Objective:To explore the chain mediating effect of anxiety and flow experience on perceived stress and mobile phone addiction in nursing college students.Methods:In December 2021, a cross-sectional design survey was conducted on 4 179 freshmen and sophomores in a nursing college in Heilongjiang Province. The Chinese perceived stress scale, generalized anxiety disorder-7, flow state scale, and mobile phone addiction tendency scale were selected separately to assess perceived stress, anxiety symptoms, flow experience and mobile phone addiction. SPSS 26.0 software was used for descriptive analysis, independent sample t-test, Spearman correlation analysis, and AMOS 24.0 software was used for mediating effect test. Results:(1) Among the 3 050 nursing students, there were 714(23.41%) students who were addicted to mobile phones. (2) Spearman correlation analysis indicated that perceived stress(27.31±9.56) was positively correlated with anxiety(7.00(1.00, 10.00), r=0.441, P<0.05), flow experience((12.00±3.40), r=0.517, P<0.05), and mobile phone addiction((42.42±13.05), r=0.476, P<0.05).Anxiety was positively correlated with flow experience ( r=0.430, P<0.01) and mobile phone addiction ( r=0.538, P<0.01).Flow experience was positively correlated with mobile phone addiction ( r=0.490, P<0.01). (3) Anxiety and flow experience played seperate mediating and chain mediating roles between perceived stress and mobile phone addiction, accounting for 26.06%(0.165/0.633), 23.54%(0.149/0.633) and 3.48%(0.022/0.633) of the total effect. Conclusion:Perceived stress not only directly affects the mobile phone addiction of nursing students, but also indirectly affects mobile phone addiction through the independent and chain mediating effects of anxiety and flow experience.

The count and recognition of white blood cells in blood smear images play an important role in the diagnosis of blood diseases including leukemia. Traditional manual test results are easily disturbed by many factors. It is necessary to develop an automatic leukocyte analysis system to provide doctors with auxiliary diagnosis, and blood leukocyte segmentation is the basis of automatic analysis. In this paper, we improved the U-Net model and proposed a segmentation algorithm of leukocyte image based on dual path and atrous spatial pyramid pooling. Firstly, the dual path network was introduced into the feature encoder to extract multi-scale leukocyte features, and the atrous spatial pyramid pooling was used to enhance the feature extraction ability of the network. Then the feature decoder composed of convolution and deconvolution was used to restore the segmented target to the original image size to realize the pixel level segmentation of blood leukocytes. Finally, qualitative and quantitative experiments were carried out on three leukocyte data sets to verify the effectiveness of the algorithm. The results showed that compared with other representative algorithms, the proposed blood leukocyte segmentation algorithm had better segmentation results, and the mIoU value could reach more than 0.97. It is hoped that the method could be conducive to the automatic auxiliary diagnosis of blood diseases in the future.
Algorithms ; Leukocytes