The rapid advancement of artificial intelligence (AI) has begun to challenge traditional assumptions of corporate organization, governance, and commerce. While AI is widely recognized as a tool for enhancing decision-making and operational efficiency, an emerging possibility lies in the concept of AI corporations—autonomous economic entities capable of engaging in trade, investment, and contractual relationships without direct human intervention. This paper explores the rise of AI corporations and their potential to redefine global commerce through autonomous economic agents. The study adopts a descriptive and analytical framework, drawing on secondary data, global case studies of decentralized autonomous organizations (DAOs), AI-driven financial institutions, and blockchain-enabled smart contracts. Findings suggest that AI corporations could significantly reduce transaction costs, enable borderless 24/7 trade, and enhance economic efficiency while simultaneously raising profound challenges concerning legal identity, accountability, taxation, and regulatory oversight. Unlike traditional corporations that rely on human managers and shareholders, AI corporations operate on algorithmic autonomy, raising questions about liability, ethical conduct, and governance in the absence of human decision-makers. The implications are both economic and policy-oriented: while the integration of AI corporations could accelerate global trade and investment, unchecked autonomy could lead to monopolistic control, systemic risks, and destabilization of labor markets. The paper argues for the urgent development of international regulatory frameworks, AI-specific corporate laws, and hybrid human–AI governance models to harness the opportunities while mitigating risks. By positioning AI corporations as the next stage in the evolution of commerce—from traditional enterprises to digital platforms and now autonomous entities—this study contributes to the discourse on the future of global business, law, and economic systems.
ABSTRACT The combination of blockchain technology with Industrial Internet of Things (IIoT) frameworks is promising in terms of building trust, data authenticity, and resilience. However, the efficiency and feasibility of integration largely rely upon the consensus mechanisms used. The present study is an overview of four renowned blockchain consensus schemes, namely Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof of Stake (DPoS), and the corresponding performance, security, efficiency, as well as compatibility under IIoT. The results reveal that low‐latency and lightweight consensus, such as PBFT and DPoS, will be helpful in IIoT applications, especially in applications with scarce resources. The paper offers practical guidance on the development of IIoT systems with integrated blockchain customized based on the requirements of the industry.
The rise of Decentralized Finance (DeFi), enabled by blockchain technology, has introduced open and transparent financial ecosystems that contrast sharply with traditional financial systems. While DeFi expands the financial landscape and democratizes participation in global markets, it also introduces new complexities. Classic valuation models used in traditional finance often fall short in this context. However, DeFis transparency---where all transactions are publicly recorded---offers a unique opportunity to model and understand market behavior using modern analytical tools. Motivated by the challenges and opportunities of DeFi, the main goal of this thesis is to propose novel methods to understand market dynamics through the lens of network science and machine learning. To this end, we focus on four specific objectives: (i) assess whether structural information from blockchain transaction networks provides predictive signals beyond traditional indicators; (ii) develop robust trust-based valuation metrics for DeFi protocols; (iii) develop a framework for forecasting financial time series through uncertainty-aware machine learning architectures; (iv) construct diversified portfolios using network-based representations of asset relationships. First, using Ethereum as a case study, we analyze the influence of the transaction network on market trends by comparing the performance of two machine learning models: one that uses technical analysis and social media indicators commonly found in the literature and another that incorporates structural properties of the transaction network. We found that by including transaction network information, we can anticipate 46% more uptrends and 19% more downtrends, highlighting the predictive power of the transaction network. Second, we introduce the TVL/MCAP bands as a tool to identify periods of overconfidence and underconfidence in the DeFi market. We show that extreme values of this indicator can signal price movements: values above the 95th percentile are associated with a 15\% higher return in the following month, while values below the 5th percentile anticipate declines, highlighting investor confidence as a key market driver. Third, we address the need for forecasts that not only anticipate market trends but also quantify the uncertainty surrounding them. To this end, we integrate Reservoir Computing (RC) with conformal prediction methods to provide statistically rigorous forecasts along with prediction intervals. We found that