Blockchain Papers

Pavani Aravalapalli

No abstract is available for this record.

Sushmita Chakraborty

No abstract is available for this record.

Dung Cao, Palaash Gang

No abstract is available for this record.

Athar Kharal, Sanaa Anjum, SyedA Yasmeen

No abstract is available for this record.

Dr Mu’azu Omeiza Musa, Professor Olugbenga-Bello Adenike, MBBS, PhD, Adah Patrick Eneojo, Dr Onoja-Alexander Mary Ojonema, MBBS, PhD FWACP · 7 authors

Strengthening Primary Health Care (PHC) financing, governance, and operational readiness is fundamental to achieving resilient health systems and sustainable health security in low- and middle-income countries. Between 2022 and 2025, the Kogi State Government implemented a package of PHC reforms comprising Decentralized Facility Financing (DFF), the Minimum Service Package (MSP), and Continuous Quality Improvement (CQI) interventions to improve service delivery, strengthen facility readiness, stabilize commodity supply systems, and expand equitable access to vulnerable and hard-to-reach populations. We evaluated the Health Systems for Health Security success coefficients in Kogi State using a facility month DHIS2 panel of n=96 PHCs (January 2019–December 2025) and BHCPF Monthly Report Forms (2024–2025). The quasi experimental mixed methods design combined an augmented two way fixed effects Difference in Differences (DiD) estimator for average treatment effects, Interrupted Time Series (ITS) segmented regression to decompose immediate (level) and sustained (slope) impacts, multilevel mixed effects models for heterogeneity, and bootstrap causal mediation to quantify operational pathways. Models adjusted for seasonality, HRH density, environmental risk, and facility fixed effects; inference used cluster robust standard errors and bootstrap confidence intervals. Primary analysis used R (4.3.2) with lme4, fixest, brms/rstanarm, INLA, MatchIt/WeightIt, CausalImpact, sf, spdep; confirmatory DiD and event study checks used Stata/MP 18.0. All code was versioned in Git and analysis notebooks and key outputs were archived. DFF with CQI produced statistically and programmatically meaningful gains across core BMPHS indicators: DPT3 +6.2 percentage points (95% CI 3.9–8.5); ANC1 +5.1 pp (95% CI 2.8–7.4); SBA +4.8 pp (95% CI 1.9–7.7); PNC +4.3 pp (95% CI 1.6–7.0). ITS decomposition for DPT3 showed an immediate level increase of +3.7 pp (95% CI 1.9–5.5) and a sustained slope of +0.12 pp/month (95% CI 0.06–0.18). Mediation analysis attributed large shares of the DPT3 gain to facility readiness, functional Ward Development Committees, tracer drug availability, and IPC compliance as the largest contributors. Predictable facility financing coupled with CQI and targeted investments in readiness, governance, and supply chain resilience yields rapid and sustained improvements in immunization and maternal health coverage. Policy priorities include protecting cold chain and tracer drug lines, institutionalizing WDC governance and IPC audits, and targeting surge HRH and outreach financing to high risk LGAs to close equity gaps. The findings demonstrate the predictability of decentralized financing combined with CQI, governance strengthening, outreach expansion, and operational readiness investments towards the improvement of PHC utilization, immunization coverage, maternal health services, and health system resilience. The study provided epidemiologic evidence to test integrated PHC financing reforms relevance in the strengthening of Health Systems for Health Security (HSFORSHS) in improving accessibility, equity, preparedness, surveillance functionality, and continuity of essential services in vulnerable populations.

Christopher Staples

No abstract is available for this record.

David Krause

No abstract is available for this record.

Sergei Solovev

Predicting short-term mid-price movements from limit order book (LOB) data is a fundamental problem in quantitative finance and market microstructure research, with direct applicability to both traditional exchanges and cryptocurrency markets—including centralized exchanges (CEXs) and emerging on-chain LOB protocols in decentralized finance (DeFi). We present three contributions to this domain. First, we propose DA-BiGRU-CNN, a domain-aware dual-branch architecture that decomposes LOB features into price and volume information channels, processes them through dedicated bidirectional GRU encoders with shared microstructure features, and fuses temporal representations via a multi-scale convolutional bottleneck (Conv1d with kernels k = 3,5,7). Second, we provide empirical evidence for a "feature sufficiency" hypothesis: a unidirectional GRU trained on 53 basic features achieves performance statistically equivalent to one trained on 219 extensively engineered features—including rolling statistics, exponential moving averages, and lag/difference features—suggesting that recurrent hidden states implicitly learn these temporal patterns. Third, we document a "negative ensemble effect" where combining sequential (GRU) and tabular (gradient boosting) models consistently degrades prediction quality, contradicting the widely-held assumption that model diversity improves ensemble performance. On a large-scale dataset of 12,165 LOB sequences (12.1M timesteps), our GRU baseline achieves a weighted Pearson correlation of 0.266, outperforming LightGBM by 58%, while our domain-aware architecture offers an architecturally principled alternative that naturally separates price dynamics from liquidity dynamics.

Luca Bassil

No abstract is available for this record.

Dinis Araujo, Ian Scott, Miguel de Castro Neto

No abstract is available for this record.

Gregory Komansky

No abstract is available for this record.

