This paper first analyzes the features of two classes of numerical methods for global analysis of nonlinear dynamical systems, which regard state space respectively as continuous and discrete ones. On basis of this un...This paper first analyzes the features of two classes of numerical methods for global analysis of nonlinear dynamical systems, which regard state space respectively as continuous and discrete ones. On basis of this understanding it then points out that the previously proposed method of point mapping under cell reference (PMUCR), has laid a frame work for the development of a two scaled numerical method suitable for the global analysis of high dimensional nonlinear systems, which may take the advantages of both classes of single scaled methods but will release the difficulties induced by the disadvantages of them. The basic ideas and main steps of implementation of the two scaled method, namely extended PMUCR, are elaborated. Finally, two examples are presented to demonstrate the capabilities of the proposed method.展开更多
Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating du...Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.展开更多
Curcumin,a safe natural yellow pigment with a wide range of pharmacological activities,is used both in herbal drugs and as a food coloring agents.Studies have shown that curcumin would suffer from extensive metabolism...Curcumin,a safe natural yellow pigment with a wide range of pharmacological activities,is used both in herbal drugs and as a food coloring agents.Studies have shown that curcumin would suffer from extensive metabolism in vivoand the predominant metabolic pathways are reduction and conjugation.In order to comprehensively study the metabolism and enrich the metabolic profile of cxurcumin in vivo,we carried out this research.A systematic method with highly sensitive UPLC-Q/TOF-MS was established to analyze different biological samples of rats after展开更多
Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to ...Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.展开更多
The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can i...The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.展开更多
All the way in the “area”, “comprehensive economic partnership agreement” (RCEP), Association of Southeast Asian Nations (ASEAN) free trade area, the main economic corridor construction under the background of suc...All the way in the “area”, “comprehensive economic partnership agreement” (RCEP), Association of Southeast Asian Nations (ASEAN) free trade area, the main economic corridor construction under the background of success, the mainland and Taiwan of China and southeast Asia has established the important relations of cooperation, industries are beginning to consider labor costs, raw materials, using the regional to invest policy and market comparative advantage. This paper starts from the investigation of Topline’s core competitiveness in China and Myanmar, and focuses on the analysis of lingerie industry in China and Myanmar, and the analysis of women’s underwear industry from the perspective of global value chain (GVC). Through the data analysis of the questionnaire survey, this paper summarizes the problems existing in the current situation of the industry, uses the intermediary analysis to analyze the correlation between the two variables, reveals the role of the core competitiveness of enterprises, and uses the GVC theory to analyze the problems existing in the industry of enterprises and their causes. According to relevant theories, the optimization path of enterprise value chain is put forward.展开更多
In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest...In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.展开更多
In this study, an intelligent monitoring platform is established for continuous quantification of soil,vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and windspeed) t...In this study, an intelligent monitoring platform is established for continuous quantification of soil,vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and windspeed) to provide an efficient dataset for modeling suction response through machine learning. Twocharacteristic parameters representing suction response during wetting processes, i.e. response time andmean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) usingeight selected influential parameters including depth, initial soil suction, vegetation- and atmosphererelated parameters. An error standardebased performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivityanalysis revealed the importance of tree canopy and mean wind speed to estimation of response timeand indicated that initial soil suction and rainfall amount have an important effect on the estimatedsuction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGPmodels describing suction response after rainfall are reliable and robust under uncertain conditions. Indepth analysis of spatial variations in suction response validated the robustness of two obtained MGGPmodels in prediction of suction variation characteristics under natural conditions.展开更多
Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties.A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on th...Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties.A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on the dynamic performance of the structure using the finite element method(FEM).To reduce the heavy computational burden,a surrogate model of a dome structure was constructed to solve this problem.The dynamic global sensitivity of elastic and elastoplastic structures was analyzed in the uncertainty quantification framework using fully quantitative variance-and distribution-based methods through the surrogate model.The model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome structure.The effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance states.Finally,the effects of the sample size and correlation function on the accuracy of the surrogate model as well as the effects of the surrogate accuracy and failure probability on the sensitivity index values are discussed.The results show that surrogate modeling has high computational efficiency and acceptable accuracy in the uncertainty quantification of large-scale structures subjected to earthquakes in comparison to the conventional FEM.展开更多
