The rapid aging of the global population necessitates automated healthcare environments, yet current Intelligent Room architectures relying on centralized cloud servers face critical challenges regarding data opacity and single points of failure. This paper proposes a novel architecture that synergizes Distributed Ledger Technology (DLT) with Federated Learning (FL) to create a trustless, immutable audit trail for patient monitoring. Unlike traditional FL approaches, we introduce a blockchain-based aggregation mechanism that eliminates the central authority. Furthermore, to address the resource constraints of edge devices such as smartphones, we implement a specific Lightweight Neural Network (L-CNN) utilizing depthwise separable convolutions. The proposed system ensures that patient data remains local while model updates are cryptographically verified on-chain, offering a scalable, low-cost solution for resource-constrained healthcare environments.
This paper discusses the economic principles and policy requirements of cryptocurrency regulation in a more complex and fast developing digital financial ecosystem. It examines how decentralized blockchain-based assets, though having immense advantages such as financial inclusion, efficiency, and innovation, can also create enormous regulatory issues such as market volatility, information asymmetry, illegal financial transactions, and systemic risk. Basing the analysis on the concepts of economic analysis, the authors assess regulation as a method that addresses market failures, distributes resources efficiently, and maximizes social welfare in general. The paper expounds the relevance of cost-benefit modeling in regulatory design, with the emphasis on the fact that policy-makers need to strike a balance between the cost of compliance, administrative burden, and possible limitations on innovation and the benefits of regulatory transparency, investor protection, and financial stability. It also examines incentive systems in international cryptocurrency markets, and especially the so-called regulatory arbitrage, where national regulatory differences affect the geographical location of digital asset practices. One of the priorities is the trade-off between financial privacy and regulatory enforcement. Even though privacy-related aspects of cryptocurrencies can secure the autonomy of users and the safety of their data, they also make it more challenging to trace and intercept illegal financial activities. This article proposes the combination of risk-based and technology-neutral regulatory frameworks that will be capable of adapting to a variety of blockchain applications, such as decentralized finance (DeFi), stablecoins, and tokenized assets. Also, the paper highlights the increasing significance of cross-border coordination and new regulatory technologies (RegTech) to handle cross-border problems and increase the efficiency of compliance. It concludes that to have good cryptocurrency governance, there must be an interdisciplinary approach that comprises of economic theory, legal analysis, and understanding of technology. This balancing and adaptive action is what is needed to encourage innovation and still maintain financial stability, investor protection, and overall interest of the digital economy by the general population.
This paper examines the evolving relationship between blockchain architecture and financial privacy, focusing on the inherent tension between transparency, pseudonymity, and regulatory oversight. It begins by analysing the structural foundations of blockchain systems, including distributed ledgers, cryptographic security, and decentralized consensus mechanisms, which collectively replace institution-based trust with system-based verification. While such architecture enhances transparency and immutability, it simultaneously generates new privacy challenges. Through a comparative analysis of Bitcoin, Monero, and Zcash, the paper highlights a spectrum of privacy designs within the cryptocurrency ecosystem. Bitcoin represents a model of transparent yet pseudonymous transactions, where public ledger visibility enables traceability despite the absence of explicit identity markers. In contrast, Monero adopts a privacy-centric approach using ring signatures, stealth addresses, and confidential transactions to obscure sender, receiver, and transaction value. Zcash introduces a hybrid model, employing zero-knowledge proofs (zk-SNARKs) to reconcile transactional confidentiality with verifiability, alongside selective disclosure mechanisms. The study further explores the limitations of transparent blockchains, including risks of transaction traceability, address clustering, and linkage to real-world identities through regulatory touchpoints such as exchanges. It also evaluates the regulatory implications of privacy-enhancing technologies, particularly their impact on anti-money laundering (AML) and counter-terrorism financing (CTF) frameworks. The paper underscores the growing role of international standards and regulatory bodies in shaping compliance mechanisms within decentralized ecosystems. Finally, the paper considers emerging solutions such as privacy-preserving smart contracts, decentralized identity systems, hybrid blockchain models, and regulatory technologies (RegTech), which aim to balance user privacy with legal accountability. It argues that the future of blockchain governance lies not in choosing between transparency and privacy, but in developing adaptive frameworks that integrate both. The study concludes that achieving this balance will require sustained interdisciplinary collaboration and coordinated global regulatory efforts.
