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
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
Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driv...Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driven soybean mapping algorithms often rely on extensive,representative datasets,which can limit their applicability across different regions and periods.In contrast,existing sample-free soybean mapping methods have yet to fully exploit key physiological traits,such as chlorophyll content,canopy greenness,and water content,that are essential for distinguishing soybeans from other crops,particularly during peak growth stages when many crops share similar spectral characteristics.To address these limitations,this study introduces an innovative approach:the spectral Gaussian mixture model(SGMM)for globalscale soybean mapping.Specifically,the SGMM develops a novel Bhattacharyya coefficient weighting method to optimize spectral probabilistic separability between soybeans and other crops.Moreover,it identifies an accurate soybean mapping timeframe,named the optimal time window,to refine spectral feature extraction across varying environmental conditions and crop calendars.Unlike previous methods that rely on fixed thresholds or a limited set of spectral indices,our SGMM offers a probabilistic mapping framework that dynamically adapts to regional variations in soybean growth.The SGMM was validated across multiple soybean-producing regions,showing high accuracy with average overall accuracies of 0.875 in China,0.907 in the United States,0.895 in Argentina,and 0.884 in Brazil.Furthermore,the provincial-level estimates of soybean areas correlated strongly with official statistics,highlighting the model’s reliability and scalability for global soybean mapping.By leveraging key physiological insights and optimizing spectral feature extraction,the SGMM provides an efficient,scalable solution for global agricultural monitoring and can serve as a reference for mapping other crops.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
BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recogn...BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.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
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
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
Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with...Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection),a hybrid intelligent framework designed to mitigate concept drift without retraining.SCAN integrates Gaussian Mixture Models(GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster.A key challenge in clusteringbased malware detection is cluster-wise class imbalance,where clusters contain disproportionate distributions of benign and malicious samples.SCAN addresses this issue through adaptive thresholding,which dynamically adjusts the decision boundary of each cluster according to its malicious-to-benign ratio.In the final training stage,four supervised learning algorithms—Random Forest(RF),Support Vector Machine(SVM),k-NN,and XGBoost—are applied within the GMM-defined clusters.We train SCAN on Android applications collected from 2014-2017 and test it with applications from 2018-2023.Experimental results demonstrate that SCAN combined with RF consistently achieves superior performance,with both average accuracy and average F1-score exceeding 91%.These findings confirm SCAN’s robustness to concept drift and highlight its potential as a sustainable and intelligent solution for long-term Android malware detection in the real world.展开更多
Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backg...Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backgrounds,small target sizes,and high annotation costs,which limit its practical application and widespread adoption.To address these issues,a semi-supervised spore detection framework is proposed for use under complex background conditions.Firstly,a difficulty perception scoring function is designed to quantify the detection difficulty of each image region.For regions with higher difficulty scores,a masking strategy is applied,while the remaining regions are adversarial augmentation is applied to encourage the model to learn fromchallenging areasmore effectively.Secondly,a Gaussian Mixture Model is employed to dynamically adjust the allocation threshold for pseudo-labels,thereby reducing the influence of unreliable supervision signals and enhancing the stability of semi-supervised learning.Finally,the Wasserstein distance is introduced for object localization refinement,offering a more robust positioning approach.Experimental results demonstrate that the proposed framework achieves 88.9% mAP50 and 60.7% mAP50-95,surpassing the baseline method by 4.2% and 4.6%,respectively,using only 10% of labeled data.In comparison with other state-of-the-art semi-supervised detection models,the proposed method exhibits superior detection accuracy and robustness.In conclusion,the framework not only offers an efficient and reliable solution for plant pathogen spore detection but also provides strong algorithmic support for real-time spore detection and early disease warning systems,with significant engineering application potential.展开更多
Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and colla...Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.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
In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into...In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood(ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization(EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts(PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.展开更多
Highly versatile machines, such as wheel loaders, forklifts, and mining haulers, are subject to many kinds of working conditions, as well as indefinite factors that lead to the complexity of the load. The load probabi...Highly versatile machines, such as wheel loaders, forklifts, and mining haulers, are subject to many kinds of working conditions, as well as indefinite factors that lead to the complexity of the load. The load probability distribution function （PDF） of transmission gears has many distributions centers; thus, its PDF cannot be well represented by just a single-peak function. For the purpose of representing the distribution characteristics of the complicated phenomenon accurately, this paper proposes a novel method to establish a mixture model. Based on linear regression models and correlation coefficients, the proposed method can be used to automatically select the best-fitting function in the mixture model. Coefficient of determination, the mean square error, and the maximum deviation are chosen and then used as judging criteria to describe the fitting precision between the theoretical distribution and the corresponding histogram of the available load data. The applicability of this modeling method is illustrated by the field testing data of a wheel loader. Meanwhile, the load spectra based on the mixture model are compiled. The comparison results show that the mixture model is more suitable for the description of the load-distribution characteristics. The proposed research improves the flexibility and intelligence of modeling, reduces the statistical error and enhances the fitting accuracy, and the load spectra complied by this method can better reflect the actual load characteristic of the gear component.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 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supported by the National Natural Science Foundation of China(Project No.42371363)the National Key Research and Development Program of China(Project No.2023YFB3907603)funded by the Fundamental Research Funds for the Central Universities and the Independent Innovation Research Funds for Graduate Students of China Agricultural University.
