Maria Christodimitropoulou, John Douvis, Panagiotis Alexopoulos, Panagiota Antonopoulou
This research examines the multifaceted digital transformation of tennis, analyzing the impact of new technologies on four key pillars: athlete training and performance, officiating and "smart" courts, fan experience and engagement, and emerging business models and governance. The integration of Artificial Intelligence, the Internet of Things through wearable sensors and "smart" equipment, and blockchain technology is radically reshaping how the sport is trained, played, watched, and managed. Technologies such as motion analysis systems, "smart" racquets, electronic officiating systems, personalized content platforms for fans, and Non-Fungible Tokens are analyzed. The research demonstrates that while these technologies offer unprecedented opportunities for performance optimization, objectivity in officiating, and deeper fan connection, they also present challenges related to adoption, regulation, commercial viability, and the need for unified governance. The research concludes that successfully navigating this new landscape requires a strategic approach that balances innovation with tradition, ensuring that technology acts as an enhancing factor for the sport rather than an end in itself.
The relevance of the study is substantiated by the need to reform the penitentiary system of Ukraine and to identify an optimal model for its institutional development in the context of European integration. Based on the synthesis of legislation and practice of the execution of criminal penalties in the EU Member States, the author has developed an original typology of public administration models in the penitentiary sphere. Four basic models are identified: 1) centralized (ministerial), characterized by a rigid vertical hierarchy; 2) the autonomous (executive) agency model, implying the operational independence of a specialized agency; 3) decentralized (federal), where management is exercised at the regional level; and 4) hybrid (asymmetric), which combines a state-wide system with autonomous jurisdictions. The advantages and disadvantages of each model are analyzed. It is established that a common European trend is the separation of policy-making functions (the prerogative of ministries) from operational management functions. The methodological basis of the study is a combination of general scientific and specialized research methods, including comparative-legal, formal-legal, and institutional analyses. The application of these methodological tools allowed for a deeper study of the European experience of penitentiary institutions. It has been shown that, regardless of the chosen organizational structure, the primary criterion for management efficiency is the state’s ability to ensure reliable, dynamic security, strict adherence to human rights standards, and the creation of the most favorable conditions for the successful social reintegration of offenders. Attention is given to the prospects of transforming the domestic penitentiary system. It is noted that the current Ukrainian penitentiary system retains signs of Soviet institutional inertia and excessive centralization. Based on the analysis, the expediency of the institutional transformation of the penitentiary system of Ukraine through the implementation of the autonomous (executive) agency model is substantiated. The study demonstrates that current Ukrainian legislation creates the necessary legal basis for the functioning of the penitentiary service as an autonomous central executive body accountable to the Ministry of Justice.
The relevance of the study is substantiated by the need to reform the penitentiary system of Ukraine and to identify an optimal model for its institutional development in the context of European integration. Based on the synthesis of legislation and practice of the execution of criminal penalties in the EU Member States, the author has developed an original typology of public administration models in the penitentiary sphere. Four basic models are identified: 1) centralized (ministerial), characterized by a rigid vertical hierarchy; 2) the autonomous (executive) agency model, implying the operational independence of a specialized agency; 3) decentralized (federal), where management is exercised at the regional level; and 4) hybrid (asymmetric), which combines a state-wide system with autonomous jurisdictions. The advantages and disadvantages of each model are analyzed. It is established that a common European trend is the separation of policy-making functions (the prerogative of ministries) from operational management functions. The methodological basis of the study is a combination of general scientific and specialized research methods, including comparative-legal, formal-legal, and institutional analyses. The application of these methodological tools allowed for a deeper study of the European experience of penitentiary institutions. It has been shown that, regardless of the chosen organizational structure, the primary criterion for management efficiency is the state’s ability to ensure reliable, dynamic security, strict adherence to human rights standards, and the creation of the most favorable conditions for the successful social reintegration of offenders. Attention is given to the prospects of transforming the domestic penitentiary system. It is noted that the current Ukrainian penitentiary system retains signs of Soviet institutional inertia and excessive centralization. Based on the analysis, the expediency of the institutional transformation of the penitentiary system of Ukraine through the implementation of the autonomous (executive) agency model is substantiated. The study demonstrates that current Ukrainian legislation creates the necessary legal basis for the functioning of the penitentiary service as an autonomous central executive body accountable to the Ministry of Justice.