RC outperform traditional econometric models, particularly in anticipating the trend of financial time series. Furthermore, we show that conformal methods, especially quantile-conformal variants, significantly improve forecast reliability while adapting to market volatility. Finally, we address the challenge of portfolio optimization using network-based methods. Specifically, we model the network of relationships between cryptocurrencies to obtain a market representation that enables selecting a more diversified portfolio. We find that peripheral assets enhance portfolio stability and returns, while links bridging network communities carry higher risk. Thereby, these results highlight the importance of structural diversification in volatile markets. In addition, we contribute to refining pairs trading strategies by proposing the Hurst exponent to identify rapid mean-reversion opportunities. We show that anti-persistent values of H lead to faster reversion and consistent returns---minimizing trading costs and enabling broader portfolio construction. In conclusion, this thesis provides an interdisciplinary analytical framework that advances our understanding of DeFi markets. By introducing network-based indicators, trust metrics, uncertainty-aware forecasts, and diversification strategies grounded in market structure, we provide new tools for investors and researchers to navigate the complexity and volatility inherent in decentralized financial systems. RESUMEN El auge de las Finanzas Descentralizadas (DeFi), impulsado por la tecnología blockchain, ha dado lugar a ecosistemas financieros más accesibles y transparentes que contrastan con los sistemas financieros tradicionales. DeFi amplía el panorama financiero actual e introduce nuevos retos, como la necesidad de un nuevo modelo de valoración de los activos. No obstante, el hecho de que todas las transacciones son públicas, ofrece una oportunidad única para modelar y comprender la dinámica del mercado mediante nuevas herramientas analíticas. Esta tesis tiene como objetivo principal proponer nuevos métodos para comprender la dinámica del mercado desde la perspectiva de los sistemas complejos y el aprendizaje automático. Para ello, nos centramos en cuatro objetivos específicos: (i) evaluar si la información estructural de las redes de transacciones aporta señales predictivas más allá de los indicadores tradicionales; (ii) desarrollar métricas de valoración de los protocolos DeFi basadas en la confianza de los inversores; (iii) construir un marco metodológico para predecir series temporales financieras mediante arquitecturas de aprendizaje automático que incorporen incertidumbre; (iv) construir portfolios diversificados utilizando representaciones de la red de relaciones entre criptomonedas. En primer lugar, utilizando Ethereum como caso de estudio, analizamos la influencia de la red de transacciones sobre la tendencia del mercado comparando dos modelos de aprendizaje automático: uno que emplea indicadores de análisis técnico y de redes sociales comunes en la literatura, y otro incluyendo propiedades estructurales de la red de transacciones. Los resultados muestran que incluyendo información de la red podemos anticipar un 46% más de tendencias alcistas y un 19% más de tendencias bajistas, lo que subraya el poder predictivo de la red de transacciones. En segundo lugar, introducimos las bandas TVL/MCAP para identificar períodos de sobreconfianza y desconfianza en el mercado DeFi. Demostramos que valores extremos de este indicador anticipan movimientos en el precio: valores por encima del percentil 95 se asocian con un rendimiento 15% superior en el mes siguiente, mientras que valores por debajo del percentil 5 anticipan caídas. En tercer lugar, abordamos la necesidad de predicciones que no solo anticipen tendencias del mercado, sino que también cuantifiquen la incertidumbre. Para ello, integramos Reservoir Computing (RC) con métodos de predicción conforme para generar predicciones estadísticamente rigurosas junto con intervalos de confianza. Mostramos que RC supera a los modelos econométricos tradicionales, especialmente anticipando la tendencia del precio. Además, los métodos conformes ---en particular las variantes de cuantiles--- mejoran significativamente la fiabilidad de las predicciones al adaptarse a la volatilidad del mercado. Por último, abordamos el problema de optimización de portfolios mediante métodos basados en redes. Específicamente, modelamos la red de relaciones entre criptomonedas para seleccionar un portfolio más diversificado. Observamos que evitar pares que conectan distintas comunidades en la red y priorizar activos periféricos aumenta el rendimiento y disminuye el riesgo, demostrando así la importancia de una diversificación estructural. Además, proponemos el uso del exponente de Hurst para identificar oportunidades que revierten antes a la media en estrategias de pairs trading. En conclusión, esta tesis propone un marco analítico interdisciplinar que contribuye al entendimiento de los mercados DeFi. Al introducir indicadores basados en redes, métricas de confianza, predicciones con estimación de incertidumbre y estrategias de diversificación basadas en la estructura del mercado, ofrecemos nuevas herramientas para que inversores e investigadores naveguen la complejidad y volatilidad propias de los sistemas financieros descentralizados.
