Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemio...Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.展开更多
Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications,yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions.This study establishe...Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications,yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions.This study establishes a pore-scale numerical framework integratingMonte Carlo-generated fractal porousmedia with Volume of Fluid(VOF)simulations to unravel the coupling among pore distribution characterized by fractal dimension(Df),flow dynamics,and displacement efficiency.A pore-scale model based on the computed tomography(CT)microstructure of Berea sandstone is established,and the simulation results are compared with experimental data.Good agreement is found in phase distribution,breakthrough behavior,and flow path morphology,confirming the reliability of the numerical simulation method.Ten fractal porous media models with Df ranging from 1.25~1.7 were constructed using a Monte-Carlo approach.The gas-liquid two-phase flow dynamics was characterized using the VOF solver across gas injection rates of 0.05-5m/s,inwhich the time-resolved two-phase distribution patternswere systematically recorded.The results reveal that smaller fractal dimensions(Df=1.25~1.45)accelerate fingering breakthrough(peak velocity is 1.73 m/s at Df=1.45)due to a bimodal pore size distribution dominated by narrow channels.Increasing Df amplifies vorticity generation by about 3 times(eddy viscosity is 0.033 Pa⋅s at Df=1.7)through reduced interfacial curvature,while tortuosity-driven pressure differentials transition from sharp increases(0.4~6.3 Pa at Df=1.25~1.3)to inertial plateaus(4.8 Pa at Df=1.7).A nonlinear increase in equilibrium gas volume fraction(fav=0.692 at Df=1.7)emerges from residual gas saturation and turbulence-enhanced dispersion.This behavior is further modulated by flow velocity,with fav peaking at 0.72 under capillary-dominated conditions(0.05 m/s),but decreasing to 0.65 in the inertial regime(0.5 m/s).The work quantitatively links fractal topology to multiphase flow regimes,demonstrating the critical role of Df in governing preferential pathways,energy dissipation,and phase distribution.展开更多
Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer...Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer solutions exhibit non-Newtonian and nonlinear flow behavior including shear thinning and shear thickening,polymer convection,diffusion,adsorption,retention,inaccessible pore volume,and reduced effective permeability.However,available well test model of polymer flooding wells generally simplifies these characteristics on pressure transient response,which may lead to inaccurate results.This work proposes a novel two-phase numerical well test model to better describe the polymer viscoelasticity and nonlinear flow behavior.Different influence factors that related to near-well blockage during polymer flooding process,including the degree of blockage(inner zone permeability),the extent of blockage(composite radius),and polymer flooding front radius are explored to investigate these impacts on bottom hole pressure responses.Results show that polymer viscoelasticity has a significant impact on the transitional flow segment of type curves,and the effects of near-well formation blockage and polymer concentration distribution on well test curves are very similar.Thus,to accurately interpret the degree of near-well blockage in injection wells,it is essential to first eliminate the influence of polymer viscoelasticity.Finally,a field case is comprehensively analyzed and discussed to illustrate the applicability of the proposed model.展开更多
The generalized mixture rule （GMR） is usually applied in determining mechanical properties such as the rheological property and Young’s modulus of multi-phase rocks. However, it is rarely used to determine electric...The generalized mixture rule （GMR） is usually applied in determining mechanical properties such as the rheological property and Young’s modulus of multi-phase rocks. However, it is rarely used to determine electrical conductivity of multi-phase rocks presently. In this paper, we calculate the effective conductivity using the 3D finite element method for a large number of two-phase medium stochastic models. The GMR is then employed as an effective conductivity model to fit the data. It shows a very close relationship between the parameter J of GMR and the ratio of conductivities of the two phases. We obtain the equations of the parameter J with the ratio of conductivity of two phases for the first time. On this basis, we can quickly predict （or calculate） the effective conductivity of any twophase medium stochastic model. The result is much more accurate than two other available effective conductivity models for the stochastic medium, which are the random model and effective medium theory model, laying a solid base for detailed evaluation of oil reservoirs.展开更多
