Sandeep Kumar Mathariya, Sai Priyanka Tirumalasetty, Ajit More, P. Dinesh Kumar · 6 authors
As more and more forensic investigations use blockchain technology to preserve digital data, we will need to build systems that are both contextually optimal for investigations and impossible to break into. Most current blockchain forensic frameworks have a monolithic approach to consensus and static assessment models. This makes them not ideal for the ever-changing forensic context of different event sensitivity, legality, and auditability needs. There are currently barriers to the successful application in high-stakes forensic environments. This work presents the Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), an extensive simulation-based evaluation framework designed to compare Proof-of-Stake (PoS), Directed Acyclic Graph (DAG), and Byzantine Fault Tolerant (BFT) blockchains against forensic key performance indicators (KPIs) to address existing deficiencies. This framework has five new modules. The Context-Aware KPI Weighting Engine (CAKWE) first makes dynamic KPI weight vector creation by using forensic event metadata and a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) makes richer DAGs by adding investigator metadata and transaction timings. This makes it easier to find traces in the future. Third, the Hybrid Ledger Simulation Module will simulate how evidence moves between Hyperledger, IOTA, and Ethereum 2.0 using different KPIs that take forensic factors into account. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs Pareto analysis and utility-based optimization to figure out if a design is good enough by weighing the pros and cons of performance and forensic depth. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) combines smart contracts and zero-knowledge proofs to make sure that the evidence is safe and can be used in court. The experiments hardly show that the forensic efficacy has improved, that trace reconstruction is accurate to 98.1 %, and that tampering is detected 100% of the time. This study presents the inaugural paradigm for forensic-aware blockchain evaluation. The system enables ongoing digital investigations that are precise, legally compliant, and contextually aware.
With the rapid advancement of blockchain technology and the expanding influence of decentralized finance (DeFi), centralized trading systems (CEX) increasingly confront critical challenges including single points of failure, insufficient data transparency, and elevated transaction costs. Therefore, it is imperative to develop and explore decentralized trading systems that are compatible with and adaptive to quantitative trading strategies. This study focuses on constructing a framework for decentralized trading systems suitable for quantitative strategies, aiming to reduce transaction costs, enhance strategy transparency, and improve risk resistance capabilities. By analyzing the technical architecture (including public chain adaptations: Solana for high-frequency strategies and Ethereum Layer 2 for medium-frequency strategies), innovations in decentralized exchanges (DEX) (such as order-book based dYdX and AMM based Uniswap V3), and the application of on-chain quantitative tools (like Nansen for whale behavior tracking and automated execution tools based on Aave smart contracts), the study confirms the feasibility and advantages of decentralized design in quantitative scenarios.
Transparent and secure communication is crucial in the digital age. The use of blockchain algorithms to improve communication security and transparency is examined in this article. We solve flaws in conventional communication networks by utilizing decentralized ledger technology, which guarantees data integrity, secrecy, and authenticity. We assess different consensus algorithms and show how well they work to secure communication channels, such as Proof of Stake and Proof of Work. We demonstrate the benefits and possible drawbacks of utilizing blockchain technology for secure communication through empirical investigation. This study sheds light on potential uses of blockchain in the communication industry in the future.
Abstract The current academic landscape is often shaped by political and financial influences, restricting access to education and compromising research independence. To address these challenges, we introduce “FreeUniversity.dao”, a university model grounded in the principles of a Decentralized Autonomous Organization (DAO). FreeUniversity.dao emphasizes academic freedom, openness, and equitable access to education, leveraging blockchain and DeFi technologies to establish a transparent and sustainable framework for global learning and research. It envisions a politically neutral and corruption-free governance model that prioritizes transparency, fairness, and accessibility. Decision-making processes are designed to be participatory, democratic, rewarding merits and contributions, and involving the community in all major decisions, including rule changes and further developments. For traditional university institutions FreeUniversity.dao offers a migration path to DAO-based structures. While this paper does not present a fully realized DAO design, it outlines the core concepts, coordination challenges, and foundational elements required to build such a system. It aims to be a starting point for further exploration and discussion about the future of academic governance. An example workflow illustrates how the DAO could work in practice and motivates how current processes can be improved to reduce interventions that could jeopardize the integrity of scientific research.
