With the development of consumer-grade drone products, drones have been good carriers for many IoT (Internet of Things) applications, especially as a part of edge computing to deliver things, collect data, or monitor objects. This paper develops a Unity3D-based quadrotor dynamics simulation component on the original platform. In order to complete the simulation, a force analysis is performed for the quadrotor to establish a physical model based on Newtonian mechanics and Euler angles, and a PID (Proportion Integral Differential)-based control model is developed based on this physical model, which performs a fixed-point control for the simulation. The component introduces weather impact factors into the physical model through an environmental computational model and presents weather effects to the user interface. In addition, we add a scene customization function and a multi-aircraft formation flight function to the original simulation, and optimize the interface presentation scheme for multi-aircraft formation flight. The quadrotor simulation model in this paper solves the problem that the original simulation system is not realistic enough and significantly improves the usability and stability of quadrotors working in the IoT area.

In recent years, the development of computer technology has promoted the informatization and intelligentization of hospital management systems and thus produced a large amount of medical data. These medical data are valuable resources for research. We can obtain inducers and unknown symptoms that can help discover diseases and make earlier diagnoses. Hypertensive disorder in pregnancy (HDP) is a common obstetric complication in pregnant women, which has severe adverse effects on the life safety of pregnant women and fetuses. However, the early and mid-term symptoms of HDP are not obvious, and there is no effective solution for it except for terminating the pregnancy. Therefore, detecting and preventing HDP is of great importance. This study aims at the preprocessing of pregnancy examination data, which serves as a part of HDP prediction. We found that the problem of missing data has a large impact on HDP prediction. Unlike general data, pregnancy examination data have high dimension and a high missing rate, are in a time series, and often have many non-linear relations. Current methods are not able to process the data effectively. To this end, we propose an improved bi-LSTM-based missing value imputation approach. It combines traditional machine learning and bidirectional LSTM to deal with missing data of pregnancy examination data. Our missing value imputation method obtains a good effect and improves the accuracy of the later prediction of HDP using examination data.

This paper provides a new method to build a virtual simulation experiment platform. The platform is based on cloud computing, which has the ability to support elastic demands on different numbers of students or different experiments. Compared with traditional similar platforms, the platform raises resource utilization rate, requires less hardware, needs less management and has a better performance. The platform also provides many other functions related to virtual simulation experiments, such as course management, reports management, reports assessment, student management and so on, which means one single platform can satisfy almost all demands during the experiment teaching process.

The convergence of different fields is crucial for scientific advancement, and interdisciplinary collaborations among scholars are necessary for fostering this development. However, identifying and recommending potential interdisciplinary collaborators systematically is a challenging task. This paper proposes an interdisciplinary collaboration discovery and recommendation model for scholars in different fields. We utilized clustering algorithms, including K-Means, DBSCAN, and Affinity, to generate a scholarly interdisciplinary collaboration discovery graph. To recommend potential collaborators, we proposed an algorithm that considers both the suitability between scholars and the individual comprehensive influence of scholars. The effectiveness of the model was validated using data from 126 scholars at the Beijing University of Posts and Telecommunications.

Gestational diabetes mellitus and hypertension are two common pregnancy complications, which seriously threaten the life safety of pregnant women and adversely affect the growth and development of the fetus. Therefore, it is of great significance to detect and prevent hypertension and diabetes at an early stage of pregnancy. Each pregnant woman will undergo multiple tests at different gestational weeks. This progress produces lots of pregnancy examination data. These data can reflect the dynamic changes of pregnant women’s health indicators during pregnancy. This study aims to establish gestational diabetes and hypertension prediction model with a machine learning method based on real pregnancy examination data from the hospital. We use Logistic Regression, XGBoost, LightGBM, and Neural Network Model based on LSTM to do the prediction, respectively, and compare the performance. We check the prediction accuracy at different stages of pregnancy. We found that with pregnancy examination data at all gestational weeks, the predictive AUCs for diabetes and hypertension can reach 0.92 and 0.87, respectively. At 16th gestational week, the AUCs are 0.68 for diabetes and 0.70 for hypertension. We extract the checking items which are most important and get a simplified model with a modest reduction in predictive accuracy. This study demonstrates that based on several routine pregnancy examination items we can establish a machine learning model to detect and predict gestational diabetes and hypertension. This can be used as a diagnostic aid and is conducive to early prevention and treatment.