Grand canonical model predictions for nuclear fragmentation

Physical Review C ◽

10.1103/physrevc.70.044611 ◽

2004 ◽

Vol 70 (4) ◽

Author(s):

C. B. Das ◽

S. Das Gupta ◽

B. K. Jennings

Keyword(s):

Canonical Model ◽

Nuclear Fragmentation ◽

Model Predictions ◽

Grand Canonical

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A Combinatorial Toolbox for Protein Sequence Design and Landscape Analysis in the Grand Canonical Model

Algorithms and Computation - Lecture Notes in Computer Science ◽

10.1007/3-540-45678-3_35 ◽

2001 ◽

pp. 403-415

Author(s):

James Aspnes ◽

Julia Hartling ◽

Kao Ming-Yang ◽

Junhyong Kim ◽

Gauri Shah

Keyword(s):

Protein Sequence ◽

Canonical Model ◽

Landscape Analysis ◽

Sequence Design ◽

Grand Canonical

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A Combinatorial Toolbox for Protein Sequence Design and Landscape Analysis in the Grand Canonical Model

Journal of Computational Biology ◽

10.1089/106652702761034154 ◽

2002 ◽

Vol 9 (5) ◽

pp. 721-741

Author(s):

James Aspnes ◽

Julia Hartling ◽

Ming-Yang Kao ◽

Junhyong Kim ◽

Gauri Shah

Keyword(s):

Protein Sequence ◽

Canonical Model ◽

Landscape Analysis ◽

Sequence Design ◽

Grand Canonical

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Grand-Canonical Model of Electrochemical Double Layers from a Hybrid Density–Potential Functional

Journal of Chemical Theory and Computation ◽

10.1021/acs.jctc.1c00098 ◽

2021 ◽

Author(s):

Jun Huang ◽

Shengli Chen ◽

Michael Eikerling

Keyword(s):

Canonical Model ◽

Double Layers ◽

Grand Canonical

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A grand canonical ensemble Monte Carlo study of confined planar and homeotropically anchored Gay—Berne films

Molecular Physics ◽

10.1080/00268979809482254 ◽

1998 ◽

Vol 93 (4) ◽

pp. 681-692

Author(s):

THOMAS GRUHN ◽

MARTIN SCHOEN

Keyword(s):

Monte Carlo ◽

Canonical Ensemble ◽

Monte Carlo Study ◽

Grand Canonical Ensemble ◽

Ensemble Monte Carlo ◽

Grand Canonical

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The REM Model: Predictions for Implicit and Explicit Memory

PsycEXTRA Dataset ◽

10.1037/e413802005-786 ◽

2001 ◽

Author(s):

Richard Shiffrin

Keyword(s):

Explicit Memory ◽

Model Predictions ◽

Implicit And Explicit Memory ◽

Implicit And Explicit

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The new approach to improvement oil reservoir proxy model predictions using machine learning algorithms

Neftyanoe khozyaystvo - Oil Industry ◽

10.24887/0028-2448-2019-12-60-63 ◽

2019 ◽

Vol 12 ◽

pp. 60-63

Author(s):

O.V. Zotkin ◽

◽

M.V. Simonov ◽

A.E. Osokina ◽

A.M. Andrianova ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Oil Reservoir ◽

New Approach ◽

Proxy Model ◽

Model Predictions

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Model predictions of fan response to inlet temperature transients and spatial temperature distortion

10.2514/6.1989-2686 ◽

1989 ◽

Author(s):

CONSTANCE DOWLER ◽

KEITH BOYER ◽

NORMAN POTI

Keyword(s):

Inlet Temperature ◽

Model Predictions ◽

Spatial Temperature

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Reduced-order Model Predictions of Wind Turbines via Mode Decomposition and Sparse Sampling

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-1602 ◽

2021 ◽

Author(s):

Ala' E. Qatramez ◽

Daniel Foti

Keyword(s):

Wind Turbines ◽

Sparse Sampling ◽

Reduced Order Model ◽

Order Model ◽

Reduced Order ◽

Mode Decomposition ◽

Model Predictions

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Lag model predictions of the NASA Junction flow

AIAA Scitech 2021 Forum ◽

10.2514/6.2021-1429 ◽

2021 ◽

Author(s):

Michael E. Olsen

Keyword(s):

Junction Flow ◽

Model Predictions ◽

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CORRELATION TECHNOLOGY OF NUCLEAR FRAGMENTATION WITH ULTRASOUND PARAMETERS DURING PHACOEMULSIFICATION IN PATIENTS WITH PSEUDOEXFOLIATION SYNDROME

The Journal of scientific articles Health and Education millennium ◽

10.26787/nydha-2226-7425-2017-19-5-12-15 ◽

2017 ◽

Vol 19 (5) ◽

pp. 12-15

Author(s):

A.E. Gorbunov ◽

Keyword(s):

Pseudoexfoliation Syndrome ◽

Nuclear Fragmentation ◽

Ultrasound Parameters

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