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.

Uniform design ray in the assessment of combined toxicities of multi-component mixtures

Multi-component mixture(MCM) is a complicated chemical system that contains a great deal of mixture rays with various mixture ratios, and each ray includes many mixtures with different concentration levels.Currently, in combined toxicity field, almost all studies on MCM focus on the mixtures designed by the equivalenteffect concentration ratio(EECR) procedure. However, the EECR mixtures cannot represent the whole mixture system because the EECR mixtures are located on one mixture ray in concentration space formed by multiple components. In our view, some optimal experimental design such as the uniform design(UD) should be used to effectively select many representative mixture rays from the MCM system,instead of single EECR ray. The uniform design ray(UDray) integrating UD idea with fixed-ratio ray design can systematically and comprehensively measure the combined toxicity changes in the MCM system. This review introduces the operation method, construction of uniform table and corresponding usable table, and some cases of application of the UD-ray to help readers easily use UD-ray in their MCM toxicity assessment.

A micropolar mixture theory of multi-component porous media

A mixture theory is developed for multi-component micropolar porous media with a combination of the hybrid mixture theory and the micropolar continuum theory. The system is modeled as multi-component micropolar elastic solids saturated with multi- component micropolar viscous fluids. Balance equations are given through the mixture theory. Constitutive equations are developed based on the second law of thermodynamics and constitutive assumptions. Taking account of compressibility of solid phases, the volume fraction of fluid as an independent state variable is introduced in the free energy function, and the dynamic compatibility condition is obtained to restrict the change of pressure difference on the solid-fluid interface. The constructed constitutive equations are used to close the field equations. The linear field equations are obtained using a linearization procedure, and the micropolar thermo-hydro-mechanical component transport model is established. This model can be applied to practical problems, such as contaminant, drug, and pesticide transport. When the proposed model is supposed to be porous media, and both fluid and solid are single-component, it will almost agree with Eringen＇s model.

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.

Experimental study on shear properties and resistivity change of soil-rock mixture

The deterioration of shear resistance in rock and soil masses has resulted in numerous severe natural disasters,highlighting the significance of long-term monitoring for disaster prevention and mitigation.This study explores the use of a non-destructive method to quickly and accurately evaluate the shear properties of soil-rock mixture.The shear stress,shear strain,and resistivity of the soil-rock mixture were tested simultaneously using a combination of direct shear and resistivity tests.The test results show that the resistivity of the soil-rock mixture gradually decreases with increasing shear strain.The resistivity of all specimens ranged approximately from 60 to 130Ω.m throughout the shear process.At the end of the shear test,the vertical failure resistivity showed an irregular“W”shape with increasing rock content.It exhibited a significant negative linear functional relationship with the shear strength.With reference to the determination of cohesion and internal friction angle on the shear strength envelope,the horizontal angle of the vertical failure resistivity-normal stress curve is defined as the resistivity angle,and the intercept of the curve is the resistivity at the initial moment of shear.It has been observed that the resistivity angle is negatively and linearly correlated with the internal friction angle.At the same time,there is a linear growth relationship between resistivity at the initial moment of shear and cohesion.It has been demonstrated that an increase in rock content contributes to a general escalation in both the average structure factor and average shape factor.Meanwhile,a decrease in the anisotropy coefficient has also been noted.These alterations are indicative of the extent of microstructural transformations occurring during the deformation process of the soil-rock mixture.The research results verify the feasibility of real-time deformation monitoring and characterization of shear strength parameters using resistivity.

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.

Gaussian Mixture-Learned Approximate Message Passing(GM-LAMP)Based Hybrid Precoders for mmWave Massive MIMO Systems

Hybrid precoder design is a key technique providing better antenna gain and reduced hardware complexity in millimeter-wave(mmWave)massive multiple-input multiple-output(MIMO)systems.In this paper,Gaussian Mixture learned approximate message passing(GM-LAMP)network is presented for the design of optimal hybrid precoders suitable for mmWave Massive MIMO systems.Optimal hybrid precoder designs using a compressive sensing scheme such as orthogonal matching pursuit(OMP)and its derivatives results in high computational complexity when the dimensionality of the sparse signal is high.This drawback can be addressed using classical iterative algorithms such as approximate message passing(AMP),which has comparatively low computational complexity.The drawbacks of AMP algorithm are fixed shrinkage parameter and non-consideration of prior distribution of the hybrid precoders.In this paper,the fixed shrinkage parameter problem of the AMP algorithm is addressed using learned AMP(LAMP)network,and is further enhanced as GMLAMP network using the concept of Gaussian Mixture distribution of the hybrid precoders.The simula-tion results show that the proposed GM-LAMP network achieves optimal hybrid precoder design with enhanced achievable rates,better accuracy and low computational complexity compared to the existing algorithms.

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.

Enhanced Evaporation of Ternary Mixtures in Porous Medium with Microcolumn Configuration

The high surface area of porous media enhances its efficacy for evaporative cooling,however,the evaporation of pure substances often encounters issues including local overheating and unstable heat transfer.To address these challenges,a volume of fluid(VOF)model integrated with a species transport model was developed to predict the evaporation processes of ternary mixtures(water,glycerol,and 1,2-propylene glycol)in porous ceramics in this study.It reveals that the synergistic effects of thermal conduction and convective heat transfer significantly influence the mixtures evaporation,causing the fluctuations in evaporation rates.The obtained result shows a significant increase in water evaporation rates with decreasing the microcolumn size.At a pore size of 30μm and a porosity of 30%,an optimal balance between capillary forces and flow resistance yields a peak water release rate of 96.0%.Furthermore,decreasing the glycerol content from 70%to 60%enhances water release by 10.6%.The findings in this work propose the approaches to optimize evaporative cooling technologies by controlling the evaporation of mixtures in porous media.

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.