Dynamic Behavior of Interacting between Epidemics and Cascades on Heterogeneous Networks

Epidemic spreading and cascading failure are two important dynamical processes over complex networks. They have been investigated separately for a long history. But in the real world, these two dynamics sometimes may interact with each other. In this paper, we explore a model combined with SIR epidemic spreading model and local loads sharing cascading failure model. There exists a critical value of tolerance parameter that whether the epidemic with high infection probability can spread out and infect a fraction of the network in this model. When the tolerance parameter is smaller than the critical value, cascading failure cuts off abundant of paths and blocks the spreading of epidemic locally. While the tolerance parameter is larger than the critical value, epidemic spreads out and infects a fraction of the network. A method for estimating the critical value is proposed. In simulation, we verify the effectiveness of this method in Barab\'asi-Albert (BA) networks

A Scale-Free Topology Construction Model for Wireless Sensor Networks

A local-area and energy-efficient (LAEE) evolution model for wireless sensor networks is proposed. The process of topology evolution is divided into two phases. In the first phase, nodes are distributed randomly in a fixed region. In the second phase, according to the spatial structure of wireless sensor networks, topology evolution starts from the sink, grows with an energy-efficient preferential attachment rule in the new node's local-area, and stops until all nodes are connected into network. Both analysis and simulation results show that the degree distribution of LAEE follows the power law. This topology construction model has better tolerance against energy depletion or random failure than other non-scale-free WSN topologies.Comment: 13pages, 3 figure

Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.Comment: 11 pages, 4 figure

Growth Inhibition and Apoptosis Induced by Osthole, A Natural Coumarin, in Hepatocellular Carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most commonly diagnosed tumors worldwide and is known to be resistant to conventional chemotherapy. New therapeutic strategies are urgently needed for treating HCC. Osthole, a natural coumarin derivative, has been shown to have anti-tumor activity. However, the effects of osthole on HCC have not yet been reported. METHODS AND FINDINGS: HCC cell lines were treated with osthole at various concentrations for 24, 48 and 72 hours. The proliferations of the HCC cells were measured by MTT assays. Cell cycle distribution and apoptosis were determined by flow cytometry. HCC tumor models were established in mice by subcutaneously injection of SMMC-7721 or Hepa1-6 cells and the effect of osthole on tumor growths in vivo and the drug toxicity were studied. NF-κB activity after osthole treatment was determined by electrophoretic mobility shift assays and the expression of caspase-3 was measured by western blotting. The expression levels of other apoptosis-related genes were also determined by real-time PCR (PCR array) assays. Osthole displayed a dose- and time-dependent inhibition of the HCC cell proliferations in vitro. It also induced apoptosis and caused cell accumulation in G2 phase. Osthole could significantly suppress HCC tumor growth in vivo with no toxicity at the dose we used. NF-κB activity was significantly suppressed by osthole at the dose- and time-dependent manner. The cleaved caspase-3 was also increased by osthole treatment. The expression levels of some apoptosis-related genes that belong to TNF ligand family, TNF receptor family, Bcl-2 family, caspase family, TRAF family, death domain family, CIDE domain and death effector domain family and CARD family were all increased with osthole treatment. CONCLUSION: Osthole could significantly inhibit HCC growth in vitro and in vivo through cell cycle arrest and inducing apoptosis by suppressing NF-κB activity and promoting the expressions of apoptosis-related genes

Node Deployment Algorithm Based on Connected Tree for Underwater Sensor Networks

Designing an efficient deployment method to guarantee optimal monitoring quality is one of the key topics in underwater sensor networks. At present, a realistic approach of deployment involves adjusting the depths of nodes in water. One of the typical algorithms used in such process is the self-deployment depth adjustment algorithm (SDDA). This algorithm mainly focuses on maximizing network coverage by constantly adjusting node depths to reduce coverage overlaps between two neighboring nodes, and thus, achieves good performance. However, the connectivity performance of SDDA is irresolute. In this paper, we propose a depth adjustment algorithm based on connected tree (CTDA). In CTDA, the sink node is used as the first root node to start building a connected tree. Finally, the network can be organized as a forest to maintain network connectivity. Coverage overlaps between the parent node and the child node are then reduced within each sub-tree to optimize coverage. The hierarchical strategy is used to adjust the distance between the parent node and the child node to reduce node movement. Furthermore, the silent mode is adopted to reduce communication cost. Simulations show that compared with SDDA, CTDA can achieve high connectivity with various communication ranges and different numbers of nodes. Moreover, it can realize coverage as high as that of SDDA with various sensing ranges and numbers of nodes but with less energy consumption. Simulations under sparse environments show that the connectivity and energy consumption performances of CTDA are considerably better than those of SDDA. Meanwhile, the connectivity and coverage performances of CTDA are close to those depth adjustment algorithms base on connected dominating set (CDA), which is an algorithm similar to CTDA. However, the energy consumption of CTDA is less than that of CDA, particularly in sparse underwater environments

A Noise-Filtering Method for Link Prediction in Complex Networks.

Link prediction plays an important role in both finding missing links in networked systems and complementing our understanding of the evolution of networks. Much attention from the network science community are paid to figure out how to efficiently predict the missing/future links based on the observed topology. Real-world information always contain noise, which is also the case in an observed network. This problem is rarely considered in existing methods. In this paper, we treat the existence of observed links as known information. By filtering out noises in this information, the underlying regularity of the connection information is retrieved and then used to predict missing or future links. Experiments on various empirical networks show that our method performs noticeably better than baseline algorithms

A New Node Deployment and Location Dispatch Algorithm for Underwater Sensor Networks

Considering that deployment strategies for underwater sensor networks should contribute to fully connecting the networks, a Guaranteed Full Connectivity Node Deployment (GFCND) algorithm is proposed in this study. The GFCND algorithm attempts to deploy the coverage nodes according to the greedy iterative strategy, after which the connectivity nodes are used to improve network connectivity and fully connect the whole network. Furthermore, a Location Dispatch Based on Command Nodes (LDBCN) algorithm is proposed, which accomplishes the location adjustment of the common nodes with the help of the SINK node and the command nodes. The command nodes then dispatch the common nodes. Simulation results show that the GFCND algorithm achieves a comparatively large coverage percentage and a fully connected network; furthermore, the LDBCN algorithm helps the common nodes preserve more total energy when they reach their destination locations

Change of network load due to node removal

The interplay between topology changes and the redistribution of traffic plays a significant role in many real-world networks. In this paper we study how the load of the remaining network changes when nodes are removed. This removal operation can model attacks and errors in networks, or the planned control of network topology. We consider a scenario similar to the data communication networks, and measure the load of a node by its betweenness centrality. By analysis and simulations, we show that when a single node is removed, the change of the remaining network’s load is positively correlated with the degree of the removed node. In multiple-node removal, by comparing several node removal schemes, we show in detail how significantly different the change of the remaining network’s load will be between starting the removal from small degree/betweenness nodes and from large degree/betweenness nodes. Moreover, when starting the removal from small degree/betweenness nodes, we not only observe that the remaining network’s load decreases, which is consistent with previous studies, but also find that the load of hubs keeps decreasing. These results help us to make a deeper understanding about the dynamics after topology changes, and are useful in planned control of network topology