Locating influential nodes via dynamics-sensitive centrality

With great theoretical and practical significance, locating influential nodes of complex networks is a promising issues. In this paper, we propose a dynamics-sensitive (DS) centrality that integrates topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is much more accurate than degree, $k$-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure

An IDEF0 Design For PDM-based Die Integrated Intelligent Design System Functional Model

AbstractThis paper establishes a PDM–based integrated design system IDEF0 functional model. The system consist four charts: A-0 chart, A0 chart, A1chart and A2chart. A-0chart defines the range of the system; A0 chart includes 4 modules (task granting, outline design, detail design, evaluation); A1chart and A2 chart decompose and illuminate detail design module and machining processing design module. The system can be easily adapt and applied to many domains of product design and engineering design

Assessment of Drought Resistance for 17 Alfalfa Varieties at the Seedling Stage

Alfalfa is a crop of relative water demand, and the good quality, the high and stable yield could be obtained directly depending on water supplies and strong or weak drought resistance ability of alfalfa cultivars (Geng 1995; Chen and Jia 2000). To guide steady and effectively development of alfalfa industrialization in the arid and semi-arid regions of China, we systematically selected the excellent drought resistant alfalfa cultivars, and then identified and assessed their drought resistant capacity. In this study, a pot experiment was conducted by simulating drought stress in a greenhouse to assess the drought resistance of seventeen alfalfa cultivars at home and abroad during seedlings period, which aim at selecting a high quality and yield perfectly suited to growing alfalfa cultivars

Impacts of Sample Size on Calculation of Pavement Texture Indicators with 1mm 3D Surface Data

The emerging 1mm resolution 3D data collection technology is capable of covering the entire pavement surface, and provides more data sets than traditional line-of-sight data collection systems. As a result, quantifying the impact of sample size including sample width and sample length on the calculation of pavement texture indicators is becoming possible. In this study, 1mm 3D texture data are collected and processed at seven test sites using the PaveVision3D Ultra system. Analysis of Variance (ANOVA) test and linear regression models are developed to investigate various sample length and width on the calculation of three widely used texture indicators: Mean Profile Depth (MPD), Mean Texture Depth (MTD) and Power Spectra Density (PSD). Since the current ASTM standards and other procedures cannot be directly applied to 3D surface for production due to a lack of definitions, the results from this research are beneficial in the process to standardize texture indicators’ computations with 1mm 3D surface data of pavements