As a core technology of blockchain ecosystems, smart contracts are fundamentally reshaping the operational logic and commercial landscape of the digital economy. This paper systematically analyzes the current economic ecosystem of smart contracts from four dimensions: business value creation, multi-dimensional industry applications, legal and regulatory challenges, and frontier technological evolution. Research indicates that smart contracts, by reducing transaction costs and enhancing trust mechanisms, have achieved maturity in financial applications (DeFi) and are demonstrating significant enabling effects in real-economy sectors such as supply chains, healthcare, and energy internet. However, their widespread implementation still faces institutional obstacles including ambiguous jurisdictional boundaries, compatibility between code and law, and liability attribution. Looking ahead, Layer-2 scaling solutions have significantly improved cost-effectiveness, while the integration of cross-chain interoperability and AI-driven intelligent decision-making will become key trends driving the expansion of "contractability" boundaries. Smart contracts are not merely technical tools but rather institutional infrastructure driving the transformation of business models from intermediary-dominated to algorithm-autonomous paradigms.
P. Subramanya Sai, Bhukya Niranjan, Akula Tejaswini, A Varshitha · 5 authors
The exponential increase in digital data exchange and online communication has intensified the need for secure, transparent, and dependable file-sharing systems. Critical information such as financial records, healthcare data, confidential documents, and research outputs is frequently transmitted across distributed networks, where conventional centralized storage models introduce significant vulnerabilities. These traditional systems rely on single-point control, making them prone to data breaches, unauthorized access, service disruptions, and integrity violations. Furthermore, they lack transparency and robust audit mechanisms, raising concerns about data reliability and trustworthiness during storage and transmission. To overcome these limitations, this work proposes a decentralized and secure file-sharing framework that integrates blockchain technology, the Inter-Planetary File System (IPFS), and cryptographic techniques. The system ensures data confidentiality by encrypting files using the Elliptic Curve Integrated Encryption Scheme (ECIES) before storing them in the distributed IPFS network. Instead of placing the actual data on-chain, only essential metadata including file hash, ownership details, timestamps, and access permissions is maintained within a smart contract on the Ethereum blockchain. This design guarantees immutability, traceability, and protection against tampering, while enabling fine-grained access control. In addition to secure storage, the framework incorporates ChaCha20-based symmetric encryption to evaluate and compare computational performance with asymmetric methods. The combination of decentralized storage, cryptographic security, and immutable ledger technology eliminates reliance on centralized authorities, thereby reducing single points of failure and enhancing system resilience. The proposed approach ensures that only authorized entities can access and decrypt shared content while maintaining transparency of file transactions.
This paper examines the complex and evolving relationship between blockchain technology and the General Data Protection Regulation (GDPR), focusing on the fundamental tension between blockchain’s immutability and the data protection rights of individuals. While blockchain offers transformative advantages such as decentralization, transparency, security, and trustless verification, its core architectural feature—immutability—poses significant challenges to compliance with key GDPR principles, particularly the right to erasure, rectification, and data minimization. The study critically analyzes how decentralized and borderless blockchain networks disrupt traditional legal frameworks that rely on identifiable data controllers and territorially bounded regulation. It explores the difficulties in assigning legal responsibility within distributed systems, as well as the complications arising from cross-border data transfers and jurisdictional ambiguities. Further, the paper evaluates emerging technical and regulatory responses aimed at reconciling these conflicts, including off-chain data storage models, encryption-based deletion (crypto-shredding), pseudonymization, and advanced privacy-preserving techniques such as zero-knowledge proofs. It also considers the role of privacy-by-design principles and the need for adaptive regulatory frameworks tailored to decentralized technologies. The paper concludes that the interaction between blockchain and GDPR represents a broader challenge in contemporary law: balancing technological innovation with the protection of fundamental rights. It argues for a coordinated, interdisciplinary approach involving legal scholars, policymakers, and technologists to develop flexible and forward-looking governance models capable of addressing the unique characteristics of decentralized digital ecosystems.
