Falling objects from façades often lead to serious accidents, which has become a technical problem to be solved urgently. This paper established a database of potential safety hazard of falling objects from façades via the statistics of detection façades in Shanghai. Some detailed insufficiencies and swelling defects were analyzed. A risk assessment system of falling objects from façades was established using the Fault Tree Analysis (FTA) method. The weight coefficient was determined by the Analytic Hierarchy Process (AHP). The beta distribution was used to fit the probability distribution of the occurrence probability of the elementary risk event. Based on the Monte Carlo model, the risk of falling objects from façades was assessed. A probability distribution of the risk probability of falling objects from façades and the importance of elementary risk factors were obtained. Some risk control measures of falling objects from façades were proposed.

Resulting from severe inflammation and cell destruction, COVID-19 patients could develop pulmonary fibrosis (PF), which remains in the convalescent stage. Nevertheless, how immune response participates in the pathogenesis of PF progression is not well defined. To investigate that question, 12 patients with severe COVID-19 were included in the study. Peripheral mononuclear cell (PBMC) samples were collected shortly after their admission and proceeded for single-cell RNA sequencing (scRNA-seq). After 14 days of discharge, the patients were revisited for chest CT scan. PF index (FI) was computed by AI-assisted CT images. Patients were categorized into FI^hi and FI^lo based on median of FI. By scRNA-seq analysis, our data demonstrated that frequency of CD4+ activated T cells and Treg cells were approximately 3-fold higher in FI^hi patients compared with FI^lo ones (p < 0.034 for all). By dissecting the differentially expressed genes, we found an overall downregulation of IFN-responsive genes (STAT1, IRF7, ISG15, ISG20, IFIs, and IFITMs) and S100s alarmins (S100A8, S100A9, S100A12, etc.) in all T-cell clusters, and cytotoxicity-related genes (GZMB, PRF1, and GNLY) in CTLs and γδ T cells in the FI^hi cohort, compared with FI^lo subjects. The GSEA analysis illustrated decreased expression of genes enriched in IFN signaling, innate immune response, adaptive immune response in T cells, NK cells, and monocytes in FI^hi patients compared with FI^lo ones. In conclusion, these data indicated that the attenuated IFN-responsive genes and their related signaling pathways could be critical for PF progression in COVID-19 patients.

This paper proposes a prefabricated frame rocking wall (PFRW) structure system in which beams, columns, and rocking walls are all prefabricated components. The rocking wall and the frame are connected by energy-dissipating connectors, and three prestressed tendons are arranged inside the rocking wall. A quasi-static test for the PFRW structure and a conventional frame (CF) structure was conducted. The research results show that the seismic load-resisting capacity of the PFRW structure is increased by about 190% relative to the CF structure, and the energy dissipation coefficient of the PFRW structure is increased to twice that of the CF structure.

Unmanned Aerial Vehicles (UAVs) have been widely used in the fields of military and civilian due to their small size, low cost, and high flexibility. However, various security threats gradually increase by continuously expanding the scopes of UAVs’ applications. Among all the security threats, the false spoofing attack is a typical network threat faced by UAVs. In real scenes, there is inevitably a large amount of random noise in the collected signals of UAVs, which leads to interference of original signals of UAVs, thus affecting the detection accuracy of abnormal signals for UAVs. This paper proposes an abnormal signals detection method for UAVs based on bimodal fusion. First, the collected signals of UAVs are modally transformed to generate waveform images. Second, the Double Shortcuts Zero-Bias ResNet is used to extract bimodal features from the waveform image modal data and the frequency-domain signal modal data of original signals for UAVs. Then, the bimodal fusion classifier is used to fuse the classification results of different modal data to alleviate the interference of random noises to the original signal of UAVs and improve the detection accuracy of abnormal signals on UAVs. Simulation results indicate that based on the original ADS-B signal data set of UAVs with random noises, the proposed method can improve the detection accuracy compared with the existing abnormal signal detection methods.