Taki E. M. Abedesselam, Fabio Giacomelli, Francesco Pasquale, Michele Salvi
We study a random process over graphs inspired by the way payments are executed in the Lightning Network, the main layer-two solution on top of Bitcoin. We first prove almost tight upper and lower bounds on the time it takes for a payment failure to occur, as a function of the number of nodes and the edge capacities, when the underlying graph is complete. Then, we show how such a random process is related to the edge-betweenness centrality measure and we prove upper and lower bounds for arbitrary graphs as a function of edge-betweenness and capacity. Finally, we validate our theoretical results by running extensive simulations over some classes of graphs, including snapshots of the real Lightning Network.
The maintenance of rail vehicles and infrastructure plays a critical role in reducing delays, preventing malfunctions, and ensuring the economic efficiency of rail transportation companies. Predictive maintenance systems powered by supervised machine learning offer a promising approach by detecting failures before they occur, reducing unscheduled downtime, and improving operational efficiency. However, the success of such systems depends on high quality labeled data, necessitating user centered labeling interfaces tailored to annotators needs for Usability and User Experience. This study introduces a cost effective predictive maintenance system developed in the federally funded project DigiOnTrack, which combines structure borne noise measurement with supervised learning to provide monitoring and maintenance recommendations for rail vehicles and infrastructure in rural Germany. The system integrates wireless sensor networks, distributed ledger technology for secure data transfer, and a dockerized container infrastructure hosting the labeling interface and dashboard. Train drivers and workshop foremen labeled faults on infrastructure and vehicles to ensure accurate recommendations. The Usability and User Experience evaluation showed that the locomotive drivers interface achieved Excellent Usability, while the workshop foremans interface was rated as Good. These results highlight the systems potential for integration into daily workflows, particularly in labeling efficiency. However, areas such as Perspicuity require further optimization for more data intensive scenarios. The findings offer insights into the design of predictive maintenance systems and labeling interfaces, providing a foundation for future guidelines in Industry 4.0 applications, particularly in rail transportation.
Andrea Venturi, Imanol Jerico-Yoldi, Francesco Zola, Raul Orduna
As Law Enforcement Agencies advance in cryptocurrency forensics, criminal actors aiming to conceal illicit fund movements increasingly turn to "mixin" services or privacy-based cryptocurrencies. Monero stands out as a leading choice due to its strong privacy preserving and untraceability properties, making conventional blockchain analysis ineffective. Understanding the behavior and operational patterns of criminal actors within Monero is therefore challenging and it is essential to support future investigative strategies and disrupt illicit activities. In this work, we propose a case study in which we leverage a novel graph-based methodology to extract structural and temporal patterns from Monero transactions linked to already discovered criminal activities. By building Address-Ring-Transaction graphs from flagged transactions, we extract structural and temporal features and use them to train Machine Learning models capable of detecting similar behavioral patterns that could highlight criminal modus operandi. This represents a first partial step toward developing analytical tools that support investigative efforts in privacy-preserving blockchain ecosystems
The rapid growth of quantum computing poses a threat to the cryptographic foundations of digital systems, requiring the development of secure and scalable electronic voting (evoting) frameworks. We introduce a post-quantum-secure evoting architecture that integrates Falcon lattice-based digital signatures, biometric authentication via MobileNetV3 and AdaFace, and a permissioned blockchain for tamper-proof vote storage. Voter registration involves capturing facial embeddings, which are digitally signed using Falcon and stored on-chain to ensure integrity and non-repudiation. During voting, real-time biometric verification is performed using anti-spoofing techniques and cosine-similarity matching. The system demonstrates low latency and robust spoof detection, monitored through Prometheus and Grafana for real-time auditing. The average classification error rates (ACER) are below 3.5% on the CelebA Spoof dataset and under 8.2% on the Wild Face Anti-Spoofing (WFAS) dataset. Blockchain anchoring incurs minimal gas overhead, approximately 3.3% for registration and 0.15% for voting, supporting system efficiency, auditability, and transparency. The experimental results confirm the system's scalability, efficiency, and resilience under concurrent loads. This approach offers a unified solution to address key challenges in voter authentication, data integrity, and quantum-resilient security for digital systems.
