Sample records for variable modeling approach

  1. Eutrophication Modeling Using Variable Chlorophyll Approach

    International Nuclear Information System (INIS)

    Abdolabadi, H.; Sarang, A.; Ardestani, M.; Mahjoobi, E.


    In this study, eutrophication was investigated in Lake Ontario to identify the interactions among effective drivers. The complexity of such phenomenon was modeled using a system dynamics approach based on a consideration of constant and variable stoichiometric ratios. The system dynamics approach is a powerful tool for developing object-oriented models to simulate complex phenomena that involve feedback effects. Utilizing stoichiometric ratios is a method for converting the concentrations of state variables. During the physical segmentation of the model, Lake Ontario was divided into two layers, i.e., the epilimnion and hypolimnion, and differential equations were developed for each layer. The model structure included 16 state variables related to phytoplankton, herbivorous zooplankton, carnivorous zooplankton, ammonium, nitrate, dissolved phosphorus, and particulate and dissolved carbon in the epilimnion and hypolimnion during a time horizon of one year. The results of several tests to verify the model, close to 1 Nash-Sutcliff coefficient (0.98), the data correlation coefficient (0.98), and lower standard errors (0.96), have indicated well-suited model’s efficiency. The results revealed that there were significant differences in the concentrations of the state variables in constant and variable stoichiometry simulations. Consequently, the consideration of variable stoichiometric ratios in algae and nutrient concentration simulations may be applied in future modeling studies to enhance the accuracy of the results and reduce the likelihood of inefficient control policies.

  2. Bayesian approach to errors-in-variables in regression models (United States)

    Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad


    In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.

  3. Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations. (United States)

    Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O


    Parameter variation in pharmacometric analysis studies can be characterized as within subject parameter variability (WSV) in pharmacometric models. WSV has previously been successfully modeled using inter-occasion variability (IOV), but also stochastic differential equations (SDEs). In this study, two approaches, dynamic inter-occasion variability (dIOV) and adapted stochastic differential equations, were proposed to investigate WSV in pharmacometric count data analysis. These approaches were applied to published count models for seizure counts and Likert pain scores. Both approaches improved the model fits significantly. In addition, stochastic simulation and estimation were used to explore further the capability of the two approaches to diagnose and improve models where existing WSV is not recognized. The results of simulations confirmed the gain in introducing WSV as dIOV and SDEs when parameters vary randomly over time. Further, the approaches were also informative as diagnostics of model misspecification, when parameters changed systematically over time but this was not recognized in the structural model. The proposed approaches in this study offer strategies to characterize WSV and are not restricted to count data.

  4. The Matrix model, a driven state variables approach to non-equilibrium thermodynamics

    NARCIS (Netherlands)

    Jongschaap, R.J.J.


    One of the new approaches in non-equilibrium thermodynamics is the so-called matrix model of Jongschaap. In this paper some features of this model are discussed. We indicate the differences with the more common approach based upon internal variables and the more sophisticated Hamiltonian and GENERIC

  5. Optimal speech motor control and token-to-token variability: a Bayesian modeling approach. (United States)

    Patri, Jean-François; Diard, Julien; Perrier, Pascal


    The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the central nervous system selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

  6. Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables (United States)

    Pek, Jolynn; Losardo, Diane; Bauer, Daniel J.


    Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…

  7. A new approach for modelling variability in residential construction projects

    Directory of Open Access Journals (Sweden)

    Mehrdad Arashpour


    Full Text Available The construction industry is plagued by long cycle times caused by variability in the supply chain. Variations or undesirable situations are the result of factors such as non-standard practices, work site accidents, inclement weather conditions and faults in design. This paper uses a new approach for modelling variability in construction by linking relative variability indicators to processes. Mass homebuilding sector was chosen as the scope of the analysis because data is readily available. Numerous simulation experiments were designed by varying size of capacity buffers in front of trade contractors, availability of trade contractors, and level of variability in homebuilding processes. The measurements were shown to lead to an accurate determination of relationships between these factors and production parameters. The variability indicator was found to dramatically affect the tangible performance measures such as home completion rates. This study provides for future analysis of the production homebuilding sector, which may lead to improvements in performance and a faster product delivery to homebuyers.

  8. A new approach for modelling variability in residential construction projects

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    Mehrdad Arashpour


    Full Text Available The construction industry is plagued by long cycle times caused by variability in the supply chain. Variations or undesirable situations are the result of factors such as non-standard practices, work site accidents, inclement weather conditions and faults in design. This paper uses a new approach for modelling variability in construction by linking relative variability indicators to processes. Mass homebuilding sector was chosen as the scope of the analysis because data is readily available. Numerous simulation experiments were designed by varying size of capacity buffers in front of trade contractors, availability of trade contractors, and level of variability in homebuilding processes. The measurements were shown to lead to an accurate determination of relationships between these factors and production parameters. The variability indicator was found to dramatically affect the tangible performance measures such as home completion rates. This study provides for future analysis of the production homebuilding sector, which may lead to improvements in performance and a faster product delivery to homebuyers. 

  9. A new approach to hazardous materials transportation risk analysis: decision modeling to identify critical variables. (United States)

    Clark, Renee M; Besterfield-Sacre, Mary E


    We take a novel approach to analyzing hazardous materials transportation risk in this research. Previous studies analyzed this risk from an operations research (OR) or quantitative risk assessment (QRA) perspective by minimizing or calculating risk along a transport route. Further, even though the majority of incidents occur when containers are unloaded, the research has not focused on transportation-related activities, including container loading and unloading. In this work, we developed a decision model of a hazardous materials release during unloading using actual data and an exploratory data modeling approach. Previous studies have had a theoretical perspective in terms of identifying and advancing the key variables related to this risk, and there has not been a focus on probability and statistics-based approaches for doing this. Our decision model empirically identifies the critical variables using an exploratory methodology for a large, highly categorical database involving latent class analysis (LCA), loglinear modeling, and Bayesian networking. Our model identified the most influential variables and countermeasures for two consequences of a hazmat incident, dollar loss and release quantity, and is one of the first models to do this. The most influential variables were found to be related to the failure of the container. In addition to analyzing hazmat risk, our methodology can be used to develop data-driven models for strategic decision making in other domains involving risk.

  10. Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

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    Dirk Temme


    Full Text Available Integrated choice and latent variable (ICLV models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

  11. State-Space Modeling and Performance Analysis of Variable-Speed Wind Turbine Based on a Model Predictive Control Approach

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    H. Bassi


    Full Text Available Advancements in wind energy technologies have led wind turbines from fixed speed to variable speed operation. This paper introduces an innovative version of a variable-speed wind turbine based on a model predictive control (MPC approach. The proposed approach provides maximum power point tracking (MPPT, whose main objective is to capture the maximum wind energy in spite of the variable nature of the wind’s speed. The proposed MPC approach also reduces the constraints of the two main functional parts of the wind turbine: the full load and partial load segments. The pitch angle for full load and the rotating force for the partial load have been fixed concurrently in order to balance power generation as well as to reduce the operations of the pitch angle. A mathematical analysis of the proposed system using state-space approach is introduced. The simulation results using MATLAB/SIMULINK show that the performance of the wind turbine with the MPC approach is improved compared to the traditional PID controller in both low and high wind speeds.

  12. How to get rid of W: a latent variables approach to modelling spatially lagged variables

    NARCIS (Netherlands)

    Folmer, H.; Oud, J.


    In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

  13. How to get rid of W : a latent variables approach to modelling spatially lagged variables

    NARCIS (Netherlands)

    Folmer, Henk; Oud, Johan


    In this paper we propose a structural equation model (SEM) with latent variables to model spatial dependence. Rather than using the spatial weights matrix W, we propose to use latent variables to represent spatial dependence and spillover effects, of which the observed spatially lagged variables are

  14. Statistical Modeling Approach to Quantitative Analysis of Interobserver Variability in Breast Contouring

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Jinzhong, E-mail: [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Woodward, Wendy A.; Reed, Valerie K.; Strom, Eric A.; Perkins, George H.; Tereffe, Welela; Buchholz, Thomas A. [Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Zhang, Lifei; Balter, Peter; Court, Laurence E. [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Li, X. Allen [Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin (United States); Dong, Lei [Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Scripps Proton Therapy Center, San Diego, California (United States)


    Purpose: To develop a new approach for interobserver variability analysis. Methods and Materials: Eight radiation oncologists specializing in breast cancer radiation therapy delineated a patient's left breast “from scratch” and from a template that was generated using deformable image registration. Three of the radiation oncologists had previously received training in Radiation Therapy Oncology Group consensus contouring for breast cancer atlas. The simultaneous truth and performance level estimation algorithm was applied to the 8 contours delineated “from scratch” to produce a group consensus contour. Individual Jaccard scores were fitted to a beta distribution model. We also applied this analysis to 2 or more patients, which were contoured by 9 breast radiation oncologists from 8 institutions. Results: The beta distribution model had a mean of 86.2%, standard deviation (SD) of ±5.9%, a skewness of −0.7, and excess kurtosis of 0.55, exemplifying broad interobserver variability. The 3 RTOG-trained physicians had higher agreement scores than average, indicating that their contours were close to the group consensus contour. One physician had high sensitivity but lower specificity than the others, which implies that this physician tended to contour a structure larger than those of the others. Two other physicians had low sensitivity but specificity similar to the others, which implies that they tended to contour a structure smaller than the others. With this information, they could adjust their contouring practice to be more consistent with others if desired. When contouring from the template, the beta distribution model had a mean of 92.3%, SD ± 3.4%, skewness of −0.79, and excess kurtosis of 0.83, which indicated a much better consistency among individual contours. Similar results were obtained for the analysis of 2 additional patients. Conclusions: The proposed statistical approach was able to measure interobserver variability quantitatively

  15. Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model

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    Ge-Jin Chu


    Full Text Available Variable selection is an important issue in regression and a number of variable selection methods have been proposed involving nonconvex penalty functions. In this paper, we investigate a novel harmonic regularization method, which can approximate nonconvex Lq  (1/2model using microarray gene expression data. The harmonic regularization method can be efficiently solved using our proposed direct path seeking approach, which can produce solutions that closely approximate those for the convex loss function and the nonconvex regularization. Simulation results based on the artificial datasets and four real microarray gene expression datasets, such as real diffuse large B-cell lymphoma (DCBCL, the lung cancer, and the AML datasets, show that the harmonic regularization method can be more accurate for variable selection than existing Lasso series methods.

  16. Variable selection in Logistic regression model with genetic algorithm. (United States)

    Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi


    Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.

  17. A Novel Approach to model EPIC variable background (United States)

    Marelli, M.; De Luca, A.; Salvetti, D.; Belfiore, A.


    One of the main aim of the EXTraS (Exploring the X-ray Transient and variable Sky) project is to characterise the variability of serendipitous XMM-Newton sources within each single observation. Unfortunately, 164 Ms out of the 774 Ms of cumulative exposure considered (21%) are badly affected by soft proton flares, hampering any classical analysis of field sources. De facto, the latest releases of the 3XMM catalog, as well as most of the analysis in literature, simply exclude these 'high background' periods from analysis. We implemented a novel SAS-indipendent approach to produce background-subtracted light curves, which allows to treat the case of very faint sources and very bright proton flares. EXTraS light curves of 3XMM-DR5 sources will be soon released to the community, together with new tools we are developing.

  18. Spatial variability and parametric uncertainty in performance assessment models

    International Nuclear Information System (INIS)

    Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo


    The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)

  19. A Nonlinear Mixed Effects Approach for Modeling the Cell-To-Cell Variability of Mig1 Dynamics in Yeast.

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    Joachim Almquist

    Full Text Available The last decade has seen a rapid development of experimental techniques that allow data collection from individual cells. These techniques have enabled the discovery and characterization of variability within a population of genetically identical cells. Nonlinear mixed effects (NLME modeling is an established framework for studying variability between individuals in a population, frequently used in pharmacokinetics and pharmacodynamics, but its potential for studies of cell-to-cell variability in molecular cell biology is yet to be exploited. Here we take advantage of this novel application of NLME modeling to study cell-to-cell variability in the dynamic behavior of the yeast transcription repressor Mig1. In particular, we investigate a recently discovered phenomenon where Mig1 during a short and transient period exits the nucleus when cells experience a shift from high to intermediate levels of extracellular glucose. A phenomenological model based on ordinary differential equations describing the transient dynamics of nuclear Mig1 is introduced, and according to the NLME methodology the parameters of this model are in turn modeled by a multivariate probability distribution. Using time-lapse microscopy data from nearly 200 cells, we estimate this parameter distribution according to the approach of maximizing the population likelihood. Based on the estimated distribution, parameter values for individual cells are furthermore characterized and the resulting Mig1 dynamics are compared to the single cell times-series data. The proposed NLME framework is also compared to the intuitive but limited standard two-stage (STS approach. We demonstrate that the latter may overestimate variabilities by up to almost five fold. Finally, Monte Carlo simulations of the inferred population model are used to predict the distribution of key characteristics of the Mig1 transient response. We find that with decreasing levels of post-shift glucose, the transient

  20. A state-and-transition simulation modeling approach for estimating the historical range of variability

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    Kori Blankenship


    Full Text Available Reference ecological conditions offer important context for land managers as they assess the condition of their landscapes and provide benchmarks for desired future conditions. State-and-transition simulation models (STSMs are commonly used to estimate reference conditions that can be used to evaluate current ecosystem conditions and to guide land management decisions and activities. The LANDFIRE program created more than 1,000 STSMs and used them to assess departure from a mean reference value for ecosystems in the United States. While the mean provides a useful benchmark, land managers and researchers are often interested in the range of variability around the mean. This range, frequently referred to as the historical range of variability (HRV, offers model users improved understanding of ecosystem function, more information with which to evaluate ecosystem change and potentially greater flexibility in management options. We developed a method for using LANDFIRE STSMs to estimate the HRV around the mean reference condition for each model state in ecosystems by varying the fire probabilities. The approach is flexible and can be adapted for use in a variety of ecosystems. HRV analysis can be combined with other information to help guide complex land management decisions.

  1. Study The role of latent variables in lost working days by Structural Equation Modeling Approach

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    Meysam Heydari


    Full Text Available Background: Based on estimations, each year about 250 million work-related injuries and many temporary or permanent disabilities occur which most are preventable. Oil and Gas industries are among industries with high incidence of injuries in the world. The aim of this study has investigated  the role and effect of different risk management variables on lost working days (LWD in the seismic projects. Methods: This study was a retrospective, cross-sectional and systematic analysis, which was carried out on occupational accidents between 2008-2015(an 8 years period in different seismic projects for oilfield exploration at Dana Energy (Iranian Seismic Company. The preliminary sample size of the study were 487accidents. A systems analysis approach were applied by using root case analysis (RCA and structural equation modeling (SEM. Tools for the data analysis were included, SPSS23 and AMOS23  software. Results: The mean of lost working days (LWD, was calculated 49.57, the final model of structural equation modeling showed that latent variables of, safety and health training factor(-0.33, risk assessment factor(-0.55 and risk control factor (-0.61 as direct causes significantly affected of lost working days (LWD in the seismic industries (p< 0.05. Conclusion: The finding of present study revealed that combination of variables affected in lost working days (LWD. Therefore,the role of these variables in accidents should be investigated and suitable programs should be considered for them.

  2. A spray flamelet/progress variable approach combined with a transported joint PDF model for turbulent spray flames (United States)

    Hu, Yong; Olguin, Hernan; Gutheil, Eva


    A spray flamelet/progress variable approach is developed for use in spray combustion with partly pre-vaporised liquid fuel, where a laminar spray flamelet library accounts for evaporation within the laminar flame structures. For this purpose, the standard spray flamelet formulation for pure evaporating liquid fuel and oxidiser is extended by a chemical reaction progress variable in both the turbulent spray flame model and the laminar spray flame structures, in order to account for the effect of pre-vaporised liquid fuel for instance through use of a pilot flame. This new approach is combined with a transported joint probability density function (PDF) method for the simulation of a turbulent piloted ethanol/air spray flame, and the extension requires the formulation of a joint three-variate PDF depending on the gas phase mixture fraction, the chemical reaction progress variable, and gas enthalpy. The molecular mixing is modelled with the extended interaction-by-exchange-with-the-mean (IEM) model, where source terms account for spray evaporation and heat exchange due to evaporation as well as the chemical reaction rate for the chemical reaction progress variable. This is the first formulation using a spray flamelet model considering both evaporation and partly pre-vaporised liquid fuel within the laminar spray flamelets. Results with this new formulation show good agreement with the experimental data provided by A.R. Masri, Sydney, Australia. The analysis of the Lagrangian statistics of the gas temperature and the OH mass fraction indicates that partially premixed combustion prevails near the nozzle exit of the spray, whereas further downstream, the non-premixed flame is promoted towards the inner rich-side of the spray jet since the pilot flame heats up the premixed inner spray zone. In summary, the simulation with the new formulation considering the reaction progress variable shows good performance, greatly improving the standard formulation, and it provides new

  3. A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling. (United States)

    Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao


    In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website:

  4. Evaluating two model reduction approaches for large scale hedonic models sensitive to omitted variables and multicollinearity

    DEFF Research Database (Denmark)

    Panduro, Toke Emil; Thorsen, Bo Jellesmark


    Hedonic models in environmental valuation studies have grown in terms of number of transactions and number of explanatory variables. We focus on the practical challenge of model reduction, when aiming for reliable parsimonious models, sensitive to omitted variable bias and multicollinearity. We...

  5. Straight line fitting and predictions: On a marginal likelihood approach to linear regression and errors-in-variables models (United States)

    Christiansen, Bo


    Linear regression methods are without doubt the most used approaches to describe and predict data in the physical sciences. They are often good first order approximations and they are in general easier to apply and interpret than more advanced methods. However, even the properties of univariate regression can lead to debate over the appropriateness of various models as witnessed by the recent discussion about climate reconstruction methods. Before linear regression is applied important choices have to be made regarding the origins of the noise terms and regarding which of the two variables under consideration that should be treated as the independent variable. These decisions are often not easy to make but they may have a considerable impact on the results. We seek to give a unified probabilistic - Bayesian with flat priors - treatment of univariate linear regression and prediction by taking, as starting point, the general errors-in-variables model (Christiansen, J. Clim., 27, 2014-2031, 2014). Other versions of linear regression can be obtained as limits of this model. We derive the likelihood of the model parameters and predictands of the general errors-in-variables model by marginalizing over the nuisance parameters. The resulting likelihood is relatively simple and easy to analyze and calculate. The well known unidentifiability of the errors-in-variables model is manifested as the absence of a well-defined maximum in the likelihood. However, this does not mean that probabilistic inference can not be made; the marginal likelihoods of model parameters and the predictands have, in general, well-defined maxima. We also include a probabilistic version of classical calibration and show how it is related to the errors-in-variables model. The results are illustrated by an example from the coupling between the lower stratosphere and the troposphere in the Northern Hemisphere winter.

  6. Control approach development for variable recruitment artificial muscles (United States)

    Jenkins, Tyler E.; Chapman, Edward M.; Bryant, Matthew


    This study characterizes hybrid control approaches for the variable recruitment of fluidic artificial muscles with double acting (antagonistic) actuation. Fluidic artificial muscle actuators have been explored by researchers due to their natural compliance, high force-to-weight ratio, and low cost of fabrication. Previous studies have attempted to improve system efficiency of the actuators through variable recruitment, i.e. using discrete changes in the number of active actuators. While current variable recruitment research utilizes manual valve switching, this paper details the current development of an online variable recruitment control scheme. By continuously controlling applied pressure and discretely controlling the number of active actuators, operation in the lowest possible recruitment state is ensured and working fluid consumption is minimized. Results provide insight into switching control scheme effects on working fluids, fabrication material choices, actuator modeling, and controller development decisions.

  7. Predictive and Descriptive CoMFA Models: The Effect of Variable Selection. (United States)

    Sepehri, Bakhtyar; Omidikia, Nematollah; Kompany-Zareh, Mohsen; Ghavami, Raouf


    Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields. Copyright© Bentham Science Publishers; For any queries, please email at

  8. Inverse Ising problem in continuous time: A latent variable approach (United States)

    Donner, Christian; Opper, Manfred


    We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

  9. Iwamoto-Harada coalescence/pickup model for cluster emission: state density approach including angular momentum variables

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    Běták Emil


    Full Text Available For low-energy nuclear reactions well above the resonance region, but still below the pion threshold, statistical pre-equilibrium models (e.g., the exciton and the hybrid ones are a frequent tool for analysis of energy spectra and the cross sections of cluster emission. For α’s, two essentially distinct approaches are popular, namely the preformed one and the different versions of coalescence approaches, whereas only the latter group of models can be used for other types of cluster ejectiles. The original Iwamoto-Harada model of pre-equilibrium cluster emission was formulated using the overlap of the cluster and its constituent nucleons in momentum space. Transforming it into level or state densities is not a straigthforward task; however, physically the same model was presented at a conference on reaction models five years earlier. At that time, only the densities without spin were used. The introduction of spin variables into the exciton model enabled detailed calculation of the γ emission and its competition with nucleon channels, and – at the same time – it stimulated further developments of the model. However – to the best of our knowledge – no spin formulation has been presented for cluster emission till recently, when the first attempts have been reported, but restricted to the first emission only. We have updated this effort now and we are able to handle (using the same simplifications as in our previous work pre-equilibrium cluster emission with spin including all nuclei in the reaction chain.

  10. Gene variants associated with antisocial behaviour: a latent variable approach. (United States)

    Bentley, Mary Jane; Lin, Haiqun; Fernandez, Thomas V; Lee, Maria; Yrigollen, Carolyn M; Pakstis, Andrew J; Katsovich, Liliya; Olds, David L; Grigorenko, Elena L; Leckman, James F


    The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a 15-year follow-up of a randomized trial of a prenatal and infancy nurse-home visitation programme in Elmira, New York. We then investigated, via a novel latent variable approach, 450 informative genetic polymorphisms in 71 genes previously associated with antisocial behaviour, drug use, affiliative behaviours and stress response in 241 consenting individuals for whom DNA was available. Haplotype and Pathway analyses were also performed. Eight single-nucleotide polymorphisms (SNPs) from eight genes contributed to the latent genetic variable that in turn accounted for 16.0% of the variance within the latent antisocial phenotype. The number of risk alleles was linearly related to the latent antisocial variable scores. Haplotypes that included the putative risk alleles for all eight genes were also associated with higher latent antisocial variable scores. In addition, 33 SNPs from 63 of the remaining genes were also significant when added to the final model. Many of these genes interact on a molecular level, forming molecular networks. The results support a role for genes related to dopamine, norepinephrine, serotonin, glutamate, opioid and cholinergic signalling as well as stress response pathways in mediating susceptibility to antisocial behaviour. This preliminary study supports use of relevant behavioural indicators and latent variable approaches to study the potential 'co-action' of gene variants associated with antisocial behaviour. It also underscores the cumulative relevance of common genetic variants for understanding the aetiology of complex behaviour. If replicated in future studies, this approach may allow the identification of a

  11. A Variable Flow Modelling Approach To Military End Strength Planning (United States)


    function. The MLRPS is more complex than the variable flow model as it has to cater for a force structure that is much larger than just the MT branch...essential positions in a Ship’s complement, or by the biggest current deficit in forecast end strength. The model can be adjusted to cater for any of unlikely that the RAN will be able to cater for such an increase in hires, so this scenario is not likely to solve their problem. Each transition

  12. Multiscale thermohydrologic model: addressing variability and uncertainty at Yucca Mountain

    International Nuclear Information System (INIS)

    Buscheck, T; Rosenberg, N D; Gansemer, J D; Sun, Y


    Performance assessment and design evaluation require a modeling tool that simultaneously accounts for processes occurring at a scale of a few tens of centimeters around individual waste packages and emplacement drifts, and also on behavior at the scale of the mountain. Many processes and features must be considered, including non-isothermal, multiphase-flow in rock of variable saturation and thermal radiation in open cavities. Also, given the nature of the fractured rock at Yucca Mountain, a dual-permeability approach is needed to represent permeability. A monolithic numerical model with all these features requires too large a computational cost to be an effective simulation tool, one that is used to examine sensitivity to key model assumptions and parameters. We have developed a multi-scale modeling approach that effectively simulates 3D discrete-heat-source, mountain-scale thermohydrologic behavior at Yucca Mountain and captures the natural variability of the site consistent with what we know from site characterization and waste-package-to-waste-package variability in heat output. We describe this approach and present results examining the role of infiltration flux, the most important natural-system parameter with respect to how thermohydrologic behavior influences the performance of the repository

  13. A regression modeling approach for studying carbonate system variability in the northern Gulf of Alaska (United States)

    Evans, Wiley; Mathis, Jeremy T.; Winsor, Peter; Statscewich, Hank; Whitledge, Terry E.


    northern Gulf of Alaska (GOA) shelf experiences carbonate system variability on seasonal and annual time scales, but little information exists to resolve higher frequency variability in this region. To resolve this variability using platforms-of-opportunity, we present multiple linear regression (MLR) models constructed from hydrographic data collected along the Northeast Pacific Global Ocean Ecosystems Dynamics (GLOBEC) Seward Line. The empirical algorithms predict dissolved inorganic carbon (DIC) and total alkalinity (TA) using observations of nitrate (NO3-), temperature, salinity and pressure from the surface to 500 m, with R2s > 0.97 and RMSE values of 11 µmol kg-1 for DIC and 9 µmol kg-1 for TA. We applied these relationships to high-resolution NO3- data sets collected during a novel 20 h glider flight and a GLOBEC mesoscale SeaSoar survey. Results from the glider flight demonstrated time/space along-isopycnal variability of aragonite saturations (Ωarag) associated with a dicothermal layer (a cold near-surface layer found in high latitude oceans) that rivaled changes seen vertically through the thermocline. The SeaSoar survey captured the uplift to aragonite saturation horizon (depth where Ωarag = 1) shoaled to a previously unseen depth in the northern GOA. This work is similar to recent studies aimed at predicting the carbonate system in continental margin settings, albeit demonstrates that a NO3--based approach can be applied to high-latitude data collected from platforms capable of high-frequency measurements.

  14. A variable resolution right TIN approach for gridded oceanographic data (United States)

    Marks, David; Elmore, Paul; Blain, Cheryl Ann; Bourgeois, Brian; Petry, Frederick; Ferrini, Vicki


    Many oceanographic applications require multi resolution representation of gridded data such as for bathymetric data. Although triangular irregular networks (TINs) allow for variable resolution, they do not provide a gridded structure. Right TINs (RTINs) are compatible with a gridded structure. We explored the use of two approaches for RTINs termed top-down and bottom-up implementations. We illustrate why the latter is most appropriate for gridded data and describe for this technique how the data can be thinned. While both the top-down and bottom-up approaches accurately preserve the surface morphology of any given region, the top-down method of vertex placement can fail to match the actual vertex locations of the underlying grid in many instances, resulting in obscured topology/bathymetry. Finally we describe the use of the bottom-up approach and data thinning in two applications. The first is to provide thinned, variable resolution bathymetry data for tests of storm surge and inundation modeling, in particular hurricane Katrina. Secondly we consider the use of the approach for an application to an oceanographic data grid of 3-D ocean temperature.

  15. Coupled variable selection for regression modeling of complex treatment patterns in a clinical cancer registry. (United States)

    Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H


    For determining a manageable set of covariates potentially influential with respect to a time-to-event endpoint, Cox proportional hazards models can be combined with variable selection techniques, such as stepwise forward selection or backward elimination based on p-values, or regularized regression techniques such as component-wise boosting. Cox regression models have also been adapted for dealing with more complex event patterns, for example, for competing risks settings with separate, cause-specific hazard models for each event type, or for determining the prognostic effect pattern of a variable over different landmark times, with one conditional survival model for each landmark. Motivated by a clinical cancer registry application, where complex event patterns have to be dealt with and variable selection is needed at the same time, we propose a general approach for linking variable selection between several Cox models. Specifically, we combine score statistics for each covariate across models by Fisher's method as a basis for variable selection. This principle is implemented for a stepwise forward selection approach as well as for a regularized regression technique. In an application to data from hepatocellular carcinoma patients, the coupled stepwise approach is seen to facilitate joint interpretation of the different cause-specific Cox models. In conditional survival models at landmark times, which address updates of prediction as time progresses and both treatment and other potential explanatory variables may change, the coupled regularized regression approach identifies potentially important, stably selected covariates together with their effect time pattern, despite having only a small number of events. These results highlight the promise of the proposed approach for coupling variable selection between Cox models, which is particularly relevant for modeling for clinical cancer registries with their complex event patterns. Copyright © 2014 John Wiley & Sons

  16. Evaporator modeling - A hybrid approach

    International Nuclear Information System (INIS)

    Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun


    In this paper, a hybrid modeling approach is proposed to model two-phase flow evaporators. The main procedures for hybrid modeling includes: (1) Based on the energy and material balance, and thermodynamic principles to formulate the process fundamental governing equations; (2) Select input/output (I/O) variables responsible to the system performance which can be measured and controlled; (3) Represent those variables existing in the original equations but are not measurable as simple functions of selected I/Os or constants; (4) Obtaining a single equation which can correlate system inputs and outputs; and (5) Identify unknown parameters by linear or nonlinear least-squares methods. The method takes advantages of both physical and empirical modeling approaches and can accurately predict performance in wide operating range and in real-time, which can significantly reduce the computational burden and increase the prediction accuracy. The model is verified with the experimental data taken from a testing system. The testing results show that the proposed model can predict accurately the performance of the real-time operating evaporator with the maximum error of ±8%. The developed models will have wide applications in operational optimization, performance assessment, fault detection and diagnosis

  17. Estimating variability in functional images using a synthetic resampling approach

    International Nuclear Information System (INIS)

    Maitra, R.; O'Sullivan, F.


    Functional imaging of biologic parameters like in vivo tissue metabolism is made possible by Positron Emission Tomography (PET). Many techniques, such as mixture analysis, have been suggested for extracting such images from dynamic sequences of reconstructed PET scans. Methods for assessing the variability in these functional images are of scientific interest. The nonlinearity of the methods used in the mixture analysis approach makes analytic formulae for estimating variability intractable. The usual resampling approach is infeasible because of the prohibitive computational effort in simulating a number of sinogram. datasets, applying image reconstruction, and generating parametric images for each replication. Here we introduce an approach that approximates the distribution of the reconstructed PET images by a Gaussian random field and generates synthetic realizations in the imaging domain. This eliminates the reconstruction steps in generating each simulated functional image and is therefore practical. Results of experiments done to evaluate the approach on a model one-dimensional problem are very encouraging. Post-processing of the estimated variances is seen to improve the accuracy of the estimation method. Mixture analysis is used to estimate functional images; however, the suggested approach is general enough to extend to other parametric imaging methods

  18. Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review (United States)

    Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed


    Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.

  19. Variable importance in latent variable regression models

    NARCIS (Netherlands)

    Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.


    The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

  20. Uncertainty and variability in computational and mathematical models of cardiac physiology. (United States)

    Mirams, Gary R; Pathmanathan, Pras; Gray, Richard A; Challenor, Peter; Clayton, Richard H


    Mathematical and computational models of cardiac physiology have been an integral component of cardiac electrophysiology since its inception, and are collectively known as the Cardiac Physiome. We identify and classify the numerous sources of variability and uncertainty in model formulation, parameters and other inputs that arise from both natural variation in experimental data and lack of knowledge. The impact of uncertainty on the outputs of Cardiac Physiome models is not well understood, and this limits their utility as clinical tools. We argue that incorporating variability and uncertainty should be a high priority for the future of the Cardiac Physiome. We suggest investigating the adoption of approaches developed in other areas of science and engineering while recognising unique challenges for the Cardiac Physiome; it is likely that novel methods will be necessary that require engagement with the mathematics and statistics community. The Cardiac Physiome effort is one of the most mature and successful applications of mathematical and computational modelling for describing and advancing the understanding of physiology. After five decades of development, physiological cardiac models are poised to realise the promise of translational research via clinical applications such as drug development and patient-specific approaches as well as ablation, cardiac resynchronisation and contractility modulation therapies. For models to be included as a vital component of the decision process in safety-critical applications, rigorous assessment of model credibility will be required. This White Paper describes one aspect of this process by identifying and classifying sources of variability and uncertainty in models as well as their implications for the application and development of cardiac models. We stress the need to understand and quantify the sources of variability and uncertainty in model inputs, and the impact of model structure and complexity and their consequences for

  1. A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses (United States)

    Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini


    The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate…

  2. A geostatistical approach to the change-of-support problem and variable-support data fusion in spatial analysis (United States)

    Wang, Jun; Wang, Yang; Zeng, Hui


    A key issue to address in synthesizing spatial data with variable-support in spatial analysis and modeling is the change-of-support problem. We present an approach for solving the change-of-support and variable-support data fusion problems. This approach is based on geostatistical inverse modeling that explicitly accounts for differences in spatial support. The inverse model is applied here to produce both the best predictions of a target support and prediction uncertainties, based on one or more measurements, while honoring measurements. Spatial data covering large geographic areas often exhibit spatial nonstationarity and can lead to computational challenge due to the large data size. We developed a local-window geostatistical inverse modeling approach to accommodate these issues of spatial nonstationarity and alleviate computational burden. We conducted experiments using synthetic and real-world raster data. Synthetic data were generated and aggregated to multiple supports and downscaled back to the original support to analyze the accuracy of spatial predictions and the correctness of prediction uncertainties. Similar experiments were conducted for real-world raster data. Real-world data with variable-support were statistically fused to produce single-support predictions and associated uncertainties. The modeling results demonstrate that geostatistical inverse modeling can produce accurate predictions and associated prediction uncertainties. It is shown that the local-window geostatistical inverse modeling approach suggested offers a practical way to solve the well-known change-of-support problem and variable-support data fusion problem in spatial analysis and modeling.

  3. Gait variability: methods, modeling and meaning

    Directory of Open Access Journals (Sweden)

    Hausdorff Jeffrey M


    Full Text Available Abstract The study of gait variability, the stride-to-stride fluctuations in walking, offers a complementary way of quantifying locomotion and its changes with aging and disease as well as a means of monitoring the effects of therapeutic interventions and rehabilitation. Previous work has suggested that measures of gait variability may be more closely related to falls, a serious consequence of many gait disorders, than are measures based on the mean values of other walking parameters. The Current JNER series presents nine reports on the results of recent investigations into gait variability. One novel method for collecting unconstrained, ambulatory data is reviewed, and a primer on analysis methods is presented along with a heuristic approach to summarizing variability measures. In addition, the first studies of gait variability in animal models of neurodegenerative disease are described, as is a mathematical model of human walking that characterizes certain complex (multifractal features of the motor control's pattern generator. Another investigation demonstrates that, whereas both healthy older controls and patients with a higher-level gait disorder walk more slowly in reduced lighting, only the latter's stride variability increases. Studies of the effects of dual tasks suggest that the regulation of the stride-to-stride fluctuations in stride width and stride time may be influenced by attention loading and may require cognitive input. Finally, a report of gait variability in over 500 subjects, probably the largest study of this kind, suggests how step width variability may relate to fall risk. Together, these studies provide new insights into the factors that regulate the stride-to-stride fluctuations in walking and pave the way for expanded research into the control of gait and the practical application of measures of gait variability in the clinical setting.

  4. The Effects of Climate Variability on Phytoplankton Composition in the Equatorial Pacific Ocean using a Model and a Satellite-Derived Approach (United States)

    Rousseaux, C. S.; Gregg, W. W.


    Compared the interannual variation in diatoms, cyanobacteria, coccolithophores and chlorophytes from the NASA Ocean Biogeochemical Model with those derived from satellite data (Hirata et al. 2011) between 1998 and 2006 in the Equatorial Pacific. Using NOBM, La Ni a events were characterized by an increase in diatoms (correlation with MEI, r=-0.81, Pphytoplankton community in response to climate variability. However, satellite-derived phytoplankton groups were all negatively correlated with climate variability (r ranged from -0.39 for diatoms to -0.64 for coccolithophores, Pphytoplankton groups except diatoms than NOBM. However, the different responses of phytoplankton to intense interannual events in the Equatorial Pacific raises questions about the representation of phytoplankton dynamics in models and algorithms: is a phytoplankton community shift as in the model or an across-the-board change in abundances of all phytoplankton as in the satellite-derived approach.

  5. On the Use of Variability Operations in the V-Modell XT Software Process Line

    DEFF Research Database (Denmark)

    Kuhrmann, Marco; Méndez Fernández, Daniel; Ternité, Thomas


    . In this article, we present a study on the feasibility of variability operations to support the development of software process lines in the context of the V-Modell XT. We analyze which variability operations are defined and practically used. We provide an initial catalog of variability operations...... as an improvement proposal for other process models. Our findings show that 69 variability operation types are defined across several metamodel versions of which, however, 25 remain unused. The found variability operations allow for systematically modifying the content of process model elements and the process......Software process lines provide a systematic approach to develop and manage software processes. It defines a reference process containing general process assets, whereas a well-defined customization approach allows process engineers to create new process variants, e.g., by extending or modifying...

  6. Variable system: An alternative approach for the analysis of mediated moderation. (United States)

    Kwan, Joyce Lok Yin; Chan, Wai


    Mediated moderation (meMO) occurs when the moderation effect of the moderator (W) on the relationship between the independent variable (X) and the dependent variable (Y) is transmitted through a mediator (M). To examine this process empirically, 2 different model specifications (Type I meMO and Type II meMO) have been proposed in the literature. However, both specifications are found to be problematic, either conceptually or statistically. For example, it can be shown that each type of meMO model is statistically equivalent to a particular form of moderated mediation (moME), another process that examines the condition when the indirect effect from X to Y through M varies as a function of W. Consequently, it is difficult for one to differentiate these 2 processes mathematically. This study therefore has 2 objectives. First, we attempt to differentiate moME and meMO by proposing an alternative specification for meMO. Conceptually, this alternative specification is intuitively meaningful and interpretable, and, statistically, it offers meMO a unique representation that is no longer identical to its moME counterpart. Second, using structural equation modeling, we propose an integrated approach for the analysis of meMO as well as for other general types of conditional path models. VS, a computer software program that implements the proposed approach, has been developed to facilitate the analysis of conditional path models for applied researchers. Real examples are considered to illustrate how the proposed approach works in practice and to compare its performance against the traditional methods. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  7. Impulsive synchronization and parameter mismatch of the three-variable autocatalator model

    International Nuclear Information System (INIS)

    Li, Yang; Liao, Xiaofeng; Li, Chuandong; Huang, Tingwen; Yang, Degang


    The synchronization problems of the three-variable autocatalator model via impulsive control approach are investigated; several theorems on the stability of impulsive control systems are also investigated. These theorems are then used to find the conditions under which the three-variable autocatalator model can be asymptotically controlled to the equilibrium point. This Letter derives some sufficient conditions for the stabilization and synchronization of a three-variable autocatalator model via impulsive control with varying impulsive intervals. Furthermore, we address the chaos quasi-synchronization in the presence of single-parameter mismatch. To illustrate the effectiveness of the new scheme, several numerical examples are given

  8. A novel approach to modeling and diagnosing the cardiovascular system

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.; Kangas, L.J.; Hashem, S.; Kouzes, R.T. [Pacific Northwest Lab., Richland, WA (United States); Allen, P.A. [Life Link, Richland, WA (United States)


    A novel approach to modeling and diagnosing the cardiovascular system is introduced. A model exhibits a subset of the dynamics of the cardiovascular behavior of an individual by using a recurrent artificial neural network. Potentially, a model will be incorporated into a cardiovascular diagnostic system. This approach is unique in that each cardiovascular model is developed from physiological measurements of an individual. Any differences between the modeled variables and the variables of an individual at a given time are used for diagnosis. This approach also exploits sensor fusion to optimize the utilization of biomedical sensors. The advantage of sensor fusion has been demonstrated in applications including control and diagnostics of mechanical and chemical processes.

  9. Modeling Psychological Attributes in Psychology – An Epistemological Discussion: Network Analysis vs. Latent Variables (United States)

    Guyon, Hervé; Falissard, Bruno; Kop, Jean-Luc


    Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. PMID:28572780

  10. FinFET centric variability-aware compact model extraction and generation technology supporting DTCO


    Wang, Xingsheng; Cheng, Binjie; Reid, David; Pender, Andrew; Asenov, Plamen; Millar, Campbell; Asenov, Asen


    In this paper, we present a FinFET-focused variability-aware compact model (CM) extraction and generation technology supporting design-technology co-optimization. The 14-nm CMOS technology generation silicon on insulator FinFETs are used as testbed transistors to illustrate our approach. The TCAD simulations include a long-range process-induced variability using a design of experiment approach and short-range purely statistical variability (mismatch). The CM extraction supports a hierarchical...

  11. Comparison of climate envelope models developed using expert-selected variables versus statistical selection (United States)

    Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.


    Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable

  12. Modelling food-web mediated effects of hydrological variability and environmental flows. (United States)

    Robson, Barbara J; Lester, Rebecca E; Baldwin, Darren S; Bond, Nicholas R; Drouart, Romain; Rolls, Robert J; Ryder, Darren S; Thompson, Ross M


    Environmental flows are designed to enhance aquatic ecosystems through a variety of mechanisms; however, to date most attention has been paid to the effects on habitat quality and life-history triggers, especially for fish and vegetation. The effects of environmental flows on food webs have so far received little attention, despite food-web thinking being fundamental to understanding of river ecosystems. Understanding environmental flows in a food-web context can help scientists and policy-makers better understand and manage outcomes of flow alteration and restoration. In this paper, we consider mechanisms by which flow variability can influence and alter food webs, and place these within a conceptual and numerical modelling framework. We also review the strengths and weaknesses of various approaches to modelling the effects of hydrological management on food webs. Although classic bioenergetic models such as Ecopath with Ecosim capture many of the key features required, other approaches, such as biogeochemical ecosystem modelling, end-to-end modelling, population dynamic models, individual-based models, graph theory models, and stock assessment models are also relevant. In many cases, a combination of approaches will be useful. We identify current challenges and new directions in modelling food-web responses to hydrological variability and environmental flow management. These include better integration of food-web and hydraulic models, taking physiologically-based approaches to food quality effects, and better representation of variations in space and time that may create ecosystem control points. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

  13. Technical note: Comparison of methane ebullition modelling approaches used in terrestrial wetland models (United States)

    Peltola, Olli; Raivonen, Maarit; Li, Xuefei; Vesala, Timo


    Emission via bubbling, i.e. ebullition, is one of the main methane (CH4) emission pathways from wetlands to the atmosphere. Direct measurement of gas bubble formation, growth and release in the peat-water matrix is challenging and in consequence these processes are relatively unknown and are coarsely represented in current wetland CH4 emission models. In this study we aimed to evaluate three ebullition modelling approaches and their effect on model performance. This was achieved by implementing the three approaches in one process-based CH4 emission model. All the approaches were based on some kind of threshold: either on CH4 pore water concentration (ECT), pressure (EPT) or free-phase gas volume (EBG) threshold. The model was run using 4 years of data from a boreal sedge fen and the results were compared with eddy covariance measurements of CH4 fluxes.Modelled annual CH4 emissions were largely unaffected by the different ebullition modelling approaches; however, temporal variability in CH4 emissions varied an order of magnitude between the approaches. Hence the ebullition modelling approach drives the temporal variability in modelled CH4 emissions and therefore significantly impacts, for instance, high-frequency (daily scale) model comparison and calibration against measurements. The modelling approach based on the most recent knowledge of the ebullition process (volume threshold, EBG) agreed the best with the measured fluxes (R2 = 0.63) and hence produced the most reasonable results, although there was a scale mismatch between the measurements (ecosystem scale with heterogeneous ebullition locations) and model results (single horizontally homogeneous peat column). The approach should be favoured over the two other more widely used ebullition modelling approaches and researchers are encouraged to implement it into their CH4 emission models.

  14. Automated optimization and construction of chemometric models based on highly variable raw chromatographic data. (United States)

    Sinkov, Nikolai A; Johnston, Brandon M; Sandercock, P Mark L; Harynuk, James J


    Direct chemometric interpretation of raw chromatographic data (as opposed to integrated peak tables) has been shown to be advantageous in many circumstances. However, this approach presents two significant challenges: data alignment and feature selection. In order to interpret the data, the time axes must be precisely aligned so that the signal from each analyte is recorded at the same coordinates in the data matrix for each and every analyzed sample. Several alignment approaches exist in the literature and they work well when the samples being aligned are reasonably similar. In cases where the background matrix for a series of samples to be modeled is highly variable, the performance of these approaches suffers. Considering the challenge of feature selection, when the raw data are used each signal at each time is viewed as an individual, independent variable; with the data rates of modern chromatographic systems, this generates hundreds of thousands of candidate variables, or tens of millions of candidate variables if multivariate detectors such as mass spectrometers are utilized. Consequently, an automated approach to identify and select appropriate variables for inclusion in a model is desirable. In this research we present an alignment approach that relies on a series of deuterated alkanes which act as retention anchors for an alignment signal, and couple this with an automated feature selection routine based on our novel cluster resolution metric for the construction of a chemometric model. The model system that we use to demonstrate these approaches is a series of simulated arson debris samples analyzed by passive headspace extraction, GC-MS, and interpreted using partial least squares discriminant analysis (PLS-DA). Copyright © 2011 Elsevier B.V. All rights reserved.

