Air pollution is a pressing issue that poses significant threats to human health and the ecological environment. The accurate prediction of air quality is crucial to enable management authorities and vulnerable populations to take measures to minimize their exposure to hazardous pollutants. Although many methods have been developed to predict air quality data, the spatio-temporal correlation of air quality data is complex and nonstationary, which makes air quality prediction still challenging. To address this, we propose a novel spatio-temporal neural network, GCNInformer, that combines the graph convolution network with Informer to predict air quality data. GCNInformer incorporates information about the spatial correlations among different monitoring sites through GCN layers and acquires both short-term and long-term temporal information in air quality data through Informer layers. Moreover, GCNInformer uses MLP layers to learn low-dimensional representations from meteorological and air quality data. These designs give GCNInformer the ability to capture the complex and nonstationary relationships between air pollutants and their surrounding environment, allowing for more accurate predictions. The experimental results demonstrate that GCNInformer outperforms other methods in predicting both short-term and long-term air quality data. Thus, the use of GCNInformer can provide useful information for air pollutant prevention and management, which can greatly improve public health by alerting individuals and communities to potential air quality hazards.

The exoskeleton is often regarded as a tool for rehabilitation and assistance of human movement. The control schemes were conventionally implemented by developing accurate physical and kinematic models, which often lack robustness to external variational disturbing forces. This paper presents a virtual neuromuscular control for robotic ankle exoskeleton standing balance. The robustness of the proposed method was improved by applying a specific virtual neuromuscular model to estimate the desired ankle torques for ankle exoskeleton standing balance control. In specialty, the proposed control method has two key components, including musculoskeletal mechanics and neural control. A simple version of the ankle exoskeleton was designed, and three sets of comparative experiments were carried out. The experimentation results demonstrated that the proposed virtual neuromuscular control could effectively reduce the wearer’s lower limb muscle activation, and improve the robustness of the different external disturbances.