In the industrial process of producing the strong phosphoric acid(SPA),clarification of the solution is essential to the ultimate product.However,the large viscosity of sediment and the induced interface interaction r...In the industrial process of producing the strong phosphoric acid(SPA),clarification of the solution is essential to the ultimate product.However,the large viscosity of sediment and the induced interface interaction result in difficulties when the SPA is clarified.CFD numerical methodology was applied to simulate internal flow field and performance of the low speed scraper based on Mixture solidliquid two-phase flow model.Sediment deposition was generated by loading solid particles at the bottom of clarifying vessel.The moving mesh and RNG k-εmodel were used to simulate the rotational turbulent flow in clarifying tank.Variables studied,amongst others,were the scraper rotation speed and the mounting height,which could affect the solid suspension height.Features of flow field and solid volume fraction distribution in computational domain were presented and analyzed.The numerical reports of the scraper torque and velocities of inlet and outlet filed were obtained.It seems the torque value of rotatio-nal axis and particle suspending height augment with an increasing rotating speed.Meanwhile,a high revolving speed is good for the deposition discharge.The particle fraction distribution in meridional surface and horizontal surface at fixed rotation speed were analyzed to determine the corresponding optimal installation height.The simulating results reflect the flow field is marginally stirred by the scraper and proper working parameters are obtained,in which case the comprehensive properties of the scraper and the clarifying tank are superior.展开更多
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,an...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.展开更多
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 netw...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.展开更多
Density(p),speed of sound(u),viscosity(η),and refractive index(n_(D))were measured for pure acetonitrile,trichloroethene,and tetrachloroethene,as well as their binary mixtures at temperatures T=(293.15,298.15,303.15)...Density(p),speed of sound(u),viscosity(η),and refractive index(n_(D))were measured for pure acetonitrile,trichloroethene,and tetrachloroethene,as well as their binary mixtures at temperatures T=(293.15,298.15,303.15)K and at ambient pressure(81.5 kPa).From the experimental data,excess molar volume(V_(m)~E),thermal expansion coefficients(α),deviations in isentropic compressibility(Δκ_(S)),viscosity(Δ_η),and refractive index(Δn_(D))were calculated.These values were then correlated using the Redlich-Kister polynomial equation,with fitting coefficients and standard deviations determined.Additionally,the Prigogine-Flory-Patterson(PFP)theory and the Extended Real Associated Solution(ERAS)model were employed to correlate the excess molar volume,while the Perturbed Chain Statistical Associating Fluid Theory(PC-SAFT)was used to predict the density of mixtures.展开更多
With the increasing miniaturization of systems and surging demand for power density,accurate prediction and control of two-phase flow pressure drop have become a core challenge restricting the performance of microchan...With the increasing miniaturization of systems and surging demand for power density,accurate prediction and control of two-phase flow pressure drop have become a core challenge restricting the performance of microchannel heat exchangers.Pressure drop,a critical hydraulic characteristic,serves as both a natural constraint for cooling systems and determines the power required to pump the working fluid through microchannels.This paper reviews the characteristics,prediction models,and optimization measures of two-phase flow pressure drop for low-boiling-point working fluids in microchannels.It systematically analyzes key influencing factors such as fluid physical properties,operating conditions,channel geometry,and flow patterns,and discusses the complex mechanisms of pressure drop under the coupling effect of multi-physical fields.Mainstream prediction models are reviewed:the homogeneous flow model simplifies calculations but shows large deviations at low quality;the separated flow model considers interphase interactions and can be applied to micro-scales after modification;the flow-pattern-based model performs zoned modeling but relies on subjective classification;machine learning improves prediction accuracy but faces the“black-box”problem.In terms of optimization,channel designs are improved through porous structures and micro-rib arrays,and flow rate distribution is optimized using splitters to balance pressure drop and heat transfer performance.This study provides theoretical support for microchannel thermal management in high-power-density devices.展开更多
Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristic...Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.展开更多