Offshore steel structures are a common investment in oil and gas industries operating in shallow to medium depth seas.These structures have become increasingly popular since the mid-19th century,with a typical design ...Offshore steel structures are a common investment in oil and gas industries operating in shallow to medium depth seas.These structures have become increasingly popular since the mid-19th century,with a typical design life of 30-50 years.Despite their popularity,the structural integrity of existing offshore structures remains a controversial topic.Environmental loads and material degradation have been identified as significant factors that can compromise the structural integrity of offshore structures.To address this issue,this study aims to investigate the reserved strength capacity of a selected offshore structure located in the Malaysian Seas.The study will explore the effect of oceanographic data,variations in vertical load,and corrosion on the structure’s main members.To determine the impact of each variable on the reserved strength ratio(RSR)of the structure,several pushover analyses were conducted with different variables.Previous literature has shown little or no relationship between seawater wave height,gravity loads,and corrosion allowance on submerged steel members and the RSR of offshore structures.However,this study aims tofill this gap in knowledge by examining these variables’effects on the RSR of offshore structures.The study’sfindings indicate that even a slight increase in wave height can significantly impact the structure’s RSR due to the increase in lateral loading,potentially leading to severe damage to structural components and the foundation model.Additionally,gravity loads had an adverse effect on the RSR of the structure when more than double the vertical load was added.Corrosion allowance was also found to impact the RSR,particularly when assuming significant wall thickness corrosion in primary members.Overall,thefindings of this study have important implications for the design and maintenance of offshore structures.The results suggest that engineers and operators should pay close attention to the potential impacts of environmental loads,such as wave height and gravity loads,and material degradation,such as corrosion allowance,on the structural integrity of offshore structures.This information can be used to optimize the design and maintenance of offshore structures,leading to safer and more efficient operations.展开更多
This paper shows the usefulness of discrete differential geometry in global analysis. Using the discrete differential geometry of triangles, we could consider the global structure of closed trajectories (of triangles)...This paper shows the usefulness of discrete differential geometry in global analysis. Using the discrete differential geometry of triangles, we could consider the global structure of closed trajectories (of triangles) on a triangular mesh consisting of congruent isosceles triangles. As an example, we perform global analysis of an Escher-style trick art, i.e., a simpler version of “Ascending and Descending”. After defining the local structure on the trick art, we analyze its global structure and attribute its paradox to a singular point (i.e., a singular triangle) at the center. Then, the endless “Penrose stairs” is described as a closed trajectory around the isolated singular point. The approach fits well with graphical projection and gives a simple and intuitive example of the interaction between global and local structures. We could deal with higher dimensional objects as well by considering n-simplices (n > 2) instead of triangles.展开更多
A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitu...A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitude and the phase shift angle. A well defined Kriging model is used to substitute the time-consuming high fidelity model, and a multi-objective genetic algorithm is employed as the search algorithm. The optimization results show that the propulsive efficiency can be improved by reducing the plunging amplitude and the phase shift angle in a proper way. The results of global sensitivity analysis using the Sobol's method show that both of the time-averaged thrust coefficient and the propulsive efficiency are most sensitive to the plunging amplitude, and second most sensitive to the pitching amplitude. It is also observed that the phase shift angle has an un-negligible influence on the propulsive efficiency, and has little effect on the time-averaged thrust coefficient.展开更多
We propose a novel rumor propagation model with guidance mechanism in hetero geneous complex networks.Firstly,the sharp threshold of rumor propagation,global stability of the information-equilibrium and information-pr...We propose a novel rumor propagation model with guidance mechanism in hetero geneous complex networks.Firstly,the sharp threshold of rumor propagation,global stability of the information-equilibrium and information-prevailingequilibrium under R_(0)<1 and R_(0)>1 is carried out by Lyapunov method and LaSalle's invariant principle.Next,we design an aperiodically intermittent stochastic stabilization method to suppress the rumor propagation.By using the Ito formula and exponential martingale inequality,the expression of the minimum control intensity is calculated.This method can effectively stabilize the rumor propagation by choosing a suitable perturb intensity and a perturb time ratio,while minimizing the control cost.Finally,numerical examples are given to illustrate the analysis and method of the paper.展开更多
The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less se...The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.展开更多
