Blockchain Papers

Klaus Jaffé

No abstract is available for this record.

David Krause

No abstract is available for this record.

Salvatore Furnari, R Lener

No abstract is available for this record.

Ming Sen Thong

No abstract is available for this record.

Anass Mohamed Amallah, Noreddine Abghour, Zouhair Chiba

No abstract is available for this record.

Jayanth Kumar Narayana

No abstract is available for this record.

AKATERINH XENOPOULOU-TYROKOMOU, Epameinondas Xenopoulos

A Case Study Application of the Xenopoulos Genetic‑Historical Logic System (X‑GHLS) https://github.com/kxenopoulou/epameinondas_xenopoulos_epistemology-of-logic_genetic-historical-logic Author: Katerina XenopoulouORCID: 0009‑0004‑9057‑7432Version: 4.0 (Complete)Publication Date: February 25, 2026 Data and Experimental Setup Dataset: Our World in Data — COVID‑19 GreeceTime Span: January 5, 2020 – August 4, 2024Total Observations: 1,674 daily recordsOut‑of‑Sample Predictions: 1,667Overall Forecast Accuracy: 98.31%Evaluation Metrics: MAPE 1.69% | R² 0.999 | RMSE 120 cases ABSTRACT We present the first complete empirical validation of the Xenopoulos Genetic‑Historical Logic System (X‑GHLS) on real‑world epidemiological data. While the theoretical framework of X‑GHLS establishes 33 philosophical principles and the XEPTQLRI metric for quantifying dialectical tension, this study demonstrates its practical application in forecasting COVID‑19 dynamics in Greece over a 4.5‑year period (January 2020 – August 2024, N = 1,674 days). The system achieves exceptional predictive performance: MAPE: 1.69% (Mean Absolute Percentage Error) R²: 0.999 (Coefficient of Determination) RMSE: 120 cases (Root Mean Square Error) Overall Accuracy: 98.31% Total Predictions: 1,667 Phase analysis reveals that the pandemic was in crisis mode (τ₅ and above) for 1,212 days (72.7% of the total), explaining why conventional statistical models struggle with such highly nonlinear dynamics. The system successfully detects all major COVID‑19 waves in Greece and provides early warning signals through the XEPTQLRI index. Comparative analysis with state‑of‑the‑art models (2026) demonstrates that X‑GHLS outperforms: TimesFM (Google): 3.2% MAPE Chronos‑2: 3.5% MAPE TiRex: 3.8% MAPE Transformer architectures: 4.2% MAPE LSTM networks: 5.8% MAPE ARIMA: 8.5% MAPE The 33rd Principle (Advanced Dialectical Negation) proves crucial for qualitative jump detection, enabling the system to adapt to regime changes that cause other models to fail. The complete mathematical formalization of all 33 principles is provided, with full reproducibility through the open‑source implementation. Environmental and economic advantages are equally striking: zero training cost, 0.001 kWh per prediction (vs 200 kWh for foundation models), zero carbon footprint (vs 100+ tons CO₂), and full interpretability through the 10 dialectical phases (τ₀–τ₉). This work constitutes the first large‑scale empirical validation of a dialectical logic system on real‑world time series data, demonstrating that philosophical principles can be mathematically formalized into predictive models that outperform state‑of‑the‑art machine learning architectures. Keywords: X‑GHLS; dialectical logic; COVID‑19 forecasting; time series analysis; XEPTQLRI index; 33 principles; phase transition detection; qualitative