In response to escalating challenges in energy conservation and emission reduction,this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired ...In response to escalating challenges in energy conservation and emission reduction,this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired boilers.Utilizing a fusion of hybrid modeling and automation technologies,we develop soft measurement models for key combustion parameters,such as the net calorific value of coal,flue gas oxygen content,and fly ash carbon content,within theDistributedControl System(DCS).Validated with performance test data,thesemodels exhibit controlled root mean square error(RMSE)and maximum absolute error(MAXE)values,both within the range of 0.203.Integrated into their respective automatic control systems,thesemodels optimize two-phase flow heat transfer,finetune combustion conditions,and mitigate incomplete combustion.Furthermore,this paper conducts an in-depth exploration of the generationmechanismof nitrogen oxides(NO_(x))and low oxygen emission reduction technology in coal-fired boilers,demonstrating a substantial reduction in furnace exit NO_(x) generation by 30%to 40%and the power supply coal consumption decreased by 1.62 g/(kW h).The research outcomes highlight the model’s rapid responsiveness,enabling prompt reflection of transient variations in various economic indicator parameters.This provides a more effective means for real-time monitoring of crucial variables in coal-fired boilers and facilitates timely combustion adjustments,underscoring notable achievements in boiler combustion.The research not only provides valuable and practical insights into the intricacies of two-phase flow heat transfer and heat exchange but also establishes a pioneering methodology for tackling industry challenges.展开更多
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 s...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.展开更多
A test study on 50% lightning impulse breakdown voltage in two-phase mixture of gas and solid particles has been carried out in a specially designed discharge cabinet. A mechanical sieve is set up for sifting differen...A test study on 50% lightning impulse breakdown voltage in two-phase mixture of gas and solid particles has been carried out in a specially designed discharge cabinet. A mechanical sieve is set up for sifting different solid particles into the discharge space uniformly. The lightning impulse voltage according with international electro-technical commission （IEC） standard is applied to the electrodes inside the discharge cabinet by the rule of up-down method in a total of 40 times. The results showed that the 50% lightning impulse breakdown voltage in two-phase mixture of gas and solid particles has its own features and is much different from that in air.展开更多
Temperature sensitivity of waxy crude oils makes it difficult to study their flow behaviour in the presence of water especially near their wax appearance temperature （WAT）. In this study a method was proposed and im...Temperature sensitivity of waxy crude oils makes it difficult to study their flow behaviour in the presence of water especially near their wax appearance temperature （WAT）. In this study a method was proposed and implemented to mitigate such difficulties which was applied in predicting mixture temperatures （Tin） of a typical Malaysian waxy crude oil and water flow in a horizontal pipe. To this end, two analytical models were derived firstly from calorimetry equation which based on developed two correlations for defining crude oil heat capacity actualized from the existed specific heat capacities of crude oils. The models were then applied for a set of experiments to reach the defined three predetermined Tm （26 ℃, 28 ℃ and 30 ℃）. The comparison between the predicted mixture temperatures （Tin,1 and Tin,2） from the two models and the experimental results displayed acceptable absolute average errors （0.80% 0.62%, 0.53% for model 1; 0,74%, 0.54%, 0.52% for model 2）. Moreover, the average errors for both models are in the range of standard error limits （4-0,75Z） according to ASTM E230. Conclusively, the proposed model showed the ease of obtaining mixture temperatures close to WAT as predetermined with accuracy of ± 0.5 ℃approximately for over 84% of the examined cases. The method is seen as a practical reference point to further study the flow behaviour of waxy crudes in oil-water two-phase flow system near sensitive temperatures.展开更多
In order to study the viscoelastic characteristics of asphalt mixtures in the whole-frequency range, a dynamic shearing rheometer is used to carry out dynamic frequency sweeps for asphalt mixtures, which obtains the t...In order to study the viscoelastic characteristics of asphalt mixtures in the whole-frequency range, a dynamic shearing rheometer is used to carry out dynamic frequency sweeps for asphalt mixtures, which obtains the three-dimensional relationships among G^＊, temperature and frequency. Then the time-temperature shift principle is used to translate the relationships from three dimensions to two dimensions, so master curves are obtained regarding dynamic modules changing along with the frequency over 15 amount levels. The Christensen- Anderson-Marasteanu （CAM） model, a kind of the rheological model, is used to analyze and compare rheological performances of several asphalt mixtures based on the obtained master curves. The results indicate that the dynamic theological test is an effective method for obtaining the master curve. Besides, the CAM model can describe well the viscoelastic deformation characteristics of asphalt mixtures.展开更多
