The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies.In this paper,we study the high-dimensional accelerated failure time(AFT)model under the case–cohort d...The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies.In this paper,we study the high-dimensional accelerated failure time(AFT)model under the case–cohort design.Based on?0-regularization and a newly defined weight function,we propose a weighted least squares procedure for variable selection and parameter estimation.Computationally,we develop a support detection and root finding(SDAR)algorithm,where the support is first determined based on the primal and dual information,then the estimator is obtained by solving the weighted least squares problem restricted to the estimated support.We show the proposed algorithm is essentially one Newton-type algorithm,thus it is more efficient and stable compared with other regularized methods.Theoretically,we establish a sharp error bound for the solution sequences generated from the proposed method.Furthermore,we propose an adaptive version of the proposed SDAR algorithm,which determines the support size of the estimated coefficient in a data-driven manner.Extensive simulation studies demonstrate the superior performance of the proposed procedures,especially for the computational efficiency.As an illustration,we apply the proposed method to a malignant breast tumor gene expression data.展开更多
A pre-selection space time model was proposed to estimate the traffic condition at poor-data-detector,especially non-detector locations.The space time model is better to integrate the spatial and temporal information ...A pre-selection space time model was proposed to estimate the traffic condition at poor-data-detector,especially non-detector locations.The space time model is better to integrate the spatial and temporal information comprehensibly.Firstly,the influencing factors of the "cause nodes" were studied,and then the pre-selection "cause nodes" procedure which utilizes the Pearson correlation coefficient to evaluate the relevancy of the traffic data was introduced.Finally,only the most relevant data were collected to compose the space time model.The experimental results with the actual data demonstrate that the model performs better than other three models.展开更多
An identification-based approach for aircraft engine modeling using the nonlinear HammersteinWiener representation was proposed.Hammerstein-Wiener modeling for both limited flight envelope and extended flight envelope...An identification-based approach for aircraft engine modeling using the nonlinear HammersteinWiener representation was proposed.Hammerstein-Wiener modeling for both limited flight envelope and extended flight envelope was investigated.Simulation shows that the resulting model can be valid over 10%variation of rotational speed of the engine,compared with those linear models that are only valid over 3%—5%change of rotational speed.It is further demonstrated that the proposed method can be utilized over large envelope up to 20% variation of rotational speed of the engine.The fundamental idea is to use nonlinear models to extend the feasible/valid region rather than those linear models.This may consequently simplify the switching logic in the onboard digital control units.This is often overlooked in aircraft engine control community,but has been emphasized in the research.展开更多
In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property...In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.展开更多
Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interactio...Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.展开更多
This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used...This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.展开更多
Computational fluid dynamics （CFD） modeling of the complex processes that occur within the burner of a gas turbine engine has become a critical step in the design process. However, due to computer limitations, it is...Computational fluid dynamics （CFD） modeling of the complex processes that occur within the burner of a gas turbine engine has become a critical step in the design process. However, due to computer limitations, it is very difficult to completely couple the fluid mechanics solver with the full combustion chemistry. Therefore, simplified chemistry models are required, and the topic of this research was to provide reduced chemistry models for CH4/O2 gas turbine flow fields to be integrated into CFD codes for the simulation of flow fields of natural gas-fueled burners. The reduction procedure for the CH4/O2 model utilized a response modeling technique wherein the full mechanism was solved over a range of temperatures, pressures, and mixture ratios to establish the response of a particular variable, namely the chemical reaction time. The conditions covered were between 1000 and 2500 K for temperature, 0.1 and 2 for equivalence ratio in air, and 0.1 and 50 atm for pressure. The kinetic time models in the form of ignition time correlations are given in Arrhenius-type formulas as functions of equivalence ratio, temperature, and pressure; or fuel-to-air ratio, temperature, and pressure. A single ignition time model was obtained for the entire range of conditions, and separate models for the low-temperature and high-temperature regions as well as for fuel-lean and rich cases were also derived. Predictions using the reduced model were verified using results from the full mechanism and empirical correlations from experiments. The models are intended for （but not limited to） use in CFD codes for flow field simulations of gas turbine combustors in which initial conditions and degree of mixedness of the fuel and air are key factors in achieving stable and robust combustion processes and acceptable emission levels. The chemical time model was utilized successfully in CFD simulations of a generic gas turbine combustor with four different cases with various levels of fuel-air premixing.展开更多