The current research introduces the Decentralized Autonomous MetaUniversity (DAMU), a novel framework for redefining academic governance through Blockchain, Machine Learning, and IoT. Unlike prior blockchain-in-education efforts which are limited to certificate verification, DAMU supports the complete academic lifecycle right from university creation and instructor assignment to student enrollment, assessment recording and credit redemption. The framework contributes a role-based DAO governance model, tokenized academic credits and NFT-based learning passports integrated with IoT-verified activity tracking to name a few. Performance validation across Goerli, Polygon PoS, and zkSync Era demonstrates up to 62% gas cost reduction, 48.6% faster DApp responsiveness and improved scalability thereby making DAMU a pioneering step toward Education 5.0 in a Web3 ecosystem.
Phi Dinh Hoang, Emmanuel L. C. VI M. Plan, Nga T. H. Nguyen
NFT market is nascent and thus prone to manipulative behavior. This paper examines the impact of wash trading on the relationships between NFT returns, volume, and volatility via Mixture of Distributions Hypothesis (MDH) and Sequential Information Arrival Hypothesis (SIAH), and the role of collection characteristics in these dynamics via Hedonic Pricing Theory (HPT). By comparing the full dataset and those devoid of cyclical wash trades, we find that MDH and SIAH hold across samples. Notably, the return-volatility relationship shifts from significantly negative to significantly positive post-cleaning, confirming that manipulative trades distort true market risk-return dynamics. In contrast, support for HPT weakens after applying stricter wash trade detection, suggesting collection features had overstated influence due to manipulation. These findings highlight the need for robust wash trading detection to ensure data reliability. Policymakers should consider ensuring market data reliability by enhancing transparency regulations around suspected wash trade transactions.
Ensuring the correctness of smart contracts is critical, as even subtle flaws can lead to severe financial losses. While bug detection tools able to spot common vulnerability patterns can serve as a first line of defense, most real-world exploits and losses stem from errors in the contract business logic. Formal verification tools such as SolCMC and the Certora Prover address this challenge, but their impact remains limited by steep learning curves and restricted specification languages. Recent works have begun to explore the use of large language models (LLMs) for security-related tasks such as vulnerability detection and test generation. Yet, a fundamental question remains open: can LLMs aid in assessing the validity of arbitrary contract-specific properties? In this paper, we provide the first systematic empirical evaluation of GPT-5, a state-of-the-art reasoning LLM, in this role. We benchmark its performance on a large dataset of verification tasks, compare its outputs against those of established formal verification tools, and assess its practical effectiveness in real-world auditing scenarios. Our study combines quantitative metrics with qualitative analysis, and shows that recent reasoning-oriented LLMs - although lacking soundness guarantees - can be surprisingly effective at predicting the (in)validity of complex properties, suggesting a new frontier in the convergence of AI and formal methods for secure smart contract development and auditing.
With the proliferation of new blockchain-based cryptocurrencies/assets and platforms that make it possible to transact across them, it becomes important to consider not just whether the transfer of coins/assets can be tracked within their respective transaction ledger, but also if they can be tracked as they move across ledgers. This is especially important given that there are documented cases of criminals attempting to use these cross-ledger trades to obscure the flow of their coins/assets. In this paper, we perform a systematic review of the various tracing techniques for blockchain transactions proposed in literature, categorize them using multiple criteria (such as tracing approach and targeted objective) and compare them. Based on the above categorization, we provide insights on the state of blockchain transaction tracing literature and identify the limitations of existing approaches. Finally, we suggest directions for future research in this area based on our analysis.
The Healthcare Revenue Cycle Management (RCM) landscape is struggling with issues such as coding complexity, payer diversity, labor shortage, and data silos. The Collaborative Core proposes a Human-in-the-Loop (HITL) Artificial Intelligence model that will be used to strategically combine AI efficiency and human judgment throughout the RCM lifecycle. This model is cost-effective as it directs mundane work to AI automation, leaving human insight to make complex decisions, which would congruently improve both efficiency and accuracy. The framework makes use of interoperability standards, distributed ledger technologies, and sophisticated AI tools, along with explicit handoff procedures between fully automated and human processes. The results of the implementation have shown positive changes in the charge capture process, claim processing, posting of payments, and the ability to manage denials, and improve provider, insurer, and patient outcomes. The Collaborative Core is an innovative solution to the complex problem of healthcare financial management, which is used in collaboration with current trends toward technological control by ensuring that it is not lost in the algorithmic bias and human adjustment to working with technology.
