Blockchain technology has witnessed remarkable growth, especially with the advent of Ethereum and its smart contract capabilities, spurring widespread applications across diverse industries. However, the scalability issue remains a significant bottleneck, primarily due to the prevalent serial transaction execution approach in many blockchain systems, leading to high latency and low throughput. Deterministic parallelism and optimistic parallelism are currently the two types of excellent parallel schemes to improve transaction execution efficiency. But there are still some problems and challenges in parallel execution. To address these challenges, this paper presents a novel parallel execution model TCHAO, which leverages historical access objects to classify and group transactions. It allows for serial execution within groups and parallel execution between groups, thereby reducing transaction execution time. The model eliminates the need for pre-defining read–write sets or pre-executing transactions, which are common limitations in existing parallelism schemes. Through the implementation of a prototype system and comprehensive experiments, TCHAO demonstrates superior performance compared to SE (serial execution), EV (Execute-Validate) architecture and sharding schemes in terms of applicable blockchain types, throughput, latency, condition and limitation. This research provides a new perspective and solution for enhancing the scalability of blockchain systems.

NoSQL databases are vital for modern big data applications, yet traditional teaching methods struggle with lagging content, insufficient practice, and low student engagement. To address these issues, this paper proposes the WWH-integrated teaching method “Why learn, What learn, How learn” for a NoSQL database course. WWH combines three core approaches: the general–special method, which structures knowledge from foundational concepts to specialized technologies; the comparative method, which contextualizes NoSQL value via real-scenario analysis; and the theory–practice combination method, which links concepts to hands-on tasks, supplemented by the problem-guidance and key-highlighting strategies. A quasi-experiment with two cohorts (80 students each; 2023 cohort as control, 2024 as experimental) validated WWH. Quantitative results showed significant improvements: theoretical exam scores rose by 9.2 points (t(158) = 9.21, p < 0.001) and experimental scores by 10.3 points (t(158) = 7.92, p < 0.001), and classroom discussion rates increased from 45.2% to 82.7% (χ²(1) = 28.90, p < 0.001). Qualitative analysis of student essays and project reports further confirmed deeper conceptual understanding, stronger tradeoff awareness, and enhanced knowledge integration in the experimental cohort. This study provides an evidence-based, student-centered framework for modernizing NoSQL instruction, better preparing students for industry data management needs.

Scalability is often considered the "Achilles' heel" of blockchain technology. With a traditional chain-based structure, blocks cannot be generated concurrently, thus limiting the throughput and slowing transaction confirmation. Recently emerged graph-based blockchains generally excel in on-chain performance but are weaker in structural flexibility, which is essential for off-chain or cross-chain scaling. In this paper, we propose Fractal Ledger, a novel blockchain model to scale out the ledger in a fractal manner. Fractal Ledger is comprised of fractal units that synthesize both chain and graph structure. Through conversions and interactions of fractal units, Fractal Ledger can share the advantages of both types of blockchains and offer scalability in structure and storage. We introduce the deduction mechanisms for flexible structure scalability and a three-directional folding scheme of lossless on-chain data compression for storage scalability. Our experimental results reveal the effectiveness of Fractal Ledger in enhancing structure and storage scalability.