Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.
Photosensitization
is a promising way to make UiO-66 visible light
responsive. In this work, zirconium (Zr) precursors and synthetic
methods (solvent-assisted or nonsolvent-assisted) were found to significantly
affect the electric charge and the defect sites of UiO-66. UiO-66
with enhanced adsorption and photosensitized photocatalytic ability
was then obtained. When ZrOCl2·8H2O was
applied as Zr-precursor in a nonsolvent method, defect rich UiO-66
(UiO–OCl–N) was achieved with higher Cr(VI) adsorbing
ability and improved photosensitized photocatalytic ability. Moreover,
with the coexistence of Rhodamine B in Cr(VI) solution, photocatalytic
performance of UiO-66 can be further enhanced.
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