A central question of the Ethereum ecosystem is where Maximal Extractable Value (MEV)revenue originates and to what extent it stems from harming unsuspecting users. It is acceptable if MEV arises from arbitrages between centralised and decentralised exchanges (CEX-DEX). Yet theoretical models have significantly underestimated the scale of these arbitrages, while empirical studies have highlighted their importance - though these remain conservative estimates, constrained by numerous debatable heuristic assumptions. Revisiting the theoretical model, we found that CEX-DEX arbitrages require trading volumes on the order of the total activity of major liquidity pools and yield profits comparable to MEV. Most prior AMM models utilised the Black-Scholes (BS) stochastic differential equation (SDE) - i.e., geometric Brownian motion - and assumed continuous price trajectories where asset prices move in small increments only.We argue that BS underestimates arbitrage profits by ignoring price jumps, which are precisely the points at which arbitrage opportunities tend to arise. To address this gap, we present an extended discrete-time AMM model in which the price process is the sum of a diffusive component and stochastic jumps that can have arbitrary noise distributions. Although mathematically more involved this framework allows us to employ a general discrete-time SDE and compute the stationary probability distribution via function iteration with geometric convergence. We further prove that the resulting mispricing process is an ergodic Markov chain. We implement our model in C++, collect spot prices and AMM exchange data from the Ethereum blockchain and fit the model parameters to the observed prices. The estimates derived from our model closely match empirical observations and provide a natural theoretical explanation for several fundamental questions in the blockchain ecosystem.
O estudo investiga barreiras de usabilidade em aplicações de Finanças Descentralizadas (DeFi) executadas em redes compatíveis com a Ethereum Virtual Machine (EVM), mostrando que problemas de fluxo, terminologia e feedback comprometem a adoção, especialmente entre iniciantes. Para enfrentar essas limitações, o trabalho propõe uma interface de usuário aprimorada e a compara a uma versão não otimizada usando métricas de desempenho, número de cliques e o questionário NASA-TLX. Os resultados indicam que a interface melhorada elevou a taxa de conclusão de tarefas de 76% para 89%, reduziu os cliques excedentes de 221 para 186 e diminuiu a carga cognitiva global aferida pelo NASA-TLX em todas as seis dimensões avaliadas, com destaque para demanda mental e frustração, inclusive entre usuários experientes, que relataram maior fluidez e previsibilidade. O artigo conclui que refinamentos de usabilidade voltados para aplicações financeiras descentralizadas são determinantes para elevar confiança e adoção, recomendando a padronização de processos, mensagens menos técnicas e a redução de etapas críticas para mitigar a fadiga de operações e ampliar o alcance da Web3.
The article examines the problem of formalizing investment cash flow in a distributed ledger environment. Within the framework of the digital transformation of financial relations, the cash flow of an investment project can be represented as a digital twin, recorded in the distributed ledger infrastructure and implemented through smart contracts. The aim of the study is to develop a mathematical model of the digital twin of investment cash flow and an algorithm for its forecasting using neural networks. Theoretical approaches to the interpretation of digital twins are systematized, and the limitations of the classical discounted cash flow model in relation to the digital environment are analyzed. A formalized model of digital cash flow is proposed, taking into account transaction fees of the distributed ledger, algorithmically accrued income, and an extended discount rate structure including technological and regulatory risk premiums. An algorithm for neural network forecasting of the digital twin is developed based on a feature vector integrating financial and infrastructure parameters. A comparative analysis of the digital and classical models is performed, which allowed establishing the structural modification of the investment process in the digital environment. The obtained results can be used in the valuation of digital financial assets and the construction of adaptive systems for forecasting their cash flows.
