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Atmospheric corrosion is ubiquitous in China but varies a lot among different regions covering the cold, temperate, and tropical zones. Categorizing the atmospheric corrosivity and plotting precise atmospheric corrosion map remain key interest for a variety of industries. The present work proposed an atmospheric corrosion map of China for hot-dip galvanized steels, which was constructed by inverse distance weighting (IDW) interpolation algorithm based on both the measured corrosion rates of coupons exposed at 2393 inland test stations and calculated corrosion rates from a prevalent dose-response function in 2918 sites in coastal regions. When the corrosion category was used as the criterion, the IDW interpolation algorithm of power 2 performed best. Cross-validation results confirmed that the prediction accuracy of IDW interpolation reached 85.6%. Based on the corrosion map, the categories of atmospheric corrosivity in China could be determined.

Background The latest guidance on chronic fatigue syndrome (CFS) recommends exercise therapy. Tai Chi, an exercise method in traditional Chinese medicine, is reportedly helpful for CFS. However, the mechanism remains unclear. The present longitudinal study aimed to detect the influence of Tai Chi on functional brain connectivity in CFS. Methods The study recruited 20 CFS patients and 20 healthy controls to receive eight sessions of Tai Chi exercise over a period of one month. Before the Tai Chi exercise, an abnormal functional brain connectivity for recognizing CFS was generated by a linear support vector model. The prediction ability of the structure was validated with a random forest classification under a permutation test. Then, the functional connections (FCs) of the structure were analyzed in the large-scale brain network after Tai Chi exercise while taking the changes in the Fatigue Scale-14, Pittsburgh Sleep Quality Index (PSQI), and the 36-item short-form health survey (SF-36) as clinical effectiveness evaluation. The registration number is ChiCTR2000032577 in the Chinese Clinical Trial Registry. Results 1) The score of the Fatigue Scale-14 decreased significantly in the CFS patients, and the scores of the PSQI and SF-36 changed significantly both in CFS patients and healthy controls. 2) Sixty FCs were considered significant to discriminate CFS (P = 0.000, best accuracy 90%), with 80.5% ± 9% average accuracy. 3) The FCs that were majorly related to the left frontoparietal network (FPN) and default mode network (DMN) significantly increased (P = 0.0032 and P = 0.001) in CFS patients after Tai Chi exercise. 4) The change of FCs in the left FPN and DMN were positively correlated (r = 0.40, P = 0.012). Conclusion These results demonstrated that the 60 FCs we found using machine learning could be neural biomarkers to discriminate between CFS patients and healthy controls. Tai Chi exercise may improve CFS patients’ fatigue syndrome, sleep quality, and body health statement by strengthening the functional connectivity of the left FPN and DMN under these FCs. The findings promote our understanding of Tai Chi exercise’s value in treating CFS.

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