With the rapid iteration of blockchain technology, smart contracts, as core components of decentralized applications, directly impact the stability of on-chain assets and ecosystems through their security. Traditional vulnerability detection methods primarily rely on expert rules and static analysis, facing bottlenecks such as high false positive rates and poor adaptability to complex logical vulnerabilities. In recent years, Large Language Models (LLMs), with their exceptional code understanding and reasoning capabilities, have provided new technical pathways for smart contract security auditing. This paper focuses on LLM-driven smart contract vulnerability detection technologies, systematically reviewing mainstream application paradigms from prompt engineering to model fine-tuning. The paper first reviews the current state of smart contract security and the limitations of traditional methods; subsequently, it provides in-depth analysis of the architectural design and core mechanisms of representative frameworks such as GPTLens and SmartVD, evaluating their performance in detection accuracy and recall rate; finally, addressing current challenges including data scarcity, model hallucinations, and computational overhead, it proposes future evolution directions such as multimodal fusion and human-in-the-loop auditing, providing reference for research and practice in related fields.
The modern educational prison has no bars. Students attend, comply, pass tests, receive diplomas — and emerge unable to use what those diplomas certify. This paper formalizes that decoupling as CM-001 (Idolatry Failure) in educational governance using the Zero Leap Theory (ZLT) framework. When the Alignment Gate (Φ) closes — through mandatory curricula, high Forced Exposure Ratios (FER), and assessment-driven instruction — the system enters an Idolatry Failure regime: the proxy (standardized scores, compliance metrics) is optimized while the structural target (transferable learning capacity, V_real) drifts. The Structural Delusion Index for Education (SDI_edu) is positive and growing. Internal metrics report success. External audit reveals divergence. This paper is the structural complement of ZLT-P15 (The Credential Standardization Trap). P15 addresses the quadrant where exit is blocked (C → 0): coercion is visible. P16 addresses the orthogonal quadrant: exit is available (C > 0), but Φ has closed. Students remain. Metrics stay green. The structural damage is invisible — precisely because assessment pressure simultaneously suppresses the behavioral signals (dropout, resistance) that would trigger correction. This is the Invisible Prison: a five-criterion observable configuration detectable with existing OECD instruments. Core empirical result: Using PISA 2022 and the OECD Survey of Adult Skills 2023 across 33 OECD countries: ρ(FER, SDI_edu) = 0.795, p < 0.0001. Higher Forced Exposure Ratio is strongly associated with greater proxy/competence decoupling. t-test high-FER vs low-FER countries: t = 4.986, p = 0.0008. Prediction 3 of ZLT-P16 confirmed. The structural triangle: South Korea presents the canonical case of sophisticated proxy/competence decoupling: top-3 PISA globally, 79% school belonging (above OECD average), but 22% life dissatisfaction (above OECD average). In the Survey of Adult Skills 2023, Korean adults (16-65) score below the OECD average in numeracy and adaptive problem-solving. Among Korean youth (16-24), literacy is above OECD average, but adaptive problem-solving falls below — the structural signature of compliance-type learning: succeeds where assessment trains, fails specifically on transfer competence. Finland is the proof-of-concept: comparable proxy outcomes, SDI_edu near zero, adult skills above OECD average across all three domains including youth 16-24. Two core corollaries derived from CM-001/P75 and I-3/P_SYNTH: P16.C1 (Alignment-Divergence): when Φ < Φ_min, SDI_edu grows monotonically — proxy improves while ρ(proxy, V_real) declines. P16.C2 (Invisible Acquiescence): the conditions that close Φ simultaneously suppress the behavioral signals that would alert the system to its own failure. The prison self-conceals. New constructs: SDI_edu three-layer audit family (SDI-1 within-system, SDI-2 young-adult orthogonal, SDI-3 long-run structural); Compliance Cascade Onset Point (CCOP); Invisible Prison as five-criterion observable state; IAS-CUR v2.1 audit standard; Attribution Inversion Principle (P16.AIP). This Zenodo record includes: ZLT_P16_v2_2_zenodo.pdf — full paper (14 pages, DOI embedded) ZLT_P16_v2_2.docx — Word source ZLT_P16_v2_1_with_appendices.docx — version with embedded figures and Appendices D–F zlt_cur_audit.py — IAS-CUR v2.1 executable audit tool (4/4 canonical tests passing; run: python zlt_cur_audit.py --test) zlt_p16_phase_diagram.py — Phase Diagram Suite: FER vs SDI_edu for 33 OECD countries (run: python