Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples acco...Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.展开更多
Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain,spinal cord and optic nerve leading to demyelination....Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain,spinal cord and optic nerve leading to demyelination.Focal demyelination is associated with relapsing-remitting multiple sclerosis,while progressive forms of the disease show axonal degeneration and neuronal loss.The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity.MicroRNAs(miRNAs)are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases.A review of recent studies with the experimental autoimmune encephalomyelitis animal model(mostly female mice 6–12 weeks of age)has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset(asymptomatic)stage when assessed in blood plasma and urine exosomes,and spinal cord tissue.The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes,brain and spinal cord tissue,and at the post-peak(chronic)stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue.Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease.Interestingly,experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a,miR-23b,miR-497,miR-26a,and miR-20b,or by suppression of miR-182,miR-181c,miR-223,miR-155,and miR-873.Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course.Additionally,studies should be performed with male mice of a similar age,and with aged male and female mice.展开更多
An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple ...An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.展开更多
Background:Multiple mitochondrial dysfunction syndromes(MMDS)presents as complex mitochondrial damage,thus impairing a variety of metabolic pathways.Heart dysplasia has been reported in MMDS patients;however,the speci...Background:Multiple mitochondrial dysfunction syndromes(MMDS)presents as complex mitochondrial damage,thus impairing a variety of metabolic pathways.Heart dysplasia has been reported in MMDS patients;however,the specific clinical symptoms and pathogenesis remain unclear.More urgently,there is a lack of an animal model to aid research.Therefore,we selected a reported MMDS causal gene,Isca1,and established an animal model of MMDS complicated with cardiac dysplasia.Methods:The myocardium-specific Isca1 knockout heterozygote(Isca1 HET)rat was obtained by crossing the Isca1 conditional knockout(Isca1 cKO)rat with theαmyosin heavy chain Cre(α-MHC-Cre)rat.Cardiac development characteristics were determined by ECG,blood pressure measurement,echocardiography and histopatho-logical analysis.The responsiveness to pathological stimuli were observed through adriamycin treatment.Mitochondria and metabolism disorder were determined by activity analysis of mitochondrial respiratory chain complex and ATP production in myocardium.Results:ISCA1 expression in myocardium exhibited a semizygous effect.Isca1 HET rats exhibited dilated cardiomyopathy characteristics,including thin-walled ventri-cles,larger chambers,cardiac dysfunction and myocardium fibrosis.Downregulated ISCA1 led to deteriorating cardiac pathological processes at the global and organiza-tional levels.Meanwhile,HET rats exhibited typical MMDS characteristics,including damaged mitochondrial morphology and enzyme activity for mitochondrial respira-tory chain complexesⅠ,ⅡandⅣ,and impaired ATP production.Conclusion:We have established a rat model of MMDS complicated with cardiomyopathy,it can also be used as model of myocardial energy metabolism dysfunction and mitochondrial cardiomyopathy.This model can be applied to the study of the mechanism of energy metabolism in cardiovascular diseases,as well as research and development of drugs.展开更多
In this paper,based on the SVIQR model we develop a stochastic epidemic model with multiple vaccinations and time delay.Firstly,we prove the existence and uniqueness of the global positive solution of the model,and co...In this paper,based on the SVIQR model we develop a stochastic epidemic model with multiple vaccinations and time delay.Firstly,we prove the existence and uniqueness of the global positive solution of the model,and construct suitable functions to obtain sufficient conditions for disease extinction.Secondly,in order to effectively control the spread of the disease,appropriate control strategies are formulated by using optimal control theory.Finally,the results are verified by numerical simulation.展开更多
The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of ...The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.展开更多
Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in t...Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.展开更多
In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching th...In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.展开更多
In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community ca...In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.展开更多
In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GP...In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GPS real-time deformation series with a high sampling rate contain coloured noise, the multiple Kalman filter model requires the white noise, and the multiple Kalman filters model is augmented by a shaping filter in order to reduce the colored noise; secondly, the multiple Kalman filters model with shaping filter can detect the deformation epoch in real-time and improve the quality of GPS measurements for the real-time deformation applications. Based on the comparisons of the applications in different GPS time series with different models, the advantages of the proposed model were illustrated. The proposed model can reduce the colored noise, detect the smaller changes, and improve the precision of the detected deformation epoch.展开更多
To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（...To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.展开更多
In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimi...In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.展开更多
To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm i...To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.展开更多
Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the of...Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.展开更多
冷水机组是建筑暖通系统中供冷/供热的核心设备,其运行工况复杂多变,且易受环境波动影响。对此,该文提出了一种基于多模型自适应预测控制(multiple model adaptive predictive control,MMAPC)的冷水机组智能调控策略。首先,根据热力学...冷水机组是建筑暖通系统中供冷/供热的核心设备,其运行工况复杂多变,且易受环境波动影响。对此,该文提出了一种基于多模型自适应预测控制(multiple model adaptive predictive control,MMAPC)的冷水机组智能调控策略。首先,根据热力学机理分析建立了冷水机组的机理模型;然后,基于模型预测控制理论,结合自适应加权的控制变量融合方法,设计了冷水机组的多模型自适应预测控制策略;最后,为方便多模型自适应控制策略的部署与测试,研发了可实现数据解析及自主决策的冷水机组调控实验平台。在三种典型工况的对比实验中,所提出多模型自适应预测控制策略比传统控制的平均跟踪误差降低了70%。展开更多
It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(M...It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter,the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect(TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.展开更多
Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calcu...Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.展开更多
基金Supported by the National High Technology Research and Development Program of China (2006AA040309)National BasicResearch Program of China (2007CB714000)
文摘Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.
