The pair, in its most absolute sense, is not just two objects but a minimal structure—a dyad—in which two poles define each other through a single opposition. This article builds a self‑contained formal model of the dyad: a set equipped with an operation that exchanges the two poles without leaving any pole unchanged, together with a sign that distinguishes them. A proof by contradiction shows that any perfect binary distinction necessarily obeys this model. The proof uses only the notions of exhaustivity, exclusivity, and symmetry, making the law scale‑invariant and independent of any particular scientific domain. We then tour the natural and human sciences, tracing the dyadic law from the spin of an electron and the positive‑negative charge pair, through left‑ and right‑handed molecules, the complementary strands of DNA, male and female gametes, the opponent colours of vision, mirror neurons, the day–night cycle, the Earth’s magnetic reversals, binary stars, the warm and cool hemispheres of the cosmic microwave background, to Boolean logic, error‑correcting codes, mathematical dualities, zero‑sum games, and the I–Thou relation. Each example is explained in plain terms to make the article self‑contained. The dyad emerges as a universal structural law—a law of form that prescribes the necessary architecture of otherness across all scales of reality and all branches of knowledge.
We present ENI6MA and Rosario Cypher as a proof-based identity and authorization architecture for emerging cybersecurity threats involving shadow AI, deepfakes, prompt injection, autonomous agents, credential theft, privacy exposure, and post-quantum risk. The paper responds to major 2026 cybersecurity forecasts by identifying a common root cause across many attack surfaces: conventional systems depend on reusable, stealable artifacts such as credentials, tokens, private keys, sessions, API keys, and stored personal data. ENI6MA replaces possession-based authentication with per-event proof of knowledge, policy-bound authorization, privacy-clean auditability, and contract enforcement behind cryptographically secure proof. Special attention is given to autonomous-agent security. The paper explains how ENI6MA constrains agents through per-action proof, verifier allowlists, policy identifiers, scoped pass credentials, and immutable validation records, reducing the risk of hijacked agents, excessive privilege, non-human identity sprawl, and zero-click prompt-injection exfiltration. The white paper also describes ENI6MA’s flexible deployment and capability model, including passwordless single sign-on, PII validation without disclosure, agent-to-agent authentication, proof-gated signing and custody, post-quantum sealing, sovereign/offline operation, and public verifier anchoring. This document is intended for cybersecurity leaders, AI governance teams, identity architects, privacy and compliance stakeholders, investors, technology partners, and researchers evaluating post-credential identity systems for human and autonomous-agent workflows.
Prof. Suvarna A. Bahir, Tejas Vaidya, Ranjeet Waghmode, , Abhishek Gavand, · 5 authors
Electronic voting systems have gained significant attention due to their ability to improve the efficiency and accessibility of elections. However, traditional voting methods and centralized electronic voting systems face challenges such as vote tampering, lack of transparency, unauthorized access, and delayed result generation. Blockchain technology offers a decentralized and secure solution to address these limitations. This paper presents a Secure Blockchain-Based E-Voting System Using Smart Contracts that leverages Ethereum blockchain technology to provide transparent, secure, and tamper-resistant elections. The proposed system integrates voter authentication, election management, candidate registration, vote recording, and real-time result monitoring within a single platform. Smart contracts developed using Solidity are used to automate election operations and ensure the integrity of voting transactions. The system is implemented using HTML, CSS, JavaScript, FastAPI, MySQL, Ethereum, Ganache, and MetaMask. Votes are securely recorded on the blockchain, preventing unauthorized modifications and improving election transparency. The proposed framework enhances voter trust, reduces dependency on centralized authorities, and simplifies election management. This solution can be effectively used for academic institutions, organizations, and small-scale election environments requiring secure and reliable voting processes. Keywords: Blockchain, Electronic Voting, Ethereum, Smart Contracts, Solidity, Decentralized Voting.
Strategic alliances have long required their participants to combine the certainty of formal contracts with the adaptive flexibility of relational mechanisms, and the substitutes-complements debate in alliance governance has spent decades trying to clarify how these two qualities can be combined.The recent emergence of blockchain-enabled smart contracts complicates this picture in interesting ways.This article asks how smart contracts interact with the contractual and relational governance mechanisms documented in the strategic alliance literature, what conditions shape this interaction, and what the implications are for alliance theory.Drawing on the alliance governance literature and the blockchain governance literature in roughly equal measure, the paper develops a framework that positions smart contracts as a third governance mechanism alongside contractual and relational forms, producing a hybrid arrangement termed algorithmic-relational governance.Three propositions are derived and illustrated through a case study of Walmart Canada's DL Freight platform, one of the larger production-grade smart contract deployments in a multi-party alliance setting.The findings suggest that smart contracts function primarily as governance complements rather than substitutes, that they alter alliance dynamics in ways transaction cost economics alone cannot predict, and that their effectiveness depends on deliberate architectural design choices that are themselves products of relational negotiation between alliance partners.