jump; Our World in Data Data Source: Our World in Data — COVID‑19 Greece DatasetCode Availability: Upon request for academic collaborationCorresponding Author: Katerina Xenopoulou (katerinaxenopoulou@gmail.com) 📊 Summary Table (for Abstract) Metric Value Comparison MAPE 1.69% 3.2% (TimesFM) R² 0.999 0.99 (Chronos‑2) Accuracy 98.31% 96.8% (TimesFM) Days Analyzed 1,674 — Predictions 1,667 — Crisis Phases (τ₅+) 1,212 days 72.7% of total 📊 KEY RESULTS Metric Value MAPE 1.69% R² 0.999 RMSE 120 cases Accuracy 98.31% Predictions 1,667 Time span 2020–2024 (1,674 days) 📈 GRAPHICAL RESULTS 1: COVID-19 Cases in Greece (2020–2024)] 2: Dialectical Phases (τ₀–τ₉) with XEPTQLRI Coloring] 3: XEPTQLRI Index with Phase Thresholds] 4: Actual vs Predicted Cases] 🏆 COMPARISON WITH STATE-OF-THE-ART MODELS (2026) Model MAPE Training Cost Energy / Prediction CO₂ Emissions Interpretability XENOPOULOS 1.69% €0 0.001 kWh 0 kg Full (33 principles) TimesFM (Google) ~3.2% €200,000+ 200 kWh 100+ tons Black box Chronos-2 ~3.5% €50,000+ 50 kWh 25 tons Black box TiRex ~3.8% €15,000+ 15 kWh 7.5 tons Limited Transformer ~4.2% €100,000+ 100 kWh 50 tons Black box LSTM ~5.8% €5,000+ 5 kWh 2.5 tons Limited ARIMA ~8.5% €0 0.001 kWh 0 kg Statistical 🔬 DETAILED ANALYSIS BY PHASE Phase Days Mean XEPTQLRI Mean Tension Confidence Description τ₀ 64 0.40 0.064 0.85 Stability τ₁ 35 1.23 0.153 0.85 Stability τ₂ 28 1.71 0.213 0.75 Pattern repetition τ₃ 14 2.88 0.360 0.65 Growing instability τ₄ 14 4.00 0.499 0.55 System saturation τ₅ 147 5.15 0.644 0.40 QUALITATIVE JUMP τ₆ 154 6.02 0.752 0.30 Paradoxical state τ₇ 462 7.06 0.883 0.20 Transcendence τ₈ 749 7.83 0.978 0.20 Transcendence Key observation: The pandemic was in crisis mode (τ₅ and above) for 1,212 days (72.7% of the total), explaining why conventional models struggled to adapt. 🌍 ENVIRONMENTAL & ECONOMIC IMPACT Model Training Cost CO₂ Emissions Equivalent XENOPOULOS €0 0 kg 0 flights TimesFM €200,000+ 100+ tons 200 flights Athens–London Chronos-2 €50,000+ 25 tons 50 flights LSTM €5,000+ 2.5 tons 5 flights 🎯 WHY THIS IS REVOLUTIONARY # Advantage XENOPOULOS Other Models 1 Accuracy 98.31% 91.5% – 96.8% 2 Training Cost €0 €5,000 – €200,000+ 3 Energy per Prediction 0.001 kWh 5 – 200 kWh 4 CO₂ Footprint 0 kg 2.5 – 100+ tons 5 Interpretability Full (33 principles) Black box / Limited 6 Phase Detection Yes (τ₀–τ₉) No 📖 THE 33 PRINCIPLES A. Dialectical Principles (1–4, 12, 16, 18, 26) # Principle 1 Synthesis of Formal and Dialectical Logic 2 Dialectical Contradiction as Creative Force 3 Dialectic of Stasis and Motion 4 Integration of Otherness 12 Dialectical Perception of Infinity 16 Logic of Process 18 Law of State Succession 26 The Concept of Aufhebung B. Theory of Knowledge (5–7, 13, 17, 19, 27, 28) # Principle 5 Historical-Genetic Approach 6 Dialectic of Theory and Practice 7 Transitional Nature of Truth 13 Genetic Logic 17 Restructuring of Dialectical Thought 19 Repetition and Historical Dialectic 27 Triple Coincidence (Sπ, Sα, f(x)) 28 Suszko Triad (L, B, Θ) C. Mathematical Formalization (21–25, 32) # Principle 21 The N[Fi(Gj)] Operator 22 INRC Group (Piaget) 23 XEPTQLRI Index 24 Ten Dialectical Stages (τ₀–τ₉) 25 Dubarle Operators (△, ▼, ▽, ▲) 32 Rogowski Np Operator D. Innovative Applications (8–11, 14–15, 20, 29–31) # Principle 8 Interdisciplinary Application of Dialectics 9 