High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four typ...High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four types of typical yaw damper layouts for a high-speed locomotive(Bo-Bo)and compares,by using the multi-objective optimization method,the influences of those layouts on the lateral dynamics performance of the locomotive;the linear stability indexes under lowconicity and high-conicity conditions are selected as optimization objectives.Furthermore,the radial basis function-based highdimensional model representation(RBF-HDMR)method is used to conduct a global sensitivity analysis(GSA)between key suspension parameters and the lateral dynamics performance of the locomotive,including the lateral ride comfort on straight tracks under the low-conicity condition,and also the operational safety on curved tracks.It is concluded that the layout of yaw dampers has a considerable impact on low-conicity stability and lateral ride comfort but has little influence on curving performance.There is also an important finding that only when the locomotive adopts the layout with opening outward,the difference in lateral ride comfort between the front and rear ends of the carbody can be eliminated by adjusting the lateral installation angle of the yaw dampers.Finally,force analysis and modal analysis methods are adopted to explain the influence mechanism of yaw damper layouts on the lateral stability and differences in lateral ride comfort between the front and rear ends of the carbody.展开更多
Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since th...Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.展开更多
Sentinel-2 scenes are increasingly being used in operational Earth observation(EO)applications at regional,continental and global scales,in near-real time applications,and with multi-temporal approaches.On a broader s...Sentinel-2 scenes are increasingly being used in operational Earth observation(EO)applications at regional,continental and global scales,in near-real time applications,and with multi-temporal approaches.On a broader scale,they are therefore one of the most important facilitators of the Digital Earth.However,the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness,the position on the Earth or the acquisition plan.The spatiotemporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider.This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017,enabling the spatio-temporal coverage and availability to be quantified,including scene availability and cloudiness.Spatial exploratory analysis of the global,multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues,e.g.systematically incorrect cloud cover estimation in high,nonvegetated altitudes.The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass.It contributes to a better understanding and selection of Sentinel-2 scenes,and improves the planning and interpretation of remote sensing analyses.展开更多
One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated wit...One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.展开更多
Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the pa...Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures(DVAS)for EVs driven by in-wheel motors(IWM).Firstly,a DVAS-based quarter suspension model is developed for distributed-drive EVs,from which nine parameters and five responses are selected for the coupling effect analysis.A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses.The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables,and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints.A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration,and two optimized parameter sets for these two objects are provided at last.Simulation results provide in-depth conclusions for the coupling effects between parameters and responses,as well as a guideline on how to design system parameters for contradictory objectives.It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36%and 15%by properly changing the IWM suspension system parameters.展开更多
The Arlequin framework proposed by Ben Dhia in 1998 is a flexible and robust method for conducting global/local analysis of structures and materials.A penalty version of the Arlequin framework for the study of structu...The Arlequin framework proposed by Ben Dhia in 1998 is a flexible and robust method for conducting global/local analysis of structures and materials.A penalty version of the Arlequin framework for the study of structural problems involving large deformation is considered here.The implementation of the penalty-based Arlequin framework into ABAQUS is then explored and the corresponding Arlequin user element subroutine is developed.Geometric nonlinear simulations of a cantilever beam and a shallow arch are conducted and the choice of the coupling operator with an appropriate penalty parameter is studied.The numerical results justify the feasibility of the proposed method,ensuring its further application to more complicated problems involving geometric or material nonlinearities.展开更多
基金supported by the National Natural Science Foundation of China (NSFC) (10872155)
文摘This paper first analyzes the features of two classes of numerical methods for global analysis of nonlinear dynamical systems, which regard state space respectively as continuous and discrete ones. On basis of this understanding it then points out that the previously proposed method of point mapping under cell reference (PMUCR), has laid a frame work for the development of a two scaled numerical method suitable for the global analysis of high dimensional nonlinear systems, which may take the advantages of both classes of single scaled methods but will release the difficulties induced by the disadvantages of them. The basic ideas and main steps of implementation of the two scaled method, namely extended PMUCR, are elaborated. Finally, two examples are presented to demonstrate the capabilities of the proposed method.
