Fisher information as thermodynamic entropy model in a classical fluid

2003 ◽  
Vol 36 (10) ◽  
pp. 2443-2453 ◽  
Author(s):  
R E Nettleton
Keyword(s):  
Fisher Information ◽  
Entropy Model ◽  
Classical Fluid ◽  
Thermodynamic Entropy

Fiber energy efficiency Part I: Extended entropy model

2014 ◽  
Vol 29 (2) ◽  
pp. 322-331 ◽  
Author(s):  
Anders Karlström ◽  
Karin Eriksson
Keyword(s):  
Energy Efficiency ◽  
Temperature Profile ◽  
Dynamic Models ◽  
Fluid Dynamic ◽  
Multiscale Model ◽  
Process Conditions ◽  
Entropy Model ◽  
Refining Zone ◽  
Energy Balances ◽  
Flat Zone

Abstract This is the first in a series of papers presenting the development of a comprehensive multiscale model with focus on fiber energy efficiency in thermo mechanical pulp processes. The fiber energy efficiency is related to the defibration and fibrillation work obtained when fibers and fiber bundles interact with the refining bars. The fiber energy efficiency differs from the total refining energy efficiency which includes the thermodynamical work as well. Extracting defibration and fibrillation work along the radius in the refining zone gives information valuable for fiber development studies.Models for this process must handle physical variables as well as machine specific parameters at different scales. To span the material and energy balances, spatial measurements from the refining zone must be available. In this paper, measurements of temperature profile and plate gaps from a full-scale CD-refiner are considered as model inputs together with a number of process variables. This enables the distributed consistency in the refining zone as well as the split of the total work between the flat zone and the CD-zone to be derived. As the temperature profile and the plate gap are available in the flat zone and the CD-zone at different process conditions it is also shown that the distributed pulp dynamic viscosity can be obtained. This is normally unknown in refining processes but certainly useful for all fluid dynamic models describing the bar-to-fiber interactions. Finally, it is shown that the inclusion of the machine parameters will be vital to get good estimates of the refining conditions and especially the split between the thermodynamical work and the defibration/fibrillation work.

Entropy Model for Multiplex Drug-Target Interaction Endpoints of Drug Immunotoxicity

2013 ◽  
Vol 13 (14) ◽  
pp. 1636-1649 ◽  
Author(s):  
Esvieta Tenorio-Borroto ◽  
Xerardo Garcia-Mera ◽  
Claudia Penuelas-Rivas ◽  
Juan Vasquez-Chagoyan ◽  
Francisco Prado-Prado ◽  
...  
Keyword(s):  
Drug Target ◽  
Entropy Model ◽  
Target Interaction

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals A Volatility Estimator of Stock Market Indices Based on the Intrinsic Entropy Model

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 484
Author(s):  
Claudiu Vințe ◽  
Marcel Ausloos ◽  
Titus Felix Furtună
Keyword(s):  
Stock Market ◽  
Time Frame ◽  
Data Sets ◽  
Entropy Model ◽  
Daily Data ◽  
Volatility Models ◽  
Derivative Instruments ◽  
Securities Industry ◽  
Time Frames ◽  
Financial Securities

Grasping the historical volatility of stock market indices and accurately estimating are two of the major focuses of those involved in the financial securities industry and derivative instruments pricing. This paper presents the results of employing the intrinsic entropy model as a substitute for estimating the volatility of stock market indices. Diverging from the widely used volatility models that take into account only the elements related to the traded prices, namely the open, high, low, and close prices of a trading day (OHLC), the intrinsic entropy model takes into account the traded volumes during the considered time frame as well. We adjust the intraday intrinsic entropy model that we introduced earlier for exchange-traded securities in order to connect daily OHLC prices with the ratio of the corresponding daily volume to the overall volume traded in the considered period. The intrinsic entropy model conceptualizes this ratio as entropic probability or market credence assigned to the corresponding price level. The intrinsic entropy is computed using historical daily data for traded market indices (S&P 500, Dow 30, NYSE Composite, NASDAQ Composite, Nikkei 225, and Hang Seng Index). We compare the results produced by the intrinsic entropy model with the volatility estimates obtained for the same data sets using widely employed industry volatility estimators. The intrinsic entropy model proves to consistently deliver reliable estimates for various time frames while showing peculiarly high values for the coefficient of variation, with the estimates falling in a significantly lower interval range compared with those provided by the other advanced volatility estimators.

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