implementations under testing. The generated code is consistently simpler, with significantly lower complexity and gas consumption, often due to omitted validation logic. Retrieval-Augmented Generation markedly improves performance, boosting functional correctness by up to 45% and yielding more concise and efficient code. Our findings reveal a significant gap between semantic similarity and functional plausibility in LLM-generated Smart Contracts. We conclude that while RAG is a powerful enhancer, achieving robust, production-ready code generation remains a substantial challenge, necessitating careful expert validation.
This study investigates the application of the Light Gradient Boosting Machine (LGBM) model for both deterministic and probabilistic forecasting of Bitcoin realized volatility. Utilizing a comprehensive set of 69 predictors -- encompassing market, behavioral, and macroeconomic indicators -- we evaluate the performance of LGBM-based models and compare them with both econometric and machine learning baselines. For probabilistic forecasting, we explore two quantile-based approaches: direct quantile regression using the pinball loss function, and a residual simulation method that transforms point forecasts into predictive distributions. To identify the main drivers of volatility, we employ gain-based and permutation feature importance techniques, consistently highlighting the significance of trading volume, lagged volatility measures, investor attention, and market capitalization. The results demonstrate that LGBM models effectively capture the nonlinear and high-variance characteristics of cryptocurrency markets while providing interpretable insights into the underlying volatility dynamics.
This study explores Bitcoin's value formation through the Granular Interaction Thinking Theory-Value Theory (GITT-VT). Rather than stemming from material utility or cash flows, Bitcoin's value arises from informational attributes and interactions of multiple factors, including cryptographic order, decentralization-enabled autonomy, trust embedded in the consensus mechanism, and socio-narrative coherence that reduce entropy within decentralized value-exchange processes. To empirically assess this perspective, a Bayesian linear model was estimated using daily data from 2022 to 2025, operationalizing four informational value dimensions: Store-of-Value (SOV), Autonomy (AUT), Social-Signal Value (SSV), and Hedonic-Sentiment Value (HSV). Results indicate that only SSV exerts a highly credible positive effect on next-day returns, highlighting the dominant role of high-entropy social information in short-term pricing dynamics. In contrast, SOV and AUT show moderately reliable positive associations, reflecting their roles as low-entropy structural anchors of long-term value. HSV displays no credible predictive effect. The study advances interdisciplinary value theory and demonstrates Bitcoin as a dual-layer entropy-regulating socio-technological ecosystem. The findings offer implications for digital asset valuation, investment education, and future research on entropy dynamics across non-cash-flow digital assets.
Srinivas P M, Ruthvik M T, Sanjay UG, Shiva Kumar S · 6 authors
Secure digital identity management is every emerging concern. Traditional authentication which includes use of passwords, central databases, and third party recovery present great security and also usability issues. Zero- Knowledge Proofs (ZKPs) and blockchain have put forth as very good for Decentralized identity systems. But also many present solutions have large compute requirements, don’t scale well, and have poor user recovery. This review puts forth that in present ZKP based identity systems we see the lack of password less login, human readable identities, and self sovereign recovery. We look at recent systems which we note have a heavy use of complex crypto credentials, central verification which is a point of failure, and extensive infrastructure which in turn do not see wide scale adoption. To present solutions to these issues we have put forth a UID based identity which uses ZKPs for authentication which does not require storage of passwords or private keys. We introduce a novel recovery which uses a human readable phrase from private key, salt, and UID which in turn is a user controlled method. What we did is we put the UID on the blockchain which in turn improves privacy and scale. Our analysis which we present improves on issues of usability, scale and security which in turn we present a very simple and practical solution for today’s identity management issues. Also this study we present which we put forth to be the base for what we think will be future works in the development of useable ZKP based identity systems.