A finite-element model of the thermosetting epoxy asphalt mixture（EAM） microstructure is developed to simulate the indirect tension test（IDT）.Image techniques are used to capture the EAM microstructure which is di...A finite-element model of the thermosetting epoxy asphalt mixture（EAM） microstructure is developed to simulate the indirect tension test（IDT）.Image techniques are used to capture the EAM microstructure which is divided into two phases：aggregates and mastic.A viscoelastic constitutive relationship,which is obtained from the results of a creep test,is used to represent the mastic phase at intermittent temperatures.Model simulation results of the stiffness modulus in IDT compare favorably with experimental data.Different loading directions and velocities are employed in order to account for their influence on the modulus and the localized stress of the microstructure model.It is pointed out that the modulus is not consistent when the loading direction changes since the heterogeneous distribution of the mixture internal structure,and the loading velocity affects the localized stress as a result of the viscoelasticity of the mastic.The study results can provide a theoretical basis for the finite-element method,which can be extended to the numerical simulations of asphalt mixture micromechanical behavior.展开更多
Biot-flow and squirt-flow are the two most important fluid flow mechanisms in porous media containing fluids. Based on the BISQ （Biot-Squirt） model where the two mechanisms are treated simultaneously, the elastic wa...Biot-flow and squirt-flow are the two most important fluid flow mechanisms in porous media containing fluids. Based on the BISQ （Biot-Squirt） model where the two mechanisms are treated simultaneously, the elastic wave-field simulation in the porous medium is limited to two-dimensions and two-components （2D2C） or two-dimensions and three-components （2D3C）. There is no previous report on wave simulation in three- dimensions and three-components. Only through three dimensional numerical simulations can we have an overall understanding of wave field coupling relations and the spatial distribution characteristics between the solid and fluid phases in the dual-phase anisotropic medium. In this paper, based on the BISQ equation, we present elastic wave propagation in a three dimensional dual-phase anisotropic medium simulated by the staggered-grid high-order finite-difference method. We analyze the resulting wave fields and show that the results are an improvement.展开更多
A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are...A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.展开更多
In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions ...In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.展开更多
Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM ...Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.展开更多
基金supported in part by the Young Scientists Fund of the National Natural Science Foundation of China(Grant Nos.82304253)(and 82273709)the Foundation for Young Talents in Higher Education of Guangdong Province(Grant No.2022KQNCX021)the PhD Starting Project of Guangdong Medical University(Grant No.GDMUB2022054).
文摘Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
基金funded by the National Key R&D Program of China,China(Grant No.2023YFB4005500)National Natural Science Foundation of China,China(Grant Nos.52379113 and 52379114).
文摘Gas-liquid two-phase flow in fractal porous media is pivotal for engineering applications,yet it remains challenging to be accurately characterized due to complex microstructure-flow interactions.This study establishes a pore-scale numerical framework integratingMonte Carlo-generated fractal porousmedia with Volume of Fluid(VOF)simulations to unravel the coupling among pore distribution characterized by fractal dimension(Df),flow dynamics,and displacement efficiency.A pore-scale model based on the computed tomography(CT)microstructure of Berea sandstone is established,and the simulation results are compared with experimental data.Good agreement is found in phase distribution,breakthrough behavior,and flow path morphology,confirming the reliability of the numerical simulation method.Ten fractal porous media models with Df ranging from 1.25~1.7 were constructed using a Monte-Carlo approach.The gas-liquid two-phase flow dynamics was characterized using the VOF solver across gas injection rates of 0.05-5m/s,inwhich the time-resolved two-phase distribution patternswere systematically recorded.The results reveal that smaller fractal dimensions(Df=1.25~1.45)accelerate fingering breakthrough(peak velocity is 1.73 m/s at Df=1.45)due to a bimodal pore size distribution dominated by narrow channels.Increasing Df amplifies vorticity generation by about 3 times(eddy viscosity is 0.033 Pa⋅s at Df=1.7)through reduced interfacial curvature,while tortuosity-driven pressure differentials transition from sharp increases(0.4~6.3 Pa at Df=1.25~1.3)to inertial plateaus(4.8 Pa at Df=1.7).A nonlinear increase in equilibrium gas volume fraction(fav=0.692 at Df=1.7)emerges from residual gas saturation and turbulence-enhanced dispersion.This behavior is further modulated by flow velocity,with fav peaking at 0.72 under capillary-dominated conditions(0.05 m/s),but decreasing to 0.65 in the inertial regime(0.5 m/s).The work quantitatively links fractal topology to multiphase flow regimes,demonstrating the critical role of Df in governing preferential pathways,energy dissipation,and phase distribution.
