ULTRAVIOLET SPECTROPHOTOMETRY OF MULTI-COMPONENT MIXTURES USING ARTIFICIAL NEURAL NETWORKS

Three—layer back—propagation neural networks were used for processing the information provided by the ultraviolet spectra of five—component mixtures, affecting the simultaneous determination of the components with satisfactory results.

Elutriation characteristics of multi-component mixtures in a fluidized bed pyrolysis process

Multi-component mixtures were prepared to simulate a bed of walnut shells undergoing pyrolysis in acold fluidized bed.Four mixtures with different ratios of walnut shell,semi-chars,and clean char were used.Batch elutriation experiments using these mixtures were performed in a transparent fluidized bed to allow direct observation of the flow patterns and particle composition.The results indicated that the bubble wake rather than bubble nose was primarily responsible for particle entrainment at higher gas velocities,and that coarser particles would be“transformed”into elutriable particles.Dimensionless gas velocities ranged from 0.2 to 1.0 and an exponential decrease in particle concentration with respect to bed height was observed.The transport disengagement height(TDH)gradually increased with the gas velocity until the entire bed layer enters a pneumatic transport state.Notably,larger TDHs were required when the bed contained a larger fraction of light components.Two characteristic parameters were used to evaluate particle elutriation:the elutriation rate constant(K),and the residual volatile content of the elutriated particles(v′).These parameters were used to optimize operating gas velocity for the fluidized bed.

Joint Effects of Multipollutant Mixtures on Mortality in Chengdu,China

Significant epidemiological research has revealed that exposure to air pollution is substantially associated with numerous detrimental health consequences^([1-3]).The negative health effects of individual air pollutants(e.g.,fine particulate matter:PM_(2.5);nitrogen dioxide:NO_(2);carbon monoxide,CO;or ozone:O_(3))have been widely explored^([4]).However,humans are constantly exposed to multipollutant mixtures in real life,and biological responses to inhaled pollutants are likely to depend on the interplay of pollutant mixtures.Therefore,it is critical and imperative to explore the joint effects of multipollutant mixtures on human beings.

Hydrogen release of NaBH_(4) below 60 ℃ with binary eutectic mixture of xylitol and erythritol additive

NaBH_(4) was widely regarded as a low-cost hydrogen storage material due to its high-mass hydrogen capacity of approximately 10.8%(mass)and high volumetric hydrogen capacity of around 115 g·L^(–1).However,it exhibits strong stability and requires temperatures above 500℃ for hydrogen release in practical applications.In this study,two polyhydric alcohols,xylitol and erythritol(XE),were prepared as a binary eutectic sugar alcohol through a grinding-melting method.This binary eutectic sugar alcohol was used as a proton-hydrogen carrier to destabilize NaBH_(4).The 19NaBH_(4)-16XE composite material prepared by ball milling could start releasing hydrogen at 57.5℃,and the total hydrogen release can reach over 88.8%(4.45%(mass))of the theoretical capacity.When the 19NaBH_(4)-16XE composite was pressed into solid blocks,the volumetric hydrogen capacity of the block-shaped composite could reach 67.2 g·L^(–1).By controlling the temperature,the hydrogen desorption capacity of the NaBH_(4)-XE composite material was controllable,which has great potential for achieving solid-state hydrogen production from NaBH_(4).

Research on shell-side heat and mass transfer with multi-component in LNG spiral-wound heat exchanger under sloshing conditions

The spiral-wound heat exchanger(SWHE) is the primary low-temperature heat exchanger for large-scale LNG plants due to its high-pressure resistance, compact structure, and high heat exchange efficiency. This paper studied the shell-side heat and mass transfer characteristics of vapor-liquid two-phase mixed refrigerants in an SWHE by combining a multi-component model in FLUENT software with a customized multicomponent mass transfer model. Besides, the mathematical model under the sloshing condition was obtained through mathematical derivation, and the corresponding UDF code was loaded into FLUENT as the momentum source term. The results under the sloshing conditions were compared with the relevant parameters under the steady-state condition. The shell-side heat and mass transfer characteristics of the SWHE were investigated by adjusting the component ratio and other working conditions. It was found that the sloshing conditions enhance the heat transfer performance and sometimes have insignificant effects. The sloshing condition is beneficial to reduce the flow resistance. The comprehensive performance of multi-component refrigerants has been improved and the improvement is more significant under sloshing conditions, considering both the heat transfer and pressure drop.These results will provide theoretical support for the research and design of multi-component heat and mass transfer enhancement of LNG SWHE under ocean sloshing conditions.