zlt_p16_phase_diagram.py) zlt_p16_knowledge_decay.py — Knowledge Decay Index (KDI) exploratory tool with honest null result zlt_p16_ias_cur_interactive.html — IAS-CUR v2.1 interactive browser-based audit (open in any browser, no server needed) 9 companion PNG figures (phase diagram suite + KDI suite) All scripts run in Google Colab without API keys or external downloads. No authentication required. Part of the Zero Leap Theory (ZLT) series. Upstream: ZLT-P75 (CM-001 / doi:10.5281/zenodo.18940742), ZLT-P_SYNTH (I-3, OD-1 / doi:10.5281/zenodo.19498184), ZLT-P15 (structural complement / doi:10.5281/zenodo.18344309). Series root: doi:10.5281/zenodo.18264279. v2.2 changelog: Corrects empirical overclaim on Korea 16-24 youth data present in v2.1. Accurate data per OECD Survey of Adult Skills 2023: literacy above OECD average, numeracy near average, adaptive problem-solving below OECD average. Domain-specific decoupling framing (compliance-type learning fails specifically on transfer competence) replaces the v2.1 maximal claim. Theory, structure, and all other empirical claims unchanged. Companion tools added in this version.
This research explores how blockchain technology can support better fraud detection in modern financial systems. Financial institutions regularly encounter issues such as identity theft, unauthorized access, and manipulation of transaction records. As financial activities increasingly move to digital platforms, these risks have become more complex and difficult to manage. Many traditional financial systems rely on centralized databases and manual monitoring processes, which may delay the identification of suspicious activities and allow fraudulent transactions to occur before they are detected.Blockchain technology provides a possible solution to these challenges. It operates through a distributed ledger where transaction data are recorded across a network rather than stored in a single central system. Because the information is verified through cryptographic methods and shared among multiple participants, altering or manipulating financial records becomes extremely difficult. This structure allows financial institutions to track transactions more clearly and detect unusual patterns more quickly.In addition, blockchain creates a transparent and reliable record of financial activities, which improves auditing and strengthens accountability among network participants. By introducing decentralized verification and secure record keeping, blockchain systems can support real-time monitoring of financial operations. As a result, integrating blockchain into financial infrastructures may improve fraud detection, reduce financial risks, and increase trust among institutions and users
The quick proliferation of cryptocurrency markets has essentially transformed the financial frameworks of the globe in that it has made it possible to initiate the means of value transfer across borders that are decentralized, borderless, and technologically advanced. Cryptocurrencies are based on blockchain and cryptographic protocols and enable peer-to-peer transactions without use of traditional financial intermediaries, which improves efficiency, lowers the costs of transactions, and increases financial inclusion, especially in underserved areas. In addition to payments, the technologies have stimulated innovation in fields like decentralised finance, smart contracts, and systems of digital identity. But the very same characteristics which render cryptocurrencies appealing also pose serious threats to regulation and law enforcement. The anonymity of transactions combined with the decentralized and cross-border structure of blockchain networks make it difficult to determine who the users are and apply jurisdiction-specific legislation. As a result, cryptocurrencies have become more and more related to different types of financial crime such as money laundering, terrorist financing, tax evasion, ransomware attacks, and illegal trading in darknet markets. The paper discusses the principal types of crime in the context of cryptocurrency and evaluates the challenges encountered by regulatory bodies and law enforcement agencies that might need to overcome these challenges. It also discusses the international regulation reaction, including the involvement of the international standard-setting organizations and the development of compliance systems, including anti-money laundering (AML) and know-your-customer (KYC) systems. Furthermore, the paper also mentions that technological solutions, such as blockchain analytics, are increasingly gaining significance in enhancing investigative potential. The paper concludes that, regardless of the revolutionary potential of cryptocurrencies in terms of financial innovation, their productive regulation involves a moderate and coordinated strategy, incorporating legal and regulatory models, technological progress, and global collaboration.