文摘Multiple sclerosis is an autoimmune neurodegenerative disease of the central nervous system characterized by pronounced inflammatory infiltrates entering the brain,spinal cord and optic nerve leading to demyelination.Focal demyelination is associated with relapsing-remitting multiple sclerosis,while progressive forms of the disease show axonal degeneration and neuronal loss.The tests currently used in the clinical diagnosis and management of multiple sclerosis have limitations due to specificity and sensitivity.MicroRNAs(miRNAs)are dysregulated in many diseases and disorders including demyelinating and neuroinflammatory diseases.A review of recent studies with the experimental autoimmune encephalomyelitis animal model(mostly female mice 6–12 weeks of age)has confirmed miRNAs as biomarkers of experimental autoimmune encephalomyelitis disease and importantly at the pre-onset(asymptomatic)stage when assessed in blood plasma and urine exosomes,and spinal cord tissue.The expression of certain miRNAs was also dysregulated at the onset and peak of disease in blood plasma and urine exosomes,brain and spinal cord tissue,and at the post-peak(chronic)stage of experimental autoimmune encephalomyelitis disease in spinal cord tissue.Therapies using miRNA mimics or inhibitors were found to delay the induction and alleviate the severity of experimental autoimmune encephalomyelitis disease.Interestingly,experimental autoimmune encephalomyelitis disease severity was reduced by overexpression of miR-146a,miR-23b,miR-497,miR-26a,and miR-20b,or by suppression of miR-182,miR-181c,miR-223,miR-155,and miR-873.Further studies are warranted on determining more fully miRNA profiles in blood plasma and urine exosomes of experimental autoimmune encephalomyelitis animals since they could serve as biomarkers of asymptomatic multiple sclerosis and disease course.Additionally,studies should be performed with male mice of a similar age,and with aged male and female mice.
基金the University of Tabriz through a Grant scheme No.808.
文摘An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.
基金The present work was supported in part by the Beijing Natural Science Foundation(5212017)CAMS Innovation Fund for Medical Sciences(CIFMS,2016-I2M-1-015)National Natural Science Foundation(31872314 and 31970508).
文摘Background:Multiple mitochondrial dysfunction syndromes(MMDS)presents as complex mitochondrial damage,thus impairing a variety of metabolic pathways.Heart dysplasia has been reported in MMDS patients;however,the specific clinical symptoms and pathogenesis remain unclear.More urgently,there is a lack of an animal model to aid research.Therefore,we selected a reported MMDS causal gene,Isca1,and established an animal model of MMDS complicated with cardiac dysplasia.Methods:The myocardium-specific Isca1 knockout heterozygote(Isca1 HET)rat was obtained by crossing the Isca1 conditional knockout(Isca1 cKO)rat with theαmyosin heavy chain Cre(α-MHC-Cre)rat.Cardiac development characteristics were determined by ECG,blood pressure measurement,echocardiography and histopatho-logical analysis.The responsiveness to pathological stimuli were observed through adriamycin treatment.Mitochondria and metabolism disorder were determined by activity analysis of mitochondrial respiratory chain complex and ATP production in myocardium.Results:ISCA1 expression in myocardium exhibited a semizygous effect.Isca1 HET rats exhibited dilated cardiomyopathy characteristics,including thin-walled ventri-cles,larger chambers,cardiac dysfunction and myocardium fibrosis.Downregulated ISCA1 led to deteriorating cardiac pathological processes at the global and organiza-tional levels.Meanwhile,HET rats exhibited typical MMDS characteristics,including damaged mitochondrial morphology and enzyme activity for mitochondrial respira-tory chain complexesⅠ,ⅡandⅣ,and impaired ATP production.Conclusion:We have established a rat model of MMDS complicated with cardiomyopathy,it can also be used as model of myocardial energy metabolism dysfunction and mitochondrial cardiomyopathy.This model can be applied to the study of the mechanism of energy metabolism in cardiovascular diseases,as well as research and development of drugs.
