The Immense Impact of Reverse Edges on Large Hierarchical Networks

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

Risk Management of Clinical Reference Dosimetry of a Large Hospital Network Using Statistical Process Control

Managing TG-51 reference dosimetry in a large hospital network can be a challenging task. The objectives of this study are to investigate the effectiveness of using Statistical Process Control (SPC) to manage TG-51 workflow in such a network. All the sites in the network performed the annual reference dosimetry in water according to TG-51. These data were used to cross-calibrate the same ion chambers in plastic phantoms for monthly QA output measurements. An energy-specific dimensionless beam quality cross-calibration factor, , was derived to monitor the process across multiple sites. The SPC analysis was then performed to obtain the mean, , standard deviation, σk, the Upper Control Limit (UCL) and Lower Control Limit (LCL) in each beam. This process was first applied to 15 years of historical data at the main campus to assess the effectiveness of the process. A two-year prospective study including all 30 linear accelerators spread over the main campus and seven satellites in the network followed. The ranges of the control limits (±3σ) were found to be in the range of 1.7% - 2.6% and 3.3% - 4.2% for the main campus and the satellite sites respectively. The wider range in the satellite sites was attributed to variations in the workflow. Standardization of workflow was also found to be effective in narrowing the control limits. The SPC is effective in identifying variations in the workflow and was shown to be an effective tool in managing large network reference dosimetry.

A Fast Method for Shortest-Path Cover Identification in Large Complex Networks

Fast identifying the amount of information that can be gained by measuring a network via shortest-paths is one of the fundamental problem for networks exploration and monitoring.However,the existing methods are time-consuming for even moderate-scale networks.In this paper,we present a method for fast shortest-path cover identification in both exact and approximate scenarios based on the relationship between the identification and the shortest distance queries.The effectiveness of the proposed method is validated through synthetic and real-world networks.The experimental results show that our method is 105 times faster than the existing methods and can solve the shortest-path cover identification in a few seconds for large-scale networks with millions of nodes and edges.

AN IDENTITY-BASED KEY AGREEMENT SCHEME FOR LARGE SCALE SENSOR NETWORKS

To solve the problems of high memory occupation, low connectivity and poor resiliency against node capture, which existing in the random key pre-distribution techniques while applying to the large scale Wireless Sensor Networks （WSNs）, an Identity-Based Key Agreement Scheme （IBKAS） is proposed based on identity-based encryption and Elliptic Curve Diffie-Hellman （ECDH）. IBKAS can resist man-in-the-middle attacks and node-capture attacks through encrypting the key agreement parameters using identity-based encryption. Theoretical analysis indicates that comparing to the random key pre-distribution techniques, IBKAS achieves significant improvement in key connectivity, communication overhead, memory occupation, and security strength, and also enables efficient secure rekcying and network expansion. Furthermore, we implement IBKAS for TinyOS-2.1.2 based on the MICA2 motes, and the experiment results demonstrate that IBKAS is feasible for infrequent key distribution and rekeying for large scale sensor networks.

Estimation of Data Transfer Routes Fractal Dimension in Large Scale Networks

The article is devoted to the evaluation of fractal properties of routing data in computer large scale networks. Implemented the study of percolation network topological structures of large dimension and made their transformation into fractal macrostructure. An example of calculating the fractal dimension of the data path for the boundary of the phase transition between the states of network connectivity. The dependence of the fractal dimension of the percolation cluster on the size of the square δ-cover and conductivity value network of large dimension. It is shown that for the value of the fractal dimension of the route dc ≈ 1.5, network has a stable dynamics of development and size of clusters are optimized with respect to the current load on the network.

GBP-WAHSN:A Group-Based Protocol for Large Wireless Ad Hoc and Sensor Networks

Grouping nodes gives better performance to the whole network by diminishing the average network delay and avoiding unnecessary message forwarding and additional overhead. Many routing protocols for ad-hoc and sensor networks have been designed but none of them are based on groups. In this paper, we will start defining group-based topologies, and then we will show how some wireless ad hoc sensor networks （WAHSN） routing protocols perform when the nodes are arranged in groups. In our proposal connections between groups are established as a function of the proximity of the nodes and the neighbor＇s available capacity （based on the node＇s energy）. We describe the architecture proposal, the messages that are needed for the proper operation and its mathematical description. We have also simulated how much time is needed to propagate information between groups. Finally, we will show a comparison with other architectures.

MRHCA： a nonparametric statistics based method for hub and co-expression module identification in large gene co-expression network

Background： Gene co-expression and differential co-expression analysis has been increasingly used to study co- functional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods： In this study, we develop a nonparametric approach to identify hub genes and modules in a large co- expression network with low computational and memory cost, namely MRHCA. Results： We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential co- expression analysis to E. coil and TCGA cancer data, we have identified significant condition specific activated genes in E. coil and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion： Our analysis has demonstrated MRItCA can （i） deal with large association networks, （ii） rigorously assess statistical significance for hubs and module sizes, （iii） identify co-expression modules with low associations, （iv） detect small and significant modules, and （v） allow genes to be present in more than one modules, compared with existing methods.

Multi-Wavelength Ultra-Weak Fiber Bragg Grating Arrays for Long-Distance Quasi-Distributed Sensing

Fiber Bragg grating(FBG)array,consisting of a number of sensing units in a single optical fiber,can be practically applied in quasi-distributed sensing networks.Serious signal crosstalk occurring between large-serial of identical FBGs,however,has limited the further increase in the number of sensing units,thus restricting applications only for short-distance sensing networks.To reduce the signal crosstalk,we design two novel types of 10-kilometer-long FBG arrays with 10000 equally spaced gratings,written on-line using a customized grating inscription system,which is affiliated to a drawing tower.Main factors causing signal crosstalk,such as spectral shadowing and multiple reflections,are firstly investigated in theory.Consistent with the theoretical findings,experimental results are proving that ultra-weak(the reflectivity of—40 dB)and multi-wavelength gratings of a number more than 10000 can be readily identified,with satisfied low crosstalk.The maximum attenuation of grating signal and minimum signal-to-noise ratio(SNR)in a single-wavelength array are 10.69 dB and 5.62 dB,respectively.As a comparison,by increasing the number of central wavelengths to three,the attenuation can be effectively reduced to 5.54dB and the minimum SNR has been improved to 8.14 dB.The current study significantly enhances the multiplexing capacity of FBG arrays and demonstrates promising potentials for establishing large-capacity quasi-distributed sensing networks.