scholarly journals Conductance and its universal fluctuations in the directed network model at the crossover to the quasi-one-dimensional regime

1997 ◽  
Vol 56 (20) ◽  
pp. 13218-13226 ◽  
Author(s):  
Ilya A. Gruzberg ◽  
N. Read ◽  
Subir Sachdev
Keyword(s):  
Network Model ◽  
Directed Network ◽  
One Dimensional

NETWORK AS A CHAOTIC DYNAMICAL SYSTEM

2007 ◽  
Vol 17 (10) ◽  
pp. 3529-3533 ◽  
Author(s):  
SYUJI MIYAZAKI ◽  
YASUSHI NAGASHIMA
Keyword(s):  
Dynamical System ◽  
Network Structure ◽  
Transition Matrix ◽  
Chaotic Dynamics ◽  
Large Deviation ◽  
Piecewise Linear ◽  
Directed Network ◽  
Chaotic Dynamical System ◽  
One Dimensional ◽  
Finite Time Lyapunov Exponents

A directed network such as the WWW can be represented by a transition matrix. Comparing this matrix to a Frobenius–Perron matrix of a chaotic piecewise-linear one-dimensional map whose domain can be divided into Markov subintervals, we are able to relate the network structure itself to chaotic dynamics. Just like various large deviation properties of local expansion rates (finite-time Lyapunov exponents) related to chaotic dynamics, we can also discuss those properties of network structure.

scholarly journals How many causal pathways must symptoms form before we call them a borderline? A hierarchical network model of borderline personality disorder

2018 ◽  
Author(s):  
Zsolt Unoka ◽  
Mara J. Richman ◽  
Dániel Czégel
Keyword(s):  
Borderline Personality Disorder ◽  
Personality Disorder ◽  
Network Model ◽  
Emotion Dysregulation ◽  
Borderline Personality ◽  
Directed Network ◽  
Causal Relationships ◽  
Hierarchical Network ◽  
Cross Sectional ◽  
Causal Pathways

Borderline personality disorder (BPD) is characterized by impulsivity, emotion dysregulation, disturbed relationships, and identity disturbances. Despite the known variable co-occurrence of BPD symptoms, the possible causal relationships are not well understood. We addressed this by creating a hierarchical network model of BPD, which identifies the most likely acyclic causal pathways that are driving BPD development. Cross-sectional data was obtained from the Structured Clinical Interview-II (SCID-II), and possible causal relationships between symptoms were identified from conditional independence relations. The symptoms’ hierarchy values, assessing their role in causal pathways, was determined by a random walk-based algorithm. By analyzing the directed network of BPD symptoms, it was found that symptoms in initial stages of causal pathways were abandonment, physical fights, impulsivity, suicidal threats, identity disturbances, and affective instability. Based on the assessed role symptoms play in causal pathways of BPD development, specific symptoms can be targeted during early diagnosis and clinical assessment.

RADIAL BASIS PROBABILISTIC NEURAL NETWORKS: MODEL AND APPLICATION

1999 ◽  
Vol 13 (07) ◽  
pp. 1083-1101 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Model ◽  
Probabilistic Neural Network ◽  
Radial Basis Function Networks ◽  
Probabilistic Neural Networks ◽  
Huge Amount ◽  
Training Time ◽  
One Dimensional ◽  
Radial Basis

This paper investigates the capabilities of radial basis function networks (RBFN) and kernel neural networks (KNN), i.e. a specific probabilistic neural networks (PNN), and studies their similarities and differences. In order to avoid the huge amount of hidden units of the KNNs (or PNNs) and reduce the training time for the RBFNs, this paper proposes a new feedforward neural network model referred to as radial basis probabilistic neural network (RBPNN). This new network model inherits the merits of the two old odels to a great extent, and avoids their defects in some ways. Finally, we apply this new RBPNN to the recognition of one-dimensional cross-images of radar targets (five kinds of aircrafts), and the experimental results are given and discussed.

Theoretical and Computational Analysis of an Auxiliary Power Unit (APU) Installation Cooling System

2012 ◽  
Author(s):  
Parthiv N. Shah ◽  
Tricia Waniewski Sur ◽  
R. Scott Miskovish ◽  
Albert Robinson
Keyword(s):  
Network Model ◽  
Cooling System ◽  
Dimensional Model ◽  
Ambient Conditions ◽  
Modeling Framework ◽  
Flow Network ◽  
One Dimensional ◽  
Dimensional Flow ◽  
System Pressure ◽  
The One

This paper presents a theoretical one-dimensional model and computational fluid dynamics (CFD) simulations of a tailcone-installed APU cooling system. The work is motivated by the need to deliver sufficient cooling airflow to critical components within an aircraft tailcone compartment. The cooling system considered herein utilizes (1) an eductor system at the APU exhaust and (2) a ram air scoop near an upstream inlet to the compartment to induce the necessary cooling flow during ground and in-flight APU operation. A one-dimensional flow network model provides a framework for the quantification and matching of eductor pumping and system pressure drop characteristics. Detailed CFD models that simulate internal tailcone compartment flows driven by ambient conditions external to the aircraft in ground or flight operation support the one-dimensional model and are used to characterize component performance and assess different scoop and eductor designs. The one-dimensional flow network model is calibrated to the CFD results to predict system cooling performance under known APU loads at points on the ground and in the flight envelope. The agreement between the models is encouraging and suggests the modeling framework and CFD techniques discussed will be applicable to future designs and improvements of eductor-driven aircraft compartment cooling systems.

Sign in / Sign up

Export Citation Format

Share Document