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.

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.

With the rapid development of the Internet of Things (IoT), the growth of the Industrial Internet of Things (IIoT) applied in the industrial sector has also been swift. However, in practical applications, there are still issues such as the misalignment between theory and application, the lack of a unified standardization framework, and the frequent occurrence of data silos. These issues limit the maintainability and scalability of IIoT systems and increase the digitalization costs for enterprises. Based on this, drawing from the design principles of classic general-purpose operating systems, we propose the concept of an IoT operating system platform. As a software infrastructure aimed at the next generation of IIoT, the IoT OS platform consists of general-purpose computer operating systems and the platform software running on them. It manages computing resources and entities such as sensors, networks, and ubiquitous artificial intelligence applications or systems, and provides service support for IIoT applications upwards, aiming to improve existing issues and enhance the specificity and scalability of IIoT systems. This paper presents the current status of IIoT systems, the definition and architecture of the IoT OS platform, and validates the theoretical architecture through specific cases.

Federated learning (FL) faces significant challenges due to statistical heterogeneity, which undermines the global model's generalization capability across diverse clients. Personalized FL (pFL) has been extensively studied to address this, but most existing approaches erroneously assume all stages of the FL training process are equally critical and require the participation of all clients, leading to substantial computational and communication overhead. Addressing this flaw, we propose an efficient adaptive pFL method, FedCEA, based on critical learning periods (CLP). FedCEA tailors efficient models for each client while ensuring data privacy and security by considering CLP during the training process to guide client selection. This approach reduces client-server communications, accelerating model convergence. To enhance the global model's generalization across clients with statistical heterogeneity, we introduce an Adaptive Initialization of Local Models (AILM) module with a personalized aggregation strategy. Additionally, training parameters are dynamically adjusted based on dataset quality to ensure efficiency. FedCEA also employs a compression method that assesses the importance of model parameters, reducing communication and computational costs. To evaluate the effectiveness of FedCEA, we conduct extensive experiments with four benchmark datasets in computer vision and natural language processing domains. The results consistently showed that FedCEA achieves improved accuracy and superior communication efficiency compared to state-of-the-art methods. This makes FedCEA a promising solution for handling heterogeneity in federated learning training. Code is available at https://github.com/buaaYYC/FedCEA/tree/main.

Federated learning (FL) encounters significant challenges in heterogeneous client environments, due to statistical heterogeneity affecting the global model’s generalization. To address these issues, we introduce FedGAC, an efficient adaptive personalized FL (pFL) method focused on critical learning periods (CLP). Unlike existing methods that assume all training stages are equally critical and require participation from all clients, FedGAC strategically select clients during CLP to reduce computational and communication burdens. To enhance the global model’s generalization across statistically diverse clients, we integrate an Adaptive Initialization of Local Models (AILM) module with a personalized aggregation strategy. Training parameters are dynamically adjusted according to dataset quality, ensuring both accuracy and efficiency. Additionally, FedGAC employs parameter compression techniques to further reduce communication and computational costs. We validate the effectiveness of FedGAC through extensive experiments on four benchmark datasets in computer vision. The results demonstrate that FedGAC significantly enhances both accuracy and communication efficiency compared to state-of-the-art FL methods. This makes FedGAC a promising approach for addressing heterogeneity in pFL. The code is available at https://github.com/buaaYYC/FedGAC.git.