Synthesis of Unity and Differentiation 10 Transcendence of Static Logic 11 Dynamic Perception of Reality 14 Negation as Creative Force 15 Quantitative and Qualitative Change 20 Dual Nature of the "Now-Present" 29 Illusion of Stability 30 Application to Artificial Intelligence 31 Critical Transition Prediction E. The 33rd Principle – Advanced Dialectical Negation f(A) = -A · P · H · (1 + M) + ε Parameter Description A Dialectical tension (from thesis–antithesis conflict) P Predictive capacity of current phase H Historical memory (weight of previous predictions) M Transitional factor (proportional to XEPTQLRI) ε Stochastic noise (uncertainty modeling) 📊 THE XEPTQLRI INDEX AND PHASES τ₀–τ₉ Phase XEPTQLRI Range Description τ₀ < 0.8 Stability τ₁ 0.8 – 1.5 First deviation τ₂ 1.5 – 2.5 Pattern repetition τ₃ 2.5 – 3.5 Incompatibility τ₄ 3.5 – 4.5 System saturation τ₅ 4.5 – 5.5 Qualitative jump τ₆ 5.5 – 6.5 Paradox τ₇ 6.5 – 7.5 Transcendence τ₈ 7.5 – 8.5 Permanent dialectics τ₉ > 8.5 Absolute synthesis 🧠 INTERPRETATION OF RESULTS Feature Description Early phase change detection The system "knows" when it enters crisis mode (τ₅ and above) and adapts predictions accordingly Paradox management In phases τ₆–τ₈, where behavior becomes nonlinear, confidence decreases and stochastic factors increase Historical memory Parameter H in the 33rd Principle incorporates knowledge from previous predictions, creating dialectical learning 🔮 FUTURE DIRECTIONS Limitation Description Future Extension Phase boundaries Thresholds between phases are empirical Automatic phase boundary optimization Stochasticity Random noise introduces minor variability Advanced uncertainty modeling Generalization Tested mainly on COVID-19 data Multi-domain testing (finance, climate) 📜 SCIENTIFIC CONTRIBUTION # Contribution 1 Complete mathematical formalization of 33 philosophical principles into a functional predictive system 2 Introduction of the XEPTQLRI index as a measurable quantity of dialectical tension 3 Ten-phase typology (τ₀–τ₉) for describing system dynamics 4 The 33rd Principle as a qualitative jump operator 5 Proof that a philosophically grounded system can outperform statistical models with millions of parameters 💡 CONCLUSION Aspect XENOPOULOS Advantage Performance 98.31% accuracy — superior to all compared models Cost Zero training cost, runs on any computer Energy 0.001 kWh per prediction (vs 200 kWh) Environment Zero carbon footprint (vs 100+ tons CO₂) Transparency Full interpretability through 33 principles Philosophical foundation Dialectics meets computation — a paradigm shift 📥 CODE AVAILABILITY The system's source code is available upon request for academic collaboration.Please contact the author for further information. 🙏 ACKNOWLEDGMENTS This work is dedicated to the memory of my father, Epameinondas Xenopoulos, whose work Epistemology of Logic (1998, 2nd ed. 2024) provided the foundation for this entire endeavor. I warmly thank my family for their support, and my granddaughter who, at 9 years old, reminded me daily that dialectics is not theory but a way of life. 📚 REFERENCES # Reference 1 Xenopoulos, E. (2024). Epistemology of Logic (2nd ed.), https://www.researchgate.net/publication/359717578_Epistemology_of_Logic_Logic-Dialectic_or_Theory_of_Knowledge 2 Hegel, G.W.F. (1812). Science of Logic 3 Piaget, J.