Abstract The President of India, in her address to Parliament in June 2009, stressed on achieving the goals of equity or inclusion, innovation and accountability in public policies and the governance process. She said that the Government is committed to creating an innovation strategy for government, industry, entrepreneurs, technologists and academicians with a focus on inclusive growth and appropriate eco-system necessary to bring about generational change in our approach to development. The Thirteenth Finance Commission emphasized the need for adopting innovative methods and focus on capacity building, decentralization and public accountability in public systems. A number of citizen centric administrative innovations are introduced by the government at center and state level. The paper elaborates on various initiatives taken by the Government of India in using innovative methods and use of technology in the process of governance to make it citizen centric and facilitating good governance.
Ubrzani razvoj blockchain tehnologije i ulazak novih kriptovaluta na tržište otvorili su mogućnost primjene kripto tehnologije u različitim industrijama, uključujući turizam i hotelijerstvo. U ovom radu analiziran je potencijal implementacije kriptovaluta u hotelskoj industriji s naglaskom na korištenje kriptovaluta kao sredstva plaćanja. Teorijski dio rada pruža pregled temeljnih značajki kriptovaluta, globalnih trendova u njihovoj upotrebi te prednosti i izazova njihove primjene u hotelijerstvu. Nadalje, analiziraju se globalni trendovi upotrebe kriptovaluta, posebno rast broja korisnika i sve veća prihvaćenost digitalne imovine u platnom prometu, ali i ograničenja kao što su volatilnost te različite regulatorne prakse koje otežavaju njihovu širu primjenu. Kroz modele prihvaćanja tehnologije objašnjava se kako percipirana korisnost, jednostavnost korištenja, tehnološka osviještenost i povjerenje u sustav utječu na namjeru korištenja kripto plaćanja. Empirijski dio temelji se na anketnom istraživanju provedenom među građanima Republike Hrvatske. Anketa je ispitala razinu poznavanja kripto tehnologije, posjedovanja kriptovaluta te namjere korištenja za plaćanje hotelskih usluga kako bi se ocijenilo što će biti potrebno za uspješnu implementaciju u hotelijerskom sektoru te mogu li se uvođenjem ove tehnologije ostvariti konkurentske prednosti.
Suleiman Dahir Mohamed, Mohd Tahir Ismail, Majid Khan Majahar Ali
Bitcoin market has exhibited substantial volatility over time.Bitcoin returns exhibit high standard deviation.This study employs the GARCH (1,1) model with normal (norm), Studentt (std), and generalized error distributions (ged) to estimate Bitcoin conditional volatility.Bitcoin exhibits fat-tailed returns, volatility clustering, and a remarkably high persistence value.The GARCH (1,1)-ged model showed superior performance compared to other models when evaluated using LL, AIC, and BIC criteria.The indicator saturation (IS) method was employed to concurrently detect historical daily breaks, trend breaks, and outliers in Bitcoin volatility data.The indicator saturation approach revealed that, for the past decade, historical Bitcoin volatility has had 6 outliers, 31 breaks, and 74 trend breaks under the normal distribution, 0 outliers, 26 breaks, and 83 trend breaks under the student-t distribution, and 1 outlier, 29 breaks, and 77 trend breaks under the ged distribution.This shows that assuming a heavy tail led to fewer outliers and breaks, and as the frequency of trend breaks increases, it also shows more volatility clusters represented by GARCH.These discoveries have the potential to comprehend the influence of events on financial markets and guarantee stability in the evaluation of financial risk, management of portfolios, and modeling endeavors.
This study analyzes the two-tier local government models in France, Japan, Germany, and the United States to draw lessons for reforming the organization of state apparatus in Vietnam. Based on the theoretical foundations of local government organization, it clarifies the principles of decentralization, autonomy, accountability, and effective public governance. International experience highlights the importance oftransparent institutions, financial autonomy, competent personnel, and the application of technology in governance. In Vietnam, the two-tier model has been implemented since July 1, 2025, under Law No.72/2025/QH15, replacing the traditional three-tier structure. The article examines the challenges during the transition and proposes solutions to improve the legal framework, ensure substantive decentralization, enhance implementation capacity, reform public finance, strengthen oversight, and promote accountability - thereby contributing to building an effective, modern, and democratic local government.
