Efficiently matching patients to clinical trials is essential for advancing medical research and ensuring reliable outcomes. However, current matching methods face several challenges. These include data integrity issues from tampered records, privacy risks caused by weak anonymization, and manual processes that delay recruitment. In addition, centralized systems lack transparency, expose sensitive patient data to security vulnerabilities, and suffer from single points of failure that reduce resilience and trust. In this paper, we propose a blockchain and Large Language Models (LLMs)-driven solution for secure, trustworthy, traceable, decentralized, and transparent patientâclinical trial matching. Blockchain ensures data integrity, security, and transparency by eliminating single points of failure and enabling tamper-proof records. LLMs enhance patientâtrial matching by automating the interpretation of complex eligibility criteria, improving accuracy, and significantly reducing the time required for manual review. Our approach uses Ethereum-based smart contracts to automate workflows such as trial registration, eligibility assessment, and consent tracking. We fine-tune GPT-4, T5, and Gemini on synthetic data derived from real clinical trial records and employ majority voting to ensure consistent and unbiased eligibility decisions. A prototype Gradio interface was developed as a minimum viable product (MVP) to demonstrate seamless interaction between LLMs and smart contracts. Performance evaluation based on accuracy (0.800), precision (0.733), recall (1.000), and F1-score (0.846) demonstrates reliable eligibility prediction. Cost analysis confirms affordability, and security evaluation verifies resilience against known threats. Comparison with existing solutions highlights the frameworkâs advantages in transparency, trust, and automation. The smart contract code is publicly available on GitHub.
Current commercial Large Language Model (LLM) architectures enforce a "server-side memory" paradigm, where user cognitive state is stored, managed, and monetized by the provider. This centralization creates two critical vulnerabilities: the economic inefficiency of "token inflation" (re-processing redundant context) and the epistemological risk of "rented cognition" (lack of user sovereignty over identity). This paper proposes a disruptive architectural shift: Remember Me AI, formally defined as the Client-Side Narrative Protocol (CSNP). By integrating Cross-Session Narrative Memory (CSNM) with a novel Semantic Compression Layer and Distributed Local Storage, we demonstrate a mechanism to reduce context token costs by approximately 40x while maintaining longitudinal coherence. We argue that this architecture commoditizes the inference layer, forcing a market transition from "Memory-as-a-Service" to "Compute-as-a-Commodity." This restores epistemological sovereignty to the user and neutralizes the lock-in mechanisms of hyperscale providers. The protocol includes Merkle-CRDT synchronization for multi-device consistency, Zero-Knowledge Safety Proofs for regulatory compliance, and a Polyglot Transpiler to ensure interoperability across proprietary model endpoints.
The fast digitalization of contemporary society has changed the data into a valuable resource, and it has been the key to the innovation in the financial sector, healthcare, politics, and industries, and it has also increased risks both in terms of misusing it, stealing it, and using it. Information security through maintaining confidentiality, integrity and availability of information has thus become a pre-requisite to trust in digital infrastructures. The present paper gives a detailed discussion of how cryptography, cybersecurity, and data privacy come into convergence and have a central role to play in protecting the digital ecosystems. Basic cryptographic primitives such as symmetric and asymmetric encryption, hash functions as well as digital signatures are discussed as the foundation of secure communication. With these, more complex privacy-sensitive technology like homomorphic encryption, zero-knowledge proofs, and differential privacy is discussed as technology that could offer the opportunity to perform safe computation and share data without jeopardizing the privacy of individuals.The paper also explores the disruptive potential of quantum computing, specifically how it can render the popular public-key systems insecure by figuring out ways to break them, e.g. the Shor algorithm, and assesses the new paradigm of post-quantum cryptography as a reaction to this existential risk. The examples are discussed within various fields such as secure communication schemes, data-at-rest security, cloud computing, and the Internet of things (IoT), e.g., in which cryptographic efficiency and versatility are most crucial. It is a synthesis of these factors that the paper highlights that cryptography is not only a technical protection but it is a cornerstone enabling resiliency, trust, and privacy-by-design in the digital era. This paper then ends with a discussion on the challenges that still need to be tackled, including scalability, usability and compliance with regulations, and how future research will be needed to define the future of secure and privacy-preserving technologies in the increasingly interconnected world.