Thomas Werner

Atoms and photons, two things so different but yet so alike. The former, the building block of matter, something we learn about in school and imagine it as some tiny marbles encircled by other tinier marbles. The latter, an electromagnetic wave, a light particle or an excitation of the electromagnetic field. Quantum mechanics tells us about the properties of these two entities. And even if it sounds, looks and writes counter-intuitive, it has proven right for over a century now. In this work, I elaborate on how we tested the laws of quantum mechanics and how we used them learn more about the tiny building blocks of nature and the fields they use to talk to each other. The atoms we use, are artificial. Superconducting qubits, small electrical circuits with quantized energy levels behave like electrons that transition between different orbitals in an atom. One of the qubits' advantages, is also a big disadvantage. We design the circuits' energy levels and fabricate them in a cleanroom. This allows for arbitrary spaced energy levels but in contrast to real atoms, prevents two superconducting qubits from being alike. Still, this qubit platform is one of the frontrunners for future quantum computing technology and testing fundamental physics due to their scalability. We interface superconducting qubits, which operate in the GHz regime, with microwave photons. We use 3D aluminum cavities as mediators between qubits and photons. The cavities allow for non-destructive readout of the qubit state, they shield the qubits from noise at the qubit frequency and they give us an easy way to frequency-tune these joint systems. We need to operate superconducting qubits and their cavities at millikelvin temperatures in dilution refrigerators. At higher temperatures, superconductivity suffers and even worse, the environment is filled with thermal noise photons. This poses a fundamental limitation on the scalability of superconducting qubit devices. Also connecting multiple devices in different fridges does not work over room temperature links because the microwave photons used for this purpose will be covered in noise and the quantum information they carry, will be unusable. Infrared photons do not suffer from this noise problem since there are close to zero thermal noise photons at their frequencies at room temperature. We cannot simply interface superconducting devices with optical photons due their frequency mismatch and the destructive effect of optical photons on superconductors. Therefore, we use microwave-to-optics transducers that allow to convert microwave photons into optical ones and vice-versa. The transducers that we use are macroscopic electro-optic transducers using the Pockels effect in a disk-shaped Lithium Niobate whispering gallery mode resonator. By using a strong optical pump, photons from the two frequency domains experience a beam-splitter interaction and get converted from one to the other. We measure the generated optical photons using elaborate optical setups, optical heterodyning and single photon detectors to gain knowledge about the qubit state or the converted microwave photons. Bridging the microwave and the optical world allows us to take advantage of both of their strengths but it also requires deep knowledge about both of their working principles. In this work, we describe two experiments that our group conducted to showcase the opportunities that arise from interfacing superconducting qubits with optical photons but also the pitfalls, one may encounter on the way. In the first experiment, we managed to all-optically read out a superconducting qubit. We show that the assignment fidelity, the probability that a measurement of the qubit state matches the prepared state, is close to equal for all-optical, microwave-to-optics and conventional microwave readout. We show T1 and T2 measurements for all three readout types and give an analysis of the noise caused by the optics. Finally, we show that the infrared light does not affect the qubit performance in a negative way but that the heating it causes does. This is an important insight that we used in the next experiment. The second experiment is the upconversion of itinerant single microwave photons to the optical domain. We show that we can generate single microwave photons from a qubit-cavity system. We upconvert these single photons, measure them with a single photon detector and reconstruct their shape. By conducting a single photon Rabi measurement, we show correlations between the microwave and the optical domain. And by thorough signal-to-noise measurements and noise analysis, we find that we can generate single infrared photons with high signal-to-noise ratio 5.1 and low transducer added noise (&lt;0.012 quanta). We show that this measurement creates a path towards entanglement of a superconducting qubit and an optical photon and what parameters need to be improved to achieve it. Additionally, this experiment is a proof of principle for an on-demand infrared single photon source. More generally, it allows to link microwave quantum technology in general to the optical domain.

Sohail Ahmed Fraz

No abstract is available for this record.

Alessio Emanuele Biondo, Mauro Gallegati

We develop an agent-based model in which inflation emerges from decentralized price-setting and credit-financed production in an endogenous-money economy. Firms operate under working-capital constraints, form market-based price expectations through heterogeneous adaptive learning, and set prices via cost-plus rules with endogenous mark-ups. Bank lending simultaneously creates deposits, while heterogeneous lending rates and credit rationing shape firms' financing costs and, through unit costs, their pricing decisions. The economy features interacting production and credit networks: intermediate-input linkages propagate cost shocks across supply chains, while bank--firm relationships transmit financial conditions across firms. The interaction of network-based pass-through, state-dependent pricing incentives, and evolving credit conditions generates inflationary regimes, including episodes driven by pricing cascades and feedback loops.

Ramesh Kasarla

Federated Learning (FL) has emerged as a transformative paradigm for distributed machine learning, enabling model training across decentralized edge devices while preserving data privacy. This methodology is critical for sectors handling sensitive information, such as finance, healthcare, and the Internet of Things (IoT). Despite its benefits, the coordination and communication overhead between distributed nodes remain significant challenges. This paper evaluates the efficacy of REST and GraphQL API architectures in facilitating FL workflows. While REST APIs are favored for their statelessness and simplicity, GraphQL offers enhanced flexibility and efficiency by enabling precise data fetching—a vital feature for bandwidth-constrained decentralized systems. We provide a comparative analysis of these paradigms across performance, security, and scalability metrics, specifically regarding data synchronization and model aggregation. Finally, we propose design best practices for developing APIs that support robust, compliant, and efficient federated prediction systems.

Oluwamayowa Oyelere, Taiwo Lasisi

No abstract is available for this record.

Marcel Osmond

The proliferation of agentic artificial intelligence systems—characterized by autonomous goal-seeking, tool use, and multi-agent coordination—presents unprecedented challenges to existing legal and financial regulatory frameworks. While traditional AI governance has focused on model-level alignment through training-time interventions such as Reinforcement Learning from Human Feedback (RLHF), the deployment of large language models (LLMs) as persistent agents embedded within socio-technical systems necessitates a paradigm shift toward institutional governance structures. This paper examines the intersection of agentic AI, Retrieval-Augmented Generation (RAG), and their implications for legal accountability and financial market integrity. Through a comprehensive analysis of the Institutional AI framework proposed by Pierucci et al. [1], we argue that alignment must be reconceptualized as a mechanism design problem involving runtime governance graphs, sanction functions, and observable behavioral constraints rather than internalized constitutional values. We address the critical deficit identified by LeCun regarding the absence of world models in current agents, demonstrating how RAG architectures function as externalized epistemic infrastructure that grounds agentic cognition in verifiable data repositories. The paper subsequently interrogates the legal implications of these systems under the European Union's Artificial Intelligence Act (EU AI Act) and the regulatory thresholds established by the Financial Conduct Authority (FCA) and European Central Bank (ECB), proposing justified compliance boundaries for high-risk financial applications. Furthermore, we acknowledge significant governance gaps within Decentralized Finance (DeFi) protocols where institutional oversight mechanisms face structural limitations. By synthesizing technical insights from multi-agent systems, constitutional AI limitations, and offensive security frameworks, this work advances a jurisprudential foundation for agentic AI that prioritizes defensible audit trails, incentive-compatible compliance, and systemic stability over opaque internal alignment guarantees. The analysis concludes that the future of AI governance lies not in perfecting isolated model behavior, but in architecting institutional environments where compliant behavior emerges as the dominant strategy through carefully calibrated payoff landscapes.

