Junction matters in hydraulic circuit bio-design of microfluidics

Microfluidic channels are at micrometer scales;thus,their fluid flows are laminar,resulting in the linear dependence of pressure drop on flow rate in the length of the channel.The ratio of the pressure drop to flow rate,referred to as resistance,depends on channel size and dynamic viscosity.Usually,a microfluidic chip is analogous to an electric circuit in design,but the design is adjusted to optimize channel size.However,whereas voltage loss is negligible at the nodes of an electric circuit,hydraulic pressure drops at the nodes of microfluidic chips by a magnitude are comparable to the pressure drops in the straight channels.Here,we prove by experiment that one must fully consider the pressure drops at nodes so as to accurately design a precise microfluidic chip.In the process,we numerically calculated the pressure drops at hydraulic nodes and list their resistances in the range of flows as concerned.We resorted to machine learning to fit the calculated results for complex junctions.Finally,we obtained a library of node resistances for common junctions and used them to design three established chips that work for single-cell analysis and for precision allocation of solutes(in gradient and averaging concentration microfluidic networks).Endothelial cells were stimulated by generating concentrations of adriamycin hydrochloride from the last two microfluidic networks,and we analyzed the response of endothelial cells.The results indicate that consideration of junction resistances in design calculation brings experimental results closer to the design values than usual.This approach may therefore contribute to providing a platform for the precise design of organ chips.

Establishing a Gross Primary Productivity Model by SIF and PRI on the Rice Canopy

Solar-induced chlorophyll fluorescence(SIF)has shown remarkable results in estimating vegetation carbon cycles,and combining it with the photochemical reflectance index(PRI)has great potential for estimating gross primary productivity(GPP).However,few studies have used SIF combined with PRI to estimate crop canopy GPP.Large temporal and spatial variability between SIF,PRI,and GPP has also been found in remote sensing observations,and the observed PRI and SIF are influenced by the ratio of different observed information(e.g.,background,direct sunlit,and shaded leaves)and the physiological state of the vegetation.In this study,the PRI and SIF from a multi-angle spectrometer and the GPP from an eddy covariance system were used to assess the ability of the PRI to enhance the SIF-GPP estimation model.A semi-empirical kernel-driven Bidirectional Reflectance Distribution Function(BRDF)model was used to describe the hotspot PRI/SIF(PRIhs/SIFhs),and a modified two-leaf model was used to calculate the total canopy PRI/SIF(PRItot/SIFtot).We compared the accuracies of PRIhs/SIFhs and PRItot/SIFtot in estimating GPP.The results indicated that the PRItot+SIFtot-GPP model performed the best,with a correlation coefficient(R2)of the validation dataset of 0.88,a root mean square error(RMSE)of 3.74,and relative prediction deviation(RPD)of 2.71.The leaf area index(LAI)had a linear effect on the PRI/SIF estimation of GPP,but the temperature and vapor pressure differences had nonlinear effects.Compared with hotspot PRIhs/SIFhs,PRItot/SIFtot exhibited better consistency with GPP across different time series.Our research demonstrates that PRI is effective in enhancing SIF and PRI for estimating GPP on the rice canopy and also suggests that the two-leaf model would contribute to the vegetation index tracking the real-time crop productivity.

An information-volume-based distance measure for decision-making

D-S evidence theory,as a general framework for reasoning with uncertainty,allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure.However,the mass assignments given by unknown information sources are disordered.How to measure the difference between the mass assignments has aroused people’s interest.In this paper,inspired by the information volume,a novel distance-based measure is proposed to measure the difference between mass assignments.The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments.At the same time,it is verified that the measure not only meets the properties of distance,but also proves the superiority of the proposed Information Volume Distance(IVD)through simulation experiments.Meanwhile,in the process of information fusion,the reliability of each source could be quantified through IVD.Therefore,based on IVD,a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion.Moreover,algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.