Social media activity has become an important component of daily life for many people. Messages from Twitter (US) and Weibo (China) have shown their potential as important data sources for detecting and analyzing infectious diseases. Such emerging and dynamic new data sources allow us to predict how infectious diseases develop and evolve both spatially and temporally. We report the dynamics of dengue fever in China using messages from Weibo. We first extract and construct a list of keywords related to dengue fever in order to analyze how frequently these words appear in Weibo messages based on the Latent Dirichlet Allocation (LDA). Spatial analysis is then applied to detect how dengue fever cases cluster spatially and spread over time.

Super-resolution (SR) techniques based on deep learning have a pivotal role in improving the spatial resolution of images. However, remote sensing images exhibit ground objects characterized by diverse types, intricate structures, substantial size discrepancies, and noise. Simultaneously, variations in imaging mechanisms, imaging time, and atmospheric conditions among different sensors result in disparities in image quality and surface radiation. These factors collectively pose challenges for existing SR models to fulfill the demands of the domain. To address these challenges, we propose a novel cross-sensor SR framework (ConvFormerSR) that integrates transformers and convolutional neural networks (CNNs), catering to the heterogeneous and complex ground features in remote sensing images. Our model leverages an enhanced transformer structure to capture long-range dependencies and high-order spatial interactions, while CNNs facilitate local detail extraction and enhance model robustness. Furthermore, as a bridge between the two branches, a feature fusion module (FFM) is devised to efficiently fuse global and local information at various levels. Additionally, we introduce a spectral loss based on the remote sensing ratio index to mitigate domain shift induced by cross-sensors. The proposed method is validated on two datasets and compared against existing state-of-the-art SR models. The results show that our proposed method can effectively improve the spatial resolution of Landsat-8 images, and the model performance is significantly better than other methods. Furthermore, the SR results exhibit satisfactory spectral consistency with high-resolution (HR) images.

The regular patterns of soil erosion tend to change at different scales of observation, affecting the mechanism of soil erosion and its evolution characteristics. This phenomenon has essential scientific significance for the rational allocation of land resources and for studies on sustainable ecosystems. As an important agricultural area in China, Danjiangkou reservoir is threatened by severe soil erosion. In this study, we selected four kinds of landscape pattern metrics, including patch density, fractal dimension, Shannon diversity index, and connectivity, to analyze soil erosion intensity in the Danjiangkou reservoir area at different scales based on landscape ecological principles. In addition, we determine the optimum research scale of the experimental area by calculating the information entropy value of soil patches at different scales. The findings suggest that: (1) the landscape pattern of soil erosion in the experimental area is obviously scale-dependent, and the responses to scale differ from index to index; (2) as the scale of observation increases, the fragmentation of soil patches is weakened, the stability of different landscape components is enhanced, and the soil becomes less vulnerable to erosion; and (3) based on information entropy theory, 60 m is confirmed to be the optimum scale of this study.