This study explores the application of Self-Sovereign Digital Identity (SSDI) and blockchain technology in forest supply chain management to improve traceability, sustainability and regulatory compliance. It addresses how these technologies can overcome the limitations of traditional identity management and improve forestry operations’ transparency, efficiency, and environmental accountability. An Ethereum-based blockchain framework was used for this study, focusing on metrics such as transaction throughput and latency. Experimental tests were conducted to analyze the performance of SSDI in forest supply chains, focusing on real-time data management and secure identity control. A framework aligned with the Forest 4.0 initiative was proposed to evaluate the efficacy of SSDI. The results show that the integration of SSDI with blockchain significantly improves traceability and sustainability within forest supply chains, with high transaction rates and reduced latency. The decentralized system improves transparency and trust, promotes efficient identity management among stakeholders, and improves compliance with environmental regulations. Our study is among the first to apply SSDI in forestry, advancing digital transformation in this sector. Demonstrating SSDI’s capacity to streamline data handling and boost traceability, it offers practical recommendations for stakeholders seeking sustainable and digitally secure supply chain management practices. • Improve traceability using blockchain-enabled identity systems in forest supply chains. • Enhance transparency with decentralized records and digital identity verification. • Analyze performance through transaction rates and latency metrics in real-time. • Streamline data handling with efficient credential issuance and verification. • Promote sustainability through intelligent integration of digital and monitoring tools.
ABSTRACT This paper develops a model of a cryptocurrency by incorporating mining into the otherwise standard search‐theoretic monetary framework. As usual, multiple equilibria exist. To obtain a sharp prediction on whether a cryptocurrency' s value will last in the future, I propose a notion of equilibrium refinement based on the feature that mining uses real resources. This refinement eliminates all equilibria where the value of the cryptocurrency is zero at some point in time or converges to zero over time. This result suggests that agents can collectively sustain the value of the cryptocurrency using costly mining as a coordinating device.
Fintech modernization is a ground-up shift from traditional batch-processing infrastructure to event-driven real-time architectures that redefine financial service delivery and social mechanisms of trust. Modern financial institutions draw on advanced stream processing technologies, API-first integration, and distributed computing to support transaction throughput rates in millions of operations per second with sub-millisecond latencies for key financial transactions. Occasion-driven architectures (also known as event-driven architecture) provide instantaneous affirmation of transactions, real-time detection of fraud, and clear audit trails through immutable event recording structures that ensure end-to-end transaction traceability for regulatory purposes. Mobile-first design patterns and modern web-based packages boost access to finance for the underprivileged through offline-enabled interfaces that function across diverse device specifications and network connectivity eventualities. Advanced cryptographic algorithms, which include homomorphic encryption and zero-knowledge proofs, facilitate privacy-enhancing analytics that reconcile customized financial offerings in opposition to people’s privacy protection. Regulatory technology embedding using compliance-by-design architectures in regulatory technology help automate policy application and reporting while advanced trust protocols using biometric authentication, behavior analysis, and machine learning algorithms prevent fraud while ensuring frictionless user experiences. The intersection of distributed architectures, privacy-retaining technologies, and inclusive design styles generates financial structures that cater to various populations even as adhering to demanding safety and regulatory compliance in diverse jurisdictions.
Cloud computing facilitates scalable data sharing across multiple organizations and users, but also raises concerns about data privacy. Matchmaking encryption (ME) is a prominent technique that enforces bilateral access control in cloud services such as cloud marketplace, allowing both senders and receivers to specify policies for the encrypted data to be revealed. However, receivers may be at risk of being exposed to malicious or harmful content, thus undermining their trust in cloud service platforms. To this end, we introduce MBAC, a content-moderated bilateral access control framework for privacy-preserving cloud data sharing services, which allows receivers to acquire data from authentic senders while preserving their anonymity, and report malicious content in a verifiable manner, i.e., empowering the service provider to hold senders accountable. MBAC is built upon a novel primitive called franking broadcast ME (FBME), which generates a franking signature for the data by designating the service provider as the moderator to ensure accountability and deniability, and encrypts both the data and its franking signature while embedding the sender secret key for privacy and authenticity. We then present a concrete construction of FBME from key-private public key encryption, strongly unforgeable one time signature and non-interactive zero-knowledge proof. Formal security analysis and extensive experiments demonstrate that MBAC provides efficient bilateral access control and content moderation for cloud data sharing services.
