An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift ...An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.展开更多
Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experien...Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experience workplace violence experience post-traumatic stress. Purpose: We want to examine the longitudinal trajectories of PTSD in this population to identify possible subgroups that might be more at risk. Furthermore, we need to investigate whether certain risk factors of PTSD might identify membership in the subgroups. Method: In a sample of psychiatric staff from 18 psychiatric wards in Denmark who had reported an incident of WV, we used Latent Growth Mixture Modelling (LGMM) and further logistic regression analysis to investigate this. Results: We found three separate PTSD trajectories: a recovering, a delayed-onset, and a moderate-stable trajectory. Higher social support and negative cognitive appraisals about oneself, the world and self-blame predicted membership in the delayed-onset trajectory, while higher social support and lower accept coping predicted membership in the delayed-onset trajectory. Conclusion: Although most psychiatric staff go through a natural recovery, it is important to be aware of and identify staff members who might be struggling long-term. More focus on the factors that might predict these groups should be an important task for psychiatric departments to prevent posttraumatic symptomatology from work.展开更多
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
A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical dis...A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.展开更多
The high-temperature creep behavior of asphalt mixture was investigated based on micromechanical modeling and virtual test by using three-dimensional discrete element method（DEM）. A user-defined micromechanical mode...The high-temperature creep behavior of asphalt mixture was investigated based on micromechanical modeling and virtual test by using three-dimensional discrete element method（DEM）. A user-defined micromechanical model of asphalt mixture was established after analyzing the irregular shape and gradation of coarse aggregates, the viscoelastic property of asphalt mastic, and the random distribution of air voids within the asphalt mixture. Virtual uniaxial static creep test at 60 ℃ was conducted by using Particle Flow Code in three dimensions（PFC3D） and was validated by laboratory test. Based on virtual creep test, the micromechanical characteristics between aggregates, within asphalt mastic, and between aggregate and asphalt mastic were analyzed for the asphalt mixture. It is proved that the virtual test based on the micromechanical model can efficiently predict the creep deformation of asphalt mixture. And the high-temperature behavior of asphalt mixture was characterized from micromechanical perspective.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
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 lear...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.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
Biomass burning(BB)emits carbonaceous aerosols that significantly influence air quality in Southwest China during spring.To further understand the characteristics of spring BB and its original contribution to organic ...Biomass burning(BB)emits carbonaceous aerosols that significantly influence air quality in Southwest China during spring.To further understand the characteristics of spring BB and its original contribution to organic carbon(OC),daily fine particulate matter(PM_(2.5))samples were collected from March to May 2022 in Pu'er,Southwest China.The concentrations of OC,elemental carbon(EC),levoglucosan(Lev),and potassium from BB(K+BB)during the study period ranged from 5.3 to 31.2μg/m^(3),0.86-13.1μg/m^(3),0.06-0.82μg/m^(3),and 0.05-2.88μg/m^(3),respectively.To eliminate the effects of Lev degradation,this study uses the Aging of Air Mass(AAM)index to correct the atmospheric concentration of Lev and combines Bayesian mixture modeling with a molecular tracer method to assess the original contribution of BB to OC.The results indicated that the AAM index was 0.18±0.05,indicating that the degradation of Lev reached 82%.When considering the degradation of levoglucosan in the atmosphere,the primary source of BB aerosols was crop-straw combustion(71.1%),followed by the combustion of certain hardwoods and softwoods(24.9%)and grasses(4.0%).The original contribution of BB to OC was 62.4%,which was much greater than the contribution when levoglucosan degradation(23.7%)was ignored.The air mass inverse trajectories and Moderate Resolution Imaging Spectroradiometer(MODIS)fire hotspots indicated that the BB plume from Southeast Asia during spring could influence PM_(2.5)long-range transport in remote locations,and the contribution could reach 82%in Southwest China.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
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
A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec-...A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.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
An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the ...An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.展开更多