We present a game semantics framework for open-world safety analysis of Ethereum smart contracts. We model the interaction between a contract and its environment as a two-player game between the contract and the environment, and prove up to gas model approximations soundness: every assertion violation found corresponds to a real execution; and completeness: every open-world execution is captured. To our knowledge, this provides the first formal open-world interaction semantics for Ethereum smart contracts with mathematical guarantees of soundness and completeness. We implement this framework in YulTracer, an assertion reachability tool for real-world Solidity contracts, built on Yul, the intermediate language of the Solidity compiler. YulTracer uses concrete execution and exhaustively explores game traces within user-specified bounds. We evaluate it on reentrancy benchmarks, where YulTracer achieves 100% recall and precision -- the only tool to do so from those we examined -- and on two large real-world exploits (the DAO and PredyPool), where it detects the known vulnerabilities and produces no false positives on fixed versions. To our knowledge, YulTracer is the first tool to achieve this level of precision on real-world contracts without false positives. We additionally demonstrate generality of the approach via the examination of access control benchmarks.
This edition advances our scholarly mission to explore how frontier technologiesâranging from artificial intelligence, blockchain, tokenization, digital identity systems, and decentralized finance to advanced econometric modelingâare reshaping global financial ecosystems while addressing pressing social, economic, and environmental challenges. Building upon the intellectual foundation established in previous issues, this volume brings together empirically rigorous and conceptually innovative contributions that illuminate the dynamic interplay between digital transformation, ethical governance, institutional capacity, and sustainable development. The manuscripts featured in this issue employ a wide spectrum of analytical methods, including bibliometric mapping, qualitative case study design, and ARDL cointegration modeling, enriching our understanding of how next-generation financial technologies influence real-world socioeconomic outcomes.
The fast pace of development of cryptocurrency markets challenges classical financial theories, highlighting the importance of investor psychology and sentiment in shaping the dynamics of prices and volatility. In sharp contrast to traditional assets, the cryptoverse is also far more driven by behavioral factors with market action frequently a result of sentiment, cognitive bias and social media than fundamentals. This study examines the intersection of behavioral finance and cryptocurrency investments, and specifically how investor sentiment affects police uncertainty phenomenon, is examined on already established and emerging markets. Using a literature-based integrative review approach, we integrate empirical and theoretical research between 2017 and 2025 from peer-reviewed sources in Scopus, ScienceDirect, JSTOR, SSRN, and Google Scholar. The review also identifies behavioural patterns that are applied again and again, such as overconfidence, herding, anchoring, and loss aversion, and looks at how they manifest in the world of crypto. It is also assessing more sentiment proxiesâsuch as Google Trends, Twitter activity, and Reddit threadsâportraying their predictive link to price volatility and trading volume. The results confirm the inefficient property of the Cryptocurrency market and also justify the relevance of behavioral finance in decentralized sentiment-sensitive markets. The paper makes both theoretical contributions by enabling the application of sentiment analysis to blockchain based assets, and practical proposals to investors, regulators, and fintech developers. Highlighting the importance of hybrids, the study argues that behaviorally driven sentiment analysis, as well as artificial intelligence (AI) driven sentiment models should be integrated into market governance frameworks. The results confirm the inefficient property of the Cryptocurrency market and also justify the relevance of behavioral finance in decentralized sentiment-sensitive markets. The paper makes both theoretical contributions by enabling the application of sentiment analysis to blockchain based assets, and practical proposals to investors, regulators, and fintech developers. Highlighting the importance of hybrids, the study argues that behaviorally driven sentiment analysis, as well as artificial intelligence (AI) driven sentiment models should be integrated into market governance frameworks.
