Optimal Weighting Factor for Single-Step Trapezoidal Method

2003 ◽  
Author(s):  
Fuzhang Zhao
Keyword(s):  
Heat Conduction ◽  
Weighting Factor ◽  
Single Step ◽  
Numerical Results ◽  
Nonlinear Heat Conduction ◽  
Step Method ◽  
Benchmark Tests ◽  
Optimal Weighting ◽  
Linear And Nonlinear

A new optimal weighting factor for a single-step trapezoidal method, θ = 0.618, has been developed to solve transient linear and nonlinear heat conduction problems. The numerical results based on the optimal weighting factor developed, through both linear and nonlinear benchmark tests, are compared with other commonly used single-step trapezoidal methods with emphasis on the combination of both accuracy and efficiency in the solution. The optimal weighting factor of single-step method has been proved to be very accurate, stable, and efficient. The relevant features for the single-step trapezoidal methods are also addressed.

Purification of Urokinase from Complex Mixtures Using Immobilized Monoclonal Antibody Against Urokinase Light Chain

1983 ◽  
Vol 49 (01) ◽  
pp. 024-027 ◽  
Author(s):  
David Vetterlein ◽  
Gary J Calton
Keyword(s):  
Monoclonal Antibody ◽  
Light Chain ◽  
Culture Media ◽  
Biological Fluids ◽  
Single Step ◽  
High Yield ◽  
Step Method ◽  
Commercial Preparation ◽  
E Coli ◽  
Tissue Culture Media

SummaryThe preparation of a monoclonal antibody (MAB) against high molecular weight (HMW) urokinase light chain (20,000 Mr) is described. This MAB was immobilized and the resulting immunosorbent was used to isolate urokinase starting with an impure commercial preparation, fresh urine, spent tissue culture media, or E. coli broth without preliminary dialysis or concentration steps. Monospecific antibodies appear to provide a rapid single step method of purifying urokinase, in high yield, from a variety of biological fluids.

scholarly journals High-resolution limited-angle phase tomography of dense layered objects using deep neural networks

2019 ◽  
Vol 116 (40) ◽  
pp. 19848-19856 ◽  
Author(s):  
Alexandre Goy ◽  
Girish Rughoobur ◽  
Shuai Li ◽  
Kwabena Arthur ◽  
Akintunde I. Akinwande ◽  
...  
Keyword(s):  
Integrated Circuit ◽  
Deep Neural Networks ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Single Step ◽  
Training Set ◽  
Step Method ◽  
3 Dimensional ◽  
Dimensional Reconstruction ◽  
Optical Domain

We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to ±10○. Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands.

A Single-Step Method for the Isolation of Antithrombin IIP

Vox Sanguinis ◽  
1984 ◽  
Vol 47 (6) ◽  
pp. 397-405
Author(s):  
Milan Wickerhauser ◽  
Craigenne Williams
Keyword(s):  
Single Step ◽  
Step Method
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