Nehaam Khan, Mohd. Aadil Shaikh, Atul Upadhyay, Dr. Vaishali Ramtekkar
The rapid growth of the event management industry has exposed significant challenges in traditional ticketing systems, including ticket fraud, duplication, unauthorized resale, and lack of transparency, as centralized platforms often fail to provide verifiable ownership and are vulnerable to manipulation. This paper proposes an NFT-Based Ticketing System that leverages blockchain technology to create a secure, decentralized, and transparent solution where each ticket is represented as a unique Non-Fungible Token (NFT) on the Polygon blockchain, ensuring immutability and authenticity. Smart contracts automate ticket minting, ownership transfer, resale regulation, and royalty distribution, enabling fair secondary market practices while maintaining control for event organizers. The system also incorporates a QR-based verification mechanism for real-time validation at event venues, preventing duplication and unauthorized access. Implemented as a decentralized application (DApp) using Web3 technologies and tested on the Polygon Mumbai testnet, the system demonstrates improved security, reduced fraud, efficient transaction handling, and enhanced user experience, thereby transforming traditional ticketing into a reliable and trustless digital ecosystem.
Web3 prediction markets, exemplified by Polymarket, have gained prominence for leveraging collective intelligence to forecast a wide range of social, political, and sports events. However, among the thousands of prediction market events, consensus disputes still arise due to imperfections in market mechanisms. On Polymarket alone, the trading volume involving disputed events has reached $972,370,804.71, underscoring the critical need for objective and efficient dispute resolution. In this study, we introduce large language models (LLMs) to: (1) evaluate whether web-enabled LLMs can reproduce the decision quality of UMA's on-chain voting process once a dispute has been raised, and (2) predict, based on event rules, which market events are likely to face future disputes before they occur. Our findings show that LLMs are unable to reliably predict which events will become disputed in advance; however, once a dispute is initiated, web-enabled LLMs achieve 89.58% agreement with UMA's final resolutions and demonstrate strong stability.
Manaswini Piduguralla, Souvik Sarkar, Arunmoezhi Ramachandran, Sathya Peri
Blockchain technology enhances transparency by maintaining a distributed ledger among mutually untrusting parties. Despite its advantages, scalability and availability remain critical bottlenecks that hinder widespread adoption. The increasing complexity of blockchain nodes further necessitates robust fault tolerance and high throughput to ensure seamless operations. We present BlockRaFT, a crash-tolerant distributed framework designed to improve both the scalability and reliability of blockchain node operations. BlockRaFT framework utilizes RAFT consensus protocol to elect a leader within a cluster of systems. The elected leader coordinates and distributes workloads across follower nodes, thereby optimizing resource utilization and work load balancing. We analyzed the tasks performed by blockchain nodes and partition them according to their stateful and stateless characteristics. Stateless operations are centralized at the leader, while stateful operations are replicated and coordinated across the cluster to ensure consistency and fault tolerance. We evaluate whether this distributed intra-node architecture provides measurable benefits over traditional single-node execution models in terms of scalability, availability, and performance. Additionally, we introduce a concurrent Merkle tree optimization that decouples smart contract execution from tree updates, significantly reducing one of the significant performance overheads in blockchain systems. Our design philosophy is rooted in utilizing the well-established principles of distributed computing and customizing them for the blockchain domain rather than reinventing them.