We present the Creation Ledger, a protocol for token economics grounded in verified creative process rather than computational expenditure, staked capital, or attention signals. The protocol introduces Proof of Provenance (PoP) — a consensus primitive where tokens are minted exclusively through verified human-directed creative work, as validated by Cognitive Rhythm Analysis (CRA) of embedded decision trees. The protocol addresses three structural failures that have destroyed every prior creator token platform: (1) speculation crowding out utility at launch, (2) verification systems that are cheaper to forge than to use honestly, and (3) regulatory classification as securities due to revenue-sharing mechanics. The system is protected by nine provisional patent applications (193 claims across four patent families) filed with the United States Patent and Trademark Office. Patent Support: Patents 1-9 filed March 30, 2026. 193 total claims across Dense Notation, Proxy Provenance, Self-Proving Documents, Decision-Chain Provenance, Schema Registry, Delivery Protocol, Code Closure, Format-Provenance Fusion, and Document-Directed Computation.
Peer-to-peer (P2P) payments facilitate rapid direct transactions but are frequently compromised by trust asymmetry, leading to substantial risks of non-delivery or non-payment. This study addresses these vulnerabilities by introducing a lightweight, deterministic escrow mechanism based on Ethereum smart contracts, specifically designed to bridge the regulatory gap in consumer protection. Unlike conventional escrow systems that rely on costly human intermediaries or complex decentralized autonomous organization (DAO) structures, the proposed "FairPay" model advances the state-of-the-art by offering a streamlined five-state lifecycle architecture comprising Created, Funded, WorkSubmitted, Released, and Refunded stages. The research prioritizes an analytical problem-solution flow, focusing on a state-machine design that enforces automated role-based restrictions. Methodological evaluation conducted on the Ethereum Sepolia testnet demonstrates a 100% functional success rate across all unit test scenarios. Furthermore, gas cost analysis reveals that the system is economically viable for granular transactions, with core operational functions maintaining a low execution overhead. Beyond operational success, the primary scholarly contribution lies in the design insight of balancing high cryptographic security with granular transaction accessibility, providing a scalable framework for the modern digital economy. However, the system currently assumes binary participant decisions for work verification, representing a transparency-oriented limitation in handling highly subjective service deliverables. Ultimately, this study demonstrates that algorithmic trust, mediated through a simplified state-machine, offers a more efficient and transparent alternative to existing high-complexity blockchain models, effectively resolving the tension between decentralized security and practical usability in P2P digital interactions.
Legal certainty is a fundamental prerequisite for implementing decentralized governance, particularly to ensure protection for the apparatus in developing public service innovations. However, regulatory fragmentation often leads to overlapping authority and administrative uncertainty at the local level. This research aims to examine the legal certainty construction of innovation through a vertical synchronization test of Regulation Number 20 of 2021, and to evaluate its sociological implications on the fluctuation of regional innovation performance. This research employs a normative legal method calibrated with policy evaluation through statutory and conceptual approaches, and analyzes Regional Innovation Index data for the 2020 to 2025 period. The analysis results indicate that the Regent Regulation suffered multilayered substantive defects over time. In the initial formulation phase, the budget deprivation threat sanction for innovations deemed unsuccessful contradicted the apparatus protection principle under Law Number 23 of 2014. Furthermore, the local government has been found to have committed legislative omission by failing to amend the Regent Regulation to accommodate the updates to the institutionalization obligation and affirmative financing guarantees mandated following the promulgation of Ministerial Regulation Number 91 of 2021 and Governor Regulation Number 3 of 2022. Factual evidence confirms that this static and defective legal formulation triggered a climate of bureaucratic fear, resulting in a drastic decline in the regional innovation score in the first year of the regulation’s implementation and subsequent stagnation. In conclusion, punitive innovation regulations that are unresponsive to hierarchical updates have degraded the essence of regional autonomy and created a governance paradox. Therefore, the revocation of the administrative sanction clause and the execution of the institutionalization amendment must be executed to restore a safe authorizing environment for experimental legislation.