This paper aims to carry out a systematic study on the application of blockchain technology in the field of Accounts Receivable Financing (ARF). The report first peels apart the main pain points of the traditional ARF model (factoring) from the aspect of information asymmetry, transmission of credit and confirmation of rights. Then the report does a thorough analysis on how blockchain technology (especially the characteristics of unchangeable nature, smart contracts, and asset digitization) theoretically solves these pain points, emphasizing the elaboration on the realization path of "penetration of credit". The core of this report is the in-depth study and comparison of four important cases of significance, namely, the "Dual-Chain Connect" of Ant Group, "Yi Enterprise Chain" (YQLink) of Ping An OneConnect, WeBank (based on FISCO BCOS), and "Jing Bao Bei" of JD Technology. Through the comparison of these cases in terms of their business models, technical architecture and risk control mechanism, this report summarizes three mainstream realization mode: "central enterprise-led", "fintech platform-led", and "(digital) bank-led", and reveals their basic difference regarding "source of credit". Finally, the study talks about the common problems confronting the field, such as data silo, interoperability and regulatory uncertainty, and gives its optimistic outlook regarding its future trends of integration into Artificial Intelligence (AI), Internet of Things (IoT), and evolution towards Decentralized Finance (DeFi).
The rapid expansion of blockchain-based governance has introduced new models for managing philanthropic resources, including the use of Decentralized Autonomous Organizations (DAOs) to distribute infaq and sadaqah. The decentralization of decision-making and the automation of financial flows offer potential solutions to long-standing challenges such as transparency deficits, centralization of authority, and inefficiencies in conventional charity systems. The research is motivated by the growing need to evaluate whether DAOs can enhance accountability, inclusivity, and equitable resource distribution within Islamic social finance. The study aims to assess the effectiveness of DAO-driven mechanisms in managing infaq and sadaqah, particularly their capacity to ensure fair participation, minimize human bias, and strengthen social justice outcomes. The objectives include analyzing governance structures, technological capabilities, and ethical implications of DAO integration. A mixed-methods design was adopted, combining blockchain system analysis, interviews with Islamic finance experts, and simulation of DAO-based distribution models. The study also employed comparative evaluation between traditional centralized charity workflows and smart contractâenabled automation. The results indicate that DAO-based models significantly improve transparency, reduce administrative delays, and enhance participatory decision-making among community members. Smart contracts ensure tamper-proof recordkeeping, while tokenized voting systems strengthen collective oversight and reduce elite capture. The research concludes that DAOs offer a promising pathway for decentralized social justice by aligning technological decentralization with Islamic ethical principles of fairness, trust, and communal responsibility. Strengthening digital literacy, enhancing regulatory clarity, and developing Shariah-compliant smart contract standards are essential for sustainable implementation.
Decentralization is widely promoted as a governance reform to improve efficiency, accountability, and responsiveness in public service delivery, particularly in the health sector. In Pakistan, the 18th Constitutional Amendment of 2010 marked a significant shift in governance by devolving health sector responsibilities from the federal government to provincial governments. This reform substantially transformed Public Financial Management (PFM) arrangements in the health sector, affecting budgeting, expenditure control, and accountability mechanisms. This paper examines the impact of decentralization on health sector PFM in Pakistan by situating the reform within broader theoretical and empirical literature. It analyzes changes in health financing, governance, and service delivery outcomes while identifying persistent institutional and fiscal challenges. The study argues that although decentralization has enhanced provincial autonomy and policy responsiveness, weaknesses in public financial management capacity, coordination, and equity continue to limit its effectiveness. Strengthening PFM systems is therefore essential for translating decentralized governance into improved health outcomes in Pakistan.