基金supported by the National Natural Science Foundation of China(52104049)the Young Elite Scientist Sponsorship Program by Beijing Association for Science and Technology(BYESS2023262)。
文摘Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer solutions exhibit non-Newtonian and nonlinear flow behavior including shear thinning and shear thickening,polymer convection,diffusion,adsorption,retention,inaccessible pore volume,and reduced effective permeability.However,available well test model of polymer flooding wells generally simplifies these characteristics on pressure transient response,which may lead to inaccurate results.This work proposes a novel two-phase numerical well test model to better describe the polymer viscoelasticity and nonlinear flow behavior.Different influence factors that related to near-well blockage during polymer flooding process,including the degree of blockage(inner zone permeability),the extent of blockage(composite radius),and polymer flooding front radius are explored to investigate these impacts on bottom hole pressure responses.Results show that polymer viscoelasticity has a significant impact on the transitional flow segment of type curves,and the effects of near-well formation blockage and polymer concentration distribution on well test curves are very similar.Thus,to accurately interpret the degree of near-well blockage in injection wells,it is essential to first eliminate the influence of polymer viscoelasticity.Finally,a field case is comprehensively analyzed and discussed to illustrate the applicability of the proposed model.
基金sponsored by National Natural Science Foundation of China (Grant No. 40874034)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-QN508)
文摘The generalized mixture rule （GMR） is usually applied in determining mechanical properties such as the rheological property and Young’s modulus of multi-phase rocks. However, it is rarely used to determine electrical conductivity of multi-phase rocks presently. In this paper, we calculate the effective conductivity using the 3D finite element method for a large number of two-phase medium stochastic models. The GMR is then employed as an effective conductivity model to fit the data. It shows a very close relationship between the parameter J of GMR and the ratio of conductivities of the two phases. We obtain the equations of the parameter J with the ratio of conductivity of two phases for the first time. On this basis, we can quickly predict （or calculate） the effective conductivity of any twophase medium stochastic model. The result is much more accurate than two other available effective conductivity models for the stochastic medium, which are the random model and effective medium theory model, laying a solid base for detailed evaluation of oil reservoirs.
基金Graduate Research and Innovation Program in Jiangsu Province(KYZZ16_0286)
文摘In the industrial process of producing the strong phosphoric acid(SPA),clarification of the solution is essential to the ultimate product.However,the large viscosity of sediment and the induced interface interaction result in difficulties when the SPA is clarified.CFD numerical methodology was applied to simulate internal flow field and performance of the low speed scraper based on Mixture solidliquid two-phase flow model.Sediment deposition was generated by loading solid particles at the bottom of clarifying vessel.The moving mesh and RNG k-εmodel were used to simulate the rotational turbulent flow in clarifying tank.Variables studied,amongst others,were the scraper rotation speed and the mounting height,which could affect the solid suspension height.Features of flow field and solid volume fraction distribution in computational domain were presented and analyzed.The numerical reports of the scraper torque and velocities of inlet and outlet filed were obtained.It seems the torque value of rotatio-nal axis and particle suspending height augment with an increasing rotating speed.Meanwhile,a high revolving speed is good for the deposition discharge.The particle fraction distribution in meridional surface and horizontal surface at fixed rotation speed were analyzed to determine the corresponding optimal installation height.The simulating results reflect the flow field is marginally stirred by the scraper and proper working parameters are obtained,in which case the comprehensive properties of the scraper and the clarifying tank are superior.