A laboratory experiment was carried out to determine the effect of different constant temperatures on germination and early seedling establishment and to study the variation among parameters of thermal time model para...A laboratory experiment was carried out to determine the effect of different constant temperatures on germination and early seedling establishment and to study the variation among parameters of thermal time model parameters for two contrasting chickpea cultivars . Seeds were subjected to six constant temperatures from 10 o C to 35 o C . A complete randomized design was used with four replication. Analysis of variance showed significant differences among treatments for all characters studied. The final germination percentage significantly increased with increasing temperature up to 25 ° C, and thereafter there was a sharp decrease in final germination at 30 ° and 35 ° C. Desi type cultivar (small seeded) “Jabel Marra” significantly exhibited higher final germination percentage and lower germination rate compared with the kabui type cultivar “Shendi” at all temperatures. The median (θ T(50) ) of the thermal time was significantly differ between the two chickpea cultivars. The large seeded cultivars (shendi) recorded significantly higher median thermal time than the small seeded cultivars (Jabel Marra). The results also revealed a significant differences between the two cultivars in all thermal time model parameters. The small seeded cultivar (Jabel Marra) scored lower total dry matter and temperature tolerance index (TTI) compared to the large seeded cultivar (Shendi) at all temperatures studied.展开更多
This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estim...This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.展开更多
This paper proposes a Bayesian semiparametric accelerated failure time model for doubly censored data with errors-in-covariates. The authors model the distributions of the unobserved covariates and the regression erro...This paper proposes a Bayesian semiparametric accelerated failure time model for doubly censored data with errors-in-covariates. The authors model the distributions of the unobserved covariates and the regression errors via the Dirichlet processes. Moreover, the authors extend the Bayesian Lasso approach to our semiparametric model for variable selection. The authors develop the Markov chain Monte Carlo strategies for posterior calculation. Simulation studies are conducted to show the performance of the proposed method. The authors also demonstrate the implementation of the method using analysis of PBC data and ACTG 175 data.展开更多
This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are...This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains When the stochastic disturbance occurs on the railway.展开更多
In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies f...In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.展开更多
Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportati...Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services,financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time(GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma's impact on Florida as a case study, we examined:(1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives;(2) the relationship between the duration of power outage and power system variables;and(3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.展开更多
Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are...Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.展开更多
In this paper, we set up continuous time model with Poisson Process to analyze demand of investment-oriented life insurance. Individual life time is assumed random, and he is received fixed income, investment-oriented...In this paper, we set up continuous time model with Poisson Process to analyze demand of investment-oriented life insurance. Individual life time is assumed random, and he is received fixed income, investment-oriented life insurance is an important financial asset under this model. Dynamic programming is applied to analyze this problem. The optimal explicit solutions are obtained in the case of CRRA utilities, and draw its demand curve with numerical simulation.展开更多
The research is: by using Wdolkowski's Time Continuum Model throughout a lesson plan enables the teacher to increase students'motivation and help them move closer to success in a learning environment. This res...The research is: by using Wdolkowski's Time Continuum Model throughout a lesson plan enables the teacher to increase students'motivation and help them move closer to success in a learning environment. This research supports the theory that instruction is a network of interactions between the teacher and learner that promotes a successful learning experience. It identifies a three-part learning sequence-a beginning, middle and an end. Each part has two of six key motivational factors that when applied correctly by the teacher will maximize the success and continued motivation of the learner.展开更多
