A Novel Design Method of Stream Ciphers Based on Table-Element Permutation

In this paper, a new stream ciphers design method (named TEP) is proposed to base on the table-element nonlinear permutation. A number of words are generated by n-LFSRs(linear feedback shift register) input to a table. In the table, every word is dealt with by the nonlinear transforms and the several words are combined with nonlinear function to produce keystream words. The algorithm is simplicity and the secret key is generated rapidly. The result of many simulation experiments show that the keystream by TEP method generating can meet DIEHARD statistics tests. The approach is efficient to design stream ciphers

Effect of human activated NRAS on replication of delNS1 H5N1 influenza virus in MDCK cells

<p>Abstract</p> <p>Background</p> <p>RAS, coded by <it>ras </it>proto-oncogenes, played an important role in signal transmission to regulate cell growth and differentiation. Host activation of RAS was significant for IFN-sensitive vaccinia virus (delE3L) or attenuate influenza virus in unallowable cells.</p> <p>Results</p> <p>Huamn <it>NRAS </it>gene was activated by mutating in codon 61. Then the activation of NRAS was detected by western blot in MDCK cells. The delNS1 H5N1 influenza virus with deletion of NS1 eIF4GI binding domain was weak multiplication in MDCK cells. And the replication of delNS1 virus and expression of IFN-beta and IRF-3 were detected by Real-time PCR in MDCK cells infected with delNS1 virus. It was found that the delNS1 virus had a significant increase in MDCK cells when the NRAS was activated, and yet, expression of IRF-3 and IFN-beta were restrained.</p> <p>Conclusions</p> <p>The study demonstrated that activated NRAS played an important part for delNS1 virus replication in MDCK cells. Activated NRAS might be down-regulating the expression of antiviral cellular factors in delNS1 virus infected cells.</p

Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions

Quantitative photoacoustic tomography (QPAT) is a valuable tool in characterizing ovarian lesions for accurate diagnosis. However, accurately reconstructing a lesion\u27s optical absorption distributions from photoacoustic signals measured with multiple wavelengths is challenging because it involves an ill-posed inverse problem with three unknowns: the Grüneisen paramete

Research on detection of transmission line corridor external force object containing random feature targets

With the objective of achieving “double carbon,” the power grid is placing greater importance on the security of transmission lines. The transmission line corridor has complex situations with external force targets and irregularly featured objects including smoke. For this reason, in this paper, the high-performance YOLOX-S model is selected for transmission line corridor external force object detection and improved to enhance model multi-object detection capability and irregular feature extraction capability. Firstly, to enhance the perception capability of external force objects in complex environment, we improve the feature output capability by adding the global context block after the output of the backbone. Then, we integrate convolutional block attention module into the feature fusion operation to enhance the recognition of objects with random features, among the external force targets by incorporating attention mechanism. Finally, we utilize EIoU to enhance the accuracy of object detection boxes, enabling the successful detection of external force targets in transmission line corridors. Through training and validating the model with the established external force dataset, the improved model demonstrates the capability to successfully detect external force objects and achieves favorable results in multi-class target detection. While there is improvement in the detection capability of external force objects with random features, the results indicate the need to enhance smoke recognition, particularly in further distinguishing targets between smoke and fog

MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors

High-fidelity 3D scene reconstruction from monocular videos continues to be challenging, especially for complete and fine-grained geometry reconstruction. The previous 3D reconstruction approaches with neural implicit representations have shown a promising ability for complete scene reconstruction, while their results are often over-smooth and lack enough geometric details. This paper introduces a novel neural implicit scene representation with volume rendering for high-fidelity online 3D scene reconstruction from monocular videos. For fine-grained reconstruction, our key insight is to incorporate geometric priors into both the neural implicit scene representation and neural volume rendering, thus leading to an effective geometry learning mechanism based on volume rendering optimization. Benefiting from this, we present MonoNeuralFusion to perform the online neural 3D reconstruction from monocular videos, by which the 3D scene geometry is efficiently generated and optimized during the on-the-fly 3D monocular scanning. The extensive comparisons with state-of-the-art approaches show that our MonoNeuralFusion consistently generates much better complete and fine-grained reconstruction results, both quantitatively and qualitatively.Comment: 12 pages, 12 figure