A kinetic moment-closed model(KMCM), derived from the Vlasov–Fokker–Planck(VFP) equation with spherically symmetric velocity space, is introduced as a general relaxation model for homogeneous plasmas. The closed for...A kinetic moment-closed model(KMCM), derived from the Vlasov–Fokker–Planck(VFP) equation with spherically symmetric velocity space, is introduced as a general relaxation model for homogeneous plasmas. The closed form of this model is presented by introducing a set of new functions called R function and R integration. This nonlinear model, based on the finitely distinguishable independent features(FDIF) hypothesis, enables the capture of the nature of the equilibrium state and non-equilibrium state. From this relaxation model, a general temperature relaxation model is derived when the velocity space exhibits spherical symmetry, and the general characteristic frequency of temperature relaxation is presented.展开更多
We derive the transport equations from the Vlasov–Fokker–Planck equation when the velocity space is spherically symmetric.The Shkarofsky's form of Fokker–Planck–Rosenbluth collision operator is employed in the...We derive the transport equations from the Vlasov–Fokker–Planck equation when the velocity space is spherically symmetric.The Shkarofsky's form of Fokker–Planck–Rosenbluth collision operator is employed in the Vlasov–Fokker–Planck equation.A closed-form relaxation model for homogeneous plasmas could be presented in terms of Gauss hypergeometric2F1functions.This has been accomplished based on the Maxwellian mixture model.Furthermore,we demonstrate that classic models such as two-temperature thermal equilibrium model and thermodynamic equilibrium model are special cases of our relaxation model and the zeroth-order Braginskii heat transfer model can also be derived.The present relaxation model is a nonequilibrium model based on the hypothesis that the plasmas system possesses finitely distinguishable independent features,without relying on the conventional near-equilibrium assumption.展开更多
Characterization of vegetation effect on soil response is essential for comprehending site-specific hydrological processes.Traditional research often relies on sensors or remote sensing data to examine the hydrologica...Characterization of vegetation effect on soil response is essential for comprehending site-specific hydrological processes.Traditional research often relies on sensors or remote sensing data to examine the hydrological properties of vegetation zones,yet these methods are limited by either measurement sparsity or spatial inaccuracy.Therefore,this paper is the first to propose a data-driven approach that incorporates high-temporal-resolution electrical resistivity tomography(ERT)to quantify soil hydrological response.Time-lapse ERT is deployed on a vegetated slope site in Foshan,China,during a discontinuous rainfall induced by Typhoon Haikui.A total of 97 ERT measurements were collected with an average time interval of 2.7 hours.The Gaussian Mixture Model(GMM)is applied to quantify the level of response and objectively classify impact zones based on features extracted directly from the ERT data.The resistivity-moisture content correlation is established based on on-site sensor data to characterize infiltration and evapotranspiration across wet-dry conditions.The findings are compared with the Normalized Difference Vegetation Index(NDVI),a common indicator for vegetation quantification,to reveal potential spatial errors in remote sensing data.In addition,this study provides discussions on the potential applications and future directions.This paper showcases significant spatio-temporal advantages over existing studies,providing a more detailed and accurate characterization of superficial soil hydrological response.展开更多
Process data recorded by computer-based assessments reflect how respondents solve problems and thus contain rich information about respondents as well as tasks.Considering that different respondents may exhibit differ...Process data recorded by computer-based assessments reflect how respondents solve problems and thus contain rich information about respondents as well as tasks.Considering that different respondents may exhibit different behavioral characteristics during problem-solving process,in this study,we propose a mixture one-parameter state response(Mix1P-SR)measurement model.This model assumes that respondents belong to discrete latent classes with different propensities towards responses to task states during the problem-solving process,and the varying response propensities are captured by different state parameters across classes.A Markov Chain Monte Carlo algorithm for the estimation of model parameters and classification of respondents is described.The simulation study shows that the Mix1P-SR model could recover parameters well on the premise that the average sequence length was not too short.Moreover,larger sample size,longer sequences,more uniform mixing proportions,and lower interclass similarity facilitated model convergence,model selection,and parameter estimation accuracy,with sequence length being particularly important.Based on the empirical data from PISA 2012,the Mix1P-SR model identified two latent classes of respondents.They had different patterns of state easiness parameters and exhibited different state response patterns,which affected their problem solving results.Implications for model application and future research directions are discussed.展开更多
Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex en...Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method.展开更多
For understanding more about the water exchange between the Kuroshio and the East China Sea,We studied the variability of the Kuroshio in the East China Sea(ECS) in the period of 1991 to 2008 using a three-dimensional...For understanding more about the water exchange between the Kuroshio and the East China Sea,We studied the variability of the Kuroshio in the East China Sea(ECS) in the period of 1991 to 2008 using a three-dimensional circulation model,and calculated Kuroshio onshore volume transport in the ECS at the minimum of 0.48 Sv(1 Sv ;106 m3/s) in summer and the maximum of 1.69 Sv in winter.Based on the data of WOA05 and NCEP,The modeled result indicates that the Kuroshio transport east of Taiwan Island decreased since 2000.Lateral movements tended to be stronger at two ends of the Kuroshio in the ECS than that of the middle segment.In addition,we applied a spectral mixture model(SMM) to determine the exchange zone between the Kuroshio and the shelf water of the ECS.The result reveals a significantly negative correlation(coefficient of-0.78) between the area of exchange zone and the Kuroshio onshore transport at 200 m isobath in the ECS.This conclusion brings a new view for the water exchange between the Kuroshio and the East China Sea.Additional to annual and semi-annual signals,intra-seasonal signal of probably the Pacific origin may trigger the events of Kuroshio intrusion and exchange in the ECS.展开更多
文摘An improved approach for J-value segmentation （JSEG） is presented for unsupervised color image segmentation. Instead of color quantization algorithm, an automatic classification method based on adaptive mean shift （AMS） based clustering is used for nonparametric clustering of image data set. The clustering results are used to construct Gaussian mixture modelling （GMM） of image data for the calculation of soft J value. The region growing algorithm used in JSEG is then applied in segmenting the image based on the multiscale soft J-images. Experiments show that the synergism of JSEG and the soft classification based on AMS based clustering and GMM overcomes the limitations of JSEG successfully and is more robust.
文摘Background: Workplace violence (WV) towards psychiatric staff has commonly been associated with Posttraumatic Stress Disorder (PTSD). However, prospective studies have shown that not all psychiatric staff who experience workplace violence experience post-traumatic stress. Purpose: We want to examine the longitudinal trajectories of PTSD in this population to identify possible subgroups that might be more at risk. Furthermore, we need to investigate whether certain risk factors of PTSD might identify membership in the subgroups. Method: In a sample of psychiatric staff from 18 psychiatric wards in Denmark who had reported an incident of WV, we used Latent Growth Mixture Modelling (LGMM) and further logistic regression analysis to investigate this. Results: We found three separate PTSD trajectories: a recovering, a delayed-onset, and a moderate-stable trajectory. Higher social support and negative cognitive appraisals about oneself, the world and self-blame predicted membership in the delayed-onset trajectory, while higher social support and lower accept coping predicted membership in the delayed-onset trajectory. Conclusion: Although most psychiatric staff go through a natural recovery, it is important to be aware of and identify staff members who might be struggling long-term. More focus on the factors that might predict these groups should be an important task for psychiatric departments to prevent posttraumatic symptomatology from work.
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supported by the Key R&D Program of Shandong Province,China(No.2021ZLGX04)the National Natural Science Foundation of China(No.52171284)。
文摘A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.
基金Funded by the National Natural Science Foundation of China(No.51378006)the Huoyingdong Foundation of China(No.141076)+1 种基金the Fundamental Research Funds for the Central Universities(No.2242015R30027)the Natural Science Foundation of Jiangsu Province(BK20161421 and BK20140109)
文摘The high-temperature creep behavior of asphalt mixture was investigated based on micromechanical modeling and virtual test by using three-dimensional discrete element method（DEM）. A user-defined micromechanical model of asphalt mixture was established after analyzing the irregular shape and gradation of coarse aggregates, the viscoelastic property of asphalt mastic, and the random distribution of air voids within the asphalt mixture. Virtual uniaxial static creep test at 60 ℃ was conducted by using Particle Flow Code in three dimensions（PFC3D） and was validated by laboratory test. Based on virtual creep test, the micromechanical characteristics between aggregates, within asphalt mastic, and between aggregate and asphalt mastic were analyzed for the asphalt mixture. It is proved that the virtual test based on the micromechanical model can efficiently predict the creep deformation of asphalt mixture. And the high-temperature behavior of asphalt mixture was characterized from micromechanical perspective.
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.
文摘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.
