Integration of Diffusion Transformer and Knowledge Graph for Efficient Cucumber Disease Detection in Agriculture.

In this study, a deep learning method combining knowledge graph and diffusion Transformer has been proposed for cucumber disease detection. By incorporating the diffusion attention mechanism and diffusion loss function, the research aims to enhance the model's ability to recognize complex agricultural disease features and to address the issue of sample imbalance efficiently. Experimental results demonstrate that the proposed method outperforms existing deep learning models in cucumber disease detection tasks.

Implementation and Evaluation of Spatial Attention Mechanism in Apricot Disease Detection Using Adaptive Sampling Latent Variable Network.

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model's capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP).

Urban Seismic Risk Assessment by Integrating Direct Economic Loss and Loss of Statistical Life: An Empirical Study in Xiamen, China.

The growing densities of human and economic activities in cities lead to more severe consequences when a catastrophe such as an earthquake occurs. This study on urban seismic risk evaluation is carried out from the perspective of the direct loss caused by disasters in urban areas, including the measurement of both the expected direct economic loss and loss of life in the face of characteristic earthquakes. Aiming to estimate, quantify and visualize the earthquake risk in each unit of urban space, this research proposes to assess urban seismic risk by integrating the direct economic loss and the loss of statistical life in a disaster, with consideration of diverse earthquake frequencies.

How Can Cities Respond to Flood Disaster Risks under Multi-Scenario Simulation? A Case Study of Xiamen, China.

Flood disasters often have serious impacts on cities. Disaster prevention and mitigation schemes for flood disasters must be based on risk assessment. We constructed an indicator system for flood disaster risk assessment from the aspects of hazard factors, sensitivity to the environment, disaster vulnerability, flood disaster prevention, and resilience.

How Can Cities Adapt to a Multi-Disaster Environment? Empirical Research in Guangzhou (China).

Urban disaster risk assessment is the most basic and important part of urban safety development. Conducting disaster prevention and mitigation on the basis of urban disaster risk assessment requires an understanding of the relationship between the city and the natural environment. This enhances the city's ability to withstand various types of disasters and achieves the development of a safe city.

Insight into the Earthquake Risk Information Seeking Behavior of the Victims: Evidence from Songyuan, China.

Efficient risk communication is a vital way to reduce the vulnerability of individuals when facing emergency risks, especially regarding earthquakes. Efficient risk communication aims at improving the supply of risk information and fulfilling the need for risk information by individuals. Therefore, an investigation into individual-level information seeking behavior within earthquake risk contexts is very important for improved earthquake risk communication.