Weimin CHEN, Xiapu Luo

Decentralized finance (DeFi) is an emerging financial service on blockchain, enabling automatic and anonymous transactions.Within DeFi, decentralized exchanges (DEXs) maintain reserves of a pair of tokens and determine the exchange rate to swap tokens.However, DEXs also create opportunities for Maximal Extractable Value (MEV), where attackers include, exclude, or reorder DEX transactions to exploit price discrepancies of tokens and extract profit.Uncovering MEV opportunities requires high throughput, as the 12-second block interval and the vast search space impose strict time constraints.However, existing tools suffer from low throughput, as they rely on CPU-bound execution, which is hindered by frequent state forking and slow DEX execution.In this paper, we take the first step in leveraging GPU parallel computing power to boost MEV-search throughput in arbitrage and sandwich strategies.More precisely, we compile an MEV bot into a GPU application and then launch thousands of GPU threads to search for profit in parallel.To this end, we design new solutions to address three major challenges: designing cheatcodes to simulate transactions on GPU, proposing a memory manager to reduce GPU memory usage, and designing strategyaware mutations to improve input diversity.We implement a prototype named MeVisor that runs DEXs on GPUs and searches for MEV using a parallel genetic algorithm.Evaluated on 3,941 real MEV cases from Ethereum, MeVisor achieves 3.3M-5.1Mtransactions per second, outperforming the CPU baseline by 100,000x.In a large-scale study of Q1 2025 data, MeVisor estimates MEV opportunities ranging from 2 to 14 transactions, yielding at most $1.1 million in MEV profit.

Julius Juette

No abstract is available for this record.

Dr. Pankaj Malik, Mohit Kapoor, Akshat Gupta, Aman Singhai · 5 authors

The rapid expansion of decentralized finance (DeFi) platforms has been accompanied by a surge in rug pull scams, where malicious actors exploit liquidity pools and abandon projects, causing substantial investor losses. Existing detection approaches are largely centralized and platform-specific, limiting their effectiveness due to privacy constraints, fragmented data sources, and the dynamic behavior of blockchain ecosystems. This paper proposes a novel Federated Time-Series Learning (FTSL) framework for cross-platform rug pull detection that enables collaborative model training without sharing raw transaction data. The proposed system integrates federated learning with advanced time-series modeling to capture temporal patterns in token price volatility, liquidity changes, transaction frequency, and smart contract activities. A hybrid deep learning architecture combining Long Short-Term Memory (LSTM) networks with an attention mechanism is employed to effectively learn sequential dependencies and identify early indicators of fraudulent behavior. The federated setup ensures privacy preservation while enabling knowledge sharing across multiple decentralized platforms. Experimental results on multi-chain DeFi datasets demonstrate that the proposed FTSL model achieves 96.3% detection accuracy, outperforming traditional centralized models (91.2%) and single-platform approaches (88.7%). The model also improves precision (95.1%), recall (94.6%), and F1-score (94.8%), indicating robust and balanced performance. Furthermore, the system is capable of detecting rug pull events 6–12 hours earlier than baseline methods, providing critical early warning signals. Communication overhead is reduced by approximately 28% through optimized federated aggregation, while maintaining scalability across heterogeneous platforms. These findings highlight that Federated Time-Series Learning offers a scalable, privacy-preserving, and highly effective solution for real-time rug pull detection, contributing to enhanced security, transparency, and trust in decentralized financial ecosystems.

David Martin

No abstract is available for this record.

Frederico C Montenegro

No abstract is available for this record.

Murillo Campello, Angela Gallo, Lira Mota, Tammaro Terracciano

No abstract is available for this record.

Frederico C Montenegro

No abstract is available for this record.

David Krause

No abstract is available for this record.

Jiaochen Liang

• DeFi stablecoin yields track FFR/SOFR, but with a distinct T+3 structural lag. • A settlement-friction framework links fiat rails to the T+3 transmission lag. • The lag is universal for both compliant USDC and offshore, unregulated USDT. • Basis regressions reveal a predictable settlement wedge after policy moves. • Robust tests rule out protocol outliers, macro trends, and weekend artifacts. Decentralized Finance (DeFi) stablecoin markets increasingly function as a shadow overnight dollar system, yet the speed at which U.S. monetary policy transmits to on-chain yields remains unclear. Focusing on the recent “High-for-Long” regime (2023–2025), I study this pass-through using daily Aave V3 deposit rates for USDC and USDT. Guided by a simple conceptual framework of settlement frictions and arbitrage constraints, I estimate an ordered VAR that controls for equity- and crypto-market cycles. The results show that DeFi yields are tightly anchored to the Federal Funds Rate (and, in robustness, SOFR), challenging the “crypto-decoupling” narrative. However, transmission exhibits a distinct T+3 structural latency, universal across both compliant USDC and unregulated USDT, indicating an infrastructural, systemic friction rather than issuer-specific constraints. Robustness tests, alternative-explanations analysis, and quantity-based mechanism checks rule out protocol outliers, broader macro trends, and weekend artifacts, supporting an interpretation based on delayed settlement and execution across fiat rails. Complementary basis regressions provide a direct pricing implication: the on/off-chain spread exhibits a significant, predictable wedge during the settlement window that dissipates thereafter. The findings imply that despite algorithmic immediacy, DeFi remains constrained by fiat infrastructure, and that improving on-chain capital efficiency may require modernizing payment rails alongside issuer-focused regulation.

Yizhou Wen, Kani Chen

No abstract is available for this record.

Gavin Persaud

No abstract is available for this record.

David Krause

No abstract is available for this record.

Nicolas Fahel

No abstract is available for this record.