文摘Traditional global sensitivity analysis(GSA)neglects the epistemic uncertainties associated with the probabilistic characteristics(i.e.type of distribution type and its parameters)of input rock properties emanating due to the small size of datasets while mapping the relative importance of properties to the model response.This paper proposes an augmented Bayesian multi-model inference(BMMI)coupled with GSA methodology(BMMI-GSA)to address this issue by estimating the imprecision in the momentindependent sensitivity indices of rock structures arising from the small size of input data.The methodology employs BMMI to quantify the epistemic uncertainties associated with model type and parameters of input properties.The estimated uncertainties are propagated in estimating imprecision in moment-independent Borgonovo’s indices by employing a reweighting approach on candidate probabilistic models.The proposed methodology is showcased for a rock slope prone to stress-controlled failure in the Himalayan region of India.The proposed methodology was superior to the conventional GSA(neglects all epistemic uncertainties)and Bayesian coupled GSA(B-GSA)(neglects model uncertainty)due to its capability to incorporate the uncertainties in both model type and parameters of properties.Imprecise Borgonovo’s indices estimated via proposed methodology provide the confidence intervals of the sensitivity indices instead of their fixed-point estimates,which makes the user more informed in the data collection efforts.Analyses performed with the varying sample sizes suggested that the uncertainties in sensitivity indices reduce significantly with the increasing sample sizes.The accurate importance ranking of properties was only possible via samples of large sizes.Further,the impact of the prior knowledge in terms of prior ranges and distributions was significant;hence,any related assumption should be made carefully.
基金partly supported by National Natural Science Foundation of China(No.81430095)also by Special National Program on Key Basic Research Project(No.2014CB560706)
文摘Curcumin,a safe natural yellow pigment with a wide range of pharmacological activities,is used both in herbal drugs and as a food coloring agents.Studies have shown that curcumin would suffer from extensive metabolism in vivoand the predominant metabolic pathways are reduction and conjugation.In order to comprehensively study the metabolism and enrich the metabolic profile of cxurcumin in vivo,we carried out this research.A systematic method with highly sensitive UPLC-Q/TOF-MS was established to analyze different biological samples of rats after
文摘Estimating the oil-water temperatures in flowlines is challenging especially in deepwater and ultra-deepwater offshore applications where issues of flow assurance and dramatic heat transfer are likely to occur due to the temperature difference between the fluids and the surroundings. Heat transfer analysis is very important for the prediction and prevention of deposits in oil and water flowlines, which could impede the flow and give rise to huge financial losses. Therefore, a 3D mathematical model of oil-water Newtonian flow under non-isothermal conditions is established to explore the complex mechanisms of the two-phase oil-water transportation and heat transfer in different flowline inclinations. In this work, a non-isothermal two-phase flow model is first modified and then implemented in the InterFoam solver by introducing the energy equation using OpenFOAM® code. The Low Reynolds Number (LRN) k-ε turbulence model is utilized to resolve the turbulence phenomena within the oil and water mixtures. The flow patterns and the local heat transfer coefficients (HTC) for two-phase oil-water flow at different flowlines inclinations (0°, +4°, +7°) are validated by the experimental literature results and the relative errors are also compared. Global sensitivity analysis is then conducted to determine the effect of the different parameters on the performance of the produced two-phase hydrocarbon systems for effective subsea fluid transportation. Thereafter, HTC and flow patterns for oil-water flows at downward inclinations of 4°, and 7° can be predicted by the models. The velocity distribution, pressure gradient, liquid holdup, and temperature variation at the flowline cross-sections are simulated and analyzed in detail. Consequently, the numerical model can be generally applied to compute the global properties of the fluid and other operating parameters that are beneficial in the management of two-phase oil-water transportation.
