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Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans , achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans , where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the radar signal of ballistic target is easily affected by the environment and the target recognition accuracy is low, a radar cross section (RCS) recognition method of ballistic target based on dual-tree complex wavelet transform (DTCWT) and variational autoencoder (VAE) is proposed. Firstly, the dynamic datas of ballistic target RCS are preprocessed by DTCWT. Then, the hidden variable features of target are extracted by VAE. Finally, the support vector machine (SVM) classifier is used to identify the data. Experimental results show that compared with the existing methods, the proposed method has higher recognition probability and better robustness. [ABSTRACT FROM AUTHOR]

An identification method of radar targets based on improved Dijkstra algorithm with time-frequency domain filtering is proposed to solve the problem that the accuracy of micro-Doppler feature extraction of radar targets in the middle of the trajectory is not high, resulting in low target recognition rates. The improved Dijkstra algorithm is used for extracting the instantaneous Doppler features of the strongest component in the multi-component echo signal. Then the strongest component is filtered through the time-frequency domain filter. So the instantaneous Doppler features of the multi-component signal can be extracted in proper sequence, and features are applied to identify radar targets in the middle of the trajectory. The simulation results demonstrate that this method is suitable for a variety of micro-motion forms, the micro-Doppler features of the echo signal have higher accuracy, and the average recognition rate of radar targets in the middle of the trajectory is increased effectively. [ABSTRACT FROM AUTHOR]

For the problem of target recognition in synthetic aperture radar (SAR) image, a method of SAR target recognition based on improved convolution neural network (CNN) and data augmentation is proposed. Firstly, Dropout is brought in the training phase to randomly delete some neurons, so that the generalization ability of the network is enhanced. Secondly, L2 regularization is introduced into the network to reduce the structural risk and effectively restrain the over fitting. Then, Adam is used to optimize the network to improve the convergence efficiency of the model. Finally, the preferred rotation data augmentation method is employed for expanding the data set of SAR target. Through the improved network and increased data, the recognition accuracy and generalization of the model are enhanced. Experiments on moving and stationary target acquisition and recognition (MSTAR) data set show that the proposed method has higher recognition accuracy and better generalization. [ABSTRACT FROM AUTHOR]

Coating the surface of cathode materials with Li3PO4 to improve the cycling performance is commonly practiced; however, obtaining an effective and uniform coating of Li3PO4 on cathode materials is extremely difficult due to the lack of strong bonds between them. Herein, this issue is solved by coating the surface of LiNi0.5Mn1.5O4 with Li3PO4 via a novel hydrothermal method. It is shown that a uniform Li3PO4 surface coating can only be obtained by introducing a small amount of additional metal ions, such as Co, in the starting solution. X‐ray photoelectron microscopy combined with scanning transmission electron microscopy reveals that the Co ions diffuse into the 16c sites of the surface of the spinel LiNi0.5Mn1.5O4 structure, making the surface layer act as a bridging layer that connects olivine Li3PO4 with bulk spinel LiNi0.5Mn1.5O4. The high‐voltage LiNi0.5Mn1.5O4 half‐cell with the optimal Li3PO4 coating shows a discharge capacity retention of 95.8% after 50 cycles at 55 °C, compared with the only 81.1% discharge capacity retention of the bare counterpart. [ABSTRACT FROM AUTHOR]

Mixed silicon and carbon (Si–C) materials with high capacity are ideal candidates for the substitution of graphite or other carbon anodes in lithium-ion batteries. However, the low coulombic efficiency of the Si–C anode in the first cycle due to the formation of a solid electrolyte interphase and the consumption of active lithium have hindered its commercial applications. Here, we report using Li2S as a prelithiation material to compensate for the loss of active lithium in the first cycle and, consequently, to enhance the specific energy of lithium-ion batteries. The Si–C anode has an initial discharge specific capacity of ∼738 mA h g−1 and a charge specific capacity of ∼638 mA h g−1. The prelithiation material with a core–shell structure is prepared by mixing Li2S, Ketjenblack (KB) and poly(vinylpyrrolidone) (PVP) in anhydrous ethanol, which shows a high irreversible capacity of ∼1084 mA h g−1. The effect of the compensation of lost active lithium is verified via a LiFePO4 (Li2S)/Si–C full cell, which exhibits not only a high specific capacity but also a stable cycling performance. The specific energy of the LiFePO4 (Li2S)/Si–C full cell shows a remarkable increase compared to the LiFePO4/Si–C full cell, exhibiting ∼13.4%, ∼26.7%, ∼65.0% and ∼110.2% more specific energy after the 1st, 10th, 100th and 200th cycle, respectively. [ABSTRACT FROM AUTHOR]