基金funded by the National Key Research and Development Program of China(No.2022YFD2200503-02)。
文摘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.
基金the University of Teknologi PETRONAS(UTP),Malaysia,and Ahmadu Bello University,Nigeria,for their vital help and availability of laboratory facilities that allowed this work to be conducted successfully.
文摘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.
基金Bu-Ali Sina University for providing financial support for conducting this study。
文摘Density(p),speed of sound(u),viscosity(η),and refractive index(n_(D))were measured for pure acetonitrile,trichloroethene,and tetrachloroethene,as well as their binary mixtures at temperatures T=(293.15,298.15,303.15)K and at ambient pressure(81.5 kPa).From the experimental data,excess molar volume(V_(m)~E),thermal expansion coefficients(α),deviations in isentropic compressibility(Δκ_(S)),viscosity(Δ_η),and refractive index(Δn_(D))were calculated.These values were then correlated using the Redlich-Kister polynomial equation,with fitting coefficients and standard deviations determined.Additionally,the Prigogine-Flory-Patterson(PFP)theory and the Extended Real Associated Solution(ERAS)model were employed to correlate the excess molar volume,while the Perturbed Chain Statistical Associating Fluid Theory(PC-SAFT)was used to predict the density of mixtures.
基金supported by the Beijing Municipal Science&Technology Commission(Z231100006123010).
文摘With the increasing miniaturization of systems and surging demand for power density,accurate prediction and control of two-phase flow pressure drop have become a core challenge restricting the performance of microchannel heat exchangers.Pressure drop,a critical hydraulic characteristic,serves as both a natural constraint for cooling systems and determines the power required to pump the working fluid through microchannels.This paper reviews the characteristics,prediction models,and optimization measures of two-phase flow pressure drop for low-boiling-point working fluids in microchannels.It systematically analyzes key influencing factors such as fluid physical properties,operating conditions,channel geometry,and flow patterns,and discusses the complex mechanisms of pressure drop under the coupling effect of multi-physical fields.Mainstream prediction models are reviewed:the homogeneous flow model simplifies calculations but shows large deviations at low quality;the separated flow model considers interphase interactions and can be applied to micro-scales after modification;the flow-pattern-based model performs zoned modeling but relies on subjective classification;machine learning improves prediction accuracy but faces the“black-box”problem.In terms of optimization,channel designs are improved through porous structures and micro-rib arrays,and flow rate distribution is optimized using splitters to balance pressure drop and heat transfer performance.This study provides theoretical support for microchannel thermal management in high-power-density devices.
基金the National Natural Science Foundation of China (Nos. 42302143, 42172159)China Geological Survey Project (No. DD20211350)support from the G. Albert Shoemaker endowment
文摘Clayey-silt natural gas hydrate reservoirs in the South China Sea exhibit loose and unconsolidated structures, heterogeneous pore structures, high clay mineral contents, and strong hydrophilicity. These characteristics complicate the gas-water two-phase flow process in porous media following hydrate decomposition, posing challenges for efficient development. This study examines the transport response of clayey-silt reservoir samples from the Shenhu area using gas-water two-phase flow experiments and CT scanning to explore changes in pore structure, gas-water distribution, and relative permeability under varying flow conditions. The results indicate that pore heterogeneity significantly influences flow characteristics. Gas preferentially displaces water in larger pores, forming fracture-like pores, which serve as preferential flow channels for gas migration. The preferential flow channels enhance gas-phase permeability up to 19 times that of the water phase when fluid pressures exceed total stresses. However,small pores retain liquid, leading to a high residual water saturation of 0.561. CT imaging reveals that these hydro-fractures improve gas permeability but also confine gas flow to specific channels. Pore network analysis shows that gas injection expands the pore-throat network, enhancing connectivity and forming fracture-like pores. Residual water remains trapped in smaller pores and throats, while structural changes, including new fractures, improve gas flow pathways and overall connectivity. Relative permeability curves demonstrate a narrow gas-water cocurrent-flow zone, a right-shifted iso-permeability point and high reservoir capillary pressure, indicating a strong "water-blocking" effect. The findings suggest that optimizing reservoir stimulation techniques to enhance fracture formation, reduce residual water saturation, and improve gas flow capacity is critical for efficient hydrate reservoir development.