基金This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.
文摘The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.
文摘All the way in the “area”, “comprehensive economic partnership agreement” (RCEP), Association of Southeast Asian Nations (ASEAN) free trade area, the main economic corridor construction under the background of success, the mainland and Taiwan of China and southeast Asia has established the important relations of cooperation, industries are beginning to consider labor costs, raw materials, using the regional to invest policy and market comparative advantage. This paper starts from the investigation of Topline’s core competitiveness in China and Myanmar, and focuses on the analysis of lingerie industry in China and Myanmar, and the analysis of women’s underwear industry from the perspective of global value chain (GVC). Through the data analysis of the questionnaire survey, this paper summarizes the problems existing in the current situation of the industry, uses the intermediary analysis to analyze the correlation between the two variables, reveals the role of the core competitiveness of enterprises, and uses the GVC theory to analyze the problems existing in the industry of enterprises and their causes. According to relevant theories, the optimization path of enterprise value chain is put forward.
基金The studies mentioned in this paper were supported in part by Grants R01 CA160205 and R01 CA197150 from the National Cancer Institute,National Institutes of Health,USAGrant HR15-016 from Oklahoma Center for the Advancement of Science and Technology,USA.
文摘In order to develop precision or personalized medicine,identifying new quantitative imaging markers and building machine learning models to predict cancer risk and prognosis has been attracting broad research interest recently.Most of these research approaches use the similar concepts of the conventional computer-aided detection schemes of medical images,which include steps in detecting and segmenting suspicious regions or tumors,followed by training machine learning models based on the fusion of multiple image features computed from the segmented regions or tumors.However,due to the heterogeneity and boundary fuzziness of the suspicious regions or tumors,segmenting subtle regions is often difficult and unreliable.Additionally,ignoring global and/or background parenchymal tissue characteristics may also be a limitation of the conventional approaches.In our recent studies,we investigated the feasibility of developing new computer-aided schemes implemented with the machine learning models that are trained by global image features to predict cancer risk and prognosis.We trained and tested several models using images obtained from full-field digital mammography,magnetic resonance imaging,and computed tomography of breast,lung,and ovarian cancers.Study results showed that many of these new models yielded higher performance than other approaches used in current clinical practice.Furthermore,the computed global image features also contain complementary information from the features computed from the segmented regions or tumors in predicting cancer prognosis.Therefore,the global image features can be used alone to develop new case-based prediction models or can be added to current tumor-based models to increase their discriminatory power.
基金the financial support funded by the Science and Technology Development Fund of Macao SAR (Grant Nos. 0026/2020/AFJ and SKL-IOTSC(UM)-2021-2023)the Funds for University of Macao (Grant No. MYRG2018-00173-FST)
文摘In this study, an intelligent monitoring platform is established for continuous quantification of soil,vegetation, and atmosphere parameters (e.g. soil suction, rainfall, tree canopy, air temperature, and windspeed) to provide an efficient dataset for modeling suction response through machine learning. Twocharacteristic parameters representing suction response during wetting processes, i.e. response time andmean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) usingeight selected influential parameters including depth, initial soil suction, vegetation- and atmosphererelated parameters. An error standardebased performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivityanalysis revealed the importance of tree canopy and mean wind speed to estimation of response timeand indicated that initial soil suction and rainfall amount have an important effect on the estimatedsuction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGPmodels describing suction response after rainfall are reliable and robust under uncertain conditions. Indepth analysis of spatial variations in suction response validated the robustness of two obtained MGGPmodels in prediction of suction variation characteristics under natural conditions.
基金the Key Project of the Natural Science Foundation of Tianjin City(No.19JCZDJC39300)is acknowledged.
