1 Two-Port Network Parameters

2015 ◽  
pp. 1-42
Keyword(s):  
Network Parameters

The Network Parameters Of The T-System In Frog Muscle Measured With The High Voltage Electron Microscope.

1975 ◽  
Vol 33 ◽  
pp. 550-551 ◽  
Author(s):  
Brenda R. Eisenberg ◽  
Lee D. Peachey
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
High Voltage ◽  
Sartorius Muscle ◽  
High Voltage Electron Microscopy ◽  
Network Parameters ◽  
Voltage Electron ◽  
High Voltage Electron Microscope ◽  
High Voltage Electron ◽  
T System

Analysis of the electrical properties of the t-system requires knowledge of the geometry of the t-system network. It is now possible to determine the network parameters experimentally by use of high voltage electron microscopy. The t-system was marked with exogenous peroxidase. Conventional methods of electron microscopy were used to fix and embed the sartorius muscle from four frogs. Transverse slices 0.5-1.0 μm thick were viewed at an accelerating voltage of 1000 kV using the JEM-1000 high voltage electron microscope at Boulder, Colorado and prints at x5000 were used for analysis.The length of a t-branch (t) from node to node (Fig. 1a) was measured with a magnifier; at least 150 t-branches around 30 myofibrils were measured from each frog. The mean length of t is 0.90 ± 0.11 μm and the number of branches per myofibril is 5.4 ± 0.2 (mean ± SD, n = 4 frogs).

Effect of Crosslinking on the Network Parameters, Swelling and Mechanical Properties of PVA-Borax and Poly (AM-co-HEMA) Double Network (DN) Hydrogels

2019 ◽  
Vol 35 (3) ◽  
pp. 371-391
Author(s):  
AKANSHA DIXIT ◽  
◽  
DIBYENDU S. BAG ◽  
DHIRENDRA KUMAR SHARMA ◽  
HARJEET SINGH ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Double Network ◽  
Network Parameters

scholarly journals Estimation of the influence of spiking neural network parameters on classification accuracy using a genetic algorithm

2018 ◽  
Vol 145 ◽  
pp. 488-494 ◽  
Author(s):  
Aleksandr Sboev ◽  
Alexey Serenko ◽  
Roman Rybka ◽  
Danila Vlasov ◽  
Andrey Filchenkov
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Spiking Neural Network ◽  
Network Parameters

scholarly journals Network parameters quantify loss of assemblage structure in human‐impacted lake ecosystems

2019 ◽  
Vol 25 (11) ◽  
pp. 3871-3882 ◽  
Author(s):  
Rong Wang ◽  
John A. Dearing ◽  
C. Patrick Doncaster ◽  
Xiangdong Yang ◽  
Enlou Zhang ◽  
...  
Keyword(s):  
Assemblage Structure ◽  
Lake Ecosystems ◽  
Network Parameters

scholarly journals Signal quality as Achilles’ heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johan Baijot ◽  
Stijn Denissen ◽  
Lars Costers ◽  
Jeroen Gielen ◽  
Melissa Cambron ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Signal To Noise Ratio ◽  
Pearson Correlation ◽  
Signal Quality ◽  
Network Parameters ◽  
Fmri Signal ◽  
Noise Ratio ◽  
Global And Local ◽  
The Brain ◽  
Local Brain

AbstractGraph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

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