Jianting Zhang, Sen Yang, Alberto Sonnino, Sebastián Loza · 5 authors
Directed Acyclic Graph (DAG)-based Byzantine Fault-Tolerant (BFT) protocols have emerged as promising solutions for high-throughput blockchains. By decoupling data dissemination from transaction ordering and constructing a well-connected DAG in the mempool, these protocols enable zero-message ordering and implicit view changes. However, we identify a fundamental liveness vulnerability: an adversary can trigger mempool explosions to prevent transaction commitment, ultimately compromising the protocol's liveness. In response, this work presents Lifefin, a generic and self-stabilizing protocol designed to integrate seamlessly with existing DAG-based BFT protocols and circumvent such vulnerabilities. Lifefin leverages the Agreement on Common Subset (ACS) mechanism, allowing nodes to escape mempool explosions by committing transactions with bounded resource usage even in adverse conditions. As a result, Lifefin imposes (almost) zero overhead in typical cases while effectively eliminating liveness vulnerabilities. To demonstrate the effectiveness of Lifefin, we integrate it into two state-of-the-art DAG-based BFT protocols, Sailfish and Mysticeti, resulting in two enhanced variants: Sailfish-Lifefin and Mysticeti-Lifefin. We implement these variants and compare them with the original Sailfish and Mysticeti systems. Our evaluation demonstrates that Lifefin achieves comparable transaction throughput while introducing only minimal additional latency to resist similar attacks.
Gensyn’s Verde Protocol: Technical Analysis of Decentralized ML Compute Verification is a 103-page technical deep dive into one of the most significant emerging architectures for decentralized machine learning. This paper provides a comprehensive examination of Gensyn’s Verde verification protocol, its refereed-delegation design, graph-based pinpointing system, probabilistic proof-of-learning mechanisms, and the RepOps reproducible operators framework. It analyzes the GHOSTLY problems Generalizability, Heterogeneity, Overhead, Scalability, Trustlessness, and Latency and evaluates how Verde addresses core limitations in verifying distributed ML training across heterogeneous hardware. By combining economic incentives, cryptographic commitments, and deterministic computation layers, this work outlines a practical blueprint for trustless, large-scale distributed AI training. The paper positions Gensyn within the broader ecosystem of Truebit, optimistic rollups, zero-knowledge systems, and decentralized compute networks, while highlighting open research questions and future directions. This publication aims to contribute a rigorous technical foundation for the democratization of AI infrastructure and the emergence of a global, permissionless compute marketplace.