文摘Full-scale dome structures intrinsically have numerous sources of irreducible aleatoric uncertainties.A large-scale numerical simulation of the dome structure is required to quantify the effects of these sources on the dynamic performance of the structure using the finite element method(FEM).To reduce the heavy computational burden,a surrogate model of a dome structure was constructed to solve this problem.The dynamic global sensitivity of elastic and elastoplastic structures was analyzed in the uncertainty quantification framework using fully quantitative variance-and distribution-based methods through the surrogate model.The model considered the predominant sources of uncertainty that have a significant influence on the performance of the dome structure.The effects of the variables on the structural performance indicators were quantified using the sensitivity index values of the different performance states.Finally,the effects of the sample size and correlation function on the accuracy of the surrogate model as well as the effects of the surrogate accuracy and failure probability on the sensitivity index values are discussed.The results show that surrogate modeling has high computational efficiency and acceptable accuracy in the uncertainty quantification of large-scale structures subjected to earthquakes in comparison to the conventional FEM.
文摘Offshore steel structures are a common investment in oil and gas industries operating in shallow to medium depth seas.These structures have become increasingly popular since the mid-19th century,with a typical design life of 30-50 years.Despite their popularity,the structural integrity of existing offshore structures remains a controversial topic.Environmental loads and material degradation have been identified as significant factors that can compromise the structural integrity of offshore structures.To address this issue,this study aims to investigate the reserved strength capacity of a selected offshore structure located in the Malaysian Seas.The study will explore the effect of oceanographic data,variations in vertical load,and corrosion on the structure’s main members.To determine the impact of each variable on the reserved strength ratio(RSR)of the structure,several pushover analyses were conducted with different variables.Previous literature has shown little or no relationship between seawater wave height,gravity loads,and corrosion allowance on submerged steel members and the RSR of offshore structures.However,this study aims tofill this gap in knowledge by examining these variables’effects on the RSR of offshore structures.The study’sfindings indicate that even a slight increase in wave height can significantly impact the structure’s RSR due to the increase in lateral loading,potentially leading to severe damage to structural components and the foundation model.Additionally,gravity loads had an adverse effect on the RSR of the structure when more than double the vertical load was added.Corrosion allowance was also found to impact the RSR,particularly when assuming significant wall thickness corrosion in primary members.Overall,thefindings of this study have important implications for the design and maintenance of offshore structures.The results suggest that engineers and operators should pay close attention to the potential impacts of environmental loads,such as wave height and gravity loads,and material degradation,such as corrosion allowance,on the structural integrity of offshore structures.This information can be used to optimize the design and maintenance of offshore structures,leading to safer and more efficient operations.
文摘This paper shows the usefulness of discrete differential geometry in global analysis. Using the discrete differential geometry of triangles, we could consider the global structure of closed trajectories (of triangles) on a triangular mesh consisting of congruent isosceles triangles. As an example, we perform global analysis of an Escher-style trick art, i.e., a simpler version of “Ascending and Descending”. After defining the local structure on the trick art, we analyze its global structure and attribute its paradox to a singular point (i.e., a singular triangle) at the center. Then, the endless “Penrose stairs” is described as a closed trajectory around the isolated singular point. The approach fits well with graphical projection and gives a simple and intuitive example of the interaction between global and local structures. We could deal with higher dimensional objects as well by considering n-simplices (n > 2) instead of triangles.
基金Supported by the National Science Foundation for Post-doctoral Scientists of China (20090460216 )the National Defense Fundamental Research Foundation of China(B222006060)
文摘A bi-objective optimization problem for flapping airfoils is solved to maximize the time-averaged thrust coefficient and the propulsive efficiency. Design variables include the plunging amplitude, the pitching amplitude and the phase shift angle. A well defined Kriging model is used to substitute the time-consuming high fidelity model, and a multi-objective genetic algorithm is employed as the search algorithm. The optimization results show that the propulsive efficiency can be improved by reducing the plunging amplitude and the phase shift angle in a proper way. The results of global sensitivity analysis using the Sobol's method show that both of the time-averaged thrust coefficient and the propulsive efficiency are most sensitive to the plunging amplitude, and second most sensitive to the pitching amplitude. It is also observed that the phase shift angle has an un-negligible influence on the propulsive efficiency, and has little effect on the time-averaged thrust coefficient.