Decentralized Autonomous Organizations (DAOs) represent a fundamental shift in collective action and business management, transitioning from traditional "top-down" hierarchies to blockchain-based distribution of power. This research explores how DAOs utilize smart contracts to establish autonomous, decentralized organizations governed by code rather than central leadership. By leveraging token-based voting and automated execution, DAOs address critical "pain points" in corporate governance, specifically transparency and the Principal-Agent Problem.Through a comparative analysis of traditional corporations and decentralized models like Maker DAO and Uni swap, the study highlights the benefits of public auditability and aligned financial incentives. However, the transition to this "future of management" faces significant hurdles, including regulatory uncertainty, security vulnerabilities in code, and voter apathy. This paper concludes that while DAOs offer a democratic, flat alternative to the modern firm, their ultimate success depends on evolving legal frameworks and robust technical security.
The communication protocols and data transfer mechanisms employed by IoT devices in smart buildings and corresponding digital twin systems predominantly rely on centralized architectures. Such centralized systems are vulnerable to single points of failure, where a malfunction can disrupt operational processes. This study introduces a blockchain-based decentralized protocol to enhance the cyber resilience of IoT data transfer for digital twins and enable decentralized automation of building operations. The framework incorporates public and private blockchain technologies alongside two case studies showcasing prototypes of each system. These prototypes were validated within a real-world building environment using smart home appliances and two digital twin platforms, with their performance evaluated based on cost, scalability, data security, and privacy. The findings reveal that the Hyperledger Fabric-based system excels in terms of scalability, speed, and cost-effectiveness, while both frameworks offer advantages over traditional centralized protocols in system cyber resilience, data security, and privacy.
Products of MDS codes are of major practical importance; for a recent example, they are used in Data Availability Sampling (DAS) in blockchain networks such as Celestia and as part of the Ethereum roadmap. This motivates us to consider subcodes of such codes with the goal of obtaining a larger minimum distance. In this paper, we present explicit constructions of subcodes of Reed--Solomon product codes, along with bounds on their minimum distance. In particular, they achieve an optimal or near-optimal dimension--distance tradeoff. For component codes of dimension $r$, our construction requires a field whose size is bounded linearly by the overall product code length, and attains the maximum possible minimum distance for subcode dimensions $r^2-1$, $r^2-2$, and all dimensions at most $2r-1$. Furthermore, we establish a new upper bound on the minimum distance of subcodes of the product of two codes with identical parameters.
Jian Gao, Lufeng Zhang, Ping Fang, Pu Ke · 7 authors
Fluctuation theorems show how coarse graining transforms microscopic symmetry into observable irreversibility. Here we ask whether an analogous symmetrybased diagnostic can be constructed for financial markets. At the microscopic level, each transaction pairs a buyer and a seller, whereas trading decisions are typically made from coarse-grained price histories. Using symmetric takeprofit and stop-loss rules, we compare the holding-time distributions of long and short trading ensembles generated from the same price series. Across equityindices, individual stocks and cryptocurrencies, the log-ratio of the two distributions shows a robust crossover. It remains nearly constant at short durations but becomes linear in the tail, implying an exponential directional asymmetry. The tail slope defines an effective market temperature, an operational measure of fluctuation intensity on the chosen observation scale. A Bachelier first-passage benchmark captures the exponential tails but not the asymmetry, because long and short positions share the same leading decay rate. By contrast, short-time correlations between overlapping positions provide a minimal mechanism for the asymmetry by generating direction-dependent subleading relaxation spectra in a coarse-grained Markov description. Together, these results establish a fluctuation-theorem-like diagnostic of irreversibility in financial markets and, more broadly, in complex systems accessible only through coarse-grained observables.
This study examines the factors contributing to cryptocurrency adoption in South Africa. This study utilized an exploratory research design that applied a qualitative technique. 10 key informants were selected using purposive sampling from organizations involved in the bitcoin industry in South Africa. The study demonstrates that the adoption of cryptocurrencies in the country is influenced by factors such as financial inclusion and access, innovation and entrepreneurship, economic diversification and regulatory frameworks, and teamwork. The challenges and hurdles encompass legislative ambiguity, cybersecurity risks, investor safeguarding, financial education and awareness, infrastructure limitations, and accessibility issues. The findings indicate that adopting cryptocurrencies can enhance financial inclusion, stimulate innovation and entrepreneurship, and tackle systemic problems in the financial industry. Nevertheless, the effective implementation and assimilation of cryptocurrencies in South Africa will necessitate a collaborative endeavour among all parties involved. Robust regulatory frameworks, comprehensive educational programmes, and cooperative endeavours are essential for maximizing the advantages of cryptocurrencies while minimizing the accompanying hazards.