基金supported by the Fundamental Research Funds for the Central Universities(No.3122025090)。
文摘In this paper,based on the SVIQR model we develop a stochastic epidemic model with multiple vaccinations and time delay.Firstly,we prove the existence and uniqueness of the global positive solution of the model,and construct suitable functions to obtain sufficient conditions for disease extinction.Secondly,in order to effectively control the spread of the disease,appropriate control strategies are formulated by using optimal control theory.Finally,the results are verified by numerical simulation.
基金supported by the National Key Program for Developing Basic Science(Nos.2022YFF0801702 and 2022YFE0106600)the National Natural Science Foundation of China(Nos.42175060 and 42175021)the Jiangsu Province Science Foundation(No.BK20250200302).
文摘The Madden-Julian Oscillation(MJO)is a key atmospheric component connecting global weather and climate.It func-tions as a primary source for subseasonal forecasts.Previous studies have highlighted the vital impact of oceanic processes on MJO propagation.However,few existing MJO prediction approaches adequately consider these factors.This study determines the critical region for the oceanic processes affecting MJO propagation by utilizing 22-year Climate Forecast System Reanalysis data.By intro-ducing surface and subsurface oceanic temperature within this critical region into a lagged multiple linear regression model,the MJO forecasting skill is considerably optimized.This optimization leads to a 12 h enhancement in the forecasting skill of the first principal component and efficiently decreases prediction errors for the total predictions.Further analysis suggests that,during the years in which MJO events propagate across the Maritime Continent over a more southerly path,the optimized statistical forecasting model obtains better improvements in MJO prediction.
基金supported by the Natural Science Foundation of Jiangsu province,China(BK20240937)the Belt and Road Special Foundation of the National Key Laboratory of Water Disaster Prevention(2022491411,2021491811)the Basal Research Fund of Central Public Welfare Scientific Institution of Nanjing Hydraulic Research Institute(Y223006).
文摘Understanding spatial heterogeneity in groundwater responses to multiple factors is critical for water resource management in coastal cities.Daily groundwater depth(GWD)data from 43 wells(2018-2022)were collected in three coastal cities in Jiangsu Province,China.Seasonal and Trend decomposition using Loess(STL)together with wavelet analysis and empirical mode decomposition were applied to identify tide-influenced wells while remaining wells were grouped by hierarchical clustering analysis(HCA).Machine learning models were developed to predict GWD,then their response to natural conditions and human activities was assessed by the Shapley Additive exPlanations(SHAP)method.Results showed that eXtreme Gradient Boosting(XGB)was superior to other models in terms of prediction performance and computational efficiency(R^(2)>0.95).GWD in Yancheng and southern Lianyungang were greater than those in Nantong,exhibiting larger fluctuations.Groundwater within 5 km of the coastline was affected by tides,with more pronounced effects in agricultural areas compared to urban areas.Shallow groundwater(3-7 m depth)responded immediately(0-1 day)to rainfall,primarily influenced by farmland and topography(slope and distance from rivers).Rainfall recharge to groundwater peaked at 50%farmland coverage,but this effect was suppressed by high temperatures(>30℃)which intensified as distance from rivers increased,especially in forest and grassland.Deep groundwater(>10 m)showed delayed responses to rainfall(1-4 days)and temperature(10-15 days),with GDP as the primary influence,followed by agricultural irrigation and population density.Farmland helped to maintain stable GWD in low population density regions,while excessive farmland coverage(>90%)led to overexploitation.In the early stages of GDP development,increased industrial and agricultural water demand led to GWD decline,but as GDP levels significantly improved,groundwater consumption pressure gradually eased.This methodological framework is applicable not only to coastal cities in China but also could be extended to coastal regions worldwide.