Introduction Over the past two decades, Sub-Saharan Africa has achieved remarkable progress toward the UNAIDS 95-95-95 targets through sustained donor investment, community leadership, and political commitment. However, in early 2025, abrupt funding contractions including the suspension of PEPFAR disbursements by the United States and significant cuts by other major donors, threaten to reverse gains in HIV diagnosis, treatment initiation, and viral suppression. This study examines the potential impact of these funding shifts on the HIV response and explores strategies to sustain progress in a changing financing landscape. Methodology This review employed a structured narrative synthesis approach. A comprehensive search was conducted across peer-reviewed journals, grey literature, and institutional reports published between 2020 and 2025. Databases searched were PubMed, Google Scholar, and institutional repositories of UNAIDS, PEPFAR, USAID, and the Global Fund, using terms such as âHIV/AIDS,â âSub-Saharan Africa,â â95-95-95 targets,â âdonor funding cuts,â and âhealth system resilience.â Of 99 records identified, 15 articles and reports met inclusion criteria. Data were thematically analyzed along the three pillars of the 95-95-95 framework, emphasizing health system resilience, equity, and sustainability. Findings Funding cuts have led to immediate service delivery challenges. HIV testing programs in East and Southern Africa report supply chain interruptions, staff shortages, and reduced outreach, particularly in marginalized communities. ART initiation has slowed due to clinic budget constraints and inadequate safety nets, leading to declines in patient retention and treatment uptake. Viral load monitoring systems are increasingly strained, with insufficient resources for reagents, equipment, and logistics. These disruptions are projected to cause a sixfold increase in new infections and a surge in AIDS-related mortality by 2029 if unaddressed. Furthermore, funding disparities are exacerbating inequities, with countries like Botswana and Eswatini maintaining progress due to better ability to absorb shocks, while conflict-affected and resource-poor regions face greater setbacks. Discussion The donor funding shortfall presents both a crisis and an opportunity. Immediate mitigation requires tapping emergency funds, reprogramming health budgets, and negotiating bridge financing with bilateral and multilateral partners. Long-term sustainability hinges on strengthening domestic resource mobilization through health levies, sin taxes, and diaspora bonds, integrating HIV services into primary healthcare, and scaling digital and community-led service delivery platforms for decentralized adherence support. Geospatial targeting and real-time data systems can optimize resource allocation to emerging hotspots. By fostering regional solidarity and community-driven financing, Sub-Saharan Africa can convert this funding crisis into an opportunity for resilient, locally owned HIV responses that keep the path to ending AIDS within reach.
This study examined the role of visual design in Non-Fungible Tokens (NFTs) as a strategic component of value creation and inclusive participation within the decentralized creative economy. The research addressed the growing need to understand how visuals influence identity, accessibility, and community-driven economic engagement in digital platforms. The study was conducted using a qualitative interpretive approach, employing visual rhetorical analysis and digital ethnography. Ten top NFT collections on the OpenSea marketplace were analyzed based on their visual characteristics, creator inclusion, and community interaction. The visual elements were assessed through rhetorical lenses ethos, pathos, and logos, while participation was observed through social media metrics and community discourse. The findings indicated that accessible and culturally resonant visual designs enhanced user engagement and contributed to broader economic inclusion. Collections with visually open, emotionally engaging, and culturally inclusive features attracted stronger community involvement and sustained market relevance. In contrast, NFTs with elitist or exclusive visual narratives demonstrated more limited participation and accessibility. The study concluded that visual identity plays a central role in shaping the success of NFTs not only as aesthetic artifacts but as instruments of economic democratization. It was recommended that future research explore algorithmic visual production and long-term community dynamics across diverse cultural contexts. This research highlighted the potential of visual design to foster equity, representation, and sustainable value in the evolving digital economy.
We frame Ethereum transactions reverted by invariants-require(<invariant>)/ assert(<invariant>)/if (<invariant>) revert statements in the contract implementation-as a positive signal of active on-chain defenses. Despite their value, the defensive patterns in these transactions remain undiscovered and underutilized in security research. We present Raven, a framework that aligns reverted transactions to the invariant causing the reversion in the smart contract source code, embeds these invariants using our BERT-based fine-tuned model, and clusters them by semantic intent to mine defensive invariant categories on Ethereum. Evaluated on a sample of 20,000 reverted transactions, Raven achieves cohesive and meaningful clusters of transaction-reverting invariants. Manual expert review of the mined 19 semantic clusters uncovers six new invariant categories absent from existing invariant catalogs, including feature toggles, replay prevention, proof/signature verification, counters, caller-provided slippage thresholds, and allow/ban/bot lists. To demonstrate the practical utility of this invariant catalog mining pipeline, we conduct a case study using one of the newly discovered invariant categories as a fuzzing oracle to detect vulnerabilities in a real-world attack. Raven thus can map Ethereum's successful defenses. These invariant categories enable security researchers to develop analysis tools based on data-driven security oracles extracted from the smart contracts' working defenses.