Rizqi Akbar Makarim, Desinta Maheswari, Aqila Dina Pramustiwi, Kartika Ayu Rahmawati · 5 authors
The volatility of cryptocurrency markets has increased substantially in recent years, particularly for Ethereum (ETH), which exhibits fat-tailed distributions and persistent volatility clustering that traditional linear models are unable to capture. This study aims to analyze and model the volatility of ETH/USD using high-frequency hourly data to determine the most appropriate volatility model for describing Ethereum’s intraday market dynamics. The dataset consists of 8,760 hourly closing prices from October 31, 2024 to October 31, 2025, obtained through the CryptoCompare API. The methodological framework includes data preprocessing, log-return transformation, stationarity analysis using the Augmented Dickey–Fuller test, detection of heteroskedasticity via the ARCH–LM test, and estimation of several ARCH and GARCH model specifications. The results show that ETH/USD returns are stationary, non-normally distributed, and exhibit clear volatility clustering. Among the ARCH models, only ARCH(1) adequately captures short-term fluctuations, while ARCH(2) provides no additional benefit. In contrast, GARCH models demonstrate superior performance in capturing both short-term shocks and long-term persistence. Based on AIC, BIC, and log-likelihood values, GARCH(1,2) emerges as the best-performing model, offering the highest flexibility in representing Ethereum’s persistent and reactive volatility patterns. These findings confirm that ETH/USD volatility is predictable and can be modeled statistically. Future research may incorporate asymmetric GARCH extensions or external explanatory variables to improve predictive performance.
Jorge Garcia-Diaz, Daniel Escánez-Expósito, Pino Caballero-Gil, Jezabel Miriam Molina-Gil
Abstract Zero Knowledge Proofs based on computational hardness assumptions are fundamental primitives for secure user authentication. This paper proposes a novel Designated Verifier Zero Knowledge Proof that leverages the inherent randomness of quantum bits. Unlike traditional constructions relying on computationally intractable NP problems, the proposed protocol derives its security from the physical layer, specifically the uncertainty principle of quantum state projections in misaligned measurement bases. A rigorous formal mathematical analysis establishes the completeness, soundness and zero knowledge properties of the scheme. Furthermore, the protocol’s performance is evaluated via a quantum simulator under realistic error-prone conditions. The results demonstrate that the construction is robust against dishonest parties while remaining feasible under constrained quantum resources, offering a scalable approach for secure quantum authentication.
Decentralized applications (DApps) represent a paradigm shift in software architecture, leveraging blockchain technology and distributed consensus mechanisms to eliminate single points of failure and centralized control. As the adoption of DApps accelerates across sectors such as finance, supply chain, healthcare, and governance, ensuring their functional correctness and behavioral reliability has become a critical engineering challenge. Unlike traditional software, DApps operate in adversarial, permissionless environments where smart contracts execute autonomously and immutably on distributed nodes, making post-deployment correction extremely costly or impossible. This review systematically examines the landscape of functional testing methodologies tailored for decentralized applications, analyzing their suitability, limitations, and practical applicability in modern DApp development workflows. We survey research spanning smart contract verification, consensus protocol testing, oracle interaction validation, cross-chain interoperability testing, and user-layer functional testing of Web3 interfaces. The review identifies four dominant testing paradigms: (1) unit testing of smart contract functions, (2) integration testing of DApp components, (3) property-based testing using formal specifications, and (4) end-to-end simulation on testnets. Through comparative analysis across 13 seminal studies, we evaluate each approach along dimensions of automation feasibility, coverage depth, gas efficiency awareness, and scalability to complex DApp ecosystems. Our findings indicate that while static analysis and symbolic execution tools such as Mythril, Slither, and Manticore offer strong vulnerability detection, they address security properties more than functional correctness. Conversely, framework-based testing tools like Hardhat, Truffle, and Foundry provide adequate unit-level coverage but struggle with cross-contract orchestration and event-driven logic verification. A critical gap exists in testing oracle-dependent and DAO governance workflows. This review concludes with a synthesis of best practices, open research challenges, and a directional roadmap for developing holistic functional testing frameworks suited to the evolving complexity of decentralized systems.