文摘In response to escalating challenges in energy conservation and emission reduction,this study delves into the complexities of heat transfer in two-phase flows and adjustments to combustion processes within coal-fired boilers.Utilizing a fusion of hybrid modeling and automation technologies,we develop soft measurement models for key combustion parameters,such as the net calorific value of coal,flue gas oxygen content,and fly ash carbon content,within theDistributedControl System(DCS).Validated with performance test data,thesemodels exhibit controlled root mean square error(RMSE)and maximum absolute error(MAXE)values,both within the range of 0.203.Integrated into their respective automatic control systems,thesemodels optimize two-phase flow heat transfer,finetune combustion conditions,and mitigate incomplete combustion.Furthermore,this paper conducts an in-depth exploration of the generationmechanismof nitrogen oxides(NO_(x))and low oxygen emission reduction technology in coal-fired boilers,demonstrating a substantial reduction in furnace exit NO_(x) generation by 30%to 40%and the power supply coal consumption decreased by 1.62 g/(kW h).The research outcomes highlight the model’s rapid responsiveness,enabling prompt reflection of transient variations in various economic indicator parameters.This provides a more effective means for real-time monitoring of crucial variables in coal-fired boilers and facilitates timely combustion adjustments,underscoring notable achievements in boiler combustion.The research not only provides valuable and practical insights into the intricacies of two-phase flow heat transfer and heat exchange but also establishes a pioneering methodology for tackling industry challenges.
文摘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.
基金National Natural Science Foundation of China.(No.50237010)
文摘A test study on 50% lightning impulse breakdown voltage in two-phase mixture of gas and solid particles has been carried out in a specially designed discharge cabinet. A mechanical sieve is set up for sifting different solid particles into the discharge space uniformly. The lightning impulse voltage according with international electro-technical commission （IEC） standard is applied to the electrodes inside the discharge cabinet by the rule of up-down method in a total of 40 times. The results showed that the 50% lightning impulse breakdown voltage in two-phase mixture of gas and solid particles has its own features and is much different from that in air.
基金Supported by the Malaysia's Ministry of Higher Education(FRGS/4F136)the University Teknologi Malaysia(RUG/01H68)
文摘Temperature sensitivity of waxy crude oils makes it difficult to study their flow behaviour in the presence of water especially near their wax appearance temperature （WAT）. In this study a method was proposed and implemented to mitigate such difficulties which was applied in predicting mixture temperatures （Tin） of a typical Malaysian waxy crude oil and water flow in a horizontal pipe. To this end, two analytical models were derived firstly from calorimetry equation which based on developed two correlations for defining crude oil heat capacity actualized from the existed specific heat capacities of crude oils. The models were then applied for a set of experiments to reach the defined three predetermined Tm （26 ℃, 28 ℃ and 30 ℃）. The comparison between the predicted mixture temperatures （Tin,1 and Tin,2） from the two models and the experimental results displayed acceptable absolute average errors （0.80% 0.62%, 0.53% for model 1; 0,74%, 0.54%, 0.52% for model 2）. Moreover, the average errors for both models are in the range of standard error limits （4-0,75Z） according to ASTM E230. Conclusively, the proposed model showed the ease of obtaining mixture temperatures close to WAT as predetermined with accuracy of ± 0.5 ℃approximately for over 84% of the examined cases. The method is seen as a practical reference point to further study the flow behaviour of waxy crudes in oil-water two-phase flow system near sensitive temperatures.