Modern cloud-based AI training relies on extensive telemetry and logs to ensure accountability. While these audit trails enable retrospective inspection, they struggle to address the inherent non-determinism of deep learning. Stochastic operations, such as dropout, create an ambiguity surface where attackers can mask malicious manipulations as natural random variance, granting them plausible deniability. Consequently, existing logging mechanisms cannot verify whether stochastic values were generated and applied honestly without exposing sensitive training data. To close this integrity gap, we introduce Verifiable Dropout, a privacy-preserving mechanism based on zero-knowledge proofs. We treat stochasticity not as an excuse but as a verifiable claim. Our approach binds dropout masks to a deterministic, cryptographically verifiable seed and proves the correct execution of the dropout operation. This design enables users to audit the integrity of stochastic training steps post-hoc, ensuring that randomness was neither biased nor cherry-picked, while strictly preserving the confidentiality of the model and data.
Temporal-Angular Quantum Addressing (TAQA) specifies a practical coordination layer for distributed quantum systems that operationalizes cycle-anchored phase-window execution. TAQA is designed for architectures where long-horizon absolute timestamp synchronization cannot be guaranteed and where continuous external timing infrastructure (GNSS, dedicated timing links, etc.) is undesirable, unavailable, or untrusted. Core idea Instead of scheduling actions at an absolute time, TAQA schedules actions by phase conditions on a shared cyclic phase convention \( \phi(t)\in[0,1)\cong \mathbb{S}^1 \) together with an explicit cycle index. Nodes execute when their locally estimated phase enters an agreed wrap-around-safe acceptance window within the intended cycle. This avoids âsame phase / wrong cycleâ ambiguity and supports deterministic coordination under explicit short-horizon error assumptions. What TAQA defines TAQA defines how to express and execute distributed quantum-network actions using classical metadata: Execution primitive (Q-Address style): TAQA expresses each executable action as a macro window + micro slot instruction. The macro window encodes the intended cycle and phase acceptance window; the micro slot provides local sequencing/offset ordering within that window using local hardware timing. Tick-canonical semantics: For interoperability and verification, TAQA adopts fixed-point ticks (integers) as canonical semantics (no floating-point boundary checks). Human-facing displays (HS degrees, HS index, SWT labels, etc.) are derived-only and must not be used for verification or boundary gating. Cycle anchoring: Every executable instruction is explicitly anchored to an intended cycle index to prevent ambiguous interpretation across repeated cycles. Optional audit hook: TAQA supports an optional post-execution signed audit receipt (TSAE-style) using the same tick-canonical context fields, suitable for optional anchoring (e.g., a ledger/Clockchain pattern). What TAQA does NOT define TAQA is a control-plane / metadata layer and does not modify quantum mechanics: It does not introduce a quantum time operator and does not change the Hilbert space. It does not define bootstrapping or clock-parameter estimation algorithms (offset/drift). These are handled by external initialization/tracking layers (e.g., bootstrapping protocols). It does not define cryptographic primitives or threat models. Security is defined by external, versioned security profiles. Applications enabled by TAQA TAQA provides a deterministic coordination layer for common distributed-quantum workflows, including: Phase-aligned distributed gate execution: remote node actions are triggered in the same cycle-anchored window; micro timing is local. Entanglement distribution scheduling: photon emission windows and BSM windows can be scheduled to coincide without continuous absolute-time synchronization. Temporal routing labels: cycle-anchored contexts can be used as temporal labels for routing, prioritization, and scheduling in repeater networks and distributed workflows. Security model (plug-in interface) TAQA treats Timeverse/Q-Address/TSAE fields as public context (not secrets). Security (signatures, nonce policy, anti-replay rules, canonical encoding, algorithm suites) is provided by an external Security Profile selected via a suite identifier (e.g., security_profile_id). TAQA fields may be bound as associated data (domain separation), but confidentiality and integrity are provided by the security layer. Normative dependencies (DOIs) TAQA is interoperable by construction and relies on the following published normative specifications: Phase-Coordination Series Conventions:https://doi.org/10.5281/zenodo.18068999 Q-Address: Macro Phase + Micro Slot:https://doi.org/10.5281/zenodo.18068997 Timeverse Security Profile:https://doi.org/10.5281/zenodo.18069423 Related context Theorem of Temporal Resolution Limitation and the Phase-Coordination Principle (v1.1):https://doi.org/10.5281/zenodo.17955430 Quantum Bootstrapping Protocol (QBP) v1.2:https://doi.org/10.5281/zenodo.18064435 Keywords: TAQA, distributed quantum computing, quantum networks, phase coordination, phase windows, cycle anchoring, Q-Address, ticks, interoperability, control plane, audit receipts, security profiles.
