sys_steady

Change the values of the constraints step by step and record the number of iterations to reach the steady state.

There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of  feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs.

Xiting Wang

sysAgNPs

Systematic Quantification of AgNPs to Unleash their Potential
for Applicability

Longfei Mao

Jiamin Hu

sys_steady function

<dl><dt>binary_dataset</dt>
<dd>A binary dataframe of datasets used to establish evaluation criteria.</dd>
<dt>tran_matrix</dt>
<dd>A transfer probability matrix.</dd>
<dt>tol_vec</dt>
<dd>A smaller constants used as constraints.</dd></dl>

Arguments

Iterate to obtain the steady state probability — sys_steady

<dl>

<dt>binary_dataset</dt>
<dd>A binary dataframe of datasets used to establish evaluation criteria.</dd>


<dt>tran_matrix</dt>
<dd>A transfer probability matrix.</dd>


<dt>tol_vec</dt>
<dd>A smaller constants used as constraints.</dd>

</dl>

sys_steady: Iterate to obtain the steady state probability

Description

Usage

Value

Arguments

Examples