Modeling and Detecting Network Communities with the Fusion of Node Attributes

As a fundamental structure in real-world networks, communities can be reflected by abundant node attributes with the fusion of graph topology. In attribute-aware community detection, probabilistic generative models (PGMs) have become the mainstream fusion method due to their principled characterization and interpretation. Here, we propose a novel PGM without imposing any distributional assumptions on attributes, which is superior to existing PGMs that require attributes to be categorical or Gaussian distributed. Based on the famous block model of graph structure, our model fuses the attribute by describing its effect on node popularity using an additional term. To characterize the effect quantitatively, we analyze the detectability of communities for the proposed model and then establish the requirements of the attribute-popularity term, which leads to a new scheme for the model selection problem in attribute-aware community detection. With the model determined, an efficient algorithm is developed to estimate the parameters and to infer the communities. The proposed method is validated from two aspects. First, the effectiveness of our algorithm is theoretically guaranteed by the detectability condition, whose correctness is verified by numerical experiments on artificial graphs. Second, extensive experiments show that our method outperforms the competing approaches on a variety of real-world networks.Comment: other authors do not want to preprin

Reinforcement learning based anti-jamming schedule in cyber-physical systems

In this paper, the security issue of cyber-physical systems is investigated, where the observation data is transmitted from a sensor to an estimator through wireless channels disturbed by an attacker. The failure of this data transmission occurs, when the sensor accesses the channel that happens to be attacked by the jammer. Since the system performance measured by the estimation error depends on whether the data transmission is a success, the problem of selecting the channel to alleviate the attack effect is studied. Moreover, the state of each channel is time-variant due to various factors, such as path loss and shadowing. Motivated by energy conservation, the problem of selecting the channel with the best state is also considered. With the help of cognitive radio technique, the sensor has the ability of selecting a sequence of channels dynamically. Based on this, the problem of selecting the channel is resolved by means of reinforcement learning to jointly avoid the attack and enjoy the channel with the best state. A corresponding algorithm is presented to obtain the sequence of channels for the sensor, and its effectiveness is proved analytically. Numerical simulations further verify the derived results

Fabrication Process Simulation of a PEM Fuel Cell Catalyst Layer and Its Microscopic Structure Characteristics

The catalyst layers (CLs) in proton exchange membrane fuel cells (PEMFCs) are porous composites of complex microstructures of the building blocks, i.e., Pt nano-particles, carbonaceous substrates and Nafion ionomers. It is important to understand the factors that control the microstructure formation in the fabrication process. A coarse-grained molecular dynamics (CG-MD) method is employed to investigate the fabrication process of CLs, which depends on the type and amount of components and also the type of the dispersion medium (ethylene glycol, isopropanol or hexanol) used during ink preparation of the catalyst-coated membranes (CCMs). The dynamical behaviors of all the components are outlined and analyzed following the fabrication steps. In addition, the Pt nano-particle size distribution is evaluated and compared with the labor testing. Furthermore, the primary pore size distributions in the final formations of three cases are shown and compared with the experiments. The sizes of the reconstructed agglomerates are also considered on the effect of solvent polarity. (C) 2012 The Electrochemical Society. [DOI: 10.1149/2.064203jes] All rights reserved

LTNE Approach and Simulation for Anode-Supported SOFCs

Fuel cells are promising for future energy systems, since they are energy efficient and, when hydrogen is used as fuel, there are no emissions of greenhouse gases. Fuel cells have during recent years various improvements, however the technology is still in the early phases of development, this can be noted by the lack of dominant design both for singe fuel cells, stacks and for entire fuel cell systems. In this study a CFD approach (COMSOL Multiphysics) is employed to investigate the effect on temperature distribution from inlet temperature, oxygen surplus, ionic conductivity and current density for an anode-supported intermediate temperature solid oxide fuel cell (IT-SOFC). The developed model is based on the governing equations of heat-, mass- and momentum transport. A local temperature non equilibrium (LTNE) approach is introduced to calculate the temperature distribution in the gas- and solid phase separately. The results show that the temperature increasing along the flow direction is controlled by the degree of surplus air. It is also found that the ohmic polarization in the electrolyte and the activation polarization in the anode and cathode have major influence on the performance. If a count flow approach is employed the inlet temperature for the fuel stream should be close to the outlet temperature for the air flow to avoid a too high temperature gradient

Short communication: QTL mapping for ear tip-barrenness in maize

Barren tip on corn ear is an important agronomic trait in maize, which is highly associated with grain yield. Understanding the genetic basis of tip-barrenness may help to reduce the ear tip-barrenness in breeding programs. In this study, ear tip-barrenness was evaluated in two environments in a F2:3 population, and it showed significant genotypic variation for ear tip-barrenness in both environments. Using mixed-model composite interval mapping method, three additive effects quantitative trait loci (QTL) for ear tip-barrenness were mapped on chromosomes 2, 3 and 6, respectively. They explained 16.6% of the phenotypic variation, and no significant QTL × Environment interactions and digenic interactions were detected. The results indicated that additive effect was the main genetic basis for ear tip-barrenness in maize. This is the first report of QTL mapped for ear tip-barrenness in maize