文摘Soybeans are a globally important crop,both commercially and nutritionally.Accurate mapping of soybean cultivation is essential for optimizing production and informing market strategies.However,traditional sample-driven soybean mapping algorithms often rely on extensive,representative datasets,which can limit their applicability across different regions and periods.In contrast,existing sample-free soybean mapping methods have yet to fully exploit key physiological traits,such as chlorophyll content,canopy greenness,and water content,that are essential for distinguishing soybeans from other crops,particularly during peak growth stages when many crops share similar spectral characteristics.To address these limitations,this study introduces an innovative approach:the spectral Gaussian mixture model(SGMM)for globalscale soybean mapping.Specifically,the SGMM develops a novel Bhattacharyya coefficient weighting method to optimize spectral probabilistic separability between soybeans and other crops.Moreover,it identifies an accurate soybean mapping timeframe,named the optimal time window,to refine spectral feature extraction across varying environmental conditions and crop calendars.Unlike previous methods that rely on fixed thresholds or a limited set of spectral indices,our SGMM offers a probabilistic mapping framework that dynamically adapts to regional variations in soybean growth.The SGMM was validated across multiple soybean-producing regions,showing high accuracy with average overall accuracies of 0.875 in China,0.907 in the United States,0.895 in Argentina,and 0.884 in Brazil.Furthermore,the provincial-level estimates of soybean areas correlated strongly with official statistics,highlighting the model’s reliability and scalability for global soybean mapping.By leveraging key physiological insights and optimizing spectral feature extraction,the SGMM provides an efficient,scalable solution for global agricultural monitoring and can serve as a reference for mapping other crops.
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 High-level Professional Groups in Gangdong Province,No.GSPZYQ2020101Guangdong Province Educational Research Planning Project,No.2024GXJK742。
文摘BACKGROUND Paternal perinatal depression(PPD)is closely associated with maternal mental health challenges,marital strain,and adverse child developmental outcomes.Despite its significant impact,PPD remains under-recognized in family-centered clinical practice.Concurrently,against the backdrop of rising rates of delayed marriage and China’s Maternity Incentive Policy,the proportion of women giving birth at an advanced maternal age is increasing.Nevertheless,research specifically examining PPD among spouses of older mothers remains critically scarce,both in China and globally.AIM To investigate PPD and its influencing factors in Chinese advanced maternal age families.METHODS This cross-sectional study included 358 participants;it was conducted among fathers of pregnant women of advanced maternal age at five hospitals in the Pearl River Delta region of China from September 2023 to June 2024.Data were collected via a general information questionnaire,the Social Support Rating Scale,and the Edinburgh Postnatal Depression Scale.Latent profile analysis and regression mixture models(RMMs)were adopted to analyze the latent PPD types and factors that influenced PPD.RESULTS The incidence of PPD was 16.48%,and three profiles were identified:Low-symptomatic(175 cases,48.89%),monophasic(140 cases,39.10%),and high-symptomatic(43 cases,12.01%).The RMM analysis revealed that first pregnancy,low income(<¥3000/month),part-time work,and a history of abnormal pregnancy were positively associated with the high-symptomatic type(P<0.05).Conversely,high subjective support and support utilization were negatively associated with the high-symptomatic type compared with the low-symptomatic type(P<0.05).Good couple relationships,high objective and subjective support,and high support utilization were negatively associated with monophasic disorder(P<0.05).CONCLUSION PPD incidence is high among Chinese fathers with advanced maternal age partners,and the characteristics of depression are varied.Healthcare practitioners should prioritize individuals with low levels of social support.
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 in part by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(No.2021R1A2C2012574)in part by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259967).