Provable security is a cornerstone of modern cryptography: Due to ubiquitous and diverse applications of cryptography, a proof of security gives us the necessary confidence to deploy a cryptographic protocol. In most cases, such a security proof comes in the form of a black-box reduction, which bases the security of a potentially complex protocol on a small set of simple and abstract assumptions that are much easier to analyse. However, proving a black-box reduction can be quite complicated, and we do not have proofs for every protocol used in practice. Here, analysing the protocols relative to oracles, a technique from computational complexity theory, can provide insights: Oracles provide the ability to compute functionalities in one computational step that otherwise might not be efficiently computable, e.g., provide access to a truly random function or solve any NP-complete problem. These oracles now allow us to replace some parts in the protocol with abstract, idealized primitives that are easier to analyse, e.g., to replace a one-way function with a truly random function. In this thesis, we utilize oracles in two different ways. In the first part, we use oracles to prove lower bounds for cryptographic primitives, i.e., showing that certain assumptions are not sufficient to build this primitive securely. The essential idea here, going back to Impagliazzo and Rudich, is to replace the assumption with an oracle, i.e., replacing a one-way function with a truly random function, and then showing that relative to this oracle, it is impossible to build the primitive. From this impossibility result relative to the oracle, we can now conclude that the primitive cannot be built from the assumption in a black-box way. We use this technique to prove a lower bound on the efficiency of constructing strong from weak one-way functions, to show that we cannot construct collision-resistant hash functions from distributional collision-resistant hash functions in a fully black-box way, and to prove that extremely lossy functions cannot be built from a large class of symmetric primitives in a black-box way. In the second part of this thesis, we use oracles as idealized models that can be used to provide heuristic security arguments for protocols.These idealized models, starting with the random oracle model (short ROM) introduced and defined by Fiat and Shamir as well as Bellare and Rogaway, were motivated by the existence of very efficient cryptographic protocols used in practice, but for which no proof of security existed. Using idealized models, it was now possible to give at least a heuristic security argument for them. In this thesis, we first focus on the common random string model, an idealized model introduced to circumvent impossibility results for non-interactive zero-knowledge proofs. We show how to reuse a single common random string for polynomially many non-interactive statistical zero-knowledge arguments, as well as analyze the relation between different soundness definitions used in literature. In a second result, we introduce an alternative notion for the ROM, the universal random oracle model, which brings this idealized model closer to reality.
Standard electrodynamics relies on two free-space parameters, vacuum permittivity ($\epsilon_0$) and vacuum permeability ($\mu_0$), to govern the speed of light. These constants act as scalar correction factors without providing geometric insight into the fabric of space. This paper demonstrates that in the Quantum Measurement Units (QMU) system, these abstract constants are replaced by a single geometric ledger governed by the Aether unit ($A_u$) and the curl unit ($\mathrm{curl}$). We show that the Maxwell wave equation resolves naturally into the Aether's rotational and torsional limits, where the propagation velocity is exactly the product of the quantum frequency ($F_q$) and the Compton wavelength ($\lambda_C$). Furthermore, we derive the Impedance of Free Space ($Z_0$) as a direct function of the QMU conductance unit ($\mathrm{cond}$), proving that vacuum impedance is the geometric ratio of magnetic flux density to distributed charge: $$Z_0 = \frac{1}{2\alpha \cdot \mathrm{cond}}$$ This derivation removes the need for arbitrary free-space constants, reducing the Maxwell equations to a closed geometric identity perfectly consistent with experimental data.
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.
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.
‘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.
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.
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.
Mohammad Madine, Khaled Salah, Raja Jayaraman, Ibrar Yaqoob
In recent years, the healthcare sector has been increasingly challenged in securing patient identities and medical records on blockchain due to rising privacy demands and strict regulatory requirements. Although advanced techniques like self-sovereign identity and zero-knowledge proofs (ZKPs) show promise, these solutions fail to limit unwarranted patient data disclosure effectively. In this paper, we propose a ZKP-based solution that combines STARKs and anonymous credentials to enable anonymous authentication and enhance privacy across both public and private blockchains. Leveraging transparent ZKP schemes and anonymous credentials, our approach ensures unlinkability by preventing the correlation of multiple patient interactions. We present sequence diagrams of real-world interactions, detailed algorithms for on- and off-chain computations, and implement the system on Ethereum and Starknet blockchains. We present a rigorous evaluation of the proposed solution, encompassing smart contract testing on Starknet networks, transaction cost analysis, performance benchmarking, scalability assessment, and static security auditing. The results demonstrate consistent and economically viable transaction costs, millisecond-level execution times for credential issuance, presentation generation, and verification, linear scalability with increasing claim count and size. We compare our solution with state-of-the-art ZKP-based identity systems to demonstrate its superiority. We further discuss its broader applicability beyond healthcare, including domains such as finance, education, and supply chain management. We make the smart contract codes publicly available on GitHub.