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Abstract Background Suboptimal health status (SHS) is an intermediate status between ideal heath and illness, it is characterized by the perception of health complaints, general weakness, decreased immunity and low energy. An increasing number of Chinese middle school students are suffering from psychological symptoms (PS), particularly anxiety and depression. The relationship between SHS and PS is unclear in adolescents. This study aimed to investigate the prevalence of SHS and the correlation between SHS and PS among Chinese middle school students and to identify the SHS-related risk factors from the perspective of public health. Methods A cross-sectional study was conducted with the cluster sampling method among 1955 middle school students in Shantou, China. SHS was assessed by Suboptimal Health Status Questionnaire-25 (SHSQ-25). And the PS of anxiety and depression were assessed with Generalized Anxiety Disorder Scale (GAD-7) and Beck Depression Inventory-II Scale (BDI-II) self-assessment questionnaires. Variate logistic analysis was applied to explore risk factors of SHS. The relationship between SHS and PS among Chinese middle school students was subsequently analyzed. Results Among the 1955 participants, 1904 middle school students were finally included in the analysis, the effective response rate was 97.39%. The prevalence of SHS was 10.3% (197/1904) while the prevalence of anxiety and depression was 30.7% (585/1904) and 34.1% (649/1904), respectively. A strong correlation was identified between SHS and PS among middle school students. With the aggravation of anxiety and depression, the probability of suffering from SHS increased (both P

Abstract Due to the large computational and GPUs memory cost of semantic segmentation, some works focus on designing a lite weight model to achieve a good trade‐off between computational cost and accuracy. A common method is to combined CNN and vision transformer. However, these methods ignore the contextual information of multi receptive fields. And existing methods often fail to inject detailed information losses in the downsampling of multi‐scale feature. To fix these issues, we propose AG Self‐Attention, which is Enhanced Atrous Self‐Attention (EASA), and Gate Attention. AG Self‐Attention adds the contextual information of multi receptive fields into the global semantic feature. Specifically, the Enhanced Atrous Self‐Attention uses weight shared atrous convolution with different atrous rates to get the contextual information under the specific different receptive fields. Gate Attention introduces gating mechanism to inject detailed information into the global semantic feature and filter detailed information by producing “fusion” gate and “update” gate. In order to prove our insight. We conduct numerous experiments in common semantic segmentation datasets, consisting of ADE20 K, COCO‐stuff, PASCAL Context, Cityscapes, to show that our method achieves state‐of‐the‐art performance and achieve a good trade‐off between computational cost and accuracy.

A superpixel is a group of pixels with similar low-level and mid-level properties, which can be seen as a basic unit in the pre-processing of remote sensing images. Therefore, superpixel segmentation can reduce the computation cost largely. However, all the deep-learning-based methods still suffer from the under-segmentation and low compactness problem of remote sensing images. To fix the problem, we propose EAGNet, an enhanced atrous extractor and self-dynamic gate network. The enhanced atrous extractor is used to extract the multi-scale superpixel feature with contextual information. The multi-scale superpixel feature with contextual information can solve the low compactness effectively. The self-dynamic gate network introduces the gating and dynamic mechanisms to inject detailed information, which solves the under-segmentation effectively. Massive experiments have shown that our EAGNet can achieve the state-of-the-art performance between k-means and deep-learning-based methods. Our methods achieved 97.61 in ASA and 18.85 in CO on the BSDS500. Furthermore, we also conduct the experiment on the remote sensing dataset to show the generalization of our EAGNet in remote sensing fields.