  15. Modeling Source Water TOC Using Hydroclimate Variables and Local Polynomial Regression. (United States)

    Samson, Carleigh C; Rajagopalan, Balaji; Summers, R Scott


    To control disinfection byproduct (DBP) formation in drinking water, an understanding of the source water total organic carbon (TOC) concentration variability can be critical. Previously, TOC concentrations in water treatment plant source waters have been modeled using streamflow data. However, the lack of streamflow data or unimpaired flow scenarios makes it difficult to model TOC. In addition, TOC variability under climate change further exacerbates the problem. Here we proposed a modeling approach based on local polynomial regression that uses climate, e.g. temperature, and land surface, e.g., soil moisture, variables as predictors of TOC concentration, obviating the need for streamflow. The local polynomial approach has the ability to capture non-Gaussian and nonlinear features that might be present in the relationships. The utility of the methodology is demonstrated using source water quality and climate data in three case study locations with surface source waters including river and reservoir sources. The models show good predictive skill in general at these locations, with lower skills at locations with the most anthropogenic influences in their streams. Source water TOC predictive models can provide water treatment utilities important information for making treatment decisions for DBP regulation compliance under future climate scenarios.

  16. Efficient Business Service Consumption by Customization with Variability Modelling

    Directory of Open Access Journals (Sweden)

    Michael Stollberg


    Full Text Available The establishment of service orientation in industry determines the need for efficient engineering technologies that properly support the whole life cycle of service provision and consumption. A central challenge is adequate support for the efficient employment of komplex services in their individual application context. This becomes particularly important for large-scale enterprise technologies where generic services are designed for reuse in several business scenarios. In this article we complement our work regarding Service Variability Modelling presented in a previous publication. There we presented an approach for the customization of services for individual application contexts by creating simplified variants, based on model-driven variability management. That work presents our revised service variability metamodel, new features of the variability tools and an applicability study, which reveals that substantial improvements on the efficiency of standard business service consumption under both usability and economic aspects can be achieved.

  17. A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model

    Energy Technology Data Exchange (ETDEWEB)

    Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)


    This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.

  18. Explicit estimating equations for semiparametric generalized linear latent variable models

    KAUST Repository

    Ma, Yanyuan


    We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the semiparametric estimating equations. We further show that the explicit estimators have the usual root n consistency and asymptotic normality. We explain the computational implementation of our method and illustrate the numerical performance of the estimators in finite sample situations via extensive simulation studies. The advantage of our estimators over the existing likelihood approach is also shown via numerical comparison. We employ the method to analyse a real data example from economics. © 2010 Royal Statistical Society.

  19. The Structure of Character Strengths: Variable- and Person-Centered Approaches

    Directory of Open Access Journals (Sweden)

    Małgorzata Najderska


    Full Text Available This article examines the structure of character strengths (Peterson and Seligman, 2004 following both variable-centered and person-centered approaches. We used the International Personality Item Pool-Values in Action (IPIP-VIA questionnaire. The IPIP-VIA measures 24 character strengths and consists of 213 direct and reversed items. The present study was conducted in a heterogeneous group of N = 908 Poles (aged 18–78, M = 28.58. It was part of a validation project of a Polish version of the IPIP-VIA questionnaire. The variable-centered approach was used to examine the structure of character strengths on both the scale and item levels. The scale-level results indicated a four-factor structure that can be interpreted based on four of the five personality traits from the Big Five theory (excluding neuroticism. The item-level analysis suggested a slightly different and limited set of character strengths (17 not 24. After conducting a second-order analysis, a four-factor structure emerged, and three of the factors could be interpreted as being consistent with the scale-level factors. Three character strength profiles were found using the person-centered approach. Two of them were consistent with alpha and beta personality metatraits. The structure of character strengths can be described by using categories from the Five Factor Model of personality and metatraits. They form factors similar to some personality traits and occur in similar constellations as metatraits. The main contributions of this paper are: (1 the validation of IPIP-VIA conducted in variable-centered approach in a new research group (Poles using a different measurement instrument; (2 introducing the person-centered approach to the study of the structure of character strengths.

  20. Assessing multiscale complexity of short heart rate variability series through a model-based linear approach (United States)

    Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe


    We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.

  1. The Effect of Macroeconomic Variables on Value-Added Agriculture: Approach of Vector Autoregresive Bayesian Model (BVAR

    Directory of Open Access Journals (Sweden)

    E. Pishbahar


    Full Text Available There are different ideas and opinions about the effects of macroeconomic variables on real and nominal variables. To answer the question of whether changes in macroeconomic variables as a political tool is useful over a business cycle, understanding the effect of macroeconomic variables on economic growth is important. In the present study, the Bayesian Vector autoregresive model and seasonality data for the years between 1991 and 2013 was used to determine the impact of monetary policy on value-added agriculture. Predicts of Vector autoregresive model are usually divertaed due to a lot of parameters in the model. Bayesian vector autoregresive model estimates more reliable predictions due to reducing the number of included parametrs and considering the former models. Compared to the Vector Autoregressive model, the coefficients are estimated more accurately. Based on the results of RMSE in this study, previous function Nrmal-Vyshart was identified as a suitable previous disteribution. According to the results of the impulse response function, the sudden effects of shocks in macroeconomic variables on the value added in agriculture and domestic venture capital are stable. The effects on the exchange rates, tax revenues and monetary will bemoderated after 7, 5 and 4periods. Monetary policy shocks ,in the first half of the year, increased the value added of agriculture, while in the second half of the year had a depressing effect on the value added.

  2. Validation of an employee satisfaction model: A structural equation model approach

    Directory of Open Access Journals (Sweden)

    Ophillia Ledimo


    Full Text Available The purpose of this study was to validate an employee satisfaction model and to determine the relationships between the different dimensions of the concept, using the structural equation modelling approach (SEM. A cross-sectional quantitative survey design was used to collect data from a random sample of (n=759 permanent employees of a parastatal organisation. Data was collected using the Employee Satisfaction Survey (ESS to measure employee satisfaction dimensions. Following the steps of SEM analysis, the three domains and latent variables of employee satisfaction were specified as organisational strategy, policies and procedures, and outcomes. Confirmatory factor analysis of the latent variables was conducted, and the path coefficients of the latent variables of the employee satisfaction model indicated a satisfactory fit for all these variables. The goodness-of-fit measure of the model indicated both absolute and incremental goodness-of-fit; confirming the relationships between the latent and manifest variables. It also indicated that the latent variables, organisational strategy, policies and procedures, and outcomes, are the main indicators of employee satisfaction. This study adds to the knowledge base on employee satisfaction and makes recommendations for future research.

  3. Functionally relevant climate variables for arid lands: Aclimatic water deficit approach for modelling desert shrub distributions (United States)

    Thomas E. Dilts; Peter J. Weisberg; Camie M. Dencker; Jeanne C. Chambers


    We have three goals. (1) To develop a suite of functionally relevant climate variables for modelling vegetation distribution on arid and semi-arid landscapes of the Great Basin, USA. (2) To compare the predictive power of vegetation distribution models based on mechanistically proximate factors (water deficit variables) and factors that are more mechanistically removed...

  4. Modeling Time-Dependent Association in Longitudinal Data: A Lag as Moderator Approach (United States)

    Selig, James P.; Preacher, Kristopher J.; Little, Todd D.


    We describe a straightforward, yet novel, approach to examine time-dependent association between variables. The approach relies on a measurement-lag research design in conjunction with statistical interaction models. We base arguments in favor of this approach on the potential for better understanding the associations between variables by…

  5. A latent class distance association model for cross-classified data with a categorical response variable. (United States)

    Vera, José Fernando; de Rooij, Mark; Heiser, Willem J


    In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

  6. A Poisson regression approach to model monthly hail occurrence in Northern Switzerland using large-scale environmental variables (United States)

    Madonna, Erica; Ginsbourger, David; Martius, Olivia


    In Switzerland, hail regularly causes substantial damage to agriculture, cars and infrastructure, however, little is known about its long-term variability. To study the variability, the monthly number of days with hail in northern Switzerland is modeled in a regression framework using large-scale predictors derived from ERA-Interim reanalysis. The model is developed and verified using radar-based hail observations for the extended summer season (April-September) in the period 2002-2014. The seasonality of hail is explicitly modeled with a categorical predictor (month) and monthly anomalies of several large-scale predictors are used to capture the year-to-year variability. Several regression models are applied and their performance tested with respect to standard scores and cross-validation. The chosen model includes four predictors: the monthly anomaly of the two meter temperature, the monthly anomaly of the logarithm of the convective available potential energy (CAPE), the monthly anomaly of the wind shear and the month. This model well captures the intra-annual variability and slightly underestimates its inter-annual variability. The regression model is applied to the reanalysis data back in time to 1980. The resulting hail day time series shows an increase of the number of hail days per month, which is (in the model) related to an increase in temperature and CAPE. The trend corresponds to approximately 0.5 days per month per decade. The results of the regression model have been compared to two independent data sets. All data sets agree on the sign of the trend, but the trend is weaker in the other data sets.

  7. A Variational Approach to the Modeling of MIMO Systems

    Directory of Open Access Journals (Sweden)

    Jraifi A


    Full Text Available Motivated by the study of the optimization of the quality of service for multiple input multiple output (MIMO systems in 3G (third generation, we develop a method for modeling MIMO channel . This method, which uses a statistical approach, is based on a variational form of the usual channel equation. The proposed equation is given by with scalar variable . Minimum distance of received vectors is used as the random variable to model MIMO channel. This variable is of crucial importance for the performance of the transmission system as it captures the degree of interference between neighbors vectors. Then, we use this approach to compute numerically the total probability of errors with respect to signal-to-noise ratio (SNR and then predict the numbers of antennas. By fixing SNR variable to a specific value, we extract informations on the optimal numbers of MIMO antennas.

  8. Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling. (United States)

    Dick, Thomas E; Molkov, Yaroslav I; Nieman, Gary; Hsieh, Yee-Hsee; Jacono, Frank J; Doyle, John; Scheff, Jeremy D; Calvano, Steve E; Androulakis, Ioannis P; An, Gary; Vodovotz, Yoram


    Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

  9. Quantifying variability in earthquake rupture models using multidimensional scaling: application to the 2011 Tohoku earthquake

    KAUST Repository

    Razafindrakoto, Hoby


    Finite-fault earthquake source inversion is an ill-posed inverse problem leading to non-unique solutions. In addition, various fault parametrizations and input data may have been used by different researchers for the same earthquake. Such variability leads to large intra-event variability in the inferred rupture models. One way to understand this problem is to develop robust metrics to quantify model variability. We propose a Multi Dimensional Scaling (MDS) approach to compare rupture models quantitatively. We consider normalized squared and grey-scale metrics that reflect the variability in the location, intensity and geometry of the source parameters. We test the approach on two-dimensional random fields generated using a von Kármán autocorrelation function and varying its spectral parameters. The spread of points in the MDS solution indicates different levels of model variability. We observe that the normalized squared metric is insensitive to variability of spectral parameters, whereas the grey-scale metric is sensitive to small-scale changes in geometry. From this benchmark, we formulate a similarity scale to rank the rupture models. As case studies, we examine inverted models from the Source Inversion Validation (SIV) exercise and published models of the 2011 Mw 9.0 Tohoku earthquake, allowing us to test our approach for a case with a known reference model and one with an unknown true solution. The normalized squared and grey-scale metrics are respectively sensitive to the overall intensity and the extension of the three classes of slip (very large, large, and low). Additionally, we observe that a three-dimensional MDS configuration is preferable for models with large variability. We also find that the models for the Tohoku earthquake derived from tsunami data and their corresponding predictions cluster with a systematic deviation from other models. We demonstrate the stability of the MDS point-cloud using a number of realizations and jackknife tests, for

  10. Quantifying variability in earthquake rupture models using multidimensional scaling: application to the 2011 Tohoku earthquake

    KAUST Repository

    Razafindrakoto, Hoby; Mai, Paul Martin; Genton, Marc G.; Zhang, Ling; Thingbaijam, Kiran Kumar


    Finite-fault earthquake source inversion is an ill-posed inverse problem leading to non-unique solutions. In addition, various fault parametrizations and input data may have been used by different researchers for the same earthquake. Such variability leads to large intra-event variability in the inferred rupture models. One way to understand this problem is to develop robust metrics to quantify model variability. We propose a Multi Dimensional Scaling (MDS) approach to compare rupture models quantitatively. We consider normalized squared and grey-scale metrics that reflect the variability in the location, intensity and geometry of the source parameters. We test the approach on two-dimensional random fields generated using a von Kármán autocorrelation function and varying its spectral parameters. The spread of points in the MDS solution indicates different levels of model variability. We observe that the normalized squared metric is insensitive to variability of spectral parameters, whereas the grey-scale metric is sensitive to small-scale changes in geometry. From this benchmark, we formulate a similarity scale to rank the rupture models. As case studies, we examine inverted models from the Source Inversion Validation (SIV) exercise and published models of the 2011 Mw 9.0 Tohoku earthquake, allowing us to test our approach for a case with a known reference model and one with an unknown true solution. The normalized squared and grey-scale metrics are respectively sensitive to the overall intensity and the extension of the three classes of slip (very large, large, and low). Additionally, we observe that a three-dimensional MDS configuration is preferable for models with large variability. We also find that the models for the Tohoku earthquake derived from tsunami data and their corresponding predictions cluster with a systematic deviation from other models. We demonstrate the stability of the MDS point-cloud using a number of realizations and jackknife tests, for

  11. A fuzzy-logic-based approach to qualitative safety modelling for marine systems

    International Nuclear Information System (INIS)

    Sii, H.S.; Ruxton, Tom; Wang Jin


    Safety assessment based on conventional tools (e.g. probability risk assessment (PRA)) may not be well suited for dealing with systems having a high level of uncertainty, particularly in the feasibility and concept design stages of a maritime or offshore system. By contrast, a safety model using fuzzy logic approach employing fuzzy IF-THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. A fuzzy-logic-based approach may be more appropriately used to carry out risk analysis in the initial design stages. This provides a tool for working directly with the linguistic terms commonly used in carrying out safety assessment. This research focuses on the development and representation of linguistic variables to model risk levels subjectively. These variables are then quantified using fuzzy sets. In this paper, the development of a safety model using fuzzy logic approach for modelling various design variables for maritime and offshore safety based decision making in the concept design stage is presented. An example is used to illustrate the proposed approach

  12. Structural identifiability of cyclic graphical models of biological networks with latent variables. (United States)

    Wang, Yulin; Lu, Na; Miao, Hongyu


    Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and

  13. Quantifying uncertainty, variability and likelihood for ordinary differential equation models

    LENUS (Irish Health Repository)

    Weisse, Andrea Y


    Abstract Background In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space. Results The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability. Conclusions While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.

  14. Gene Variants Associated with Antisocial Behaviour: A Latent Variable Approach (United States)

    Bentley, Mary Jane; Lin, Haiqun; Fernandez, Thomas V.; Lee, Maria; Yrigollen, Carolyn M.; Pakstis, Andrew J.; Katsovich, Liliya; Olds, David L.; Grigorenko, Elena L.; Leckman, James F.


    Objective: The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Methods: Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a…

  15. Hidden Markov latent variable models with multivariate longitudinal data. (United States)

    Song, Xinyuan; Xia, Yemao; Zhu, Hongtu


    Cocaine addiction is chronic and persistent, and has become a major social and health problem in many countries. Existing studies have shown that cocaine addicts often undergo episodic periods of addiction to, moderate dependence on, or swearing off cocaine. Given its reversible feature, cocaine use can be formulated as a stochastic process that transits from one state to another, while the impacts of various factors, such as treatment received and individuals' psychological problems on cocaine use, may vary across states. This article develops a hidden Markov latent variable model to study multivariate longitudinal data concerning cocaine use from a California Civil Addict Program. The proposed model generalizes conventional latent variable models to allow bidirectional transition between cocaine-addiction states and conventional hidden Markov models to allow latent variables and their dynamic interrelationship. We develop a maximum-likelihood approach, along with a Monte Carlo expectation conditional maximization (MCECM) algorithm, to conduct parameter estimation. The asymptotic properties of the parameter estimates and statistics for testing the heterogeneity of model parameters are investigated. The finite sample performance of the proposed methodology is demonstrated by simulation studies. The application to cocaine use study provides insights into the prevention of cocaine use. © 2016, The International Biometric Society.


    Directory of Open Access Journals (Sweden)

    Andrei OGREZEANU


    Full Text Available The interdisciplinary study of information technology adoption has developed rapidly over the last 30 years. Various theoretical models have been developed and applied such as: the Technology Acceptance Model (TAM, Innovation Diffusion Theory (IDT, Theory of Planned Behavior (TPB, etc. The result of these many years of research is thousands of contributions to the field, which, however, remain highly fragmented. This paper develops a theoretical model of technology adoption by integrating major theories in the field: primarily IDT, TAM, and TPB. To do so while avoiding mess, an approach that goes back to basics in independent variable type’s development is proposed; emphasizing: 1 the logic of classification, and 2 psychological mechanisms behind variable types. Once developed these types are then populated with variables originating in empirical research. Conclusions are developed on which types are underpopulated and present potential for future research. I end with a set of methodological recommendations for future application of the model.

  17. Fracture in quasi-brittle materials: experimental and numerical approach for the determination of an incremental model with generalized variables

    International Nuclear Information System (INIS)

    Morice, Erwan


    Fracture in quasi-brittle materials, such as ceramics or concrete, can be represented schematically by series of events of nucleation and coalescence of micro-cracks. Modeling this process is an important challenge for the reliability and life prediction of concrete structures, in particular the prediction of the permeability of damaged structures. A multi-scale approach is proposed. The global behavior is modeled within the fracture mechanics framework and the local behavior is modeled by the discrete element method. An approach was developed to condense the non linear behavior of the mortar. A model reduction technic is used to extract the relevant information from the discrete elements method. To do so, the velocity field is partitioned into mode I, II, linear and non-linear components, each component being characterized by an intensity factor and a fixed spatial distribution. The response of the material is hence condensed in the evolution of the intensity factors, used as non-local variables. A model was also proposed to predict the behavior of the crack for proportional and non-proportional mixed mode I+II loadings. An experimental campaign was finally conducted to characterize the fatigue and fracture behavior of mortar. The results show that fatigue crack growth can be of significant importance. The experimental velocity field determined, in the crack tip region, by DIC, were analyzed using the same technic as that used for analyzing the fields obtained by the discrete element method showing consistent results. (author)

  18. Characterizing uncertainty and population variability in the toxicokinetics of trichloroethylene and metabolites in mice, rats, and humans using an updated database, physiologically based pharmacokinetic (PBPK) model, and Bayesian approach

    International Nuclear Information System (INIS)

    Chiu, Weihsueh A.; Okino, Miles S.; Evans, Marina V.


    We have developed a comprehensive, Bayesian, PBPK model-based analysis of the population toxicokinetics of trichloroethylene (TCE) and its metabolites in mice, rats, and humans, considering a wider range of physiological, chemical, in vitro, and in vivo data than any previously published analysis of TCE. The toxicokinetics of the 'population average,' its population variability, and their uncertainties are characterized in an approach that strives to be maximally transparent and objective. Estimates of experimental variability and uncertainty were also included in this analysis. The experimental database was expanded to include virtually all available in vivo toxicokinetic data, which permitted, in rats and humans, the specification of separate datasets for model calibration and evaluation. The total combination of these approaches and PBPK analysis provides substantial support for the model predictions. In addition, we feel confident that the approach employed also yields an accurate characterization of the uncertainty in metabolic pathways for which available data were sparse or relatively indirect, such as GSH conjugation and respiratory tract metabolism. Key conclusions from the model predictions include the following: (1) as expected, TCE is substantially metabolized, primarily by oxidation at doses below saturation; (2) GSH conjugation and subsequent bioactivation in humans appear to be 10- to 100-fold greater than previously estimated; and (3) mice had the greatest rate of respiratory tract oxidative metabolism as compared to rats and humans. In a situation such as TCE in which there is large database of studies coupled with complex toxicokinetics, the Bayesian approach provides a systematic method of simultaneously estimating model parameters and characterizing their uncertainty and variability. However, care needs to be taken in its implementation to ensure biological consistency, transparency, and objectivity.

  19. Bayesian Population Physiologically-Based Pharmacokinetic (PBPK Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations.

    Directory of Open Access Journals (Sweden)

    Markus Krauss

    Full Text Available Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to

  20. R Package multiPIM: A Causal Inference Approach to Variable Importance Analysis

    Directory of Open Access Journals (Sweden)

    Stephan J Ritter


    Full Text Available We describe the R package multiPIM, including statistical background, functionality and user options. The package is for variable importance analysis, and is meant primarily for analyzing data from exploratory epidemiological studies, though it could certainly be applied in other areas as well. The approach taken to variable importance comes from the causal inference field, and is different from approaches taken in other R packages. By default, multiPIM uses a double robust targeted maximum likelihood estimator (TMLE of a parameter akin to the attributable risk. Several regression methods/machine learning algorithms are available for estimating the nuisance parameters of the models, including super learner, a meta-learner which combines several different algorithms into one. We describe a simulation in which the double robust TMLE is compared to the graphical computation estimator. We also provide example analyses using two data sets which are included with the package.

  1. Multi-infill strategy for kriging models used in variable fidelity optimization

    Directory of Open Access Journals (Sweden)

    Chao SONG


    Full Text Available In this paper, a computationally efficient optimization method for aerodynamic design has been developed. The low-fidelity model and the multi-infill strategy are utilized in this approach. Low-fidelity data is employed to provide a good global trend for model prediction, and multiple sample points chosen by different infill criteria in each updating cycle are used to enhance the exploitation and exploration ability of the optimization approach. Take the advantages of low-fidelity model and the multi-infill strategy, and no initial sample for the high-fidelity model is needed. This approach is applied to an airfoil design case and a high-dimensional wing design case. It saves a large number of high-fidelity function evaluations for initial model construction. What’s more, faster reduction of an aerodynamic function is achieved, when compared to ordinary kriging using the multi-infill strategy and variable-fidelity model using single infill criterion. The results indicate that the developed approach has a promising application to efficient aerodynamic design when high-fidelity analyses are involved. Keywords: Aerodynamics, Infill criteria, Kriging models, Multi-infill, Optimization

  2. Joint Bayesian variable and graph selection for regression models with network-structured predictors (United States)

    Peterson, C. B.; Stingo, F. C.; Vannucci, M.


    In this work, we develop a Bayesian approach to perform selection of predictors that are linked within a network. We achieve this by combining a sparse regression model relating the predictors to a response variable with a graphical model describing conditional dependencies among the predictors. The proposed method is well-suited for genomic applications since it allows the identification of pathways of functionally related genes or proteins which impact an outcome of interest. In contrast to previous approaches for network-guided variable selection, we infer the network among predictors using a Gaussian graphical model and do not assume that network information is available a priori. We demonstrate that our method outperforms existing methods in identifying network-structured predictors in simulation settings, and illustrate our proposed model with an application to inference of proteins relevant to glioblastoma survival. PMID:26514925

  3. Influences of variables on ship collision probability in a Bayesian belief network model

    International Nuclear Information System (INIS)

    Hänninen, Maria; Kujala, Pentti


    The influences of the variables in a Bayesian belief network model for estimating the role of human factors on ship collision probability in the Gulf of Finland are studied for discovering the variables with the largest influences and for examining the validity of the network. The change in the so-called causation probability is examined while observing each state of the network variables and by utilizing sensitivity and mutual information analyses. Changing course in an encounter situation is the most influential variable in the model, followed by variables such as the Officer of the Watch's action, situation assessment, danger detection, personal condition and incapacitation. The least influential variables are the other distractions on bridge, the bridge view, maintenance routines and the officer's fatigue. In general, the methods are found to agree on the order of the model variables although some disagreements arise due to slightly dissimilar approaches to the concept of variable influence. The relative values and the ranking of variables based on the values are discovered to be more valuable than the actual numerical values themselves. Although the most influential variables seem to be plausible, there are some discrepancies between the indicated influences in the model and literature. Thus, improvements are suggested to the network.

  4. Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll

    KAUST Repository

    Dreano, Denis


    Phytoplankton is at the basis of the marine food chain and therefore play a fundamental role in the ocean ecosystem. However, the large-scale phytoplankton dynamics of the Red Sea are not well understood yet, mainly due to the lack of historical in situ measurements. As a result, our knowledge in this area relies mostly on remotely-sensed observations and large-scale numerical marine ecosystem models. Models are very useful to identify the mechanisms driving the variations in chlorophyll concentration and have practical applications for fisheries operation and harmful algae blooms monitoring. Modelling approaches can be divided between physics- driven (dynamical) approaches, and data-driven (statistical) approaches. Dynamical models are based on a set of differential equations representing the transfer of energy and matter between different subsets of the biota, whereas statistical models identify relationships between variables based on statistical relations within the available data. The goal of this thesis is to develop, implement and test novel dynamical and statistical modelling approaches for studying and forecasting the variability of chlorophyll concentration in the Red Sea. These new models are evaluated in term of their ability to efficiently forecast and explain the regional chlorophyll variability. We also propose innovative synergistic strategies to combine data- and physics-driven approaches to further enhance chlorophyll forecasting capabilities and efficiency.

  5. The productivity of mental health care: an instrumental variable approach. (United States)

    Lu, Mingshan


    BACKGROUND: Like many other medical technologies and treatments, there is a lack of reliable evidence on treatment effectiveness of mental health care. Increasingly, data from non-experimental settings are being used to study the effect of treatment. However, as in a number of studies using non-experimental data, a simple regression of outcome on treatment shows a puzzling negative and significant impact of mental health care on the improvement of mental health status, even after including a large number of potential control variables. The central problem in interpreting evidence from real-world or non-experimental settings is, therefore, the potential "selection bias" problem in observational data set. In other words, the choice/quantity of mental health care may be correlated with other variables, particularly unobserved variables, that influence outcome and this may lead to a bias in the estimate of the effect of care in conventional models. AIMS OF THE STUDY: This paper addresses the issue of estimating treatment effects using an observational data set. The information in a mental health data set obtained from two waves of data in Puerto Rico is explored. The results using conventional models - in which the potential selection bias is not controlled - and that from instrumental variable (IV) models - which is what was proposed in this study to correct for the contaminated estimation from conventional models - are compared. METHODS: Treatment effectiveness is estimated in a production function framework. Effectiveness is measured as the improvement in mental health status. To control for the potential selection bias problem, IV approaches are employed. The essence of the IV method is to use one or more instruments, which are observable factors that influence treatment but do not directly affect patient outcomes, to isolate the effect of treatment variation that is independent of unobserved patient characteristics. The data used in this study are the first (1992

  6. Confounding of three binary-variables counterfactual model


    Liu, Jingwei; Hu, Shuang


    Confounding of three binary-variables counterfactual model is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three counterfactual models: the control variable is independent of the covariate variable, the control variable has the effect on the covariate variable and the covariate variable affects the control variable. Using the ancillary information based on conditional independence hypotheses, the sufficient conditions...

  7. Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale (United States)

    Kreibich, Heidi; Schröter, Kai; Merz, Bruno


    Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

  8. A New Variable Selection Method Based on Mutual Information Maximization by Replacing Collinear Variables for Nonlinear Quantitative Structure-Property Relationship Models

    Energy Technology Data Exchange (ETDEWEB)

    Ghasemi, Jahan B.; Zolfonoun, Ehsan [Toosi University of Technology, Tehran (Korea, Republic of)


    Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

  9. A New Variable Selection Method Based on Mutual Information Maximization by Replacing Collinear Variables for Nonlinear Quantitative Structure-Property Relationship Models

    International Nuclear Information System (INIS)

    Ghasemi, Jahan B.; Zolfonoun, Ehsan


    Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms

  10. Developing Baltic cod recruitment models II : Incorporation of environmental variability and species interaction

    DEFF Research Database (Denmark)

    Köster, Fritz; Hinrichsen, H.H.; St. John, Michael


    We investigate whether a process-oriented approach based on the results of field, laboratory, and modelling studies can be used to develop a stock-environment-recruitment model for Central Baltic cod (Gadus morhua). Based on exploratory statistical analysis, significant variables influencing...... cod in these areas, suggesting that key biotic and abiotic processes can be successfully incorporated into recruitment models....... survival of early life stages and varying systematically among spawning sites were incorporated into stock-recruitment models, first for major cod spawning sites and then combined for the entire Central Baltic. Variables identified included potential egg production by the spawning stock, abiotic conditions...

  11. A Comparison of Approaches for the Analysis of Interaction Effects between Latent Variables Using Partial Least Squares Path Modeling (United States)

    Henseler, Jorg; Chin, Wynne W.


    In social and business sciences, the importance of the analysis of interaction effects between manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS) to analyze interaction effects between latent variables need an overview of the available approaches as well as their suitability. This article…

  12. On the ""early-time"" evolution of variables relevant to turbulence models for the Rayleigh-Taylor instability

    Energy Technology Data Exchange (ETDEWEB)

    Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory


    We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant variables before fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of mixing between two interpenetrating fluids to define the initial profiles for the turbulence model variables. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted profiles for the turbulence model variables and profiles of the variables obtained from low Atwood number three dimensional simulations show reasonable agreement.

  13. A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts. (United States)

    Brunwasser, Steven M; Gebretsadik, Tebeb; Gold, Diane R; Turi, Kedir N; Stone, Cosby A; Datta, Soma; Gern, James E; Hartert, Tina V


    The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0-2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders. We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with

  14. Efficient family-based model checking via variability abstractions

    DEFF Research Database (Denmark)

    Dimovski, Aleksandar; Al-Sibahi, Ahmad Salim; Brabrand, Claus


    with the abstract model checking of the concrete high-level variational model. This allows the use of Spin with all its accumulated optimizations for efficient verification of variational models without any knowledge about variability. We have implemented the transformations in a prototype tool, and we illustrate......Many software systems are variational: they can be configured to meet diverse sets of requirements. They can produce a (potentially huge) number of related systems, known as products or variants, by systematically reusing common parts. For variational models (variational systems or families...... of related systems), specialized family-based model checking algorithms allow efficient verification of multiple variants, simultaneously, in a single run. These algorithms, implemented in a tool Snip, scale much better than ``the brute force'' approach, where all individual systems are verified using...

  15. Analysis and modeling of wafer-level process variability in 28 nm FD-SOI using split C-V measurements (United States)

    Pradeep, Krishna; Poiroux, Thierry; Scheer, Patrick; Juge, André; Gouget, Gilles; Ghibaudo, Gérard


    This work details the analysis of wafer level global process variability in 28 nm FD-SOI using split C-V measurements. The proposed approach initially evaluates the native on wafer process variability using efficient extraction methods on split C-V measurements. The on-wafer threshold voltage (VT) variability is first studied and modeled using a simple analytical model. Then, a statistical model based on the Leti-UTSOI compact model is proposed to describe the total C-V variability in different bias conditions. This statistical model is finally used to study the contribution of each process parameter to the total C-V variability.



    Nobuoki, Eshima; Minoru, Tabata; Geng, Zhi; Department of Medical Information Analysis, Faculty of Medicine, Oita Medical University; Department of Applied Mathematics, Faculty of Engineering, Kobe University; Department of Probability and Statistics, Peking University


    This paper discusses path analysis of categorical variables with logistic regression models. The total, direct and indirect effects in fully recursive causal systems are considered by using model parameters. These effects can be explained in terms of log odds ratios, uncertainty differences, and an inner product of explanatory variables and a response variable. A study on food choice of alligators as a numerical exampleis reanalysed to illustrate the present approach.

  17. Incorporation of expert variability into breast cancer treatment recommendation in designing clinical protocol guided fuzzy rule system models. (United States)

    Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O


    It has been often demonstrated that clinicians exhibit both inter-expert and intra-expert variability when making difficult decisions. In contrast, the vast majority of computerized models that aim to provide automated support for such decisions do not explicitly recognize or replicate this variability. Furthermore, the perfect consistency of computerized models is often presented as a de facto benefit. In this paper, we describe a novel approach to incorporate variability within a fuzzy inference system using non-stationary fuzzy sets in order to replicate human variability. We apply our approach to a decision problem concerning the recommendation of post-operative breast cancer treatment; specifically, whether or not to administer chemotherapy based on assessment of five clinical variables: NPI (the Nottingham Prognostic Index), estrogen receptor status, vascular invasion, age and lymph node status. In doing so, we explore whether such explicit modeling of variability provides any performance advantage over a more conventional fuzzy approach, when tested on a set of 1310 unselected cases collected over a fourteen year period at the Nottingham University Hospitals NHS Trust, UK. The experimental results show that the standard fuzzy inference system (that does not model variability) achieves overall agreement to clinical practice around 84.6% (95% CI: 84.1-84.9%), while the non-stationary fuzzy model can significantly increase performance to around 88.1% (95% CI: 88.0-88.2%), psystems in any application domain. Copyright © 2012 Elsevier Inc. All rights reserved.

  18. Stage-by-Stage and Parallel Flow Path Compressor Modeling for a Variable Cycle Engine (United States)

    Kopasakis, George; Connolly, Joseph W.; Cheng, Larry


    This paper covers the development of stage-by-stage and parallel flow path compressor modeling approaches for a Variable Cycle Engine. The stage-by-stage compressor modeling approach is an extension of a technique for lumped volume dynamics and performance characteristic modeling. It was developed to improve the accuracy of axial compressor dynamics over lumped volume dynamics modeling. The stage-by-stage compressor model presented here is formulated into a parallel flow path model that includes both axial and rotational dynamics. This is done to enable the study of compressor and propulsion system dynamic performance under flow distortion conditions. The approaches utilized here are generic and should be applicable for the modeling of any axial flow compressor design.

  19. Crime Modeling using Spatial Regression Approach (United States)

    Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.


    Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

  20. Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

    Directory of Open Access Journals (Sweden)

    H. Kreibich


    Full Text Available Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB.In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

  1. Ensembling Variable Selectors by Stability Selection for the Cox Model

    Directory of Open Access Journals (Sweden)

    Qing-Yan Yin


    Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

  2. A Synergetic Approach to Describe the Stability and Variability of Motor Behavior (United States)

    Witte, Kersttn; Bock, Holger; Storb, Ulrich; Blaser, Peter

    At the beginning of the 20th century, the Russian physiologist and biomechanist Bernstein developed his cyclograms, in which he showed in the non-repetition of the same movement under constant conditions. We can also observe this phenomenon when we analyze several cyclic sports movements. For example, we investigated the trajectories of single joints and segments of the body in breaststroke, walking, and running. The problem of the stability and variability of movement, and the relation between the two, cannot be satisfactorily tackled by means of linear methods. Thus, several authors (Turvey, 1977; Kugler et al., 1980; Haken et al., 1985; Schöner et al., 1986; Mitra et al., 1997; Kay et al., 1991; Ganz et al., 1996; Schöllhorn, 1999) use nonlinear models to describe human movement. These models and approaches have shown that nonlinear theories of complex systems provide a new understanding of the stability and variability of motor control. The purpose of this chapter is a presentation of a common synergetic model of motor behavior and its application to foot tapping, walking, and running.


    Directory of Open Access Journals (Sweden)

    Juan Antonio Cutillas Espinosa


    Full Text Available Most approaches to variability in Optimality Theory have attempted to make variation possible within the OT framework, i.e. to reformulate constraints and rankings to accommodate variable and gradient linguistic facts. Sociolinguists have attempted to apply these theoretical advances to the study of language variation, with an emphasis on language-interna1 variables (Auger 2001, Cardoso 2001. Little attention has been paid to the array of externa1 factors that influence the patterning of variation. In this paper, we argue that some variation pattems-specially those that are socially meaningful- are actually the result of a three-grarnmar system. G, is the standard grammar, which has to be available to the speaker to obtain these variation patterns. G; is the vernacular grammar, which the speaker is likely to have acquired in his local community. Finally, G, is an intergrammar, which is used by the speaker as his 'default' constraint set. G is a continuous ranking (Boersma & Hayes 2001 and domination relations are consciously altered by the speakers to shape the appropriate and variable linguistic output. We illustrate this model with analyses of English and Spanish.

  4. Two-step variable selection in quantile regression models

    Directory of Open Access Journals (Sweden)

    FAN Yali


    Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.

  5. Variable sound speed in interacting dark energy models (United States)

    Linton, Mark S.; Pourtsidou, Alkistis; Crittenden, Robert; Maartens, Roy


    We consider a self-consistent and physical approach to interacting dark energy models described by a Lagrangian, and identify a new class of models with variable dark energy sound speed. We show that if the interaction between dark energy in the form of quintessence and cold dark matter is purely momentum exchange this generally leads to a dark energy sound speed that deviates from unity. Choosing a specific sub-case, we study its phenomenology by investigating the effects of the interaction on the cosmic microwave background and linear matter power spectrum. We also perform a global fitting of cosmological parameters using CMB data, and compare our findings to ΛCDM.

  6. New approaches for examining associations with latent categorical variables: applications to substance abuse and aggression. (United States)

    Feingold, Alan; Tiberio, Stacey S; Capaldi, Deborah M


    Assessments of substance use behaviors often include categorical variables that are frequently related to other measures using logistic regression or chi-square analysis. When the categorical variable is latent (e.g., extracted from a latent class analysis [LCA]), classification of observations is often used to create an observed nominal variable from the latent one for use in a subsequent analysis. However, recent simulation studies have found that this classical 3-step analysis championed by the pioneers of LCA produces underestimates of the associations of latent classes with other variables. Two preferable but underused alternatives for examining such linkages-each of which is most appropriate under certain conditions-are (a) 3-step analysis, which corrects the underestimation bias of the classical approach, and (b) 1-step analysis. The purpose of this article is to dissuade researchers from conducting classical 3-step analysis and to promote the use of the 2 newer approaches that are described and compared. In addition, the applications of these newer models-for use when the independent, the dependent, or both categorical variables are latent-are illustrated through substantive analyses relating classes of substance abusers to classes of intimate partner aggressors.

  7. Estimation of exhaust gas aerodynamic force on the variable geometry turbocharger actuator: 1D flow model approach

    International Nuclear Information System (INIS)

    Ahmed, Fayez Shakil; Laghrouche, Salah; Mehmood, Adeel; El Bagdouri, Mohammed


    Highlights: • Estimation of aerodynamic force on variable turbine geometry vanes and actuator. • Method based on exhaust gas flow modeling. • Simulation tool for integration of aerodynamic force in automotive simulation software. - Abstract: This paper provides a reliable tool for simulating the effects of exhaust gas flow through the variable turbine geometry section of a variable geometry turbocharger (VGT), on flow control mechanism. The main objective is to estimate the resistive aerodynamic force exerted by the flow upon the variable geometry vanes and the controlling actuator, in order to improve the control of vane angles. To achieve this, a 1D model of the exhaust flow is developed using Navier–Stokes equations. As the flow characteristics depend upon the volute geometry, impeller blade force and the existing viscous friction, the related source terms (losses) are also included in the model. In order to guarantee stability, an implicit numerical solver has been developed for the resolution of the Navier–Stokes problem. The resulting simulation tool has been validated through comparison with experimentally obtained values of turbine inlet pressure and the aerodynamic force as measured at the actuator shaft. The simulator shows good compliance with experimental results

  8. Constructing a justice model based on Sen's capability approach


    Yüksel, Sevgi; Yuksel, Sevgi


    The thesis provides a possible justice model based on Sen's capability approach. For this goal, we first analyze the general structure of a theory of justice, identifying the main variables and issues. Furthermore, based on Sen (2006) and Kolm (1998), we look at 'transcendental' and 'comparative' approaches to justice and concentrate on the sufficiency condition for the comparative approach. Then, taking Rawls' theory of justice as a starting point, we present how Sen's capability approach em...

  9. Variable selection and model choice in geoadditive regression models. (United States)

    Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard


    Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

  10. Thermodynamic consistency of viscoplastic material models involving external variable rates in the evolution equations for the internal variables

    International Nuclear Information System (INIS)

    Malmberg, T.


    The objective of this study is to derive and investigate thermodynamic restrictions for a particular class of internal variable models. Their evolution equations consist of two contributions: the usual irreversible part, depending only on the present state, and a reversible but path dependent part, linear in the rates of the external variables (evolution equations of ''mixed type''). In the first instance the thermodynamic analysis is based on the classical Clausius-Duhem entropy inequality and the Coleman-Noll argument. The analysis is restricted to infinitesimal strains and rotations. The results are specialized and transferred to a general class of elastic-viscoplastic material models. Subsequently, they are applied to several viscoplastic models of ''mixed type'', proposed or discussed in the literature (Robinson et al., Krempl et al., Freed et al.), and it is shown that some of these models are thermodynamically inconsistent. The study is closed with the evaluation of the extended Clausius-Duhem entropy inequality (concept of Mueller) where the entropy flux is governed by an assumed constitutive equation in its own right; also the constraining balance equations are explicitly accounted for by the method of Lagrange multipliers (Liu's approach). This analysis is done for a viscoplastic material model with evolution equations of the ''mixed type''. It is shown that this approach is much more involved than the evaluation of the classical Clausius-Duhem entropy inequality with the Coleman-Noll argument. (orig.) [de

  11. A Core Language for Separate Variability Modeling

    DEFF Research Database (Denmark)

    Iosif-Lazăr, Alexandru Florin; Wasowski, Andrzej; Schaefer, Ina


    Separate variability modeling adds variability to a modeling language without requiring modifications of the language or the supporting tools. We define a core language for separate variability modeling using a single kind of variation point to define transformations of software artifacts in object...... hierarchical dependencies between variation points via copying and flattening. Thus, we reduce a model with intricate dependencies to a flat executable model transformation consisting of simple unconditional local variation points. The core semantics is extremely concise: it boils down to two operational rules...

  12. Demonstration of a geostatistical approach to physically consistent downscaling of climate modeling simulations

    KAUST Repository

    Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason P.; McCabe, Matthew


    A downscaling approach based on multiple-point geostatistics (MPS) is presented. The key concept underlying MPS is to sample spatial patterns from within training images, which can then be used in characterizing the relationship between different variables across multiple scales. The approach is used here to downscale climate variables including skin surface temperature (TSK), soil moisture (SMOIS), and latent heat flux (LH). The performance of the approach is assessed by applying it to data derived from a regional climate model of the Murray-Darling basin in southeast Australia, using model outputs at two spatial resolutions of 50 and 10 km. The data used in this study cover the period from 1985 to 2006, with 1985 to 2005 used for generating the training images that define the relationships of the variables across the different spatial scales. Subsequently, the spatial distributions for the variables in the year 2006 are determined at 10 km resolution using the 50 km resolution data as input. The MPS geostatistical downscaling approach reproduces the spatial distribution of TSK, SMOIS, and LH at 10 km resolution with the correct spatial patterns over different seasons, while providing uncertainty estimates through the use of multiple realizations. The technique has the potential to not only bridge issues of spatial resolution in regional and global climate model simulations but also in feature sharpening in remote sensing applications through image fusion, filling gaps in spatial data, evaluating downscaled variables with available remote sensing images, and aggregating/disaggregating hydrological and groundwater variables for catchment studies.

  13. Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling (United States)

    Raykov, Tenko; Marcoulides, George A.


    A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or…

  14. A dual model approach to ground water recovery trench design

    International Nuclear Information System (INIS)

    Clodfelter, C.L.; Crouch, M.S.


    The design of trenches for contaminated ground water recovery must consider several variables. This paper presents a dual-model approach for effectively recovering contaminated ground water migrating toward a trench by advection. The approach involves an analytical model to determine the vertical influence of the trench and a numerical flow model to determine the capture zone within the trench and the surrounding aquifer. The analytical model is utilized by varying trench dimensions and head values to design a trench which meets the remediation criteria. The numerical flow model is utilized to select the type of backfill and location of sumps within the trench. The dual-model approach can be used to design a recovery trench which effectively captures advective migration of contaminants in the vertical and horizontal planes

  15. In search of control variables : A systems approach

    NARCIS (Netherlands)

    Dalenoort, GJ


    Motor processes cannot be modeled by a single (unified) model. Instead, a number of models at different levels of description are needed. The concepts of control and control variable only make sense at the functional level. A clear distinction must be made between external models and internal

  16. Variability of orogenic magmatism during Mediterranean-style continental collisions : A numerical modelling approach

    NARCIS (Netherlands)

    Andrić, N.; Vogt, K.; Matenco, L.; Cvetković, V.; Cloetingh, S.; Gerya, T.