基金Project supported by the Guangzhou Science and Technology Project(Grant No.20210202710)Scientific Research Project of Guangzhou University(Grant No.YG2020010)。
文摘We propose a novel rumor propagation model with guidance mechanism in hetero geneous complex networks.Firstly,the sharp threshold of rumor propagation,global stability of the information-equilibrium and information-prevailingequilibrium under R_(0)<1 and R_(0)>1 is carried out by Lyapunov method and LaSalle's invariant principle.Next,we design an aperiodically intermittent stochastic stabilization method to suppress the rumor propagation.By using the Ito formula and exponential martingale inequality,the expression of the minimum control intensity is calculated.This method can effectively stabilize the rumor propagation by choosing a suitable perturb intensity and a perturb time ratio,while minimizing the control cost.Finally,numerical examples are given to illustrate the analysis and method of the paper.
基金The study is supported by the National Numerical Wind tunnel project(No.2019ZT2-A05)the Nature Science Foundation of China(No.11902254).
文摘The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.
基金supported by the National Railway Group Science and Technology Program(Nos.N2020J026 and N2021J028)the Independent Research and Development Project of State Key Laboratory of Traction Power,China(No.2022TPL_Q02)。
文摘High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four types of typical yaw damper layouts for a high-speed locomotive(Bo-Bo)and compares,by using the multi-objective optimization method,the influences of those layouts on the lateral dynamics performance of the locomotive;the linear stability indexes under lowconicity and high-conicity conditions are selected as optimization objectives.Furthermore,the radial basis function-based highdimensional model representation(RBF-HDMR)method is used to conduct a global sensitivity analysis(GSA)between key suspension parameters and the lateral dynamics performance of the locomotive,including the lateral ride comfort on straight tracks under the low-conicity condition,and also the operational safety on curved tracks.It is concluded that the layout of yaw dampers has a considerable impact on low-conicity stability and lateral ride comfort but has little influence on curving performance.There is also an important finding that only when the locomotive adopts the layout with opening outward,the difference in lateral ride comfort between the front and rear ends of the carbody can be eliminated by adjusting the lateral installation angle of the yaw dampers.Finally,force analysis and modal analysis methods are adopted to explain the influence mechanism of yaw damper layouts on the lateral stability and differences in lateral ride comfort between the front and rear ends of the carbody.
文摘Urban Building Energy Modelling(UBEM)allows us to simulate buildings’energy performances at a larger scale.However,creating a reliable urban-scale energy model of new or existing urban areas can be difficult since the model requires overly detailed input data,which is not necessarily publicly unavailable.Model calibration is a necessary step to reduce the uncertainties and simulation results in order to develop a reliable and accurate UBEM.Due to the concerns over computational resources and the time needed for calibration,a sensitivity analysis is often required to identify the key parameters with the most substantial impact before the calibration is deployed in UBEM.Here,we study the sensitivity of uncertain input parameters that affect the annual heating and cooling energy demand by employing an urban-scale energy model,CitySim.Our goal is to determine the relative influence of each set of input parameters and their interactions on heating and cooling loads for various building forms under different climates.First,we conduct a global sensitivity analysis for annual cooling and heating consumption under different climate conditions.Building upon this,we investigate the changes in input sensitivity to different building forms,focusing on the indices with the largest Total-order sensitivity.Finally,we determine First-order indices and Total-order effects of each input parameter included in the urban building energy model.We also provide tables,showing the important parameters on the annual cooling and heating demand for each climate and each building form.We find that if the desired calibration process require to decrease the number of the inputs to save the computational time and cost,calibrating 5 parameters;temperature set-point,infiltration rate,floor U-value,avg.walls U-value and roof U-value would impact the results over 55%for any climate and any building form.
基金the Austrian Science Fund(FWF)through the Doctoral College GIScience(DK W1237-N23)the Austrian Research Promotion Agency(Österreichische Forschungsförderungsgesellschaft,FFG)under the Austrian Space Application Programme(ASAP)within the project Sen2Cube.at(project no.:866016).