This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is intro...This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.展开更多
In this paper,the vibration signals in the fatigue crack growth process in a chinese steel used in a mining machinery were analyzed by the frequency spectrum, the time series and grey system model,and the critical cri...In this paper,the vibration signals in the fatigue crack growth process in a chinese steel used in a mining machinery were analyzed by the frequency spectrum, the time series and grey system model,and the critical criterion for crack initiation was proposed.展开更多
The numerical simulation of modern aero-engine combustion chamber needs accurate description of the interaction between turbulence and chemical reaction mechanism. The Large Eddy Simulation(LES) method with the Transp...The numerical simulation of modern aero-engine combustion chamber needs accurate description of the interaction between turbulence and chemical reaction mechanism. The Large Eddy Simulation(LES) method with the Transported Probability Density Function(TPDF) turbulence combustion model is promising in engineering applications. In flame region, the impact of chemical reaction should be considered in TPDF molecular mixing model. Based on pioneer research, three new TPDF turbulence-chemistry dual time scale molecular mixing models were proposed tentatively by adding the chemistry time scale in molecular mixing model for nonpremixed flame. The Aero-Engine Combustor Simulation Code(AECSC) which is based on LES-TPDF method was combined with the three new models. Then the Sandia laboratory's methane-air jet flames: Flame D and Flame E were simulated. Transient simulation results show that all the three new models can predict the instantaneous combustion flow pattern of the jet flames. Furthermore,the average scalar statistical results were compared with the experimental data. The simulation result of the new TPDF arithmetic mean modification model is the closest to the experimental data:the average error in Flame D is 7.6% and 6.6% in Flame E. The extinction and re-ignition phenomena of the jet flames especially Flame E were captured. The turbulence time scale and the chemistry time scale are in different order in the whole flow field. The dual time scale TPDF combustion model has ability to deal with both the turbulence effect and the chemistry reaction effect, as well as their interaction more accurately for nonpremixed flames.展开更多
Objective To construct a model of Seasonal Autoregressive Integrated Moving Average （SARIMA） for forecasting the epidemic of Japanese encephalitis （JE） in Xianyang, Shaanxi, China, and provide valuable reference ...Objective To construct a model of Seasonal Autoregressive Integrated Moving Average （SARIMA） for forecasting the epidemic of Japanese encephalitis （JE） in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention. Methods Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015. Results SARIMA （1, 1, 1） （2, 1, 1）12 was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R2, and a lower Mean Absolute Percent Error. SARIMA （1, 1, 1） （2, 1, 1）12 was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA （1, 1, 1） （2, 1, 1）12 appeared to be reliable and accurate and could be applied to incidence prediction. Conclusions The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally （≥0.4/100 000）. According to the predicted results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.The authors wish to thank the staff from the CDCs from 13 counties of Xianyang, Shaanxi province, China, for their contribution to Japanese encephalitis cases reporting.展开更多
基金Supported by the National Natural Science Foundation of China(Grant Nos.12371274,12271459)National Social Science Foundation of China(Grant No.24CTJ036)+1 种基金Natural Science Foundation of Hubei Province(Grant No.2021CFB502)the Fundamental Research Funds for the Central Universities,Zhongnan University of Economics and Law(Grant No.2722024BY024)。
文摘The case–cohort design has been widely used to reduce the cost of covariate measurements in large cohort studies.In this paper,we study the high-dimensional accelerated failure time(AFT)model under the case–cohort design.Based on?0-regularization and a newly defined weight function,we propose a weighted least squares procedure for variable selection and parameter estimation.Computationally,we develop a support detection and root finding(SDAR)algorithm,where the support is first determined based on the primal and dual information,then the estimator is obtained by solving the weighted least squares problem restricted to the estimated support.We show the proposed algorithm is essentially one Newton-type algorithm,thus it is more efficient and stable compared with other regularized methods.Theoretically,we establish a sharp error bound for the solution sequences generated from the proposed method.Furthermore,we propose an adaptive version of the proposed SDAR algorithm,which determines the support size of the estimated coefficient in a data-driven manner.Extensive simulation studies demonstrate the superior performance of the proposed procedures,especially for the computational efficiency.As an illustration,we apply the proposed method to a malignant breast tumor gene expression data.