Smart contracts exhibit increasingly complex semantics and interactions, yet existing vulnerability detection methods rely on single-granularity representations, limiting their ability to capture semantic mechanisms across bytecode execution and cross-contract interactions. In addition, vulnerability data are scarce and imbalanced, and most deep learning-based approaches lack semantic interpretability. To address these limitations, a semantic-aware cross multi-granularity representation enhanced detection framework (CMR-ED) is proposed. CMR-ED models opcode execution semantics, function-level behaviors, and cross-contract interactions, aligning multi-level semantic information within a unified representation space. A structure-aware vulnerability pattern generator mitigates data scarcity through semantically consistent sample synthesis, while an explainable detection mechanism provides triggering paths and reasoning-chain explanations. Extensive experiments show that CMR-ED outperforms state-of-the-art methods while improving interpretability under semantically complex scenarios.
Rohith Singh, Mr. Charan Singh, Abdul Rashad, Md. Abdur Rasheed · 6 authors
Prompt injection is a foundational security vulnerability in large language models (LLMs) deployed as autonomous agents with tool access and multi-step reasoning capabilities. Existing defenses rely on heuristic filters that fail under obfuscation, indirect injection, and multi-agent propagation. We present a Unified Cryptographic-Control Architecture (UCCA), a principled framework that integrates five complementary guarantees: (1) information-theoretic leakage bounds derived via Fano's inequality, (2) certified robustness via randomized smoothing, (3) token-level rejection via erase-and-check, (4) runtime trajectory enforcement via control barrier functions (CBFs), and (5) verifiable inference via zero-knowledge proofs (ZK-SNARKs). We formally prove that any successful prompt injection attack must simultaneously bypass all five mechanisms, a condition we show has probability at most δ under stated assumptions. We evaluate UCCA on three real LLMs (GPT-4o, Claude 3.5 Sonnet, Mistral-7B) across four established attack benchmarks (INJECAGENT, TensorTrust, PromptBench, HarmBench), achieving attack success rates below 8% while maintaining median latency overhead under 340 ms. Our framework bridges formal security guarantees and deployable system architecture, establishing a foundation for provably secure autonomous AI. • Information-theoretic bounds on system prompt leakage using mutual information and Fano's inequality. • Certified robustness for safety-critical classification through randomized smoothing, where the robustness radius R is determined from output probability gaps. • Token-level rejection guarantees using an erase-and-check procedure capable of detecting adversarial subsets of size ≤ k. • Runtime safety enforcement through control barrier functions (CBFs), ensuring LLM outputs remain within a verified safe set. • Verifiable inference using ZK-SNARKs, allowing cryptographic attestation of model outputs without revealing model weights. • UCCA, a deployable system integrating all five mechanisms, evaluated on real LLMs and standard benchmarks.
The rapid emergence of contemporary financial concepts—such as decentralized finance, cryptocurrency, and algorithmic trading—has necessitated an advanced level of digital literacy to maintain and achieve financial well-being. This paper presents a comprehensive mixed-methods study to explore the intersection of these domains. The qualitative phase utilizes a News-Reflection Analysis (NRA) of 150 mainstream financial news articles from 2021 to 2025, yielding a robust coding framework and foundational propositions. Building upon these qualitative insights, the quantitative phase employs Partial Least Squares Structural Equation Modelling (PLS-SEM) on a simulated dataset of 450 respondents. We test a conceptual model integrating Contemporary Financial Concepts (CFC), Digital Literacy (DL), Financial Behavior (FB), and Financial Well-Being (FWB). Findings reveal that while CFC positively influences financial behaviour, digital literacy serves as a critical moderator, significantly amplifying the translation of complex financial knowledge into tangible well-being. This paper provides a Q1-journal-ready framework, complete with qualitative coding schemes, an advanced SEM path diagram, simulate hypothesis testing, and a rigorously validated 22-item measurement instrument.