Programmable money—digital currency whose behaviour is controlled by code—creates new design space for dynamic, data-driven monetary policy. This paper proposes a framework for AI-adjusted interest rates in programmable monetary systems, w here machine-learning models continuously calibrate interest-rate parameters in response to real-time economic and network conditions. We formally describe the architecture of such systems, illustrate how AI-driven mechanisms can extend existing algorithmic interest-rate models in decentralized finance (DeFi), and discuss their potential integration with central bank digital currencies (CBDCs). Using stylized simulation data calibrated to typical DeFi lending dynamics, we compare baseline algorithmic rate m odels with an AI-adjusted variant, showing reduced volatility and smoother utilization patterns. A case study on Compound and Aave interest-rate mechanisms demonstrates how AI- based forecasting and reinforcement learning could enhance stability and policy precision. We conclude by outlining governance, regulatory, and ethical considerations, and propose a research agenda for AI-driven algorithmic monetary policy.
Nazneen Fatema, Abdullah Mohammed Ibrahim, Jesmin Sabnam, Abdullah Mohammad Ismail
This bibliometric study maps research trends in cybersecurity and data breaches within the financial sector from 2020 to 2024, analyzing 7355 documents from the Web of Science. The findings reveal a rapidly expanding and interdisciplinary field, driven by the digital transformation of finance, heightened cyber threats, and the impact of global events such as the COVID-19 pandemic. The research landscape has evolved from descriptive, technical studies to sophisticated analyses incorporating network theory, econometrics, and risk management. Most prolific authors and sources, such as IEEE, demonstrate strong international collaboration and significant citation impact, with China, the USA, and the UK leading in citations. Co-citation network analysis identifies three major intellectual clusters: economic modeling of cyber risk, network-based risk propagation, and systemic macro-financial implications of cyberattacks. The study highlights an increasing focus on quantifying the financial and reputational impacts of cyber incidents, making research directly relevant to business and regulatory stakeholders. Limitations include reliance on a single database and quantitative methods. Future research directions emphasize the security implications of emerging technologies (e.g., quantum computing, decentralized finance, artificial intelligence), behavioral and cultural aspects of cybersecurity, and systemic regulatory challenges. The field is dynamic, reflecting the financial sector’s evolving risk landscape.
This paper investigates the impact of uncertainty on investor overconfidence in the Bitcoin market. While prior studies mainly focus on returns and volatility, limited attention has been paid to behavioral responses. Using a nonlinear autoregressive distributed lag (NARDL) model and monthly data from June 2011 to August 2022, we examine the asymmetric effects of major U.S. uncertainty indices (EPU, GPR, CPU, TEU and EURQ). The results reveal significant asymmetries. In the short run, increases in EPU and GPR reduce investor overconfidence, while decreases have the opposite effect. TEU and EURQ negatively affect investor confidence in both the short and long run. These findings highlight the key role of information-based uncertainty in shaping investor behavior and contribute to the behavioral finance literature by providing new evidence from cryptocurrency markets.
Blockchain-based electronic voting systems have been identified as a solution to enhance the transparency, security, and efficiency of modern electoral processes. However, the existing system has three major problems, which include scalability, privacy issues, and cybersecurity attacks. The researcher proposed an innovative solution to develop an electronic voting system with enhanced security, scalability, and transparency of voters' information. This paper introduced the Hybrid Cryptographic and Enforced Blockchain (HCE VoteChain) framework, which combines Hyperledger Fabric with various sophisticated forms of cryptography, including SHA256 hashing, Advanced Encryption Standard (AES256) encryption, Elliptic Curve Digital Signature Algorithm (ECDSA) and digital signatures, Paillier Homomorphic Encryption, and Zero Knowledge Proof (ZKP) auditing. The experimental evaluation demonstrated that the system achieved a throughput of 288 Transactions Per Second (TPS) while maintaining an average latency of 2.521 seconds, a transaction speed of 0.13 votes per second, and a data immutability score of 0.999 and security resilience of 10000 and fault tolerance of 0.96, which proved its high reliability and robustness across different operational conditions. The results indicate that the framework suggested is a big step up in terms of security, scalability, and transparency over the existing solutions. Besides, it does not compromise the voter's privacy and auditability. The innovation of this work is the combination of multi-layer cryptographic mechanisms with the permission blockchain architecture to not only come up with a balanced compromise between performance and security but also to make the system capable of handling large-scale real-world digital elections.