Optimization of Biofuel Formulation by Mixture Design

With the full growth of energy needs in the world, several studies are now focused on finding renewable sources. The aim of this work is to optimise biofuel formulation from a mixture design by studying physical properties, such as specific gravity and kinematic viscosity of various formulated mixtures. Optimization from the mixture plan revealed that in the chosen experimental domain, the optimal conditions are: 40% for used frying oil (UFO), 50% for bioethanol and 10% for diesel. These experimental conditions lead to a biofuel with a density of 0.84 and a kinematic viscosity of 2.97 cSt. These parameters are compliant with the diesel quality certificate in tropical areas. These density and viscosity values were determined according to respective desirability values of 0.68 and 0.75.

Probabilistic seismic inversion based on physics-guided deep mixture density network

Deterministic inversion based on deep learning has been widely utilized in model parameters estimation.Constrained by logging data,seismic data,wavelet and modeling operator,deterministic inversion based on deep learning can establish nonlinear relationships between seismic data and model parameters.However,seismic data lacks low-frequency and contains noise,which increases the non-uniqueness of the solutions.The conventional inversion method based on deep learning can only establish the deterministic relationship between seismic data and parameters,and cannot quantify the uncertainty of inversion.In order to quickly quantify the uncertainty,a physics-guided deep mixture density network(PG-DMDN)is established by combining the mixture density network(MDN)with the deep neural network(DNN).Compared with Bayesian neural network(BNN)and network dropout,PG-DMDN has lower computing cost and shorter training time.A low-frequency model is introduced in the training process of the network to help the network learn the nonlinear relationship between narrowband seismic data and low-frequency impedance.In addition,the block constraints are added to the PG-DMDN framework to improve the horizontal continuity of the inversion results.To illustrate the benefits of proposed method,the PG-DMDN is compared with existing semi-supervised inversion method.Four synthetic data examples of Marmousi II model are utilized to quantify the influence of forward modeling part,low-frequency model,noise and the pseudo-wells number on inversion results,and prove the feasibility and stability of the proposed method.In addition,the robustness and generality of the proposed method are verified by the field seismic data.

Optimal preparation of Bose and Fermi atomic gas mixtures of ^(87)Rb and ^(40)K in a crossed optical dipole trap

We report on the optimal production of the Bose and Fermi mixtures with ^(87) Rb and ^(40)K in a crossed optical dipole trap(ODT).We measure the atomic number and lifetime of the mixtures in combination of the spin state |F=9/2,m_(F)=9/2> of^(40)K and |1,1>of ^(87) Rb in the ODT,which is larger and longer compared with the combination of the spin state |9/2,9/2> of^(40)K and 12,2) of ^(87)Rb in the ODT.We observe the atomic numbers of ^(87)Rb and ^(40)K shown in each stage of the sympathetic cooling process while gradually reducing the depth of the optical trap.By optimizing the relative loading time of atomic mixtures in the MOT,we obtain the large atomic number of ^(40)K(~6 ×10^(6)) or the mixtures of atoms with an equal number(~1.6 × 10^(6)) at the end of evaporative cooling in the ODT.We experimentally investigate the evaporative cooling in an enlarged volume of the ODT via adding a third laser beam to the crossed ODT and found that more atoms(8 × 10^(6)) and higher degeneracy(T/T_(F)=0.25) of Fermi gases are obtained.The ultracold atomic gas mixtures pave the way to explore phenomena such as few-body collisions and the Bose-Fermi Hubbard model,as well as for creating ground-state molecules of ^(87)Rb^(40)K.

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors,site quality,and aridity index

The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

A Bayesian Mixture Model Approach to Disparity Testing

The topic of this article is one-sided hypothesis testing for disparity, i.e., the mean of one group is larger than that of another when there is uncertainty as to which group a datum is drawn. For each datum, the uncertainty is captured with a given discrete probability distribution over the groups. Such situations arise, for example, in the use of Bayesian imputation methods to assess race and ethnicity disparities with certain insurance, health, and financial data. A widely used method to implement this assessment is the Bayesian Improved Surname Geocoding (BISG) method which assigns a discrete probability over six race/ethnicity groups to an individual given the individual’s surname and address location. Using a Bayesian framework and Markov Chain Monte Carlo sampling from the joint posterior distribution of the group means, the probability of a disparity hypothesis is estimated. Four methods are developed and compared with an illustrative data set. Three of these methods are implemented in an R-code and one method in WinBUGS. These methods are programed for any number of groups between two and six inclusive. All the codes are provided in the appendices.

