Background Skin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited. Objective This study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases. Methods Each image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences. Results Xiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%). Conclusions Xiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.

In order to research the aircraft impact damage, one symbolic fitting method for analyzing and forecasting the damage data is proposed based on genetic programming (GP). The method can be used to forecast the impact damage by recognizing the rule in some groups of actual data including impact parameters and damage hole size. The principle of GP symbolic fitting method is briefly introduced. The fitting model is created with some sample data respectively for training and testing from Sorenson experiential equation. The computation with Matlab program indicates the model has a good performance to fit and forecast the damage data with avoiding the noise. The application of GP symbolic fitting method can help to decrease the times of fire experiments. Since the method can recognize the complicated nonlinear relationship between the impact parameters and damage data, it is more applicable than theoretical analysis and experiential equation to forecast the aircraft impact damage.