Federated Learning (FL) is increasingly deployed in healthcare to enable collaborative intelligence while keeping sensitive data privately at local institutions. However, existing healthcare-oriented FL frameworks still suffer from several limitations: they are vulnerable to adversarial model updates, provide limited transparency and verifiable auditability, and often lack predictable performance under constrained resources. We present IIN-Health, a blockchain-enhanced intelligent fusion network tailored for dependable healthcare FL. IIN-Health adopts a dual-chain architecture with policy-aware access control and auditable provenance tracking to integrate learning, security, and governance in a unified framework. Evidence-Carrying Access Tokens (ECATs), combined with zero-knowledge proofs, are introduced to enforce patient-defined policies and validate access decisions without disclosing sensitive information. In addition, we design MedBFT-Δ, a domain-specific Byzantine fault-tolerant protocol that ensures reliable system behavior in the presence of faulty or malicious participants. We conduct several experiments to validate its feasibility and accuracy on the MNIST dataset. The results demonstrate that IIN-Health achieves smooth and stable convergence, exhibits strong resilience against poisoning attacks, and maintains graceful performance degradation under resource constraints, while preserving verifiable auditability of model updates and data flows. These observations indicate that IIN-Health can provide a practical balance among performance, security, and regulatory compliance, and thus offers a promising foundation for trustworthy and scalable FL deployments in healthcare.
Imran Hasan, Abdullah All Ahhad, Md Zamilur Rahman, Bikash Chandra Singh
Smart contracts enable decentralized applications across domains such as finance, logistics, and healthcare, but their immutable nature and complex execution logic make them highly susceptible to vulnerabilities, including reentrancy, integer overflows, and access control flaws. These weaknesses can lead to severe financial and operational losses. Traditional static or rule-based detection tools lack scalability and adaptability, while existing deep learning models often struggle with limited data, poor generalization, and the absence of actionable mitigation guidance. This paper proposes a hybrid multi-task learning framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for smart contract vulnerability detection, coupled with a transformer-based Large Language Model (LLM) for root cause analysis and dynamic mitigation generation. The framework extracts spatial opcode features using CNNs and captures temporal execution patterns via LSTMs, supported by preprocessing steps that include opcode extraction, positional encoding, static and dynamic analysis features, and data augmentation. A feature fusion module consolidates spatial and temporal information, while SHAP and LIME provide interpretability by identifying features driving model predictions. The mitigation layer employs an encoder–decoder transformer to map detected vulnerabilities to their underlying causes and generate context-aware remediation strategies. Experimental results show strong performance, achieving 93% accuracy, 90% precision, and an AUC-ROC of up to 90% across multiple vulnerability categories. Beyond accurate detection, the framework delivers explainable root cause insights and tailored mitigations, offering a scalable and adaptive solution for enhancing smart contract security in modern blockchain ecosystems.
Financial technology (FinTech) has emerged as a key driver of financial inclusion, transforming access to payments, credit, savings, and insurance for households, small businesses, and underserved populations worldwide. This study synthesizes a decade of Scopus-indexed bibliometric and systematic-review research on FinTech and financial inclusion published between 2015 and 2025. Rather than conducting a new bibliometric extraction, it provides a comparative synthesis of major peer-reviewed review studies, consolidating evidence on publication trends, intellectual structure, geographic distribution, and emerging research themes. The findings reveal rapid growth in scholarly output since 2016, led by China, India, the United States, and the United Kingdom. Dominant themes include digital payments, mobile money, regulatory technology, artificial intelligence, decentralized finance, financial literacy, SME finance, and sustainability-oriented digital finance. The review identifies persistent gaps in low-income regions and limited integration of AI and ESG perspectives. It offers a consolidated evidence base and proposes directions for future research, policy formulation, and practice.
ABSTRACT In the rapidly evolving field of information technology (IT), blockchain‐based smart contracts have emerged as a transformative force, redefining the mechanisms of digital agreements and transactions. These self‐executing contracts, with terms directly written into code on a blockchain, promise to enhance the automation, transparency and security of business processes. The integration of smart contracts into IT businesses stands as a critical innovation, aiming to streamline operations and foster trust in digital interactions. This paper aims to identify and prioritize the key variables influencing the adoption of blockchain‐based smart contracts within IT businesses. To achieve the stated objective, initially, a systematic literature review (SLR) was conducted to identify the variables of blockchain‐based smart contracts in IT businesses. Secondly, questionnaire‐based survey was conducted with IT professionals and experts, to get the experts perceptions on identified variables. The study further applied the fuzzy analytic hierarchy process (AHP) to evaluate and prioritize the identified variables based on their significance and impact on the adoption process. The research successfully identified 16 critical variables that significantly influence the adoption of blockchain‐based smart contracts in IT businesses. These variables were categorized and analyzed to understand their roles and interdependencies in the adoption process. The fuzzy AHP results revealed a hierarchical ranking of these variables, highlighting those with the most substantial impact on successful adoption, such as “security and privacy concerns,” “technical complexity,” and “regulatory and legal challenges.” The integration of blockchain‐based smart contracts presents both opportunities and challenges for IT businesses. The identified variables and their prioritization offer a roadmap for organizations to navigate the complexities of adopting this technology.
Jyotiplaban Talukdar, Ferdous Ahmed Barbhuiya, Shouraya Mishra, Shubhashish Shukla
Asset ownership tracking is fundamental to economic activity, yet it remains fragmented across paper-based deeds, centralised registries, and siloed databases. Public blockchains provide immutability but suffer from scalability limitations and inflexible consensus mechanisms unsuitable for enterprise deployment. We present Keychain Signet (KCS), a decentralised architecture designed as a notarised asset ledger that strictly separates application logic, consensus, and persistence. KCS distributes responsibilities: service providers manage application workflows, notaries enforce validity and uniqueness via BLS signatures, and storage nodes execute BFT-Raft consensus to order blocks. This architecture enforces a "notarised append" model where authorised writes are persisted and ordered by the storage cluster. Unlike standard blockchains, our architecture resolves the latest asset state in O(1) time via a disc-backed Distributed Hash Table while maintaining an immutable, cryptographically linked history for auditability. Experimental evaluation demonstrates 401.1 TPS throughput, notary signing latency of 1.6–10.2 ms scaling linearly with committee size, signature verification under 0.81 ms, and storage persistence under 700μs. The system tolerates Byzantine faults, storage corruption, and node crashes while preserving safety and liveness guarantees.
Energy demand in urban and metropolitan regions has been growing rapidly, often exceeding production capacity, leading to imbalances in energy distribution. Existing peer-to-peer (P2P) energy trading models, along with classical algorithms like FCFS and best-fit frameworks in smart cities, address some of these issues. However, they often face challenges such as limited transaction success percentage, inefficiencies in price matching, and privacy concerns during trades. To overcome these limitations, a framework is proposed that integrates game-theoretic pricing-based collaborative trading with Nash equilibrium and an additional pricing mechanism (CoGap) to enhance fairness and transaction success percentage in decentralized energy markets. The proposed framework is implemented on an Ethereum-based blockchain using Solidity smart contracts, incorporating cryptographic security through the Keccak-256 hash function and privacy-preserving zero-knowledge proofs (ZKPs). Moreover, it ensures security and price negotiations while maximizing transaction efficiency. Simulation results demonstrate that CoGap consistently achieves higher transaction success rates compared to four state-of-the-art collaborative energy trading schemes.
The rapid expansion of cryptocurrency markets has fundamentally transformed the global financial system and challenged traditional approaches to financial regulation. Cryptocurrency exchanges have emerged as key intermediaries facilitating the purchase, sale, transfer, and storage of digital assets across jurisdictions. However, the borderless and decentralized nature of cryptocurrencies has generated significant legal concerns relating to anti-money laundering compliance, counter-terrorist financing measures, consumer protection, taxation, cybersecurity, market manipulation, and regulatory enforcement. This article examines international legal frameworks governing cryptocurrency exchanges, analyzes regulatory approaches adopted by leading jurisdictions, including the European Union and the United States, and evaluates major enforcement actions involving Binance and FTX. The study further explores emerging challenges associated with decentralized finance (DeFi) and proposes recommendations aimed at strengthening international cooperation and harmonizing legal standards for digital asset regulation.
