Community Asynchrony Increased Its Stability by Mediating the Relationship of Diversity–Stability Relationships in Loess Plateau, China

Extreme weather such as heavy rainfall and drought are threatening the global grassland and its potential to mitigate climate change. Therefore, understanding the drivers that promote the stability of grassland ecosystems is considered to be critical to mitigate the adverse effects of climate change on grasslands. Here, we use precipitation addition (PA) + grazing experiment to explain how species richness, aboveground biomass, species asynchrony, functional group level stability, drought tolerance and grazing tolerance can maintain grassland productivity stability. The results showed that grazing counteracted the promoting effect of rainfall on vegetation to a certain extent, and weakened the sensitivity of species of grazing tolerant functional group to rainfall. Rainfall and grazing affect the asynchrony of the community through the influence of drought tolerance and grazing tolerance functional groups, and then affect the stability of the community through the mediation of the relationship between aboveground biomass and species richness. This effect was significantly correlated with the differences of vegetation characteristics and resource acquisition strategies, but not with the community species richness. This study provides more explanations for the maintenance mechanism of community stability

AI-driven optimization of blockchain scalability, security, and privacy protection

With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. Most of the existing related reviews explore the application of AI in blockchain as a whole but lack in-depth classification and discussion on how AI can empower the core aspects of blockchain. This paper explores the application of artificial intelligence technologies in addressing core challenges of blockchain systems, specifically in terms of scalability, security, and privacy protection. Instead of claiming a deep theoretical integration, we focus on how AI methods, such as machine learning and deep learning, have been effectively adopted to optimize blockchain consensus algorithms, improve smart contract vulnerability detection, and enhance privacy-preserving mechanisms like federated learning and differential privacy. Through comprehensive classification and discussion, this paper provides a structured overview of the current research landscape and identifies potential directions for further technical collaboration between AI and blockchain technologies