The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest touris...The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.展开更多
Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urg...Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.展开更多
Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system....Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.展开更多
The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation proce...The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.展开更多
Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary...Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary to find a tradeoff between power consumption and communication latency. So we propose an analytical latency model which can show us the relationship of them. The proposed model to analyze latency is based on the M/G/1 queuing model, which is suitable for dynamic frequency scaling. The experiment results show that the accuracy of this model is more than 90%.展开更多
This article studied on five elements system and set general and strict model expectations combining with Traditional Chinese Medicine Zang-fu organs theory,in which absolute stable state,conditional stable state or t...This article studied on five elements system and set general and strict model expectations combining with Traditional Chinese Medicine Zang-fu organs theory,in which absolute stable state,conditional stable state or transient stability and instability in mathematical models were corresponding to human healthy state,sub-healthy state(pathological plateau phase) and health deterioration state respectively.Model parameters were set up according to the mutual generation and restriction relations among five elements.The dynamic model of Five-Elements System was built,of which impulse responses were corresponding to human response under TCM treatment.Analyses of initial value and excitation response were conducted by numerical simulation and results turned out to meet the requirements of general model expectation:five elements system dynamic model had self-organization function;there existed only one non-global stable point and instability region in the five-dimensional space consisting of variables,in which instability was corresponding to pathological deterioration;system stable region was an unbounded domain and it included the stable sub-regions of special straight line-type,ray-type and line segment-type.Among those ray-types,some contained "Regression Peak" were classed as conditional stable regions while others as absolute ones.The existence of this peak indicates that our body must exceed a "Regression Threshold" when transiting from sub-healthy state(pathological plateau phase) to healthy state through self-regulation mechanism.Impulse excitation can reduce certain threshold and then increase the system health level,which is complied with the operating principle of Five-Elements System and the empirical rule of TCM clinical practice.This model has revealed qualitatively the inherent movement law of Five-Elements System and thus provides a new analysis tool for basic theoretical study on TCM.展开更多
In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), ob...In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.展开更多
Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water r...Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.展开更多
Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression pr...Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression process remain substantial challenges.This study proposes the adaptive backward stepwise selection of fast SINDy(ABSS-FSINDy),which integrates statistical learning-based estimation and technical advancements to significantly reduce simulation time.This approach not only provides insights into the conditions under which SINDy performs optimally but also highlights potential failure points,particularly in the context of backward stepwise selection(BSS).By decoding predefined features into textual expressions,ABSS-FSINDy significantly reduces the simulation time compared with conventional symbolic regression methods.We validate the proposed method through a series of numerical experiments involving both planar/spatial dynamics and high-dimensional chaotic systems,including Lotka-Volterra,hyperchaotic Rossler,coupled Lorenz,and Lorenz 96 benchmark systems.The experimental results demonstrate that ABSS-FSINDy autonomously determines optimal hyperparameters within the SINDy framework,overcoming the curse of dimensionality in high-dimensional simulations.This improvement is substantial across both lowand high-dimensional systems,yielding efficiency gains of one to three orders of magnitude.For instance,in a 20D dynamical system,the simulation time is reduced from 107.63 s to just 0.093 s,resulting in a 3-order-of-magnitude improvement in simulation efficiency.This advancement broadens the applicability of SINDy for the identification and reconstruction of high-dimensional dynamical systems.展开更多
We analyze COVID-19 surveillance data from Ontario,Canada,using state-space modelling techniques to address key challenges in understanding disease transmission dynamics.The study applies component linear Gaussian sta...We analyze COVID-19 surveillance data from Ontario,Canada,using state-space modelling techniques to address key challenges in understanding disease transmission dynamics.The study applies component linear Gaussian state-space models to capture periodicity,trends,and random fluctuations in case counts.We explore the relationships between COVID-19 cases,hospitalizations,workdays,and wastewater viral loads through dynamic regression models,offering insights into how these factors influence public health outcomes.Our analysis extends to multivariate covariance estimation,utilizing a novel methodology to provide time-varying correlation estimates that account for non-stationary data.Results demonstrate the significance of incorporating environmental covariates,such as wastewater data,in improving model robustness and uncovering the complex interplay between epidemiological factors.This work highlights the limitations of simpler models and emphasizes the advantages of state-space approaches for analyzing dynamic infectious disease data.By illustrating the application of advanced modelling techniques,this study contributes to a deeper understanding of disease transmission and informs public health interventions.展开更多
Quantitative traits whose phenotypic values change with time or other quantitative factor are called dynamic quantitative traits. Genetic analyses of dynamic traits are usually conducted in one of two ways. One is to ...Quantitative traits whose phenotypic values change with time or other quantitative factor are called dynamic quantitative traits. Genetic analyses of dynamic traits are usually conducted in one of two ways. One is to treat phenotypic values collected at different time points as repeated measurements of the same trait, which are analyzed in the framework of multivariate theory. Alternatively, a growth curve may be fit to the phenotypes at multiple time points and inference can be made through the parameters of the growth trajectories. The latter has been used in QTL mapping for developmental traits and resulted in an appearance of the functional mapping strategy. Aiming at the disadvantages of functional mapping strategy, we propose to replace the nonlinear and non-additive model biological meaningful by the orthogonal polynomial or B-Spline model to fit dynamic curves with arbitrary shape and analyze arbitrary complicated data, and the constant residual covariance matrix by the alterable one calculated by using auto-correlation function to deal with discrepancies in measurement schedule of phenotype among progenies. A novel RRM mapping strategy was developed for mapping QTL of dynamic traits, which performs higher detecting efficiency than functional mapping, especially for detection of multiple QTL, has been proved by our simulations and data analysis. Finally, a simplified and effective mapping strategy was further discussed by integrating functional mapping and RRM mapping strategies.展开更多
This paper discusses a physics-informed methodology aimed at reconstructing efficiently the fluid state of a system.Herein,the generation of an accurate reduced order model of twodimensional unsteady flows from data l...This paper discusses a physics-informed methodology aimed at reconstructing efficiently the fluid state of a system.Herein,the generation of an accurate reduced order model of twodimensional unsteady flows from data leverages on sparsity-promoting statistical learning techniques.The cornerstone of the approach is l_(1) regularised regression,resulting in sparselyconnected models where only the important quadratic interactions between modes are retained.The original dynamical behaviour is reproduced at low computational costs,as few quadratic interactions need to be evaluated.The approach has two key features.First,interactions are selected systematically as a solution of a convex optimisation problem and no a priori assumptions on the physics of the flow are required.Second,the presence of a regularisation term improves the predictive performance of the original model,generally affected by noise and poor data quality.Test cases are for two-dimensional lid-driven cavity flows,at three values of the Reynolds number for which the motion is chaotic and energy interactions are scattered across the spectrum.It is found that:(A)the sparsification generates models maintaining the original accuracy level but with a lower number of active coefficients;this becomes more pronounced for increasing Reynolds numbers suggesting that extension of these techniques to real-life flow configurations is possible;(B)sparse models maintain a good temporal stability for predictions.The methodology is ready for more complex applications without modifications of the underlying theory,and the integration into a cyberphysical model is feasible.展开更多
Sand-dust weather has become an international social-environmental issue of common concern, and constitutes a serious threat to human lives and economic development. In order to explore the responses of natural desert...Sand-dust weather has become an international social-environmental issue of common concern, and constitutes a serious threat to human lives and economic development. In order to explore the responses of natural desert sand and dust to the dynamics of water in desertification, we extracted long-term monitoring data related to precipitation, soil water, groundwater, and sand-dust weather. These data originated from the test stations for desertification control in desert areas of the middle reaches of the Heihe River. We used an algorithm of characteristic parameters, correlations, and multiple regression analysis to establish a regression model for the duration of sand-dust weather. The response char-acteristics of the natural desert sand and dust and changes of the water inter-annual and annual variance were also examined. Our results showed： （1） From 2006 to 2014 the frequency, duration, and volatility trends of sand-dust weather obviously increased, but the change amplitudes of precipitation, soil water, and groundwater level grew smaller. （2） In the vegetative growth seasons from March to November, the annual variance rates of the soil moisture content in each of four studied layers of soil samples were similar, and the changes in the frequency and duration of sand-dust weather were similar. （3） Our new regression equation for the duration of sand-dust weather passed the R test, F test, and t test. By this regression model we could predict the duration of sand-dust weather with an accuracy of 42.9%. This study can thus provide technological support and reference data for water resource management and re-search regarding sand-dust weather mechanisms.展开更多
The objective of this work is to model statistically the ultraviolet radiation index (UV Index) to make forecast (extrapolate) and analyze trends. The task is relevant, due to increased UV flux and high rate of cases ...The objective of this work is to model statistically the ultraviolet radiation index (UV Index) to make forecast (extrapolate) and analyze trends. The task is relevant, due to increased UV flux and high rate of cases non-melanoma skin cancer in northeast of Brazil. The methodology utilized an Autoregressive Distributed Lag model (ADL) or Dynamic Linear Regression model. The monthly data of UV index were measured in east coast of the Brazilian Northeast (City of Natal-Rio Grande do Norte). The Total Ozone is single explanatory variable to model and was obtained from the TOMS and OMI/AURA instruments. The Predictive Mean Matching (PMM) method was used to complete the missing data of UV Index. The results mean squared error (MSE) between the observed UV index and interpolated data by model was of 0.36 and for extrapolation was of 0.30 with correlations of 0.90 and 0.91 respectively. The forecast/extrapolation performed by model for a climatological period (2012-2042) indicated a trend of increased UV (Seasonal Man-Kendall test scored τ = 0.955 and p-value 0.001) if the Total Ozone remain on this tendency to reduce. In those circumstances, the model indicated an increase of almost one unit of UV index to year 2042.展开更多
文摘The global pandemic,coronavirus disease 2019(COVID-19),has significantly affected tourism,especially in Spain,as it was among the first countries to be affected by the pandemic and is among the world’s biggest tourist destinations.Stock market values are responding to the evolution of the pandemic,especially in the case of tourist companies.Therefore,being able to quantify this relationship allows us to predict the effect of the pandemic on shares in the tourism sector,thereby improving the response to the crisis by policymakers and investors.Accordingly,a dynamic regression model was developed to predict the behavior of shares in the Spanish tourism sector according to the evolution of the COVID-19 pandemic in the medium term.It has been confirmed that both the number of deaths and cases are good predictors of abnormal stock prices in the tourism sector.
文摘Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.
文摘Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.
基金Supported by the Natural Science Foundation of Jiangsu Province of China(BK20130531)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD[2011]6)Jiangsu Government Scholarship
文摘The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.
基金supported by the National Natural Science Foundation of China under Grant No.61376024 and No.61306024Natural Science Foundation of Guangdong Province under Grant No.S2013040014366Basic Research Programme of Shenzhen No.JCYJ20140417113430642 and JCYJ20140901003939020
文摘Modulating both the clock frequency and supply voltage of the network-on-chip （NoC） during runtime can reduce the power consumption and heat flux, but will lead to the increase of the latency of NoC. It is necessary to find a tradeoff between power consumption and communication latency. So we propose an analytical latency model which can show us the relationship of them. The proposed model to analyze latency is based on the M/G/1 queuing model, which is suitable for dynamic frequency scaling. The experiment results show that the accuracy of this model is more than 90%.
基金supported by the National Basic Research Program of China (973 Program, No. 2006CB504509)the Science Foundation of Shanghai Municipal Commission of Science and Technology (No. 2007DZ19722, 2008DZ19733000, 2009DZ1974303)+1 种基金the Doctoral Fund of Ministry of Education of China (No. 200802461152)the Fudan Young Teacher's Research Foundation (No. 09FQ07)
文摘This article studied on five elements system and set general and strict model expectations combining with Traditional Chinese Medicine Zang-fu organs theory,in which absolute stable state,conditional stable state or transient stability and instability in mathematical models were corresponding to human healthy state,sub-healthy state(pathological plateau phase) and health deterioration state respectively.Model parameters were set up according to the mutual generation and restriction relations among five elements.The dynamic model of Five-Elements System was built,of which impulse responses were corresponding to human response under TCM treatment.Analyses of initial value and excitation response were conducted by numerical simulation and results turned out to meet the requirements of general model expectation:five elements system dynamic model had self-organization function;there existed only one non-global stable point and instability region in the five-dimensional space consisting of variables,in which instability was corresponding to pathological deterioration;system stable region was an unbounded domain and it included the stable sub-regions of special straight line-type,ray-type and line segment-type.Among those ray-types,some contained "Regression Peak" were classed as conditional stable regions while others as absolute ones.The existence of this peak indicates that our body must exceed a "Regression Threshold" when transiting from sub-healthy state(pathological plateau phase) to healthy state through self-regulation mechanism.Impulse excitation can reduce certain threshold and then increase the system health level,which is complied with the operating principle of Five-Elements System and the empirical rule of TCM clinical practice.This model has revealed qualitatively the inherent movement law of Five-Elements System and thus provides a new analysis tool for basic theoretical study on TCM.
文摘In this paper, a logistical regression statistical analysis (LR) is presented for a set of variables used in experimental measurements in reversed field pinch (RFP) machines, commonly known as “slinky mode” (SM), observed to travel around the torus in Madison Symmetric Torus (MST). The LR analysis is used to utilize the modified Sine-Gordon dynamic equation model to predict with high confidence whether the slinky mode will lock or not lock when compared to the experimentally measured motion of the slinky mode. It is observed that under certain conditions, the slinky mode “locks” at or near the intersection of poloidal and/or toroidal gaps in MST. However, locked mode cease to travel around the torus;while unlocked mode keeps traveling without a change in the energy, making it hard to determine an exact set of conditions to predict locking/unlocking behaviour. The significant key model parameters determined by LR analysis are shown to improve the Sine-Gordon model’s ability to determine the locking/unlocking of magnetohydrodyamic (MHD) modes. The LR analysis of measured variables provides high confidence in anticipating locking versus unlocking of slinky mode proven by relational comparisons between simulations and the experimentally measured motion of the slinky mode in MST.
文摘Possible changes in the structure and seasonal variability of the subtropical ridge may lead to changes in the rainfall’s variability modes over Caribbean region. This generates additional difficulties around water resource planning, therefore, obtaining seasonal prediction models that allow these variations to be characterized in detail, it’s a concern, specially for island states. This research proposes the construction of statistical-dynamic models based on PCA regression methods. It is used as predictand the monthly precipitation accumulated, while the predictors (6) are extracted from the ECMWF-SEAS5 ensemble mean forecasts with a lag of one month with respect to the target month. In the construction of the models, two sequential training schemes are evaluated, obtaining that only the shorter preserves the seasonal characteristics of the predictand. The evaluation metrics used, where cell-point and dichotomous methodologies are combined, suggest that the predictors related to sea surface temperatures do not adequately represent the seasonal variability of the predictand, however, others such as the temperature at 850 hPa and the Outgoing Longwave Radiation are represented with a good approximation regardless of the model chosen. In this sense, the models built with the nearest neighbor methodology were the most efficient. Using the individual models with the best results, an ensemble is built that allows improving the individual skill of the models selected as members by correcting the underestimation of precipitation in the dynamic model during the wet season, although problems of overestimation persist for thresholds lower than 50 mm.
基金Project supported by the National Natural Science Foundation of China(Nos.12172291,12472357,and 12232015)the Shaanxi Province Outstanding Youth Fund Project(No.2024JC-JCQN-05)the 111 Project(No.BP0719007)。
文摘Sparse identification of nonlinear dynamics(SINDy)has made significant progress in data-driven dynamics modeling.However,determining appropriate hyperparameters and addressing the time-consuming symbolic regression process remain substantial challenges.This study proposes the adaptive backward stepwise selection of fast SINDy(ABSS-FSINDy),which integrates statistical learning-based estimation and technical advancements to significantly reduce simulation time.This approach not only provides insights into the conditions under which SINDy performs optimally but also highlights potential failure points,particularly in the context of backward stepwise selection(BSS).By decoding predefined features into textual expressions,ABSS-FSINDy significantly reduces the simulation time compared with conventional symbolic regression methods.We validate the proposed method through a series of numerical experiments involving both planar/spatial dynamics and high-dimensional chaotic systems,including Lotka-Volterra,hyperchaotic Rossler,coupled Lorenz,and Lorenz 96 benchmark systems.The experimental results demonstrate that ABSS-FSINDy autonomously determines optimal hyperparameters within the SINDy framework,overcoming the curse of dimensionality in high-dimensional simulations.This improvement is substantial across both lowand high-dimensional systems,yielding efficiency gains of one to three orders of magnitude.For instance,in a 20D dynamical system,the simulation time is reduced from 107.63 s to just 0.093 s,resulting in a 3-order-of-magnitude improvement in simulation efficiency.This advancement broadens the applicability of SINDy for the identification and reconstruction of high-dimensional dynamical systems.
基金funded by the NSERC/Mitacs/Sanofi Alliance program.
文摘We analyze COVID-19 surveillance data from Ontario,Canada,using state-space modelling techniques to address key challenges in understanding disease transmission dynamics.The study applies component linear Gaussian state-space models to capture periodicity,trends,and random fluctuations in case counts.We explore the relationships between COVID-19 cases,hospitalizations,workdays,and wastewater viral loads through dynamic regression models,offering insights into how these factors influence public health outcomes.Our analysis extends to multivariate covariance estimation,utilizing a novel methodology to provide time-varying correlation estimates that account for non-stationary data.Results demonstrate the significance of incorporating environmental covariates,such as wastewater data,in improving model robustness and uncovering the complex interplay between epidemiological factors.This work highlights the limitations of simpler models and emphasizes the advantages of state-space approaches for analyzing dynamic infectious disease data.By illustrating the application of advanced modelling techniques,this study contributes to a deeper understanding of disease transmission and informs public health interventions.
基金Item supported by national natural sciencfoundation (No.30471236)
文摘Quantitative traits whose phenotypic values change with time or other quantitative factor are called dynamic quantitative traits. Genetic analyses of dynamic traits are usually conducted in one of two ways. One is to treat phenotypic values collected at different time points as repeated measurements of the same trait, which are analyzed in the framework of multivariate theory. Alternatively, a growth curve may be fit to the phenotypes at multiple time points and inference can be made through the parameters of the growth trajectories. The latter has been used in QTL mapping for developmental traits and resulted in an appearance of the functional mapping strategy. Aiming at the disadvantages of functional mapping strategy, we propose to replace the nonlinear and non-additive model biological meaningful by the orthogonal polynomial or B-Spline model to fit dynamic curves with arbitrary shape and analyze arbitrary complicated data, and the constant residual covariance matrix by the alterable one calculated by using auto-correlation function to deal with discrepancies in measurement schedule of phenotype among progenies. A novel RRM mapping strategy was developed for mapping QTL of dynamic traits, which performs higher detecting efficiency than functional mapping, especially for detection of multiple QTL, has been proved by our simulations and data analysis. Finally, a simplified and effective mapping strategy was further discussed by integrating functional mapping and RRM mapping strategies.
文摘This paper discusses a physics-informed methodology aimed at reconstructing efficiently the fluid state of a system.Herein,the generation of an accurate reduced order model of twodimensional unsteady flows from data leverages on sparsity-promoting statistical learning techniques.The cornerstone of the approach is l_(1) regularised regression,resulting in sparselyconnected models where only the important quadratic interactions between modes are retained.The original dynamical behaviour is reproduced at low computational costs,as few quadratic interactions need to be evaluated.The approach has two key features.First,interactions are selected systematically as a solution of a convex optimisation problem and no a priori assumptions on the physics of the flow are required.Second,the presence of a regularisation term improves the predictive performance of the original model,generally affected by noise and poor data quality.Test cases are for two-dimensional lid-driven cavity flows,at three values of the Reynolds number for which the motion is chaotic and energy interactions are scattered across the spectrum.It is found that:(A)the sparsification generates models maintaining the original accuracy level but with a lower number of active coefficients;this becomes more pronounced for increasing Reynolds numbers suggesting that extension of these techniques to real-life flow configurations is possible;(B)sparse models maintain a good temporal stability for predictions.The methodology is ready for more complex applications without modifications of the underlying theory,and the integration into a cyberphysical model is feasible.
基金supported by the Science and Technology Innovation Service Platform of Qilian mountains in Gansu Province (No. 144JTCG254)the Innovation Groups of Basic Research of Gansu Province (No. 145RJIG337)the National Natural Science Foundation of China (No. 41461004)
文摘Sand-dust weather has become an international social-environmental issue of common concern, and constitutes a serious threat to human lives and economic development. In order to explore the responses of natural desert sand and dust to the dynamics of water in desertification, we extracted long-term monitoring data related to precipitation, soil water, groundwater, and sand-dust weather. These data originated from the test stations for desertification control in desert areas of the middle reaches of the Heihe River. We used an algorithm of characteristic parameters, correlations, and multiple regression analysis to establish a regression model for the duration of sand-dust weather. The response char-acteristics of the natural desert sand and dust and changes of the water inter-annual and annual variance were also examined. Our results showed： （1） From 2006 to 2014 the frequency, duration, and volatility trends of sand-dust weather obviously increased, but the change amplitudes of precipitation, soil water, and groundwater level grew smaller. （2） In the vegetative growth seasons from March to November, the annual variance rates of the soil moisture content in each of four studied layers of soil samples were similar, and the changes in the frequency and duration of sand-dust weather were similar. （3） Our new regression equation for the duration of sand-dust weather passed the R test, F test, and t test. By this regression model we could predict the duration of sand-dust weather with an accuracy of 42.9%. This study can thus provide technological support and reference data for water resource management and re-search regarding sand-dust weather mechanisms.
文摘The objective of this work is to model statistically the ultraviolet radiation index (UV Index) to make forecast (extrapolate) and analyze trends. The task is relevant, due to increased UV flux and high rate of cases non-melanoma skin cancer in northeast of Brazil. The methodology utilized an Autoregressive Distributed Lag model (ADL) or Dynamic Linear Regression model. The monthly data of UV index were measured in east coast of the Brazilian Northeast (City of Natal-Rio Grande do Norte). The Total Ozone is single explanatory variable to model and was obtained from the TOMS and OMI/AURA instruments. The Predictive Mean Matching (PMM) method was used to complete the missing data of UV Index. The results mean squared error (MSE) between the observed UV index and interpolated data by model was of 0.36 and for extrapolation was of 0.30 with correlations of 0.90 and 0.91 respectively. The forecast/extrapolation performed by model for a climatological period (2012-2042) indicated a trend of increased UV (Seasonal Man-Kendall test scored τ = 0.955 and p-value 0.001) if the Total Ozone remain on this tendency to reduce. In those circumstances, the model indicated an increase of almost one unit of UV index to year 2042.