Objective : to critically analyze the possibility of extending the existing spatial criminal law principles to acts committed in the decentralized virtual worlds of the metaverse, and to develop proposals that include updating the approach to establishing jurisdiction over such virtual crimes. Methods : the methodological basis of the research is a set of general scientific methods and approaches of scientific cognition â dialectical, formal logical (analysis and synthesis, induction and deduction), systematic, as well as private scientific methods â formal legal, legal modeling, interpretation. The study relies on an analysis of judicial practice, foreign legislation, technical features of blockchain technologies and decentralized autonomous organizations, which makes it possible to identify gaps in legal regulation and propose conceptually new solutions for determining the crime scene in a virtual environment. Results : the study revealed a limited implementation of the current generally accepted principles of determining jurisdiction in relation to virtual crimes that do not have physical coordinates. The proposed multifactorial jurisdiction model redefines the âcrime sceneâ taking into account factors such as the offenderâs digital identity, the nature and location of digital assets, platform management protocols, and the actual damage caused. Assumingly, the immutable and verifiable nature of blockchain transactions can serve as a legal equivalent of a physical presence to establish personal jurisdiction, allowing criminal prosecution to be initiated even in cases where the actual location of the offender remains unknown. Scientific novelty : the paper presents an approach that implies the fundamental transformation of reactive, adaptive legal regulation principles into a proactive, comprehensive framework designed specifically for the unique challenges of the metaverse. A paradigm-changing hypothesis was put forward: that a permanent (stable) digital footprint of the offender in virtual spaces can serve to exercise jurisdiction. The model systematically presents the idea of harm as the most important link between virtual offenses and their consequences in the real world. Practical significance : it is currently impossible to apply legal norms and rules to relations in the metaverse, taking into account their specifics. The main provisions and conclusions of the study can be used to improve the mechanisms of legal regulation of the metaverse and to form international protocols on data exchange and mutual legal assistance for searching and collecting evidence based on blockchain technology. They may help to develop legislative initiatives aimed at creating integrated legal mechanisms that are scalable and resistant to rapid technological changes, characteristic for the digital environment.
Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs...
Bitcoin price forecasting is characterized by extreme volatility and non-stationarity, often defying traditional univariate time-series models over long horizons. This paper addresses a critical gap by integrating Global M2 Liquidity, aggregated from 18 major economies, as a leading exogenous variable with a 12-week lag structure. Using the TimeXer architecture, we compare a liquidity-conditioned forecasting model (TimeXer-Exog) against state-of-the-art benchmarks including LSTM, N-BEATS, PatchTST, and a standard univariate TimeXer. Experiments conducted on daily Bitcoin price data from January 2020 to August 2025 demonstrate that explicit macroeconomic conditioning significantly stabilizes long-horizon forecasts. At a 70-day forecast horizon, the proposed TimeXer-Exog model achieves a mean squared error (MSE) 1.08e8, outperforming the univariate TimeXer baseline by over 89 percent. These results highlight that conditioning deep learning models on global liquidity provides substantial improvements in long-horizon Bitcoin price forecasting.