Sensing the heavy water concentration in an H_(2)O-D_(2)O mixture by solid-solid phononic crystals

A new method based on phononic crystals is presented to detect the concentration of heavy water(D_(2)O)in an H_(2)O-D_(2)O mixture.Results have been obtained and analyzed in the concentration range of 0%-10%and 90%-100%D_(2)O.A proposed structure of tungsten scatterers in an aluminum host is studied.In order to detect the target material,a cavity region is considered as a sound wave resonator in which the target material with different concentrations of D_(2)O is embedded.By changing the concentration of D_(2)O in the H_(2)O-D_(2)O mixture,the resonance frequency undergoes a frequency shift.Each 1%change in D_(2)O concentration in the H_(2)O-D_(2)O mixture causes a frequency change of about 120 Hz.The finite element method is used as the numerical method to calculate and analyze the natural frequencies and transmission spectra of the proposed sensor.The performance evaluation index shows a high Q factor up to 1475758 and a high sensitivity up to 13075,which are acceptable values for sensing purposes.The other figures of merit related to the detection performance also indicate high-quality performance of the designed sensor.

Multiscale Study on Low Temperature Crack Resistance Mechanism of Steel Slag Asphalt Mixture

The objective of this paper was to study low temperature crack resistance mechanism of steel slag asphalt mixture(SAM).Thermal stress restrained specimen test(TSRST)and three-point bending test were carried out to evaluate the low-temperature crack resistance of SAM and basalt asphalt mixture(BAM).Based on the digital image correlation technique(DIC),the strain field distribution and crack propagation of SAM were analyzed from the microscopic point of view,and a new index,crack length factor(C),was proposed to evaluate the crack resistance of the asphalt mixture.The crystal phase composition and microstructure of steel slag aggregate(SA)and basalt aggregate(BA)were studied by X-ray diffraction(XRD)and scanning electron microscopy(SEM)to explore the low-temperature crack resistance mechanism of SAM.Results show that the low-temperature crack resistance of SAM is better than that of BAM;SAM has good integrity and persistent elastic deformation,and its bending failure mode is a hysteretic quasi-brittle failure;The SA surface is evenly distributed with pores and has surface roughness.SA has the composition phase of alkaline aggregate-calcite(CaCO3),so it has good adhesion to asphalt,which reveals the mechanism of excellent low-temperature crack resistance of SAM.

Long-term performance of recycled asphalt mixtures containing high RAP and RAS

The application of reclaimed asphalt pavement(RAP)and reclaimed asphalt shingles(RAS)on asphalt pavement can reduce the asphalt paving cost,conserve energy and protect the environment.However,the use of high contents of RAP and RAS in asphalt pavement may lead to durability issues,especially the fatigue cracking and thermal cracking.It is necessary to conduct a series of analyses on asphalt mixtures containing high RAP and RAS,and seek methods to enhance their long-term performance.This paper provides a comprehensive over-view of the long-term performance of recycled asphalt mixtures containing high contents of RAP and RAS.The findings in this research show that rutting resistance of high recycled asphalt mixtures is not a concern,whereas their resistance to fatigue and thermal cracking is not conclusive.Recycling agents can be used to improve the thermal cracking resistance of high recycled asphalt mixtures.An optimum decision on recycling agents will improve the durability properties of high recycled asphalt mixtures.It is recommended that to use a balanced mixture design approach with testing of the blended asphalt binders will provide better understanding of long-term performance of recycled asphalt mixtures containing high RAP and RAS.

Predictive modelling of volumetric and Marshall properties of asphalt mixtures modified with waste tire-derived char:A statistical neural network approach

The goals of this study are to assess the viability of waste tire-derived char(WTDC)as a sustainable,low-cost fine aggregate surrogate material for asphalt mixtures and to develop the statistically coupled neural network(SCNN)model for predicting volumetric and Marshall properties of asphalt mixtures modified with WTDC.The study is based on experimental data acquired from laboratory volumetric and Marshall properties testing on WTDCmodified asphalt mixtures(WTDC-MAM).The input variables comprised waste tire char content and asphalt binder content.The output variables comprised mixture unit weight,total voids,voids filled with asphalt,Marshall stability,and flow.Statistical coupled neural networks were utilized to predict the volumetric and Marshall properties of asphalt mixtures.For predictive modeling,the SCNN model is employed,incorporating a three-layer neural network and preprocessing techniques to enhance accuracy and reliability.The optimal network architecture,using the collected dataset,was a 2:6:5 structure,and the neural network was trained with 60%of the data,whereas the other 20%was used for cross-validation and testing respectively.The network employed a hyperbolic tangent(tanh)activation function and a feed-forward backpropagation.According to the results,the network model could accurately predict the volumetric and Marshall properties.The predicted accuracy of SCNN was found to be as high value>98%and low prediction errors for both volumetric and Marshall properties.This study demonstrates WTDC's potential as a low-cost,sustainable aggregate replacement.The SCNN-based predictive model proves its efficiency and versatility and promotes sustainable practices.