文摘Sentinel-2 scenes are increasingly being used in operational Earth observation(EO)applications at regional,continental and global scales,in near-real time applications,and with multi-temporal approaches.On a broader scale,they are therefore one of the most important facilitators of the Digital Earth.However,the data quality and availability are not spatially and temporally homogeneous due to effects related to cloudiness,the position on the Earth or the acquisition plan.The spatiotemporal inhomogeneity of the underlying data may therefore affect any big remote sensing analysis and is important to consider.This study presents an assessment of the metadata for all accessible Sentinel-2 Level-1C scenes acquired in 2017,enabling the spatio-temporal coverage and availability to be quantified,including scene availability and cloudiness.Spatial exploratory analysis of the global,multi-temporal metadata also reveals that higher acquisition frequencies do not necessarily yield more cloud-free scenes and exposes metadata quality issues,e.g.systematically incorrect cloud cover estimation in high,nonvegetated altitudes.The continuously updated datasets and analysis results are accessible as a Web application called EO-Compass.It contributes to a better understanding and selection of Sentinel-2 scenes,and improves the planning and interpretation of remote sensing analyses.
文摘One of the major scientific challenges and societal concerns is to make informed decisions to ensure sustainable groundwater availability when facing deep uncertainties.A major computational requirement associated with this is on-demand computing for risk analysis to support timely decision.This paper presents a scientific modeling service called‘ModflowOnAzure’which enables large-scale ensemble runs of groundwater flow models to be easily executed in parallel in the Windows Azure cloud.Several technical issues were addressed,including the conjunctive use of desktop tools in MATLAB to avoid license issues in the cloud,integration of Dropbox with Azure for improved usability and‘Drop-and-Compute,’and automated file exchanges between desktop and the cloud.Two scientific use cases are presented in this paper using this service with significant computational speedup.One case is from Arizona,where six plausible alternative conceptual models and a streamflow stochastic model are used to evaluate the impacts of different groundwater pumping scenarios.Another case is from Texas,where a global sensitivity analysis is performed on a regional groundwater availability model.Results of both cases show informed uncertainty analysis results that can be used to assist the groundwater planning and sustainability study.
基金This study was supported by Young Scientists Fund(Grant No.51805028)Postdoctoral Research Foundation of China(Grant No.BX201600017).
文摘Distributed-drive electric vehicles(EVs)replace internal combustion engine with multiple motors,and the novel configura-tion results in new dynamic-related issues.This paper studies the coupling effects between the parameters and responses of dynamic vibration-absorbing structures(DVAS)for EVs driven by in-wheel motors(IWM).Firstly,a DVAS-based quarter suspension model is developed for distributed-drive EVs,from which nine parameters and five responses are selected for the coupling effect analysis.A two-stage global sensitivity analysis is then utilized to investigate the effect of each parameter on the responses.The control of the system is then converted into a multiobjective optimization problem with the defined system parameters being the optimization variables,and three dynamic limitations regarding both motor and suspension subsystems are taken as the constraints.A particle swarm optimization approach is then used to either improve ride comfort or mitigate IWM vibration,and two optimized parameter sets for these two objects are provided at last.Simulation results provide in-depth conclusions for the coupling effects between parameters and responses,as well as a guideline on how to design system parameters for contradictory objectives.It can be concluded that either passenger comfort or motor lifespan can be reduced up to 36%and 15%by properly changing the IWM suspension system parameters.
基金Project supported by the National Natural Science Foundation of China (No. 10725210)the National Basic Research Program (973) of China (No. 2009CB623200)
文摘The Arlequin framework proposed by Ben Dhia in 1998 is a flexible and robust method for conducting global/local analysis of structures and materials.A penalty version of the Arlequin framework for the study of structural problems involving large deformation is considered here.The implementation of the penalty-based Arlequin framework into ABAQUS is then explored and the corresponding Arlequin user element subroutine is developed.Geometric nonlinear simulations of a cantilever beam and a shallow arch are conducted and the choice of the coupling operator with an appropriate penalty parameter is studied.The numerical results justify the feasibility of the proposed method,ensuring its further application to more complicated problems involving geometric or material nonlinearities.
