Photonic recurrent ising sampler (PRIS) for photonic parallel networks (Conference Presentation)

2020 ◽  
Author(s):  
Yichen Shen
Keyword(s):  
Parallel Networks

scholarly journals A nonlinear strain-rate dependent constitutive model for uncured rubber materials under large deformation

2020 ◽  
Vol 37 ◽  
pp. 118-125
Author(s):  
Weihua Zhou ◽  
Changqing Fang ◽  
Huifeng Tan ◽  
Huiyu Sun
Keyword(s):  
Constitutive Model ◽  
Large Deformation ◽  
Mechanical Response ◽  
Uniaxial Tensile ◽  
Hysteresis Phenomenon ◽  
Mechanical Behaviors ◽  
Nonlinear Constitutive Model ◽  
Rate Dependent ◽  
Parallel Networks ◽  
Non Gaussian

Abstract Uncured rubber possesses remarkable hyperelastic and viscoelastic properties while it undergoes large deformation; therefore, it has wide application prospects and attracts great research interests from academia and industry. In this paper, a nonlinear constitutive model with two parallel networks is developed to describe the mechanical response of uncured rubber. The constitutive model is incorporated with the Eying model to describe the hysteresis phenomenon and viscous flow criterion, and the hyperelastic properties under large deformation are captured by a non-Gaussian chain molecular network model. Based on the model, the mechanical behaviors of hyperelasticity, viscoelasticity and hysteresis under different strain rates are investigated. Furthermore, the constitutive model is employed to estimate uniaxial tensile, cyclic loading–unloading and multistep tensile relaxation mechanical behaviors of uncured rubber, and the prediction results show good agreement with the test data. The nonlinear mechanical constitutive model provides an efficient method for predicting the mechanical response of uncured rubber materials.

scholarly journals Configurations of series-parallel networks with maximum reliability

1996 ◽  
Vol 36 (2) ◽  
pp. 247-253 ◽  
Author(s):  
Walter J. Gutjahr ◽  
Georg Ch. Pflug ◽  
Andrzej Ruszczyński
Keyword(s):  
Parallel Networks ◽  
Maximum Reliability

Neural Biases to Covert and Overt Signals of Fear: Dissociation by Trait Anxiety and Depression

2007 ◽  
Vol 19 (10) ◽  
pp. 1595-1608 ◽  
Author(s):  
Leanne M. Williams ◽  
Andrew H. Kemp ◽  
Kim Felmingham ◽  
Belinda J. Liddell ◽  
Donna M. Palmer ◽  
...  
Keyword(s):  
Trait Anxiety ◽  
Time Course ◽  
Event Related Potentials ◽  
Anxiety And Depression ◽  
Controlled Processing ◽  
Parallel Networks ◽  
Related Potentials ◽  
Conscious Attention ◽  
The Impact ◽  
Emotion Encoding

Although biases toward signals of fear may be an evolutionary adaptation necessary for survival, heightened biases may be maladaptive and associated with anxiety or depression. In this study, event-related potentials (ERPs) were used to examine the time course of neural responses to facial fear stimuli (versus neutral) presented overtly (for 500 msec with conscious attention) and covertly (for 10 msec with immediate masking to preclude conscious awareness) in 257 nonclinical subjects. We also examined the impact of trait anxiety and depression, assessed using psychometric ratings, on the time course of ERPs. In the total subject group, controlled biases to overtly processed fear were reflected in an enhancement of ERPs associated with structural encoding (120–220 msec) and sustained evaluation persisting from 250 msec and beyond, following a temporo-occipital to frontal topography. By contrast, covert fear processing elicited automatic biases, reflected in an enhancement of ERPs prior to structural encoding (80–180 msec) and again in the period associated with automatic orienting and emotion encoding (230–330 msec), which followed the reverse frontal to temporo-occipital topography. Higher levels of trait anxiety (in the clinical range) were distinguished by a heightened bias to covert fear (speeding of early ERPs), compared to higher depression which was associated with an opposing bias to overt fear (slowing of later ERPs). Anxiety also heightened early responses to covert fear, and depression to overt fear, with subsequent deficits in emotion encoding in each case. These findings are consistent with neural biases to signals of fear which operate automatically and during controlled processing, feasibly supported by parallel networks. Heightened automatic biases in anxiety may contribute to a cycle of hypervigilance and anxious thoughts, whereas depression may represent a “burnt out” emotional state in which evaluation of fear stimuli is prolonged only when conscious attention is allocated.

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