scholarly journals LEVEL STATISTICS IN SUPERCONDUCTIVITY IN ULTRASMALL METALLIC GRAINS

2000 ◽  
Vol 49 (5) ◽  
pp. 969
Author(s):  
CHEN ZHI-QIAN ◽  
ZHENG REN-RONG ◽  
CHEN HONG ◽  
YAO CHUN-QING
Keyword(s):  
Level Statistics ◽  
Metallic Grains

scholarly journals Effect of Level Statistics on Superconductivity in Ultrasmall Metallic Grains

1996 ◽  
Vol 77 (24) ◽  
pp. 4962-4965 ◽  
Author(s):  
Robert A. Smith ◽  
Vinay Ambegaokar
Keyword(s):  
Level Statistics ◽  
Metallic Grains

SUPERCONDUCTIVITY ENHANCEMENTS IN SMALL METALLIC GRAINS

2005 ◽  
Vol 19 (15n17) ◽  
pp. 2369-2374
Author(s):  
RENRONG ZHENG ◽  
ZHI QIAN CHEN ◽  
SHUN QUAN ZHU
Keyword(s):  
Mean Field Theory ◽  
Mean Field ◽  
Pairing Correlation ◽  
Gap Equation ◽  
Gaussian Unitary Ensemble ◽  
Level Statistics ◽  
Gaussian Orthogonal Ensemble ◽  
Metallic Grains ◽  
Unitary Ensemble ◽  
Small Metallic Grains

The reasons for superconductivity enhancement in small metallic grains including hundreds of thousand electrons are investigated by solving the generalized gap equation based on BCS mean field theory. The analysis suggests that the superconductivity enhancement in small metallic grains are the results caused by the pairing correlation and the level statistics in the Gaussian orthogonal ensemble (GOE) and the Gaussian unitary ensemble (GUE).

A Bayesian adjusted plus-minus analysis for the esport Dota 2

2020 ◽  
Vol 16 (4) ◽  
pp. 325-341
Author(s):  
Nicholas Clark ◽  
Brian Macdonald ◽  
Ian Kloo
Keyword(s):  
Professional Sports ◽  
Hierarchical Regression ◽  
Sports Analytics ◽  
Bayesian Hierarchical ◽  
The Past ◽  
Score Statistics ◽  
Level Statistics ◽  
Traditional Sports ◽  
Level Information ◽  
Individual Player

AbstractAnalytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team’s performance, and then use a Bayesian APM model to estimate how players contribute to their team’s gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as “box score statistics”). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports.

Arithmetical chaos and violation of universality in energy level statistics

1992 ◽  
Vol 69 (15) ◽  
pp. 2188-2191 ◽  
Author(s):  
J. Bolte ◽  
G. Steil ◽  
F. Steiner
Keyword(s):  
Energy Level ◽  
Level Statistics

Towards Practical Examinations in Advanced Level Statistics

1990 ◽  
Vol 12 (3) ◽  
pp. 82-83
Author(s):  
Christopher Reynolds ◽  
Nigel Walkey
Keyword(s):  
Advanced Level ◽  
Level Statistics
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