White blood cells (WBCs) are the cells of immune system, protecting against infective diseases and invasion of viruses and bacteria. Their aberrant number, both abnormal increase and decrease, is a sign of an ongoing pathology, a precise evaluation of their number is of the utmost importance as the first step of assessing a potential disease. In blood cell microscopic images, since red blood cells and platelets are similar in color with WBCs, and WBCs are partially adhesive, WBC segmentation for counting is often not resulting in a good performance. Therefore, in this work, a color space transformation is proposed to filter out red blood cells and platelets, which is transforming the blood cell microscopic images of patients with acute lymphoblastic leukemia from RGB color space to HSV to detect and extract WBCs. For precisely segmenting adhesive WBCs in extraction results, we set cell border to the third class, in addition to foreground and background. A weighted cross-entropy loss function based on class weight and distance transformation weight enhanced U-Net to learn cell border features. Our results showed that the method proposed in this paper for WBC segmentation using the data set ALL_IDB1 could achieve an accuracy of 97.92%.

Thanks to its high transparency, high carrier mobility, and thermal conductivity, graphene is often used as transparent conductive electrode (TCE) in optoelectronic devices. However, the low carrier concentration and high resistance caused by vacancy defects, grain boundaries, and superposed folds in typical graphene films limit its application. In this study, we propose a method to increase both the conductivity and carrier concentration in single-layer graphene (SLG) by blending it with silver nanowires (AgNWs). AgNWs provide connections between grain boundaries of graphene to improve charge-carrier transport. The AgNWs in this study can reduce the resistance of SLG from 650 Ω/◻ to 27 Ω/◻ yet still maintain a transmittance of 86.7% (at 550 nm). Flexible organic light-emitting diode, with a maximum 15000 cd m⁻² luminance was successfully fabricated using such graphene and AgNWs composite transparent electrodes.

A prerequisite for solving many agricultural problems is to accurately estimate the area affected by crop disasters and its severity rating. In this letter, we propose a pipeline to segment the drought area and distinguish the severity rating of the maize on RGB images accessed by an unmanned aerial vehicle (UAV) through a semantic segmentation method based on deep learning. First, the ground truth is created through expert evaluation and visual interpretation with the aid of the Normalized Difference Vegetation Index (NDVI). The neural network structure that was used is based on U-Net. Some structural and parameter improvements on U-net were made using SE-ResNeXt-50 as the backbone with the atrous spatial pyramid pooling (ASPP) module. By using RGB images as the input of the neural network for training, the final trained network can work on RGB images captured by a consumer UAV. The experimental results showed that our pipeline achieved an F1-score of 0.9034 and a Jaccard index of 0.8287 on the test set.