文摘Many machine learning-based Android malware detection often suffers from concept drift,where models trained on historical data fail to generalize to evolving threats.This paper proposes SCAN(Structural Clustering with Adaptive thresholds for iNtelligent Android malware detection),a hybrid intelligent framework designed to mitigate concept drift without retraining.SCAN integrates Gaussian Mixture Models(GMMs)-based clustering with cluster-wise adaptive thresholding and supervised classifiers tailored to each cluster.A key challenge in clusteringbased malware detection is cluster-wise class imbalance,where clusters contain disproportionate distributions of benign and malicious samples.SCAN addresses this issue through adaptive thresholding,which dynamically adjusts the decision boundary of each cluster according to its malicious-to-benign ratio.In the final training stage,four supervised learning algorithms—Random Forest(RF),Support Vector Machine(SVM),k-NN,and XGBoost—are applied within the GMM-defined clusters.We train SCAN on Android applications collected from 2014-2017 and test it with applications from 2018-2023.Experimental results demonstrate that SCAN combined with RF consistently achieves superior performance,with both average accuracy and average F1-score exceeding 91%.These findings confirm SCAN’s robustness to concept drift and highlight its potential as a sustainable and intelligent solution for long-term Android malware detection in the real world.
基金supported by Development of asparagus price database based on agricultural big data(381724).
文摘Asparagus stem blight is a devastating crop disease,and the early detection of its pathogenic spores is essential for effective disease control and prevention.However,spore detection is still hindered by complex backgrounds,small target sizes,and high annotation costs,which limit its practical application and widespread adoption.To address these issues,a semi-supervised spore detection framework is proposed for use under complex background conditions.Firstly,a difficulty perception scoring function is designed to quantify the detection difficulty of each image region.For regions with higher difficulty scores,a masking strategy is applied,while the remaining regions are adversarial augmentation is applied to encourage the model to learn fromchallenging areasmore effectively.Secondly,a Gaussian Mixture Model is employed to dynamically adjust the allocation threshold for pseudo-labels,thereby reducing the influence of unreliable supervision signals and enhancing the stability of semi-supervised learning.Finally,the Wasserstein distance is introduced for object localization refinement,offering a more robust positioning approach.Experimental results demonstrate that the proposed framework achieves 88.9% mAP50 and 60.7% mAP50-95,surpassing the baseline method by 4.2% and 4.6%,respectively,using only 10% of labeled data.In comparison with other state-of-the-art semi-supervised detection models,the proposed method exhibits superior detection accuracy and robustness.In conclusion,the framework not only offers an efficient and reliable solution for plant pathogen spore detection but also provides strong algorithmic support for real-time spore detection and early disease warning systems,with significant engineering application potential.
基金supported by the National Natural Science Foundation of China Youth Fund(No.62101579)。
文摘Multi-Agent Systems(MAS),which consist of multiple interacting agents,are crucial in Cyber-Physical Systems(CPS),because they improve system adaptability,efficiency,and robustness through parallel processing and collaboration.However,most existing unsupervised meta-learning methods are centralized and not suitable for multi-agent systems where data are distributed stored and inaccessible to all agents.Meta-GMVAE,based on Variational Autoencoder(VAE)and set-level variational inference,represents a sophisticated unsupervised meta-learning model that improves generative performance by efficiently learning data representations across various tasks,increasing adaptability and reducing sample requirements.Inspired by these advancements,we propose a novel Distributed Unsupervised Meta-Learning(DUML)framework based on Meta-GMVAE and a fusion strategy.Furthermore,we present a DUML algorithm based on Gaussian Mixture Model(DUMLGMM),where the parameters of the Gaussian-mixture are solved by an Expectation-Maximization algorithm.Simulations on Omniglot and Mini Image Net datasets show that DUMLGMM can achieve the performance of the corresponding centralized algorithm and outperform non-cooperative algorithm.
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.
基金Project (No. 2011AA110304) supported by the National High-Tech R&D Program of China (863 program)
文摘In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood(ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization(EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts(PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.
基金supported by National Natural Science Foundation of China (Grant Nos. 50805065, 51075179)
文摘Highly versatile machines, such as wheel loaders, forklifts, and mining haulers, are subject to many kinds of working conditions, as well as indefinite factors that lead to the complexity of the load. The load probability distribution function （PDF） of transmission gears has many distributions centers; thus, its PDF cannot be well represented by just a single-peak function. For the purpose of representing the distribution characteristics of the complicated phenomenon accurately, this paper proposes a novel method to establish a mixture model. Based on linear regression models and correlation coefficients, the proposed method can be used to automatically select the best-fitting function in the mixture model. Coefficient of determination, the mean square error, and the maximum deviation are chosen and then used as judging criteria to describe the fitting precision between the theoretical distribution and the corresponding histogram of the available load data. The applicability of this modeling method is illustrated by the field testing data of a wheel loader. Meanwhile, the load spectra based on the mixture model are compiled. The comparison results show that the mixture model is more suitable for the description of the load-distribution characteristics. The proposed research improves the flexibility and intelligence of modeling, reduces the statistical error and enhances the fitting accuracy, and the load spectra complied by this method can better reflect the actual load characteristic of the gear component.
