The Design, Practice and Effects of a SPOC

The 2022 “Frontier of Educational Technology” summer school was held by the Department of Educational Technology, Graduate School of Education, Peking University from July 4th 2022 to July 8th 2022. In order to ensure the learning quality, a small private online course was designed for the first time. A series of learning activities such as ice-breaking trip, expert lectures, group discussions, and group presentation were carried out with the help of the course management system, live broadcast platform and social media groups. We analyzed the data recorded by the course management system and found that more than half of the students participated in all learning activities, and the final completion rate of the summer school is as high as 89.86%. An online questionnaire was used to examine students’ subjective responses to the pedagogy and course performance. The results showed that the average score of almost all the questions in the questionnaire is greater than 4.3 points (out of 5.0), indicating that most students are satisfied or very satisfied with this pedagogy. Suggestions for improvement and future expectations are put forward, and can provide a reference for the full-online SPOC practice and research that are being widely carried out around the world

Prediction of Type 2 Diabetes Risk and Its Effect Evaluation Based on the XGBoost Model

In view of the harm of diabetes to the population, we have introduced an ensemble learning algorithm—EXtreme Gradient Boosting (XGBoost) to predict the risk of type 2 diabetes and compared it with Support Vector Machines (SVM), the Random Forest (RF) and K-Nearest Neighbor (K-NN) algorithm in order to improve the prediction effect of existing models. The combination of convenient sampling and snowball sampling in Xicheng District, Beijing was used to conduct a questionnaire survey on the personal data, eating habits, exercise status and family medical history of 380 middle-aged and elderly people. Then, we trained the models and obtained the disease risk index for each sample with 10-fold cross-validation. Experiments were made to compare the commonly used machine learning algorithms mentioned above and we found that XGBoost had the best prediction effect, with an average accuracy of 0.8909 and the area under the receiver’s working characteristic curve (AUC) was 0.9182. Therefore, due to the superiority of its architecture, XGBoost has more outstanding prediction accuracy and generalization ability than existing algorithms in predicting the risk of type 2 diabetes, which is conducive to the intelligent prevention and control of diabetes in the future

AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK

Effect of Surface Dispersion of Fe Nanoparticles on the Room-Temperature Flash Sintering Behavior of 3YSZ

Arc floating in surface flashover can be controlled by reducing the interfacial charge-transfer resistance of ceramics. However, thus far, only a few studies have been conducted on methods of treating ceramic surfaces directly to reduce the interfacial charge-transfer resistance. Herein, we explore the flash sintering behavior of a ceramic surface (3 mol% yttria-stabilized zirconia (3YSZ)) onto which loose metal (iron) powder was spread prior to flash sintering at room temperature (25 °C). The iron powder acts as a conductive phase that accelerates the start of flash sintering while also doping the ceramic phase during the sintering process. Notably, the iron powder substantially reduces the transition time from the arc stage to the flash stage from 13.50 to 8.22 s. The surface temperature (~1600 °C) of the ceramic substrate is sufficiently high to melt the iron powder. The molten metal then reacts with the ceramic surface, causing iron ions to substitute Zr4+ ions and promoting rapid densification. The YSZ grains in the metal-infiltrated area grow exceptionally fast. The results demonstrate that spreading metal powder onto a ceramic surface prior to flash sintering can enable the metal to enter the ceramic pores, which will be of significance in developing and enhancing ceramic–metal powder processing techniques