Transaction fee plays an important role in determining the priority of transaction processing in public blockchain systems. Owing to the observability of unconfirmed transactions, a strategic user can postpone his transaction broadcasting time and set a fee as low as possible by prying into his mempool that stores them. However, the stochastic mining interval may cause the delayed transaction to miss the next valid block. Meanwhile, a new feature (i.e. fee bumping) emerges that allows each user to increase his transaction fee before confirmation, making the fee setting more challenging. In this paper, we investigate a novel transaction policy from the perspective of a single strategic user that determines the broadcasting time and the transaction fee simultaneously. Two representative scenarios are considered, in which a number of coexisting ordinary users are mempool-oblivious that set their fees according to certain distribution, and are semi-strategic that check their mempools at a Poisson rate and update their fees. In the former, we compute the optimal broadcasting time and transaction fee that adapts to the arbitrary distribution of mining interval. When the block interval is exponentially distributed in Bitcoin-like PoW systems, the strategic user needs to broadcast his transaction immediately after its creation. And when the block interval is fixed in Ethereum-like PoS systems, he finds it profitable to wait until the last moment before block generation. In the latter, we formulate a continuous-time Markov chain to characterize the dynamics of mempool states, and derive the optimal fee adjusting frequency of the strategic user when the block interval is exponentially distributed. In both theory and simulations, we show that this strategic user should immediately increase his fee whenever it falls behind the minimum fee of being included.
ABSTRACT This paper examines the impact of regulatory controls on Bitcoin's excess returns and volatility. The paper innovates by proxying changes in the regulatory environment using global Google search volume intensity data. The generated regulatory indices accurately identify episodes of regulatory tightening within cryptocurrency markets. A threeâfactor modelâincorporating market, momentum, and size factorsâis employed to evaluate the effects of regulation on Bitcoin returns. The study also assesses the influence of changes in the regulatory environment on volatility using additional controls. Findings reveal that increased regulation significantly reduces monthly Bitcoin returns and increases return volatility. These effects are both statistically and economically significant, robust across multiple proxies for regulatory activity, and persist even when accounting for the effects of the COVIDâ19 pandemic. The results highlight the real regulatory risks associated with Bitcoin investments, particularly for riskâaverse investors, and underscore the importance of policy developments in shaping cryptocurrency market dynamics.
Traditional lucky draw systems often face trust issues due to a lack of transparency and potential for manipulation. This study addresses these challenges by developing and evaluating a blockchain-based lucky draw prototype named "LuckyDraw." A mixed-method approach was employed, combining an applied research method with the Agile Scrum framework for system development, and a quantitative survey to evaluate user acceptance. The quantitative analysis, using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique on data from 100 respondents, confirmed the instrument's validity and reliability. The results showed that Perceived Usefulness (PU) was the strongest predictor of Behavioral Intention (BIU), followed by Trust (TRT). Furthermore, Perceived Ease of Use (PEOU) had a significant positive effect on PU. These findings indicate that a transparent, trustworthy, and easy-to-use system is highly accepted by users, offering a viable solution to the shortcomings of traditional systems.
The rapid growth of the digital economy has brought unprecedented advantages, enabling seamless transactions, real-time data exchange and global connectivity for the businesses. However, this digital expansion has also exposed businesses, governments and individuals to an evolving landscape of cyber threats. Traditional cybersecurity frameworks which rely heavily on centralized models are increasingly proving inadequate in the face of sophisticated cyber intrusions. Blockchain technology is a decentralized, cryptographically secure and immutable ledger system that introduces an innovative approach to cybersecurity. This research article examines the role of blockchain technology in enhancing cybersecurity, discussing its capabilities in securing online transactions, ensuring data integrity, preventing cyber threats and facilitating a proactive security mechanism against cyberattacks for businesses by integrating the CHIPS framework (ConnectâHarnessâInnovateâProtectâSustain). This framework emphasizes the ability of blockchain to Connect stakeholders via trust less networks, Harness distributed ledgers to ensure data integrity, Innovate mechanisms for secure transactions, Protect digital assets with tamper resistant architectures and Sustain long term cyber resilience through scalable and adaptable systems. This study also highlights the effectiveness of this integration in securing digital transactions, thwarting cyberattacks and facilitating proactive cybersecurity strategies in business operations.