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supported by the Basic Research Key Project of Science and Technology Department of Yunnan Province(No.202401AS070116)the National Natural Science Foundation of China(No.21966016)。
文摘Biomass burning(BB)emits carbonaceous aerosols that significantly influence air quality in Southwest China during spring.To further understand the characteristics of spring BB and its original contribution to organic carbon(OC),daily fine particulate matter(PM_(2.5))samples were collected from March to May 2022 in Pu'er,Southwest China.The concentrations of OC,elemental carbon(EC),levoglucosan(Lev),and potassium from BB(K+BB)during the study period ranged from 5.3 to 31.2μg/m^(3),0.86-13.1μg/m^(3),0.06-0.82μg/m^(3),and 0.05-2.88μg/m^(3),respectively.To eliminate the effects of Lev degradation,this study uses the Aging of Air Mass(AAM)index to correct the atmospheric concentration of Lev and combines Bayesian mixture modeling with a molecular tracer method to assess the original contribution of BB to OC.The results indicated that the AAM index was 0.18±0.05,indicating that the degradation of Lev reached 82%.When considering the degradation of levoglucosan in the atmosphere,the primary source of BB aerosols was crop-straw combustion(71.1%),followed by the combustion of certain hardwoods and softwoods(24.9%)and grasses(4.0%).The original contribution of BB to OC was 62.4%,which was much greater than the contribution when levoglucosan degradation(23.7%)was ignored.The air mass inverse trajectories and Moderate Resolution Imaging Spectroradiometer(MODIS)fire hotspots indicated that the BB plume from Southeast Asia during spring could influence PM_(2.5)long-range transport in remote locations,and the contribution could reach 82%in Southwest China.
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 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.
基金Supported by the National Natural Science Foundation of China(60505004,60773061)~~
文摘A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.
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 National Natural Science Foundation of China(No.61105048,60972165)the Doctoral Fund of Ministry of Education of China(No.20110092120034)+2 种基金the Natural Science Foundation of Jiangsu Province(No.BK2010240)the Technology Foundation for Selected Overseas Chinese Scholar,Ministry of Human Resources and Social Security of China(No.6722000008)the Open Fund of Jiangsu Province Key Laboratory for Remote Measuring and Control(No.YCCK201005)
文摘An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.
基金supported by the Shuangchuang Ph.D Award (from World Prestigious Universities) (Grant No. JSSCBS20211303)Lianyungang Postdoctoral Science Foundation (Grant No. LYG20220014)the National Natural Science Foundation of China (Grant No.120051410)。
文摘A kinetic moment-closed model(KMCM), derived from the Vlasov–Fokker–Planck(VFP) equation with spherically symmetric velocity space, is introduced as a general relaxation model for homogeneous plasmas. The closed form of this model is presented by introducing a set of new functions called R function and R integration. This nonlinear model, based on the finitely distinguishable independent features(FDIF) hypothesis, enables the capture of the nature of the equilibrium state and non-equilibrium state. From this relaxation model, a general temperature relaxation model is derived when the velocity space exhibits spherical symmetry, and the general characteristic frequency of temperature relaxation is presented.
基金Project supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDB0500302 and LSKJ202300305)。
文摘We derive the transport equations from the Vlasov–Fokker–Planck equation when the velocity space is spherically symmetric.The Shkarofsky's form of Fokker–Planck–Rosenbluth collision operator is employed in the Vlasov–Fokker–Planck equation.A closed-form relaxation model for homogeneous plasmas could be presented in terms of Gauss hypergeometric2F1functions.This has been accomplished based on the Maxwellian mixture model.Furthermore,we demonstrate that classic models such as two-temperature thermal equilibrium model and thermodynamic equilibrium model are special cases of our relaxation model and the zeroth-order Braginskii heat transfer model can also be derived.The present relaxation model is a nonequilibrium model based on the hypothesis that the plasmas system possesses finitely distinguishable independent features,without relying on the conventional near-equilibrium assumption.
基金support from the National Key R&D Program of China(2021YFC3001003)Guangdong Provincial Department of Science and Technology(2022A0505030019)Science and Technology Development Fund,Macao SAR(File nos.0056/2023/RIB2,001/2024/SKL).
