Quantify Self-Organized Storage Capacity in Supporting Infrastructure-less Transportation Operation

2015 ◽  
Author(s):  
Xiaoyan Hong ◽  
Yingyan Lou ◽  
Meng Kuai ◽  
Shuoping Wang
Keyword(s):  
Storage Capacity ◽  
Self Organized

Oxygen deficient, carbon coated self-organized TiO2 nanotubes as anode material for Li-ion intercalation

2015 ◽  
Vol 3 (32) ◽  
pp. 16469-16477 ◽  
Author(s):  
J. Brumbarov ◽  
J. P. Vivek ◽  
S. Leonardi ◽  
C. Valero-Vidal ◽  
E. Portenkirchner ◽  
...  
Keyword(s):  
Anode Material ◽  
Tio2 Nanotubes ◽  
Storage Capacity ◽  
Self Organized ◽  
Carbon Coated ◽  
Li Ion ◽  
Ion Intercalation ◽  
Li Storage ◽  
C Nanotubes

Anatase TiO2−x–C nanotubes demonstrate a superior Li storage capacity as high as 320(±68) mA h g−1 compared to 180(±38) mA h g−1 for TiO2−x.

scholarly journals Efficiency of local learning rules in threshold-linear associative networks

2020 ◽  
Author(s):  
Francesca Schönsberg ◽  
Yasser Roudi ◽  
Alessandro Treves
Keyword(s):  
Storage Capacity ◽  
Hebbian Learning ◽  
Back Propagation ◽  
Local Learning ◽  
Associative Networks ◽  
Empirical Distributions ◽  
Self Organized ◽  
Optimal Capacity ◽  
Learning Rules ◽  
Non Local

We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.

Problem behavior in autism spectrum disorders: A paradigmatic self-organized perspective of network structures

2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Problem Behaviors ◽  
Autism Spectrum ◽  
The Self ◽  
Network Structures ◽  
Self Organized ◽  
Critical Equilibrium ◽  
Self Organized Criticality ◽  
Acute Agitation ◽  
Spectrum Disorders

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

The B.A.M. Storage Capacity

1995 ◽  
Vol 5 (1) ◽  
pp. 85-96 ◽  
Author(s):  
H. Englisch ◽  
V. Mastropietro ◽  
B. Tirozzi
Keyword(s):  
Storage Capacity

Simulation of the internal temperature in the Passol Laboratory, University of Debrecen

2012 ◽  
Vol 3 (1) ◽  
pp. 63-73 ◽  
Author(s):  
I. Csáky ◽  
F. Kalmár
Keyword(s):  
Temperature Variation ◽  
Air Temperature ◽  
Indoor Air ◽  
Storage Capacity ◽  
Building Structures ◽  
Internal Temperature ◽  
The One ◽  
Heavy Structure ◽  
East West ◽  
Made In

Abstract Nowadays the facades of newly built buildings have significant glazed surfaces. The solar gains in these buildings can produce discomfort caused by direct solar radiation on the one hand and by the higher indoor air temperature on the other hand. The amplitude of the indoor air temperature variation depends on the glazed area, orientation of the facade and heat storage capacity of the building. This paper presents the results of a simulation, which were made in the Passol Laboratory of University of Debrecen in order to define the internal temperature variation. The simulation proved that the highest amplitudes of the internal temperature are obtained for East orientation of the facade. The upper acceptable limit of the internal air temperature is exceeded for each analyzed orientation: North, South, East, West. Comparing different building structures, according to the obtained results, in case of the heavy structure more cooling hours are obtained, but the energy consumption for cooling is lower.

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