Dr. P. C. Prabhu Kumar, P. Poojitha, K. Satheesh Kumar, M. Charan Kumar · 6 authors
The rapid digital transformation of the healthcare and drug sector has increased the reliance on cloud infrastructures for storing and exchanging Electronic Health Records (EHRs), raising significant concerns regarding privacy breaches, unauthorized access, and data integrity. To overcome these challenges, this project proposes a secure, patient-centric medical and drug data-sharing framework that integrates blockchain technology with distributed cloud storage. In this system, patients upload encrypted Personal Health Records (PHRs) to an untrusted cloud server while maintaining complete control over access permissions. A semi-trusted Setup and Re-Encryption Server (SRS) manage cryptographic key generation and re-encryption processes, enabling healthcare providers to access only the data explicitly authorized by the patient. All access requests, key operations, and permission updates are immutably recorded on a blockchain ledger, ensuring transparency, traceability, and accountability. The design further enforces forward and backward access control, automatically revoking past privileges when permissions are modified. Experimental evaluation demonstrates that the framework effectively ensures confidentiality, integrity, and access control while resisting tampering and supporting efficient real-time medical services, making it a promising solution for secure and scalable e-Health data exchange.
Cross-chain bridges are critical for decentralized finance (DeFi) to enable asset interoperability across heterogeneous blockchains. They are based on a complex hybrid architecture that involves on-chain contracts and off-chain relayers. In the recent past, several major attacks exploited vulnerabilities in cross-chain bridges. However, existing analysis tools have limited detection effectiveness as they focus on individual contracts and do not capture the complex interaction chain in cross-chain bridges. In this paper, we present BridgeFuzz, the first fuzzing framework for cross-chain bridge developers capable of detecting vulnerabilities such as balance mismatches, protocol errors, and off-chain denial-of-service bugs. BridgeFuzz is the first step towards bridging the gap between smart contract vulnerability research and the holistic vulnerability analysis of cross-chain bridges.
William Fernando Martínez Luna, Ana María Moreno Ballesteros, Edgar José Ruiz Dorantes
Non-fungible tokens (NFTs) are transforming the commercialisation of digital art by establishing unique blockchain identifiers that ensure authenticity and certify subsequent transactions. However, the transfer of control over an NFT does not automatically include the transfer of the associated copyrights, thereby creating legal uncertainty as to what rights are actually acquired. This interdisciplinary project between engineering and law proposes the design of a smart contract, based on the ERC-721 standard, to manage the transfer of property rights linked to digital artworks represented as NFTs. The accompanying legal contract incorporates essential clauses covering the identification of the parties, a description of the artwork and its link to the token, pricing, royalties, and the terms of rights transfer. The proposal seeks to integrate blockchain technology with existing legal frameworks, offering an innovative solution that strengthens legal certainty in the transfer of copyright within digital environments.