基金Project(D101106049710005) supported by the Beijing Science Foundation Program,ChinaProject(61104164) supported by the National Natural Science Foundation,China
文摘A pre-selection space time model was proposed to estimate the traffic condition at poor-data-detector,especially non-detector locations.The space time model is better to integrate the spatial and temporal information comprehensibly.Firstly,the influencing factors of the "cause nodes" were studied,and then the pre-selection "cause nodes" procedure which utilizes the Pearson correlation coefficient to evaluate the relevancy of the traffic data was introduced.Finally,only the most relevant data were collected to compose the space time model.The experimental results with the actual data demonstrate that the model performs better than other three models.
文摘An identification-based approach for aircraft engine modeling using the nonlinear HammersteinWiener representation was proposed.Hammerstein-Wiener modeling for both limited flight envelope and extended flight envelope was investigated.Simulation shows that the resulting model can be valid over 10%variation of rotational speed of the engine,compared with those linear models that are only valid over 3%—5%change of rotational speed.It is further demonstrated that the proposed method can be utilized over large envelope up to 20% variation of rotational speed of the engine.The fundamental idea is to use nonlinear models to extend the feasible/valid region rather than those linear models.This may consequently simplify the switching logic in the onboard digital control units.This is often overlooked in aircraft engine control community,but has been emphasized in the research.
文摘In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.
基金supported by the National Natural Science Foundation of China(Grant Nos.11261034,71561020,61503203,and 11326239)the Higher School Science and Technology Research Project of Inner Mongolia,China(Grant No.NJZY13119)the Natural Science Foundation of Inner Mongolia,China(Grant Nos.2015MS0103 and 2014BS0105)
文摘Community detection in signed networks has been studied widely in recent years. In this paper, a discrete difference equation is proposed to imitate the consistently changing phases of the nodes. During the interaction, each node will update its phase based on the difference equation. Each node has many different nodes connected with it, and these neighbors have different influences on it. The similarity between two nodes is applied to describe the influences between them. Nodes with high positive similarities will get together and nodes with negative similarities will be far away from each other.Communities are detected ultimately when the phases of the nodes are stable. Experiments on real world and synthetic signed networks show the efficiency of detection performance. Moreover, the presented method gains better detection performance than two existing good algorithms.
文摘This paper provides a new obstacle avoidance control method for cars based on big data and just-in-time modeling. Just-in-time modeling is a new kind of data-driven control technique in the age of big data and is used in various real systems. The main property of the proposed method is that a gain and a control time which are parameters in the control input to avoid an encountered obstacle are computed from a database which includes a lot of driving data in various situations. Especially, the important advantage of the method is small computation time, and hence it realizes real-time obstacle avoidance control for cars. From some numerical simulations, it is showed that the new control method can make the car avoid various obstacles efficiently in comparison with the previous method.
