Random-network models of the conductance of disordered condensed matter

1976 ◽  
Vol 13 (4) ◽  
pp. 1720-1727 ◽  
Author(s):  
Paul Erdös ◽  
Stephen B. Haley
Keyword(s):  
Random Network ◽  
Condensed Matter ◽  
Network Models

Application of Lattice Imaging Techniques to Amorphous Films

1975 ◽  
Vol 33 ◽  
pp. 8-9
Author(s):  
S. R. Herd ◽  
P. Chaudhari
Keyword(s):  
Nearest Neighbor ◽  
Random Network ◽  
Imaging Techniques ◽  
Network Models ◽  
Accurate Method ◽  
Direct Transmission ◽  
Lattice Imaging ◽  
Transmission Electron ◽  
Lattice Planes ◽  
Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

Visualization of tetrahedral disordering in amorphous germanium through local atomic motifs

2018 ◽  
Vol 51 (6) ◽  
pp. 1544-1550
Author(s):  
Aly Rahemtulla ◽  
Bruno Tomberli ◽  
Stefan Kycia
Keyword(s):  
Random Network ◽  
Network Models ◽  
Refined Model ◽  
Amorphous Germanium ◽  
Crystalline Materials ◽  
X Ray ◽  
Alignment Procedure ◽  
X Ray Scattering ◽  
Structural Details ◽  
Continuous Random Network

The atomic arrangements in amorphous solids, unlike those in crystalline materials, remain elusive. The details of atom ordering are under debate even in simplistic random network models. This work presents further advancements in the local atomic motif (LAM) method, first through the introduction of an optimized alignment procedure providing a clearer image of the angular ordering of atoms in a model. Secondly, by applying stereographic projections with LAMs, the angular ordering within coordination shells can be quantified and investigated. To showcase the new capabilities, the LAM method is applied to amorphous germanium, the archetype of covalent amorphous systems. The method is shown to dissect structural details of amorphous germanium (a-Ge) from the continuous random network (CRN) model and a reverse Monte Carlo (RMC) refined model fitted to high-resolution X-ray scattering measurements. The LAMs reveal well defined dihedral ordering in the second shell. The degree of dihedral ordering is observed to be coupled to bond length distances in the CRN model. This coupling is clearly not present within the RMC refined model. The LAMs reveal inclusions of third-shell atoms occupying interstitial positions in the second shell in both models.

scholarly journals Random Network Models to Predict the Long-Term Impact of HPV Vaccination on Genital Warts

Viruses ◽  
2017 ◽  
Vol 9 (10) ◽  
pp. 300 ◽  
Author(s):  
Javier Díez-Domingo ◽  
Víctor Sánchez-Alonso ◽  
Rafael-J. Villanueva ◽  
Luis Acedo ◽  
José-Antonio Moraño ◽  
...  
Keyword(s):  
Random Network ◽  
Hpv Vaccination ◽  
Network Models ◽  
Genital Warts ◽  
Long Term Impact

scholarly journals Stochastic Models of Emerging Infectious Disease Transmission on Adaptive Random Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Navavat Pipatsart ◽  
Wannapong Triampo ◽  
Charin Modchang
Keyword(s):  
Infectious Disease ◽  
Disease Transmission ◽  
Random Network ◽  
Network Models ◽  
Transmission Probability ◽  
Stochastic Simulations ◽  
Random Networks ◽  
Cumulative Number ◽  
Network Adaptation ◽  
Infectious Disease Dynamics

We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur.

scholarly journals Modeling Network Dynamics

2020 ◽  
pp. 145-171
Keyword(s):  
Communication Networks ◽  
Random Network ◽  
Network Dynamics ◽  
Network Models ◽  
Random Networks ◽  
Social Phenomena ◽  
Disease Propagation ◽  
The Social ◽  
Wide Range ◽  
Model Dynamics

Social relationships and the social networks over these relationships do not occur arbitrarily. However, the random networks dealt with in this chapter are important tools for modeling the networks of these systems. The authors use random networks to understand and to model dynamics regarding the whole social structure. Random network models became the topic of several studies independently from social network analysis in the 1950s. These models were used in the analysis of a wide range of social and non-social phenomena, from electrical and communication networks to the speed and manner of disease propagation. This chapter explores the modeling network dynamics of random networks.

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

scholarly journals Random network models with variable disorder of geometry

2019 ◽  
Vol 100 (14) ◽  
Author(s):  
A. Klümper ◽  
W. Nuding ◽  
A. Sedrakyan
Keyword(s):  
Random Network ◽  
Network Models

scholarly journals Entropy Characterization of Random Network Models

Entropy ◽  
2017 ◽  
Vol 19 (7) ◽  
pp. 321 ◽  
Author(s):  
Pedro Zufiria ◽  
Iker Barriales-Valbuena
Keyword(s):  
Random Network ◽  
Network Models
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