Historical tillage promotes grass-legume mixtures establishment and accelerates soil microbial activity and organic carbon decomposition

Perennial grass-legume mixtures have been extensively used to restore degraded grasslands,increasing grassland productivity and forage quality.Tillage is crucial for seedbed preparation and sustainable weed management for the establishment of grass-legume mixtures.However,a common concern is that intensive tillage may alter soil characteristics,leading to losses in soil organic carbon(SOC).We investigated the plant community composition,SOC,soil microbial biomass carbon(MBC),soil enzyme activities,and soil properties in long-term perennial grass-legume mixtures under two different tillage intensities(once and twice)as well as in a fenced grassland(FG).The establishment of grass-legume mixtures increased plant species diversity and plant community coverage,compared with FG.Compared with once tilled grassland(OTG),twice tilled grassland(TTG)enhanced the coverage of high-quality leguminous forage species by 380.3%.Grass-legume mixtures with historical tillage decreased SOC and dissolved organic carbon(DOC)concentrations,whereas soil MBC concentrations in OTG and TTG increased by 16.0%and 16.4%,respectively,compared with FG.TTG significantly decreased the activity of N-acetyl-β-D-glucosaminidase(NAG)by 72.3%,whereas soil enzymeβ-glucosidase(βG)in OTG and TTG increased by 55.9%and 27.3%,respectively,compared with FG.Correlation analysis indicated a close association of the increase in MBC andβG activities with the rapid decline in SOC.This result suggested that MBC was a key driving factor in soil carbon storage dynamics,potentially accelerating soil carbon cycling and facilitating biogeochemical cycling.The establishment of grass-legume mixtures effectively improves forage quality and boosts plant diversity,thereby facilitating the restoration of degraded grasslands.Although tillage assists in establishing legume-grass mixtures by controlling weeds,it accelerates microbial activity and organic carbon decomposition.Our findings provide a foundation for understanding the process and effectiveness of restoration management in degraded grasslands.

Thermodynamic Performance Analysis of Geothermal Power Plant Based on Organic Rankine Cycle (ORC) Using Mixture of Pure Working Fluids

The selection of working fluid significantly impacts the geothermal ORC’s Efficiency.Using a mixture as a working fluid is a strategy to improve the output of geothermal ORC.In the current study,modelling and thermodynamic analysis of ORC,using geothermal as a heat source,is carried out at fixed operating conditions.The model is simulated in the Engineering Equation Solver(EES).An environment-friendly mixture of fluids,i.e.,R245fa/R600a,with a suitable mole fraction,is used as the operating fluid.The mixture provided the most convenient results compared to the pure working fluid under fixed operating conditions.The impact of varying the evaporator pressure on the performance parameters,including energy efficiency,exergy efficiency and net power output is investigated.The system provided the optimal performance once the evaporator pressure reached the maximum value.The efficiencies:Energy and Exergy,and Net Power output of the system are 16.62%,64.08%and 2199 kW for the basic cycle and 20.72%,67.76%and 2326 kW respectively for the regenerative cycle.

Effect of Guanxin-V Mixture Combined with Sacubitril Valsartan on Cardiac Function after PCI in STEMI Patients

[Objectives]To observe the effect of Guanxin-V Mixture combined with Sacubitril Valsartan on cardiac function in patients after PCI for acute ST-segment elevation myocardial infarction(STE-MI).[Methods]41 cases of STEMI patients(qi and yin deficiency and blood stasis and obstruction)hospitalized in Nanjing Hospital of Chinese Medicine affiliated to Nanjing University of Chinese Medicine from January 2020 to June 2021 were randomly divided into 21 cases in the treatment group and 20 cases in the control group,and the two groups were given standardized Western medicine treatment as soon as possible after PCI.The control group was treated with Sacubitril Valsartan,and the treatment group was treated with Guanxin-V Mixture on the basis of treatment in the control group.The patients in the two groups were treated for 3 months,and the TCM syndrome score,left ventricular ejection fraction(LVEF),and N-Terminal Pro-Brain Natriuretic Peptide(NT-proBNP),interleukin-6(IL-6),and high-sensitivity C-reactive protein(hs-CRP)levels,and the incidence of heart failure and adverse reactions in the two groups after treatment were recorded.[Results]After the treatment,the TCM syndrome score and serum NT-proBNP,IL-6 and hs-CRP levels of the two groups significantly decreased(P<0.05),and the levels of the treatment group were significantly lower than those of the control group(P<0.05);the LVEF of the two groups significantly increased(P<0.05),and the level of the treatment group was significantly higher than that of the control group(P<0.05).Comparison of the incidence of heart failure and adverse reactions in the two groups showed no statistically significant differences(P>0.05).[Conclusions]Guanxin-V Mixture combined with Sacubitril Valsartan could significantly improve cardiac function in STEMI patients undergoing PCI,and its effect may be related to the suppression of inflammatory response.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