基金The National Natural Science Foundation of China (No.50278037)
文摘In order to study the viscoelastic characteristics of asphalt mixtures in the whole-frequency range, a dynamic shearing rheometer is used to carry out dynamic frequency sweeps for asphalt mixtures, which obtains the three-dimensional relationships among G^＊, temperature and frequency. Then the time-temperature shift principle is used to translate the relationships from three dimensions to two dimensions, so master curves are obtained regarding dynamic modules changing along with the frequency over 15 amount levels. The Christensen- Anderson-Marasteanu （CAM） model, a kind of the rheological model, is used to analyze and compare rheological performances of several asphalt mixtures based on the obtained master curves. The results indicate that the dynamic theological test is an effective method for obtaining the master curve. Besides, the CAM model can describe well the viscoelastic deformation characteristics of asphalt mixtures.
基金Program for New Century Excellent Talents in University(No. NCET-08-0118)Specialized Research Fund for the Doctoral Program of Higher Education (No. 20090092110049)
文摘A finite-element model of the thermosetting epoxy asphalt mixture（EAM） microstructure is developed to simulate the indirect tension test（IDT）.Image techniques are used to capture the EAM microstructure which is divided into two phases：aggregates and mastic.A viscoelastic constitutive relationship,which is obtained from the results of a creep test,is used to represent the mastic phase at intermittent temperatures.Model simulation results of the stiffness modulus in IDT compare favorably with experimental data.Different loading directions and velocities are employed in order to account for their influence on the modulus and the localized stress of the microstructure model.It is pointed out that the modulus is not consistent when the loading direction changes since the heterogeneous distribution of the mixture internal structure,and the loading velocity affects the localized stress as a result of the viscoelasticity of the mastic.The study results can provide a theoretical basis for the finite-element method,which can be extended to the numerical simulations of asphalt mixture micromechanical behavior.
基金National Natural Science Foundation (Project number 40604013).
文摘Biot-flow and squirt-flow are the two most important fluid flow mechanisms in porous media containing fluids. Based on the BISQ （Biot-Squirt） model where the two mechanisms are treated simultaneously, the elastic wave-field simulation in the porous medium is limited to two-dimensions and two-components （2D2C） or two-dimensions and three-components （2D3C）. There is no previous report on wave simulation in three- dimensions and three-components. Only through three dimensional numerical simulations can we have an overall understanding of wave field coupling relations and the spatial distribution characteristics between the solid and fluid phases in the dual-phase anisotropic medium. In this paper, based on the BISQ equation, we present elastic wave propagation in a three dimensional dual-phase anisotropic medium simulated by the staggered-grid high-order finite-difference method. We analyze the resulting wave fields and show that the results are an improvement.
基金The National Natural Science Foundation of China(No.61231002,61273266,51075068,61271359)Doctoral Fund of Ministry of Education of China(No.20110092130004)
文摘A cascaded projection of the Gaussian mixture model algorithm is proposed.First,the marginal distribution of the Gaussian mixture model is computed for different feature dimensions, and a number of sub-classifiers are generated using the marginal distribution model.Each sub-classifier is based on different feature sets.The cascaded structure is adopted to fuse the sub-classifiers dynamically to achieve sample adaptation ability.Secondly,the effectiveness of the proposed algorithm is verified on electrocardiogram emotional signal and speech emotional signal.Emotional data including fidgetiness,happiness and sadness is collected by induction experiments.Finally,the emotion feature extraction method is discussed,including heart rate variability, the chaotic electrocardiogram feature and utterance level static feature.The emotional feature reduction methods are studied, including principle component analysis,sequential forward selection, the Fisher discriminant ratio and maximal information coefficient.The experimental results show that the proposed classification algorithm can effectively improve recognition accuracy in two different scenarios.
基金The National Natural Science Foundation of China (No.61172135,61101198)the Aeronautical Foundation of China (No.20115152026)
文摘In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.
基金The US National Science Foundation (No. CMMI-0408390,CMMI-0644552)the American Chemical Society Petroleum Research Foundation (No.PRF-44468-G9)+3 种基金the Research Fellowship for International Young Scientists (No.51050110143)the Fok Ying-Tong Education Foundation (No.114024)the Natural Science Foundation of Jiangsu Province (No.BK2009015)the Postdoctoral Science Foundation of Jiangsu Province (No.0901005C)
文摘Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc.
