scholarly journals Structural transformation in GexS100−x (10≤x≤40) network glasses: Structural varieties in short-range, medium-range, and nanoscopic scale

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Y. Sakaguchi ◽  
T. Hanashima ◽  
K. Ohara ◽  
Al-Amin A. Simon ◽  
M. Mitkova
2015 ◽  
Vol 153 ◽  
pp. 432-442 ◽  
Author(s):  
R. Golovchak ◽  
P. Lucas ◽  
J. Oelgoetz ◽  
A. Kovalskiy ◽  
J. York-Winegar ◽  
...  

1998 ◽  
Vol 232-234 ◽  
pp. 427-433 ◽  
Author(s):  
G Herms ◽  
J Sakowski ◽  
W Gerike ◽  
U Hoppe ◽  
D Stachel

2014 ◽  
Vol 29 (3) ◽  
pp. 489-504 ◽  
Author(s):  
David R. Novak ◽  
Christopher Bailey ◽  
Keith F. Brill ◽  
Patrick Burke ◽  
Wallace A. Hogsett ◽  
...  

Abstract The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%–40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)−1 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%–15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)−1] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.


Author(s):  
Thomas Proffen ◽  
Katharine L. Page

AbstractThe knowledge of the detailed atomic structure of modern materials is the key to understanding the their macroscopic properties. The atomic pair distribution function (PDF) reveals short-range and medium-range structural information. In this paper we present an overview of refinement and modelling techniques. In short, we will be trying to answer the question: What can I learn from my PDF?


2001 ◽  
Vol 293-295 ◽  
pp. 182-186 ◽  
Author(s):  
Th. Halm ◽  
J. Nomssi Nzali ◽  
W. Hoyer ◽  
R.P. May ◽  
M. Bionducci

2010 ◽  
Vol 107 (8) ◽  
pp. 083511 ◽  
Author(s):  
Zhao-Yang Hou ◽  
Li-Xia Liu ◽  
Rang-Su Liu ◽  
Ze-An Tian ◽  
Jin-Guo Wang

Fractals ◽  
2007 ◽  
Vol 15 (02) ◽  
pp. 105-126 ◽  
Author(s):  
YINGCHUN ZHOU ◽  
MURAD S. TAQQU

Bucket random permutations (shuffling) are used to modify the dependence structure of a time series, and this may destroy long-range dependence, when it is present. Three types of bucket permutations are considered here: external, internal and two-level permutations. It is commonly believed that (1) an external random permutation destroys the long-range dependence and keeps the short-range dependence, (2) an internal permutation destroys the short-range dependence and keeps the long-range dependence, and (3) a two-level permutation distorts the medium-range dependence while keeping both the long-range and short-range dependence. This paper provides a theoretical basis for investigating these claims. It extends the study started in Ref. 1 and analyze the effects that these random permutations have on a long-range dependent finite variance stationary sequence both in the time domain and in the frequency domain.


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