文摘Characterization of vegetation effect on soil response is essential for comprehending site-specific hydrological processes.Traditional research often relies on sensors or remote sensing data to examine the hydrological properties of vegetation zones,yet these methods are limited by either measurement sparsity or spatial inaccuracy.Therefore,this paper is the first to propose a data-driven approach that incorporates high-temporal-resolution electrical resistivity tomography(ERT)to quantify soil hydrological response.Time-lapse ERT is deployed on a vegetated slope site in Foshan,China,during a discontinuous rainfall induced by Typhoon Haikui.A total of 97 ERT measurements were collected with an average time interval of 2.7 hours.The Gaussian Mixture Model(GMM)is applied to quantify the level of response and objectively classify impact zones based on features extracted directly from the ERT data.The resistivity-moisture content correlation is established based on on-site sensor data to characterize infiltration and evapotranspiration across wet-dry conditions.The findings are compared with the Normalized Difference Vegetation Index(NDVI),a common indicator for vegetation quantification,to reveal potential spatial errors in remote sensing data.In addition,this study provides discussions on the potential applications and future directions.This paper showcases significant spatio-temporal advantages over existing studies,providing a more detailed and accurate characterization of superficial soil hydrological response.
基金supported by National Natural Science Foundation of China(Grant 32300938).
文摘Process data recorded by computer-based assessments reflect how respondents solve problems and thus contain rich information about respondents as well as tasks.Considering that different respondents may exhibit different behavioral characteristics during problem-solving process,in this study,we propose a mixture one-parameter state response(Mix1P-SR)measurement model.This model assumes that respondents belong to discrete latent classes with different propensities towards responses to task states during the problem-solving process,and the varying response propensities are captured by different state parameters across classes.A Markov Chain Monte Carlo algorithm for the estimation of model parameters and classification of respondents is described.The simulation study shows that the Mix1P-SR model could recover parameters well on the premise that the average sequence length was not too short.Moreover,larger sample size,longer sequences,more uniform mixing proportions,and lower interclass similarity facilitated model convergence,model selection,and parameter estimation accuracy,with sequence length being particularly important.Based on the empirical data from PISA 2012,the Mix1P-SR model identified two latent classes of respondents.They had different patterns of state easiness parameters and exhibited different state response patterns,which affected their problem solving results.Implications for model application and future research directions are discussed.
基金support from the National Natural Science Foundation of China(Grant No.52175130)the Sichuan Science and Technology Program(Grant No.2021YFS0336)+4 种基金the China Postdoctoral Science Foundation(Grant No.2021M700693)the 2021 Open Project of Failure Mechanics and Engineering Disaster Prevention,Key Lab of Sichuan Province(Grant No.FMEDP202104)the Fundamental Research Funds for the Central Universities(Grant No.ZYGX2019J035)the Sichuan Science and Technology Innovation Seedling Project Funding Project(Grant No.2021112)the Sichuan Special Equipment Inspection and Research Institute(YNJD-02-2020)are gratefully acknowledged.
文摘Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method.
基金Supported by the National Basic Research Program of China (973 Program) (Nos. 2005CB422300,2007CB411804,2010CB428904)the National Natural Science Foundation of China (Nos. 40976001,40940025,41006002)+2 种基金Tianjin Municipal Science and Technology Commission Project (No. 09JCYBJC07400)the "111 Project" (No.B07036)the Program for New Century Excellent Talents in University (No. NECT-07-0781)
文摘For understanding more about the water exchange between the Kuroshio and the East China Sea,We studied the variability of the Kuroshio in the East China Sea(ECS) in the period of 1991 to 2008 using a three-dimensional circulation model,and calculated Kuroshio onshore volume transport in the ECS at the minimum of 0.48 Sv(1 Sv ;106 m3/s) in summer and the maximum of 1.69 Sv in winter.Based on the data of WOA05 and NCEP,The modeled result indicates that the Kuroshio transport east of Taiwan Island decreased since 2000.Lateral movements tended to be stronger at two ends of the Kuroshio in the ECS than that of the middle segment.In addition,we applied a spectral mixture model(SMM) to determine the exchange zone between the Kuroshio and the shelf water of the ECS.The result reveals a significantly negative correlation(coefficient of-0.78) between the area of exchange zone and the Kuroshio onshore transport at 200 m isobath in the ECS.This conclusion brings a new view for the water exchange between the Kuroshio and the East China Sea.Additional to annual and semi-annual signals,intra-seasonal signal of probably the Pacific origin may trigger the events of Kuroshio intrusion and exchange in the ECS.