Johannes Stephanus Stapelberg

The South African energy market is undergoing a fundamental shift toward renewable energy integration. In response to supply constraints and the global focus on sustainability, Eskom has proposed and is in the process of developing the Virtual Wheeling system, enabling independent power producers (IPPs) to sell energy via energy buyers– intermediaries matching off-taker energy requirements with IPP capacity– to off-takers through Eskom's grid infrastructure. While this system presents significant opportunities to open the energy market, foster competition, and accelerate renewable energy adoption, it also introduces risks for off-takers. These risks stem from the requirement for off-takers to continue paying their traditional electricity bills while simultaneously settling accounts with IPPs for alternative energy supply. The refunding process, which offsets the off-takers' double payment, follows a sequential payment process: first, distributors– typically municipalities– settle their Eskom bill. Eskom then calculates refunds and allocates funds to energy buyers. Finally, energy buyers allocate refunds proportionally to each off-taker in its portfolio, and ultimately off-takers are reimbursed. Any default in this process could jeopardise the entire system, while delays or estimations in refund calculations could impose temporary financial burdens on off-takers, discouraging participation and limiting the overall success of the system. This study explores the potential of blockchain-based smart contracts to address off-taker risks by automating the reconciliation and settlement of energy transactions within the Virtual Wheeling system. A prototype smart contract is developed to automatically calculate fees for each stakeholder and allocate funds in a single transaction upon off-taker payment, streamlining the multistep refunding process. The proposed system not only mitigates inherent process risks, but also enhances efficiency, transparency and trust in the Virtual Wheeling system. The research methodology includes a risk assessment of the current Virtual Wheeling system, the design and development of a smart contract prototype and the evaluation of its effectiveness in mitigating identified risks. The findings indicate that blockchain-enabled automation could significantly reduce default risks, enhance cash flow certainty for off-takers, and improve overall trust in the Virtual Wheeling system. However, regulatory challenges, interoperability with legacy infrastructure and scalability considerations remain critical factors for widespread adoption. This study contributes to the growing body of research on blockchain applications in energy markets and provides practical insights into how decentralised technologies can improve financial resilience in billing and settlement processes.

Michael Aaron Russell

No abstract is available for this record.

Lu Liu

Smart contracts have become the backbone of decentralized ecosystems, managing billions of dollars in assets across applications ranging from Decentralized Finance (DeFi) to digital governance. Given the immutable and autonomous nature of blockchains, the security of these contracts is paramount. A single vulnerability can lead to catastrophic and irreversible financial losses. However, despite these high stakes, a significant gap exists in understanding how developers utilize exception-handling mechanisms to enforce correctness and the specific types of logic flaws that arise from their misuse. This thesis aims to enhance smart contract security through comprehensive studies, beginning with an empirical characterization of defensive programming practices, followed by a systematic analysis of associated faults, and finally, the proposal of a novel vulnerability detection framework. It consists of the following three studies. The first study focuses on the fundamental safeguards of contract logic: state-reverting state-ments (i.e., require, revert, and throw). While these statements serve as the principal mechanisms for exception handling in Solidity, there is a lack of empirical understanding regarding their prevalence and usage patterns in the wild. To address this, the study conducts the first empirical study across thousands of real-world contracts. The results reveal that these statements are pervasive, appearing even more frequently than general-purpose if statements. The analysis further demonstrates that developers primarily use these statements to perform seven types of authority verification and input validity checks. This study establishes an understanding of how developers intend to secure contract logic. The second study investigates the landscape of faults arising from the improper use of these state-reverting statements. Although developers rely on these statements for security, incorrect implementation results in subtle bugs that traditional testing often misses. To understand these failures and benchmark detection capabilities, this study constructs the first comprehensive dataset of 320 real-world faults, curated from open-source project histories and security audit reports Through manual analysis, the study derives a taxonomy of 17 distinct fault types and distills 12 common fixing strategies. A subsequent evaluation of 12 state-of-the-art security tools against this benchmark reveals an average detection rate of only 14.4%, highlighting that existing tools are ineffective at identifying these critical logic flaws. The third study addresses the limitations of existing approaches in identifying high-level semantic vulnerabilities, specifically Price Manipulation. As indicated by the second study, traditional tools struggle with logic flaws because they often lack the ability to interpret complex economic context. To bridge this gap, this study proposes PMDETECTOR, a hybrid framework designed to proactively detect price manipulation. The framework employs a three-stage pipeline to model economic semantics: (1) static taint analysis to identify potentially vulnerable paths, (2) a two-stage Large Language Model (LLM) analysis to filter effective defenses and simulate exploitation, and (3) a final static checker to validate findings. Evaluated on 73 vulnerable and 288 benign contracts, PMDETECTOR achieves up to 100% precision and 88% recall, with GPT-4o achieving a state-of-the-art F1-score of 0.91. Furthermore, in a large-scale scan of over 8,000 recently deployed contracts, it identified 4 previously unknown vulnerabilities, confirming its practical utility in securing the DeFi ecosystem. In summary, this thesis advances the field of smart contract security by bridging the gap be-tween empirical study and automated tool development. By characterizing defensive practices and investigating the limitations of existing security tools, this work paves the way for more effective detection methods. The proposed hybrid framework demonstrates that integrating static analysis with the semantic reasoning of LLMs can effectively identify complex semantic smart contract vulnerabilities, providing the community with insights and tools to safeguard decentralized applications.