    The relationship between magma generation and the tectonic evolution of orogens during subduction and subsequent collision requires self-consistent numerical modelling approaches predicting volumes and compositions of the produced magmatic rocks. Here, we use a 2D magmatic-thermomechanical numerical

  17. Evaluating the role of soil variability on groundwater pollution and recharge at regional scale by integrating a process-based vadose zone model in a stochastic approach (United States)

    Coppola, Antonio; Comegna, Alessandro; Dragonetti, Giovanna; Lamaddalena, Nicola; Zdruli, Pandi


    the lack of information on vertical variability of soil properties. It is our opinion that, with sufficient information on soil horizonation and with an appropriate horizontal resolution, it may be demonstrated that model outputs may be largely sensitive to the vertical variability of stream tubes, even at applicative scales. Horizon differentiation is one of the main observations made by pedologists while describing soils and most analytical data are given according to soil horizons. Over the last decades, soil horizonation has been subjected to regular monitoring for mapping soil variation at regional scales. Accordingly, this study mainly aims to developing a regional-scale simulation approach for vadose zone flow and transport that use real soil profiles data based on information on vertical variability of soils. As to the methodology, the parallel column concept was applied to account for the effect of vertical heterogeneity on variability of water flow and solute transport in the vadose zone. Even if the stream tube approach was mainly introduced for (unrealistic) vertically homogeneous soils, we extended their use to real vertically variable soils. The approach relies on available datasets coming from different sources and offers quantitative answers to soil and groundwater vulnerability to non-point source of chemicals and pathogens at regional scale within a defined confidence interval. This result will be pursued through the design and building up of a spatial database containing 1). Detailed pedological information, 2). Hydrological properties mainly measured in the investigated area in different soil horizons, 3). Water table depth, 4). Spatially distributed climatic temporal series, and 5). Land use. The area of interest for the study is located in the sub-basin of Metaponto agricultural site, located in southern Basilicata Region in Italy, covering approximately 11,698 hectares, crossed by two main rivers, Sinni and Agri and from many secondary water

  18. A phenomenological approach to modeling chemical dynamics in nonlinear and two-dimensional spectroscopy. (United States)

    Ramasesha, Krupa; De Marco, Luigi; Horning, Andrew D; Mandal, Aritra; Tokmakoff, Andrei


    We present an approach for calculating nonlinear spectroscopic observables, which overcomes the approximations inherent to current phenomenological models without requiring the computational cost of performing molecular dynamics simulations. The trajectory mapping method uses the semi-classical approximation to linear and nonlinear response functions, and calculates spectra from trajectories of the system's transition frequencies and transition dipole moments. It rests on identifying dynamical variables important to the problem, treating the dynamics of these variables stochastically, and then generating correlated trajectories of spectroscopic quantities by mapping from the dynamical variables. This approach allows one to describe non-Gaussian dynamics, correlated dynamics between variables of the system, and nonlinear relationships between spectroscopic variables of the system and the bath such as non-Condon effects. We illustrate the approach by applying it to three examples that are often not adequately treated by existing analytical models--the non-Condon effect in the nonlinear infrared spectra of water, non-Gaussian dynamics inherent to strongly hydrogen bonded systems, and chemical exchange processes in barrier crossing reactions. The methods described are generally applicable to nonlinear spectroscopy throughout the optical, infrared and terahertz regions.

  19. Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies. (United States)

    Savitsky, Terrance; Vannucci, Marina; Sha, Naijun


    This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data. Our specific interest is in the analysis of data sets with predictors that have an a priori unknown form of possibly nonlinear associations to the response. The modeling approach we describe incorporates Gaussian processes in a generalized linear model framework to obtain a class of nonparametric regression models where the covariance matrix depends on the predictors. We consider, in particular, continuous, categorical and count responses. We also look into models that account for survival outcomes. We explore alternative covariance formulations for the Gaussian process prior and demonstrate the flexibility of the construction. Next, we focus on the important problem of selecting variables from the set of possible predictors and describe a general framework that employs mixture priors. We compare alternative MCMC strategies for posterior inference and achieve a computationally efficient and practical approach. We demonstrate performances on simulated and benchmark data sets.

  20. Bayesian modeling of measurement error in predictor variables

    NARCIS (Netherlands)

    Fox, Gerardus J.A.; Glas, Cornelis A.W.


    It is shown that measurement error in predictor variables can be modeled using item response theory (IRT). The predictor variables, that may be defined at any level of an hierarchical regression model, are treated as latent variables. The normal ogive model is used to describe the relation between

  1. Polynomial Chaos Expansion Approach to Interest Rate Models

    Directory of Open Access Journals (Sweden)

    Luca Di Persio


    Full Text Available The Polynomial Chaos Expansion (PCE technique allows us to recover a finite second-order random variable exploiting suitable linear combinations of orthogonal polynomials which are functions of a given stochastic quantity ξ, hence acting as a kind of random basis. The PCE methodology has been developed as a mathematically rigorous Uncertainty Quantification (UQ method which aims at providing reliable numerical estimates for some uncertain physical quantities defining the dynamic of certain engineering models and their related simulations. In the present paper, we use the PCE approach in order to analyze some equity and interest rate models. In particular, we take into consideration those models which are based on, for example, the Geometric Brownian Motion, the Vasicek model, and the CIR model. We present theoretical as well as related concrete numerical approximation results considering, without loss of generality, the one-dimensional case. We also provide both an efficiency study and an accuracy study of our approach by comparing its outputs with the ones obtained adopting the Monte Carlo approach, both in its standard and its enhanced version.

  2. Error-in-variables models in calibration (United States)

    Lira, I.; Grientschnig, D.


    In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration process and from the possible inaccuracy of the calibration model. However, the premise that the stimuli are completely known is never strictly fulfilled and in some instances it may be grossly inadequate. Then, error-in-variables (EIV) regression models have to be employed. In metrology, these models have been approached mostly from the frequentist perspective. In contrast, not much guidance is available on their Bayesian analysis. In this paper, we first present a brief summary of the conventional statistical techniques that have been developed to deal with EIV models in calibration. We then proceed to discuss the alternative Bayesian framework under some simplifying assumptions. Through a detailed example about the calibration of an instrument for measuring flow rates, we provide advice on how the user of the calibration function should employ the latter framework for inferring the stimulus acting on the calibrated device when, in use, a certain response is measured.

  3. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

    Directory of Open Access Journals (Sweden)

    Osadolor Ebhuoma


    Full Text Available Malaria is a serious public health threat in Sub-Saharan Africa (SSA, and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI Web of KnowledgeSM databases (PubMed, Web of Science and ScienceDirect was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI derived from either the National Oceanic and Atmospheric Administration (NOAA Advanced Very High Resolution Radiometer (AVHRR or Moderate-resolution Imaging Spectrometer (MODIS satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical

  4. Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa. (United States)

    Ebhuoma, Osadolor; Gebreslasie, Michael


    Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical

  5. Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food. (United States)

    Commeau, Natalie; Cornu, Marie; Albert, Isabelle; Denis, Jean-Baptiste; Parent, Eric


    Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked. © 2012 Society for Risk Analysis.

  6. Using multiple biomarkers and determinants to obtain a better measurement of oxidative stress: a latent variable structural equation model approach. (United States)

    Eldridge, Ronald C; Flanders, W Dana; Bostick, Roberd M; Fedirko, Veronika; Gross, Myron; Thyagarajan, Bharat; Goodman, Michael


    Since oxidative stress involves a variety of cellular changes, no single biomarker can serve as a complete measure of this complex biological process. The analytic technique of structural equation modeling (SEM) provides a possible solution to this problem by modelling a latent (unobserved) variable constructed from the covariance of multiple biomarkers. Using three pooled datasets, we modelled a latent oxidative stress variable from five biomarkers related to oxidative stress: F 2 -isoprostanes (FIP), fluorescent oxidation products, mitochondrial DNA copy number, γ-tocopherol (Gtoc) and C-reactive protein (CRP, an inflammation marker closely linked to oxidative stress). We validated the latent variable by assessing its relation to pro- and anti-oxidant exposures. FIP, Gtoc and CRP characterized the latent oxidative stress variable. Obesity, smoking, aspirin use and β-carotene were statistically significantly associated with oxidative stress in the theorized directions; the same exposures were weakly and inconsistently associated with the individual biomarkers. Our results suggest that using SEM with latent variables decreases the biomarker-specific variability, and may produce a better measure of oxidative stress than do single variables. This methodology can be applied to similar areas of research in which a single biomarker is not sufficient to fully describe a complex biological phenomenon.

  7. A Bayesian Hierarchical Modeling Approach to Predicting Flow in Ungauged Basins (United States)

    Gronewold, A.; Alameddine, I.; Anderson, R. M.


    Recent innovative approaches to identifying and applying regression-based relationships between land use patterns (such as increasing impervious surface area and decreasing vegetative cover) and rainfall-runoff model parameters represent novel and promising improvements to predicting flow from ungauged basins. In particular, these approaches allow for predicting flows under uncertain and potentially variable future conditions due to rapid land cover changes, variable climate conditions, and other factors. Despite the broad range of literature on estimating rainfall-runoff model parameters, however, the absence of a robust set of modeling tools for identifying and quantifying uncertainties in (and correlation between) rainfall-runoff model parameters represents a significant gap in current hydrological modeling research. Here, we build upon a series of recent publications promoting novel Bayesian and probabilistic modeling strategies for quantifying rainfall-runoff model parameter estimation uncertainty. Our approach applies alternative measures of rainfall-runoff model parameter joint likelihood (including Nash-Sutcliffe efficiency, among others) to simulate samples from the joint parameter posterior probability density function. We then use these correlated samples as response variables in a Bayesian hierarchical model with land use coverage data as predictor variables in order to develop a robust land use-based tool for forecasting flow in ungauged basins while accounting for, and explicitly acknowledging, parameter estimation uncertainty. We apply this modeling strategy to low-relief coastal watersheds of Eastern North Carolina, an area representative of coastal resource waters throughout the world because of its sensitive embayments and because of the abundant (but currently threatened) natural resources it hosts. Consequently, this area is the subject of several ongoing studies and large-scale planning initiatives, including those conducted through the United

  8. A variable resolution nonhydrostatic global atmospheric semi-implicit semi-Lagrangian model (United States)

    Pouliot, George Antoine


    -resolution topographic data set and the variable resolution grid, sets of experiments with increasing resolution were performed over specific regions of interest. Using realistic initial conditions derived from re-analysis fields, nonhydrostatic effects were significant for grid spacings on the order of 0.1 degrees with orographic forcing. If the model code was adapted for use in a message passing interface (MPI) on a parallel supercomputer today, it was estimated that a global grid spacing of 0.1 degrees would be achievable for a global model. In this case, nonhydrostatic effects would be significant for most areas. A variable resolution grid in a global model provides a unified and flexible approach to many climate and numerical weather prediction problems. The ability to configure the model from very fine to very coarse resolutions allows for the simulation of atmospheric phenomena at different scales using the same code. We have developed a dynamical core illustrating the feasibility of using a variable resolution in a global model.

  9. BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods (United States)

    Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.


    Pluvial flood events, such as in Copenhagen (Denmark) in 2011, Beijing (China) in 2012 or Houston (USA) in 2016, have caused severe losses to urban dwellings in recent years. These floods are caused by storm events with high rainfall rates well above the design levels of urban drainage systems, which lead to inundation of streets and buildings. A projected increase in frequency and intensity of heavy rainfall events in many areas and an ongoing urbanization may increase pluvial flood losses in the future. For an efficient risk assessment and adaptation to pluvial floods, a quantification of the flood risk is needed. Few loss models have been developed particularly for pluvial floods. These models usually use simple waterlevel- or rainfall-loss functions and come with very high uncertainties. To account for these uncertainties and improve the loss estimation, we present a probabilistic multi-variable loss estimation model for pluvial floods based on empirical data. The model was developed in a two-step process using a machine learning approach and a comprehensive database comprising 783 records of direct building and content damage of private households. The data was gathered through surveys after four different pluvial flood events in Germany between 2005 and 2014. In a first step, linear and non-linear machine learning algorithms, such as tree-based and penalized regression models were used to identify the most important loss influencing factors among a set of 55 candidate variables. These variables comprise hydrological and hydraulic aspects, early warning, precaution, building characteristics and the socio-economic status of the household. In a second step, the most important loss influencing variables were used to derive a probabilistic multi-variable pluvial flood loss estimation model based on Bayesian Networks. Two different networks were tested: a score-based network learned from the data and a network based on expert knowledge. Loss predictions are made

  10. Analysis of uncertainty and variability in finite element computational models for biomedical engineering:characterization and propagation

    Directory of Open Access Journals (Sweden)

    Nerea Mangado


    Full Text Available Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

  11. Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation. (United States)

    Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González


    Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

  12. ltm: An R Package for Latent Variable Modeling and Item Response Analysis

    Directory of Open Access Journals (Sweden)

    Dimitris Rizopoulos


    Full Text Available The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Birnbaum's Three-Parameter models have been implemented, whereas for polytomous data Semejima's Graded Response model is available. Parameter estimates are obtained under marginal maximum likelihood using the Gauss-Hermite quadrature rule. The capabilities and features of the package are illustrated using two real data examples.

  13. Handbook of latent variable and related models

    CERN Document Server

    Lee, Sik-Yum


    This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data- Includes illustrative examples with real data sets from business, education, medicine, public health and sociology.- Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

  14. Assessing the accuracy and stability of variable selection methods for random forest modeling in ecology. (United States)

    Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H


    Random forest (RF) modeling has emerged as an important statistical learning method in ecology due to its exceptional predictive performance. However, for large and complex ecological data sets, there is limited guidance on variable selection methods for RF modeling. Typically, either a preselected set of predictor variables are used or stepwise procedures are employed which iteratively remove variables according to their importance measures. This paper investigates the application of variable selection methods to RF models for predicting probable biological stream condition. Our motivating data set consists of the good/poor condition of n = 1365 stream survey sites from the 2008/2009 National Rivers and Stream Assessment, and a large set (p = 212) of landscape features from the StreamCat data set as potential predictors. We compare two types of RF models: a full variable set model with all 212 predictors and a reduced variable set model selected using a backward elimination approach. We assess model accuracy using RF's internal out-of-bag estimate, and a cross-validation procedure with validation folds external to the variable selection process. We also assess the stability of the spatial predictions generated by the RF models to changes in the number of predictors and argue that model selection needs to consider both accuracy and stability. The results suggest that RF modeling is robust to the inclusion of many variables of moderate to low importance. We found no substantial improvement in cross-validated accuracy as a result of variable reduction. Moreover, the backward elimination procedure tended to select too few variables and exhibited numerous issues such as upwardly biased out-of-bag accuracy estimates and instabilities in the spatial predictions. We use simulations to further support and generalize results from the analysis of real data. A main purpose of this work is to elucidate issues of model selection bias and instability to ecologists interested in

  15. Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

    Directory of Open Access Journals (Sweden)

    Liu Yufeng


    Full Text Available Abstract Background Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR to neoadjuvant chemotherapy were also built using this approach. Results We identified statistically significant prognostic models for relapse-free survival (RFS at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR predictions for the entire population. Conclusions Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA

  16. An introduction to latent variable growth curve modeling concepts, issues, and application

    CERN Document Server

    Duncan, Terry E; Strycker, Lisa A


    This book provides a comprehensive introduction to latent variable growth curve modeling (LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its various methodological extensions, including a number of practical examples of its use. It is designed to take advantage of the reader's familiarity with analysis of variance and structural equation modeling (SEM) in introducing LGM techniques. Sample data, syntax, input and output, are provided for EQS, Amos, LISREL, and Mplus on the book's CD. Throughout the book, the authors present a variety of LGM techniques that are useful for many different research designs, and numerous figures provide helpful diagrams of the examples.Updated throughout, the second edition features three new chapters-growth modeling with ordered categorical variables, growth mixture modeling, and pooled interrupted time series LGM approaches. Following a new organization, the book now covers the development of the LGM, followed by chapters on multiple-group is...

  17. A Probabilistic Model for Propagating Ungauged Basin Runoff Prediction Variability and Uncertainty Into Estuarine Water Quality Dynamics and Water Quality-Based Management Decisions (United States)

    Anderson, R.; Gronewold, A.; Alameddine, I.; Reckhow, K.


    The latest official assessment of United States (US) surface water quality indicates that pathogens are a leading cause of coastal shoreline water quality standard violations. Rainfall-runoff and hydrodynamic water quality models are commonly used to predict fecal indicator bacteria (FIB) concentrations in these waters and to subsequently identify climate change, land use, and pollutant mitigation scenarios which might improve water quality and lead to reinstatement of a designated use. While decay, settling, and other loss kinetics dominate FIB fate and transport in freshwater systems, previous authors identify tidal advection as a dominant fate and transport process in coastal estuaries. As a result, acknowledging hydrodynamic model input (e.g. watershed runoff) variability and parameter (e.g tidal dynamics parameter) uncertainty is critical to building a robust coastal water quality model. Despite the widespread application of watershed models (and associated model calibration procedures), we find model inputs and parameters are commonly encoded as deterministic point estimates (as opposed to random variables), an approach which effectively ignores potential sources of variability and uncertainty. Here, we present an innovative approach to building, calibrating, and propagating uncertainty and variability through a coupled data-based mechanistic (DBM) rainfall-runoff and tidal prism water quality model. While we apply the model to an ungauged tributary of the Newport River Estuary (one of many currently impaired shellfish harvesting waters in Eastern North Carolina), our model can be used to evaluate water quality restoration scenarios for coastal waters with a wide range of designated uses. We begin by calibrating the DBM rainfall-runoff model, as implemented in the IHACRES software package, using a regionalized calibration approach. We then encode parameter estimates as random variables (in the rainfall-runoff component of our comprehensive model) via the

  18. Variable Selection for Regression Models of Percentile Flows (United States)

    Fouad, G.


    Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

  19. A network-based approach for semi-quantitative knowledge mining and its application to yield variability (United States)

    Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph


    Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. A systematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields.

  20. An innovative statistical approach for analysing non-continuous variables in environmental monitoring: assessing temporal trends of TBT pollution. (United States)

    Santos, José António; Galante-Oliveira, Susana; Barroso, Carlos


    The current work presents an innovative statistical approach to model ordinal variables in environmental monitoring studies. An ordinal variable has values that can only be compared as "less", "equal" or "greater" and it is not possible to have information about the size of the difference between two particular values. The example of ordinal variable under this study is the vas deferens sequence (VDS) used in imposex (superimposition of male sexual characters onto prosobranch females) field assessment programmes for monitoring tributyltin (TBT) pollution. The statistical methodology presented here is the ordered logit regression model. It assumes that the VDS is an ordinal variable whose values match up a process of imposex development that can be considered continuous in both biological and statistical senses and can be described by a latent non-observable continuous variable. This model was applied to the case study of Nucella lapillus imposex monitoring surveys conducted in the Portuguese coast between 2003 and 2008 to evaluate the temporal evolution of TBT pollution in this country. In order to produce more reliable conclusions, the proposed model includes covariates that may influence the imposex response besides TBT (e.g. the shell size). The model also provides an analysis of the environmental risk associated to TBT pollution by estimating the probability of the occurrence of females with VDS ≥ 2 in each year, according to OSPAR criteria. We consider that the proposed application of this statistical methodology has a great potential in environmental monitoring whenever there is the need to model variables that can only be assessed through an ordinal scale of values.

  1. Study of solar radiation prediction and modeling of relationships between solar radiation and meteorological variables

    International Nuclear Information System (INIS)

    Sun, Huaiwei; Zhao, Na; Zeng, Xiaofan; Yan, Dong


    Highlights: • We investigate relationships between solar radiation and meteorological variables. • A strong relationship exists between solar radiation and sunshine duration. • Daily global radiation can be estimated accurately with ARMAX–GARCH models. • MGARCH model was applied to investigate time-varying relationships. - Abstract: The traditional approaches that employ the correlations between solar radiation and other measured meteorological variables are commonly utilized in studies. It is important to investigate the time-varying relationships between meteorological variables and solar radiation to determine which variables have the strongest correlations with solar radiation. In this study, the nonlinear autoregressive moving average with exogenous variable–generalized autoregressive conditional heteroscedasticity (ARMAX–GARCH) and multivariate GARCH (MGARCH) time-series approaches were applied to investigate the associations between solar radiation and several meteorological variables. For these investigations, the long-term daily global solar radiation series measured at three stations from January 1, 2004 until December 31, 2007 were used in this study. Stronger relationships were observed to exist between global solar radiation and sunshine duration than between solar radiation and temperature difference. The results show that 82–88% of the temporal variations of the global solar radiation were captured by the sunshine-duration-based ARMAX–GARCH models and 55–68% of daily variations were captured by the temperature-difference-based ARMAX–GARCH models. The advantages of the ARMAX–GARCH models were also confirmed by comparison of Auto-Regressive and Moving Average (ARMA) and neutral network (ANN) models in the estimation of daily global solar radiation. The strong heteroscedastic persistency of the global solar radiation series was revealed by the AutoRegressive Conditional Heteroscedasticity (ARCH) and Generalized Auto

  2. One Approach for Dynamic L-lysine Modelling of Repeated Fed-batch Fermentation

    Directory of Open Access Journals (Sweden)

    Kalin Todorov


    Full Text Available This article deals with establishment of dynamic unstructured model of variable volume fed-batch fermentation process with intensive droppings for L-lysine production. The presented approach of the investigation includes the following main procedures: description of the process by generalized stoichiometric equations; preliminary data processing and calculation of specific rates for main kinetic variables; identification of the specific rates as a second-order non-linear dynamic models; establishment and optimisation of dynamic model of the process; simulation researches. MATLAB is used as a research environment.

  3. Developing Baltic cod recruitment models II : Incorporation of environmental variability and species interaction

    DEFF Research Database (Denmark)

    Köster, Fritz; Hinrichsen, H.H.; St. John, Michael


    We investigate whether a process-oriented approach based on the results of field, laboratory, and modelling studies can be used to develop a stock-environment-recruitment model for Central Baltic cod (Gadus morhua). Based on exploratory statistical analysis, significant variables influencing...... affecting survival of eggs, predation by clupeids on eggs, larval transport, and cannibalism. Results showed that recruitment in the most important spawning area, the Bornholm Basin, during 1976-1995 was related to egg production; however, other factors affecting survival of the eggs (oxygen conditions......, predation) were also significant and when incorporated explained 69% of the variation in 0-group recruitment. In other spawning areas, variable hydrographic conditions did not allow for regular successful egg development. Hence, relatively simple models proved sufficient to predict recruitment of 0-group...

  4. On Approaches to Analyze the Sensitivity of Simulated Hydrologic Fluxes to Model Parameters in the Community Land Model

    Directory of Open Access Journals (Sweden)

    Jie Bao


    Full Text Available Effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash–Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.

  5. Preliminary Multi-Variable Parametric Cost Model for Space Telescopes (United States)

    Stahl, H. Philip; Hendrichs, Todd


    This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

  6. A Generalized Stability Analysis of the AMOC in Earth System Models: Implication for Decadal Variability and Abrupt Climate Change

    Energy Technology Data Exchange (ETDEWEB)

    Fedorov, Alexey V. [Yale Univ., New Haven, CT (United States)


    The central goal of this research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) as related to climate variability and abrupt climate change within a hierarchy of climate models ranging from realistic ocean models to comprehensive Earth system models. Generalized Stability Analysis, a method that quantifies the transient and asymptotic growth of perturbations in the system, is one of the main approaches used throughout this project. The topics we have explored range from physical mechanisms that control AMOC variability to the factors that determine AMOC predictability in the Earth system models, to the stability and variability of the AMOC in past climates.

  7. Weight restrictions on geography variables in the DEA benchmarking model for Norwegian electricity distribution companies

    Energy Technology Data Exchange (ETDEWEB)

    Bjoerndal, Endre; Bjoerndal, Mette; Camanho, Ana


    The DEA model for the distribution networks is designed to take into account the diverse operating conditions of the companies through so-called 'geography' variables. Our analyses show that companies with difficult operating conditions tend to be rewarded with relatively high efficiency scores, and this is the reason for introducing weight restrictions. We discuss the relative price restrictions suggested for geography and high voltage variables by NVE (2008), and we compare these to an alternative approach by which the total (virtual) weight of the geography variables is restricted. The main difference between the two approaches is that the former tends to affect more companies, but to a lesser extent, than the latter. We also discuss how to set the restriction limits. Since the virtual restrictions are at a more aggregated level than the relative ones, it may be easier to establish the limits with this approach. Finally, we discuss implementation issues, and give a short overview of available software. (Author). 18 refs., figs

  8. Internal variability of a 3-D ocean model

    Directory of Open Access Journals (Sweden)

    Bjarne Büchmann


    Full Text Available The Defence Centre for Operational Oceanography runs operational forecasts for the Danish waters. The core setup is a 60-layer baroclinic circulation model based on the General Estuarine Transport Model code. At intervals, the model setup is tuned to improve ‘model skill’ and overall performance. It has been an area of concern that the uncertainty inherent to the stochastical/chaotic nature of the model is unknown. Thus, it is difficult to state with certainty that a particular setup is improved, even if the computed model skill increases. This issue also extends to the cases, where the model is tuned during an iterative process, where model results are fed back to improve model parameters, such as bathymetry.An ensemble of identical model setups with slightly perturbed initial conditions is examined. It is found that the initial perturbation causes the models to deviate from each other exponentially fast, causing differences of several PSUs and several kelvin within a few days of simulation. The ensemble is run for a full year, and the long-term variability of salinity and temperature is found for different regions within the modelled area. Further, the developing time scale is estimated for each region, and great regional differences are found – in both variability and time scale. It is observed that periods with very high ensemble variability are typically short-term and spatially limited events.A particular event is examined in detail to shed light on how the ensemble ‘behaves’ in periods with large internal model variability. It is found that the ensemble does not seem to follow any particular stochastic distribution: both the ensemble variability (standard deviation or range as well as the ensemble distribution within that range seem to vary with time and place. Further, it is observed that a large spatial variability due to mesoscale features does not necessarily correlate to large ensemble variability. These findings bear

  9. Generalized latent variable modeling multilevel, longitudinal, and structural equation models

    CERN Document Server

    Skrondal, Anders; Rabe-Hesketh, Sophia


    This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models.

  10. A Bayesian network approach for modeling local failure in lung cancer

    International Nuclear Information System (INIS)

    Oh, Jung Hun; Craft, Jeffrey; Al Lozi, Rawan; Vaidya, Manushka; Meng, Yifan; Deasy, Joseph O; Bradley, Jeffrey D; El Naqa, Issam


    Locally advanced non-small cell lung cancer (NSCLC) patients suffer from a high local failure rate following radiotherapy. Despite many efforts to develop new dose-volume models for early detection of tumor local failure, there was no reported significant improvement in their application prospectively. Based on recent studies of biomarker proteins' role in hypoxia and inflammation in predicting tumor response to radiotherapy, we hypothesize that combining physical and biological factors with a suitable framework could improve the overall prediction. To test this hypothesis, we propose a graphical Bayesian network framework for predicting local failure in lung cancer. The proposed approach was tested using two different datasets of locally advanced NSCLC patients treated with radiotherapy. The first dataset was collected retrospectively, which comprises clinical and dosimetric variables only. The second dataset was collected prospectively in which in addition to clinical and dosimetric information, blood was drawn from the patients at various time points to extract candidate biomarkers as well. Our preliminary results show that the proposed method can be used as an efficient method to develop predictive models of local failure in these patients and to interpret relationships among the different variables in the models. We also demonstrate the potential use of heterogeneous physical and biological variables to improve the model prediction. With the first dataset, we achieved better performance compared with competing Bayesian-based classifiers. With the second dataset, the combined model had a slightly higher performance compared to individual physical and biological models, with the biological variables making the largest contribution. Our preliminary results highlight the potential of the proposed integrated approach for predicting post-radiotherapy local failure in NSCLC patients.

  11. Variable selection for confounder control, flexible modeling and Collaborative Targeted Minimum Loss-based Estimation in causal inference (United States)

    Schnitzer, Mireille E.; Lok, Judith J.; Gruber, Susan


    This paper investigates the appropriateness of the integration of flexible propensity score modeling (nonparametric or machine learning approaches) in semiparametric models for the estimation of a causal quantity, such as the mean outcome under treatment. We begin with an overview of some of the issues involved in knowledge-based and statistical variable selection in causal inference and the potential pitfalls of automated selection based on the fit of the propensity score. Using a simple example, we directly show the consequences of adjusting for pure causes of the exposure when using inverse probability of treatment weighting (IPTW). Such variables are likely to be selected when using a naive approach to model selection for the propensity score. We describe how the method of Collaborative Targeted minimum loss-based estimation (C-TMLE; van der Laan and Gruber, 2010) capitalizes on the collaborative double robustness property of semiparametric efficient estimators to select covariates for the propensity score based on the error in the conditional outcome model. Finally, we compare several approaches to automated variable selection in low-and high-dimensional settings through a simulation study. From this simulation study, we conclude that using IPTW with flexible prediction for the propensity score can result in inferior estimation, while Targeted minimum loss-based estimation and C-TMLE may benefit from flexible prediction and remain robust to the presence of variables that are highly correlated with treatment. However, in our study, standard influence function-based methods for the variance underestimated the standard errors, resulting in poor coverage under certain data-generating scenarios. PMID:26226129

  12. Process informed accurate compact modelling of 14-nm FinFET variability and application to statistical 6T-SRAM simulations


    Wang, Xingsheng; Reid, Dave; Wang, Liping; Millar, Campbell; Burenkov, Alex; Evanschitzky, Peter; Baer, Eberhard; Lorenz, Juergen; Asenov, Asen


    This paper presents a TCAD based design technology co-optimization (DTCO) process for 14nm SOI FinFET based SRAM, which employs an enhanced variability aware compact modeling approach that fully takes process and lithography simulations and their impact on 6T-SRAM layout into account. Realistic double patterned gates and fins and their impacts are taken into account in the development of the variability-aware compact model. Finally, global process induced variability and local statistical var...

  13. Feedback structure based entropy approach for multiple-model estimation

    Institute of Scientific and Technical Information of China (English)

    Shen-tu Han; Xue Anke; Guo Yunfei


    The variable-structure multiple-model (VSMM) approach, one of the multiple-model (MM) methods, is a popular and effective approach in handling problems with mode uncertainties. The model sequence set adaptation (MSA) is the key to design a better VSMM. However, MSA methods in the literature have big room to improve both theoretically and practically. To this end, we propose a feedback structure based entropy approach that could find the model sequence sets with the smallest size under certain conditions. The filtered data are fed back in real time and can be used by the minimum entropy (ME) based VSMM algorithms, i.e., MEVSMM. Firstly, the full Markov chains are used to achieve optimal solutions. Secondly, the myopic method together with particle filter (PF) and the challenge match algorithm are also used to achieve sub-optimal solutions, a trade-off between practicability and optimality. The numerical results show that the proposed algorithm provides not only refined model sets but also a good robustness margin and very high accuracy.

  14. Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors

    Directory of Open Access Journals (Sweden)

    Ruche Guy


    Full Text Available Abstract Background During the last decades, dengue viruses have spread throughout the Americas region, with an increase in the number of severe forms of dengue. The surveillance system in Guadeloupe (French West Indies is currently operational for the detection of early outbreaks of dengue. The goal of the study was to improve this surveillance system by assessing a modelling tool to predict the occurrence of dengue epidemics few months ahead and thus to help an efficient dengue control. Methods The Box-Jenkins approach allowed us to fit a Seasonal Autoregressive Integrated Moving Average (SARIMA model of dengue incidence from 2000 to 2006 using clinical suspected cases. Then, this model was used for calculating dengue incidence for the year 2007 compared with observed data, using three different approaches: 1 year-ahead, 3 months-ahead and 1 month-ahead. Finally, we assessed the impact of meteorological variables (rainfall, temperature and relative humidity on the prediction of dengue incidence and outbreaks, incorporating them in the model fitting the best. Results The 3 months-ahead approach was the most appropriate for an effective and operational public health response, and the most accurate (Root Mean Square Error, RMSE = 0.85. Relative humidity at lag-7 weeks, minimum temperature at lag-5 weeks and average temperature at lag-11 weeks were variables the most positively correlated to dengue incidence in Guadeloupe, meanwhile rainfall was not. The predictive power of SARIMA models was enhanced by the inclusion of climatic variables as external regressors to forecast the year 2007. Temperature significantly affected the model for better dengue incidence forecasting (p-value = 0.03 for minimum temperature lag-5, p-value = 0.02 for average temperature lag-11 but not humidity. Minimum temperature at lag-5 weeks was the best climatic variable for predicting dengue outbreaks (RMSE = 0.72. Conclusion Temperature improves dengue outbreaks forecasts

  15. Fatigue Crack Propagation Under Variable Amplitude Loading Analyses Based on Plastic Energy Approach

    Directory of Open Access Journals (Sweden)

    Sofiane Maachou


    Full Text Available Plasticity effects at the crack tip had been recognized as “motor” of crack propagation, the growth of cracks is related to the existence of a crack tip plastic zone, whose formation and intensification is accompanied by energy dissipation. In the actual state of knowledge fatigue crack propagation is modeled using crack closure concept. The fatigue crack growth behavior under constant amplitude and variable amplitude loading of the aluminum alloy 2024 T351 are analyzed using in terms energy parameters. In the case of VAL (variable amplitude loading tests, the evolution of the hysteretic energy dissipated per block is shown similar with that observed under constant amplitude loading. A linear relationship between the crack growth rate and the hysteretic energy dissipated per block is obtained at high growth rates. For lower growth rates values, the relationship between crack growth rate and hysteretic energy dissipated per block can represented by a power law. In this paper, an analysis of fatigue crack propagation under variable amplitude loading based on energetic approach is proposed.

  16. Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables (United States)

    Henson, Robert A.; Templin, Jonathan L.; Willse, John T.


    This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

  17. Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability (United States)

    Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.


    This study forms part II of two papers describing ECHAM6-FESOM, a newly established global climate model with a unique multi-resolution sea ice-ocean component. While part I deals with the model description and the mean climate state, here we examine the internal climate variability of the model under constant present-day (1990) conditions. We (1) assess the internal variations in the model in terms of objective variability performance indices, (2) analyze variations in global mean surface temperature and put them in context to variations in the observed record, with particular emphasis on the recent warming slowdown, (3) analyze and validate the most common atmospheric and oceanic variability patterns, (4) diagnose the potential predictability of various climate indices, and (5) put the multi-resolution approach to the test by comparing two setups that differ only in oceanic resolution in the equatorial belt, where one ocean mesh keeps the coarse 1° resolution applied in the adjacent open-ocean regions and the other mesh is gradually refined to 0.25°. Objective variability performance indices show that, in the considered setups, ECHAM6-FESOM performs overall favourably compared to five well-established climate models. Internal variations of the global mean surface temperature in the model are consistent with observed fluctuations and suggest that the recent warming slowdown can be explained as a once-in-one-hundred-years event caused by internal climate variability; periods of strong cooling in the model (`hiatus' analogs) are mainly associated with ENSO-related variability and to a lesser degree also to PDO shifts, with the AMO playing a minor role. Common atmospheric and oceanic variability patterns are simulated largely consistent with their real counterparts. Typical deficits also found in other models at similar resolutions remain, in particular too weak non-seasonal variability of SSTs over large parts of the ocean and episodic periods of almost absent

  18. On the explaining-away phenomenon in multivariate latent variable models. (United States)

    van Rijn, Peter; Rijmen, Frank


    Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.

  19. Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

    KAUST Repository

    Fernandes, José Antonio


    A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of \\'state-of-the-art\\' uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs. © 2012 Elsevier Ltd.

  20. An alternative approach to exact wave functions for time-dependent coupled oscillator model of charged particle in variable magnetic field

    International Nuclear Information System (INIS)

    Menouar, Salah; Maamache, Mustapha; Choi, Jeong Ryeol


    The quantum states of time-dependent coupled oscillator model for charged particles subjected to variable magnetic field are investigated using the invariant operator methods. To do this, we have taken advantage of an alternative method, so-called unitary transformation approach, available in the framework of quantum mechanics, as well as a generalized canonical transformation method in the classical regime. The transformed quantum Hamiltonian is obtained using suitable unitary operators and is represented in terms of two independent harmonic oscillators which have the same frequencies as that of the classically transformed one. Starting from the wave functions in the transformed system, we have derived the full wave functions in the original system with the help of the unitary operators. One can easily take a complete description of how the charged particle behaves under the given Hamiltonian by taking advantage of these analytical wave functions.

  1. Effects of environmental variables on invasive amphibian activity: Using model selection on quantiles for counts (United States)

    Muller, Benjamin J.; Cade, Brian S.; Schwarzkoph, Lin


    Many different factors influence animal activity. Often, the value of an environmental variable may influence significantly the upper or lower tails of the activity distribution. For describing relationships with heterogeneous boundaries, quantile regressions predict a quantile of the conditional distribution of the dependent variable. A quantile count model extends linear quantile regression methods to discrete response variables, and is useful if activity is quantified by trapping, where there may be many tied (equal) values in the activity distribution, over a small range of discrete values. Additionally, different environmental variables in combination may have synergistic or antagonistic effects on activity, so examining their effects together, in a modeling framework, is a useful approach. Thus, model selection on quantile counts can be used to determine the relative importance of different variables in determining activity, across the entire distribution of capture results. We conducted model selection on quantile count models to describe the factors affecting activity (numbers of captures) of cane toads (Rhinella marina) in response to several environmental variables (humidity, temperature, rainfall, wind speed, and moon luminosity) over eleven months of trapping. Environmental effects on activity are understudied in this pest animal. In the dry season, model selection on quantile count models suggested that rainfall positively affected activity, especially near the lower tails of the activity distribution. In the wet season, wind speed limited activity near the maximum of the distribution, while minimum activity increased with minimum temperature. This statistical methodology allowed us to explore, in depth, how environmental factors influenced activity across the entire distribution, and is applicable to any survey or trapping regime, in which environmental variables affect activity.

  2. Probabilistic approaches to accounting for data variability in the practical application of bioavailability in predicting aquatic risks from metals. (United States)

    Ciffroy, Philippe; Charlatchka, Rayna; Ferreira, Daniel; Marang, Laura


    The biotic ligand model (BLM) theoretically enables the derivation of environmental quality standards that are based on true bioavailable fractions of metals. Several physicochemical variables (especially pH, major cations, dissolved organic carbon, and dissolved metal concentrations) must, however, be assigned to run the BLM, but they are highly variable in time and space in natural systems. This article describes probabilistic approaches for integrating such variability during the derivation of risk indexes. To describe each variable using a probability density function (PDF), several methods were combined to 1) treat censored data (i.e., data below the limit of detection), 2) incorporate the uncertainty of the solid-to-liquid partitioning of metals, and 3) detect outliers. From a probabilistic perspective, 2 alternative approaches that are based on log-normal and Γ distributions were tested to estimate the probability of the predicted environmental concentration (PEC) exceeding the predicted non-effect concentration (PNEC), i.e., p(PEC/PNEC>1). The probabilistic approach was tested on 4 real-case studies based on Cu-related data collected from stations on the Loire and Moselle rivers. The approach described in this article is based on BLM tools that are freely available for end-users (i.e., the Bio-Met software) and on accessible statistical data treatments. This approach could be used by stakeholders who are involved in risk assessments of metals for improving site-specific studies. Copyright © 2013 SETAC.

  3. Stochastic Fractional Programming Approach to a Mean and Variance Model of a Transportation Problem

    Directory of Open Access Journals (Sweden)

    V. Charles


    Full Text Available In this paper, we propose a stochastic programming model, which considers a ratio of two nonlinear functions and probabilistic constraints. In the former, only expected model has been proposed without caring variability in the model. On the other hand, in the variance model, the variability played a vital role without concerning its counterpart, namely, the expected model. Further, the expected model optimizes the ratio of two linear cost functions where as variance model optimize the ratio of two non-linear functions, that is, the stochastic nature in the denominator and numerator and considering expectation and variability as well leads to a non-linear fractional program. In this paper, a transportation model with stochastic fractional programming (SFP problem approach is proposed, which strikes the balance between previous models available in the literature.


    Directory of Open Access Journals (Sweden)

    Ripon Kumar Chakrabortty


    Full Text Available Now-a-day's many leading manufacturing industry have started to practice Six Sigma and Lean manufacturing concepts to boost up their productivity as well as quality of products. In this paper, the Six Sigma approach has been used to reduce process variability of a food processing industry in Bangladesh. DMAIC (Define,Measure, Analyze, Improve, & Control model has been used to implement the Six Sigma Philosophy. Five phases of the model have been structured step by step respectively. Different tools of Total Quality Management, Statistical Quality Control and Lean Manufacturing concepts likely Quality function deployment, P Control chart, Fish-bone diagram, Analytical Hierarchy Process, Pareto analysis have been used in different phases of the DMAIC model. The process variability have been tried to reduce by identify the root cause of defects and reducing it. The ultimate goal of this study is to make the process lean and increase the level of sigma.

  5. Modeling flow in fractured medium. Uncertainty analysis with stochastic continuum approach

    International Nuclear Information System (INIS)

    Niemi, A.


    For modeling groundwater flow in formation-scale fractured media, no general method exists for scaling the highly heterogeneous hydraulic conductivity data to model parameters. The deterministic approach is limited in representing the heterogeneity of a medium and the application of fracture network models has both conceptual and practical limitations as far as site-scale studies are concerned. The study investigates the applicability of stochastic continuum modeling at the scale of data support. No scaling of the field data is involved, and the original variability is preserved throughout the modeling. Contributions of various aspects to the total uncertainty in the modeling prediction can also be determined with this approach. Data from five crystalline rock sites in Finland are analyzed. (107 refs., 63 figs., 7 tabs.)

  6. The mediation proportion: a structural equation approach for estimating the proportion of exposure effect on outcome explained by an intermediate variable

    DEFF Research Database (Denmark)

    Ditlevsen, Susanne; Christensen, Ulla; Lynch, John


    It is often of interest to assess how much of the effect of an exposure on a response is mediated through an intermediate variable. However, systematic approaches are lacking, other than assessment of a surrogate marker for the endpoint of a clinical trial. We review a measure of "proportion...... of several intermediate variables. Binary or categorical variables can be included directly through threshold models. We call this measure the mediation proportion, that is, the part of an exposure effect on outcome explained by a third, intermediate variable. Two examples illustrate the approach. The first...... example is a randomized clinical trial of the effects of interferon-alpha on visual acuity in patients with age-related macular degeneration. In this example, the exposure, mediator and response are all binary. The second example is a common problem in social epidemiology-to find the proportion...

  7. Linear latent variable models: the lava-package

    DEFF Research Database (Denmark)

    Holst, Klaus Kähler; Budtz-Jørgensen, Esben


    are implemented including robust standard errors for clustered correlated data, multigroup analyses, non-linear parameter constraints, inference with incomplete data, maximum likelihood estimation with censored and binary observations, and instrumental variable estimators. In addition an extensive simulation......An R package for specifying and estimating linear latent variable models is presented. The philosophy of the implementation is to separate the model specification from the actual data, which leads to a dynamic and easy way of modeling complex hierarchical structures. Several advanced features...

  8. Multi-wheat-model ensemble responses to interannual climatic variability

    DEFF Research Database (Denmark)

    Ruane, A C; Hudson, N I; Asseng, S


    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and ......-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.......We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we...... evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal...

  9. Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients. (United States)

    Hu, Chuanpu; Randazzo, Bruce; Sharma, Amarnath; Zhou, Honghui


    Exposure-response modeling plays an important role in optimizing dose and dosing regimens during clinical drug development. The modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate the level of improvement achievable by jointly modeling two such endpoints in the latent variable IDR modeling framework through the sharing of model parameters. This is illustrated with an application to the exposure-response of guselkumab, a human IgG1 monoclonal antibody in clinical development that blocks IL-23. A Phase 2b study was conducted in 238 patients with psoriasis for which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA) scores. A latent variable Type I IDR model was developed to evaluate the therapeutic effect of guselkumab dosing on 75, 90 and 100% improvement of PASI scores from baseline and PGA scores, with placebo effect empirically modeled. The results showed that the joint model is able to describe the observed data better with fewer parameters compared with the common approach of separately modeling the endpoints.

  10. Fuzzy parametric uncertainty analysis of linear dynamical systems: A surrogate modeling approach (United States)

    Chowdhury, R.; Adhikari, S.