Abstract The high rate of healthcare digitalization and the extensive use of electronic healthcare records (EHRs) have heightened the issues of data privacy, interoperability, and the ability of the system to scale. Blockchain has emerged as a promising paradigm of decentralizing trust and improving security in healthcare information systems, and its real implementation is still divided. The paper highlights a systematic review of 94 peer-reviewed articles published in the years 2019–2025, which investigate architectural designs, privacy designs, scalability designs, federated learning designs, cross-chain interoperability designs, and novel cryptography designs in health care blockchain systems. The review contributes to the research in two ways: (i) a full taxonomy of blockchain design methods of healthcare applications and (ii) an organized discussion of the gaps in the research and future trends. The results demonstrate that hybrid constructions of lightweight zero-knowledge proofs, federated learning, adaptive consensus mechanisms, and cross-chain frameworks have better potential in privacy, scalability, and regulatory compliance than blockchain-based EHR solutions.
Punjab is an agrarian society and is one of the world’s leading producers of crops such as rice and wheat. The crops rely heavily on fertilizers and different pesticides, which harm the environment. After every harvesting season, farmers burn crop residue too quickly to clear the fields for the next sowing, as short time gaps undermine the productivity of the crops. The practice of stubble burning has raised serious environmental, health and legal concerns. Stubble burning has degraded air quality by discharging harmful gases such as carbon dioxide, carbon monoxide, and sulphur oxides, which lead to respiratory illnesses, particularly among children, the elderly, and pregnant women. Thus, it has endangered public health so the study has emphasized the serious issue of stubble burning in Punjab from a legal and regulatory perspective. The chapter has also analysed the challenges faced by farmers that forced them to burn stubble. The study has employed qualitative methodology and relies on secondary sources such as statutory provisions, government reports, newspapers, articles, and studies related to stubble burning. The role of the government, courts, and regulatory institutions in addressing the serious issue of stubble burning through environmental laws, legal restrictions, penalties, and awareness campaigns to discourage stubble burning have also been thoroughly discussed. The research work has highlighted sustainable and legally viable residue management practices, effective implementation of environmental laws, farmer-centric polices, and coordinated government action to achieve the right to a clean and healthy environment.
Transaction monitoring and efficient audit management have become increasingly importantin modern financial systems. Traditional centralized databases and auditing methods often face challengesrelated to security vulnerabilities, fraudulent activities, and data manipulation. A blockchain-based financialtransaction monitoring system integrates smart contracts, decentralized ledgers, and audit trails to automatefinancial operations, enhance transparency, and reduce fraud risks. The proposed architecture is implementedon Ethereum and Hyperledger Fabric platforms, enabling automated transaction validation and executionthrough smart contracts. All transactions are stored in an immutable decentralized ledger, while audit trailsare generated and maintained automatically. Simulation results demonstrate a 40–60% reduction in fraudulentactivities and up to a 70% decrease in audit processing time compared with conventional approaches. Theapplication of cryptographic algorithms and Zero-Knowledge Proofs further strengthens data security andprivacy protection. The proposed solution contributes to the improvement of financial control and auditingsystems within the framework of the digital economy.
The growing demand for secure, transparent, and efficient financial systems has accelerated interest in blockchain technology within the financial sector. This study investigates the determinants and outcomes of financial blockchain adoption by proposing an integrated research model that links core blockchain characteristics security and transparency, smart contracts, and decentralization to financial blockchain adoption and its subsequent effects on fraud reduction, risk management, and cost efficiency. Using data collected from financial institutions operating in Jordan, the study applies Partial Least Squares Structural Equation Modeling (PLS-SEM) to empirically test the proposed hypotheses. The findings demonstrate that security and transparency, smart contracts, and decentralization significantly and positively influence financial blockchain adoption. Moreover, the results confirm that financial blockchain adoption has a strong positive impact on fraud reduction, risk management effectiveness, and operational cost efficiency. By conceptualizing blockchain adoption as a strategic organizational capability rather than a purely technological choice, this study extends existing blockchain and fintech literature. The findings provide valuable theoretical insights into how blockchain characteristics translate into tangible financial outcomes and offer practical guidance for financial institutions and policymakers seeking to enhance trust, governance, and efficiency through blockchain-based financial systems.
K Vigneshkumar, A R JayaSudha, P Nandini, Jana Murugesan · 7 authors
The system architecture presented in this work uses blockchain technology in conjunction with cryptographic authentication methods and real-time anomaly detection to validate the integrity of artificial intelligence models. By integrating tokenization-based model tracking, zero-knowledge proof verification, and machine learningbased integrity monitoring, the suggested solution fills important holes in current methods. We use Ethereum smart contracts to construct a prototype system and assess it using several AI model designs. In comparison to signature-based methods alone, experimental findings show 96.7% fewer false positives, sub-second verification latency for the real-time component, and 99.4% detection accuracy for model tampering attempts. Every day, the system effectively processes 10,000 model inference records while upholding cryptographic security requirements.