Scientific knowledge production is undergoing a dual transformation. On one front, Decentralized Science (DeSci) leverages blockchain-based infrastructures to reconfigure how research is funded, verified, and governed, disintermediating legacy gatekeepers through tokenized incentives and distributed provenance. On the other, Artificial Intelligence (AI) is automating core dimensions of science, from hypothesis generation to experimental execution and model validation. This paper introduces DeScAI, a theoretical framework that unifies these domains into a recursive, self-verifying epistemic system governed by autonomous agents operating within decentralized, trust-minimized networks. We present a five-stratum architecture for DeScAI, hypothesizing that its integration enables epistemic acceleration, pluralistic inquiry, and cryptographically auditable trust. Methods include a structured literature synthesis (2018–2025), conceptual modeling, and descriptive analysis of 14 projects. Three hypothetical trajectories for future empirical investigation are proposed concerning cycle-time compression, epistemic pluralism, and reproducibility amplification. We conclude that DeScAI is not speculative: its core components are already deployed. What remains is orchestration, stitching together decentralized ledgers, incentive protocols, self-sovereign scientific agents (SSA), and cryptographic infrastructures into a single, recursive system. If successful, DeScAI could radically reduce the latency between hypothesis and verification, reconfigure scientific legitimacy as a live, contestable signal, and transform the incentive structure of research itself.
Ch. Rupa, Sai Varshitha. G, D Divya, Thippa Reddy Gadekallu · 6 authors
The world faces a severe blood shortage, with a gap of 1.95 million units, highlighting the need for efficient blood allocation and management systems. Traditional cloud and blockchain approaches have been explored for blood bank management but faced implementation challenges. This study proposes designing and developing a decentralized Binance blockchain-based application framework to ensure transparency and security. It uses the AdaBoost algorithm to predict the availability of the nearest blood bank and blood donor. Supply chain management provides transparency without the intervention of third parties thereby preventing blood crimes. Metamask is incorporated for crypto transactions in the Binance Smart Chain test network (BSC). BSC stands out for its low transaction fees and high scalability, enabling swift transaction processing at a fraction of the cost compared to Ethereum. The smart contracts are deployed using hardhat configuration enabling BscScan as an Application Programming Interface (API) gateway to record transactions within the decentralized application (dApp). The proposed system achieved an accuracy of 99.5%, demonstrating the robustness of the AdaBoost model in predicting blood availability. The integration of blockchain technology ensures transparency, immutability, and secure traceability of blood transactions across the network.
M. Veena, K. Sravani, K. Dhanapal, G. Praveen Kumar · 6 authors
This study was done to ensure effective fisheries management while protecting marine ecosystems and livelihoods by integrating traceability and sustainability practices. Fisheries played a crucial role in food security worldwide, supporting millions of livelihoods and providing nutrition for billions. However, challenges like overfishing, Illegal fishing, and environmental degradation threatened marine ecosystems. Traceability and sustainability were vital to addressing these issues by combating Illegal, Unreported and Unregulated (IUU) fishing, ensuring food safety, supporting sustainable practices, and meeting regulatory requirements. The fish industry experienced substantial illegal, unreported, and unregulated (IUU) activities within traditional supply chain systems. Blockchain technology and the Internet of Things (IoT) were expected to transform the fish supply chain (FSC) by incorporating distributed ledger technology (DLT) to build trustworthy, transparent, decentralized traceability systems that promoted secure data sharing and employed IUU prevention and detection methods. Efficient traceability management was necessary for managing products in the fishery supply chain. Monitoring and tracking of the fishery supply chain operations assisted system stakeholders in identifying the origins and causes of product fraud and malpractice. Traceability helped prevent IUU fishing, which costs up to $23.5 billion annually, by using blockchain systems to record catch data immutably and enable real-time tracking. Technologies like RFID (Radio Frequency Identification tags) and QR (Quick Response) codes verified seafood origin, reducing fraud and enhancing food safety. Sustainability prevented overfishing, preserved biodiversity, and supported small-scale fishers through fair trade practices. With 75% of consumers favouring sustainably sourced seafood, blockchain enhanced trust by providing transparent sustainability records and QR code verification.