Yau Yim Ching, LAM Ping Him

Digital transformation is reshaping healthcare systems worldwide, with nursing practice positioned at the forefront of technology-driven innovation. As nurses increasingly engage in data management, coordination of care, and decision-making across complex health systems, the need for secure, transparent, and trustworthy digital infrastructures has become paramount (Khezr et al., 2019). Among emerging technologies, blockchain, a decentralised and tamper-resistant ledger system, and smart contracts, which enable automated and verifiable transactions, present promising opportunities to enhance trust, accountability, and interoperability within nursing workflows (Khezr et al., 2019; Saeed et al., 2022). Smart contract’s core features: immutability, transparency, and decentralisation, make it particularly suited for addressing long-standing challenges in healthcare data management and nursing administration. In nursing contexts, potential applications include secure sharing of patient records, real-time tracking of clinical documentation, automated credential verification, and protection of consent and privacy (Kuo et al., 2017; Naresh et al., 2025). It may further facilitate process automation in areas such as nursing resource allocation, performance auditing, and continuing education accreditation. Despite its promise, the adoption of blockchain technology in nursing remains in its infancy. Most studies have centered on technical or conceptual models rather than empirical evaluations, and few have examined the direct or indirect impacts on nursing efficiency, patient safety, or care coordination (Hasselgren et al., 2020). Implementation challenges, such as scalability, interoperability with existing hospital information systems, regulatory ambiguity, and user acceptance also persist (Saeed et al., 2022). Moreover, the ethical implications related to data ownership and governance in decentralised environments warrant careful consideration in nursing settings. Given these gaps, a scoping review is needed to synthesise current evidence on the applications and impacts of blockchain and smart contract technologies in nursing practice. This review will explore their roles across nursing service delivery, management, and education, with particular attention to how these technologies influence efficiency, trust, accountability, and data security. By consolidating interdisciplinary insights, this study seeks to guide future research and inform policy and practice frameworks for integrating blockchain and smart contract technologies into the nursing profession.

Yinhao Xiao, Chih-Chung Liu, Mingshu Cong, Le Yang

No abstract is available for this record.

G sowmya, Dr.R. Sridevi

No abstract is available for this record.

Samuel Fahim

This paper studies whether fast-settlement payment layers can replace secure baselayer blockchains in a search-theoretic monetary model. The Chain provides secure but costly and probabilistic settlement, while the Network provides instant, cost-free payments but exposes users to cyberattacks and requires sellers to incur adoption costs. In the Chain-only benchmark, buyers choose settlement intensity after bargaining. Because they do not internalize the full trade surplus, settlement intensity is inefficiently low, reducing trade efficiency and weakening the monetary value of tokens. Introducing the Network generates multiple payment equilibria. Under exogenous cyberattack risk, Chain and Network payments may coexist: the Network provides fast settlement and fallback liquidity when Chain settlement fails, while the Chain remains valuable for its security and universal acceptance. If cyberattack risk is sufficiently low, pure Network payments can arise, although pure Chain payments may also persist because Network acceptance is costly for sellers. When cyberattack risk is endogenous, broader Network adoption increases exposed balances and strengthens hackers’ incentives. This security externality weakens the Network’s value as fallback liquidity and eliminates the pure Network-payment equilibrium. The Chain, therefore, survives as a secure settlement anchor. The welfare analysis shows that Network adoption is not always welfare improving: its payment-efficiency gains must outweigh seller adoption costs and, under endogenous attacks, the resource costs of hacking. Fast-settlement layers can improve payment efficiency, but they do not generically replace secure base-layer settlement.

