An upper bound on the size of m-ary t-sEC/AUED codes

Zhang, Pengcheng; Fu, Fang-Wei; Chang, Zuling

doi:10.1016/s1005-8885(07)60042-9

Cited by 18 publications

(7 citation statements)

References 5 publications

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“…In the pairwise combination of different networks, scale-freescale-free (SF-SF) duplex networks (i) have the widest diffusion range, and random-scale-free (RD-SF) duplex networks (g) have the fastest diffusion speed. The experimental results are also consistent with the literature that there is a positive correlation between the speed of knowledge diffusion and the degree of randomization in the network, that is, the greater the degree of randomization of the network, the faster the speed of knowledge diffusion in the network [53]. In the process of knowledge diffusion that does not continuously generate new knowledge, random networks show better performance [54].…”

Section: B Impact Of Topological Types On Doi In Duplex Networksupporting

confidence: 88%

A Multiplex Network Perspective of Innovation Diffusion: An Information-Behavior Framework

2020

IEEE Access

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Information transmission of innovation and peer effect among firms have significant impacts on innovation adoption decisions of firms, but those effects usually depend on the different types of inter-firm networks. Therefore, the diffusion of innovation (DOI) can be regarded as a process occurring in complex systems composed of different types of interactions, which can be categorized into links belonging to different layers of multiplex networks. This study aims to shed light on the complexity of innovation diffusion from the perspective of an information-behavior framework based on multiplex networks. The process of technology innovation adoption is formulated into two stages including information perception and decision making, and a novel innovation diffusion model based on duplex inter-firm networks, in which one layer has influence on information transmission and the other carrys the peer effect, is further proposed in this study. The simulation experiments indicate that the random-scale-free duplex networks are favourable to the diffusion speed while scale-free-scale-free duplex networks are conducive to the diffusion range. Moreover, within the scale-free-scale-free duplex networks, the diffusion speed will increase with the increasing of the power law index in the information network and decrease with the increasing of the power law index of the behavior network. The study contributes to the literature by establishing duplex network model that distinguishes links between information and behavior networks, and offers insights concerning optimization of network configuration in the promotion of DOI. INDEX TERMS Multiplex networks, diffusion of innovation, information-behavior framework, agent-based modeling.

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Section: B Impact Of Topological Types On Doi In Duplex Networksupporting

confidence: 88%

A Multiplex Network Perspective of Innovation Diffusion: An Information-Behavior Framework

2020

IEEE Access

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“…Chen [15] conducted the experiment of a parabolic single tube CFST arch subjected to spatial loads in the lab of Fuzhou University. The model arch was 7.5 m in span, 1/5 rise-span ratio, 121 mm in diameter and 4.5 mm in thickness of steel tube.…”

Section: Test Setupmentioning

confidence: 99%

“…Lin [14] carried out the experimental study on a CFST (single tube) arch with one rib under spatial loads and laid the foundation for future research on the spatial load-carrying capacity of CFST arches. Chen et al [15] used the finite element (FE) method to carry out the dual non-linearity analyses for CFST arches with a single rib under spatial loads and found that the influence of geometric nonlinearity increased, compared to arches under in-plane loads. Li [16] analyzed the out-plane stability of CFST arches and proposed corresponding design formula.…”

Section: Introductionmentioning

confidence: 99%

Stressing State Analysis on a Single Tube CFST Arch Under Spatial Loads

et al. 2019

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This paper analyzes the stressing state characteristics of a concrete-filled steel tubular (CFST) arch model under spatial loads, using the method of modeling structural stressing state and the thin plate simulating interpolation (TSI) method. Firstly, the parameter-generalized strain energy density (GSED) is applied to model the stressing state of the arch. Then, the normalized GSED sum at each load plots the characteristic curve. The characteristic loads P (66 kN) and Q (85 kN) in the curve are distinguished by the Mann–Kendall (M–K) criterion. To characterize structural axial and bending stressing states, the parameters of the sectional average strain and generalized bending strain are proposed as stressing state submodes. Finally, the TSI method is used to interpolate strain data for deep analysis of internal forces. By modeling the structural stressing state, the working behavior characteristics of arch structures are greatly revealed in a particular view and the results could provide a reference for the development of bridge design.

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“…Chen [32] experimented on a parabolic single tube CFST arch under spatial loads. To distinguish this arch model from arch-A, it is described as arch-B below.…”

Section: Experiments On the Cfst Arch Under Spatial Loadsmentioning

confidence: 99%

Investigation on Single Tube CFST Arch Models by Modeling Structural Stressing State Based on NSF Method

et al. 2019

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This paper investigated the behavioral characteristics of two single tube concrete-filled steel tube (CFST) arch models under different loads. Applying the numerical shape function (NSF) method, the limited strain data of arch models were interpolated to obtain more detailed strain information at unmeasured points. By numerically modeling and characterizing the structural stressing state of arches, these interpolated strains were calculated as the normalized strain energy density (SED) sum to plot the corresponding characteristic curves. Utilizing the Mann-Kendall (M-K) criterion, the qualitative characteristic load was detected from the curve and was referred to as the failure load, updating the existing definition of structural failure. Then, from the perspective of experimental strains, strain/stress fields, and stressing state submodes of internal forces obtained based on the NSF method, the working behavioral characteristics of each respective CFST arch model under loads were embodied in detail. The mutation features were distinguished from the development trend of strain/stress fields or distribution patterns of internal forces to verify the rationality of the updated failure load. Consequently, the NSF method can have a reasonable interpolation on the limited experimental data. By modeling structural stressing state, it can conduct an accurate estimation of the structural failure load and provide a reference for the future design of arch bridges.

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An upper bound on the size of m-ary t-sEC/AUED codes

Cited by 18 publications

References 5 publications

A Multiplex Network Perspective of Innovation Diffusion: An Information-Behavior Framework

A Multiplex Network Perspective of Innovation Diffusion: An Information-Behavior Framework

Stressing State Analysis on a Single Tube CFST Arch Under Spatial Loads

Investigation on Single Tube CFST Arch Models by Modeling Structural Stressing State Based on NSF Method

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