This study provides an in-depth analysis of the dynamic connectedness between G7 stock market indices, traditional cryptocurrencies (Bitcoin, Ethereum), gold, digital gold (PAXG, XAUT), and companies specializing in artificial intelligence (AI). Covering the period from 2020 to 2024, the analysis focuses on four distinct periods: the COVID-19 pandemic, the Russia-Ukraine conflict, the banking crisis triggered by the collapse of Silicon Valley Bank in March 2023 and the speculative rise in the gold markets in 2024. The methodology employs a Quantile Vector Autoregressive (QVAR) connectivity approach, starting with the median quantile and systematically extending to various quantiles to capture the entire distribution of connectedness under different market conditions. Our results reveal significant fluctuations in the Total Connectivity Index (TCI) during the studied crises and demonstrate how the roles of key assets—Bitcoin, Ethereum, gold, PAXG, XAUT, and AI firms—shift between being net emitters and receivers of shocks. These shifts underscore the importance of asset selection in crafting effective hedging strategies. Specifically, we observe that G7 investors adopt varying diversification strategies depending on their domestic market conditions and the specific crisis period. The study highlights that assets for diversification and risk reduction vary by country and crisis. Traditional cryptocurrencies and AI companies in general emerge as promising diversification tools, especially in times of technological disruption and economic uncertainty. Several financial implications for investors and policymakers are proposed, providing insights for optimizing portfolio resilience in the face of global market volatility.
Yang Xu, Qixin Wang, Yufei Ren, Ying Hu · 7 authors
ABSTRACT Transitive signatures are a special type of homomorphic signature proposed by Turing Award winners Micali and Rivest, which are highly suitable for authenticating dynamically growing graph‐based data systems. In such a signature scheme, anyone with the signer's public key is allowed to generate a signature for a composed edge , from two signatures on adjacent edges and . To prevent the problem of malicious dissemination of signatures by verifiers leading to data privacy leakage, researchers have proposed a series of universal designated verifier transitive signature (UDVTS) schemes. However, existing work requires that the designated verifier create its own secret‐public key pair using the public key parameters provided by the signer. Besides, these schemes suffer from significant performance defects due to expensive pairing or exponentiation operations. In this work, we design a pairing‐free and exponentiation‐free UDVTS proof scheme based on the SM2 digital signature algorithm and a zero‐knowledge proof scheme. We prove the security of our construction based on rigorous cryptographic assumptions. The performance comparison with related work shows that our UDVTS proof scheme has an optimal computational cost and desirable communication cost. For example, compared to the state‐of‐the‐art work, we reduce the signing cost by and the designated verification cost by .
This study examines links between global financial stress and cryptocurrency returns from 1 January 2017 to 31 January 2025, while explicitly accounting for commodity markets. We use an econometric toolkit: unit-root and cointegration testing, ARDL bounds, Toda–Yamamoto causality, and a two-state Markov Switching model to trace long-run equilibrium and transmission mechanisms across cryptocurrencies (BGCI), systemic stress (OFR-FSI), volatility measures (VIX, VVIX, VSTOXX, VVSTOXX, MOVE), major equities and bonds, and three commodities (gold, oil, copper). Results show robust long-run cointegration between BGCI and several financial variables, including S&P/ASX 200 and the Bloomberg Barclays Bond Index; models that include commodities continue to support these long-term links. Toda–Yamamoto tests reveal that stress and volatility indices unidirectionally transmit shocks to cryptocurrencies and commodities, while gold displays a bidirectional relationship with BGCI, indicating a conditional safe haven interaction. Markov Switching estimates show amplified co-movement among BGCI, gold and bonds in stress regimes, with the model predominantly remaining in a normal state. Overall, cryptocurrencies are embedded within the broader financial system; commodities, especially gold, are used to moderate the stress crypto transmission and offer conditional diversification value during turmoil.