    Uncertainty propagation engineering systems possess significant computational challenges. This paper explores the possibility of using correlated function expansion based metamodelling approach when uncertain system parameters are modeled using Fuzzy variables. In particular, the application of High-Dimensional Model Representation (HDMR) is proposed for fuzzy finite element analysis of dynamical systems. The HDMR expansion is a set of quantitative model assessment and analysis tools for capturing high-dimensional input-output system behavior based on a hierarchy of functions of increasing dimensions. The input variables may be either finite-dimensional (i.e., a vector of parameters chosen from the Euclidean space RM) or may be infinite-dimensional as in the function space CM[0,1]. The computational effort to determine the expansion functions using the alpha cut method scales polynomially with the number of variables rather than exponentially. This logic is based on the fundamental assumption underlying the HDMR representation that only low-order correlations among the input variables are likely to have significant impacts upon the outputs for most high-dimensional complex systems. The proposed method is integrated with a commercial Finite Element software. Modal analysis of a simplified aircraft wing with Fuzzy parameters has been used to illustrate the generality of the proposed approach. In the numerical examples, triangular membership functions have been used and the results have been validated against direct Monte Carlo simulations.

  11. Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability (United States)

    Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos


    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

  12. Are revised models better models? A skill score assessment of regional interannual variability (United States)

    Sperber, Kenneth R.; Participating AMIP Modelling Groups


    Various skill scores are used to assess the performance of revised models relative to their original configurations. The interannual variability of all-India, Sahel and Nordeste rainfall and summer monsoon windshear is examined in integrations performed under the experimental design of the Atmospheric Model Intercomparison Project. For the indices considered, the revised models exhibit greater fidelity at simulating the observed interannual variability. Interannual variability of all-India rainfall is better simulated by models that have a more realistic rainfall climatology in the vicinity of India, indicating the beneficial effect of reducing systematic model error.

  13. Reduced modeling of signal transduction – a modular approach

    Directory of Open Access Journals (Sweden)

    Ederer Michael


    Full Text Available Abstract Background Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. Results We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. Conclusion The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good

  14. Exploring structural variability in X-ray crystallographic models using protein local optimization by torsion-angle sampling

    International Nuclear Information System (INIS)

    Knight, Jennifer L.; Zhou, Zhiyong; Gallicchio, Emilio; Himmel, Daniel M.; Friesner, Richard A.; Arnold, Eddy; Levy, Ronald M.


    Torsion-angle sampling, as implemented in the Protein Local Optimization Program (PLOP), is used to generate multiple structurally variable single-conformer models which are in good agreement with X-ray data. An ensemble-refinement approach to differentiate between positional uncertainty and conformational heterogeneity is proposed. Modeling structural variability is critical for understanding protein function and for modeling reliable targets for in silico docking experiments. Because of the time-intensive nature of manual X-ray crystallographic refinement, automated refinement methods that thoroughly explore conformational space are essential for the systematic construction of structurally variable models. Using five proteins spanning resolutions of 1.0–2.8 Å, it is demonstrated how torsion-angle sampling of backbone and side-chain libraries with filtering against both the chemical energy, using a modern effective potential, and the electron density, coupled with minimization of a reciprocal-space X-ray target function, can generate multiple structurally variable models which fit the X-ray data well. Torsion-angle sampling as implemented in the Protein Local Optimization Program (PLOP) has been used in this work. Models with the lowest R free values are obtained when electrostatic and implicit solvation terms are included in the effective potential. HIV-1 protease, calmodulin and SUMO-conjugating enzyme illustrate how variability in the ensemble of structures captures structural variability that is observed across multiple crystal structures and is linked to functional flexibility at hinge regions and binding interfaces. An ensemble-refinement procedure is proposed to differentiate between variability that is a consequence of physical conformational heterogeneity and that which reflects uncertainty in the atomic coordinates

  15. Coevolution of variability models and related software artifacts

    DEFF Research Database (Denmark)

    Passos, Leonardo; Teixeira, Leopoldo; Dinztner, Nicolas


    models coevolve with other artifact types, we study a large and complex real-world variant-rich software system: the Linux kernel. Specifically, we extract variability-coevolution patterns capturing changes in the variability model of the Linux kernel with subsequent changes in Makefiles and C source...

  16. Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely. (United States)

    Widaman, Keith F; Grimm, Kevin J; Early, Dawnté R; Robins, Richard W; Conger, Rand D


    Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group.

  17. A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites (United States)

    Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.


    Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

  18. Mechanical disequilibria in two-phase flow models: approaches by relaxation and by a reduced model

    International Nuclear Information System (INIS)

    Labois, M.


    This thesis deals with hyperbolic models for the simulation of compressible two-phase flows, to find alternatives to the classical bi-fluid model. We first establish a hierarchy of two-phase flow models, obtained according to equilibrium hypothesis between the physical variables of each phase. The use of Chapman-Enskog expansions enables us to link the different existing models to each other. Moreover, models that take into account small physical unbalances are obtained by means of expansion to the order one. The second part of this thesis focuses on the simulation of flows featuring velocity unbalances and pressure balances, in two different ways. First, a two-velocity two-pressure model is used, where non-instantaneous velocity and pressure relaxations are applied so that a balancing of these variables is obtained. A new one-velocity one-pressure dissipative model is then proposed, where the arising of second-order terms enables us to take into account unbalances between the phase velocities. We develop a numerical method based on a fractional step approach for this model. (author)

  19. S-variable approach to LMI-based robust control

    CERN Document Server

    Ebihara, Yoshio; Arzelier, Denis


    This book shows how the use of S-variables (SVs) in enhancing the range of problems that can be addressed with the already-versatile linear matrix inequality (LMI) approach to control can, in many cases, be put on a more unified, methodical footing. Beginning with the fundamentals of the SV approach, the text shows how the basic idea can be used for each problem (and when it should not be employed at all). The specific adaptations of the method necessitated by each problem are also detailed. The problems dealt with in the book have the common traits that: analytic closed-form solutions are not available; and LMIs can be applied to produce numerical solutions with a certain amount of conservatism. Typical examples are robustness analysis of linear systems affected by parametric uncertainties and the synthesis of a linear controller satisfying multiple, often  conflicting, design specifications. For problems in which LMI methods produce conservative results, the SV approach is shown to achieve greater accuracy...

  20. Investigation of clinical pharmacokinetic variability of an opioid antagonist through physiologically based absorption modeling. (United States)

    Ding, Xuan; He, Minxia; Kulkarni, Rajesh; Patel, Nita; Zhang, Xiaoyu


    Identifying the source of inter- and/or intrasubject variability in pharmacokinetics (PK) provides fundamental information in understanding the pharmacokinetics-pharmacodynamics relationship of a drug and project its efficacy and safety in clinical populations. This identification process can be challenging given that a large number of potential causes could lead to PK variability. Here we present an integrated approach of physiologically based absorption modeling to investigate the root cause of unexpectedly high PK variability of a Phase I clinical trial drug. LY2196044 exhibited high intersubject variability in the absorption phase of plasma concentration-time profiles in humans. This could not be explained by in vitro measurements of drug properties and excellent bioavailability with low variability observed in preclinical species. GastroPlus™ modeling suggested that the compound's optimal solubility and permeability characteristics would enable rapid and complete absorption in preclinical species and in humans. However, simulations of human plasma concentration-time profiles indicated that despite sufficient solubility and rapid dissolution of LY2196044 in humans, permeability and/or transit in the gastrointestinal (GI) tract may have been negatively affected. It was concluded that clinical PK variability was potentially due to the drug's antagonism on opioid receptors that affected its transit and absorption in the GI tract. Copyright © 2013 Wiley Periodicals, Inc.

  1. Improved variable reduction in partial least squares modelling by Global-Minimum Error Uninformative-Variable Elimination. (United States)

    Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C


    The calibration performance of Partial Least Squares regression (PLS) can be improved by eliminating uninformative variables. For PLS, many variable elimination methods have been developed. One is the Uninformative-Variable Elimination for PLS (UVE-PLS). However, the number of variables retained by UVE-PLS is usually still large. In UVE-PLS, variable elimination is repeated as long as the root mean squared error of cross validation (RMSECV) is decreasing. The set of variables in this first local minimum is retained. In this paper, a modification of UVE-PLS is proposed and investigated, in which UVE is repeated until no further reduction in variables is possible, followed by a search for the global RMSECV minimum. The method is called Global-Minimum Error Uninformative-Variable Elimination for PLS, denoted as GME-UVE-PLS or simply GME-UVE. After each iteration, the predictive ability of the PLS model, built with the remaining variable set, is assessed by RMSECV. The variable set with the global RMSECV minimum is then finally selected. The goal is to obtain smaller sets of variables with similar or improved predictability than those from the classical UVE-PLS method. The performance of the GME-UVE-PLS method is investigated using four data sets, i.e. a simulated set, NIR and NMR spectra, and a theoretical molecular descriptors set, resulting in twelve profile-response (X-y) calibrations. The selective and predictive performances of the models resulting from GME-UVE-PLS are statistically compared to those from UVE-PLS and 1-step UVE, one-sided paired t-tests. The results demonstrate that variable reduction with the proposed GME-UVE-PLS method, usually eliminates significantly more variables than the classical UVE-PLS, while the predictive abilities of the resulting models are better. With GME-UVE-PLS, a lower number of uninformative variables, without a chemical meaning for the response, may be retained than with UVE-PLS. The selectivity of the classical UVE method

  2. Improving plot- and regional-scale crop models for simulating impacts of climate variability and extremes (United States)

    Tao, F.; Rötter, R.


    Many studies on global climate report that climate variability is increasing with more frequent and intense extreme events1. There are quite large uncertainties from both the plot- and regional-scale models in simulating impacts of climate variability and extremes on crop development, growth and productivity2,3. One key to reducing the uncertainties is better exploitation of experimental data to eliminate crop model deficiencies and develop better algorithms that more adequately capture the impacts of extreme events, such as high temperature and drought, on crop performance4,5. In the present study, in a first step, the inter-annual variability in wheat yield and climate from 1971 to 2012 in Finland was investigated. Using statistical approaches the impacts of climate variability and extremes on wheat growth and productivity were quantified. In a second step, a plot-scale model, WOFOST6, and a regional-scale crop model, MCWLA7, were calibrated and validated, and applied to simulate wheat growth and yield variability from 1971-2012. Next, the estimated impacts of high temperature stress, cold damage, and drought stress on crop growth and productivity based on the statistical approaches, and on crop simulation models WOFOST and MCWLA were compared. Then, the impact mechanisms of climate extremes on crop growth and productivity in the WOFOST model and MCWLA model were identified, and subsequently, the various algorithm and impact functions were fitted against the long-term crop trial data. Finally, the impact mechanisms, algorithms and functions in WOFOST model and MCWLA model were improved to better simulate the impacts of climate variability and extremes, particularly high temperature stress, cold damage and drought stress for location-specific and large area climate impact assessments. Our studies provide a good example of how to improve, in parallel, the plot- and regional-scale models for simulating impacts of climate variability and extremes, as needed for

  3. Linear mixed-effects modeling approach to FMRI group analysis. (United States)

    Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W


    Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analyses would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity

  4. Tractable approximations for probabilistic models: The adaptive Thouless-Anderson-Palmer mean field approach

    DEFF Research Database (Denmark)

    Opper, Manfred; Winther, Ole


    We develop an advanced mean held method for approximating averages in probabilistic data models that is based on the Thouless-Anderson-Palmer (TAP) approach of disorder physics. In contrast to conventional TAP. where the knowledge of the distribution of couplings between the random variables...... is required. our method adapts to the concrete couplings. We demonstrate the validity of our approach, which is so far restricted to models with nonglassy behavior? by replica calculations for a wide class of models as well as by simulations for a real data set....

  5. From Transition Systems to Variability Models and from Lifted Model Checking Back to UPPAAL

    DEFF Research Database (Denmark)

    Dimovski, Aleksandar; Wasowski, Andrzej


    efficient lifted (family-based) model checking for real-time variability models. This reduces the cost of maintaining specialized family-based real-time model checkers. Real-time variability models can be model checked using the standard UPPAAL. We have implemented abstractions as syntactic source...

  6. The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability. (United States)

    Marković, Snežana; Kerč, Janez; Horvat, Matej


    We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

  7. Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach

    DEFF Research Database (Denmark)

    Asgharian, Hossein; Hou, Ai Jun; Javed, Farrukh


    This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term compone......This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long...

  8. Modelling Inter-relationships among water, governance, human development variables in developing countries with Bayesian networks. (United States)

    Dondeynaz, C.; Lopez-Puga, J.; Carmona-Moreno, C.


    Improving Water and Sanitation Services (WSS), being a complex and interdisciplinary issue, passes through collaboration and coordination of different sectors (environment, health, economic activities, governance, and international cooperation). This inter-dependency has been recognised with the adoption of the "Integrated Water Resources Management" principles that push for the integration of these various dimensions involved in WSS delivery to ensure an efficient and sustainable management. The understanding of these interrelations appears as crucial for decision makers in the water sector in particular in developing countries where WSS still represent an important leverage for livelihood improvement. In this framework, the Joint Research Centre of the European Commission has developed a coherent database (WatSan4Dev database) containing 29 indicators from environmental, socio-economic, governance and financial aid flows data focusing on developing countries (Celine et al, 2011 under publication). The aim of this work is to model the WatSan4Dev dataset using probabilistic models to identify the key variables influencing or being influenced by the water supply and sanitation access levels. Bayesian Network Models are suitable to map the conditional dependencies between variables and also allows ordering variables by level of influence on the dependent variable. Separated models have been built for water supply and for sanitation because of different behaviour. The models are validated if complying with statistical criteria but either with scientific knowledge and literature. A two steps approach has been adopted to build the structure of the model; Bayesian network is first built for each thematic cluster of variables (e.g governance, agricultural pressure, or human development) keeping a detailed level for interpretation later one. A global model is then built based on significant indicators of each cluster being previously modelled. The structure of the

  9. A generalised chemical precipitation modelling approach in wastewater treatment applied to calcite

    DEFF Research Database (Denmark)

    Mbamba, Christian Kazadi; Batstone, Damien J.; Flores Alsina, Xavier


    , the present study aims to identify a broadly applicable precipitation modelling approach. The study uses two experimental platforms applied to calcite precipitating from synthetic aqueous solutions to identify and validate the model approach. Firstly, dynamic pH titration tests are performed to define...... an Arrhenius-style correction of kcryst. The influence of magnesium (a common and representative added impurity) on kcryst was found to be significant but was considered an optional correction because of a lesser influence as compared to that of temperature. Other variables such as ionic strength and pH were...

  10. An Investigation into the Relationship among Psychiatric, Demographic and Socio-Economic Variables with Bayesian Network Modeling

    Directory of Open Access Journals (Sweden)

    Gunal Bilek


    Full Text Available The aim of this paper is to investigate the factors influencing the Beck Depression Inventory score, the Beck Hopelessness Scale score and the Rosenberg Self-Esteem score and the relationships among the psychiatric, demographic and socio-economic variables with Bayesian network modeling. The data of 823 university students consist of 21 continuous and discrete relevant psychiatric, demographic and socio-economic variables. After the discretization of the continuous variables by two approaches, two Bayesian networks models are constructed using the b n l e a r n package in R, and the results are presented via figures and probabilities. One of the most significant results is that in the first Bayesian network model, the gender of the students influences the level of depression, with female students being more depressive. In the second model, social activity directly influences the level of depression. In each model, depression influences both the level of hopelessness and self-esteem in students; additionally, as the level of depression increases, the level of hopelessness increases, but the level of self-esteem drops.

  11. A variable age of onset segregation model for linkage analysis, with correction for ascertainment, applied to glioma

    DEFF Research Database (Denmark)

    Sun, Xiangqing; Vengoechea, Jaime; Elston, Robert


    We propose a 2-step model-based approach, with correction for ascertainment, to linkage analysis of a binary trait with variable age of onset and apply it to a set of multiplex pedigrees segregating for adult glioma....

  12. Predictive value of EEG in postanoxic encephalopathy: A quantitative model-based approach. (United States)

    Efthymiou, Evdokia; Renzel, Roland; Baumann, Christian R; Poryazova, Rositsa; Imbach, Lukas L


    The majority of comatose patients after cardiac arrest do not regain consciousness due to severe postanoxic encephalopathy. Early and accurate outcome prediction is therefore essential in determining further therapeutic interventions. The electroencephalogram is a standardized and commonly available tool used to estimate prognosis in postanoxic patients. The identification of pathological EEG patterns with poor prognosis relies however primarily on visual EEG scoring by experts. We introduced a model-based approach of EEG analysis (state space model) that allows for an objective and quantitative description of spectral EEG variability. We retrospectively analyzed standard EEG recordings in 83 comatose patients after cardiac arrest between 2005 and 2013 in the intensive care unit of the University Hospital Zürich. Neurological outcome was assessed one month after cardiac arrest using the Cerebral Performance Category. For a dynamic and quantitative EEG analysis, we implemented a model-based approach (state space analysis) to quantify EEG background variability independent from visual scoring of EEG epochs. Spectral variability was compared between groups and correlated with clinical outcome parameters and visual EEG patterns. Quantitative assessment of spectral EEG variability (state space velocity) revealed significant differences between patients with poor and good outcome after cardiac arrest: Lower mean velocity in temporal electrodes (T4 and T5) was significantly associated with poor prognostic outcome (pEEG patterns such as generalized periodic discharges (pEEG analysis (state space analysis) provides a novel, complementary marker for prognosis in postanoxic encephalopathy. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Comparison of Two Grid Refinement Approaches for High Resolution Regional Climate Modeling: MPAS vs WRF (United States)

    Leung, L.; Hagos, S. M.; Rauscher, S.; Ringler, T.


    This study compares two grid refinement approaches using global variable resolution model and nesting for high-resolution regional climate modeling. The global variable resolution model, Model for Prediction Across Scales (MPAS), and the limited area model, Weather Research and Forecasting (WRF) model, are compared in an idealized aqua-planet context with a focus on the spatial and temporal characteristics of tropical precipitation simulated by the models using the same physics package from the Community Atmosphere Model (CAM4). For MPAS, simulations have been performed with a quasi-uniform resolution global domain at coarse (1 degree) and high (0.25 degree) resolution, and a variable resolution domain with a high-resolution region at 0.25 degree configured inside a coarse resolution global domain at 1 degree resolution. Similarly, WRF has been configured to run on a coarse (1 degree) and high (0.25 degree) resolution tropical channel domain as well as a nested domain with a high-resolution region at 0.25 degree nested two-way inside the coarse resolution (1 degree) tropical channel. The variable resolution or nested simulations are compared against the high-resolution simulations that serve as virtual reality. Both MPAS and WRF simulate 20-day Kelvin waves propagating through the high-resolution domains fairly unaffected by the change in resolution. In addition, both models respond to increased resolution with enhanced precipitation. Grid refinement induces zonal asymmetry in precipitation (heating), accompanied by zonal anomalous Walker like circulations and standing Rossby wave signals. However, there are important differences between the anomalous patterns in MPAS and WRF due to differences in the grid refinement approaches and sensitivity of model physics to grid resolution. This study highlights the need for "scale aware" parameterizations in variable resolution and nested regional models.

  14. Modelling of Sub-daily Hydrological Processes Using Daily Time-Step Models: A Distribution Function Approach to Temporal Scaling (United States)

    Kandel, D. D.; Western, A. W.; Grayson, R. B.


    erosion models. The statistical description of sub-daily variability is thus propagated through the model, allowing the effects of variability to be captured in the simulations. This results in cdfs of various fluxes, the integration of which over a day gives respective daily totals. Using 42-plot-years of surface runoff and soil erosion data from field studies in different environments from Australia and Nepal, simulation results from this cdf approach are compared with the sub-hourly (2-minute for Nepal and 6-minute for Australia) and daily models having similar process descriptions. Significant improvements in the simulation of surface runoff and erosion are achieved, compared with a daily model that uses average daily rainfall intensities. The cdf model compares well with a sub-hourly time-step model. This suggests that the approach captures the important effects of sub-daily variability while utilizing commonly available daily information. It is also found that the model parameters are more robustly defined using the cdf approach compared with the effective values obtained at the daily scale. This suggests that the cdf approach may offer improved model transferability spatially (to other areas) and temporally (to other periods).

  15. Mathematical evaluation of similarity factor using various weighing approaches on aceclofenac marketed formulations by model-independent method. (United States)

    Soni, T G; Desai, J U; Nagda, C D; Gandhi, T R; Chotai, N P


    The US Food and Drug Administration's (FDA's) guidance for industry on dissolution testing of immediate-release solid oral dosage forms describes that drug dissolution may be the rate limiting step for drug absorption in the case of low solubility/high permeability drugs (BCS class II drugs). US FDA Guidance describes the model-independent mathematical approach proposed by Moore and Flanner for calculating a similarity factor (f2) of dissolution across a suitable time interval. In the present study, the similarity factor was calculated on dissolution data of two marketed aceclofenac tablets (a BCS class II drug) using various weighing approaches proposed by Gohel et al. The proposed approaches were compared with a conventional approach (W = 1). On the basis of consideration of variability, preference is given in the order of approach 3 > approach 2 > approach 1 as approach 3 considers batch-to-batch as well as within-samples variability and shows best similarity profile. Approach 2 considers batch-to batch variability with higher specificity than approach 1.

  16. Agent-based modeling: a new approach for theory building in social psychology. (United States)

    Smith, Eliot R; Conrey, Frederica R


    Most social and psychological phenomena occur not as the result of isolated decisions by individuals but rather as the result of repeated interactions between multiple individuals over time. Yet the theory-building and modeling techniques most commonly used in social psychology are less than ideal for understanding such dynamic and interactive processes. This article describes an alternative approach to theory building, agent-based modeling (ABM), which involves simulation of large numbers of autonomous agents that interact with each other and with a simulated environment and the observation of emergent patterns from their interactions. The authors believe that the ABM approach is better able than prevailing approaches in the field, variable-based modeling (VBM) techniques such as causal modeling, to capture types of complex, dynamic, interactive processes so important in the social world. The article elaborates several important contrasts between ABM and VBM and offers specific recommendations for learning more and applying the ABM approach.


    Directory of Open Access Journals (Sweden)



    Full Text Available This paper presents a simulation model of a complex system, in this case a financial market, using a MultiAgent Based Simulation approach. Such model takes into account microlevel aspects like the Continuous Double Auction mechanism, which is widely used within stock markets, as well as investor agents reasoning who participate looking for profits. To model such reasoning several variables were considered including general stocks information like profitability and volatility, but also some agent's aspects like their risk tendency. All these variables are incorporated throughout a fuzzy logic approach trying to represent in a faithful manner the kind of reasoning that nonexpert investors have, including a stochastic component in order to model human factors.

  18. Preliminary Multi-Variable Cost Model for Space Telescopes (United States)

    Stahl, H. Philip; Hendrichs, Todd


    Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. This paper reviews the methodology used to develop space telescope cost models; summarizes recently published single variable models; and presents preliminary results for two and three variable cost models. Some of the findings are that increasing mass reduces cost; it costs less per square meter of collecting aperture to build a large telescope than a small telescope; and technology development as a function of time reduces cost at the rate of 50% per 17 years.

  19. The effect of the number of seed variables on the performance of Cooke′s classical model

    International Nuclear Information System (INIS)

    Eggstaff, Justin W.; Mazzuchi, Thomas A.; Sarkani, Shahram


    In risk analysis, Cooke′s classical model for aggregating expert judgment has been widely used for over 20 years. However, the validity of this model has been the subject of much debate. Critics assert that this model′s scoring rule may unintentionally reward experts who manipulate their quantile estimates in order to receive a greater weight. In addition, the question of the number of seed variables required to ensure adequate performance of Cooke′s classical model remains unanswered. In this study, we conduct a comprehensive examination of the model through an iterative, cross validation test to perform an out-of-sample comparison between Cooke′s classical model and the equal-weight linear opinion pool method on almost all of the expert judgment studies compiled by Cooke and colleagues to date. Our results indicate that Cooke′s classical model significantly outperforms equally weighting expert judgment, regardless of the number of seed variables used; however, there may, in fact, be a maximum number of seed variables beyond which Cooke′s model cannot outperform an equally-weighted panel. - Highlights: • We examine Cooke′s classical model through an iterative, cross validation test. • The performance-based and equally weighted decision makers are compared. • Results strengthen Cooke′s argument for a two-fold cross-validation approach. • Accuracy test results show strong support in favor of Cooke′s classical method. • There may be a maximum number of seed variables that ensures model performance

  20. Parameter Estimation of Structural Equation Modeling Using Bayesian Approach

    Directory of Open Access Journals (Sweden)

    Dewi Kurnia Sari


    Full Text Available Leadership is a process of influencing, directing or giving an example of employees in order to achieve the objectives of the organization and is a key element in the effectiveness of the organization. In addition to the style of leadership, the success of an organization or company in achieving its objectives can also be influenced by the commitment of the organization. Where organizational commitment is a commitment created by each individual for the betterment of the organization. The purpose of this research is to obtain a model of leadership style and organizational commitment to job satisfaction and employee performance, and determine the factors that influence job satisfaction and employee performance using SEM with Bayesian approach. This research was conducted at Statistics FNI employees in Malang, with 15 people. The result of this study showed that the measurement model, all significant indicators measure each latent variable. Meanwhile in the structural model, it was concluded there are a significant difference between the variables of Leadership Style and Organizational Commitment toward Job Satisfaction directly as well as a significant difference between Job Satisfaction on Employee Performance. As for the influence of Leadership Style and variable Organizational Commitment on Employee Performance directly declared insignificant.

  1. On the derivation of thermodynamic restrictions for materials with internal state variables

    International Nuclear Information System (INIS)

    Malmberg, T.


    Thermodynamic restrictions for the constitutive relations of an internal variable model are derived by evaluating the Clausius-Duhem entropy inequality with two different approaches. The classical Coleman-Noll argumentation of Rational Thermodynamics applied by Coleman and Gurtin to an internal variable model is summarized. This approach requires an arbitrary modulation of body forces and heat supply in the interior of the body which is subject to criticism. The second approach applied in this presentation is patterned after a concept of Mueller and Liu, originally developed within the context of a different entropy inequality and different classes of constitutive models. For the internal variable model the second approach requires only the modulation of initial values on the boundary of the body. In the course of the development of the second approach certain differences to the argumentation of Mueller and Liu become evident and are pointed out. Finally, the results demonstrate that the first and second approach give the same thermodynamic restrictions for the internal variable model. The derived residual entropy inequality requires further analysis. (orig.) [de

  2. Genetic programming based models in plant tissue culture: An addendum to traditional statistical approach. (United States)

    Mridula, Meenu R; Nair, Ashalatha S; Kumar, K Satheesh


    In this paper, we compared the efficacy of observation based modeling approach using a genetic algorithm with the regular statistical analysis as an alternative methodology in plant research. Preliminary experimental data on in vitro rooting was taken for this study with an aim to understand the effect of charcoal and naphthalene acetic acid (NAA) on successful rooting and also to optimize the two variables for maximum result. Observation-based modelling, as well as traditional approach, could identify NAA as a critical factor in rooting of the plantlets under the experimental conditions employed. Symbolic regression analysis using the software deployed here optimised the treatments studied and was successful in identifying the complex non-linear interaction among the variables, with minimalistic preliminary data. The presence of charcoal in the culture medium has a significant impact on root generation by reducing basal callus mass formation. Such an approach is advantageous for establishing in vitro culture protocols as these models will have significant potential for saving time and expenditure in plant tissue culture laboratories, and it further reduces the need for specialised background.

  3. Squeezing more information out of time variable gravity data with a temporal decomposition approach

    DEFF Research Database (Denmark)

    Barletta, Valentina Roberta; Bordoni, A.; Aoudia, A.


    an explorative approach based on a suitable time series decomposition, which does not rely on predefined time signatures. The comparison and validation against the fitting approach commonly used in GRACE literature shows a very good agreement for what concerns trends and periodic signals on one side......A measure of the Earth's gravity contains contributions from solid Earth as well as climate-related phenomena, that cannot be easily distinguished both in time and space. After more than 7years, the GRACE gravity data available now support more elaborate analysis on the time series. We propose...... used to assess the possibility of finding evidence of meaningful geophysical signals different from hydrology over Africa in GRACE data. In this case we conclude that hydrological phenomena are dominant and so time variable gravity data in Africa can be directly used to calibrate hydrological models....

  4. Analysis models for variables associated with breastfeeding duration

    Directory of Open Access Journals (Sweden)

    Edson Theodoro dos S. Neto


    Full Text Available OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78% children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages. RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55 and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1 increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3 and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5. However, protective factors (maternal age and family income differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

  5. The long-term variability of cosmic ray protons in the heliosphere: A modeling approach

    Directory of Open Access Journals (Sweden)

    M.S. Potgieter


    Full Text Available Galactic cosmic rays are charged particles created in our galaxy and beyond. They propagate through interstellar space to eventually reach the heliosphere and Earth. Their transport in the heliosphere is subjected to four modulation processes: diffusion, convection, adiabatic energy changes and particle drifts. Time-dependent changes, caused by solar activity which varies from minimum to maximum every ∼11 years, are reflected in cosmic ray observations at and near Earth and along spacecraft trajectories. Using a time-dependent compound numerical model, the time variation of cosmic ray protons in the heliosphere is studied. It is shown that the modeling approach is successful and can be used to study long-term modulation cycles.

  6. Introduction to statistical modelling 2: categorical variables and interactions in linear regression. (United States)

    Lunt, Mark


    In the first article in this series we explored the use of linear regression to predict an outcome variable from a number of predictive factors. It assumed that the predictive factors were measured on an interval scale. However, this article shows how categorical variables can also be included in a linear regression model, enabling predictions to be made separately for different groups and allowing for testing the hypothesis that the outcome differs between groups. The use of interaction terms to measure whether the effect of a particular predictor variable differs between groups is also explained. An alternative approach to testing the difference between groups of the effect of a given predictor, which consists of measuring the effect in each group separately and seeing whether the statistical significance differs between the groups, is shown to be misleading. © The Author 2013. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email:

  7. Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting (United States)

    Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter


    Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

  8. Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting. (United States)

    Suchting, Robert; Gowin, Joshua L; Green, Charles E; Walss-Bass, Consuelo; Lane, Scott D


    Rationale : Given datasets with a large or diverse set of predictors of aggression, machine learning (ML) provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior. Objectives : The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5) polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults. Methods : The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a) select variables from an initial set of 20 to build a model of trait aggression; and then (b) reduce that model to maximize parsimony and generalizability. Results : From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ) total score, with R 2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect), childhood trauma (physical abuse and neglect), and the FKBP5_13 gene (rs1360780). The six-factor model approximated the initial eight-factor model at 99.4% of R 2 . Conclusions : Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for

  9. Understanding and forecasting polar stratospheric variability with statistical models

    Directory of Open Access Journals (Sweden)

    C. Blume


    Full Text Available The variability of the north-polar stratospheric vortex is a prominent aspect of the middle atmosphere. This work investigates a wide class of statistical models with respect to their ability to model geopotential and temperature anomalies, representing variability in the polar stratosphere. Four partly nonstationary, nonlinear models are assessed: linear discriminant analysis (LDA; a cluster method based on finite elements (FEM-VARX; a neural network, namely the multi-layer perceptron (MLP; and support vector regression (SVR. These methods model time series by incorporating all significant external factors simultaneously, including ENSO, QBO, the solar cycle, volcanoes, to then quantify their statistical importance. We show that variability in reanalysis data from 1980 to 2005 is successfully modeled. The period from 2005 to 2011 can be hindcasted to a certain extent, where MLP performs significantly better than the remaining models. However, variability remains that cannot be statistically hindcasted within the current framework, such as the unexpected major warming in January 2009. Finally, the statistical model with the best generalization performance is used to predict a winter 2011/12 with warm and weak vortex conditions. A vortex breakdown is predicted for late January, early February 2012.

  10. Gaussian Mixture Model of Heart Rate Variability (United States)

    Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario


    Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters. PMID:22666386

  11. Verification of models for ballistic movement time and endpoint variability. (United States)

    Lin, Ray F; Drury, Colin G


    A hand control movement is composed of several ballistic movements. The time required in performing a ballistic movement and its endpoint variability are two important properties in developing movement models. The purpose of this study was to test potential models for predicting these two properties. Twelve participants conducted ballistic movements of specific amplitudes using a drawing tablet. The measured data of movement time and endpoint variability were then used to verify the models. This study was successful with Hoffmann and Gan's movement time model (Hoffmann, 1981; Gan and Hoffmann 1988) predicting more than 90.7% data variance for 84 individual measurements. A new theoretically developed ballistic movement variability model, proved to be better than Howarth, Beggs, and Bowden's (1971) model, predicting on average 84.8% of stopping-variable error and 88.3% of aiming-variable errors. These two validated models will help build solid theoretical movement models and evaluate input devices. This article provides better models for predicting end accuracy and movement time of ballistic movements that are desirable in rapid aiming tasks, such as keying in numbers on a smart phone. The models allow better design of aiming tasks, for example button sizes on mobile phones for different user populations.

  12. Latent variable modeling%建立隐性变量模型

    Institute of Scientific and Technical Information of China (English)



    @@ A latent variable model, as the name suggests,is a statistical model that contains latent, that is, unobserved, variables.Their roots go back to Spearman's 1904 seminal work[1] on factor analysis,which is arguably the first well-articulated latent variable model to be widely used in psychology, mental health research, and allied disciplines.Because of the association of factor analysis with early studies of human intelligence, the fact that key variables in a statistical model are, on occasion, unobserved has been a point of lingering contention and controversy.The reader is assured, however, that a latent variable,defined in the broadest manner, is no more mysterious than an error term in a normal theory linear regression model or a random effect in a mixed model.

  13. Patterns and Variability in Global Ocean Chlorophyll: Satellite Observations and Modeling (United States)

    Gregg, Watson


    Recent analyses of SeaWiFS data have shown that global ocean chlorophyll has increased more than 4% since 1998. The North Pacific ocean basin has increased nearly 19%. These trend analyses follow earlier results showing decadal declines in global ocean chlorophyll and primary production. To understand the causes of these changes and trends we have applied the newly developed NASA Ocean Biogeochemical Assimilation Model (OBAM), which is driven in mechanistic fashion by surface winds, sea surface temperature, atmospheric iron deposition, sea ice, and surface irradiance. The model utilizes chlorophyll from SeaWiFS in a daily assimilation. The model has in place many of the climatic variables that can be expected to produce the changes observed in SeaWiFS data. This enables us to diagnose the model performance, the assimilation performance, and possible causes for the increase in chlorophyll. A full discussion of the changes and trends, possible causes, modeling approaches, and data assimilation will be the focus of the seminar.

  14. Galactic models with variable spiral structure

    International Nuclear Information System (INIS)

    James, R.A.; Sellwood, J.A.


    A series of three-dimensional computer simulations of disc galaxies has been run in which the self-consistent potential of the disc stars is supplemented by that arising from a small uniform Population II sphere. The models show variable spiral structure, which is more pronounced for thin discs. In addition, the thin discs form weak bars. In one case variable spiral structure associated with this bar has been seen. The relaxed discs are cool outside resonance regions. (author)

  15. An approach to model validation and model-based prediction -- polyurethane foam case study.

    Energy Technology Data Exchange (ETDEWEB)

    Dowding, Kevin J.; Rutherford, Brian Milne


    Enhanced software methodology and improved computing hardware have advanced the state of simulation technology to a point where large physics-based codes can be a major contributor in many systems analyses. This shift toward the use of computational methods has brought with it new research challenges in a number of areas including characterization of uncertainty, model validation, and the analysis of computer output. It is these challenges that have motivated the work described in this report. Approaches to and methods for model validation and (model-based) prediction have been developed recently in the engineering, mathematics and statistical literatures. In this report we have provided a fairly detailed account of one approach to model validation and prediction applied to an analysis investigating thermal decomposition of polyurethane foam. A model simulates the evolution of the foam in a high temperature environment as it transforms from a solid to a gas phase. The available modeling and experimental results serve as data for a case study focusing our model validation and prediction developmental efforts on this specific thermal application. We discuss several elements of the ''philosophy'' behind the validation and prediction approach: (1) We view the validation process as an activity applying to the use of a specific computational model for a specific application. We do acknowledge, however, that an important part of the overall development of a computational simulation initiative is the feedback provided to model developers and analysts associated with the application. (2) We utilize information obtained for the calibration of model parameters to estimate the parameters and quantify uncertainty in the estimates. We rely, however, on validation data (or data from similar analyses) to measure the variability that contributes to the uncertainty in predictions for specific systems or units (unit-to-unit variability). (3) We perform statistical

  16. A variable-order fractal derivative model for anomalous diffusion

    Directory of Open Access Journals (Sweden)

    Liu Xiaoting


    Full Text Available This paper pays attention to develop a variable-order fractal derivative model for anomalous diffusion. Previous investigations have indicated that the medium structure, fractal dimension or porosity may change with time or space during solute transport processes, results in time or spatial dependent anomalous diffusion phenomena. Hereby, this study makes an attempt to introduce a variable-order fractal derivative diffusion model, in which the index of fractal derivative depends on temporal moment or spatial position, to characterize the above mentioned anomalous diffusion (or transport processes. Compared with other models, the main advantages in description and the physical explanation of new model are explored by numerical simulation. Further discussions on the dissimilitude such as computational efficiency, diffusion behavior and heavy tail phenomena of the new model and variable-order fractional derivative model are also offered.

  17. Spatial pattern evaluation of a calibrated national hydrological model - a remote-sensing-based diagnostic approach (United States)

    Mendiguren, Gorka; Koch, Julian; Stisen, Simon


    Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds

  18. Modeling the variability of solar radiation data among weather stations by means of principal components analysis

    International Nuclear Information System (INIS)

    Zarzo, Manuel; Marti, Pau


    Research highlights: →Principal components analysis was applied to R s data recorded at 30 stations. → Four principal components explain 97% of the data variability. → The latent variables can be fitted according to latitude, longitude and altitude. → The PCA approach is more effective for gap infilling than conventional approaches. → The proposed method allows daily R s estimations at locations in the area of study. - Abstract: Measurements of global terrestrial solar radiation (R s ) are commonly recorded in meteorological stations. Daily variability of R s has to be taken into account for the design of photovoltaic systems and energy efficient buildings. Principal components analysis (PCA) was applied to R s data recorded at 30 stations in the Mediterranean coast of Spain. Due to equipment failures and site operation problems, time series of R s often present data gaps or discontinuities. The PCA approach copes with this problem and allows estimation of present and past values by taking advantage of R s records from nearby stations. The gap infilling performance of this methodology is compared with neural networks and alternative conventional approaches. Four principal components explain 66% of the data variability with respect to the average trajectory (97% if non-centered values are considered). A new method based on principal components regression was also developed for R s estimation if previous measurements are not available. By means of multiple linear regression, it was found that the latent variables associated to the four relevant principal components can be fitted according to the latitude, longitude and altitude of the station where data were recorded from. Additional geographical or climatic variables did not increase the predictive goodness-of-fit. The resulting models allow the estimation of daily R s values at any location in the area under study and present higher accuracy than artificial neural networks and some conventional approaches

  19. Effects and detection of raw material variability on the performance of near-infrared calibration models for pharmaceutical products. (United States)

    Igne, Benoit; Shi, Zhenqi; Drennen, James K; Anderson, Carl A


    The impact of raw material variability on the prediction ability of a near-infrared calibration model was studied. Calibrations, developed from a quaternary mixture design comprising theophylline anhydrous, lactose monohydrate, microcrystalline cellulose, and soluble starch, were challenged by intentional variation of raw material properties. A design with two theophylline physical forms, three lactose particle sizes, and two starch manufacturers was created to test model robustness. Further challenges to the models were accomplished through environmental conditions. Along with full-spectrum partial least squares (PLS) modeling, variable selection by dynamic backward PLS and genetic algorithms was utilized in an effort to mitigate the effects of raw material variability. In addition to evaluating models based on their prediction statistics, prediction residuals were analyzed by analyses of variance and model diagnostics (Hotelling's T(2) and Q residuals). Full-spectrum models were significantly affected by lactose particle size. Models developed by selecting variables gave lower prediction errors and proved to be a good approach to limit the effect of changing raw material characteristics. Hotelling's T(2) and Q residuals provided valuable information that was not detectable when studying only prediction trends. Diagnostic statistics were demonstrated to be critical in the appropriate interpretation of the prediction of quality parameters. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

  20. Higher-dimensional cosmological model with variable gravitational ...

    Indian Academy of Sciences (India)

    We have studied five-dimensional homogeneous cosmological models with variable and bulk viscosity in Lyra geometry. Exact solutions for the field equations have been obtained and physical properties of the models are discussed. It has been observed that the results of new models are well within the observational ...

  1. Prediction of autoignition in a lifted methane/air flame using an unsteady flamelet/progress variable model

    Energy Technology Data Exchange (ETDEWEB)

    Ihme, Matthias; See, Yee Chee [Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 (United States)


    An unsteady flamelet/progress variable (UFPV) model has been developed for the prediction of autoignition in turbulent lifted flames. The model is a consistent extension to the steady flamelet/progress variable (SFPV) approach, and employs an unsteady flamelet formulation to describe the transient evolution of all thermochemical quantities during the flame ignition process. In this UFPV model, all thermochemical quantities are parameterized by mixture fraction, reaction progress parameter, and stoichiometric scalar dissipation rate, eliminating the explicit dependence on a flamelet time scale. An a priori study is performed to analyze critical modeling assumptions that are associated with the population of the flamelet state space. For application to LES, the UFPV model is combined with a presumed PDF closure to account for subgrid contributions of mixture fraction and reaction progress variable. The model was applied in LES of a lifted methane/air flame. Additional calculations were performed to quantify the interaction between turbulence and chemistry a posteriori. Simulation results obtained from these calculations are compared with experimental data. Compared to the SFPV results, the unsteady flamelet/progress variable model captures the autoignition process, and good agreement with measurements is obtained for mixture fraction, temperature, and species mass fractions. From the analysis of scatter data and mixture fraction-conditional results it is shown that the turbulence/chemistry interaction delays the ignition process towards lower values of scalar dissipation rate, and a significantly larger region in the flamelet state space is occupied during the ignition process. (author)

  2. Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach (United States)

    Ortega, Pablo; Guilyardi, Eric; Swingedouw, Didier; Mignot, Juliette; Nguyen, Sébastien


    While the Atlantic Meridional Overturning Circulation (AMOC) is thought to be a crucial component of the North Atlantic climate, past changes in its strength are challenging to quantify, and only limited information is available. In this study, we use a perfect model approach with the IPSL-CM5A-LR model to assess the performance of several surface nudging techniques in reconstructing the variability of the AMOC. Special attention is given to the reproducibility of an extreme positive AMOC peak from a preindustrial control simulation. Nudging includes standard relaxation techniques towards the sea surface temperature and salinity anomalies of this target control simulation, and/or the prescription of the wind-stress fields. Surface nudging approaches using standard fixed restoring terms succeed in reproducing most of the target AMOC variability, including the timing of the extreme event, but systematically underestimate its amplitude. A detailed analysis of the AMOC variability mechanisms reveals that the underestimation of the extreme AMOC maximum comes from a deficit in the formation of the dense water masses in the main convection region, located south of Iceland in the model. This issue is largely corrected after introducing a novel surface nudging approach, which uses a varying restoring coefficient that is proportional to the simulated mixed layer depth, which, in essence, keeps the restoring time scale constant. This new technique substantially improves water mass transformation in the regions of convection, and in particular, the formation of the densest waters, which are key for the representation of the AMOC extreme. It is therefore a promising strategy that may help to better constrain the AMOC variability and other ocean features in the models. As this restoring technique only uses surface data, for which better and longer observations are available, it opens up opportunities for improved reconstructions of the AMOC over the last few decades.

  3. Multi-scale climate modelling over Southern Africa using a variable-resolution global model

    CSIR Research Space (South Africa)

    Engelbrecht, FA


    Full Text Available -mail: Multi-scale climate modelling over Southern Africa using a variable-resolution global model FA Engelbrecht1, 2*, WA Landman1, 3, CJ Engelbrecht4, S Landman5, MM Bopape1, B Roux6, JL McGregor7 and M Thatcher7 1 CSIR Natural... improvement. Keywords: multi-scale climate modelling, variable-resolution atmospheric model Introduction Dynamic climate models have become the primary tools for the projection of future climate change, at both the global and regional scales. Dynamic...