基金supported by a University Turbine Systems Research grant from the South Carolina Institute for Energy Studies, contract number 04-01-SR114
文摘Computational fluid dynamics （CFD） modeling of the complex processes that occur within the burner of a gas turbine engine has become a critical step in the design process. However, due to computer limitations, it is very difficult to completely couple the fluid mechanics solver with the full combustion chemistry. Therefore, simplified chemistry models are required, and the topic of this research was to provide reduced chemistry models for CH4/O2 gas turbine flow fields to be integrated into CFD codes for the simulation of flow fields of natural gas-fueled burners. The reduction procedure for the CH4/O2 model utilized a response modeling technique wherein the full mechanism was solved over a range of temperatures, pressures, and mixture ratios to establish the response of a particular variable, namely the chemical reaction time. The conditions covered were between 1000 and 2500 K for temperature, 0.1 and 2 for equivalence ratio in air, and 0.1 and 50 atm for pressure. The kinetic time models in the form of ignition time correlations are given in Arrhenius-type formulas as functions of equivalence ratio, temperature, and pressure; or fuel-to-air ratio, temperature, and pressure. A single ignition time model was obtained for the entire range of conditions, and separate models for the low-temperature and high-temperature regions as well as for fuel-lean and rich cases were also derived. Predictions using the reduced model were verified using results from the full mechanism and empirical correlations from experiments. The models are intended for （but not limited to） use in CFD codes for flow field simulations of gas turbine combustors in which initial conditions and degree of mixedness of the fuel and air are key factors in achieving stable and robust combustion processes and acceptable emission levels. The chemical time model was utilized successfully in CFD simulations of a generic gas turbine combustor with four different cases with various levels of fuel-air premixing.
文摘A laboratory experiment was carried out to determine the effect of different constant temperatures on germination and early seedling establishment and to study the variation among parameters of thermal time model parameters for two contrasting chickpea cultivars . Seeds were subjected to six constant temperatures from 10 o C to 35 o C . A complete randomized design was used with four replication. Analysis of variance showed significant differences among treatments for all characters studied. The final germination percentage significantly increased with increasing temperature up to 25 ° C, and thereafter there was a sharp decrease in final germination at 30 ° and 35 ° C. Desi type cultivar (small seeded) “Jabel Marra” significantly exhibited higher final germination percentage and lower germination rate compared with the kabui type cultivar “Shendi” at all temperatures. The median (θ T(50) ) of the thermal time was significantly differ between the two chickpea cultivars. The large seeded cultivars (shendi) recorded significantly higher median thermal time than the small seeded cultivars (Jabel Marra). The results also revealed a significant differences between the two cultivars in all thermal time model parameters. The small seeded cultivar (Jabel Marra) scored lower total dry matter and temperature tolerance index (TTI) compared to the large seeded cultivar (Shendi) at all temperatures studied.
基金supported by the National Natural Science Foundation of China under Grant No.11671168the Science and Technology Developing Plan of Jilin Province under Grant No.20200201258JC。
文摘This paper discusses regression analysis of interval-censored failure time data arising from the accelerated failure time model in the presence of informative censoring.For the problem,a sieve maximum likelihood estimation approach is proposed and in the method,the copula model is employed to describe the relationship between the failure time of interest and the censoring or observation process.Also I-spline functions are used to approximate the unknown functions in the model,and a simulation study is carried out to assess the finite sample performance of the proposed approach and suggests that it works well in practical situations.In addition,an illustrative example is provided.
基金supported by the National Natural Science Foundation of China under Grant Nos.11171007/A011103,11171230,and 11471024
文摘This paper proposes a Bayesian semiparametric accelerated failure time model for doubly censored data with errors-in-covariates. The authors model the distributions of the unobserved covariates and the regression errors via the Dirichlet processes. Moreover, the authors extend the Bayesian Lasso approach to our semiparametric model for variable selection. The authors develop the Markov chain Monte Carlo strategies for posterior calculation. Simulation studies are conducted to show the performance of the proposed method. The authors also demonstrate the implementation of the method using analysis of PBC data and ACTG 175 data.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 70901006 and 60634010)the State Key Laboratory of Rail Traffic Control and Safety (Grant Nos. RCS2009ZT001 and RCS2008ZZ001)Beijing Jiaotong University, and the Innovation Foundation of Science and Technology for Excellent Doctorial Candidate of Beijing Jiaotong University (Grant No. 141034522)
文摘This paper presents a discrete-time model to describe the movements of a group of trains, in which some operational strategies, including traction operation, braking operation and impact of stochastic disturbance, are defined. To show the dynamic characteristics of train traffic flow with stochastic disturbance, some numerical experiments on a railway line are simulated. The computational results show that the discrete-time movement model can well describe the movements of trains on a rail line with the moving-block signalling system. Comparing with the results of no disturbance, it finds that the traffic capacity of the rail line will decrease with the influence of stochastic disturbance. Additionally, the delays incurred by stochastic disturbance can be propagated to the subsequent trains, and then prolong their traversing time on the rail line. It can provide auxiliary information for rescheduling trains When the stochastic disturbance occurs on the railway.