Sufian Al majmaie, Ghazal Ghajari, Niraj Prasad Bhatta, Fathi Amsaad
The integration of Fog Computing with Flying Ad-Hoc Networks (FANETs) offers promising capabilities for decentralized, low-latency intelligence in UAV-based applications. However, the distributed nature, mobility, and resource constraints of FANETs expose them to significant security and privacy challenges, particularly against quantum threats. To address these issues, this work introduces a blockchain-based, AI-enhanced key management framework designed for fog-enabled FANETs. The proposed scheme employs a Post-Quantum Multivariate Identity-Based Signature Scheme (PQ-MISS) and Zero-Knowledge Proofs (ZKPs) to achieve secure key establishment, privacy-preserving data aggregation, and integrity verification. A polynomial composition-based encryption mechanism and an aggregate signature model support secure and efficient multi-device communication across fog and UAV layers. Fog servers construct partial blockchain blocks from validated UAV data. These blocks are completed and mined by Cloud Servers (CSs). AI algorithms then analyze the verified data to generate accurate predictions and insights. NS-3 simulations validate the efficiency of PQ-MISS in reducing communication overhead while improving the speed and reliability of data aggregation and verification. Comparative analysis demonstrates the proposed scheme's advantages over existing methods in computational cost, post-quantum security, and scalability, making it a robust solution for secure, intelligent, and future-ready FANET systems.
Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to volatile market conditions. Existing risk management tools utilize static health-factor thresholds, which are reactive and fail to distinguish between administrative "dust" cleanup and genuine insolvency. In this work, we propose an autonomous agent that leverages time-to-event (survival) analysis and moves beyond prediction to execution. Unlike passive risk signals, this agent perceives risk, simulates counterfactual futures, and executes protocol-faithful interventions to proactively prevent liquidations. We introduce a return period metric derived from a numerically stable XGBoost Cox proportional hazards model to normalize risk across transaction types, coupled with a volatility-adjusted trend score to filter transient market noise. To select optimal interventions, we implement a counterfactual optimization loop that simulates potential user actions to find the minimum capital required to mitigate risk. We validate our approach using a high-fidelity, protocol-faithful Aave v3 simulator on a cohort of 4,882 high-risk user profiles. The results demonstrate the agent's ability to prevent liquidations in imminent-risk scenarios where static rules fail, effectively "saving the unsavable" while maintaining a zero worsening rate, providing a critical safety guarantee often missing in autonomous financial agents. Furthermore, the system successfully differentiates between actionable financial risks and negligible dust events, optimizing capital efficiency where static rules fail.
Permission control vulnerabilities in Non-fungible token (NFT) contracts can result in significant financial losses, as attackers may exploit these weaknesses to gain unauthorized access or circumvent critical permission checks. In this paper, we propose NFTDELTA, a framework that leverages static analysis and multi-view learning to detect permission control vulnerabilities in NFT contracts. Specifically, we extract comprehensive function Control Flow Graph (CFG) information via two views: sequence features (representing execution paths) and graph features (capturing structural control flow). These two views are then integrated to create a unified code representation. We also define three specific categories of permission control vulnerabilities and employ a custom detector to identify defects through multi-view feature similarity analysis. Our evaluation of 795 popular NFT collections identified 241 confirmed permission control vulnerabilities, comprising 214 cases of Bypass Auth Reentrancy, 15 of Weak Auth Validation, and 12 of Loose Permission Management. Manual verification demonstrates the detector's high reliability, achieving an average precision of 97.92% and an F1-score of 81.09%. Furthermore, NFTDELTA demonstrates enhanced efficiency and scalability, proving its effectiveness in securing NFT ecosystems.
India’s aspiration to emerge as a developed nation by 2047 under the vision of Viksit Bharat necessitates the identification and scaling of localized innovations. Odisha, characterized by its socio-cultural diversity and rapid technological transition, presents a compelling case as a living laboratory for future-ready developmental models. This study positions Odisha at the intersection of three transformative domains: algorithmic welfare systems, governance challenges arising from silent urbanization, and the digitization of cultural heritage through Web3 technologies such as NFTs. By integrating quantitative modeling, qualitative fieldwork, and policy analysis, the study proposes a framework for culturally rooted, inclusive, and decentralized development. The findings suggest that Odisha’s experiments in adaptive welfare delivery, the recognition of hybrid urban spaces, and digital cultural economies not only address local challenges but also offer scalable insights for national policy. The state thus emerges as a microcosm of a technologically empowered, socially equitable, and culturally vibrant Viksit Bharat.