  4. Modelling the co-evolution of indirect genetic effects and inherited variability. (United States)

    Marjanovic, Jovana; Mulder, Han A; Rönnegård, Lars; Bijma, Piter


    When individuals interact, their phenotypes may be affected not only by their own genes but also by genes in their social partners. This phenomenon is known as Indirect Genetic Effects (IGEs). In aquaculture species and some plants, however, competition not only affects trait levels of individuals, but also inflates variability of trait values among individuals. In the field of quantitative genetics, the variability of trait values has been studied as a quantitative trait in itself, and is often referred to as inherited variability. Such studies, however, consider only the genetic effect of the focal individual on trait variability and do not make a connection to competition. Although the observed phenotypic relationship between competition and variability suggests an underlying genetic relationship, the current quantitative genetic models of IGE and inherited variability do not allow for such a relationship. The lack of quantitative genetic models that connect IGEs to inherited variability limits our understanding of the potential of variability to respond to selection, both in nature and agriculture. Models of trait levels, for example, show that IGEs may considerably change heritable variation in trait values. Currently, we lack the tools to investigate whether this result extends to variability of trait values. Here we present a model that integrates IGEs and inherited variability. In this model, the target phenotype, say growth rate, is a function of the genetic and environmental effects of the focal individual and of the difference in trait value between the social partner and the focal individual, multiplied by a regression coefficient. The regression coefficient is a genetic trait, which is a measure of cooperation; a negative value indicates competition, a positive value cooperation, and an increasing value due to selection indicates the evolution of cooperation. In contrast to the existing quantitative genetic models, our model allows for co-evolution of

  5. Uni- and multi-variable modelling of flood losses: experiences gained from the Secchia river inundation event. (United States)

    Carisi, Francesca; Domeneghetti, Alessio; Kreibich, Heidi; Schröter, Kai; Castellarin, Attilio


    Flood risk is function of flood hazard and vulnerability, therefore its accurate assessment depends on a reliable quantification of both factors. The scientific literature proposes a number of objective and reliable methods for assessing flood hazard, yet it highlights a limited understanding of the fundamental damage processes. Loss modelling is associated with large uncertainty which is, among other factors, due to a lack of standard procedures; for instance, flood losses are often estimated based on damage models derived in completely different contexts (i.e. different countries or geographical regions) without checking its applicability, or by considering only one explanatory variable (i.e. typically water depth). We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 200 km2 in Northern Italy. In the aftermath of this event, local authorities collected flood loss data, together with additional information on affected private households and industrial activities (e.g. buildings surface and economic value, number of company's employees and others). Based on these data we implemented and compared a quadratic-regression damage function, with water depth as the only explanatory variable, and a multi-variable model that combines multiple regression trees and considers several explanatory variables (i.e. bagging decision trees). Our results show the importance of data collection revealing that (1) a simple quadratic regression damage function based on empirical data from the study area can be significantly more accurate than literature damage-models derived for a different context and (2) multi-variable modelling may outperform the uni-variable approach, yet it is more difficult to develop and apply due to a much higher demand of detailed data.

  6. Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

    Energy Technology Data Exchange (ETDEWEB)

    Cole, Wesley [National Renewable Energy Lab. (NREL), Golden, CO (United States); Frew, Bethany [National Renewable Energy Lab. (NREL), Golden, CO (United States); Mai, Trieu [National Renewable Energy Lab. (NREL), Golden, CO (United States); Sun, Yinong [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bistline, John [Electric Power Research Inst. (EPRI), Knoxville, TN (United States); Blanford, Geoffrey [Electric Power Research Inst. (EPRI), Knoxville, TN (United States); Young, David [Electric Power Research Inst. (EPRI), Knoxville, TN (United States); Marcy, Cara [U.S. Energy Information Administration, Washington, DC (United States); Namovicz, Chris [U.S. Energy Information Administration, Washington, DC (United States); Edelman, Risa [US Environmental Protection Agency (EPA), Washington, DC (United States); Meroney, Bill [US Environmental Protection Agency (EPA), Washington, DC (United States); Sims, Ryan [US Environmental Protection Agency (EPA), Washington, DC (United States); Stenhouse, Jeb [US Environmental Protection Agency (EPA), Washington, DC (United States); Donohoo-Vallett, Paul [Dept. of Energy (DOE), Washington DC (United States)


    Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision-makers. With the recent surge in variable renewable energy (VRE) generators — primarily wind and solar photovoltaics — the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. This report summarizes the analyses and model experiments that were conducted as part of two workshops on modeling VRE for national-scale capacity expansion models. It discusses the various methods for treating VRE among four modeling teams from the Electric Power Research Institute (EPRI), the U.S. Energy Information Administration (EIA), the U.S. Environmental Protection Agency (EPA), and the National Renewable Energy Laboratory (NREL). The report reviews the findings from the two workshops and emphasizes the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making. This research is intended to inform the energy modeling community on the modeling of variable renewable resources, and is not intended to advocate for or against any particular energy technologies, resources, or policies.

  7. An integrated approach to permeability modeling using micro-models

    Energy Technology Data Exchange (ETDEWEB)

    Hosseini, A.H.; Leuangthong, O.; Deutsch, C.V. [Society of Petroleum Engineers, Canadian Section, Calgary, AB (Canada)]|[Alberta Univ., Edmonton, AB (Canada)


    An important factor in predicting the performance of steam assisted gravity drainage (SAGD) well pairs is the spatial distribution of permeability. Complications that make the inference of a reliable porosity-permeability relationship impossible include the presence of short-scale variability in sand/shale sequences; preferential sampling of core data; and uncertainty in upscaling parameters. Micro-modelling is a simple and effective method for overcoming these complications. This paper proposed a micro-modeling approach to account for sampling bias, small laminated features with high permeability contrast, and uncertainty in upscaling parameters. The paper described the steps and challenges of micro-modeling and discussed the construction of binary mixture geo-blocks; flow simulation and upscaling; extended power law formalism (EPLF); and the application of micro-modeling and EPLF. An extended power-law formalism to account for changes in clean sand permeability as a function of macroscopic shale content was also proposed and tested against flow simulation results. There was close agreement between the model and simulation results. The proposed methodology was also applied to build the porosity-permeability relationship for laminated and brecciated facies of McMurray oil sands. Experimental data was in good agreement with the experimental data. 8 refs., 17 figs.

  8. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling (United States)

    Amatulli, Giuseppe; Domisch, Sami; Tuanmu, Mao-Ning; Parmentier, Benoit; Ranipeta, Ajay; Malczyk, Jeremy; Jetz, Walter


    Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches. While a cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at Data Citation 1 and for download and visualization at

  9. Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability (United States)

    Parsons, Luke Alexander

    Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limon) record indicates that precipitation variability in western Amazonia is 'red' (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly 'white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall

  10. Modeling and optimization of process variables of wire-cut electric discharge machining of super alloy Udimet-L605

    Directory of Open Access Journals (Sweden)

    Somvir Singh Nain


    Full Text Available This paper presents the behavior of Udimet-L605 after wire electric discharge machining and evaluating the WEDM process using sophisticated machine learning approaches. The experimental work is depicted on the basis of Taguchi orthogonal L27 array, considering six input variables and three interactions. Three models such as support vector machine algorithms based on PUK kernel, non-linear regression and multi-linear regression have been proposed to examine the variance between experimental and predicted outcome and preferred the preeminent model based on its evaluation parameters performance and graph analysis. The grey relational analysis is the relevant approach to obtain the best grouping of input variables for maximum material removal rate and minimum surface roughness. Based on statistical analysis, it has been concluded that pulse-on time, interaction between pulse-on time x pulse-off time, spark-gap voltage and wire tension are the momentous variable for surface roughness while the pulse-on time, spark-gap voltage and pulse-off time are the momentous variables for material removal rate. The micro structural and compositional changes on the surface of work material were examined by means of SEM and EDX analysis. The thickness of the white layer and the recast layer formation increases with increases in the pulse-on time duration.

  11. The necessity of connection structures in neural models of variable binding. (United States)

    van der Velde, Frank; de Kamps, Marc


    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.

  12. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.


    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  13. A Non-Gaussian Spatial Generalized Linear Latent Variable Model

    KAUST Repository

    Irincheeva, Irina


    We consider a spatial generalized linear latent variable model with and without normality distributional assumption on the latent variables. When the latent variables are assumed to be multivariate normal, we apply a Laplace approximation. To relax the assumption of marginal normality in favor of a mixture of normals, we construct a multivariate density with Gaussian spatial dependence and given multivariate margins. We use the pairwise likelihood to estimate the corresponding spatial generalized linear latent variable model. The properties of the resulting estimators are explored by simulations. In the analysis of an air pollution data set the proposed methodology uncovers weather conditions to be a more important source of variability than air pollution in explaining all the causes of non-accidental mortality excluding accidents. © 2012 International Biometric Society.

  14. Exploring a Multiresolution Modeling Approach within the Shallow-Water Equations

    Energy Technology Data Exchange (ETDEWEB)

    Ringler, Todd D.; Jacobsen, Doug; Gunzburger, Max; Ju, Lili; Duda, Michael; Skamarock, William


    The ability to solve the global shallow-water equations with a conforming, variable-resolution mesh is evaluated using standard shallow-water test cases. While the long-term motivation for this study is the creation of a global climate modeling framework capable of resolving different spatial and temporal scales in different regions, the process begins with an analysis of the shallow-water system in order to better understand the strengths and weaknesses of the approach developed herein. The multiresolution meshes are spherical centroidal Voronoi tessellations where a single, user-supplied density function determines the region(s) of fine- and coarsemesh resolution. The shallow-water system is explored with a suite of meshes ranging from quasi-uniform resolution meshes, where the grid spacing is globally uniform, to highly variable resolution meshes, where the grid spacing varies by a factor of 16 between the fine and coarse regions. The potential vorticity is found to be conserved to within machine precision and the total available energy is conserved to within a time-truncation error. This result holds for the full suite of meshes, ranging from quasi-uniform resolution and highly variable resolution meshes. Based on shallow-water test cases 2 and 5, the primary conclusion of this study is that solution error is controlled primarily by the grid resolution in the coarsest part of the model domain. This conclusion is consistent with results obtained by others.When these variable-resolution meshes are used for the simulation of an unstable zonal jet, the core features of the growing instability are found to be largely unchanged as the variation in the mesh resolution increases. The main differences between the simulations occur outside the region of mesh refinement and these differences are attributed to the additional truncation error that accompanies increases in grid spacing. Overall, the results demonstrate support for this approach as a path toward

  15. Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity. (United States)

    Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C


    The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain

  16. Integrated variable projection approach (IVAPA) for parallel magnetic resonance imaging. (United States)

    Zhang, Qiao; Sheng, Jinhua


    Parallel magnetic resonance imaging (pMRI) is a fast method which requires algorithms for the reconstructing image from a small number of measured k-space lines. The accurate estimation of the coil sensitivity functions is still a challenging problem in parallel imaging. The joint estimation of the coil sensitivity functions and the desired image has recently been proposed to improve the situation by iteratively optimizing both the coil sensitivity functions and the image reconstruction. It regards both the coil sensitivities and the desired images as unknowns to be solved for jointly. In this paper, we propose an integrated variable projection approach (IVAPA) for pMRI, which integrates two individual processing steps (coil sensitivity estimation and image reconstruction) into a single processing step to improve the accuracy of the coil sensitivity estimation using the variable projection approach. The method is demonstrated to be able to give an optimal solution with considerably reduced artifacts for high reduction factors and a low number of auto-calibration signal (ACS) lines, and our implementation has a fast convergence rate. The performance of the proposed method is evaluated using a set of in vivo experiment data. Copyright © 2012 Elsevier Ltd. All rights reserved.

  17. On the relationship between optical variability, visual saliency, and eye fixations: a computational approach. (United States)

    Garcia-Diaz, Antón; Leborán, Víctor; Fdez-Vidal, Xosé R; Pardo, Xosé M


    A hierarchical definition of optical variability is proposed that links physical magnitudes to visual saliency and yields a more reductionist interpretation than previous approaches. This definition is shown to be grounded on the classical efficient coding hypothesis. Moreover, we propose that a major goal of contextual adaptation mechanisms is to ensure the invariance of the behavior that the contribution of an image point to optical variability elicits in the visual system. This hypothesis and the necessary assumptions are tested through the comparison with human fixations and state-of-the-art approaches to saliency in three open access eye-tracking datasets, including one devoted to images with faces, as well as in a novel experiment using hyperspectral representations of surface reflectance. The results on faces yield a significant reduction of the potential strength of semantic influences compared to previous works. The results on hyperspectral images support the assumptions to estimate optical variability. As well, the proposed approach explains quantitative results related to a visual illusion observed for images of corners, which does not involve eye movements.

  18. Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction. (United States)

    Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui


    New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier

  19. Designing water demand management schemes using a socio-technical modelling approach. (United States)

    Baki, Sotiria; Rozos, Evangelos; Makropoulos, Christos


    Although it is now widely acknowledged that urban water systems (UWSs) are complex socio-technical systems and that a shift towards a socio-technical approach is critical in achieving sustainable urban water management, still, more often than not, UWSs are designed using a segmented modelling approach. As such, either the analysis focuses on the description of the purely technical sub-system, without explicitly taking into account the system's dynamic socio-economic processes, or a more interdisciplinary approach is followed, but delivered through relatively coarse models, which often fail to provide a thorough representation of the urban water cycle and hence cannot deliver accurate estimations of the hydrosystem's responses. In this work we propose an integrated modelling approach for the study of the complete socio-technical UWS that also takes into account socio-economic and climatic variability. We have developed an integrated model, which is used to investigate the diffusion of household water conservation technologies and its effects on the UWS, under different socio-economic and climatic scenarios. The integrated model is formed by coupling a System Dynamics model that simulates the water technology adoption process, and the Urban Water Optioneering Tool (UWOT) for the detailed simulation of the urban water cycle. The model and approach are tested and demonstrated in an urban redevelopment area in Athens, Greece under different socio-economic scenarios and policy interventions. It is suggested that the proposed approach can establish quantifiable links between socio-economic change and UWS responses and therefore assist decision makers in designing more effective and resilient long-term strategies for water conservation. Copyright © 2017 Elsevier B.V. All rights reserved.

  20. Modelling chloride penetration in concrete using electrical voltage and current approaches

    Directory of Open Access Journals (Sweden)

    Juan Lizarazo-Marriaga


    Full Text Available This paper reports a research programme aimed at giving a better understanding of the phenomena involved in the chloride penetration in cement-based materials. The general approach used was to solve the Nernst-Planck equation numerically for two physical ideal states that define the possible conditions under which chlorides will move through concrete. These conditions are named in this paper as voltage control and current control. For each condition, experiments and simulations were carried out in order to establish the importance of electrical variables such as voltage and current in modelling chloride transport in concrete. The results of experiments and simulations showed that if those electrical variables are included as key parameters in the modelling of chloride penetration through concrete, a better understanding of this complex phenomenon can be obtained.

  1. Modeling of carbon sequestration in coal-beds: A variable saturated simulation

    International Nuclear Information System (INIS)

    Liu Guoxiang; Smirnov, Andrei V.


    Storage of carbon dioxide in deep coal seams is a profitable method to reduce the concentration of green house gases in the atmosphere while the methane as a byproduct can be extracted during carbon dioxide injection into the coal seam. In this procedure, the key element is to keep carbon dioxide in the coal seam without escaping for a long term. It is depended on many factors such as properties of coal basin, fracture state, phase equilibrium, etc., especially the porosity, permeability and saturation of the coal seam. In this paper, a variable saturation model was developed to predict the capacity of carbon dioxide sequestration and coal-bed methane recovery. This variable saturation model can be used to track the saturation variability with the partial pressures change caused by carbon dioxide injection. Saturation variability is a key factor to predict the capacity of carbon dioxide storage and methane recovery. Based on this variable saturation model, a set of related variables including capillary pressure, relative permeability, porosity, coupled adsorption model, concentration and temperature equations were solved. From results of the simulation, historical data agree with the variable saturation model as well as the adsorption model constructed by Langmuir equations. The Appalachian basin, as an example, modeled the carbon dioxide sequestration in this paper. The results of the study and the developed models can provide the projections for the CO 2 sequestration and methane recovery in coal-beds within different regional specifics

  2. Model predictive control approach for a CPAP-device

    Directory of Open Access Journals (Sweden)

    Scheel Mathias


    Full Text Available The obstructive sleep apnoea syndrome (OSAS is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2 - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.

  3. The effects of motivational factors on car use: a multidisciplinary modelling approach

    Energy Technology Data Exchange (ETDEWEB)

    Steg, L.; Ras, M. [University of Groningen (Netherlands). Centre for Environmental and Traffic Psychology; Geurs, K. [National Institute of Public Health and Environment, Bilthoven (Netherlands)


    Current transport models usually do not take motivational factors into account, and if they do, it is only implicitly. This paper presents a modelling approach aimed at explicitly examining the effects of motivational factors on present and future car use in the Netherlands. A car-use forecasting model for the years 2010 and 2020 was constructed on the basis of (i) a multinominal regression analysis, which revealed the importance of a motivational variable (viz., problem awareness) in explaining current car-use behavior separate from socio-demographic and socio-economic variables, and (ii) a population model constructed to forecast the size and composition of the Dutch population. The results show that car use could be better explained by taking motivational factors explicitly into account, and that the level of car use forecast might change significantly if changes in motivations are assumed. The question on how motivational factors could be incorporated into current (Dutch) national transport models was also addressed. (author)

  4. Poisson regression approach for modeling fatal injury rates amongst Malaysian workers

    International Nuclear Information System (INIS)

    Kamarulzaman Ibrahim; Heng Khai Theng


    Many safety studies are based on the analysis carried out on injury surveillance data. The injury surveillance data gathered for the analysis include information on number of employees at risk of injury in each of several strata where the strata are defined in terms of a series of important predictor variables. Further insight into the relationship between fatal injury rates and predictor variables may be obtained by the poisson regression approach. Poisson regression is widely used in analyzing count data. In this study, poisson regression is used to model the relationship between fatal injury rates and predictor variables which are year (1995-2002), gender, recording system and industry type. Data for the analysis were obtained from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the data follow poisson distribution has been violated. After correction for the problem of over dispersion, the predictor variables that are found to be significant in the model are gender, system of recording, industry type, two interaction effects (interaction between recording system and industry type and between year and industry type). Introduction Regression analysis is one of the most popular

  5. Biotic interactions in the face of climate change: a comparison of three modelling approaches.

    Directory of Open Access Journals (Sweden)

    Anja Jaeschke

    Full Text Available Climate change is expected to alter biotic interactions, and may lead to temporal and spatial mismatches of interacting species. Although the importance of interactions for climate change risk assessments is increasingly acknowledged in observational and experimental studies, biotic interactions are still rarely incorporated in species distribution models. We assessed the potential impacts of climate change on the obligate interaction between Aeshna viridis and its egg-laying plant Stratiotes aloides in Europe, based on an ensemble modelling technique. We compared three different approaches for incorporating biotic interactions in distribution models: (1 We separately modelled each species based on climatic information, and intersected the future range overlap ('overlap approach'. (2 We modelled the potential future distribution of A. viridis with the projected occurrence probability of S. aloides as further predictor in addition to climate ('explanatory variable approach'. (3 We calibrated the model of A. viridis in the current range of S. aloides and multiplied the future occurrence probabilities of both species ('reference area approach'. Subsequently, all approaches were compared to a single species model of A. viridis without interactions. All approaches projected a range expansion for A. viridis. Model performance on test data and amount of range gain differed depending on the biotic interaction approach. All interaction approaches yielded lower range gains (up to 667% lower than the model without interaction. Regarding the contribution of algorithm and approach to the overall uncertainty, the main part of explained variation stems from the modelling algorithm, and only a small part is attributed to the modelling approach. The comparison of the no-interaction model with the three interaction approaches emphasizes the importance of including obligate biotic interactions in projective species distribution modelling. We recommend the use of

  6. Modeling the influence of atmospheric leading modes on the variability of the Arctic freshwater cycle (United States)

    Niederdrenk, L.; Sein, D.; Mikolajewicz, U.


    Global general circulation models show remarkable differences in modeling the Arctic freshwater cycle. While they agree on the general sinks and sources of the freshwater budget, they differ largely in the magnitude of the mean values as well as in the variability of the freshwater terms. Regional models can better resolve the complex topography and small scale processes, but they are often uncoupled, thus missing the air-sea interaction. Additionally, regional models mostly use some kind of salinity restoring or flux correction, thus disturbing the freshwater budget. Our approach to investigate the Arctic hydrologic cycle and its variability is a regional atmosphere-ocean model setup, consisting of the global ocean model MPIOM with high resolution in the Arctic coupled to the regional atmosphere model REMO. The domain of the atmosphere model covers all catchment areas of the rivers draining into the Arctic. To account for all sinks and sources of freshwater in the Arctic, we include a discharge model providing terrestrial lateral waterflows. We run the model without salinity restoring but with freshwater correction, which is set to zero in the Arctic. This allows for the analysis of a closed freshwater budget in the Artic region. We perform experiments for the second half of the 20th century and use data from the global model MPIOM/ECHAM5 performed with historical conditions, that was used within the 4th Assessment Report of the IPCC, as forcing for our regional model. With this setup, we investigate how the dominant modes of large-scale atmospheric variability impact the variability in the freshwater components. We focus on the two leading empirical orthogonal functions of winter mean sea level pressure, as well as on the North Atlantic Oscillation and the Siberian High. These modes have a large impact on the Arctic Ocean circulation as well as on the solid and liquid export through Fram Strait and through the Canadian archipelago. However, they cannot explain

  7. Multirule Based Diagnostic Approach for the Fog Predictions Using WRF Modelling Tool

    Directory of Open Access Journals (Sweden)

    Swagata Payra


    Full Text Available The prediction of fog onset remains difficult despite the progress in numerical weather prediction. It is a complex process and requires adequate representation of the local perturbations in weather prediction models. It mainly depends upon microphysical and mesoscale processes that act within the boundary layer. This study utilizes a multirule based diagnostic (MRD approach using postprocessing of the model simulations for fog predictions. The empiricism involved in this approach is mainly to bridge the gap between mesoscale and microscale variables, which are related to mechanism of the fog formation. Fog occurrence is a common phenomenon during winter season over Delhi, India, with the passage of the western disturbances across northwestern part of the country accompanied with significant amount of moisture. This study implements the above cited approach for the prediction of occurrences of fog and its onset time over Delhi. For this purpose, a high resolution weather research and forecasting (WRF model is used for fog simulations. The study involves depiction of model validation and postprocessing of the model simulations for MRD approach and its subsequent application to fog predictions. Through this approach model identified foggy and nonfoggy days successfully 94% of the time. Further, the onset of fog events is well captured within an accuracy of 30–90 minutes. This study demonstrates that the multirule based postprocessing approach is a useful and highly promising tool in improving the fog predictions.

  8. Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model (United States)

    Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef


    We address the question of quantifying uncertainty associated with autogenic pattern variability in a channelized transport system by means of a modern geostatistical method. This question has considerable relevance for practical subsurface applications as well, particularly those related to uncertainty quantification relying on Bayesian approaches. Specifically, we show how the autogenic variability in a laboratory experiment can be represented and reproduced by a multiple-point geostatistical prior uncertainty model. The latter geostatistical method requires selection of a limited set of training images from which a possibly infinite set of geostatistical model realizations, mimicking the training image patterns, can be generated. To that end, we investigate two methods to determine how many training images and what training images should be provided to reproduce natural autogenic variability. The first method relies on distance-based clustering of overhead snapshots of the experiment; the second method relies on a rate of change quantification by means of a computer vision algorithm termed the demon algorithm. We show quantitatively that with either training image selection method, we can statistically reproduce the natural variability of the delta formed in the experiment. In addition, we study the nature of the patterns represented in the set of training images as a representation of the "eigenpatterns" of the natural system. The eigenpattern in the training image sets display patterns consistent with previous physical interpretations of the fundamental modes of this type of delta system: a highly channelized, incisional mode; a poorly channelized, depositional mode; and an intermediate mode between the two.

  9. Development of a plug-in for Variability Modeling in Software Product Lines

    Directory of Open Access Journals (Sweden)

    María Lucía López-Araujo


    Full Text Available Las Líneas de Productos de Software (LPS toman ventaja económica de las similitudes y variación entre un conjunto de sistemas de software dentro de un dominio específico. La Ingeniería de Líneas de Productos de Software por lo tanto, define una serie de procesos para el desarrollo de LPS que consideran las similitudes y variación a lo largo del ciclo devida. El modelado de variabilidad, en consecuencia, es una actividad esencial en un enfoque de Ingeniería de Líneas de Productos de Software. Existen varias técnicas para modelado de variabilidad. Entre ellas resalta COVAMOF que permite modelar los puntos de variación, variantes y dependencias como entidades de primera clase, proporcionando una manera uniforme de representarlos en los diversos niveles de abstracción de una LPS. Para poder aprovechar los beneficios de COVAMOF es necesario contar con una herramienta, de otra manera el modelado y la administración de la variabilidad pueden resultar una labor ardua para el ingeniero de software. Este trabajo presenta el desarrollo de un plug-in de COVAMOF para Eclipse.Software Product Lines (SPL take economic advantage of commonality and variability among a set of software systems that exist within a specific domain. Therefore, Software Product Line Engineering defines a series of processes for the development of a SPL that consider commonality and variability during the software life cycle. Variability modeling is therefore an essential activity in a Software Product Line Engineering approach. There are several techniques for variability modeling nowadays. COVAMOF stands out among them since it allows the modeling of variation points, variants and dependencies as first class elements. COVAMOF, therefore, provides an uniform manner for representing such concepts in different levels of abstraction within a SPL. In order to take advantage of COVAMOF benefits, it is necessary to have a computer aided tool, otherwise variability modeling and

  10. Experimental verification and comparison of the rubber V- belt continuously variable transmission models (United States)

    Grzegożek, W.; Dobaj, K.; Kot, A.


    The paper includes the analysis of the rubber V-belt cooperation with the CVT transmission pulleys. The analysis of the forces and torques acting in the CVT transmission was conducted basing on calculated characteristics of the centrifugal regulator and the torque regulator. The accurate estimation of the regulator surface curvature allowed for calculation of the relation between the driving wheel axial force, the engine rotational speed and the gear ratio of the CVT transmission. Simplified analytical models of the rubber V-belt- pulley cooperation are based on three basic approaches. The Dittrich model assumes two contact regions on the driven and driving wheel. The Kim-Kim model considers, in addition to the previous model, also the radial friction. The radial friction results in the lack of the developed friction area on the driving pulley. The third approach, formulated in the Cammalleri model, assumes variable sliding angle along the wrap arch and describes it as a result the belt longitudinal and cross flexibility. Theoretical torque on the driven and driving wheel was calculated on the basis of the known regulators characteristics. The calculated torque was compared to the measured loading torque. The best accordance, referring to the centrifugal regulator range of work, was obtained for the Kim-Kim model.

  11. Mediterranean climate modelling: variability and climate change scenarios

    International Nuclear Information System (INIS)

    Somot, S.


    Air-sea fluxes, open-sea deep convection and cyclo-genesis are studied in the Mediterranean with the development of a regional coupled model (AORCM). It accurately simulates these processes and their climate variabilities are quantified and studied. The regional coupling shows a significant impact on the number of winter intense cyclo-genesis as well as on associated air-sea fluxes and precipitation. A lower inter-annual variability than in non-coupled models is simulated for fluxes and deep convection. The feedbacks driving this variability are understood. The climate change response is then analysed for the 21. century with the non-coupled models: cyclo-genesis decreases, associated precipitation increases in spring and autumn and decreases in summer. Moreover, a warming and salting of the Mediterranean as well as a strong weakening of its thermohaline circulation occur. This study also concludes with the necessity of using AORCMs to assess climate change impacts on the Mediterranean. (author)

  12. Capturing spike variability in noisy Izhikevich neurons using point process generalized linear models

    DEFF Research Database (Denmark)

    Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.


    current. We then fit these spike train datawith a statistical model (a generalized linear model, GLM, with multiplicative influences of past spiking). For different levels of noise, we show how the GLM captures both the deterministic features of the Izhikevich neuron and the variability driven...... by the noise. We conclude that the GLM captures essential features of the simulated spike trains, but for near-deterministic spike trains, goodness-of-fit analyses reveal that the model does not fit very well in a statistical sense; the essential random part of the GLM is not captured....... are separately applied; understanding the relationships between these modeling approaches remains an area of active research. In this letter, we examine this relationship using simulation. To do so, we first generate spike train data from a well-known dynamical model, the Izhikevich neuron, with a noisy input...

  13. Improved installation approach for variable spring setting on a pipe yet to be insulated

    International Nuclear Information System (INIS)

    Shah, H.H.; Chitnis, S.S.; Rencher, D.


    This paper provides an approach in setting of variable spring supports for noninsulated or partially insulated piping systems so that resetting these supports is not required when the insulation is fully installed. This approach shows a method of deriving the spring coldload setting tolerance values that can be readily utilized by craft personnel. This method is based on the percentage of the weight of the insulation compared to the total weight of the pipe and the applicable tolerance. Use of these setting tolerances eliminates reverification of the original cold-load settings, for the majority of variable springs when the insulation is fully installed


    Directory of Open Access Journals (Sweden)

    Suandi -


    Full Text Available The main problems faced by developing countries including Indonesia are not onlyeconomic problems that tend to harm, but still met the high fertility rate. The purpose ofwriting to find out the relationship between socioeconomic status to the level of fertilitythrough the "A Latent Variable Approach." The study adopts the approach of fertility oneconomic development. Economic development based on the theories of Malthus: anincrease in "income" is slower than the increase in births (fertility and is the root ofpeople falling into poverty. However, Becker made linkage model or the influence ofchildren income and price. According to Becker, viewed from the aspect of demand thatthe price of children is greater than the income effect.The study shows that (1 level of education correlates positively on income andnegatively affect fertility, (2 age structure of women (control contraceptives adverselyaffect fertility. That is, the older the age, the level of individual productivity and lowerfertility or declining, and (3 husband's employment status correlated positively to theearnings (income. Through a permanent factor income or household income referred toas a negative influence on fertility. There are differences in value orientation of childrenbetween advanced society (rich with a backward society (the poor. The poor, forexample, the value of children is more production of goods. That is, children born moreemphasis on aspects of the number or the number of children owned (quantity, numberof children born by the poor is expected to help their parents at the age of retirement orno longer productive so that the child is expected to assist them in economic, security,and social security (insurance, while the developed (rich children are moreconsumption value or quality of the child.

  15. Genetic and Psychosocial Predictors of Aggression: Variable Selection and Model Building With Component-Wise Gradient Boosting

    Directory of Open Access Journals (Sweden)

    Robert Suchting


    Full Text Available Rationale: Given datasets with a large or diverse set of predictors of aggression, machine learning (ML provides efficient tools for identifying the most salient variables and building a parsimonious statistical model. ML techniques permit efficient exploration of data, have not been widely used in aggression research, and may have utility for those seeking prediction of aggressive behavior.Objectives: The present study examined predictors of aggression and constructed an optimized model using ML techniques. Predictors were derived from a dataset that included demographic, psychometric and genetic predictors, specifically FK506 binding protein 5 (FKBP5 polymorphisms, which have been shown to alter response to threatening stimuli, but have not been tested as predictors of aggressive behavior in adults.Methods: The data analysis approach utilized component-wise gradient boosting and model reduction via backward elimination to: (a select variables from an initial set of 20 to build a model of trait aggression; and then (b reduce that model to maximize parsimony and generalizability.Results: From a dataset of N = 47 participants, component-wise gradient boosting selected 8 of 20 possible predictors to model Buss-Perry Aggression Questionnaire (BPAQ total score, with R2 = 0.66. This model was simplified using backward elimination, retaining six predictors: smoking status, psychopathy (interpersonal manipulation and callous affect, childhood trauma (physical abuse and neglect, and the FKBP5_13 gene (rs1360780. The six-factor model approximated the initial eight-factor model at 99.4% of R2.Conclusions: Using an inductive data science approach, the gradient boosting model identified predictors consistent with previous experimental work in aggression; specifically psychopathy and trauma exposure. Additionally, allelic variants in FKBP5 were identified for the first time, but the relatively small sample size limits generality of results and calls for

  16. Variable-Structure Control of a Model Glider Airplane (United States)

    Waszak, Martin R.; Anderson, Mark R.


    A variable-structure control system designed to enable a fuselage-heavy airplane to recover from spin has been demonstrated in a hand-launched, instrumented model glider airplane. Variable-structure control is a high-speed switching feedback control technique that has been developed for control of nonlinear dynamic systems.

  17. Variable population exposure and distributed travel speeds in least-cost tsunami evacuation modelling (United States)

    Fraser, Stuart A.; Wood, Nathan J.; Johnston, David A.; Leonard, Graham S.; Greening, Paul D.; Rossetto, Tiziana


    Evacuation of the population from a tsunami hazard zone is vital to reduce life-loss due to inundation. Geospatial least-cost distance modelling provides one approach to assessing tsunami evacuation potential. Previous models have generally used two static exposure scenarios and fixed travel speeds to represent population movement. Some analyses have assumed immediate departure or a common evacuation departure time for all exposed population. Here, a method is proposed to incorporate time-variable exposure, distributed travel speeds, and uncertain evacuation departure time into an existing anisotropic least-cost path distance framework. The method is demonstrated for hypothetical local-source tsunami evacuation in Napier City, Hawke's Bay, New Zealand. There is significant diurnal variation in pedestrian evacuation potential at the suburb level, although the total number of people unable to evacuate is stable across all scenarios. Whilst some fixed travel speeds approximate a distributed speed approach, others may overestimate evacuation potential. The impact of evacuation departure time is a significant contributor to total evacuation time. This method improves least-cost modelling of evacuation dynamics for evacuation planning, casualty modelling, and development of emergency response training scenarios. However, it requires detailed exposure data, which may preclude its use in many situations.

  18. A mathematical model in cellular manufacturing system considering subcontracting approach under constraints

    Directory of Open Access Journals (Sweden)

    Kamran Forghani


    Full Text Available In this paper, a new mathematical model in cellular manufacturing systems (CMSs has been presented. In order to increase the performance of manufacturing system, the production quantity of parts has been considered as a decision variable, i.e. each part can be produced and outsourced, simultaneously. This extension would be minimized the unused capacity of machines. The exceptional elements (EEs are taken into account and would be totally outsourced to the external supplier in order to remove intercellular material handling cost. The problem has been formulated as a mixed-integer programming to minimize the sum of manufacturing variable costs under budget, machines capacity and demand constraints. Also, to evaluate advantages of the model, several illustrative numerical examples have been provided to compare the performance of the proposed model with the available classical approaches in the literature.

  19. Modeling the impacts of climate variability and hurricane on carbon sequestration in a coastal forested wetland in South Carolina (United States)

    Zhaohua Dai; Carl C. Trettin; Changsheng Li; Ge Sun; Devendra M. Amatya; Harbin Li


    The impacts of hurricane disturbance and climate variability on carbon dynamics in a coastal forested wetland in South Carolina of USA were simulated using the Forest-DNDC model with a spatially explicit approach. The model was validated using the measured biomass before and after Hurricane Hugo and the biomass inventories in 2006 and 2007, showed that the Forest-DNDC...

  20. Modeling of short-term mechanism of arterial pressure control in the cardiovascular system: object-oriented and acausal approach. (United States)

    Kulhánek, Tomáš; Kofránek, Jiří; Mateják, Marek


    This letter introduces an alternative approach to modeling the cardiovascular system with a short-term control mechanism published in Computers in Biology and Medicine, Vol. 47 (2014), pp. 104-112. We recommend using abstract components on a distinct physical level, separating the model into hydraulic components, subsystems of the cardiovascular system and individual subsystems of the control mechanism and scenario. We recommend utilizing an acausal modeling feature of Modelica language, which allows model variables to be expressed declaratively. Furthermore, the Modelica tool identifies which are the dependent and independent variables upon compilation. An example of our approach is introduced on several elementary components representing the hydraulic resistance to fluid flow and the elastic response of the vessel, among others. The introduced model implementation can be more reusable and understandable for the general scientific community. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Computational Fluid Dynamics Modeling of a Supersonic Nozzle and Integration into a Variable Cycle Engine Model (United States)

    Connolly, Joseph W.; Friedlander, David; Kopasakis, George


    This paper covers the development of an integrated nonlinear dynamic simulation for a variable cycle turbofan engine and nozzle that can be integrated with an overall vehicle Aero-Propulso-Servo-Elastic (APSE) model. A previously developed variable cycle turbofan engine model is used for this study and is enhanced here to include variable guide vanes allowing for operation across the supersonic flight regime. The primary focus of this study is to improve the fidelity of the model's thrust response by replacing the simple choked flow equation convergent-divergent nozzle model with a MacCormack method based quasi-1D model. The dynamic response of the nozzle model using the MacCormack method is verified by comparing it against a model of the nozzle using the conservation element/solution element method. A methodology is also presented for the integration of the MacCormack nozzle model with the variable cycle engine.

  2. Modelling spatio-temporal variability of Mytilus edulis (L.) growth by forcing a dynamic energy budget model with satellite-derived environmental data (United States)

    Thomas, Yoann; Mazurié, Joseph; Alunno-Bruscia, Marianne; Bacher, Cédric; Bouget, Jean-François; Gohin, Francis; Pouvreau, Stéphane; Struski, Caroline


    In order to assess the potential of various marine ecosystems for shellfish aquaculture and to evaluate their carrying capacities, there is a need to clarify the response of exploited species to environmental variations using robust ecophysiological models and available environmental data. For a large range of applications and comparison purposes, a non-specific approach based on 'generic' individual growth models offers many advantages. In this context, we simulated the response of blue mussel ( Mytilus edulis L.) to the spatio-temporal fluctuations of the environment in Mont Saint-Michel Bay (North Brittany) by forcing a generic growth model based on Dynamic Energy Budgets with satellite-derived environmental data (i.e. temperature and food). After a calibration step based on data from mussel growth surveys, the model was applied over nine years on a large area covering the entire bay. These simulations provide an evaluation of the spatio-temporal variability in mussel growth and also show the ability of the DEB model to integrate satellite-derived data and to predict spatial and temporal growth variability of mussels. Observed seasonal, inter-annual and spatial growth variations are well simulated. The large-scale application highlights the strong link between food and mussel growth. The methodology described in this study may be considered as a suitable approach to account for environmental effects (food and temperature variations) on physiological responses (growth and reproduction) of filter feeders in varying environments. Such physiological responses may then be useful for evaluating the suitability of coastal ecosystems for shellfish aquaculture.

  3. Analytical Model for LLC Resonant Converter With Variable Duty-Cycle Control

    DEFF Research Database (Denmark)

    Shen, Yanfeng; Wang, Huai; Blaabjerg, Frede


    are identified and discussed. The proposed model enables a better understanding of the operation characteristics and fast parameter design of the LLC converter, which otherwise cannot be achieved by the existing simulation based methods and numerical models. The results obtained from the proposed model......In LLC resonant converters, the variable duty-cycle control is usually combined with a variable frequency control to widen the gain range, improve the light-load efficiency, or suppress the inrush current during start-up. However, a proper analytical model for the variable duty-cycle controlled LLC...... converter is still not available due to the complexity of operation modes and the nonlinearity of steady-state equations. This paper makes the efforts to develop an analytical model for the LLC converter with variable duty-cycle control. All possible operation models and critical operation characteristics...


    Directory of Open Access Journals (Sweden)

    Hajime Inamura


    Full Text Available The need of tools for design and evaluation of pedestrian areas, subways stations, entrance hall, shopping mall, escape routes, stadium etc lead to the necessity of a pedestrian model. One approach pedestrian model is Microscopic Pedestrian Simulation Model. To be able to develop and calibrate a microscopic pedestrian simulation model, a number of variables need to be considered. As the first step of model development, some data was collected using video and the coordinate of the head path through image processing were also taken. Several numbers of variables can be gathered to describe the behavior of pedestrian from a different point of view. This paper describes how to obtain variables from video taking and simple image processing that can represent the movement of pedestrians and its variables

  5. A model for AGN variability on multiple time-scales (United States)

    Sartori, Lia F.; Schawinski, Kevin; Trakhtenbrot, Benny; Caplar, Neven; Treister, Ezequiel; Koss, Michael J.; Urry, C. Megan; Zhang, C. E.


    We present a framework to link and describe active galactic nuclei (AGN) variability on a wide range of time-scales, from days to billions of years. In particular, we concentrate on the AGN variability features related to changes in black hole fuelling and accretion rate. In our framework, the variability features observed in different AGN at different time-scales may be explained as realisations of the same underlying statistical properties. In this context, we propose a model to simulate the evolution of AGN light curves with time based on the probability density function (PDF) and power spectral density (PSD) of the Eddington ratio (L/LEdd) distribution. Motivated by general galaxy population properties, we propose that the PDF may be inspired by the L/LEdd distribution function (ERDF), and that a single (or limited number of) ERDF+PSD set may explain all observed variability features. After outlining the framework and the model, we compile a set of variability measurements in terms of structure function (SF) and magnitude difference. We then combine the variability measurements on a SF plot ranging from days to Gyr. The proposed framework enables constraints on the underlying PSD and the ability to link AGN variability on different time-scales, therefore providing new insights into AGN variability and black hole growth phenomena.

  6. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. (United States)

    Hong, Haoyuan; Tsangaratos, Paraskevas; Ilia, Ioanna; Liu, Junzhi; Zhu, A-Xing; Xu, Chong


    The main objective of the present study was to utilize Genetic Algorithms (GA) in order to obtain the optimal combination of forest fire related variables and apply data mining methods for constructing a forest fire susceptibility map. In the proposed approach, a Random Forest (RF) and a Support Vector Machine (SVM) was used to produce a forest fire susceptibility map for the Dayu County which is located in southwest of Jiangxi Province, China. For this purpose, historic forest fires and thirteen forest fire related variables were analyzed, namely: elevation, slope angle, aspect, curvature, land use, soil cover, heat load index, normalized difference vegetation index, mean annual temperature, mean annual wind speed, mean annual rainfall, distance to river network and distance to road network. The Natural Break and the Certainty Factor method were used to classify and weight the thirteen variables, while a multicollinearity analysis was performed to determine the correlation among the variables and decide about their usability. The optimal set of variables, determined by the GA limited the number of variables into eight excluding from the analysis, aspect, land use, heat load index, distance to river network and mean annual rainfall. The performance of the forest fire models was evaluated by using the area under the Receiver Operating Characteristic curve (ROC-AUC) based on the validation dataset. Overall, the RF models gave higher AUC values. Also the results showed that the proposed optimized models outperform the original models. Specifically, the optimized RF model gave the best results (0.8495), followed by the original RF (0.8169), while the optimized SVM gave lower values (0.7456) than the RF, however higher than the original SVM (0.7148) model. The study highlights the significance of feature selection techniques in forest fire susceptibility, whereas data mining methods could be considered as a valid approach for forest fire susceptibility modeling

  7. The application of an internal state variable model to the viscoplastic behavior of irradiated ASTM 304L stainless steel

    Energy Technology Data Exchange (ETDEWEB)

    McAnulty, Michael J., E-mail: [Department of Energy, 1955 Fremont Avenue, Idaho Falls, ID 83402 (United States); Potirniche, Gabriel P. [Mechanical Engineering Department, University of Idaho, Moscow, ID 83844 (United States); Tokuhiro, Akira [Mechanical Engineering Department, University of Idaho, Idaho Falls, ID 83402 (United States)


    Highlights: Black-Right-Pointing-Pointer An internal state variable approach is used to predict the plastic behavior of irradiated metals. Black-Right-Pointing-Pointer The model predicts uniaxial tensile test data for irradiated 304L stainless steel. Black-Right-Pointing-Pointer The model is implemented as a user-defined material subroutine in the finite element code ABAQUS. Black-Right-Pointing-Pointer Results are compared for the unirradiated and irradiated specimens loaded in uniaxial tension. - Abstract: Neutron irradiation of metals results in decreased fracture toughness, decreased ductility, increased yield strength and increased ductile-to-brittle transition temperature. Designers use the most limiting material properties throughout the reactor vessel lifetime to determine acceptable safety margins. To reduce analysis conservatism, a new model is proposed based on an internal state variable approach for the plastic behavior of unirradiated ductile materials to support its use for analyzing irradiated materials. The proposed modeling addresses low temperature irradiation of 304L stainless steel, and predicts uniaxial tensile test data of irradiated experimental specimens. The model was implemented as a user-defined material subroutine (UMAT) in the finite element software ABAQUS. Results are compared between the unirradiated and irradiated specimens subjected to tension tests.

  8. Classification criteria of syndromes by latent variable models

    DEFF Research Database (Denmark)

    Petersen, Janne


    patient's characteristics. These methods may erroneously reduce multiplicity either by combining markers of different phenotypes or by mixing HALS with other processes such as aging. Latent class models identify homogenous groups of patients based on sets of variables, for example symptoms. As no gold......The thesis has two parts; one clinical part: studying the dimensions of human immunodeficiency virus associated lipodystrophy syndrome (HALS) by latent class models, and a more statistical part: investigating how to predict scores of latent variables so these can be used in subsequent regression...... standard exists for diagnosing HALS the normally applied diagnostic models cannot be used. Latent class models, which have never before been used to diagnose HALS, make it possible, under certain assumptions, to: statistically evaluate the number of phenotypes, test for mixing of HALS with other processes...