基金Project supported by the National Basic Research Program of China(Grant No.2012CB725400)the National Natural Science Foundation of China(Grant No.71131001-1)the Research Foundation of State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,China(Grant Nos.RCS2012ZZ001 and RCS2012ZT001)
文摘In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.
基金the U.S.National Science Foundation for the Grant CMMI-1832578 to support the research presented in this article。
文摘Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services,financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time(GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma's impact on Florida as a case study, we examined:(1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives;(2) the relationship between the duration of power outage and power system variables;and(3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.
基金supported by the National Scienceand Technology Support Program of China (No.2009BAG17B01)
文摘Bus rapid transit （BRT） systems have been shown to have many advantages including affordability, high capacity vehicles, and reliable service. Due to these attractive advantages, many cities throughout the world are in the process of planning the construction of BRT systems. To improve the performance of BRT systems, many researchers study BRT operation and control, which include the study of dwell times at bus/BRT stations. To ensure the effectiveness of real-time control which aims to avoid bus/BRT vehicles congestion, accurate dwell time models are needed. We develop our models using data from a BRT vehicle survey conducted in Changzhou, China, where BRT lines are built along passenger corridors, and BRT stations are enclosed like light rails. This means that interactions between passengers traveling on the BRT system are more frequent than those in traditional transit system who use platform stations. We statistically analyze the BRT vehicle survey data, and based on this analysis, we are able to make the following conclusions： （ I ） The delay time per passenger at a BRT station is less than that at a non-BRT station, which implies that BRT stations are efficient in the sense that they are able to move passengers quickly. （II） The dwell time follows a logarithmic normal distribution with a mean of 2.56 and a variance of 0.53. （III） The greater the number of BRT lines serviced by a station, the longer the dwell time is. （IV） Daily travel demands are highest during the morning peak interval where the dwell time, the number of passengers boarding and alighting and the number of passengers on vehicles reach their maximum values. （V） The dwell time is highly positively correlated with the total number of passengers boarding and alighting. （VI） The delay per passenger is negatively correlated with the total number of passengers boarding and alighting. We propose two dwell time models for the BRT station. The first proposed model is a linear model while the second is nonlinear. We introduce the conflict between passengers boarding and alighting into our models. Finally, by comparing our models with the models of Rajbhandari and Chien et al., and TCQSM （Transit Capacity and Quality of Service Manual）, we conclude that the proposed nonlinear model can better predict the dwell time at BRT stations.
文摘In this paper, we set up continuous time model with Poisson Process to analyze demand of investment-oriented life insurance. Individual life time is assumed random, and he is received fixed income, investment-oriented life insurance is an important financial asset under this model. Dynamic programming is applied to analyze this problem. The optimal explicit solutions are obtained in the case of CRRA utilities, and draw its demand curve with numerical simulation.
文摘The research is: by using Wdolkowski's Time Continuum Model throughout a lesson plan enables the teacher to increase students'motivation and help them move closer to success in a learning environment. This research supports the theory that instruction is a network of interactions between the teacher and learner that promotes a successful learning experience. It identifies a three-part learning sequence-a beginning, middle and an end. Each part has two of six key motivational factors that when applied correctly by the teacher will maximize the success and continued motivation of the learner.