M. Ganesh, Gaddam Richitha, B Sai Jagadeesh Goud, Gannarapu Ramani · 5 authors
Drug repurposing has gained significant attention as an efficient strategy for identifying new therapeutic applications of existing drugs, thereby reducing both development time and cost compared to traditional drug discovery processes. Current drug discovery approaches rely on experimental procedures, expert analysis, and extensive clinical trials, which are time-intensive and computationally inefficient when handling large-scale biomedical data. These methods often struggle to process complex and highdimensional datasets, resulting in slower analysis and limited predictive capability. Additionally, these systems lack robust mechanisms for secure data management, making clinical records and trial discussions susceptible to inconsistencies and unauthorized modifications. To overcome these limitations, this work proposes an intelligent drug repurposing framework that integrates Machine Learning (ML), Deep Learning (DL), and blockchain technologies. The system utilizes baseline models such as K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) for comparative analysis, along with a hybrid DrugNet model that combines Convolutional Neural Networks (CNN) for feature extraction and Random Forest (RF) for classification. This hybrid approach enhances the ability to capture complex patterns in drug-related data and improves prediction accuracy. Furthermore, blockchain integration using Web3 ensures secure storage of user data, clinical interactions, and trial information, providing transparency, immutability, and data integrity. The proposed framework enables automated prediction of potential drug–disease associations through a unified processing pipeline, supporting real-time analysis and decision-making. By combining advanced Artificial Intelligence (AI) techniques with decentralized data management, the system improves scalability, reliability, and efficiency in drug repurposing. This approach offers a practical and secure solution for accelerating pharmaceutical research and supporting data-driven medical innovation
The rapid advancement of digital technologies in healthcare has increased the need for secure, transparent, and efficient management of medical data. However, most existing systems rely on centralized architectures, where sensitive patient information is controlled by a single authority. This creates vulnerabilities such as data breaches, unauthorized access, and single points of failure, which can compromise data integrity and patient privacy. The core problem addressed in this research is the lack of a decentralized and tamper-resistant mechanism for managing Electronic Health Records (EHR). Current solutions often suffer from limited transparency, inefficient data sharing between patients and doctors, risks of data manipulation, dependency on intermediaries, and scalability issues due to large medical files like reports and prescriptions. To address these challenges, this research proposes a blockchain-based healthcare management system integrated with the Inter-Planetary File System (IPFS). Blockchain technology, implemented using Ethereum and Web3, ensures secure, immutable, and transparent transaction handling through smart contracts. IPFS is used for decentralized storage of medical files, with only cryptographic hashes stored on the blockchain to reduce storage overhead while maintaining data integrity. The system enables patients to book appointments, upload medical reports, and securely share them with doctors. Doctors can access records, provide diagnoses, and generate prescriptions, which are also stored via IPFS and linked to the blockchain. This ensures that data cannot be altered without detection, enhancing trust. The proposed system improves data security, privacy, reliability, and scalability in healthcare data management.
Immutable code and steep transaction fees make smart contract deployment uniquely unforgiving. While continuous integration (CI/CD) pipelines excel at catching standard software bugs, applying exhaustive security tests to Web3 applications severely bottlenecks development through massive computational overhead and gas consumption. This paper presents a testing architecture designed specifically to resolve this tension between security depth and execution speed. The system pipelines three core engines. First, an AI-driven pre-execution gate flags immediate vulnerabilities. Next, a structural reduction module applies the k + 1 symmetric pattern to strip out redundant test permutations. Finally, the system constrains the remaining test suite using the NSGA-II evolutionary algorithm. This multi-objective optimizer dynamically schedules execution to maximize fault detection against strict, predefined gas budgets. To evaluate the model empirically, I bridged a localized EVM sandbox with a Python optimization engine. Results confirm the framework collapses exponential test generation and throttlesexecution costs without sacrificing critical security coverage. Ultimately, it offers a highly scalable path forward for modern DevSecOps.
