An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection.

Traditional machine learning methods struggle with plant pest and disease image recognition, particularly when dealing with small sample sizes, indistinct features, and numerous categories. This paper proposes an improved ResNet34 model (ESA-ResNet34) for crop pest and disease detection. The model employs ResNet34 as its backbone and introduces an efficient spatial attention mechanism (effective spatial attention, ESA) to focus on key regions of the images.

Medical image segmentation with UNet-based multi-scale context fusion.

Histopathological examination holds a crucial role in cancer grading and serves as a significant reference for devising individualized patient treatment plans in clinical practice. Nevertheless, the distinctive features of numerous histopathological image targets frequently contribute to suboptimal segmentation performance. In this paper, we propose a UNet-based multi-scale context fusion algorithm for medical image segmentation, which extracts rich contextual information by extracting semantic information at different encoding stages and assigns different weights to the semantic information at different scales through TBSFF module to improve the learning ability of the network for features.

Molecular insights into the performance of promoters for carbon dioxide hydrate.

Hydrate-based CO storage is a cost-effective and environmentally friendly approach to reduce carbon emission, and the addition of hydrate promoters has shown a promising avenue for enhancing CO hydrate formation. In this work, the promotion mechanism and promotion performance of five different hydrate promoters (denoted as DIOX, CP, THF, THP, and CH) were investigated and compared by first-principles calculations and molecular dynamics simulations. The results show that the hydrate promoters prefer to singly occupy 56 cages of the sII hydrate, and CO molecules can singly occupy 5 cage or multiply occupy 56 cages.