基金Supported by the Shandong Natural Science Foundation(ZR2013BL008)
文摘This paper combines grey model with time series model and then dynamic model for rapid and in-depth fault prediction in chemical processes. Two combination methods are proposed. In one method, historical data is introduced into the grey time series model to predict future trend of measurement values in chemical process. These predicted measurements are then used in the dynamic model to retrieve the change of fault parameters by model based diagnosis algorithm. In another method, historical data is introduced directly into the dynamic model to retrieve historical fault parameters by model based diagnosis algorithm. These parameters are then predicted by the grey time series model. The two methods are applied to a gravity tank example. The case study demonstrates that the first method is more accurate for fault prediction.
文摘In this paper,the vibration signals in the fatigue crack growth process in a chinese steel used in a mining machinery were analyzed by the frequency spectrum, the time series and grey system model,and the critical criterion for crack initiation was proposed.
基金co-supported by the National Key R&D Program of China(Nos.2017YFB0202400 and 2017YFB0202402)the National Natural Science Foundation of China(No.91741125)the Project of Newton International Fellowship Alumnus from Royal Society(No.AL120003)
文摘The numerical simulation of modern aero-engine combustion chamber needs accurate description of the interaction between turbulence and chemical reaction mechanism. The Large Eddy Simulation(LES) method with the Transported Probability Density Function(TPDF) turbulence combustion model is promising in engineering applications. In flame region, the impact of chemical reaction should be considered in TPDF molecular mixing model. Based on pioneer research, three new TPDF turbulence-chemistry dual time scale molecular mixing models were proposed tentatively by adding the chemistry time scale in molecular mixing model for nonpremixed flame. The Aero-Engine Combustor Simulation Code(AECSC) which is based on LES-TPDF method was combined with the three new models. Then the Sandia laboratory's methane-air jet flames: Flame D and Flame E were simulated. Transient simulation results show that all the three new models can predict the instantaneous combustion flow pattern of the jet flames. Furthermore,the average scalar statistical results were compared with the experimental data. The simulation result of the new TPDF arithmetic mean modification model is the closest to the experimental data:the average error in Flame D is 7.6% and 6.6% in Flame E. The extinction and re-ignition phenomena of the jet flames especially Flame E were captured. The turbulence time scale and the chemistry time scale are in different order in the whole flow field. The dual time scale TPDF combustion model has ability to deal with both the turbulence effect and the chemistry reaction effect, as well as their interaction more accurately for nonpremixed flames.
基金Supported by the Youth Project of Shaanxi University of Chinese Medicine(2015QN05)
文摘Objective To construct a model of Seasonal Autoregressive Integrated Moving Average （SARIMA） for forecasting the epidemic of Japanese encephalitis （JE） in Xianyang, Shaanxi, China, and provide valuable reference information for JE control and prevention. Methods Theoretically epidemiologic study was employed in the research process. Monthly incidence data on JE for the period from Jan 2005 to Sep 2014 were obtained from a passive surveillance system at the Center for Diseases Prevention and Control in Xianyang, Shaanxi province. An optimal SARIMA model was developed for JE incidence from 2005 to 2013 with the Box and Jenkins approach. This SARIMA model could predict JE incidence for the year 2014 and 2015. Results SARIMA （1, 1, 1） （2, 1, 1）12 was considered to be the best model with the lowest Bayesian information criterion, Akaike information criterion, Mean Absolute Error values, the highest R2, and a lower Mean Absolute Percent Error. SARIMA （1, 1, 1） （2, 1, 1）12 was stationary and accurate for predicting JE incidence in Xianyang. The predicted incidence, around 0.3/100 000 from June to August in 2014 with low errors, was higher compared with the actual incidence. Therefore, SARIMA （1, 1, 1） （2, 1, 1）12 appeared to be reliable and accurate and could be applied to incidence prediction. Conclusions The proposed prediction model could provide clues to early identification of the JE incidence that is increased abnormally （≥0.4/100 000）. According to the predicted results in 2014, the JE incidence in Xianyang will decline slightly and reach its peak from June to August.The authors wish to thank the staff from the CDCs from 13 counties of Xianyang, Shaanxi province, China, for their contribution to Japanese encephalitis cases reporting.
