A Theoritical and Mathematical Basis Towards Dispersing Nanoparticles and Biological Agents in a Non Polar Solvent for Fabricating Porous Materials

Advances in Bioceramics and Biocomposites II, Ceramic Engineering and Science Proceedings, Volume 27, Issue 6 - Ceramic Engineering and Science Proceedings ◽

10.1002/9780470291351.ch8 ◽

2008 ◽

pp. 87-94

Author(s):

Navin J. Manjooran ◽

Gary R. Pickrell

Keyword(s):

Porous Materials ◽

Polar Solvent ◽

Biological Agents ◽

Mathematical Basis

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Porous materials from cellulose nanocrystal and mxene surfactants at the oil/water interface

10.1021/scimeetings.0c07366 ◽

2020 ◽

Author(s):

Bingqing qian ◽

Haiqiao Wang ◽

Dong Wang ◽

Hao-Bin Zhang ◽

Jessica Wu ◽

...

Keyword(s):

Porous Materials ◽

Cellulose Nanocrystal ◽

Water Interface ◽

Oil Water

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Porous materials: Magic foam

Nature China ◽

10.1038/nchina.2013.74 ◽

2013 ◽

Author(s):

Felix Cheung

Keyword(s):

Porous Materials

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On the Mathematical Basis of Zelen’s Prerandomized Designs

Methods of Information in Medicine ◽

10.1055/s-0038-1635370 ◽

1985 ◽

Vol 24 (03) ◽

pp. 120-130 ◽

Cited By ~ 28

Author(s):

E. Brunner ◽

N. Neumann

Keyword(s):

Clinical Trial ◽

Normal Distribution ◽

Random Variables ◽

Small Samples ◽

Selection Effects ◽

Sample Sizes ◽

Mathematical Basis ◽

Additional Assumptions

SummaryThe mathematical basis of Zelen’s suggestion [4] of pre randomizing patients in a clinical trial and then asking them for their consent is investigated. The first problem is to estimate the therapy and selection effects. In the simple prerandomized design (PRD) this is possible without any problems. Similar observations have been made by Anbar [1] and McHugh [3]. However, for the double PRD additional assumptions are needed in order to render therapy and selection effects estimable. The second problem is to determine the distribution of the statistics. It has to be taken into consideration that the sample sizes are random variables in the PRDs. This is why the distribution of the statistics can only be determined asymptotically, even under the assumption of normal distribution. The behaviour of the statistics for small samples is investigated by means of simulations, where the statistics considered in the present paper are compared with the statistics suggested by Ihm [2]. It turns out that the statistics suggested in [2] may lead to anticonservative decisions, whereas the “canonical statistics” suggested by Zelen [4] and considered in the present paper keep the level quite well or may lead to slightly conservative decisions, if there are considerable selection effects.

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Comparison of Rheumatological Diagnoses by a Bayesian Program and by Physicians

Methods of Information in Medicine ◽

10.1055/s-0038-1634834 ◽

1991 ◽

Vol 30 (03) ◽

pp. 187-193 ◽

Cited By ~ 7

Author(s):

H. J. Moens ◽

J. K. van der Korst

Keyword(s):

Receiver Operating Characteristics ◽

Bayesian Decision ◽

Operating Characteristics ◽

Likelihood Ratios ◽

Improve Performance ◽

Mathematical Basis ◽

Human Expert ◽

Test Population ◽

Sensitivity Specificity ◽

Diagnostic Outcome

AbstractA Bayesian decision support system was developed for the diagnosis of rheumatic disorders. Knowledge in this system is represented as evidential weights of findings. Simple weights were calculated as the logarithm of likelihood ratios on the basis of 1,000 consecutive patients from a rheumatological clinic. The effect of various methods to improve performance of the system by modification of the weights was studied. Three methods had a mathematical basis; a fourth consisted of weights adapted by a human expert, which allowed inclusion of diagnostic rules such as defined in widely accepted criteria sets. The system’s performance was measured in a test population of 570 different cases from the same clinic and compared with predictions of diagnostic outcome made by rheumatologists. The weights from a human expert gave optimal results (sensitivity 65% and specificity 96%), that were close to the physicians’ predictions (sensitivity 64% and specificity 98%). The methods to measure the performance of the various models used in this study emphasize sensitivity, specificity and the use of receiver operating characteristics.

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