Hyperspectral estimation of chlorophyll density in winter wheat using fractional-order derivative combined with machine learning.

Chlorophyll density (ChD) can reflect the photosynthetic capacity of the winter wheat population, therefore achieving real-time non-destructive monitoring of ChD in winter wheat is of great significance for evaluating the growth status of winter wheat. Derivative preprocessing has a wide range of applications in the hyperspectral monitoring of winter wheat chlorophyll. In order to research the role of fractional-order derivative (FOD) in the hyperspectral monitoring model of ChD, this study based on an irrigation experiment of winter wheat to obtain ChD and canopy hyperspectral reflectance.

Response mechanism of water status and photosynthetic characteristics of under drought stress and rehydrated conditions.

Introduction: Plant physiology response and adaptation to drought stress has become a hotspot in plant ecology and evolution. possesses high ecological, ornamental and economic benefits. It has large root system and tolerance to cold, drought and poor soil.

Efficient information exchange approach for medical IoT based on AI and DAG-enabled blockchain.

The development of artificial intelligence (AI) based medical Internet of Things (IoT) technology plays a crucial role in making the collection and exchange of medical information more convenient. However, security, privacy, and efficiency issues during information exchange have become pressing challenges. While many scholars have proposed solutions based on AI and blockchain to address these issues, few have focused on the impact of the slow consensus algorithm of blockchain on the efficiency of information exchange.

The graded heat-health risk forecast and early warning with full-season coverage across China: a predicting model development and evaluation study.

Background: Due to global climate change, high temperature and heatwaves have become critical issues that pose a threat to human health. An effective early warning system is essential to mitigate the health risks associated with high temperature and heatwaves. However, most of the current heatwave early warning systems are not adequately developed based on the heat-health risk model, and the health impact of hot weather has not been well managed in most countries.