This paper explores the application of distributed ledger technology to facilitate dynamic resource allocation in virtualized radio access networks (RANs). A novel architecture is presented that leverages blockchain to automate the exchange of network functions among diverse stakeholders. This approach addresses challenges related to trust and scalability in open RAN environments, enabling autonomous and secure sharing of infrastructure resources. The proposed system employs auction-based mechanisms and network virtualization to allow entities to transact RAN resources, such as virtual network functions (VNFs), in a decentralized manner. Simulation results demonstrate the potential of this framework to enhance flexibility and efficiency in next-generation mobile networks.
Prof. M. S. Bhosale, Abhishek Kangude, Vedant Khandare, Sunil Kajave
Abstract: In modern digital governance, ensuring secure, transparent, and tamper-proof elections remains a critical challenge. Traditional voting systems, whether paper-based or electronic, suffer from issues such as centralized control, lack of transparency, and vulnerability to tampering. This paper proposes a Blockchain-Based Decentralized E-Voting System that utilizes Ethereum smart contracts, MetaMask authentication, and a Flutter-based frontend to provide a secure and transparent voting mechanism. Each vote is recorded as an immutable blockchain transaction, ensuring integrity, verifiability, and prevention of double voting. The system eliminates the need for third-party intervention and enables real-time result verification. Experimental results demonstrate improved security, reliability, and scalability, making the system suitable for modern digital election processes. Keywords: Blockchain, E-Voting, Smart Contracts, Ethereum, MetaMask, Decentralization, Web3
The emergence of decentralized autonomous organizations (DAOs) represents a significant shift in organizational design driven by blockchain technology. Unlike traditional hierarchical structures, DAOs operate through decentralized governance, algorithmic rules, and collective coordination embedded within distributed systems. Despite growing scholarly attention, existing research remains fragmented, with limited integration across technological, governance, and organizational dimensions. This paper addresses this gap by developing a multidimensional theoretical framework that explains the organizational logic of DAOs through the interaction of blockchain architecture, governance mechanisms, and coordination processes. Drawing on an integrative review of the literature, DAOs are conceptualized as socio-technical systems in which technological infrastructure enables decentralized governance, governance mechanisms shape participation and decision-making, and coordination processes support collective action and value creation. The framework highlights the interdependencies among these dimensions and advances a set of theoretical propositions to guide future research. By offering a more integrated perspective, this study contributes to DAO scholarship and extends organizational theory to better account for decentralized and algorithmic forms of organizing. The findings also provide insights for designing and governing DAOs in the evolving digital economy.
Lyudmila Kovalchuk, Mariia Rodinko, Roman Oliynykov, Volodymyr Artemchuk
This paper studies the probability of a double-spend attack in an Ouroboros-like Proof-of-Stake (PoS) setting when confirmation decisions must be made for a finite number of blocks. Existing security analyses of Ouroboros-family protocols are mainly asymptotic and therefore do not directly provide the attack probability for a fixed confirmation depth. We consider an analytically tractable model that allows empty slots and multiple slot leaders, and assumes fixed stake distribution within an epoch, one-block growth of the public longest chain in any slot containing at least one honest leader, and next-slot block visibility. These assumptions hold when the time slot length is much greater than the network delay, and are applicable to practical deployment scenarios such as Cardano. Under these assumptions, for the first time, an exact closed-form solution for the success probability of a double-spend attack considering a realistic model with multiple leaders and empty time slots. Numerical examples illustrate how the required confirmation depth depends on the adversarial stake ratio and the active slot coefficient. The results apply to the stated analytical model and do not yet cover delayed fork resolution or the full protocol-level fork-choice and finality mechanisms of Ouroboros Praos.