基金National Natural Science Foundation of China !( No.69772 0 3 1)
文摘In fault identification, the Strong Tracking Filter (STF) has strong ability to track the change of some parameters by whitening filtering innovation. In this paper, the authors give out a modified STF by searching the fading factor based on the Least Squared Estimation. In hybrid estimation, the well known Interacting Multiple Model (IMM) Technique can model the change of the system modes. So one can design a new adaptive filter — SIMM. In this filter, our modified STF is a parameter adaptive part and IMM is a mode adaptive part. The benefit of the new filter is that the number of models can be reduced considerably. The simulations show that SIMM greatly improves accuracy of velocity and acceleration compared with the standard IMM to track the maneuvering target when 2 model conditional estimators are used in both filters. And the computation burden of SIMM increases only 6% compared with IMM.
文摘In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.
基金Project(20120022120011)supported by the Specialized Research Fund for the Doctoral Program of Higher Education of ChinaProject(2652012062)supported by the Fundamental Research Funds for the Central Universities,China
文摘In order to detect the deformation in real-time of the GPS time series and improve its reliability, the multiple Kalman filters model with shaping filter was proposed. Two problems were solved: firstly, because the GPS real-time deformation series with a high sampling rate contain coloured noise, the multiple Kalman filter model requires the white noise, and the multiple Kalman filters model is augmented by a shaping filter in order to reduce the colored noise; secondly, the multiple Kalman filters model with shaping filter can detect the deformation epoch in real-time and improve the quality of GPS measurements for the real-time deformation applications. Based on the comparisons of the applications in different GPS time series with different models, the advantages of the proposed model were illustrated. The proposed model can reduce the colored noise, detect the smaller changes, and improve the precision of the detected deformation epoch.
基金The National Natural Science Foundation of China(No.61273236)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1637),China Scholarship Council
文摘To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.
基金The National Key Technology R&D Program during the 11th Five-Year Plan Period of China (No. 2009BAG17B02)the National High Technology Research and Development Program of China (863 Program) (No. 2011AA110304)the National Natural Science Foundation of China (No. 50908100)
文摘In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.
文摘To avoid missing track caused by the target maneuvers in automatic target tracking system, a new maneuvering target tracking technique called threshold interacting multiple model （TIMM） is proposed. This algorithm is based on the interacting multiple model （IMM） method and applies a threshold controller to improve tracking accuracy. It is also applicable to other advanced algorithms of IMM. In this research, we also compare the position and velocity root mean square （RMS） errors of TIMM and IMM algorithms with two different examples. Simulation results show that the TIMM algorithm is superior to the traditional IMM alzorithm in estimation accuracy.
文摘Aim To develop a practical target tracking algorithm for different motion modes. Methods After creation of the new model, it was implemented by computer simulation to prove its performance and compared with the often-used current statistical model. Results The simulation results show that the new IMM (interactive multiple model) have low tracking error in both maneuVering segment and non^Inaneuwi segment while the current statistical model bas muCh higher tracking error in non-maneuvering segment. Conclusion In the point of trackintaccuracy, the new IMM method is much better than the current acceleration method. It can develop into a practical target hacking method.
文摘冷水机组是建筑暖通系统中供冷/供热的核心设备,其运行工况复杂多变,且易受环境波动影响。对此,该文提出了一种基于多模型自适应预测控制(multiple model adaptive predictive control,MMAPC)的冷水机组智能调控策略。首先,根据热力学机理分析建立了冷水机组的机理模型;然后,基于模型预测控制理论,结合自适应加权的控制变量融合方法,设计了冷水机组的多模型自适应预测控制策略;最后,为方便多模型自适应控制策略的部署与测试,研发了可实现数据解析及自主决策的冷水机组调控实验平台。在三种典型工况的对比实验中,所提出多模型自适应预测控制策略比传统控制的平均跟踪误差降低了70%。
基金supported by the Natural Science Foundation of Anhui Province(1708085QF149)。
文摘It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model(MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter,the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect(TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.
基金provided by the Korean Ministry of Environment and Eco Star Project
文摘Rainfall is an important factor in estimating the event mean concentration （EMC） which is used to quantify the washed-off pollutant concentrations from non-point sources （NPSs）. Pollutant loads could also be calculated using rainfall, catchment area and runoff coefficient. In this study, runoff quantity and quality data gathered from a 28-month monitoring conducted on the road and parking lot sites in Korea were evaluated using multiple linear regression （MLR） to develop equations for estimating pollutant loads and EMCs as a function of rainfall variables. The results revealed that total event rainfall and average rainfall intensity are possible predictors of pollutant loads. Overall, the models are indicators of the high uncertainties of NPSs; perhaps estimation of EMCs and loads could be accurately obtained by means of water quality sampling or a long term monitoring is needed to gather more data that can be used for the development of estimation models.
