Prevention and Trust Evaluation Scheme Based on Interpersonal Relationships for Large-Scale Peer-To-Peer Networks

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Nonlinear analysis of stress and strain for a clay core rock-fill dam with FEM

AbstractBased on the Duncan-Chang hyperbolic nonlinear elastic material model, this paper carried out the stress and strain numerical analysis of a clay core rock-fill dam, which is a certain building reservoir dam in Yunnan province. By loading on each layer step by step and with the static nonlinear finite element simulation of deposition, it obtained the results of the stress and deformation of the clay core rock-fill dam. The calculation showed that the great difference in deformation modulus causes non-smooth variations in deformation, stress and strain between the transition area and the rock-debris fill. From the analysis it can be seen that the present design of the dam is reasonable since no any abnormal stresses and deformations occurred in the dam. Moreover, this also indicated a feasible and provided a valuable evident for the optimization of cross-section zones in a project

Sparse general non-negative matrix factorization based on left semi-tensor product

The dimension reduction of large scale high-dimensional data is a challenging task, especially the dimension reduction of face data and the accuracy increment of face recognition in the large scale face recognition system, which may cause large storage space and long recognition time. In order to further reduce the recognition time and the storage space in the large scale face recognition systems, on the basis of the general non-negative matrix factorization based on left semi-tensor (GNMFL) without dimension matching constraints proposed in our previous work, we propose a sparse GNMFL/L (SGNMFL/L) to decompose a large number of face data sets in the large scale face recognition systems, which makes the decomposed base matrix sparser and suppresses the decomposed coefficient matrix. Therefore, the dimension of the basis matrix and the coefficient matrix can be further reduced. Two sets of experiments are conducted to show the effectiveness of the proposed SGNMFL/L on two databases. The experiments are mainly designed to verify the effects of two hyper-parameters on the sparseness of basis matrix factorized by SGNMFL/L, compare the performance of the conventional NMF, sparse NMF (SNMF), GNMFL, and the proposed SGNMFL/L in terms of storage space and time efficiency, and compare their face recognition accuracies with different noises. Both the theoretical derivation and the experimental results show that the proposed SGNMF/L can effectively save the storage space and reduce the computation time while achieving high recognition accuracy and has strong robustness

Overview of compressed sensing: Sensing model, reconstruction algorithm, and its applications

With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist-Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials. © 2020 by the authors

Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering

Detecting events from social media data streams is gradually attracting researchers. The innate challenge for detecting events is to extract discriminative information from social media data thereby assigning the data into different events. Due to the excessive diversity and high updating frequency of social data, using supervised approaches to detect events from social messages is hardly achieved. To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner. However, two intrinsic issues exist in benchmark methods: conventional GCL can only roughly explore partial attributes, thereby insufficiently learning the discriminative information of social messages; for benchmark methods, the learned embeddings are clustered in the latent space by taking advantage of certain specific prior knowledge, which conflicts with the principle of unsupervised learning paradigm. In this paper, we propose a novel unsupervised social media event detection method via hybrid graph contrastive learning and reinforced incremental clustering (HCRC), which uses hybrid graph contrastive learning to comprehensively learn semantic and structural discriminative information from social messages and reinforced incremental clustering to perform efficient clustering in a solidly unsupervised manner. We conduct comprehensive experiments to evaluate HCRC on the Twitter and Maven datasets. The experimental results demonstrate that our approach yields consistent significant performance boosts. In traditional incremental setting, semi-supervised incremental setting and solidly unsupervised setting, the model performance has achieved maximum improvements of 53%, 45%, and 37%, respectively.Comment: Accepted by Knowledge-Based System

The Role of Toll-Like Receptors in Skin Host Defense, Psoriasis, and Atopic Dermatitis.