Arnita Sur

The cost of convenience. Cryptocurrencies are becoming more and more ubiquitous in the financial markets but have also become a basis for all crimes. It would be quite interesting to note this work follows a research field that focuses on crimes relating to cryptocurrencies and, more particularly, market integrity and investor trust implications. We examine how common types of offenses, such as fraud, money laundering and hacking, are presented in practice and consider practical examples which illustrate how strategies associated with cybercrime are constantly evolving. An evaluation of the degree of response from regulators and the effectiveness of measures already in place is used to provide a spotlight into the challenges experienced by the law enforcement and policymakers. We’ll plead for effective cooperation concerning advancements of the technology, frameworks of legislation, and awareness by the public for enhancing security in the cryptocurrency market. From this in-depth analysis, we hope people will become more sensitive to possible risks in using digital currencies and push harder for stricter safeguards for investors and the entire financial system. DOI - https://doi.org/10.65525/SVUP.9788199651524.2026.95-105

Arnita Sur

The emergence of cryptocurrencies has introduced significant shifts in the global financial landscape, and India is no exception. This research paper examines the impact of cryptocurrency adoption on the Indian economy, focusing on three primary dimensions: economic growth, financial inclusion, and regulatory challenges. Through a comprehensive analysis of market trends, policy developments, and case studies, the paper reveals cryptocurrencies have the potential to stimulate economic growth by fostering is dual-faceted, as they also pose risks related to market volatility, financial stability, and regulatory uncertainty. The study further explores how cryptocurrencies can enhance financial inclusion by providing alternative financial services to underserved populations but also highlights the challenges in integrating these digital assets into the existing financial system. By evaluating both the opportunities and risks associated with cryptocurrency adoption, the paper offers policy recommendations aimed at harnessing the benefits while mitigating potential downsides. The findings underscore the need for a balanced approach in formulating regulations that support innovation while ensuring economic stability and investor protection. DOI - https://doi.org/10.65525/SVUP.9788199651593.2025.90-105

Giovanna MASSAROTTO

As of February 2026, the cryptocurrency market capitalization has reached approximately $2.5 trillion. Blockchain technology underlying cryptocurrencies promises a decentralized financial system as an alternative to the traditional centralized banking system, which failed systemically in 2008. Several studies demonstrate that blockchain systems are subject to collusion related to issues of centralization and security. If enough participants in the blockchain network collude the entire system and cryptocurrency application is compromised. Collusion is the most serious antitrust conduct as it can lead to price fixing in a way that harms consumers. However, only a few antitrust cases have been brought in the context of cryptocurrencies to address collusion and with little success. Is antitrust the wrong legal tool to address collusion in blockchain or does collusion have a different meaning in antitrust law and cryptocurrencies? To address this question this paper provides a roadmap of collusion across legal, economic, and computer science perspectives, laying out a framework of algorithmic collusion. It applies this framework in past antitrust cases regarding collusion in cryptocurrencies to guide courts and antitrust regulators in prosecuting collusion in a blockchain framework. It shows how conceiving collusion as a problem of centralization and security diverges from the traditional antitrust framework, which defines and prosecutes collusion in terms of agreements in restraint of trade. This article makes three important contributions to the contemporary literature. First, it examines the collusion problem and elements from an antitrust and cryptocurrency perspective. Second, it identifies similarities and differences between the legal, economic and computer science definition of collusion by providing a comprehensive framework of algorithmic collusion in cryptocurrencies. Third, it applies this framework to some antitrust cases in the context of cryptocurrencies reflecting on how collusion could be prosecuted more effectively in a blockchain context. Collusion has a different meaning in cryptocurrencies and antitrust. However, in both contexts, collusion constrains economic freedom, suggesting that the protection of economic freedom should inform cryptocurrency antitrust enforcement

David Krause

No abstract is available for this record.