  9. Uncertainties in radioecological assessment models-Their nature and approaches to reduce them

    International Nuclear Information System (INIS)

    Kirchner, G.; Steiner, M.


    Radioecological assessment models are necessary tools for estimating the radiation exposure of humans and non-human biota. This paper focuses on factors affecting their predictive accuracy, discusses the origin and nature of the different contributions to uncertainty and variability and presents approaches to separate and quantify them. The key role of the conceptual model, notably in relation to its structure and complexity, as well as the influence of the number and type of input parameters, are highlighted. Guidelines are provided to improve the degree of reliability of radioecological models

  10. Using structural equation modeling to investigate relationships among ecological variables (United States)

    Malaeb, Z.A.; Kevin, Summers J.; Pugesek, B.H.


    Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0

  11. Monte Carlo Bayesian inference on a statistical model of sub-gridcolumn moisture variability using high-resolution cloud observations. Part 1: Method (United States)

    Norris, Peter M.; da Silva, Arlindo M.


    A method is presented to constrain a statistical model of sub-gridcolumn moisture variability using high-resolution satellite cloud data. The method can be used for large-scale model parameter estimation or cloud data assimilation. The gridcolumn model includes assumed probability density function (PDF) intra-layer horizontal variability and a copula-based inter-layer correlation model. The observables used in the current study are Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure, brightness temperature and cloud optical thickness, but the method should be extensible to direct cloudy radiance assimilation for a small number of channels. The algorithm is a form of Bayesian inference with a Markov chain Monte Carlo (MCMC) approach to characterizing the posterior distribution. This approach is especially useful in cases where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach is not gradient-based and allows jumps into regions of non-zero cloud probability. The current study uses a skewed-triangle distribution for layer moisture. The article also includes a discussion of the Metropolis and multiple-try Metropolis versions of MCMC. PMID:29618847

  12. Monte Carlo Bayesian Inference on a Statistical Model of Sub-Gridcolumn Moisture Variability Using High-Resolution Cloud Observations. Part 1: Method (United States)

    Norris, Peter M.; Da Silva, Arlindo M.


    A method is presented to constrain a statistical model of sub-gridcolumn moisture variability using high-resolution satellite cloud data. The method can be used for large-scale model parameter estimation or cloud data assimilation. The gridcolumn model includes assumed probability density function (PDF) intra-layer horizontal variability and a copula-based inter-layer correlation model. The observables used in the current study are Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure, brightness temperature and cloud optical thickness, but the method should be extensible to direct cloudy radiance assimilation for a small number of channels. The algorithm is a form of Bayesian inference with a Markov chain Monte Carlo (MCMC) approach to characterizing the posterior distribution. This approach is especially useful in cases where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach is not gradient-based and allows jumps into regions of non-zero cloud probability. The current study uses a skewed-triangle distribution for layer moisture. The article also includes a discussion of the Metropolis and multiple-try Metropolis versions of MCMC.

  13. 8760-Based Method for Representing Variable Generation Capacity Value in Capacity Expansion Models: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Frew, Bethany A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Cole, Wesley J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sun, Yinong [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Mai, Trieu T [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Richards, James [National Renewable Energy Laboratory (NREL), Golden, CO (United States)


    Capacity expansion models (CEMs) are widely used to evaluate the least-cost portfolio of electricity generators, transmission, and storage needed to reliably serve demand over the evolution of many years or decades. Various CEM formulations are used to evaluate systems ranging in scale from states or utility service territories to national or multi-national systems. CEMs can be computationally complex, and to achieve acceptable solve times, key parameters are often estimated using simplified methods. In this paper, we focus on two of these key parameters associated with the integration of variable generation (VG) resources: capacity value and curtailment. We first discuss common modeling simplifications used in CEMs to estimate capacity value and curtailment, many of which are based on a representative subset of hours that can miss important tail events or which require assumptions about the load and resource distributions that may not match actual distributions. We then present an alternate approach that captures key elements of chronological operation over all hours of the year without the computationally intensive economic dispatch optimization typically employed within more detailed operational models. The updated methodology characterizes the (1) contribution of VG to system capacity during high load and net load hours, (2) the curtailment level of VG, and (3) the potential reductions in curtailments enabled through deployment of storage and more flexible operation of select thermal generators. We apply this alternate methodology to an existing CEM, the Regional Energy Deployment System (ReEDS). Results demonstrate that this alternate approach provides more accurate estimates of capacity value and curtailments by explicitly capturing system interactions across all hours of the year. This approach could be applied more broadly to CEMs at many different scales where hourly resource and load data is available, greatly improving the representation of challenges

  14. A Numerical-Analytical Approach to Modeling the Axial Rotation of the Earth (United States)

    Markov, Yu. G.; Perepelkin, V. V.; Rykhlova, L. V.; Filippova, A. S.


    A model for the non-uniform axial rotation of the Earth is studied using a celestial-mechanical approach and numerical simulations. The application of an approximate model containing a small number of parameters to predict variations of the axial rotation velocity of the Earth over short time intervals is justified. This approximate model is obtained by averaging variable parameters that are subject to small variations due to non-stationarity of the perturbing factors. The model is verified and compared with predictions over a long time interval published by the International Earth Rotation and Reference Systems Service (IERS).

  15. Electrochemo-hydrodynamics modeling approach for a uranium electrowinning cell

    Energy Technology Data Exchange (ETDEWEB)

    Kim, K.R.; Paek, S.; Ahn, D.H., E-mail:, E-mail:, E-mail: [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Park, J.Y.; Hwang, I.S., E-mail:, E-mail: [Department of Nuclear Engineering, Seoul National University (Korea, Republic of)


    This study demonstrated a simulation based on fully coupling of electrochemical kinetics with 3- dimensional transport of ionic species in a flowing molten-salt electrolyte through a simplified channel cell of uranium electro winner. Dependences of ionic electro-transport on the velocity of stationary electrolyte flow were studied using a coupling approach of electrochemical reaction model. The present model was implemented in a commercially available computational fluid dynamics (CFD) platform, Ansys-CFX, using its customization ability via user defined functions. The main parameters characterizing the effect of the turbulent flow of an electrolyte between two planar electrodes were demonstrated by means of CFD-based multiphysics simulation approach. Simulation was carried out for the case of uranium electrowinning characteristics in a stream of molten salt electrolyte. This approach was taken into account the concentration profile at the electrode surface, to represent the variation of the diffusion limited current density as a function of the flow characteristics and of applied current density. It was able to predict conventional current voltage relation in addition to details of electrolyte fluid dynamics and electrochemical variable, such as flow field, species concentrations, potential, and current distributions throughout the current driven cell. (author)

  16. Electrochemo-hydrodynamics modeling approach for a uranium electrowinning cell

    International Nuclear Information System (INIS)

    Kim, K.R.; Paek, S.; Ahn, D.H.; Park, J.Y.; Hwang, I.S.


    This study demonstrated a simulation based on fully coupling of electrochemical kinetics with 3- dimensional transport of ionic species in a flowing molten-salt electrolyte through a simplified channel cell of uranium electro winner. Dependences of ionic electro-transport on the velocity of stationary electrolyte flow were studied using a coupling approach of electrochemical reaction model. The present model was implemented in a commercially available computational fluid dynamics (CFD) platform, Ansys-CFX, using its customization ability via user defined functions. The main parameters characterizing the effect of the turbulent flow of an electrolyte between two planar electrodes were demonstrated by means of CFD-based multiphysics simulation approach. Simulation was carried out for the case of uranium electrowinning characteristics in a stream of molten salt electrolyte. This approach was taken into account the concentration profile at the electrode surface, to represent the variation of the diffusion limited current density as a function of the flow characteristics and of applied current density. It was able to predict conventional current voltage relation in addition to details of electrolyte fluid dynamics and electrochemical variable, such as flow field, species concentrations, potential, and current distributions throughout the current driven cell. (author)

  17. Optimizing variable radius plot size and LiDAR resolution to model standing volume in conifer forests (United States)

    Ram Kumar Deo; Robert E. Froese; Michael J. Falkowski; Andrew T. Hudak


    The conventional approach to LiDAR-based forest inventory modeling depends on field sample data from fixed-radius plots (FRP). Because FRP sampling is cost intensive, combining variable-radius plot (VRP) sampling and LiDAR data has the potential to improve inventory efficiency. The overarching goal of this study was to evaluate the integration of LiDAR and VRP data....

  18. Assessing doses to terrestrial wildlife at a radioactive waste disposal site: Inter-comparison of modelling approaches

    Energy Technology Data Exchange (ETDEWEB)

    Johansen, M.P., E-mail: [Australian Nuclear Science and Technology Organisation, Locked Bag 2001, Kirrawee DC, NSW, 2232 (Australia); Barnett, C.L., E-mail: [Centre for Ecology and Hydrology, Lancaster (United Kingdom); Beresford, N.A., E-mail: [Centre for Ecology and Hydrology, Lancaster (United Kingdom); Brown, J.E., E-mail: [Norwegian Radiation Protection Authority, Oesteraas (Norway); Cerne, M., E-mail: [Jozef Stefan Institute, Ljubljana (Slovenia); Howard, B.J., E-mail: [Centre for Ecology and Hydrology, Lancaster (United Kingdom); Kamboj, S., E-mail: [Argonne National Laboratory, IL (United States); Keum, D.-K., E-mail: [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Smodis, B. [Jozef Stefan Institute, Ljubljana (Slovenia); Twining, J.R., E-mail: [Australian Nuclear Science and Technology Organisation, Locked Bag 2001, Kirrawee DC, NSW, 2232 (Australia); Vandenhove, H., E-mail: [Belgian Nuclear Research Centre, Mol (Belgium); Vives i Batlle, J., E-mail: [Belgian Nuclear Research Centre, Mol (Belgium); Wood, M.D., E-mail: [University of Salford, Manchester (United Kingdom); Yu, C., E-mail: [Argonne National Laboratory, IL (United States)


    Radiological doses to terrestrial wildlife were examined in this model inter-comparison study that emphasised factors causing variability in dose estimation. The study participants used varying modelling approaches and information sources to estimate dose rates and tissue concentrations for a range of biota types exposed to soil contamination at a shallow radionuclide waste burial site in Australia. Results indicated that the dominant factor causing variation in dose rate estimates (up to three orders of magnitude on mean total dose rates) was the soil-to-organism transfer of radionuclides that included variation in transfer parameter values as well as transfer calculation methods. Additional variation was associated with other modelling factors including: how participants conceptualised and modelled the exposure configurations (two orders of magnitude); which progeny to include with the parent radionuclide (typically less than one order of magnitude); and dose calculation parameters, including radiation weighting factors and dose conversion coefficients (typically less than one order of magnitude). Probabilistic approaches to model parameterisation were used to encompass and describe variable model parameters and outcomes. The study confirms the need for continued evaluation of the underlying mechanisms governing soil-to-organism transfer of radionuclides to improve estimation of dose rates to terrestrial wildlife. The exposure pathways and configurations available in most current codes are limited when considering instances where organisms access subsurface contamination through rooting, burrowing, or using different localised waste areas as part of their habitual routines. - Highlights: Black-Right-Pointing-Pointer Assessment of modelled dose rates to terrestrial biota from radionuclides. Black-Right-Pointing-Pointer The substantial variation among current approaches is quantifiable. Black-Right-Pointing-Pointer The dominant variable was soil

  19. Fixed transaction costs and modelling limited dependent variables

    NARCIS (Netherlands)

    Hempenius, A.L.


    As an alternative to the Tobit model, for vectors of limited dependent variables, I suggest a model, which follows from explicitly using fixed costs, if appropriate of course, in the utility function of the decision-maker.

  20. Effects of Variable Production Rate and Time-Dependent Holding Cost for Complementary Products in Supply Chain Model

    Directory of Open Access Journals (Sweden)

    Mitali Sarkar


    Full Text Available Recently, a major trend is going to redesign a production system by controlling or making variable the production rate within some fixed interval to maintain the optimal level. This strategy is more effective when the holding cost is time-dependent as it is interrelated with holding duration of products and rate of production. An effort is made to make a supply chain model (SCM to show the joint effect of variable production rate and time-varying holding cost for specific type of complementary products, where those products are made by two different manufacturers and a common retailer makes them bundle and sells bundles to end customers. Demand of each product is specified by stochastic reservation prices with a known potential market size. Those players of the SCM are considered with unequal power. Stackelberg game approach is employed to obtain global optimum solution of the model. An illustrative numerical example, graphical representation, and managerial insights are given to illustrate the model. Results prove that variable production rate and time-dependent holding cost save more than existing literature.

  1. Thermodynamic approach to the inelastic state variable theories

    International Nuclear Information System (INIS)

    Dashner, P.A.


    A continuum model is proposed as a theoretical foundation for the inelastic state variable theory of Hart. The model is based on the existence of a free energy function and the assumption that a strained material element recalls two other local configurations which are, in some specified manner, descriptive of prior deformation. A precise formulation of these material hypotheses within the classical thermodynamical framework leads to the recovery of a generalized elastic law and the specification of evolutionary laws for the remembered configurations which are frame invariant and formally valid for finite strains. Moreover, the precise structure of Hart's theory is recovered when strains are assumed to be small

  2. Modeling an integrated hospital management planning problem using integer optimization approach (United States)

    Sitepu, Suryati; Mawengkang, Herman; Irvan


    Hospital is a very important institution to provide health care for people. It is not surprising that nowadays the people’s demands for hospital is increasing. However, due to the rising cost of healthcare services, hospitals need to consider efficiencies in order to overcome these two problems. This paper deals with an integrated strategy of staff capacity management and bed allocation planning to tackle these problems. Mathematically, the strategy can be modeled as an integer linear programming problem. We solve the model using a direct neighborhood search approach, based on the notion of superbasic variables.

  3. Transferability of species distribution models: a functional habitat approach for two regionally threatened butterflies. (United States)

    Vanreusel, Wouter; Maes, Dirk; Van Dyck, Hans


    Numerous models for predicting species distribution have been developed for conservation purposes. Most of them make use of environmental data (e.g., climate, topography, land use) at a coarse grid resolution (often kilometres). Such approaches are useful for conservation policy issues including reserve-network selection. The efficiency of predictive models for species distribution is usually tested on the area for which they were developed. Although highly interesting from the point of view of conservation efficiency, transferability of such models to independent areas is still under debate. We tested the transferability of habitat-based predictive distribution models for two regionally threatened butterflies, the green hairstreak (Callophrys rubi) and the grayling (Hipparchia semele), within and among three nature reserves in northeastern Belgium. We built predictive models based on spatially detailed maps of area-wide distribution and density of ecological resources. We used resources directly related to ecological functions (host plants, nectar sources, shelter, microclimate) rather than environmental surrogate variables. We obtained models that performed well with few resource variables. All models were transferable--although to different degrees--among the independent areas within the same broad geographical region. We argue that habitat models based on essential functional resources could transfer better in space than models that use indirect environmental variables. Because functional variables can easily be interpreted and even be directly affected by terrain managers, these models can be useful tools to guide species-adapted reserve management.

  4. Multi-model attribution of upper-ocean temperature changes using an isothermal approach (United States)

    Weller, Evan; Min, Seung-Ki; Palmer, Matthew D.; Lee, Donghyun; Yim, Bo Young; Yeh, Sang-Wook


    Both air-sea heat exchanges and changes in ocean advection have contributed to observed upper-ocean warming most evident in the late-twentieth century. However, it is predominantly via changes in air-sea heat fluxes that human-induced climate forcings, such as increasing greenhouse gases, and other natural factors such as volcanic aerosols, have influenced global ocean heat content. The present study builds on previous work using two different indicators of upper-ocean temperature changes for the detection of both anthropogenic and natural external climate forcings. Using simulations from phase 5 of the Coupled Model Intercomparison Project, we compare mean temperatures above a fixed isotherm with the more widely adopted approach of using a fixed depth. We present the first multi-model ensemble detection and attribution analysis using the fixed isotherm approach to robustly detect both anthropogenic and natural external influences on upper-ocean temperatures. Although contributions from multidecadal natural variability cannot be fully removed, both the large multi-model ensemble size and properties of the isotherm analysis reduce internal variability of the ocean, resulting in better observation-model comparison of temperature changes since the 1950s. We further show that the high temporal resolution afforded by the isotherm analysis is required to detect natural external influences such as volcanic cooling events in the upper-ocean because the radiative effect of volcanic forcings is short-lived.

  5. Emerging adulthood features and criteria for adulthood : Variable- and person-centered approaches

    NARCIS (Netherlands)

    Tagliabue, Semira; Crocetti, Elisabetta; Lanz, Margherita

    Reaching adulthood is the aim of the transition to adulthood; however, emerging adults differently define both adulthood and the transitional period they are living. Variable-centered and person-centered approaches were integrated in the present paper to investigate if the criteria used to define

  6. A variable capacitance based modeling and power capability predicting method for ultracapacitor (United States)

    Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang


    Methods of accurate modeling and power capability predicting for ultracapacitors are of great significance in management and application of lithium-ion battery/ultracapacitor hybrid energy storage system. To overcome the simulation error coming from constant capacitance model, an improved ultracapacitor model based on variable capacitance is proposed, where the main capacitance varies with voltage according to a piecewise linear function. A novel state-of-charge calculation approach is developed accordingly. After that, a multi-constraint power capability prediction is developed for ultracapacitor, in which a Kalman-filter-based state observer is designed for tracking ultracapacitor's real-time behavior. Finally, experimental results verify the proposed methods. The accuracy of the proposed model is verified by terminal voltage simulating results under different temperatures, and the effectiveness of the designed observer is proved by various test conditions. Additionally, the power capability prediction results of different time scales and temperatures are compared, to study their effects on ultracapacitor's power capability.

  7. Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings

    International Nuclear Information System (INIS)

    Wang, Qinpeng; Augenbroe, Godfried; Kim, Ji-Hyun; Gu, Li


    Highlights: • A meta-analysis framework for a stochastic characterization of occupancy variables. • Sensitivity ranking of occupancy variability against all other sources of uncertainty. • Sensitivity of occupant presence for building energy consumption is low. • Accurate mean knowledge is sufficient for predicting building energy consumption. • Prediction of peak demand behavior requires stochastic occupancy modeling. - Abstract: Occupants interact with buildings in various ways via their presence (passive effects) and control actions (active effects). Therefore, understanding the influence of occupants is essential if we are to evaluate the performance of a building. In this paper, we model the mean profiles and variability of occupancy variables (presence and actions) separately. We will use a multi-variate Gaussian distribution to generate mean profiles of occupancy variables, while the variability will be represented by a multi-dimensional time series model, within a framework for a meta-analysis that synthesizes occupancy data gathered from a pool of buildings. We then discuss variants of occupancy models with respect to various outcomes of interest such as HVAC energy consumption and peak demand behavior via a sensitivity analysis. Results show that our approach is able to generate stochastic occupancy profiles, requiring minimum additional input from the energy modeler other than standard diversity profiles. Along with the meta-analysis, we enable the generalization of previous research results and statistical inferences to choose occupancy variables for future buildings. The sensitivity analysis shows that for aggregated building energy consumption, occupant presence has a smaller impact compared to lighting and appliance usage. Specifically, being accumulatively 55% wrong with regard to presence, only translates to 2% error in aggregated cooling energy in July and 3.6% error in heating energy in January. Such a finding redirects focus to the

  8. Sparse modeling of spatial environmental variables associated with asthma. (United States)

    Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W


    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. New Statistical Model for Variability of Aerosol Optical Thickness: Theory and Application to MODIS Data over Ocean (United States)

    Alexandrov, Mikhail Dmitrievic; Geogdzhayev, Igor V.; Tsigaridis, Konstantinos; Marshak, Alexander; Levy, Robert; Cairns, Brian


    A novel model for the variability in aerosol optical thickness (AOT) is presented. This model is based on the consideration of AOT fields as realizations of a stochastic process, that is the exponent of an underlying Gaussian process with a specific autocorrelation function. In this approach AOT fields have lognormal PDFs and structure functions having the correct asymptotic behavior at large scales. The latter is an advantage compared with fractal (scale-invariant) approaches. The simple analytical form of the structure function in the proposed model facilitates its use for the parameterization of AOT statistics derived from remote sensing data. The new approach is illustrated using a month-long global MODIS AOT dataset (over ocean) with 10 km resolution. It was used to compute AOT statistics for sample cells forming a grid with 5deg spacing. The observed shapes of the structure functions indicated that in a large number of cases the AOT variability is split into two regimes that exhibit different patterns of behavior: small-scale stationary processes and trends reflecting variations at larger scales. The small-scale patterns are suggested to be generated by local aerosols within the marine boundary layer, while the large-scale trends are indicative of elevated aerosols transported from remote continental sources. This assumption is evaluated by comparison of the geographical distributions of these patterns derived from MODIS data with those obtained from the GISS GCM. This study shows considerable potential to enhance comparisons between remote sensing datasets and climate models beyond regional mean AOTs.

  10. Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health. (United States)

    Vasconcelos, A G; Almeida, R M; Nobre, F F


    This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

  11. On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach

    Directory of Open Access Journals (Sweden)

    Alan Fask


    Full Text Available There has been a remarkable growth in distance learning courses in higher education. Despite indications that distance learning courses are more vulnerable to cheating behavior than traditional courses, there has been little research studying whether online exams facilitate a relatively greater level of cheating. This article examines this issue by developing an approach using a latent variable to measure student cheating. This latent variable is linked to both known student mastery related variables and variables unrelated to student mastery. Grade scores from a proctored final exam and an unproctored final exam are used to test for increased cheating behavior in the unproctored exam

  12. An improved hindcast approach for evaluation and diagnosis of physical processes in global climate models (United States)

    Ma, H.-Y.; Chuang, C. C.; Klein, S. A.; Lo, M.-H.; Zhang, Y.; Xie, S.; Zheng, X.; Ma, P.-L.; Zhang, Y.; Phillips, T. J.


    We present an improved procedure of generating initial conditions (ICs) for climate model hindcast experiments with specified sea surface temperature and sea ice. The motivation is to minimize errors in the ICs and lead to a better evaluation of atmospheric parameterizations' performance in the hindcast mode. We apply state variables (horizontal velocities, temperature, and specific humidity) from the operational analysis/reanalysis for the atmospheric initial states. Without a data assimilation system, we apply a two-step process to obtain other necessary variables to initialize both the atmospheric (e.g., aerosols and clouds) and land models (e.g., soil moisture). First, we nudge only the model horizontal velocities toward operational analysis/reanalysis values, given a 6 h relaxation time scale, to obtain all necessary variables. Compared to the original strategy in which horizontal velocities, temperature, and specific humidity are nudged, the revised approach produces a better representation of initial aerosols and cloud fields which are more consistent and closer to observations and model's preferred climatology. Second, we obtain land ICs from an off-line land model simulation forced with observed precipitation, winds, and surface fluxes. This approach produces more realistic soil moisture in the land ICs. With this refined procedure, the simulated precipitation, clouds, radiation, and surface air temperature over land are improved in the Day 2 mean hindcasts. Following this procedure, we propose a "Core" integration suite which provides an easily repeatable test allowing model developers to rapidly assess the impacts of various parameterization changes on the fidelity of modeled cloud-associated processes relative to observations.

  13. A computational approach to compare regression modelling strategies in prediction research. (United States)

    Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H


    It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

  14. A modelling framework to project future climate change impacts on streamflow variability and extremes in the West River, China

    Directory of Open Access Journals (Sweden)

    Y. Fei


    Full Text Available In this study, a hydrological modelling framework was introduced to assess the climate change impacts on future river flow in the West River basin, China, especially on streamflow variability and extremes. The modelling framework includes a delta-change method with the quantile-mapping technique to construct future climate forcings on the basis of observed meteorological data and the downscaled climate model outputs. This method is able to retain the signals of extreme weather events, as projected by climate models, in the constructed future forcing scenarios. Fed with the historical and future forcing data, a large-scale hydrologic model (the Variable Infiltration Capacity model, VIC was executed for streamflow simulations and projections at daily time scales. A bootstrapping resample approach was used as an indirect alternative to test the equality of means, standard deviations and the coefficients of variation for the baseline and future streamflow time series, and to assess the future changes in flood return levels. The West River basin case study confirms that the introduced modelling framework is an efficient effective tool to quantify streamflow variability and extremes in response to future climate change.

  15. Modeling and Simulation of Variable Mass, Flexible Structures (United States)

    Tobbe, Patrick A.; Matras, Alex L.; Wilson, Heath E.


    The advent of the new Ares I launch vehicle has highlighted the need for advanced dynamic analysis tools for variable mass, flexible structures. This system is composed of interconnected flexible stages or components undergoing rapid mass depletion through the consumption of solid or liquid propellant. In addition to large rigid body configuration changes, the system simultaneously experiences elastic deformations. In most applications, the elastic deformations are compatible with linear strain-displacement relationships and are typically modeled using the assumed modes technique. The deformation of the system is approximated through the linear combination of the products of spatial shape functions and generalized time coordinates. Spatial shape functions are traditionally composed of normal mode shapes of the system or even constraint modes and static deformations derived from finite element models of the system. Equations of motion for systems undergoing coupled large rigid body motion and elastic deformation have previously been derived through a number of techniques [1]. However, in these derivations, the mode shapes or spatial shape functions of the system components were considered constant. But with the Ares I vehicle, the structural characteristics of the system are changing with the mass of the system. Previous approaches to solving this problem involve periodic updates to the spatial shape functions or interpolation between shape functions based on system mass or elapsed mission time. These solutions often introduce misleading or even unstable numerical transients into the system. Plus, interpolation on a shape function is not intuitive. This paper presents an approach in which the shape functions are held constant and operate on the changing mass and stiffness matrices of the vehicle components. Each vehicle stage or component finite element model is broken into dry structure and propellant models. A library of propellant models is used to describe the

  16. Drought resilience across ecologically dominant species: An experiment-model integration approach (United States)

    Felton, A. J.; Warren, J.; Ricciuto, D. M.; Smith, M. D.


    Poorly understood are the mechanisms contributing to variability in ecosystem recovery following drought. Grasslands of the central U.S. are ecologically and economically important ecosystems, yet are also highly sensitive to drought. Although characteristics of these ecosystems change across gradients of temperature and precipitation, a consistent feature among these systems is the presence of highly abundant, dominant grass species that control biomass production. As a result, the incorporation of these species' traits into terrestrial biosphere models may constrain predictions amid increases in climatic variability. Here we report the results of a modeling-experiment (MODEX) research approach. We investigated the physiological, morphological and growth responses of the dominant grass species from each of the four major grasslands of the central U.S. (ranging from tallgrass prairie to desert grassland) following severe drought. Despite significant differences in baseline values, full recovery in leaf physiological function was evident across species, of which was consistently driven by the production of new leaves. Further, recovery in whole-plant carbon uptake tended to be driven by shifts in allocation from belowground to aboveground structures. However, there was clear variability among species in the magnitude of this dynamic as well as the relative allocation to stem versus leaf production. As a result, all species harbored the physiological capacity to recover from drought, yet we posit that variability in the recovery of whole-plant carbon uptake to be more strongly driven by variability in the sensitivity of species' morphology to soil moisture increases. The next step of this project will be to incorporate these and other existing data on these species and ecosystems into the community land model in an effort to test the sensitivity of this model to these data.

  17. Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices (United States)

    Reichstein, Markus; Rey, Ana; Freibauer, Annette; Tenhunen, John; Valentini, Riccardo; Banza, Joao; Casals, Pere; Cheng, Yufu; Grünzweig, Jose M.; Irvine, James; Joffre, Richard; Law, Beverly E.; Loustau, Denis; Miglietta, Franco; Oechel, Walter; Ourcival, Jean-Marc; Pereira, Joao S.; Peressotti, Alessandro; Ponti, Francesca; Qi, Ye; Rambal, Serge; Rayment, Mark; Romanya, Joan; Rossi, Federica; Tedeschi, Vanessa; Tirone, Giampiero; Xu, Ming; Yakir, Dan


    Field-chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, interannual and spatial variability of soil respiration as affected by water availability, temperature, and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g., leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical nonlinear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content, and site-specific maximum leaf area index. The model explained nearly two thirds of the temporal and intersite variability of soil respiration with a mean absolute error of 0.82 μmol m-2 s-1. The parameterized model exhibits the following principal properties: (1) At a relative amount of upper-layer soil water of 16% of field capacity, half-maximal soil respiration rates are reached. (2) The apparent temperature sensitivity of soil respiration measured as Q10 varies between 1 and 5 depending on soil temperature and water content. (3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly timescale, we employed the approach by [2002] that used monthly precipitation and air temperature to globally predict soil respiration (T&P model). While this model was able to

  18. Using latent variable approach to estimate China's economy-wide energy rebound effect over 1954–2010

    International Nuclear Information System (INIS)

    Shao, Shuai; Huang, Tao; Yang, Lili


    The energy rebound effect has been a significant issue in China, which is undergoing economic transition, since it reflects the effectiveness of energy-saving policy relying on improved energy efficiency. Based on the IPAT equation and Brookes' explanation of the rebound effect, this paper develops an alternative estimation model of the rebound effect. By using the estimation model and latent variable approach, which is achieved through a time-varying coefficient state space model, we estimate China's economy-wide energy rebound effect over 1954–2010. The results show that the rebound effect evidently exists in China as a result of the annual average of 39.73% over 1954–2010. Before and after the implementation of China's reform and opening-up policy in 1978, the rebound effects are 47.24% and 37.32%, with a strong fluctuation and a circuitously downward trend, respectively, indicating that a stable political environment and the development of market economy system facilitate the effectiveness of energy-saving policy. Although the energy-saving effect of improving energy efficiency has been partly realised, there remains a large energy-saving potential in China. - Highlights: • We present an improved estimation methodology of economy-wide energy rebound effect. • We use the latent variable approach to estimate China's economy-wide rebound effect. • The rebound exists in China and varies before and after reform and opening-up. • After 1978, the average rebound is 37.32% with a circuitously downward trend. • Traditional Solow remainder method underestimates the rebound in most cases

  19. Evaluation of an unsteady flamelet progress variable model for autoignition and flame development in compositionally stratified mixtures (United States)

    Mukhopadhyay, Saumyadip; Abraham, John


    The unsteady flamelet progress variable (UFPV) model has been proposed by Pitsch and Ihme ["An unsteady/flamelet progress variable method for LES of nonpremixed turbulent combustion," AIAA Paper No. 2005-557, 2005] for modeling the averaged/filtered chemistry source terms in Reynolds averaged simulations and large eddy simulations of reacting non-premixed combustion. In the UFPV model, a look-up table of source terms is generated as a function of mixture fraction Z, scalar dissipation rate χ, and progress variable C by solving the unsteady flamelet equations. The assumption is that the unsteady flamelet represents the evolution of the reacting mixing layer in the non-premixed flame. We assess the accuracy of the model in predicting autoignition and flame development in compositionally stratified n-heptane/air mixtures using direct numerical simulations (DNS). The focus in this work is primarily on the assessment of accuracy of the probability density functions (PDFs) employed for obtaining averaged source terms. The performance of commonly employed presumed functions, such as the dirac-delta distribution function, the β distribution function, and statistically most likely distribution (SMLD) approach in approximating the shapes of the PDFs of the reactive and the conserved scalars is evaluated. For unimodal distributions, it is observed that functions that need two-moment information, e.g., the β distribution function and the SMLD approach with two-moment closure, are able to reasonably approximate the actual PDF. As the distribution becomes multimodal, higher moment information is required. Differences are observed between the ignition trends obtained from DNS and those predicted by the look-up table, especially for smaller gradients where the flamelet assumption becomes less applicable. The formulation assumes that the shape of the χ(Z) profile can be modeled by an error function which remains unchanged in the presence of heat release. We show that this

  20. Variability aware compact model characterization for statistical circuit design optimization (United States)

    Qiao, Ying; Qian, Kun; Spanos, Costas J.


    Variability modeling at the compact transistor model level can enable statistically optimized designs in view of limitations imposed by the fabrication technology. In this work we propose an efficient variabilityaware compact model characterization methodology based on the linear propagation of variance. Hierarchical spatial variability patterns of selected compact model parameters are directly calculated from transistor array test structures. This methodology has been implemented and tested using transistor I-V measurements and the EKV-EPFL compact model. Calculation results compare well to full-wafer direct model parameter extractions. Further studies are done on the proper selection of both compact model parameters and electrical measurement metrics used in the method.

  1. Interannual modes of variability of Southern Hemisphere atmospheric circulation in CMIP3 models

    International Nuclear Information System (INIS)

    Grainger, S; Frederiksen, C S; Zheng, X


    The atmospheric circulation acts as a bridge between large-scale sources of climate variability, and climate variability on regional scales. Here a statistical method is applied to monthly mean Southern Hemisphere 500hPa geopotential height to separate the interannual variability of the seasonal mean into intraseasonal and slowly varying (time scales of a season or longer) components. Intraseasonal and slow modes of variability are estimated from realisations of models from the Coupled Model Intercomparison Project Phase 3 (CMIP3) twentieth century coupled climate simulation (20c3m) and are evaluated against those estimated from reanalysis data. The intraseasonal modes of variability are generally well reproduced across all CMIP3 20c3m models for both Southern Hemisphere summer and winter. The slow modes are in general less well reproduced than the intraseasonal modes, and there are larger differences between realisations than for the intraseasonal modes. New diagnostics are proposed to evaluate model variability. It is found that differences between realisations from each model are generally less than inter-model differences. Differences between model-mean diagnostics are found. The results obtained are applicable to assessing the reliability of changes in atmospheric circulation variability in CMIP3 models and for their suitability for further studies of regional climate variability.

  2. Some considerations concerning the challenge of incorporating social variables into epidemiological models of infectious disease transmission. (United States)

    Barnett, Tony; Fournié, Guillaume; Gupta, Sunetra; Seeley, Janet


    Incorporation of 'social' variables into epidemiological models remains a challenge. Too much detail and models cease to be useful; too little and the very notion of infection - a highly social process in human populations - may be considered with little reference to the social. The French sociologist Émile Durkheim proposed that the scientific study of society required identification and study of 'social currents'. Such 'currents' are what we might today describe as 'emergent properties', specifiable variables appertaining to individuals and groups, which represent the perspectives of social actors as they experience the environment in which they live their lives. Here we review the ways in which one particular emergent property, hope, relevant to a range of epidemiological situations, might be used in epidemiological modelling of infectious diseases in human populations. We also indicate how such an approach might be extended to include a range of other potential emergent properties to represent complex social and economic processes bearing on infectious disease transmission.

  3. A MIMIC approach to modeling the underground economy in Taiwan (United States)

    Wang, David Han-Min; Lin, Jer-Yan; Yu, Tiffany Hui-Kuang


    The size of underground economy (UE) expansion usually increases the tax gap, impose a burden on the economy, and results in tax distortions. This study uses the MIMIC approach to model the causal variables and indicating variables to estimate the UE in Taiwan. We also focus on testing the data for non-stationarity and perform diagnostic tests. By using annual time-series data for Taiwan from 1961 to 2003, it is found that the estimated size of the UE varies from 11.0% to 13.1% before 1988, and from 10.6% to 11.8% from 1989 onwards. That the size of the UE experienced a substantial downward shift in 1989 indicates that there was a structural break. The UE is significantly and positively affected by such casual variables as the logarithm of real government consumption and currency inflation, but is negatively affected by the tax burden at 5% significant level. Unemployment rate and crime rate are not significantly correlated with the UE in this study.

  4. Quality by Design approach for studying the impact of formulation and process variables on product quality of oral disintegrating films. (United States)

    Mazumder, Sonal; Pavurala, Naresh; Manda, Prashanth; Xu, Xiaoming; Cruz, Celia N; Krishnaiah, Yellela S R


    The present investigation was carried out to understand the impact of formulation and process variables on the quality of oral disintegrating films (ODF) using Quality by Design (QbD) approach. Lamotrigine (LMT) was used as a model drug. Formulation variable was plasticizer to film former ratio and process variables were drying temperature, air flow rate in the drying chamber, drying time and wet coat thickness of the film. A Definitive Screening Design of Experiments (DoE) was used to identify and classify the critical formulation and process variables impacting critical quality attributes (CQA). A total of 14 laboratory-scale DoE formulations were prepared and evaluated for mechanical properties (%elongation at break, yield stress, Young's modulus, folding endurance) and other CQA (dry thickness, disintegration time, dissolution rate, moisture content, moisture uptake, drug assay and drug content uniformity). The main factors affecting mechanical properties were plasticizer to film former ratio and drying temperature. Dissolution rate was found to be sensitive to air flow rate during drying and plasticizer to film former ratio. Data were analyzed for elucidating interactions between different variables, rank ordering the critical materials attributes (CMA) and critical process parameters (CPP), and for providing a predictive model for the process. Results suggested that plasticizer to film former ratio and process controls on drying are critical to manufacture LMT ODF with the desired CQA. Published by Elsevier B.V.

  5. A novel methodology improves reservoir characterization models using geologic fuzzy variables

    Energy Technology Data Exchange (ETDEWEB)

    Soto B, Rodolfo [DIGITOIL, Maracaibo (Venezuela); Soto O, David A. [Texas A and M University, College Station, TX (United States)


    One of the research projects carried out in Cusiana field to explain its rapid decline during the last years was to get better permeability models. The reservoir of this field has a complex layered system that it is not easy to model using conventional methods. The new technique included the development of porosity and permeability maps from cored wells following the same trend of the sand depositions for each facie or layer according to the sedimentary facie and the depositional system models. Then, we used fuzzy logic to reproduce those maps in three dimensions as geologic fuzzy variables. After multivariate statistical and factor analyses, we found independence and a good correlation coefficient between the geologic fuzzy variables and core permeability and porosity. This means, the geologic fuzzy variable could explain the fabric, the grain size and the pore geometry of the reservoir rock trough the field. Finally, we developed a neural network permeability model using porosity, gamma ray and the geologic fuzzy variable as input variables. This model has a cross-correlation coefficient of 0.873 and average absolute error of 33% compared with the actual model with a correlation coefficient of 0.511 and absolute error greater than 250%. We tested different methodologies, but this new one showed dramatically be a promiser way to get better permeability models. The use of the models have had a high impact in the explanation of well performance and workovers, and reservoir simulation models. (author)

  6. Mobile phone use while driving: a hybrid modeling approach. (United States)

    Márquez, Luis; Cantillo, Víctor; Arellana, Julián


    The analysis of the effects that mobile phone use produces while driving is a topic of great interest for the scientific community. There is consensus that using a mobile phone while driving increases the risk of exposure to traffic accidents. The purpose of this research is to evaluate the drivers' behavior when they decide whether or not to use a mobile phone while driving. For that, a hybrid modeling approach that integrates a choice model with the latent variable "risk perception" was used. It was found that workers and individuals with the highest education level are more prone to use a mobile phone while driving than others. Also, "risk perception" is higher among individuals who have been previously fined and people who have been in an accident or almost been in an accident. It was also found that the tendency to use mobile phones while driving increases when the traffic speed reduces, but it decreases when the fine increases. Even though the urgency of the phone call is the most important explanatory variable in the choice model, the cost of the fine is an important attribute in order to control mobile phone use while driving. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Plasticity models of material variability based on uncertainty quantification techniques

    Energy Technology Data Exchange (ETDEWEB)

    Jones, Reese E. [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Rizzi, Francesco [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Boyce, Brad [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Templeton, Jeremy Alan [Sandia National Lab. (SNL-CA), Livermore, CA (United States); Ostien, Jakob [Sandia National Lab. (SNL-CA), Livermore, CA (United States)


    The advent of fabrication techniques like additive manufacturing has focused attention on the considerable variability of material response due to defects and other micro-structural aspects. This variability motivates the development of an enhanced design methodology that incorporates inherent material variability to provide robust predictions of performance. In this work, we develop plasticity models capable of representing the distribution of mechanical responses observed in experiments using traditional plasticity models of the mean response and recently developed uncertainty quantification (UQ) techniques. Lastly, we demonstrate that the new method provides predictive realizations that are superior to more traditional ones, and how these UQ techniques can be used in model selection and assessing the quality of calibrated physical parameters.

  8. The role of Personal Self-Regulation and Regulatory Teaching to predict motivational-affective variables, achievement and satisfaction: A structural model

    Directory of Open Access Journals (Sweden)

    Jesus ede la Fuente


    Full Text Available The present investigation examines how personal self-regulation (presage variable and regulatory teaching (process variable of teaching relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning and, finally, how they relate to performance and satisfaction with the learning process (product variables. The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models. A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching-learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching-learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching-learning context at university.

  9. The role of personal self-regulation and regulatory teaching to predict motivational-affective variables, achievement, and satisfaction: a structural model (United States)

    De la Fuente, Jesus; Zapata, Lucía; Martínez-Vicente, Jose M.; Sander, Paul; Cardelle-Elawar, María


    The present investigation examines how personal self-regulation (presage variable) and regulatory teaching (process variable of teaching) relate to learning approaches, strategies for coping with stress, and self-regulated learning (process variables of learning) and, finally, how they relate to performance and satisfaction with the learning process (product variables). The objective was to clarify the associative and predictive relations between these variables, as contextualized in two different models that use the presage-process-product paradigm (the Biggs and DEDEPRO models). A total of 1101 university students participated in the study. The design was cross-sectional and retrospective with attributional (or selection) variables, using correlations and structural analysis. The results provide consistent and significant empirical evidence for the relationships hypothesized, incorporating variables that are part of and influence the teaching–learning process in Higher Education. Findings confirm the importance of interactive relationships within the teaching–learning process, where personal self-regulation is assumed to take place in connection with regulatory teaching. Variables that are involved in the relationships validated here reinforce the idea that both personal factors and teaching and learning factors should be taken into consideration when dealing with a formal teaching–learning context at university. PMID:25964764

  10. Estimate the time varying brain receptor occupancy in PET imaging experiments using non-linear fixed and mixed effect modeling approach

    International Nuclear Information System (INIS)

    Zamuner, Stefano; Gomeni, Roberto; Bye, Alan


    Positron-Emission Tomography (PET) is an imaging technology currently used in drug development as a non-invasive measure of drug distribution and interaction with biochemical target system. The level of receptor occupancy achieved by a compound can be estimated by comparing time-activity measurements in an experiment done using tracer alone with the activity measured when the tracer is given following administration of unlabelled compound. The effective use of this surrogate marker as an enabling tool for drug development requires the definition of a model linking the brain receptor occupancy with the fluctuation of plasma concentrations. However, the predictive performance of such a model is strongly related to the precision on the estimate of receptor occupancy evaluated in PET scans collected at different times following drug treatment. Several methods have been proposed for the analysis and the quantification of the ligand-receptor interactions investigated from PET data. The aim of the present study is to evaluate alternative parameter estimation strategies based on the use of non-linear mixed effect models allowing to account for intra and inter-subject variability on the time-activity and for covariates potentially explaining this variability. A comparison of the different modeling approaches is presented using real data. The results of this comparison indicates that the mixed effect approach with a primary model partitioning the variance in term of Inter-Individual Variability (IIV) and Inter-Occasion Variability (IOV) and a second stage model relating the changes on binding potential to the dose of unlabelled drug is definitely the preferred approach

  11. On the ""early-time"" evolution of variables relevant to turbulence models for Rayleigh-Taylor instability

    Energy Technology Data Exchange (ETDEWEB)

    Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory


    We present our progress toward setting initial conditions in variable density turbulence models. In particular, we concentrate our efforts on the BHR turbulence model for turbulent Rayleigh-Taylor instability. Our approach is to predict profiles of relevant parameters before the fully turbulent regime and use them as initial conditions for the turbulence model. We use an idealized model of the mixing between two interpenetrating fluids to define the initial profiles for the turbulence model parameters. Velocities and volume fractions used in the idealized mixing model are obtained respectively from a set of ordinary differential equations modeling the growth of the Rayleigh-Taylor instability and from an idealization of the density profile in the mixing layer. A comparison between predicted initial profiles for the turbulence model parameters and initial profiles of the parameters obtained from low Atwood number three dimensional simulations show reasonable agreement.

  12. A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka (United States)

    Hapugoda, J. C.; Sooriyarachchi, M. R.


    Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

  13. Combining epidemiologic and biostatistical tools to enhance variable selection in HIV cohort analyses.

    Directory of Open Access Journals (Sweden)

    Christopher Rentsch

    Full Text Available BACKGROUND: Variable selection is an important step in building a multivariate regression model for which several methods and statistical packages are available. A comprehensive approach for variable selection in complex multivariate regression analyses within HIV cohorts is explored by utilizing both epidemiological and biostatistical procedures. METHODS: Three different methods for variable selection were illustrated in a study comparing survival time between subjects in the Department of Defense's National History Study and the Atlanta Veterans Affairs Medical Center's HIV Atlanta VA Cohort Study. The first two methods were stepwise selection procedures, based either on significance tests (Score test, or on information theory (Akaike Information Criterion, while the third method employed a Bayesian argument (Bayesian Model Averaging. RESULTS: All three methods resulted in a similar parsimonious survival model. Three of the covariates previously used in the multivariate model were not included in the final model suggested by the three approaches. When comparing the parsimonious model to the previously published model, there was evidence of less variance in the main survival estimates. CONCLUSIONS: The variable selection approaches considered in this study allowed building a model based on significance tests, on an information criterion, and on averaging models using their posterior probabilities. A parsimonious model that balanced these three approaches was found to provide a better fit than the previously reported model.

  14. A distributed approach for parameters estimation in System Biology models

    International Nuclear Information System (INIS)

    Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.


    Due to the lack of experimental measurements, biological variability and experimental errors, the value of many parameters of the systems biology mathematical models is yet unknown or uncertain. A possible computational solution is the parameter estimation, that is the identification of the parameter values that determine the best model fitting respect to experimental data. We have developed an environment to distribute each run of the parameter estimation algorithm on a different computational resource. The key feature of the implementation is a relational database that allows the user to swap the candidate solutions among the working nodes during the computations. The comparison of the distributed implementation with the parallel one showed that the presented approach enables a faster and better parameter estimation of systems biology models.

  15. A hydrochemical modelling framework for combined assessment of spatial and temporal variability in stream chemistry: application to Plynlimon, Wales

    Directory of Open Access Journals (Sweden)

    H.J. Foster


    Full Text Available Recent concern about the risk to biota from acidification in upland areas, due to air pollution and land-use change (such as the planting of coniferous forests, has generated a need to model catchment hydro-chemistry to assess environmental risk and define protection strategies. Previous approaches have tended to concentrate on quantifying either spatial variability at a regional scale or temporal variability at a given location. However, to protect biota from ‘acid episodes’, an assessment of both temporal and spatial variability of stream chemistry is required at a catchment scale. In addition, quantification of temporal variability needs to represent both episodic event response and long term variability caused by deposition and/or land-use change. Both spatial and temporal variability in streamwater chemistry are considered in a new modelling methodology based on application to the Plynlimon catchments, central Wales. A two-component End-Member Mixing Analysis (EMMA is used whereby low and high flow chemistry are taken to represent ‘groundwater’ and ‘soil water’ end-members. The conventional EMMA method is extended to incorporate spatial variability in the two end-members across the catchments by quantifying the Acid Neutralisation Capacity (ANC of each in terms of a statistical distribution. These are then input as stochastic variables to a two-component mixing model, thereby accounting for variability of ANC both spatially and temporally. The model is coupled to a long-term acidification model (MAGIC to predict the evolution of the end members and, hence, the response to future scenarios. The results can be plotted as a function of time and space, which enables better assessment of the likely effects of pollution deposition or land-use changes in the future on the stream chemistry than current methods which use catchment average values. The model is also a useful basis for further research into linkage between hydrochemistry

  16. Generalized Network Psychometrics : Combining Network and Latent Variable Models

    NARCIS (Netherlands)

    Epskamp, S.; Rhemtulla, M.; Borsboom, D.


    We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

  17. International energy market dynamics: a modelling approach. Tome 1

    International Nuclear Information System (INIS)

    Nachet, S.


    This work is an attempt to model international energy market and reproduce the behaviour of both energy demand and supply. Energy demand was represented using sector versus source approach. For developing countries, existing link between economic and energy sectors were analysed. Energy supply is exogenous for energy sources other than oil and natural gas. For hydrocarbons, exploration-production process was modelled and produced figures as production yield, exploration effort index, etc. The model built is econometric and is solved using a software that was constructed for this purpose. We explore the energy market future using three scenarios and obtain projections by 2010 for energy demand per source and oil natural gas supply per region. Economic variables are used to produce different indicators as energy intensity, energy per capita, etc. (author). 378 refs., 26 figs., 35 tabs., 11 appends

  18. International energy market dynamics: a modelling approach. Tome 2

    International Nuclear Information System (INIS)

    Nachet, S.


    This work is an attempt to model international energy market and reproduce the behaviour of both energy demand and supply. Energy demand was represented using sector versus source approach. For developing countries, existing link between economic and energy sectors were analysed. Energy supply is exogenous for energy sources other than oil and natural gas. For hydrocarbons, exploration-production process was modelled and produced figures as production yield, exploration effort index, ect. The model build is econometric and is solved using a software that was constructed for this purpose. We explore the energy market future using three scenarios and obtain projections by 2010 for energy demand per source and oil and natural gas supply per region. Economic variables are used to produce different indicators as energy intensity, energy per capita, etc. (author). 378 refs., 26 figs., 35 tabs., 11 appends

  19. Can Geostatistical Models Represent Nature's Variability? An Analysis Using Flume Experiments (United States)

    Scheidt, C.; Fernandes, A. M.; Paola, C.; Caers, J.


    The lack of understanding in the Earth's geological and physical processes governing sediment deposition render subsurface modeling subject to large uncertainty. Geostatistics is often used to model uncertainty because of its capability to stochastically generate spatially varying realizations of the subsurface. These methods can generate a range of realizations of a given pattern - but how representative are these of the full natural variability? And how can we identify the minimum set of images that represent this natural variability? Here we use this minimum set to define the geostatistical prior model: a set of training images that represent the range of patterns generated by autogenic variability in the sedimentary environment under study. The proper definition of the prior model is essential in capturing the variability of the depositional patterns. This work starts with a set of overhead images from an experimental basin that showed ongoing autogenic variability. We use the images to analyze the essential characteristics of this suite of patterns. In particular, our goal is to define a prior model (a minimal set of selected training images) such that geostatistical algorithms, when applied to this set, can reproduce the full measured variability. A necessary prerequisite is to define a measure of variability. In this study, we measure variability using a dissimilarity distance between the images. The distance indicates whether two snapshots contain similar depositional patterns. To reproduce the variability in the images, we apply an MPS algorithm to the set of selected snapshots of the sedimentary basin that serve as training images. The training images are chosen from among the initial set by using the distance measure to ensure that only dissimilar images are chosen. Preliminary investigations show that MPS can reproduce fairly accurately the natural variability of the experimental depositional system. Furthermore, the selected training images provide

  20. Static models, recursive estimators and the zero-variance approach

    KAUST Repository

    Rubino, Gerardo


    When evaluating dependability aspects of complex systems, most models belong to the static world, where time is not an explicit variable. These models suffer from the same problems than dynamic ones (stochastic processes), such as the frequent combinatorial explosion of the state spaces. In the Monte Carlo domain, on of the most significant difficulties is the rare event situation. In this talk, we describe this context and a recent technique that appears to be at the top performance level in the area, where we combined ideas that lead to very fast estimation procedures with another approach called zero-variance approximation. Both ideas produced a very efficient method that has the right theoretical property concerning robustness, the Bounded Relative Error one. Some examples illustrate the results.

  1. The place of quantitative energy models in a prospective approach

    International Nuclear Information System (INIS)

    Taverdet-Popiolek, N.


    Futurology above all depends on having the right mind set. Gaston Berger summarizes the prospective approach in 5 five main thrusts: prepare for the distant future, be open-minded (have a systems and multidisciplinary approach), carry out in-depth analyzes (draw out actors which are really determinant or the future, as well as established shed trends), take risks (imagine risky but flexible projects) and finally think about humanity, futurology being a technique at the service of man to help him build a desirable future. On the other hand, forecasting is based on quantified models so as to deduce 'conclusions' about the future. In the field of energy, models are used to draw up scenarios which allow, for instance, measuring medium or long term effects of energy policies on greenhouse gas emissions or global welfare. Scenarios are shaped by the model's inputs (parameters, sets of assumptions) and outputs. Resorting to a model or projecting by scenario is useful in a prospective approach as it ensures coherence for most of the variables that have been identified through systems analysis and that the mind on its own has difficulty to grasp. Interpretation of each scenario must be carried out in the light o the underlying framework of assumptions (the backdrop), developed during the prospective stage. When the horizon is far away (very long-term), the worlds imagined by the futurologist contain breaks (technological, behavioural and organizational) which are hard to integrate into the models. It is here that the main limit for the use of models in futurology is located. (author)

  2. The Relationships among Economic, Newsroom and Content Variables: A Path Model. (United States)

    Lacy, Stephen; And Others

    Efforts to discover what variables affect news media content have taken many approaches during the past 35 years. These approaches have emphasized psychological factors, sociological factors, cultural and social forces, and economic factors. Evidence exists that all these forces play a role in determining what becomes news. To examine how these…

  3. On the modelling and partial-load control of variable-speed wind turbines

    Energy Technology Data Exchange (ETDEWEB)

    Novak, P [Chalmers Univ. of Technology, Goeteborg (Sweden). School of Electrical and Computer Engineering


    The focus of this thesis is on modelling and variable-speed control of wind turbines. A physical model structure including the fundamental drive-train mode is derived and validated by system-identification experiments on a full-scale wind turbine. The resulting, parametrized model has been used as a basis for an evaluation of controllers for partial-load operation, validated by non-linear simulations. This evaluation, including several controller concepts, verifies that a sophisticated controller becomes necessary, when stretching the limits in power-loss minimization. This control strategy also demands the sampling frequency to be pushed to a high level. As a consequence, the angular-position measurements become time correlated and, in the limit, periodic. It is shown in the thesis how the resulting, operating-point-dependent effects on the measurement errors influence the estimation quality, using a stationary Kalman filter as an example. A gain-scheduling estimation approach is shown to improve the performance. 39 refs, 63 figs, 2 tabs

  4. Insights into the variability of nucleated amyloid polymerization by a minimalistic model of stochastic protein assembly

    Energy Technology Data Exchange (ETDEWEB)

    Eugène, Sarah, E-mail:; Doumic, Marie, E-mail:, E-mail: [INRIA de Paris, 2 Rue Simone Iff, CS 42112, 75589 Paris Cedex 12 (France); Sorbonne Universités, UPMC Université Pierre et Marie Curie, UMR 7598, Laboratoire Jacques-Louis Lions, F-75005 Paris (France); Xue, Wei-Feng, E-mail: [School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ (United Kingdom); Robert, Philippe, E-mail: [INRIA de Paris, 2 Rue Simone Iff, CS 42112, 75589 Paris Cedex 12 (France)


    Self-assembly of proteins into amyloid aggregates is an important biological phenomenon associated with human diseases such as Alzheimer’s disease. Amyloid fibrils also have potential applications in nano-engineering of biomaterials. The kinetics of amyloid assembly show an exponential growth phase preceded by a lag phase, variable in duration as seen in bulk experiments and experiments that mimic the small volumes of cells. Here, to investigate the origins and the properties of the observed variability in the lag phase of amyloid assembly currently not accounted for by deterministic nucleation dependent mechanisms, we formulate a new stochastic minimal model that is capable of describing the characteristics of amyloid growth curves despite its simplicity. We then solve the stochastic differential equations of our model and give mathematical proof of a central limit theorem for the sample growth trajectories of the nucleated aggregation process. These results give an asymptotic description for our simple model, from which closed form analytical results capable of describing and predicting the variability of nucleated amyloid assembly were derived. We also demonstrate the application of our results to inform experiments in a conceptually friendly and clear fashion. Our model offers a new perspective and paves the way for a new and efficient approach on extracting vital information regarding the key initial events of amyloid formation.

  5. Detecting relationships between the interannual variability in climate records and ecological time series using a multivariate statistical approach - four case studies for the North Sea region

    Energy Technology Data Exchange (ETDEWEB)

    Heyen, H. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Gewaesserphysik


    A multivariate statistical approach is presented that allows a systematic search for relationships between the interannual variability in climate records and ecological time series. Statistical models are built between climatological predictor fields and the variables of interest. Relationships are sought on different temporal scales and for different seasons and time lags. The possibilities and limitations of this approach are discussed in four case studies dealing with salinity in the German Bight, abundance of zooplankton at Helgoland Roads, macrofauna communities off Norderney and the arrival of migratory birds on Helgoland. (orig.) [Deutsch] Ein statistisches, multivariates Modell wird vorgestellt, das eine systematische Suche nach potentiellen Zusammenhaengen zwischen Variabilitaet in Klima- und oekologischen Zeitserien erlaubt. Anhand von vier Anwendungsbeispielen wird der Klimaeinfluss auf den Salzgehalt in der Deutschen Bucht, Zooplankton vor Helgoland, Makrofauna vor Norderney, und die Ankunft von Zugvoegeln auf Helgoland untersucht. (orig.)

  6. A data-driven approach for modeling post-fire debris-flow volumes and their uncertainty (United States)

    Friedel, Michael J.


    This study demonstrates the novel application of genetic programming to evolve nonlinear post-fire debris-flow volume equations from variables associated with a data-driven conceptual model of the western United States. The search space is constrained using a multi-component objective function that simultaneously minimizes root-mean squared and unit errors for the evolution of fittest equations. An optimization technique is then used to estimate the limits of nonlinear prediction uncertainty associated with the debris-flow equations. In contrast to a published multiple linear regression three-variable equation, linking basin area with slopes greater or equal to 30 percent, burn severity characterized as area burned moderate plus high, and total storm rainfall, the data-driven approach discovers many nonlinear and several dimensionally consistent equations that are unbiased and have less prediction uncertainty. Of the nonlinear equations, the best performance (lowest prediction uncertainty) is achieved when using three variables: average basin slope, total burned area, and total storm rainfall. Further reduction in uncertainty is possible for the nonlinear equations when dimensional consistency is not a priority and by subsequently applying a gradient solver to the fittest solutions. The data-driven modeling approach can be applied to nonlinear multivariate problems in all fields of study.

  7. CFD approach to modelling, hydrodynamic analysis and motion characteristics of a laboratory underwater glider with experimental results

    Directory of Open Access Journals (Sweden)

    Yogang Singh


    Full Text Available Underwater gliders are buoyancy propelled vehicle which make use of buoyancy for vertical movement and wings to propel the glider in forward direction. Autonomous underwater gliders are a patented technology and are manufactured and marketed by corporations. In this study, we validate the experimental lift and drag characteristics of a glider from the literature using Computational fluid dynamics (CFD approach. This approach is then used for the assessment of the steady state characteristics of a laboratory glider designed at Indian Institute of Technology (IIT Madras. Flow behaviour and lift and drag force distribution at different angles of attack are studied for Reynolds numbers varying from 105 to 106 for NACA0012 wing configurations. The state variables of the glider are the velocity, gliding angle and angle of attack which are simulated by making use of the hydrodynamic drag and lift coefficients obtained from CFD. The effect of the variable buoyancy is examined in terms of the gliding angle, velocity and angle of attack. Laboratory model of glider is developed from the final design asserted by CFD. This model is used for determination of static and dynamic properties of an underwater glider which were validated against an equivalent CAD model and simulation results obtained from equations of motion of glider in vertical plane respectively. In the literature, only empirical approach has been adopted to estimate the hydrodynamic coefficients of the AUG that are required for its trajectory simulation. In this work, a CFD approach has been proposed to estimate the hydrodynamic coefficients and validated with experimental data. A two-mass variable buoyancy engine has been designed and implemented. The equations of motion for this two-mass engine have been obtained by modifying the single mass version of the equations described in the literature. The objectives of the present study are to understand the glider dynamics adopting a CFD approach

  8. Time shift in slope failure prediction between unimodal and bimodal modeling approaches (United States)

    Ciervo, Fabio; Casini, Francesca; Nicolina Papa, Maria; Medina, Vicente


    Together with the need to use more appropriate mathematical expressions for describing hydro-mechanical soil processes, a challenge issue relates to the need of considering the effects induced by terrain heterogeneities on the physical mechanisms, taking into account the implications of the heterogeneities in affecting time-dependent hydro-mechanical variables, would improve the prediction capacities of models, such as the ones used in early warning systems. The presence of the heterogeneities in partially-saturated slopes results in irregular propagation of the moisture and suction front. To mathematically represent the "dual-implication" generally induced by the heterogeneities in describing the hydraulic terrain behavior, several bimodal hydraulic models have been presented in literature and replaced the conventional sigmoidal/unimodal functions; this presupposes that the scale of the macrostructure is comparable with the local scale (Darcy scale), thus the Richards' model can be assumed adequate to mathematically reproduce the processes. The purpose of this work is to focus on the differences in simulating flow infiltration processes and slope stability conditions originated from preliminary choices of hydraulic models and contextually between different approaches to evaluate the factor of safety (FoS). In particular, the results of two approaches are compared. The first one includes the conventional expression of the FoS under saturated conditions and the widespread used hydraulic model of van Genuchten-Mualem. The second approach includes a generalized FoS equation for infinite-slope model under variably saturated soil conditions (Lu and Godt, 2008) and the bimodal Romano et al.'s (2011) functions to describe the hydraulic response. The extension of the above mentioned approach to the bimodal context is based on an analytical method to assess the effects of the hydraulic properties on soil shear developed integrating a bimodal lognormal hydraulic function

  9. Internal variability in a regional climate model over West Africa

    Energy Technology Data Exchange (ETDEWEB)

    Vanvyve, Emilie; Ypersele, Jean-Pascal van [Universite catholique de Louvain, Institut d' astronomie et de geophysique Georges Lemaitre, Louvain-la-Neuve (Belgium); Hall, Nicholas [Laboratoire d' Etudes en Geophysique et Oceanographie Spatiales/Centre National d' Etudes Spatiales, Toulouse Cedex 9 (France); Messager, Christophe [University of Leeds, Institute for Atmospheric Science, Environment, School of Earth and Environment, Leeds (United Kingdom); Leroux, Stephanie [Universite Joseph Fourier, Laboratoire d' etude des Transferts en Hydrologie et Environnement, BP53, Grenoble Cedex 9 (France)


    Sensitivity studies with regional climate models are often performed on the basis of a few simulations for which the difference is analysed and the statistical significance is often taken for granted. In this study we present some simple measures of the confidence limits for these types of experiments by analysing the internal variability of a regional climate model run over West Africa. Two 1-year long simulations, differing only in their initial conditions, are compared. The difference between the two runs gives a measure of the internal variability of the model and an indication of which timescales are reliable for analysis. The results are analysed for a range of timescales and spatial scales, and quantitative measures of the confidence limits for regional model simulations are diagnosed for a selection of study areas for rainfall, low level temperature and wind. As the averaging period or spatial scale is increased, the signal due to internal variability gets smaller and confidence in the simulations increases. This occurs more rapidly for variations in precipitation, which appear essentially random, than for dynamical variables, which show some organisation on larger scales. (orig.)


    Directory of Open Access Journals (Sweden)

    Jakub Nieć


    Full Text Available The deep bed filtration model elaborated by Iwasaki has many applications, e.g. solids removal from wastewater. Its main parameter, filter coefficient, is directly related to removal efficiency and depends on filter depth and time of operation. In this paper the authors have proposed a new approach to modelling, describing dry organic mass from septic tank effluent and biomass distribution in a sand filter. In this approach the variable filter coefficient value was used as affected by depth and time of operation and the live biomass concentration distribution was approximated by a logistic function. Relatively stable biomass contents in deeper beds compartments were observed in empirical studies. The Iwasaki equations associated with the logistic function can predict volatile suspended solids deposition and biomass content in sand filters. The comparison between the model and empirical data for filtration lasting 10 and 20 days showed a relatively good agreement.

  11. Modeling the potential distribution of the invasive golden mussel Limnoperna fortunei in the Upper Paraguay River system using limnological variables

    Directory of Open Access Journals (Sweden)

    MD Oliveira

    Full Text Available The invasive golden mussel, Limnoperna fortunei (Dunker, 1857, was introduced into the La Plata River estuary and quickly expanded upstream to the North, into the Paraguay and Paraná rivers. An ecological niche modeling approach, based on limnological variables, was used to predict the expansion of the golden mussel in the Paraguay River and its tributaries. We used three approaches to predict the geographic distribution: 1 the spatial distribution of calcium concentration and the saturation index for calcium carbonate (calcite; 2 the Genetic Algorithm for Rule-Set Production (GARP model; and the 3 Maximum Entropy Method (Maxent model. Other limnological variables such as temperature, dissolved oxygen, pH, and Total Suspended Solids (TSS were used in the latter two cases. Important tributaries of the Paraguay River such as the Cuiabá and Miranda/Aquidauana rivers exhibit high risk of invasion, while lower risk was observed in the chemically dilute waters of the middle basin where shell calcification may be limited by low calcium concentrations and carbonate mineral undersaturation.

  12. Variable cycle control model for intersection based on multi-source information (United States)

    Sun, Zhi-Yuan; Li, Yue; Qu, Wen-Cong; Chen, Yan-Yan


    In order to improve the efficiency of traffic control system in the era of big data, a new variable cycle control model based on multi-source information is presented for intersection in this paper. Firstly, with consideration of multi-source information, a unified framework based on cyber-physical system is proposed. Secondly, taking into account the variable length of cell, hysteresis phenomenon of traffic flow and the characteristics of lane group, a Lane group-based Cell Transmission Model is established to describe the physical properties of traffic flow under different traffic signal control schemes. Thirdly, the variable cycle control problem is abstracted into a bi-level programming model. The upper level model is put forward for cycle length optimization considering traffic capacity and delay. The lower level model is a dynamic signal control decision model based on fairness analysis. Then, a Hybrid Intelligent Optimization Algorithm is raised to solve the proposed model. Finally, a case study shows the efficiency and applicability of the proposed model and algorithm.

  13. Hydrologic Model Development and Calibration: Contrasting a Single- and Multi-Objective Approach for Comparing Model Performance (United States)

    Asadzadeh, M.; Maclean, A.; Tolson, B. A.; Burn, D. H.


    Hydrologic model calibration aims to find a set of parameters that adequately simulates observations of watershed behavior, such as streamflow, or a state variable, such as snow water equivalent (SWE). There are different metrics for evaluating calibration effectiveness that involve quantifying prediction errors, such as the Nash-Sutcliffe (NS) coefficient and bias evaluated for the entire calibration period, on a seasonal basis, for low flows, or for high flows. Many of these metrics are conflicting such that the set of parameters that maximizes the high flow NS differs from the set of parameters that maximizes the low flow NS. Conflicting objectives are very likely when different calibration objectives are based on different fluxes and/or state variables (e.g., NS based on streamflow versus SWE). One of the most popular ways to balance different metrics is to aggregate them based on their importance and find the set of parameters that optimizes a weighted sum of the efficiency metrics. Comparing alternative hydrologic models (e.g., assessing model improvement when a process or more detail is added to the model) based on the aggregated objective might be misleading since it represents one point on the tradeoff of desired error metrics. To derive a more comprehensive model comparison, we solved a bi-objective calibration problem to estimate the tradeoff between two error metrics for each model. Although this approach is computationally more expensive than the aggregation approach, it results in a better understanding of the effectiveness of selected models at each level of every error metric and therefore provides a better rationale for judging relative model quality. The two alternative models used in this study are two MESH hydrologic models (version 1.2) of the Wolf Creek Research basin that differ in their watershed spatial discretization (a single Grouped Response Unit, GRU, versus multiple GRUs). The MESH model, currently under development by Environment

  14. Spectral Kernel Approach to Study Radiative Response of Climate Variables and Interannual Variability of Reflected Solar Spectrum (United States)

    Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan


    The radiative kernel approach provides a simple way to separate the radiative response to different climate parameters and to decompose the feedback into radiative and climate response components. Using CERES/MODIS/Geostationary data, we calculated and analyzed the solar spectral reflectance kernels for various climate parameters on zonal, regional, and global spatial scales. The kernel linearity is tested. Errors in the kernel due to nonlinearity can vary strongly depending on climate parameter, wavelength, surface, and solar elevation; they are large in some absorption bands for some parameters but are negligible in most conditions. The spectral kernels are used to calculate the radiative responses to different climate parameter changes in different latitudes. The results show that the radiative response in high latitudes is sensitive to the coverage of snow and sea ice. The radiative response in low latitudes is contributed mainly by cloud property changes, especially cloud fraction and optical depth. The large cloud height effect is confined to absorption bands, while the cloud particle size effect is found mainly in the near infrared. The kernel approach, which is based on calculations using CERES retrievals, is then tested by direct comparison with spectral measurements from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (a different instrument on a different spacecraft). The monthly mean interannual variability of spectral reflectance based on the kernel technique is consistent with satellite observations over the ocean, but not over land, where both model and data have large uncertainty. RMS errors in kernel ]derived monthly global mean reflectance over the ocean compared to observations are about 0.001, and the sampling error is likely a major component.

  15. Developing models to predict 8th grade students' achievement levels on timss science based on opportunity-to-learn variables (United States)

    Whitford, Melinda M.

    Science educational reforms have placed major emphasis on improving science classroom instruction and it is therefore vital to study opportunity-to-learn (OTL) variables related to student science learning experiences and teacher teaching practices. This study will identify relationships between OTL and student science achievement and will identify OTL predictors of students' attainment at various distinct achievement levels (low/intermediate/high/advanced). Specifically, the study (a) address limitations of previous studies by examining a large number of independent and control variables that may impact students' science achievement and (b) it will test hypotheses of structural relations to how the identified predictors and mediating factors impact on student achievement levels. The study will follow a multi-stage and integrated bottom-up and top-down approach to identify predictors of students' achievement levels on standardized tests using TIMSS 2011 dataset. Data mining or pattern recognition, a bottom-up approach will identify the most prevalent association patterns between different student achievement levels and variables related to student science learning experiences, teacher teaching practices and home and school environments. The second stage is a top-down approach, testing structural equation models of relations between the significant predictors and students' achievement levels according.

  16. Partitioning inter annual variability in net ecosystem exchange between climatic variability and functional change

    International Nuclear Information System (INIS)

    Hui, D.; Luo, Y.; Katul, G.


    Inter annual variability in net ecosystem exchange of carbon is investigated using a homogeneity-of-slopes model to identify the function change contributing to inter annual variability, net ecosystem carbon exchange, and night-time ecosystem respiration. Results of employing this statistical approach to a data set collected at the Duke Forest AmeriFlux site from August 1997 to December 2001 are discussed. The results demonstrate that it is feasible to partition the variation in ecosystem carbon fluxes into direct effects of seasonal and inter annual climatic variability and functional change. 51 refs., 4 tabs., 5 figs

  17. Modelin the Transport and Chemical Evolution of Onshore and Offshore Emissions and Their Impact on Local and Regional Air Quality Using a Variable-Grid-Resolution Air Quality Model

    Energy Technology Data Exchange (ETDEWEB)

    Adel Hanna


    The overall objective of this research project was to develop an innovative modeling technique to adequately model the offshore/onshore transport of pollutants. The variable-grid modeling approach that was developed alleviates many of the shortcomings of the traditionally used nested regular-grid modeling approach, in particular related to biases near boundaries and the excessive computational requirements when using nested grids. The Gulf of Mexico region contiguous to the Houston-Galveston area and southern Louisiana was chosen as a test bed for the variable-grid modeling approach. In addition to the onshore high pollution emissions from various sources in those areas, emissions from on-shore and off-shore oil and gas exploration and production are additional sources of air pollution. We identified case studies for which to perform meteorological and air quality model simulations. Our approach included developing and evaluating the meteorological, emissions, and chemistry-transport modeling components for the variable-grid applications, with special focus on the geographic areas where the finest grid resolution was used. We evaluated the performance of two atmospheric boundary layer (ABL) schemes, and identified the best-performing scheme for simulating mesoscale circulations for different grid resolutions. Use of a newly developed surface data assimilation scheme resulted in improved meteorological model simulations. We also successfully ingested satellite-derived sea surface temperatures (SSTs) into the meteorological model simulations, leading to further improvements in simulated wind, temperature, and moisture fields. These improved meteorological fields were important for variable-grid simulations, especially related to capturing the land-sea breeze circulations that are critical for modeling offshore/onshore transport of pollutants in the Gulf region. We developed SMOKE-VGR, the variable-grid version of the SMOKE emissions processing model, and tested and

  18. Variable Fidelity Aeroelastic Toolkit - Structural Model, Phase I (United States)

    National Aeronautics and Space Administration — The proposed innovation is a methodology to incorporate variable fidelity structural models into steady and unsteady aeroelastic and aeroservoelastic analyses in...

  19. Multi-Period Natural Gas Market Modeling. Applications, Stochastic Extensions and Solution Approaches

    International Nuclear Information System (INIS)

    Egging, R.G.


    This dissertation develops deterministic and stochastic multi-period mixed complementarity problems (MCP) for the global natural gas market, as well as solution approaches for large-scale stochastic MCP. The deterministic model is unique in the combination of the level of detail of the actors in the natural gas markets and the transport options, the detailed regional and global coverage, the multi-period approach with endogenous capacity expansions for transportation and storage infrastructure, the seasonal variation in demand and the representation of market power according to Nash-Cournot theory. The model is applied to several scenarios for the natural gas market that cover the formation of a cartel by the members of the Gas Exporting Countries Forum, a low availability of unconventional gas in the United States, and cost reductions in long-distance gas transportation. The results provide insights in how different regions are affected by various developments, in terms of production, consumption, traded volumes, prices and profits of market participants. The stochastic MCP is developed and applied to a global natural gas market problem with four scenarios for a time horizon until 2050 with nineteen regions and containing 78,768 variables. The scenarios vary in the possibility of a gas market cartel formation and varying depletion rates of gas reserves in the major gas importing regions. Outcomes for hedging decisions of market participants show some significant shifts in the timing and location of infrastructure investments, thereby affecting local market situations. A first application of Benders decomposition (BD) is presented to solve a large-scale stochastic MCP for the global gas market with many hundreds of first-stage capacity expansion variables and market players exerting various levels of market power. The largest problem solved successfully using BD contained 47,373 variables of which 763 first-stage variables, however using BD did not result in

  20. The Properties of Model Selection when Retaining Theory Variables

    DEFF Research Database (Denmark)

    Hendry, David F.; Johansen, Søren

    Economic theories are often fitted directly to data to avoid possible model selection biases. We show that embedding a theory model that specifies the correct set of m relevant exogenous variables, x{t}, within the larger set of m+k candidate variables, (x{t},w{t}), then selection over the second...... set by their statistical significance can be undertaken without affecting the estimator distribution of the theory parameters. This strategy returns the theory-parameter estimates when the theory is correct, yet protects against the theory being under-specified because some w{t} are relevant....

  1. Ordered LOGIT Model approach for the determination of financial distress. (United States)

    Kinay, B


    Nowadays, as a result of the global competition encountered, numerous companies come up against financial distresses. To predict and take proactive approaches for those problems is quite important. Thus, the prediction of crisis and financial distress is essential in terms of revealing the financial condition of companies. In this study, financial ratios relating to 156 industrial firms that are quoted in the Istanbul Stock Exchange are used and probabilities of financial distress are predicted by means of an ordered logit regression model. By means of Altman's Z Score, the dependent variable is composed by scaling the level of risk. Thus, a model that can compose an early warning system and predict financial distress is proposed.

  2. A data-model synthesis to explain variability in calcification observed during a CO2 perturbation mesocosm experiment (United States)

    Krishna, Shubham; Schartau, Markus


    The effect of ocean acidification on growth and calcification of the marine algae Emiliania huxleyi was investigated in a series of mesocosm experiments where enclosed water volumes that comprised a natural plankton community were exposed to different carbon dioxide (CO2) concentrations. Calcification rates observed during those experiments were found to be highly variable, even among replicate mesocosms that were subject to similar CO2 perturbations. Here, data from an ocean acidification mesocosm experiment are reanalysed with an optimality-based dynamical plankton model. According to our model approach, cellular calcite formation is sensitive to variations in CO2 at the organism level. We investigate the temporal changes and variability in observations, with a focus on resolving observed differences in total alkalinity and particulate inorganic carbon (PIC). We explore how much of the variability in the data can be explained by variations of the initial conditions and by the level of CO2 perturbation. Nine mesocosms of one experiment were sorted into three groups of high, medium, and low calcification rates and analysed separately. The spread of the three optimised ensemble model solutions captures most of the observed variability. Our results show that small variations in initial abundance of coccolithophores and the prevailing physiological acclimation states generate differences in calcification that are larger than those induced by ocean acidification. Accordingly, large deviations between optimal mass flux estimates of carbon and of nitrogen are identified even between mesocosms that were subject to similar ocean acidification conditions. With our model-based data analysis we document how an ocean acidification response signal in calcification can be disentangled from the observed variability in PIC.

  3. Oil price fluctuations and their impact on the macroeconomic variables of Kuwait: a case study using a VAR model

    International Nuclear Information System (INIS)

    Eltony, M. Nagy; Al-Awadi, Mohammad


    In this study, a vector autoregression model (VAR) and a vector error correction model (VECM) were estimated to examine the impact of oil price fluctuations on seven key macroeconomic variables for the Kuwaiti economy. Quarterly data for the period 1984-1998 were utilised. Theoretically and empirically speaking, VECM is superior to the VAR approach. Also, the results corresponding to the VECM model are closer to common sense. However, the estimated models indicate a high degree of interrelation between major macroeconomic variables. The empirical results highlight the causality running from the oil prices and oil revenues, to government development and current expenditure and then towards other variables. For the most part, the empirical evidence indicates that oil price shocks and hence oil revenues have a notable impact on government expenditure, both development and current. However, government development expenditure has been influenced relatively more. The results also point out the significant of the CPI in explaining a notable part of the variations of both types of government expenditure. On the other hand, the variations in value of imports are mostly accounted for by oil revenue fluctuations. On the other hand, the variations in value of imports are mostly accounted for by oil revenue fluctuations and then by the fluctuation in government development expenditures. Also, the results from the VECM approach indicate that a significant part of LM2 variance is explained by the variance in oil revenue. It reaches about 46 per cent in the 10th quarter, even more than its own variations. (Author)

  4. A Bayesian Approach to Person Fit Analysis in Item Response Theory Models. Research Report. (United States)

    Glas, Cees A. W.; Meijer, Rob R.

    A Bayesian approach to the evaluation of person fit in item response theory (IRT) models is presented. In a posterior predictive check, the observed value on a discrepancy variable is positioned in its posterior distribution. In a Bayesian framework, a Markov Chain Monte Carlo procedure can be used to generate samples of the posterior distribution…

  5. A Model to Couple Flow, Thermal and Reactive Chemical Transport, and Geo-mechanics in Variably Saturated Media (United States)

    Yeh, G. T.; Tsai, C. H.


    This paper presents the development of a THMC (thermal-hydrology-mechanics-chemistry) process model in variably saturated media. The governing equations for variably saturated flow and reactive chemical transport are obtained based on the mass conservation principle of species transport supplemented with Darcy's law, constraint of species concentration, equation of states, and constitutive law of K-S-P (Conductivity-Degree of Saturation-Capillary Pressure). The thermal transport equation is obtained based on the conservation of energy. The geo-mechanic displacement is obtained based on the assumption of equilibrium. Conventionally, these equations have been implicitly coupled via the calculations of secondary variables based on primary variables. The mechanisms of coupling have not been obvious. In this paper, governing equations are explicitly coupled for all primary variables. The coupling is accomplished via the storage coefficients, transporting velocities, and conduction-dispersion-diffusion coefficient tensor; one set each for every primary variable. With this new system of equations, the coupling mechanisms become clear. Physical interpretations of every term in the coupled equations will be discussed. Examples will be employed to demonstrate the intuition and superiority of these explicit coupling approaches. Keywords: Variably Saturated Flow, Thermal Transport, Geo-mechanics, Reactive Transport.

  6. Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

    Directory of Open Access Journals (Sweden)

    Christophe Coupé


    Full Text Available As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM, which address grouping of observations, and generalized linear mixed-effects models (GLMM, which offer a family of distributions for the dependent variable. Generalized additive models (GAM are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS. We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships

  7. Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape. (United States)

    Coupé, Christophe


    As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we

  8. How the 2SLS/IV estimator can handle equality constraints in structural equation models: a system-of-equations approach. (United States)

    Nestler, Steffen


    Parameters in structural equation models are typically estimated using the maximum likelihood (ML) approach. Bollen (1996) proposed an alternative non-iterative, equation-by-equation estimator that uses instrumental variables. Although this two-stage least squares/instrumental variables (2SLS/IV) estimator has good statistical properties, one problem with its application is that parameter equality constraints cannot be imposed. This paper presents a mathematical solution to this problem that is based on an extension of the 2SLS/IV approach to a system of equations. We present an example in which our approach was used to examine strong longitudinal measurement invariance. We also investigated the new approach in a simulation study that compared it with ML in the examination of the equality of two latent regression coefficients and strong measurement invariance. Overall, the results show that the suggested approach is a useful extension of the original 2SLS/IV estimator and allows for the effective handling of equality constraints in structural equation models. © 2013 The British Psychological Society.

  9. Dynamics of melanoma tumor therapy with vesicular stomatitis virus: explaining the variability in outcomes using mathematical modeling. (United States)

    Rommelfanger, D M; Offord, C P; Dev, J; Bajzer, Z; Vile, R G; Dingli, D


    Tumor selective, replication competent viruses are being tested for cancer gene therapy. This approach introduces a new therapeutic paradigm due to potential replication of the therapeutic agent and induction of a tumor-specific immune response. However, the experimental outcomes are quite variable, even when studies utilize highly inbred strains of mice and the same cell line and virus. Recognizing that virotherapy is an exercise in population dynamics, we utilize mathematical modeling to understand the variable outcomes observed when B16ova malignant melanoma tumors are treated with vesicular stomatitis virus in syngeneic, fully immunocompetent mice. We show how variability in the initial tumor size and the actual amount of virus delivered to the tumor have critical roles on the outcome of therapy. Virotherapy works best when tumors are small, and a robust innate immune response can lead to superior tumor control. Strategies that reduce tumor burden without suppressing the immune response and methods that maximize the amount of virus delivered to the tumor should optimize tumor control in this model system.

  10. Natural climate variability in a coupled model

    International Nuclear Information System (INIS)

    Zebiak, S.E.; Cane, M.A.


    Multi-century simulations with a simplified coupled ocean-atmosphere model are described. These simulations reveal an impressive range of variability on decadal and longer time scales, in addition to the dominant interannual el Nino/Southern Oscillation signal that the model originally was designed to simulate. Based on a very large sample of century-long simulations, it is nonetheless possible to identify distinct model parameter sensitivities that are described here in terms of selected indices. Preliminary experiments motivated by general circulation model results for increasing greenhouse gases suggest a definite sensitivity to model global warming. While these results are not definitive, they strongly suggest that coupled air-sea dynamics figure prominently in global change and must be included in models for reliable predictions

  11. Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration (United States)

    Doherty, John E.; Hunt, Randall J.


    Highly parameterized groundwater models can create calibration difficulties. Regularized inversion-the combined use of large numbers of parameters with mathematical approaches for stable parameter estimation-is becoming a common approach to address these difficulties and enhance the transfer of information contained in field measurements to parameters used to model that system. Though commonly used in other industries, regularized inversion is somewhat imperfectly understood in the groundwater field. There is concern that this unfamiliarity can lead to underuse, and misuse, of the methodology. This document is constructed to facilitate the appropriate use of regularized inversion for calibrating highly parameterized groundwater models. The presentation is directed at an intermediate- to advanced-level modeler, and it focuses on the PEST software suite-a frequently used tool for highly parameterized model calibration and one that is widely supported by commercial graphical user interfaces. A brief overview of the regularized inversion approach is provided, and techniques for mathematical regularization offered by PEST are outlined, including Tikhonov, subspace, and hybrid schemes. Guidelines for applying regularized inversion techniques are presented after a logical progression of steps for building suitable PEST input. The discussion starts with use of pilot points as a parameterization device and processing/grouping observations to form multicomponent objective functions. A description of potential parameter solution methodologies and resources available through the PEST software and its supporting utility programs follows. Directing the parameter-estimation process through PEST control variables is then discussed, including guidance for monitoring and optimizing the performance of PEST. Comprehensive listings of PEST control variables, and of the roles performed by PEST utility support programs, are presented in the appendixes.

  12. Drought variability in six catchments in the UK (United States)

    Kwok-Pan, Chun; Onof, Christian; Wheater, Howard


    Drought is fundamentally related to consistent low precipitation levels. Changes in global and regional drought patterns are suggested by numerous recent climate change studies. However, most of the climate change adaptation measures are at a catchment scale, and the development of a framework for studying persistence in precipitation is still at an early stage. Two stochastic approaches for modelling drought severity index (DSI) are proposed to investigate possible changes in droughts in six catchments in the UK. They are the autoregressive integrated moving average (ARIMA) and the generalised linear model (GLM) approach. Results of ARIMA modelling show that mean sea level pressure and possibly the North Atlantic Oscillation (NAO) index are important climate variables for short term drought forecasts, whereas relative humidity is not a significant climate variable despite its high correlation with the DSI series. By simulating rainfall series, the generalised linear model (GLM) approach can provide the probability density function of the DSI. GLM simulations indicate that the changes in the 10th and 50th quantiles of drought events are more noticeable than in the 90th extreme droughts. The possibility of extending the GLM approach to support risk-based water management is also discussed.

  13. A multi-model approach to X-ray pulsars

    Directory of Open Access Journals (Sweden)

    Schönherr G.


    Full Text Available The emission characteristics of X-ray pulsars are governed by magnetospheric accretion within the Alfvén radius, leading to a direct coupling of accretion column properties and interactions at the magnetosphere. The complexity of the physical processes governing the formation of radiation within the accreted, strongly magnetized plasma has led to several sophisticated theoretical modelling efforts over the last decade, dedicated to either the formation of the broad band continuum, the formation of cyclotron resonance scattering features (CRSFs or the formation of pulse profiles. While these individual approaches are powerful in themselves, they quickly reach their limits when aiming at a quantitative comparison to observational data. Too many fundamental parameters, describing the formation of the accretion columns and the systems’ overall geometry are unconstrained and different models are often based on different fundamental assumptions, while everything is intertwined in the observed, highly phase-dependent spectra and energy-dependent pulse profiles. To name just one example: the (phase variable line width of the CRSFs is highly dependent on the plasma temperature, the existence of B-field gradients (geometry and observation angle, parameters which, in turn, drive the continuum radiation and are driven by the overall two-pole geometry for the light bending model respectively. This renders a parallel assessment of all available spectral and timing information by a compatible across-models-approach indispensable. In a collaboration of theoreticians and observers, we have been working on a model unification project over the last years, bringing together theoretical calculations of the Comptonized continuum, Monte Carlo simulations and Radiation Transfer calculations of CRSFs as well as a General Relativity (GR light bending model for ray tracing of the incident emission pattern from both magnetic poles. The ultimate goal is to implement a

  14. A Deep Learning based Approach to Reduced Order Modeling of Fluids using LSTM Neural Networks (United States)

    Mohan, Arvind; Gaitonde, Datta


    Reduced Order Modeling (ROM) can be used as surrogates to prohibitively expensive simulations to model flow behavior for long time periods. ROM is predicated on extracting dominant spatio-temporal features of the flow from CFD or experimental datasets. We explore ROM development with a deep learning approach, which comprises of learning functional relationships between different variables in large datasets for predictive modeling. Although deep learning and related artificial intelligence based predictive modeling techniques have shown varied success in other fields, such approaches are in their initial stages of application to fluid dynamics. Here, we explore the application of the Long Short Term Memory (LSTM) neural network to sequential data, specifically to predict the time coefficients of Proper Orthogonal Decomposition (POD) modes of the flow for future timesteps, by training it on data at previous timesteps. The approach is demonstrated by constructing ROMs of several canonical flows. Additionally, we show that statistical estimates of stationarity in the training data can indicate a priori how amenable a given flow-field is to this approach. Finally, the potential and limitations of deep learning based ROM approaches will be elucidated and further developments discussed.

  15. A Quantitative Approach to Variables Affecting Production of Short Films in Turkey

    Directory of Open Access Journals (Sweden)

    Vedat Akman


    Full Text Available This study aims to explore the influence of various variables affecting the production of migration themed short films in Turkey. We proceeded to our analysis using descriptive statistics to describe the main futures of the sample data quantitatively. Due to non-uniformity of the data available, we were unable to use inductive statistics. Our basic sample statistical results indicated that short film producers prefered to produce short films on domestic migration theme rather than international. Gender and university seemed on surface as significant determinants to the production of migration themed short films in Turkey. We also looked at the demografic variables to provide more insights into our quantitative approach.