As the key defense molecules originally identified in Drosophila, Toll-like receptor (TLR) superfamily members play a fundamental role in detecting invading pathogens or damage and initiating the innate immune system of mammalian cells. The skin, the largest organ of the human body, protects the human body by providing a critical physical and immunological active multilayered barrier against invading pathogens and environmental factors. At the first line of defense, the skin is constantly exposed to pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), and TLRs, expressed in a cell type-specific manner by various skin cells, serve as key molecules to recognize PAMPs and DAMPs and to initiate downstream innate immune host responses. While TLR-initiated inflammatory responses are necessary for pathogen clearance and tissue repair, aberrant activation of TLRs will exaggerate T cell-mediated autoimmune activation, leading to unwanted inflammation, and the development of several skin diseases, including psoriasis, atopic dermatitis, systemic lupus erythematosus, diabetic foot ulcers, fibrotic skin diseases, and skin cancers. Together, TLRs are at the interface between innate immunity and adaptive immunity. In this review, we will describe current understanding of the role of TLRs in skin defense and in the pathogenesis of psoriasis and atopic dermatitis, and we will also discuss the development and therapeutic effect of TLR-targeted therapies

A Novel Whole-Cell Biocatalyst with NAD+ Regeneration for Production of Chiral Chemicals

Background: The high costs of pyridine nucleotide cofactors have limited the applications of NAD(P)-dependent oxidoreductases on an industrial scale. Although NAD(P)H regeneration systems have been widely studied, NAD(P) + regeneration, which is required in reactions where the oxidized form of the cofactor is used, has been less well explored, particularly in whole-cell biocatalytic processes. Methodology/Principal Findings: Simultaneous overexpression of an NAD + dependent enzyme and an NAD + regenerating enzyme (H2O producing NADH oxidase from Lactobacillus brevis) in a whole-cell biocatalyst was studied for application in the NAD +-dependent oxidation system. The whole-cell biocatalyst with (2R,3R)-2,3-butanediol dehydrogenase as the catalyzing enzyme was used to produce (3R)-acetoin, (3S)-acetoin and (2S,3S)-2,3-butanediol. Conclusions/Significance: A recombinant strain, in which an NAD + regeneration enzyme was coexpressed, displayed significantly higher biocatalytic efficiency in terms of the production of chiral acetoin and (2S,3S)-2,3-butanediol. The application of this coexpression system to the production of other chiral chemicals could be extended by using differen

Involvement of Lysosome Membrane Permeabilization and Reactive Oxygen Species Production in the Necrosis Induced by Chlamydia muridarum Infection in L929 Cells

Chlamydiae, obligate intracellular bacteria, are associated with a variety of human diseases. The chlamydial life cycle undergoes a biphasic development: replicative reticulate bodies (RBs) phase and infectious elementary bodies (EBs) phase. At the end of the chlamydial intracellular life cycle, EBs have to be released to the surrounded cells. Therefore, the interactions between Chlamydiae and cell death pathways could greatly influence the outcomes of Chlamydia infection. However, the underlying molecular mechanisms remain elusive. Here, we investigated host cell death after Chlamydia infection in vitro, in L929 cells, and showed that Chlamydia infection induces cell necrosis, as detected by the propidium iodide (PI)-Annexin V double-staining flow-cytometric assay and Lactate dehydrogenase (LDH) release assay. The production of reactive oxygen species (ROS), an important factor in induction of necrosis, was increased after Chlamydia infection, and inhibition of ROS with specific pharmacological inhibitors, diphenylene iodonium (DPI) or butylated hydroxyanisole (BHA), led to significant suppression of necrosis. Interestingly, live-cell imaging revealed that Chlamydia infection induced lysosome membrane permeabilization (LMP). When an inhibitor upstream of LMP, CA-074-Me, was added to cells, the production of ROS was reduced with concomitant inhibition of necrosis. Taken together, our results indicate that Chlamydia infection elicits the production of ROS, which is dependent on LMP at least partially, followed by induction of host-cell necrosis. To our best knowledge, this is the first live-cell-imaging observation of LMP post Chlamydia infection and report on the link of LMP to ROS to necrosis during Chlamydia infection. </p