In an age where knowledge is power and credentials are the currency of trust, securing academic qualifications while preserving individual privacy has become paramount. Traditional verification methods are costly, slow, and prone to fraud, and centralized digital systems risk exposing sensitive personal data. To address these challenges, we propose a novel framework the Blockchain Academic Credential Interoperability Protocol (BACIP) - that leverages blockchain technology and zero-knowledge proofs (ZKPs). BACIP integrates smart contracts and a dual-blockchain architecture with privacy-preserving ZKP circuits (implemented via Circom/SnarkJS) to automate issuance, storage, and cross-border verification of educational credentials. Our methodology combines on-chain integrity (via Ethereum/Polygon smart contracts) with off-chain confidentiality (using AES encryption and IPFS storage) and self-sovereign identities (DIDs). The distinguishing innovation of BACIP is the seamless integration of ZKPs throughout the credential lifecycle, enabling verifiers to validate specific academic attributes-such as degree completion or GPA-without accessing or exposing any underlying personal data. This approach ensures cryptographic trust while upholding strict privacy standards. Initial results demonstrate a considerably well proof success rate, improved compliance with data protection regulations such as GDPR, and a significant reduction in on-chain computational load. By uniting the transparency of blockchain with the confidentiality of zero-knowledge techniques, BACIP offers a scalable and interoperable framework for secure academic credentialing. Institutions and employers benefit from faster, automated verification workflows, while learners maintain full control over their digital identities and credentials. Ultimately, BACIP paves the way for trustworthy, efficient, and privacy-respecting academic mobility across borders and platforms.
In the dynamic landscape of e-commerce, fostering customer loyalty is critical for sustainable growth and profitability, given the ease with which consumers can switch platforms and the high cost of acquiring new customers. This study explores multifaceted strategies for enhancing customer retention, including loyalty programs, gamification, customer lifetime value (CLV) and churn analytics, and community-based approaches. It examines how data-driven personalization, psychological reward systems, and emotional connections through brand communities drive loyalty. Examples such as Amazon Prime, Sephora’s Beauty Insider, and Nike Run Club illustrate the effectiveness of tailored rewards, gamification, and social engagement. The integration of CLV and churn analytics enables businesses to optimize resources by targeting high-value customers and predicting churn risk. Community strategies, leveraging social media, user-generated content, and events, foster a sense of belonging, particularly among younger demographics. Ethical considerations, including data privacy and transparency, are highlighted as essential for maintaining trust. The study underscores the evolving role of technology, such as AI and Web3, in shaping innovative, customer-centric loyalty strategies for both large and small e-commerce businesses.
Once a playground for tech enthusiasts, the crypto space has shifted to a financial field that is increasingly on policymakers’ radar due to the increasing adoption of crypto-assets, and also some significant crypto-related collapses. In this context, it is crucial to propose monitoring frameworks to assess the potential integration of the crypto sphere into traditional financial systems. We propose the use of the TVP-VAR approach as a strategic instrument for policymakers to analyze the connectedness between major financial markets and relevant crypto systems, such as the emerging centralized finance sector and the increasingly relevant decentralized finance ecosystem. Our findings indicate that the financial integration between the crypto space and traditional financial markets remains weak. Nonetheless, we report a very slight increase in connectedness since 2020, suggesting that while the crypto space is still far from being fully integrated, it has begun to establish modest but persistent links with conventional financial markets. • We examine dynamic connectedness between crypto and global equity markets. • TVP-VAR shows crypto–TradFi integration remains weak but rising since 2020. • DeFi and broad crypto indices transmit more spillovers than Bitcoin or CeFi. • Results highlight regulatory priority on DeFi and full-market monitoring.
The study explores the impact of non-fungible tokens on asset liquidity within the decentralized finance system, based on a systematic review of thirty articles retrieved from major scholarly databases. We analyzed how NFTs contribute to increasing liquidity and facilitate a shift in digital asset ownership. Findings show NFTs improve asset liquidity by permitting fractional ownership and trade of assets that were previously illiquid, like real estate and digital art. NFTs’ unique characteristics and market volatility may make them less liquid. Blockchain technology that underpins NFTs offers transparent and unchangeable ownership records. The ramifications show how developers, investors, and regulators may take advantage of NFTs’ while resolving obstacles, including scalability problems and regulatory uncertainty.