Neuraminidase (NA) is a membrane surface antigen which helps in the release of influenza viruses from the host cells after replication. Anti-influenza drugs such as zanamivir bind with eight highly conserved functional residues (R118, D151, R152, R224, E276, R292, R371, and Y406) in the active site of NA, thus restricting the viral release the from host cells. Binding of the drug in active site inhibits the ability of enzyme to cleave sialic acid residues on the cell membrane. Reports on the emergence of zanamivir-resistant strains of H1N1 Influenza virus necessitated a search for alternative drug candidates, preferably from plant source due to their known benefits such as less or no side effects, availability, and low cost. Withaferin A (WA), an active constituent of Withania somnifera ayurvedic herb, has been shown to have a broad range of medicinal properties including its anti-viral activity. The present study demonstrated that WA has the potential to attenuate the neuraminidase of H1N1 influenza. Our docking and simulation results predicted high binding affinity of the WA toward NA and revealed several interesting molecular interactions with the residues which are catalytically important during molecular dynamic simulations. The results presented in the article could be of high importance for further designing of target-specific anti-influenza drug candidates.
In practical airborne radar, the interference signals in training snapshots usually lead to inaccurate estimation of the clutter covariance matrix (CCM) in space-time adaptive processing (STAP), which seriously degrade radar performance and even occur target self-nulling phenomenon. To solve this problem, a knowledge-aided sparse recovery (SR) STAP algorithm based on Gaussian kernel function is developed. The proposed method distinguishes clutter components and interference signals in training snapshots by the priori knowledge that the clutter components are distributed along the clutter ridge, which dislodges interference signals from training snapshots by Gaussian kernel similar degree. Thus, the CCM is estimated by utilizing these snapshots. Finally, the proposed STAP weight vector is built, which is convenient for the subsequent signal processing. The experimental results were performed to verify the effectiveness and superiority of the proposed method. The test results also show that the proposed algorithm completely removes the interference signals, accurately estimates CCM, and improves the moving target detection performance in small-sample and non-homogeneous clutter environments.
This study is concerned with motion analysis and hydroelastic response of a floating offshore wind turbine to wave loads. The novel floating structure, made of prestressed concrete, is designed to support multiple wind turbines, and it rotates according to the environmental loads to face the incoming wind. The floating structure is attached to a mooring line that allows the rotation of the structure in response to the environmental loads. The floating structure is an equilateral triangular platform. The wind turbines are located at the vertices. Due to the dimensional characteristics of the structure, elasticity of the floating platform plays an important role in its dynamics. While the dynamic response of the structure is driven by both aerodynamic and hydrodynamic loads, this study focuses on the motion and elastic response of the novel floating structure to the hydrodynamic loads only. The three dimensional hydrodynamic loads on the floating structure are obtained by use of the constant panel approach of the Green function method, subject to linear mooring loads. A finite element analysis is carried out for the calculation of the elastic response of the structure. Computations of the integrated linear structure-fluid-structure interaction problem are performed in frequency domain using HYDRAN, a computer program written for the linear dynamic analysis of rigid and flexible bodies. Results presented here include the response amplitude operators of both the rigid and flexible bodies to incoming waves of various frequencies and directions. Also presented are the wave-induced stresses on the floating body, and the elastic deformations.
Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suffer information leakage and equipment damage when being attacked by ICPS intrusion. Therefore, the network security of ICPS cannot be ignored, and researchers have conducted in-depth research on network intrusion detection for ICPS. Though machine learning and deep learning methods are often used for network intrusion detection, the problem of data imbalance can cause the model to pay attention to the misclassification cost of the prevalent class, but ignore that of the rare class, which seriously affects the classification performance of network intrusion detection models. Considering the powerful generative power of the diffusion model, we propose an ICPS Intrusion Detection system based on the Diffusion model (IDD). Firstly, data corresponding to the rare class is generated by the diffusion model, which makes the training dataset of different classes balanced. Then, the improved BiLSTM classification network is trained on the balanced training set. Extensive experiments are conducted to show that the IDD method outperforms the existing baseline method on several available datasets.
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