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Porcine deltacoronavirus (PDCoV) is highly pathogenic to piglets, and no specific drugs or vaccines are available for the prevention and treatment of PDCoV infection, the need for antiviral therapies is pressing. HSP90 inhibitors have potent inhibitory effects against the replication of numerous viruses, hence we evaluated three HSP90 inhibitors, 17-AAG, VER-82576, and KW-2478, for their effects on PDCoV infection in vitro. We evaluated their effectivenesses at suppressing PDCoV by qRT-PCR, western blot, and TCID

In this study, three machine learning techniques, the XGBoost (Extreme Gradient Boosting), LSTM (Long Short-Term Memory Networks), and ARIMA (Autoregressive Integrated Moving Average Model), are utilized to deal with the time series prediction tasks for coastal bridge engineering. The performance of these techniques is comparatively demonstrated in three typical cases, the wave-load-on-deck under regular waves, structural displacement under combined wind and wave loads, and wave height variation along with typhoon/hurricane approaching. To enhance the prediction accuracy, a typical data preprocessing method is adopted and an improved prediction framework for the LSTM model after the rolling forecast prediction is proposed. The obtained results show that: (a) When making a prediction on data featured with periodic regularity, both the XGBoost and ARIMA models perform well, and the XGBoost model can make predictions multi-step ahead, (b) The ARIMA model can predict just one step ahead based on aperiodic dataset with limited amplitude more accurately, while the XGBoost and LSTM models can predict multi-step ahead with appropriate data preprocessing, and (c) All the three models can predict the data tendency with model updating over time, but the prediction accuracy of the LSTM model is more favorable. The successful application of these three machine learning techniques can provide guidance to resolve engineering problems with time-history prediction requirements.

English billboards are common in our daily work and life, and how to effectively recognize them is a problem worthy of study. This paper mainly uses Progressive Scale Expansion Network (PSENet) and Convolutional Recurrent Neural Network (CRNN) to conduct text recognition experiments on English billboards. The English billboards are divided into four categories: text distortion, larger background, special text font, and normal format. PSENet is used for text detection, and CRNN is used for text recognition. The identification results of PSENet and CRNN revealed differences among the four groups of English billboards: normal format text recognition is the best, and large background text recognition is the worst, and the recognition effect of text distortion and special text font is less accurate. Finally, the work of this paper is analyzed and summarized, and the future work is prospected.

As long-span cross-sea bridges extend to deeper sea areas, the bridge pile tends to increase in its slenderness ratio and becomes more susceptible to waves. To improve the structural stability at the construction stage, this study analyses wave-induced response of foundations. The wave theory and the method used for computing wave forces on foundations are first introduced. Then, a pile group foundation is taken as the research object, and different pile lengths ranging from 16 m to 46 m are considered. The wave-induced response of the piles and the cap is calculated. After understanding the effect of the pile length, three optimized foundations are proposed with the aim of reducing the free length of the pile, and the corresponding finite element models are established to compare their wave-induced response. The results show that the displacement at the top of the foundation increases with the increase in the pile length until the cap partly emerges from water and so does the internal force at the bottom. Setting a constraint in the middle of the piles can reduce their free lengths and is favourable to the wave-induced response of the foundation except for the shearing force. A stronger constraint shows better effects on improvement of the stability of the foundation. The conclusions provide reference for optimization on pile foundations of deepwater bridges.

In mountainous areas, more challenges are expected for the construction of long-span bridges. The flutter instability during erection is an outstanding issue due to flexible structural characteristics and strong winds with large angles of attack. Taking the suspension bridge as an example, the flutter stability of the bridge with different suspending sequences was investigated. First, the dynamic characteristics of the bridge during erection were computed by the finite element software ANSYS, along with the effects on flutter stability discussed. Then, different aerodynamic shapes of the bridge girder during erection were considered. The aerodynamic coefficients and the critical flutter state were determined by wind tunnel tests. Based on the above analysis, some structural measures are proposed for improving the flutter stability of the bridge during erection. The results show that the flutter stability of the bridge during erection is related to the suspending sequence and the aerodynamic shape of the girder. Owing to the structural dynamic characteristics, the bridge has better flutter stability when the girder segments are suspended symmetrically from the two towers to the mid-span. Considering the construction requirement that the bridge deck should be laid without intervals, this structural superiority is seriously weakened by the unfavorable aerodynamic shape of the girder. In order to improve the flutter stability of the bridge during erection, an effective way is to adopt some temporary structural strengthening measures.