Abu Dhabi University

Abstract Decentralized blockchain oracles are critical for bridging on-chain smart contracts with off-chain real-world data. However, existing reputation systems often rely on static cumulative profiling, leading to a phenomenon we define as Reputation Inertia. In this state, an oracle's accumulated historical honesty acts as a buffer that masks potential malicious behavior (Whitewashing attacks). It simultaneously fails to account for benign disagreement during market volatility (Flash Crash scenarios). To address this dilemma, this paper proposes a novel graph-based profiling mechanism utilizing an Adaptive Exponential Weighted Moving Average (AEWMA). Unlike typical static models, our approach introduces a Dynamic Trust Decay factor that regulates the weight of historical reputation based on real-time network volatility. This allows the system to act in a highly sensitive manner to deviations during stable market conditions in order to rapidly detect sleeper cells, whilst also dampening penalty mechanisms during high-volatility events to prevent false positives. We provide a complete Solidity smart contract implementation demonstrating the on-chain feasibility of the AEWMA mechanism with $O(1)$ storage per edge. We validated the proposed method through discrete-event simulations using historical cryptocurrency market data. Experimental results demonstrate that our dynamic approach reduces the Time-to-Detection (TtD) for whitewashing attacks by approximately \textbf{81\%} compared to static baseline (from 9.0 to 1.7 rounds). It also maintained a False Positive Rate (FPR) of near \textbf{0.4\%} even under extreme volatility conditions ($5\times$ standard deviation), compared to 7.1\% for the static approach. These findings suggest that volatility-aware profiling significantly enhances the security and incentive compatibility of decentralized oracle networks.

In this paper, we introduce a newly proposed probability distribution, the Gamma Lindley \(q\)- distribution (\(q\)-GLD). We explore its structural properties and derive parameter estimation procedures. The proposed model is then applied in the context of quantum mechanics, where we demonstrate its suitability as a potential solution to the \(q\)-deformed Schrödinger equation. These findings reveal the distribution’s effectiveness in modeling complex physical systems within a \(q\)-deformed framework.

Artificial intelligence (AI)-powered technology integration in social fintech has transformative potential to advance social responsibility and support sustainable development. This research examines a Blockchain-based lending mechanism that integrates centralized exchanges (CEX) and decentralized exchanges (DEX) to facilitate seamless financial transactions and equitable resource allocation. AI-driven tools are utilized to enhance transparency, accuracy, and security, while smart contracts facilitate the efficient management and verification of loan distribution. The proposed system focuses on helping underserved communities, poor regions, and green businesses, promoting fair and sustainable finance in line with the Sustainable Development Goals (SDGs). The hybrid ecosystem combines the liquidity and regulatory compliance of centralized exchanges with the autonomy and reduced intermediary involvement of decentralized exchanges. AI enhances loan processing, reducing biases and inefficiencies. This framework with smart contracts is to provide scalable, auditable lending aligned with sustainable goals. Machine Learning (ML) algorithms verified loan eligibility with the borrower dataset. The performance of Random Forest algorithms is good due to their robustness and ensemble learning features. Then, Optuna enhanced model tuning, and SHapley Additive exPlanations (SHAP) identified key parameters. Finally, Smart contracts ensured secure, autonomous execution of green loans based on ML verification and sustainability criteria.

State losses from corruption cases in Indonesia are commonly interpreted solely as financial losses, while broader economic losses are largely overlooked. Corruption generates impacts that extend beyond fiscal depletion, affecting economic stability, social welfare, ecological integrity, and other systemic dimensions. This study therefore aims, first, to identify and examine the challenges arising from the ambiguous definition of state loss in Indonesia’s legal framework, particularly in price-related corruption cases; second, to analyse Singapore’s conduct-based model of corruption enforcement, which does not treat state loss as an element of the offence; and third, to formulate an ideal normative framework that can strengthen Indonesia’s anti-corruption regime. Using a normative legal research method with statutory, comparative, and conceptual approaches, this study finds that the ambiguity of the state-loss concept has made it difficult to establish economic losses in court, leading judges to focus exclusively on financial loss and leaving broader economic harm unaddressed in judicial decisions. In contrast, Singapore’s approach, by excluding state loss as an element of corruption, allows enforcement to centre on gratification and corrupt intent, resulting in a more coherent and efficient process than the Indonesian model. Consequently, a reframing of the state-loss concept is required, including more precise parameters of economic loss within anti-corruption law, standardised methodologies for its calculation, and broader asset-recovery mechanisms to enhance legal certainty and improve the overall effectiveness of corruption enforcement.

Purpose This research investigates how circular economy practices and sustainable digital closed-loop supply chains impact economic performance and intellectual capital development in the manufacturing sector of Gulf Cooperation Council countries. The geographical region faces unique sustainability and financial challenges when addressing critical gaps in understanding how circular practices integrate with sustainable digital and closed-loop supply chains and leverage structural, human and relational capital. Design/methodology/approach Modeling sustainable digital closed-loop supply chain as a multidimensional construct mediating the relationship between the circularity practices and economic outcomes through the lens of intellectual capital, using 200 respondents from manufacturing firms, is the core of this research. The study applies the structural equation modeling approach and analyzes the model pathways that significantly enhance sustainable digital performance. Findings The results indicate that circular economy practices significantly enhance sustainable digital closed-loop supply chain performance, which in turn has a strong positive impact on economic performance. The mediating role of closed-loop supply chain performance indicates how structural and relational capital help in generating economic value. This indirect effect exceeds the direct effect of circular practices on economic performance. Practical implications The findings of this research offer guidance to managers and policymakers to consider circular economic initiatives as necessary investments toward developing intangible intellectual assets. Strengthening digital infrastructure, employee competencies and supply chain partnerships are critical in leveraging the circular economy–driven intellectual capital. Originality/value While earlier studies consider circular economy and digital closed-loop supply chain as only operational strategies to minimize waste and improve efficient use of resources, this study conceptualizes them as primary mechanisms for intellectual capital formation. This research is among the first empirical studies to examine the integration of circular practices and digital closed-loop supply chain performance through the intellectual capital lenses. This integrative approach extends intellectual capital theory into the sustainability and digitalization domains underpinning the economic performance in Gulf Cooperation Council manufacturing.

Introduction: Hypertrophic cardiomyopathy (HCM), especially the obstructive variant, can lead to serious manifestations including cardiac arrest and refractory heart failure. Veno-arterial extracorporeal membrane oxygenation (VA-ECMO) can be a valuable tool for patients with prolonged cardiac arrest or cardiogenic shock. However, no previous reviews have studied the outcomes of HCM patients that required VA-ECMO support. Methods: A systemic search was done across three databases to identify adult cases of HCM that were placed on ECMO of whom the outcomes were reported. The age cutoff was 16 years or older. All variants of HCM were included. Demographic data such as age and gender, as well as relevant clinical data which includes the indication of ECMO, were collected. Results: Out of 263 abstracts screened, 111 underwent full text review. 22 studies which reported mortality outcome were included. These studies were mainly case reports, and it included 23 patients. The average age was 47 years (median:49, IQR: 16-78). 13/23 were females (57%). 3/23 of patients died, yielding an overall mortality of 13% in these studies. The most common indications of ECMO were cardiogenic shock (10/23, 43%), decompensated heart failure (5/23, 22%) and cardiac arrest (4/23, 17%). Conclusions: Veno-arterial extramembrane oxygenation appears to be an effective modality for the management of complicated HCM especially in cases of cardiogenic shock and decompensated heart failure. It appears to improve survival related to cardiac arrest secondary to HCM, but further analyses are needed to confirm these observations.