scholarly journals Characterization of the Long-Range Internal Dynamics of Pyrene-Labeled Macromolecules by Pyrene Excimer Fluorescence

2016 ◽  
Vol 49 (24) ◽  
pp. 9597-9604 ◽  
Author(s):  
Shiva Farhangi ◽  
Remi Casier ◽  
Lu Li ◽  
Janine Lydia Thoma ◽  
Jean Duhamel
Keyword(s):  
Long Range ◽  
Internal Dynamics ◽  
Excimer Fluorescence

scholarly journals How wide is the window opened by high-resolution relaxometry on the internal dynamics of proteins in solution?

2021 ◽  
Vol 75 (2-3) ◽  
pp. 119-131
Author(s):  
Albert A. Smith ◽  
Nicolas Bolik-Coulon ◽  
Matthias Ernst ◽  
Beat H. Meier ◽  
Fabien Ferrage
Keyword(s):  
Nuclear Magnetic Resonance ◽  
High Resolution ◽  
High Field ◽  
Rotational Diffusion ◽  
Relaxation Data ◽  
Internal Dynamics ◽  
Nmr Relaxometry ◽  
Analysis Of Dynamics

AbstractThe dynamics of molecules in solution is usually quantified by the determination of timescale-specific amplitudes of motions. High-resolution nuclear magnetic resonance (NMR) relaxometry experiments—where the sample is transferred to low fields for longitudinal (T1) relaxation, and back to high field for detection with residue-specific resolution—seeks to increase the ability to distinguish the contributions from motion on timescales slower than a few nanoseconds. However, tumbling of a molecule in solution masks some of these motions. Therefore, we investigate to what extent relaxometry improves timescale resolution, using the “detector” analysis of dynamics. Here, we demonstrate improvements in the characterization of internal dynamics of methyl-bearing side chains by carbon-13 relaxometry in the small protein ubiquitin. We show that relaxometry data leads to better information about nanosecond motions as compared to high-field relaxation data only. Our calculations show that gains from relaxometry are greater with increasing correlation time of rotational diffusion.

Long range, non-destructive characterization of GaN substrates for power devices

2019 ◽  
Vol 506 ◽  
pp. 178-184 ◽  
Author(s):  
J.C. Gallagher ◽  
T.J. Anderson ◽  
L.E. Luna ◽  
A.D. Koehler ◽  
J.K. Hite ◽  
...  
Keyword(s):  
Long Range ◽  
Power Devices ◽  
Non Destructive ◽  
Non Destructive Characterization

scholarly journals Architecture of Artificial Neural Network in Identification of Internal Dynamics and Prediction of Dynamic System Rainfall Data Time Series

2012 ◽  
pp. 34-42
Author(s):  
Sanjeev Karmakar ◽  
Manoj Kumar Kowar ◽  
Pulak Guhathakurta
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Dynamic System ◽  
Long Range ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Global Minima ◽  
Internal Dynamics ◽  
Artificial Neural

The objective of this study is to expand and evaluate the back-propagation artificial neural network (BPANN) and to apply in the identification of internal dynamics of very high dynamic system such long-range total rainfall data time series. This objective is considered via comprehensive review of literature (1978-2011). It is found that, detail of discussion concerning the architecture of ANN for the same is rarely visible in the literature; however various applications of ANN are available. The detail architecture of BPANN with its parameters, i.e., learning rate, number of hidden layers, number of neurons in hidden layers, number of input vectors in input layer, initial and optimized weights etc., designed learning algorithm, observations of local and global minima, and results have been discussed. It is observed that obtaining global minima is almost complicated and always a temporal nervousness. However, achievement of global minima for the period of the training has been discussed. It is found that, the application of the BPANN on identification for internal dynamics and prediction for the long-range total annual rainfall has produced good results. The results are explained through the strong association between rainfall predictors i.e., climate parameter (independent parameter) and total annual rainfall (dependent parameter) are presented in this paper as well.

Representative Time Series Fractal Analysis of the Traffic Flows of a Dedicated High-Speed Link

2021 ◽  
Author(s):  
Ginno Millan ◽  
manuel vargas ◽  
Guillermo Fuertes
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Long Range ◽  
Computer Networks ◽  
Fractal Analysis ◽  
High Speed ◽  
Long Range Dependence ◽  
Traffic Flows ◽  
Speed Computer

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link exhibited fractal behavior since the Hurst exponent was in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between -0.5, 0. Based on these results, it is ideal to characterize both the singularities of the fractal traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyzes, the fact that the traffic flows of current computer networks exhibited fractal behavior with a long-range dependence was reaffirmed.

Characterization of phase transformations, long range order and thermal properties of Ni _{2} MnGa alloys

2001 ◽  
Vol 12 (1-2) ◽  
pp. 9-17 ◽  
Author(s):  
Hideki Hosoda ◽  
Tsuyoshi Sugimoto ◽  
Kenji Ohkubo ◽  
Seiji Miura ◽  
Tetsuo Mohri ◽  
...  
Keyword(s):  
Thermal Properties ◽  
Phase Transformations ◽  
Long Range ◽  
Range Order ◽  
Long Range Order
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