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Low visibility has significant influences on highways, ferries, civil aviation, and other modes of transportation, and the visibility observation of meteorological departments is not dense enough to meet the monitoring needs of low visibility weather. Using existing video surveillance equipment to extract visibility data can save a significant amount of money on visibility instrument deployment and maintenance, improve data density, and provide finer data support for traffic and urban safety operations. Based on video live image conversion, a simple convolutional neural network classification approach is suggested to extract visibility levels. The algorithm assumes that the video devices are installed horizontally and have an open view, and it creates a new fixed-size image by dividing the original video image into horizontal chunks and extracting the gradient, color saturation, and brightness information from each horizontal chunk. A simple convolutional neural network is used to learn and develop a visibility level recognition model from the converted images. The model is trained by 29668 video images of Yangshan Port Weather Station in Shanghai from September 2019 to December 2020, and then tested with 5757 video images from January to May in 2021. The comparison indicates the recognition model generated with this technique has a greater accuracy than the recognition model built directly with AlexNet network. The model has an overall accuracy of 87.99% during daytime and 81.32% during nighttime when the observed visibility is classified into five levels of fog-free, light fog, fog, dense fog, and thick fog according to the fog forecasting level. The model's identification ability for no fog and light fog is high. However, because the scenery becomes nearly indistinguishable once dense fog appears at night, the model's recognition ability for dense fog level at night is poor, and it is easy to categorize it as a fog level mistakenly. Taking 1000 meters as the criterion of low visibility weather, the algorithm's accuracy is 96.18% during daytime and 96.14% during nighttime. The algorithm features a quick learning rate and ease of application, making it suitable for low visibility video image recognition in most open-field scenarios. The model is applied during a radiation fog in Shanghai on 13 April 2021. The video images of the sparse area of the automatic weather station installation are collected for visibility identification, and the visibility distribution map formed together with the existing automatic weather station visibility meter data is more complete and accurate, which demonstrates that the model established by this algorithm can effectively compensate the problem of insufficient density of the existing automatic station visibility meter data, and has certain application value in meteorological operations.

The new era puts forward higher requirements on the quality of hospital management personnel, so it is significant to improve their health management research capabilities and encourage them to solve practical difficulties through scientific research as well as make innovations to their management thinking. In this article, a questionnaire survey of 100 hospital management personnel was carried out in a tertiary hospital in Guangzhou, China, to identify their health management research capabilities and learning needs for related knowledge. The results showed that most of the interviewees were young and middle-aged women with relatively high academic qualifications, mainly in medical and management majors. Their scientific research knowledge base and learning needs are on the average, and their scientific research method foundation is relatively ideal. There are significant differences in scientific research knowledge base, scientific research training needs, and scientific research methods among those with different ages and educational levels (p

In this study, we explored inter-ocular asymmetry (between the two eyes of the same patient) using optical coherence tomography angiography (OCTA) in patients with diabetes mellitus (DM) at different retinopathy stages. A total of 258 patients were divided into four groups: no DM, DM without diabetic retinopathy (DR), non-proliferative DR (NPDR), and proliferative DR (PDR). Superficial and deep vessel density (SVD, DVD), superficial and deep perfusion density (SPD, DPD), foveal avascular zone (FAZ) area, perimeter and circularity were calculated, and asymmetry index (AI) was used to evaluate the asymmetry of two eyes of the same subject. AIs of SPD, SVD, FAZ area and FAZ perimeter in the PDR group were larger than all other 3 groups (all p

The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies. [ABSTRACT FROM AUTHOR]