Measuring Significance of Community Structure in Complex Networks

Many complex systems can be represented as networks and separating a network into communities could simplify the functional analysis considerably. Recently, many approaches have been proposed for finding communities, but none of them can evaluate the communities found are significant or trivial definitely. In this paper, we propose an index to evaluate the significance of communities in networks. The index is based on comparing the similarity between the original community structure in network and the community structure of the network after perturbed, and is defined by integrating all the similarities. Many artificial networks and real-world networks are tested. The results show that the index is independent from the size of network and the number of communities. Moreover, we find the clear communities always exist in social networks, but don't find significative communities in proteins interaction networks and metabolic networks.Comment: 6 pages, 4 figures, 1 tabl

An improved particle swarm optimization combined with double-chaos search

Particle swarm optimization (PSO) has been successfully applied to various complex optimization problems due to its simplicity and efficiency. However, the update strategy of the standard PSO algorithm is to learn from the global best particle, making it difficult to maintain diversity in the population and prone to premature convergence due to being trapped in local optima. Chaos search mechanism is an optimization technique based on chaotic dynamics, which utilizes the randomness and nonlinearity of a chaotic system for global search and can escape from local optima. To overcome the limitations of PSO, an improved particle swarm optimization combined with double-chaos search (DCS-PSO) is proposed in this paper. In DCS-PSO, we first introduce double-chaos search mechanism to narrow the search space, which enables PSO to focus on the neighborhood of the optimal solution and reduces the probability that the swarm gets trapped into a local optimum. Second, to enhance the population diversity, the logistic map is employed to perform a global search in the narrowed search space and the best solution found by both the logistic and population search guides the population to converge. Experimental results show that DCS-PSO can effectively narrow the search space and has better convergence accuracy and speed in most cases

BDS+: An Inter-Datacenter Data Replication System With Dynamic Bandwidth Separation

Many important cloud services require replicating massive data from one datacenter (DC) to multiple DCs. While the performance of pair-wise inter-DC data transfers has been much improved, prior solutions are insufficient to optimize bulk-data multicast, as they fail to explore the rich inter-DC overlay paths that exist in geo-distributed DCs, as well as the remaining bandwidth reserved for online traffic under fixed bandwidth separation scheme. To take advantage of these opportunities, we present BDS+, a near-optimal network system for large-scale inter-DC data replication. BDS+ is an application-level multicast overlay network with a fully centralized architecture, allowing a central controller to maintain an up-to-date global view of data delivery status of intermediate servers, in order to fully utilize the available overlay paths. Furthermore, in each overlay path, it leverages dynamic bandwidth separation to make use of the remaining available bandwidth reserved for online traffic. By constantly estimating online traffic demand and rescheduling bulk-data transfers accordingly, BDS+ can further speed up the massive data multicast. Through a pilot deployment in one of the largest online service providers and large-scale real-trace simulations, we show that BDS+ can achieve 3-5 x speedup over the provider's existing system and several well-known overlay routing baselines of static bandwidth separation. Moreover, dynamic bandwidth separation can further reduce the completion time of bulk data transfers by 1.2 to 1.3 times

Co-Production of Nattokinase and Poly (γ-Glutamic Acid) Under Solid-State Fermentation Using Soybean and Rice Husk

ABSTRACTThe aim of this work was to study the co-production of nattokinase and poly (γ-glutamic acid) by Bacillus subtilis natto with soybean and rice husk under solid-state fermentation (SSF). The results showed that the size of soybean particle and rice husk significantly improved the co-production of nattokinase and poly (γ-glutamic acid), yielding 2503.4 IU/gs and 320 mg/gs, respectively in the improved culture medium composed of 16.7% soybean flour and 13.3% rice husk with 70% water content. The yields increased by approximate 7- and 2-fold factor relative to their original ones. Thus, the co-production of nattokinase and poly (γ-glutamic acid) under SSF could be considered as an efficient method to exploit agro-residues for economical production of some higher-value products

List of genes showing significantly different expression in the comparison RWT <i>vs</i>. CT.

<p>List of genes showing significantly different expression in the comparison RWT <i>vs</i>. CT.</p

Top 10 statistically enriched KEGG pathways in the comparison groups.

<p>sly01100: Metabolic pathways, sly01110: Biosynthesis of secondary metabolites, sly03010: Ribosome, sly01200: Carbon metabolism, sly00190: Oxidative phosphorylation, sly04141: Protein processing in endoplasmic reticulum, sly00195: Photosynthesis, sly04075: Plant hormone signal transduction, sly01230: Biosynthesis of amino acids, sly00500: Starch and sucrose metabolism, sly04626: Plant-pathogen interaction, sly00010: Glycolysis/Gluconeogenesis, sly00710: Carbon fixation in photosynthetic organisms, sly03040: Spliceosome.</p