The spread of corona virus disease 2019 (COVID-19) has coincided with the rise of Transformer and graph neural networks, leading several studies to propose using them to better predict the evolution of a pandemic. The inconveniences of infectious diseases make it important to predict their spread. However, the single deep learning (DL) model has the problems of unstable prediction effect and poor convergence. When calculating the relationship between different positions within a sequence, Transformer does not consider the local context in which each position is located, which can make the prediction vulnerable to outliers, so the integration of the graph convolutional network (GCN) to capture local information is considered. In this paper, we use Transformer to encode the time sequence information of COVID-19 and GCN to decode the time sequence information with graph structure, so that Transformer and GCN are perfectly combined and spatial information is used to further study the integration of these two methods. In addition, we improve the traditional positional encoding structure and propose a dynamic positional encoding technique to extract dynamic temporal information effectively, which is proved to be the key to capture spatial and temporal patterns in data. To make our predictions more useful, we only focused on three states in the United States, covering one of the most affected states, one of the least affected states, and one intermediate state. We used mean absolute percentage error and mean square error as evaluation indexes. Experimental results show that the proposed time series model has better predictive performance than the current DL models. Moreover, the convergence of our model is also better than the current DL models, providing a more accurate reference for the prevention of epidemics.

The coronavirus disease 2019 (COVID-19) has spread rapidly around the world since 2020, with a significant fatality rate. Until recently, numerous countries were unable to adequately control the pandemic. As a result, COVID-19 trend prediction has become a hot topic in academic circles. Both traditional models and existing deep learning (DL) models have the problem of low prediction accuracy. In this paper, we propose a hybrid model based on an improved Transformer and graph convolution network (GCN) for COVID-19 forecasting. The salient feature of the model in this paper is that rich temporal sequence information is extracted by the multi-head attention mechanism, and then the correlation of temporal sequence information is further aggregated by GCN. In addition, to solve the problem of the high time complexity of the existing Transformer, we use the cosine function to replace the softmax calculation, so that the calculation of query, key and value can be split, and the time complexity is reduced from the original $O(N^2)$ to $O(N)$. We only concentrated on three states in the United States, one of which was the most affected, one of which was the least affected, and one intermediate state, in order to make our predictions more meaningful. We use mean absolute percentage error and mean absolute error as evaluation indexes. The experimental results show that the proposed time series model has a better predictive performance than the current DL models and traditional models. Additionally, our model’s convergence outperforms that of the current DL models, offering a more precise benchmark for the control of epidemics.

The vehicle scanning method (VSM) is widely used for bridge damage identification (BDI) because it relies solely on vehicle dynamic responses. The recently introduced contact point response, which is derived from vehicle dynamics but devoid of vehicle-related natural frequencies, shows great potential for application in the vehicle scanning method. However, its application in bridge damage detection remains understudied. The aim of this paper is to propose a new bridge damage identification method based on the contact point response. The method uses variational modal decomposition (VMD) to solve the problem of mode mixing and spurious frequencies in the signal. The continuous wavelet transform (CWT) is then utilized for damage identification. The introduction of variational modal decomposition makes the extracted signal more accurate, thus enabling more accurate damage identification. Numerical simulations validate the method’s robustness under varying conditions, including the vehicle speed, wavelet scale factors, the number of bridge spans, and pavement roughness. The results demonstrate that variational modal decomposition eliminates signal artifacts, producing smooth variational modal decomposition–continuous wavelet transform curves for accurate damage detection. In this study, we offer a robust and practical solution for bridge health monitoring using the vehicle scanning method.

The traditional identification methods have limited ability to identify damage location of bridge structures. Therefore, a bridge structural damage location identification method based on deep learning is proposed. In addition, the sigmoid function is the activation function, and the cross entropy is the cost function. Meanwhile, take the Gaussian noise as the addition method and take the softmax as the classifier. So the constructed SDAE deep learning model can realize damage location identification of the simply supported the continuous beam bridges. Compared with the traditional identification methods of bridge structures, namely BP network and SVM, the proposed method shows higher identification accuracy and antinoise performance. Here, the average identification accuracy of the method for continuous beam bridge is 99.8%. As can be seen that the proposed method is more suitable for practical bridge structure damage location identification.

In this study, the pore structure of a hardened phosphorous building gypsum body was optimised by blending an air-entraining agent with the appropriate water–paste ratio. The response surface test was designed according to the test results of the hardened phosphorous building gypsum body treated with an air-entraining agent and an appropriate water–paste ratio. Moreover, the optimal process parameters were selected to prepare a porous phosphorous building gypsum skeleton, which was used as a paraffin carrier to prepare energy-storage phosphorous building gypsum. The results indicate that if the ratio of the air-entraining agent to the water–paste ratio is reasonable, the hardened body of phosphorous building gypsum can form a better pore structure. With the influx of paraffin, its accumulated pore volume and specific surface area decrease, and the pore size distribution is uniform. The paraffin completely occupies the pores, causing the compressive strength of energy-storage phosphorous building gypsum to be better than that of similar gypsum energy-storing materials. The heat energy further captured by energy-storage phosphorous building gypsum in the endothermic and exothermic stages is 28.19 J/g and 28.64 J/g, respectively, which can be used to prepare energy-saving building materials.