Yangyin Huowei mixture alleviates chronic atrophic gastritis by inhibiting the IL-10/JAK1/STAT3 pathway

BACKGROUND The Chinese medicine Yangyin Huowei mixture(YYHWM)exhibits good clinical efficacy in the treatment of chronic atrophic gastritis(CAG),but the mechanisms underlying its activity remain unclear.AIM To investigate the therapeutic effects of YYHWM and its underlying mechanisms in a CAG rat model.METHODS Sprague-Dawley rats were allocated into control,model,vitacoenzyme,and low,medium,and high-dose YYHWM groups.CAG was induced in rats using Nmethyl-N′-nitro-N-nitrosoguanidine,ranitidine hydrochloride,hunger and satiety perturbation,and ethanol gavage.Following an 8-wk intervention period,stomach samples were taken,stained,and examined for histopathological changes.ELISA was utilized to quantify serum levels of PG-I,PG-II,G-17,IL-1β,IL-6,and TNF-α.Western blot analysis was performed to evaluate protein expression of IL-10,JAK1,and STAT3.RESULTS The model group showed gastric mucosal layer disruption and inflammatory cell infiltration.Compared with the blank control group,serum levels of PGI,PGII,and G-17 in the model group were significantly reduced(82.41±3.53 vs 38.52±1.71,23.06±0.96 vs 11.06±0.70,and 493.09±12.17 vs 225.52±17.44,P<0.01 for all),whereas those of IL-1β,IL-6,and TNF-αwere significantly increased(30.15±3.07 vs 80.98±4.47,69.05±12.72 vs 110.85±6.68,and 209.24±11.62 vs 313.37±36.77,P<0.01 for all),and the protein levels of IL-10,JAK1,and STAT3 were higher in gastric mucosal tissues(0.47±0.10 vs 1.11±0.09,0.49±0.05 vs 0.99±0.07,and 0.24±0.05 vs 1.04±0.14,P<0.01 for all).Compared with the model group,high-dose YYHWM treatment significantly improved the gastric mucosal tissue damage,increased the levels of PGI,PGII,and G-17(38.52±1.71 vs 50.41±3.53,11.06±0.70 vs 15.33±1.24,and 225.52±17.44 vs 329.22±29.11,P<0.01 for all),decreased the levels of IL-1β,IL-6,and TNF-α(80.98±4.47 vs 61.56±4.02,110.85±6.68 vs 89.20±8.48,and 313.37±36.77 vs 267.30±9.31,P<0.01 for all),and evidently decreased the protein levels of IL-10 and STAT3 in gastric mucosal tissues(1.11±0.09 vs 0.19±0.07 and 1.04±0.14 vs 0.55±0.09,P<0.01 for both).CONCLUSION YYHWM reduces the release of inflammatory factors by inhibiting the IL-10/JAK1/STAT3 pathway,alleviating gastric mucosal damage,and enhancing gastric secretory function,thereby ameliorating CAG development and cancer transformation.

UV,five wavelengths fusion and electrochemical fingerprints combined with antioxidant activity for quality control of antiviral mixture

Aiming to ensure the consistency of quality control of Traditional Chinese Medicines(TCMs),a combination method of high-performance liquid chromatography(HPLC),ultraviolet(UV),electrochemical(EC)was developed in this study to comprehensively evaluate the quality of Antiviral Mixture(AM),and Comprehensive Linear Quantification Fingerprint Method(CLQFM)was used to process the data.Quantitative analysis of three active substances in TCM was conducted.A fivewavelength fusion fingerprint(FWFF)was developed,using second-order derivatives of UV spectral data to differentiate sample levels effectively.The combination of HPLC and UV spectrophotometry,along with electrochemical fingerprinting(ECFP),successfully evaluated total active substances.Ultimately,a multidimensional profiling analytical system for TCM was developed.