Objective: Non-alcoholic fatty liver disease (NAFLD), previously thought to predominantly affect obese individuals, has also been shown to occur in subjects who have a relatively normal body mass index (BMI). Due to the normal BMI, non-obese NAFLD are easily to be ignored and eventually lead to potential liver injuries. Methods: A population-based cross-sectional study was conducted on 1608 cases with normal serum alanine aminotransferase (ALT) levels who were divided into an obese group (BMI ≥ 25 kg/m²) and a non-obese group (BMI < 25 kg/m²). NAFLD was diagnosed by ultrasound and Fibro Scan examination. Non-obese populations were divided into NAFLD group (CAP ≥ 240 db/m) and non-NAFLD group (CAP < 240 db/m). The incidence of NAFLD in the obese and non-obese populations and constituent ratios of genders, age, and serum levels of triglycerides (TG), cholesterol (CHOL), and blood glucose were compared. Risk factors of NAFLD in non-obese people were analyzed by multivariate logistics regression. Results: The occurrence of NAFLD was higher in the obese group than in the non-obese group, regardless of gender (P < 0.001). In the non-obese group, the occurrence of NAFLD in female patients was lower than that in male (P=0.001). The occurrence of NAFLD increased with age, with 50– 59 years being the peak age of incidence in both male and female. The peak age of NAFLD occurrence in non-obese male patients was more delayed than that in obese male patients. BMI (OR=1.311, P=0.000) and TG (OR=2.545, P=0.000) were risk factors for NAFLD in the non-obese population. Conclusion: Compared with obese population, the incidence of NAFLD in non-obese population was relatively low and more frequently in male than in female, the peak age of NAFLD occurrence in non-obese male patients was also delayed. BMI and TG should still be controlled to avoid the occurrence of NAFLD although the BMI of such patients is normal.

Magnetic compensation is a necessary step in the aeromagnetic data processing. While the aeromagnetic compensation model is a linear regression model, the multicollinearity of the variables in the model reduces the accuracy of the compensation model. To solve this problem, we propose a deep autoencoder (DAE) aeromagnetic compensation algorithm. The DAE searches the direction of maximum change in the data by using the gradient descent backpropagation algorithm. The special structure of the encoder can compress the representation of the coefficient matrix and extract data features, thereby weakening the correlation between the coefficient matrix variables. The features obtained after dimension reduction are used in the compensation calculation. We validate the DAE algorithm by applying it to data collected by an unmanned aerial vehicle and demonstrate that the DAE magnetic compensation results are better than those of the principal component analysis algorithm.

Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability.