To reveal the high-resolution atmospheric and statistical characteristics of haze events within the boundary layer (BL) in different months, this study conducted a combined detection experiment using a wind-profiling radar, a microwave radiometer, and an ambient particulate monitor on 1230 haze events occurring at Xianyang Airport from 2016 to 2021. First, the boundary layer heights (BLHs) of the haze events were calculated using the atmospheric refractive index structure constant, wind direction and speed, and these were verified against reanalysis data from ERA-Interim. Spatial–temporal evolution and statistical characteristics of temperature, and relative humidity and horizontal wind during haze events, were then analyzed. Finally, the relationships between the BLH and AQI (air quality index) and during the haze events were analyzed. The results indicate that the average BLHs during haze events at Xianyang Airport were generally lower than 1000 m. Moreover, the average BLHs in December and January were distributed in the range of 200–600 m, and lower than that in June and July, in a range of 500–1100 m. Furthermore, the maximum value of the average BLH appears at 13:00–15:00. When the temperature was low in the morning, the stratification difference was small and the sensible heat flux between ground and air was still weak, leading to a low BLH value. Meanwhile, when the air quality was poor, the relative humidity was relatively large, and the corresponding AQI and were very large. Subsequently, when the temperature gradually increased with time, the heat flux and the average BLH also gradually increased. Moreover, the relative humidity within the BL decreased, and the corresponding AQI and also gradually decreased, with the corresponding air quality improving accordingly. The results obtained herein provide a key reference for the preparedness of haze events.
Purpose: Calcification nodules in thyroid can be found in thyroid disease. Current clinical computed tomography systems can be used to detect calcification nodules. Our aim is to identify the nature of thyroid calcification nodule based on plain CT images. Method: Sixty-three
patients (36 benign and 27 malignant nodules) found thyroid calcification nodules were retrospectively analyzed, together with computed tomography images and pathology finding. The regions of interest (ROI) of 6464 pixels containing calcification nodules were manually delineated by radiologists
in CT plain images. We extracted thirty-one texture features from each ROI. And nineteen texture features were picked up after feature optimization by logistic regression analysis. All the texture features were normalized to [0, 1]. Four classification algorithms, including ensemble learning,
support vector machine, K-nearest neighbor, decision tree, were used as classification algorithms to identity the benign and malignant nodule. Accuracy, PPV, NPV, SEN, and AUC were calculated to evaluate the performance of different classifiers. Results: Nineteen texture features were
selected after feature optimization by logistic regression analysis (P <0.05). Both Ensemble Learning and Support Vector Machine achieved the highest accuracy of 97.1%. The PPV, NPV, SEN, and SPC are 96.9%, 97.4%, 98.4%, and 95.0%, respectively. The AUC was 1. Conclusion: Texture
features extracted from calcification nodules could be used as biomarkers to identify benign or malignant thyroid calcification.
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