  16. Flexible software process lines in practice: A metamodel-based approach to effectively construct and manage families of software process models

    DEFF Research Database (Denmark)

    Kuhrmann, Marco; Ternité, Thomas; Friedrich, Jan


    Process flexibility and adaptability is frequently discussed, and several proposals aim to improve software processes for a given organization-/project context. A software process line (SPrL) is an instrument to systematically construct and manage variable software processes, by combining pre-def......: A metamodel-based approach to effectively construct and manage families of software process models [Ku16]. This paper was published as original research article in the Journal of Systems and Software.......Process flexibility and adaptability is frequently discussed, and several proposals aim to improve software processes for a given organization-/project context. A software process line (SPrL) is an instrument to systematically construct and manage variable software processes, by combining pre...... to construct flexible SPrLs and show its practical application in the German V-Modell XT. We contribute a proven approach that is presented as metamodel fragment for reuse and implementation in further process modeling approaches. This summary refers to the paper Flexible software process lines in practice...

  17. An adaptive sampling method for variable-fidelity surrogate models using improved hierarchical kriging (United States)

    Hu, Jiexiang; Zhou, Qi; Jiang, Ping; Shao, Xinyu; Xie, Tingli


    Variable-fidelity (VF) modelling methods have been widely used in complex engineering system design to mitigate the computational burden. Building a VF model generally includes two parts: design of experiments and metamodel construction. In this article, an adaptive sampling method based on improved hierarchical kriging (ASM-IHK) is proposed to refine the improved VF model. First, an improved hierarchical kriging model is developed as the metamodel, in which the low-fidelity model is varied through a polynomial response surface function to capture the characteristics of a high-fidelity model. Secondly, to reduce local approximation errors, an active learning strategy based on a sequential sampling method is introduced to make full use of the already required information on the current sampling points and to guide the sampling process of the high-fidelity model. Finally, two numerical examples and the modelling of the aerodynamic coefficient for an aircraft are provided to demonstrate the approximation capability of the proposed approach, as well as three other metamodelling methods and two sequential sampling methods. The results show that ASM-IHK provides a more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.

  18. Characteristics of quantum open systems: free random variables approach

    International Nuclear Information System (INIS)

    Gudowska-Nowak, E.; Papp, G.; Brickmann, J.


    Random Matrix Theory provides an interesting tool for modelling a number of phenomena where noises (fluctuations) play a prominent role. Various applications range from the theory of mesoscopic systems in nuclear and atomic physics to biophysical models, like Hopfield-type models of neural networks and protein folding. Random Matrix Theory is also used to study dissipative systems with broken time-reversal invariance providing a setup for analysis of dynamic processes in condensed, disordered media. In the paper we use the Random Matrix Theory (RMT) within the formalism of Free Random Variables (alias Blue's functions), which allows to characterize spectral properties of non-Hermitean ''Hamiltonians''. The relevance of using the Blue's function method is discussed in connection with application of non-Hermitean operators in various problems of physical chemistry. (author)

  19. From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions (United States)

    Fenicia, Fabrizio; Kavetski, Dmitri; Savenije, Hubert H. G.; Pfister, Laurent


    This paper explores the development and application of distributed hydrological models, focusing on the key decisions of how to discretize the landscape, which model structures to use in each landscape element, and how to link model parameters across multiple landscape elements. The case study considers the Attert catchment in Luxembourg—a 300 km2 mesoscale catchment with 10 nested subcatchments that exhibit clearly different streamflow dynamics. The research questions are investigated using conceptual models applied at hydrologic response unit (HRU) scales (1-4 HRUs) on 6 hourly time steps. Multiple model structures are hypothesized and implemented using the SUPERFLEX framework. Following calibration, space/time model transferability is tested using a split-sample approach, with evaluation criteria including streamflow prediction error metrics and hydrological signatures. Our results suggest that: (1) models using geology-based HRUs are more robust and capture the spatial variability of streamflow time series and signatures better than models using topography-based HRUs; this finding supports the hypothesis that, in the Attert, geology exerts a stronger control than topography on streamflow generation, (2) streamflow dynamics of different HRUs can be represented using distinct and remarkably simple model structures, which can be interpreted in terms of the perceived dominant hydrologic processes in each geology type, and (3) the same maximum root zone storage can be used across the three dominant geological units with no loss in model transferability; this finding suggests that the partitioning of water between streamflow and evaporation in the study area is largely independent of geology and can be used to improve model parsimony. The modeling methodology introduced in this study is general and can be used to advance our broader understanding and prediction of hydrological behavior, including the landscape characteristics that control hydrologic response, the

  20. Modeling variably saturated subsurface solute transport with MODFLOW-UZF and MT3DMS (United States)

    Morway, Eric D.; Niswonger, Richard G.; Langevin, Christian D.; Bailey, Ryan T.; Healy, Richard W.


    The MT3DMS groundwater solute transport model was modified to simulate solute transport in the unsaturated zone by incorporating the unsaturated-zone flow (UZF1) package developed for MODFLOW. The modified MT3DMS code uses a volume-averaged approach in which Lagrangian-based UZF1 fluid fluxes and storage changes are mapped onto a fixed grid. Referred to as UZF-MT3DMS, the linked model was tested against published benchmarks solved analytically as well as against other published codes, most frequently the U.S. Geological Survey's Variably-Saturated Two-Dimensional Flow and Transport Model. Results from a suite of test cases demonstrate that the modified code accurately simulates solute advection, dispersion, and reaction in the unsaturated zone. Two- and three-dimensional simulations also were investigated to ensure unsaturated-saturated zone interaction was simulated correctly. Because the UZF1 solution is analytical, large-scale flow and transport investigations can be performed free from the computational and data burdens required by numerical solutions to Richards' equation. Results demonstrate that significant simulation runtime savings can be achieved with UZF-MT3DMS, an important development when hundreds or thousands of model runs are required during parameter estimation and uncertainty analysis. Three-dimensional variably saturated flow and transport simulations revealed UZF-MT3DMS to have runtimes that are less than one tenth of the time required by models that rely on Richards' equation. Given its accuracy and efficiency, and the wide-spread use of both MODFLOW and MT3DMS, the added capability of unsaturated-zone transport in this familiar modeling framework stands to benefit a broad user-ship.

  1. A bivariate measurement error model for semicontinuous and continuous variables: Application to nutritional epidemiology. (United States)

    Kipnis, Victor; Freedman, Laurence S; Carroll, Raymond J; Midthune, Douglas


    Semicontinuous data in the form of a mixture of a large portion of zero values and continuously distributed positive values frequently arise in many areas of biostatistics. This article is motivated by the analysis of relationships between disease outcomes and intakes of episodically consumed dietary components. An important aspect of studies in nutritional epidemiology is that true diet is unobservable and commonly evaluated by food frequency questionnaires with substantial measurement error. Following the regression calibration approach for measurement error correction, unknown individual intakes in the risk model are replaced by their conditional expectations given mismeasured intakes and other model covariates. Those regression calibration predictors are estimated using short-term unbiased reference measurements in a calibration substudy. Since dietary intakes are often "energy-adjusted," e.g., by using ratios of the intake of interest to total energy intake, the correct estimation of the regression calibration predictor for each energy-adjusted episodically consumed dietary component requires modeling short-term reference measurements of the component (a semicontinuous variable), and energy (a continuous variable) simultaneously in a bivariate model. In this article, we develop such a bivariate model, together with its application to regression calibration. We illustrate the new methodology using data from the NIH-AARP Diet and Health Study (Schatzkin et al., 2001, American Journal of Epidemiology 154, 1119-1125), and also evaluate its performance in a simulation study. © 2015, The International Biometric Society.

  2. Reactive transport modeling in variably saturated porous media with OGS-IPhreeqc (United States)

    He, W.; Beyer, C.; Fleckenstein, J. H.; Jang, E.; Kalbacher, T.; Shao, H.; Wang, W.; Kolditz, O.


    Worldwide, sustainable water resource management becomes an increasingly challenging task due to the growth of population and extensive applications of fertilizer in agriculture. Moreover, climate change causes further stresses to both water quantity and quality. Reactive transport modeling in the coupled soil-aquifer system is a viable approach to assess the impacts of different land use and groundwater exploitation scenarios on the water resources. However, the application of this approach is usually limited in spatial scale and to simplified geochemical systems due to the huge computational expense involved. Such computational expense is not only caused by solving the high non-linearity of the initial boundary value problems of water flow in the unsaturated zone numerically with rather fine spatial and temporal discretization for the correct mass balance and numerical stability, but also by the intensive computational task of quantifying geochemical reactions. In the present study, a flexible and efficient tool for large scale reactive transport modeling in variably saturated porous media and its applications are presented. The open source scientific software OpenGeoSys (OGS) is coupled with the IPhreeqc module of the geochemical solver PHREEQC. The new coupling approach makes full use of advantages from both codes: OGS provides a flexible choice of different numerical approaches for simulation of water flow in the vadose zone such as the pressure-based or mixed forms of Richards equation; whereas the IPhreeqc module leads to a simplification of data storage and its communication with OGS, which greatly facilitates the coupling and code updating. Moreover, a parallelization scheme with MPI (Message Passing Interface) is applied, in which the computational task of water flow and mass transport is partitioned through domain decomposition, whereas the efficient parallelization of geochemical reactions is achieved by smart allocation of computational workload over

  3. Characterising an intense PM pollution episode in March 2015 in France from multi-site approach and near real time data: Climatology, variabilities, geographical origins and model evaluation (United States)

    Petit, J.-E.; Amodeo, T.; Meleux, F.; Bessagnet, B.; Menut, L.; Grenier, D.; Pellan, Y.; Ockler, A.; Rocq, B.; Gros, V.; Sciare, J.; Favez, O.


    During March 2015, a severe and large-scale particulate matter (PM) pollution episode occurred in France. Measurements in near real-time of the major chemical composition at four different urban background sites across the country (Paris, Creil, Metz and Lyon) allowed the investigation of spatiotemporal variabilities during this episode. A climatology approach showed that all sites experienced clear unusual rain shortage, a pattern that is also found on a longer timescale, highlighting the role of synoptic conditions over Wester-Europe. This episode is characterized by a strong predominance of secondary pollution, and more particularly of ammonium nitrate, which accounted for more than 50% of submicron aerosols at all sites during the most intense period of the episode. Pollution advection is illustrated by similar variabilities in Paris and Creil (distant of around 100 km), as well as trajectory analyses applied on nitrate and sulphate. Local sources, especially wood burning, are however found to contribute to local/regional sub-episodes, notably in Metz. Finally, simulated concentrations from Chemistry-Transport model CHIMERE were compared to observed ones. Results highlighted different patterns depending on the chemical components and the measuring site, reinforcing the need of such exercises over other pollution episodes and sites.

  4. AMOC decadal variability in Earth system models: Mechanisms and climate impacts

    Energy Technology Data Exchange (ETDEWEB)

    Fedorov, Alexey [Yale Univ., New Haven, CT (United States)


    This is the final report for the project titled "AMOC decadal variability in Earth system models: Mechanisms and climate impacts". The central goal of this one-year research project was to understand the mechanisms of decadal and multi-decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) within a hierarchy of climate models ranging from realistic ocean GCMs to Earth system models. The AMOC is a key element of ocean circulation responsible for oceanic transport of heat from low to high latitudes and controlling, to a large extent, climate variations in the North Atlantic. The questions of the AMOC stability, variability and predictability, directly relevant to the questions of climate predictability, were at the center of the research work.

  5. Higher-dimensional cosmological model with variable gravitational ...

    Indian Academy of Sciences (India)

    variable G and bulk viscosity in Lyra geometry. Exact solutions for ... a comparative study of Robertson–Walker models with a constant deceleration .... where H is defined as H =(˙A/A)+(1/3)( ˙B/B) and β0,H0 are representing present values of β ...

  6. Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon (United States)

    Dons, Evi; Van Poppel, Martine; Kochan, Bruno; Wets, Geert; Int Panis, Luc


    Land use regression (LUR) modeling is a statistical technique used to determine exposure to air pollutants in epidemiological studies. Time-activity diaries can be combined with LUR models, enabling detailed exposure estimation and limiting exposure misclassification, both in shorter and longer time lags. In this study, the traffic related air pollutant black carbon was measured with μ-aethalometers on a 5-min time base at 63 locations in Flanders, Belgium. The measurements show that hourly concentrations vary between different locations, but also over the day. Furthermore the diurnal pattern is different for street and background locations. This suggests that annual LUR models are not sufficient to capture all the variation. Hourly LUR models for black carbon are developed using different strategies: by means of dummy variables, with dynamic dependent variables and/or with dynamic and static independent variables. The LUR model with 48 dummies (weekday hours and weekend hours) performs not as good as the annual model (explained variance of 0.44 compared to 0.77 in the annual model). The dataset with hourly concentrations of black carbon can be used to recalibrate the annual model, resulting in many of the original explaining variables losing their statistical significance, and certain variables having the wrong direction of effect. Building new independent hourly models, with static or dynamic covariates, is proposed as the best solution to solve these issues. R2 values for hourly LUR models are mostly smaller than the R2 of the annual model, ranging from 0.07 to 0.8. Between 6 a.m. and 10 p.m. on weekdays the R2 approximates the annual model R2. Even though models of consecutive hours are developed independently, similar variables turn out to be significant. Using dynamic covariates instead of static covariates, i.e. hourly traffic intensities and hourly population densities, did not significantly improve the models' performance.

  7. Variable selection for mixture and promotion time cure rate models. (United States)

    Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng


    Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.

  8. Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty: STRUCTURAL UNCERTAINTY DIAGNOSTICS

    Energy Technology Data Exchange (ETDEWEB)

    Moges, Edom [Civil and Environmental Engineering Department, Washington State University, Richland Washington USA; Demissie, Yonas [Civil and Environmental Engineering Department, Washington State University, Richland Washington USA; Li, Hong-Yi [Hydrology Group, Pacific Northwest National Laboratory, Richland Washington USA


    In most water resources applications, a single model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integrate expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses are used to assess the presence of multiple dominant processes and the adequacy of a single model, as well as to identify the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas, the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response.

  9. Generating temporal model using climate variables for the prediction of dengue cases in Subang Jaya, Malaysia (United States)

    Dom, Nazri Che; Hassan, A Abu; Latif, Z Abd; Ismail, Rodziah


    Objective To develop a forecasting model for the incidence of dengue cases in Subang Jaya using time series analysis. Methods The model was performed using the Autoregressive Integrated Moving Average (ARIMA) based on data collected from 2005 to 2010. The fitted model was then used to predict dengue incidence for the year 2010 by extrapolating dengue patterns using three different approaches (i.e. 52, 13 and 4 weeks ahead). Finally cross correlation between dengue incidence and climate variable was computed over a range of lags in order to identify significant variables to be included as external regressor. Results The result of this study revealed that the ARIMA (2,0,0) (0,0,1)52 model developed, closely described the trends of dengue incidence and confirmed the existence of dengue fever cases in Subang Jaya for the year 2005 to 2010. The prediction per period of 4 weeks ahead for ARIMA (2,0,0)(0,0,1)52 was found to be best fit and consistent with the observed dengue incidence based on the training data from 2005 to 2010 (Root Mean Square Error=0.61). The predictive power of ARIMA (2,0,0) (0,0,1)52 is enhanced by the inclusion of climate variables as external regressor to forecast the dengue cases for the year 2010. Conclusions The ARIMA model with weekly variation is a useful tool for disease control and prevention program as it is able to effectively predict the number of dengue cases in Malaysia.

  10. Model Predictive Control of a Nonlinear System with Known Scheduling Variable

    DEFF Research Database (Denmark)

    Mirzaei, Mahmood; Poulsen, Niels Kjølstad; Niemann, Hans Henrik


    Model predictive control (MPC) of a class of nonlinear systems is considered in this paper. We will use Linear Parameter Varying (LPV) model of the nonlinear system. By taking the advantage of having future values of the scheduling variable, we will simplify state prediction. Consequently...... the control problem of the nonlinear system is simplied into a quadratic programming. Wind turbine is chosen as the case study and we choose wind speed as the scheduling variable. Wind speed is measurable ahead of the turbine, therefore the scheduling variable is known for the entire prediction horizon....

  11. A geometric model for magnetizable bodies with internal variables

    Directory of Open Access Journals (Sweden)

    Restuccia, L


    Full Text Available In a geometrical framework for thermo-elasticity of continua with internal variables we consider a model of magnetizable media previously discussed and investigated by Maugin. We assume as state variables the magnetization together with its space gradient, subjected to evolution equations depending on both internal and external magnetic fields. We calculate the entropy function and necessary conditions for its existence.

  12. Social interactions and college enrollment: A combined school fixed effects/instrumental variables approach. (United States)

    Fletcher, Jason M


    This paper provides some of the first evidence of peer effects in college enrollment decisions. There are several empirical challenges in assessing the influences of peers in this context, including the endogeneity of high school, shared group-level unobservables, and identifying policy-relevant parameters of social interactions models. This paper addresses these issues by using an instrumental variables/fixed effects approach that compares students in the same school but different grade-levels who are thus exposed to different sets of classmates. In particular, plausibly exogenous variation in peers' parents' college expectations are used as an instrument for peers' college choices. Preferred specifications indicate that increasing a student's exposure to college-going peers by ten percentage points is predicted to raise the student's probability of enrolling in college by 4 percentage points. This effect is roughly half the magnitude of growing up in a household with married parents (vs. an unmarried household). Copyright © 2015 Elsevier Inc. All rights reserved.

  13. Examples of EOS Variables as compared to the UMM-Var Data Model (United States)

    Cantrell, Simon; Lynnes, Chris


    In effort to provide EOSDIS clients a way to discover and use variable data from different providers, a Unified Metadata Model for Variables is being created. This presentation gives an overview of the model and use cases we are handling.

  14. A multi-objective approach to improve SWAT model calibration in alpine catchments (United States)

    Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele


    Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.

  15. Speech-discrimination scores modeled as a binomial variable. (United States)

    Thornton, A R; Raffin, M J


    Many studies have reported variability data for tests of speech discrimination, and the disparate results of these studies have not been given a simple explanation. Arguments over the relative merits of 25- vs 50-word tests have ignored the basic mathematical properties inherent in the use of percentage scores. The present study models performance on clinical tests of speech discrimination as a binomial variable. A binomial model was developed, and some of its characteristics were tested against data from 4120 scores obtained on the CID Auditory Test W-22. A table for determining significant deviations between scores was generated and compared to observed differences in half-list scores for the W-22 tests. Good agreement was found between predicted and observed values. Implications of the binomial characteristics of speech-discrimination scores are discussed.

  16. Optimal variable-grid finite-difference modeling for porous media

    International Nuclear Information System (INIS)

    Liu, Xinxin; Yin, Xingyao; Li, Haishan


    Numerical modeling of poroelastic waves by the finite-difference (FD) method is more expensive than that of acoustic or elastic waves. To improve the accuracy and computational efficiency of seismic modeling, variable-grid FD methods have been developed. In this paper, we derived optimal staggered-grid finite difference schemes with variable grid-spacing and time-step for seismic modeling in porous media. FD operators with small grid-spacing and time-step are adopted for low-velocity or small-scale geological bodies, while FD operators with big grid-spacing and time-step are adopted for high-velocity or large-scale regions. The dispersion relations of FD schemes were derived based on the plane wave theory, then the FD coefficients were obtained using the Taylor expansion. Dispersion analysis and modeling results demonstrated that the proposed method has higher accuracy with lower computational cost for poroelastic wave simulation in heterogeneous reservoirs. (paper)

  17. Parameter estimation of variable-parameter nonlinear Muskingum model using excel solver (United States)

    Kang, Ling; Zhou, Liwei


    Abstract . The Muskingum model is an effective flood routing technology in hydrology and water resources Engineering. With the development of optimization technology, more and more variable-parameter Muskingum models were presented to improve effectiveness of the Muskingum model in recent decades. A variable-parameter nonlinear Muskingum model (NVPNLMM) was proposed in this paper. According to the results of two real and frequently-used case studies by various models, the NVPNLMM could obtain better values of evaluation criteria, which are used to describe the superiority of the estimated outflows and compare the accuracies of flood routing using various models, and the optimal estimated outflows by the NVPNLMM were closer to the observed outflows than the ones by other models.

  18. Modeling the Power Variability of Core Speed Scaling on Homogeneous Multicore Systems

    Directory of Open Access Journals (Sweden)

    Zhihui Du


    Full Text Available We describe a family of power models that can capture the nonuniform power effects of speed scaling among homogeneous cores on multicore processors. These models depart from traditional ones, which assume that individual cores contribute to power consumption as independent entities. In our approach, we remove this independence assumption and employ statistical variables of core speed (average speed and the dispersion of the core speeds to capture the comprehensive heterogeneous impact of subtle interactions among the underlying hardware. We systematically explore the model family, deriving basic and refined models that give progressively better fits, and analyze them in detail. The proposed methodology provides an easy way to build power models to reflect the realistic workings of current multicore processors more accurately. Moreover, unlike the existing lower-level power models that require knowledge of microarchitectural details of the CPU cores and the last level cache to capture core interdependency, ours are easier to use and scalable to emerging and future multicore architectures with more cores. These attributes make the models particularly useful to system users or algorithm designers who need a quick way to estimate power consumption. We evaluate the family of models on contemporary x86 multicore processors using the SPEC2006 benchmarks. Our best model yields an average predicted error as low as 5%.

  19. Analyzing Unsaturated Flow Patterns in Fractured Rock Using an Integrated Modeling Approach

    International Nuclear Information System (INIS)

    Y.S. Wu; G. Lu; K. Zhang; L. Pan; G.S. Bodvarsson


    Characterizing percolation patterns in unsaturated fractured rock has posed a greater challenge to modeling investigations than comparable saturated zone studies, because of the heterogeneous nature of unsaturated media and the great number of variables impacting unsaturated flow. This paper presents an integrated modeling methodology for quantitatively characterizing percolation patterns in the unsaturated zone of Yucca Mountain, Nevada, a proposed underground repository site for storing high-level radioactive waste. The modeling approach integrates a wide variety of moisture, pneumatic, thermal, and isotopic geochemical field data into a comprehensive three-dimensional numerical model for modeling analyses. It takes into account the coupled processes of fluid and heat flow and chemical isotopic transport in Yucca Mountain's highly heterogeneous, unsaturated fractured tuffs. Modeling results are examined against different types of field-measured data and then used to evaluate different hydrogeological conceptualizations and their results of flow patterns in the unsaturated zone. In particular, this model provides a much clearer understanding of percolation patterns and flow behavior through the unsaturated zone, both crucial issues in assessing repository performance. The integrated approach for quantifying Yucca Mountain's flow system is demonstrated to provide a practical modeling tool for characterizing flow and transport processes in complex subsurface systems

  20. BehavePlus fire modeling system, version 5.0: Variables (United States)

    Patricia L. Andrews


    This publication has been revised to reflect updates to version 4.0 of the BehavePlus software. It was originally published as the BehavePlus fire modeling system, version 4.0: Variables in July, 2008.The BehavePlus fire modeling system is a computer program based on mathematical models that describe wildland fire behavior and effects and the...

  1. Selection bias in species distribution models: An econometric approach on forest trees based on structural modeling (United States)

    Martin-StPaul, N. K.; Ay, J. S.; Guillemot, J.; Doyen, L.; Leadley, P.


    Species distribution models (SDMs) are widely used to study and predict the outcome of global changes on species. In human dominated ecosystems the presence of a given species is the result of both its ecological suitability and human footprint on nature such as land use choices. Land use choices may thus be responsible for a selection bias in the presence/absence data used in SDM calibration. We present a structural modelling approach (i.e. based on structural equation modelling) that accounts for this selection bias. The new structural species distribution model (SSDM) estimates simultaneously land use choices and species responses to bioclimatic variables. A land use equation based on an econometric model of landowner choices was joined to an equation of species response to bioclimatic variables. SSDM allows the residuals of both equations to be dependent, taking into account the possibility of shared omitted variables and measurement errors. We provide a general description of the statistical theory and a set of applications on forest trees over France using databases of climate and forest inventory at different spatial resolution (from 2km to 8km). We also compared the outputs of the SSDM with outputs of a classical SDM (i.e. Biomod ensemble modelling) in terms of bioclimatic response curves and potential distributions under current climate and climate change scenarios. The shapes of the bioclimatic response curves and the modelled species distribution maps differed markedly between SSDM and classical SDMs, with contrasted patterns according to species and spatial resolutions. The magnitude and directions of these differences were dependent on the correlations between the errors from both equations and were highest for higher spatial resolutions. A first conclusion is that the use of classical SDMs can potentially lead to strong miss-estimation of the actual and future probability of presence modelled. Beyond this selection bias, the SSDM we propose represents

  2. Internal Interdecadal Variability in CMIP5 Control Simulations (United States)

    Cheung, A. H.; Mann, M. E.; Frankcombe, L. M.; England, M. H.; Steinman, B. A.; Miller, S. K.


    Here we make use of control simulations from the CMIP5 models to quantify the amplitude of the interdecadal internal variability component in Atlantic, Pacific, and Northern Hemisphere mean surface temperature. We compare against estimates derived from observations using a semi-empirical approach wherein the forced component as estimated using CMIP5 historical simulations is removed to yield an estimate of the residual, internal variability. While the observational estimates are largely consistent with those derived from the control simulations for both basins and the Northern Hemisphere, they lie in the upper range of the model distributions, suggesting the possibility of differences between the amplitudes of observed and modeled variability. We comment on some possible reasons for the disparity.

  3. Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling (United States)

    Peleg, Nadav; Blumensaat, Frank; Molnar, Peter; Fatichi, Simone; Burlando, Paolo


    The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP - Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM - the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the

  4. Modeling Turbulent Combustion for Variable Prandtl and Schmidt Number (United States)

    Hassan, H. A.


    This report consists of two abstracts submitted for possible presentation at the AIAA Aerospace Science Meeting to be held in January 2005. Since the submittal of these abstracts we are continuing refinement of the model coefficients derived for the case of a variable Turbulent Prandtl number. The test cases being investigated are a Mach 9.2 flow over a degree ramp and a Mach 8.2 3-D calculation of crossing shocks. We have developed an axisymmetric code for treating axisymmetric flows. In addition the variable Schmidt number formulation was incorporated in the code and we are in the process of determining the model constants.

  5. A Structural Equation Approach to Models with Spatial Dependence

    NARCIS (Netherlands)

    Oud, Johan H. L.; Folmer, Henk

    We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

  6. A structural equation approach to models with spatial dependence

    NARCIS (Netherlands)

    Oud, J.H.L.; Folmer, H.


    We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

  7. A Structural Equation Approach to Models with Spatial Dependence

    NARCIS (Netherlands)

    Oud, J.H.L.; Folmer, H.


    We introduce the class of structural equation models (SEMs) and corresponding estimation procedures into a spatial dependence framework. SEM allows both latent and observed variables within one and the same (causal) model. Compared with models with observed variables only, this feature makes it

  8. Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling (United States)

    Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.


    Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed

  9. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method. (United States)

    Yang, Jun-He; Cheng, Ching-Hsue; Chan, Chia-Pan


    Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir's water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir's water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  10. Variable Selection via Partial Correlation. (United States)

    Li, Runze; Liu, Jingyuan; Lou, Lejia


    Partial correlation based variable selection method was proposed for normal linear regression models by Bühlmann, Kalisch and Maathuis (2010) as a comparable alternative method to regularization methods for variable selection. This paper addresses two important issues related to partial correlation based variable selection method: (a) whether this method is sensitive to normality assumption, and (b) whether this method is valid when the dimension of predictor increases in an exponential rate of the sample size. To address issue (a), we systematically study this method for elliptical linear regression models. Our finding indicates that the original proposal may lead to inferior performance when the marginal kurtosis of predictor is not close to that of normal distribution. Our simulation results further confirm this finding. To ensure the superior performance of partial correlation based variable selection procedure, we propose a thresholded partial correlation (TPC) approach to select significant variables in linear regression models. We establish the selection consistency of the TPC in the presence of ultrahigh dimensional predictors. Since the TPC procedure includes the original proposal as a special case, our theoretical results address the issue (b) directly. As a by-product, the sure screening property of the first step of TPC was obtained. The numerical examples also illustrate that the TPC is competitively comparable to the commonly-used regularization methods for variable selection.

  11. A GIS Approach to Evaluate Infrastructure Variables Influencing the Occurrence of Traffic Accidents in Urban Roads

    Directory of Open Access Journals (Sweden)

    Murat Selim Çepni


    Full Text Available Several studies worldwide have been developed that seek to explain the occurrence of traffic accidents from different perspectives. The analyses have addressed legal perspectives, technical attributes of vehicles and infrastructure as well as the psychological, behavioral and socio-economic components of the road system users. Recently, some analysis techniques based on the use of Geographic Information Systems (GIS have been used, which allow the generation of spatial distribution maps, models and risk estimates from a spatial perspective. Sometimes analyses of traffic accidents are performed using quantitative statistical techniques, which place significant importance on the evolution of accidents. Studies such as those in references have shown that conventional statistical models are sometimes inadequate to model the frequency of traffic accidents, as they may provide erroneous inferences. GIS approach has been used to explore different spatial and temporal visualization technologies to reveal accident patterns and significant factors relating to vehicle crashes, or as a management system for accident analysis and the determination of hot spots. This paper examines the relationship between urban road accidents and variables related to road infrastructure, environment and traffic volumes. Some accident-prone sections in the city of Kocaeli are specifically identified by GIS tools. Urban road accidents in Kocaeli are a serious problem and it is believed that accidents can be related to infrastructure characteristics. The study aimed to establish the relationship between urban road accidents and the road infrastructure variables and revealed some possible accident prone locations for the period of 2013 and 2015 in Kocaeli city

  12. Purposeful selection of variables in logistic regression

    Directory of Open Access Journals (Sweden)

    Williams David Keith


    Full Text Available Abstract Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.

  13. A Model for Positively Correlated Count Variables

    DEFF Research Database (Denmark)

    Møller, Jesper; Rubak, Ege Holger


    An α-permanental random field is briefly speaking a model for a collection of non-negative integer valued random variables with positive associations. Though such models possess many appealing probabilistic properties, many statisticians seem unaware of α-permanental random fields...... and their potential applications. The purpose of this paper is to summarize useful probabilistic results, study stochastic constructions and simulation techniques, and discuss some examples of α-permanental random fields. This should provide a useful basis for discussing the statistical aspects in future work....

  14. Interacting ghost dark energy models with variable G and Λ (United States)

    Sadeghi, J.; Khurshudyan, M.; Movsisyan, A.; Farahani, H.


    In this paper we consider several phenomenological models of variable Λ. Model of a flat Universe with variable Λ and G is accepted. It is well known, that varying G and Λ gives rise to modified field equations and modified conservation laws, which gives rise to many different manipulations and assumptions in literature. We will consider two component fluid, which parameters will enter to Λ. Interaction between fluids with energy densities ρ1 and ρ2 assumed as Q = 3Hb(ρ1+ρ2). We have numerical analyze of important cosmological parameters like EoS parameter of the composed fluid and deceleration parameter q of the model.

  15. Representing general theoretical concepts in structural equation models: The role of composite variables (United States)

    Grace, J.B.; Bollen, K.A.


    Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically-based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling heterogeneous concepts of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially-reduced-form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influence of suites of variables are often of interest. ?? Springer Science+Business Media, LLC 2007.

  16. Inter-model variability and biases of the global water cycle in CMIP3 coupled climate models

    International Nuclear Information System (INIS)

    Liepert, Beate G; Previdi, Michael


    Observed changes such as increasing global temperatures and the intensification of the global water cycle in the 20th century are robust results of coupled general circulation models (CGCMs). In spite of these successes, model-to-model variability and biases that are small in first order climate responses, however, have considerable implications for climate predictability especially when multi-model means are used. We show that most climate simulations of the 20th and 21st century A2 scenario performed with CMIP3 (Coupled Model Inter-comparison Project Phase 3) models have deficiencies in simulating the global atmospheric moisture balance. Large biases of only a few models (some biases reach the simulated global precipitation changes in the 20th and 21st centuries) affect the multi-model mean global moisture budget. An imbalanced flux of −0.14 Sv exists while the multi-model median imbalance is only −0.02 Sv. Moreover, for most models the detected imbalance changes over time. As a consequence, in 13 of the 18 CMIP3 models examined, global annual mean precipitation exceeds global evaporation, indicating that there should be a ‘leaking’ of moisture from the atmosphere whereas for the remaining five models a ‘flooding’ is implied. Nonetheless, in all models, the actual atmospheric moisture content and its variability correctly increases during the course of the 20th and 21st centuries. These discrepancies therefore imply an unphysical and hence ‘ghost’ sink/source of atmospheric moisture in the models whose atmospheres flood/leak. The ghost source/sink of moisture can also be regarded as atmospheric latent heating/cooling and hence as positive/negative perturbation of the atmospheric energy budget or non-radiative forcing in the range of −1 to +6 W m −2 (median +0.1 W m −2 ). The inter-model variability of the global atmospheric moisture transport from oceans to land areas, which impacts the terrestrial water cycle, is also quite high and ranges

  17. How ocean lateral mixing changes Southern Ocean variability in coupled climate models (United States)

    Pradal, M. A. S.; Gnanadesikan, A.; Thomas, J. L.


    The lateral mixing of tracers represents a major uncertainty in the formulation of coupled climate models. The mixing of tracers along density surfaces in the interior and horizontally within the mixed layer is often parameterized using a mixing coefficient ARedi. The models used in the Coupled Model Intercomparison Project 5 exhibit more than an order of magnitude range in the values of this coefficient used within the Southern Ocean. The impacts of such uncertainty on Southern Ocean variability have remained unclear, even as recent work has shown that this variability differs between different models. In this poster, we change the lateral mixing coefficient within GFDL ESM2Mc, a coarse-resolution Earth System model that nonetheless has a reasonable circulation within the Southern Ocean. As the coefficient varies from 400 to 2400 m2/s the amplitude of the variability varies significantly. The low-mixing case shows strong decadal variability with an annual mean RMS temperature variability exceeding 1C in the Circumpolar Current. The highest-mixing case shows a very similar spatial pattern of variability, but with amplitudes only about 60% as large. The suppression of mixing is larger in the Atlantic Sector of the Southern Ocean relatively to the Pacific sector. We examine the salinity budgets of convective regions, paying particular attention to the extent to which high mixing prevents the buildup of low-saline waters that are capable of shutting off deep convection entirely.

  18. Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia (United States)

    Prahutama, Alan; Suparti; Wahyu Utami, Tiani


    Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

  19. Higher Energy Intake Variability as Predisposition to Obesity: Novel Approach Using Interquartile Range. (United States)

    Forejt, Martin; Brázdová, Zuzana Derflerová; Novák, Jan; Zlámal, Filip; Forbelská, Marie; Bienert, Petr; Mořkovská, Petra; Zavřelová, Miroslava; Pohořalá, Aneta; Jurášková, Miluše; Salah, Nabil; Bienertová-Vašků, Julie


    It is known that total energy intake and its distribution during the day influences human anthropometric characteristics. However, possible association between variability in total energy intake and obesity has thus far remained unexamined. This study was designed to establish the influence of energy intake variability of each daily meal on the anthropometric characteristics of obesity. A total of 521 individuals of Czech Caucasian origin aged 16–73 years (390 women and 131 men) were included in the study, 7-day food records were completed by all study subjects and selected anthropometric characteristics were measured. The interquartile range (IQR) of energy intake was assessed individually for each meal of the day (as a marker of energy intake variability) and subsequently correlated with body mass index (BMI), body fat percentage (%BF), waist-hip ratio (WHR), and waist circumference (cW). Four distinct models were created using multiple logistic regression analysis and backward stepwise logistic regression. The most precise results, based on the area under the curve (AUC), were observed in case of the %BF model (AUC=0.895) and cW model (AUC=0.839). According to the %BF model, age (p<0.001) and IQR-lunch (p<0.05) seem to play an important prediction role for obesity. Likewise, according to the cW model, age (p<0.001), IQR-breakfast (p<0.05) and IQR-dinner (p <0.05) predispose patients to the development of obesity. The results of our study show that higher variability in the energy intake of key daily meals may increase the likelihood of obesity development. Based on the obtained results, it is necessary to emphasize the regularity in meals intake for maintaining proper body composition. Copyright© by the National Institute of Public Health, Prague 2017

  20. Input variable selection for data-driven models of Coriolis flowmeters for two-phase flow measurement

    International Nuclear Information System (INIS)

    Wang, Lijuan; Yan, Yong; Wang, Xue; Wang, Tao


    Input variable selection is an essential step in the development of data-driven models for environmental, biological and industrial applications. Through input variable selection to eliminate the irrelevant or redundant variables, a suitable subset of variables is identified as the input of a model. Meanwhile, through input variable selection the complexity of the model structure is simplified and the computational efficiency is improved. This paper describes the procedures of the input variable selection for the data-driven models for the measurement of liquid mass flowrate and gas volume fraction under two-phase flow conditions using Coriolis flowmeters. Three advanced input variable selection methods, including partial mutual information (PMI), genetic algorithm-artificial neural network (GA-ANN) and tree-based iterative input selection (IIS) are applied in this study. Typical data-driven models incorporating support vector machine (SVM) are established individually based on the input candidates resulting from the selection methods. The validity of the selection outcomes is assessed through an output performance comparison of the SVM based data-driven models and sensitivity analysis. The validation and analysis results suggest that the input variables selected from the PMI algorithm provide more effective information for the models to measure liquid mass flowrate while the IIS algorithm provides a fewer but more effective variables for the models to predict gas volume fraction. (paper)

  1. Multi-period natural gas market modeling Applications, stochastic extensions and solution approaches (United States)

    Egging, Rudolf Gerardus

    This dissertation develops deterministic and stochastic multi-period mixed complementarity problems (MCP) for the global natural gas market, as well as solution approaches for large-scale stochastic MCP. The deterministic model is unique in the combination of the level of detail of the actors in the natural gas markets and the transport options, the detailed regional and global coverage, the multi-period approach with endogenous capacity expansions for transportation and storage infrastructure, the seasonal variation in demand and the representation of market power according to Nash-Cournot theory. The model is applied to several scenarios for the natural gas market that cover the formation of a cartel by the members of the Gas Exporting Countries Forum, a low availability of unconventional gas in the United States, and cost reductions in long-distance gas transportation. 1 The results provide insights in how different regions are affected by various developments, in terms of production, consumption, traded volumes, prices and profits of market participants. The stochastic MCP is developed and applied to a global natural gas market problem with four scenarios for a time horizon until 2050 with nineteen regions and containing 78,768 variables. The scenarios vary in the possibility of a gas market cartel formation and varying depletion rates of gas reserves in the major gas importing regions. Outcomes for hedging decisions of market participants show some significant shifts in the timing and location of infrastructure investments, thereby affecting local market situations. A first application of Benders decomposition (BD) is presented to solve a large-scale stochastic MCP for the global gas market with many hundreds of first-stage capacity expansion variables and market players exerting various levels of market power. The largest problem solved successfully using BD contained 47,373 variables of which 763 first-stage variables, however using BD did not result in

  2. Global energy modeling - A biophysical approach

    Energy Technology Data Exchange (ETDEWEB)

    Dale, Michael


    This paper contrasts the standard economic approach to energy modelling with energy models using a biophysical approach. Neither of these approaches includes changing energy-returns-on-investment (EROI) due to declining resource quality or the capital intensive nature of renewable energy sources. Both of these factors will become increasingly important in the future. An extension to the biophysical approach is outlined which encompasses a dynamic EROI function that explicitly incorporates technological learning. The model is used to explore several scenarios of long-term future energy supply especially concerning the global transition to renewable energy sources in the quest for a sustainable energy system.

  3. Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach (United States)

    Hamid, Mohd Fahmi Abdul; Shabri, Ani


    Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

  4. A novel approach for modeling malaria incidence using complex categorical household data: The minimum message length (MML method applied to Indonesian data

    Directory of Open Access Journals (Sweden)

    Gerhard Visser


    Full Text Available We investigated the application of a Minimum Message Length (MML modeling approach to identify the simplest model that would explain two target malaria incidence variables: incidence in the short term and on the average longer term, in two areas in Indonesia, based on a range of ecological variables including environmental and socio-economic ones. The approach is suitable for dealing with a variety of problems such as complexity and where there are missing values in the data. It can detect weak relations, is resistant to overfittingand can show the way in which many variables, working together, contribute to explaining malaria incidence. This last point is a major strength of the method as it allows many variables to be analysed. Data were obtained at household level by questionnaire for villages in West Timor and Central Java. Data were collected on 26 variables in nine categories: stratum (a village-level variable based on the API/AMI categories, ecology, occupation, preventative measures taken, health care facilities, the immediate environment, household characteristics, socio-economic status and perception of malaria cause. Several models were used and the simplest (best model, that is the one with the minimum message length was selected for each area. The results showed that consistent predictors of malaria included combinations of ecology (coastal, preventative (clean backyard and environment (mosquito breeding place, garden and rice cultivation. The models also showed that most of the other variables were not good predictors and this is discussed in the paper. We conclude that the method has potential for identifying simple predictors of malaria and that it could be used to focus malaria management on combinations of variables rather than relying on single ones that may not be consistently reliable.

  5. Statistical Dependence of Pipe Breaks on Explanatory Variables

    Directory of Open Access Journals (Sweden)

    Patricia Gómez-Martínez


    Full Text Available Aging infrastructure is the main challenge currently faced by water suppliers. Estimation of assets lifetime requires reliable criteria to plan assets repair and renewal strategies. To do so, pipe break prediction is one of the most important inputs. This paper analyzes the statistical dependence of pipe breaks on explanatory variables, determining their optimal combination and quantifying their influence on failure prediction accuracy. A large set of registered data from Madrid water supply network, managed by Canal de Isabel II, has been filtered, classified and studied. Several statistical Bayesian models have been built and validated from the available information with a technique that combines reference periods of time as well as geographical location. Statistical models of increasing complexity are built from zero up to five explanatory variables following two approaches: a set of independent variables or a combination of two joint variables plus an additional number of independent variables. With the aim of finding the variable combination that provides the most accurate prediction, models are compared following an objective validation procedure based on the model skill to predict the number of pipe breaks in a large set of geographical locations. As expected, model performance improves as the number of explanatory variables increases. However, the rate of improvement is not constant. Performance metrics improve significantly up to three variables, but the tendency is softened for higher order models, especially in trunk mains where performance is reduced. Slight differences are found between trunk mains and distribution lines when selecting the most influent variables and models.

  6. Empirical Correction to the Likelihood Ratio Statistic for Structural Equation Modeling with Many Variables. (United States)

    Yuan, Ke-Hai; Tian, Yubin; Yanagihara, Hirokazu


    Survey data typically contain many variables. Structural equation modeling (SEM) is commonly used in analyzing such data. The most widely used statistic for evaluating the adequacy of a SEM model is T ML, a slight modification to the likelihood ratio statistic. Under normality assumption, T ML approximately follows a chi-square distribution when the number of observations (N) is large and the number of items or variables (p) is small. However, in practice, p can be rather large while N is always limited due to not having enough participants. Even with a relatively large N, empirical results show that T ML rejects the correct model too often when p is not too small. Various corrections to T ML have been proposed, but they are mostly heuristic. Following the principle of the Bartlett correction, this paper proposes an empirical approach to correct T ML so that the mean of the resulting statistic approximately equals the degrees of freedom of the nominal chi-square distribution. Results show that empirically corrected statistics follow the nominal chi-square distribution much more closely than previously proposed corrections to T ML, and they control type I errors reasonably well whenever N ≥ max(50,2p). The formulations of the empirically corrected statistics are further used to predict type I errors of T ML as reported in the literature, and they perform well.

  7. A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

    Directory of Open Access Journals (Sweden)

    Jun-He Yang


    Full Text Available Reservoirs are important for households and impact the national economy. This paper proposed a time-series forecasting model based on estimating a missing value followed by variable selection to forecast the reservoir’s water level. This study collected data from the Taiwan Shimen Reservoir as well as daily atmospheric data from 2008 to 2015. The two datasets are concatenated into an integrated dataset based on ordering of the data as a research dataset. The proposed time-series forecasting model summarily has three foci. First, this study uses five imputation methods to directly delete the missing value. Second, we identified the key variable via factor analysis and then deleted the unimportant variables sequentially via the variable selection method. Finally, the proposed model uses a Random Forest to build the forecasting model of the reservoir’s water level. This was done to compare with the listing method under the forecasting error. These experimental results indicate that the Random Forest forecasting model when applied to variable selection with full variables has better forecasting performance than the listing model. In addition, this experiment shows that the proposed variable selection can help determine five forecast methods used here to improve the forecasting capability.

  8. Uncertainty analysis of pollutant build-up modelling based on a Bayesian weighted least squares approach

    International Nuclear Information System (INIS)

    Haddad, Khaled; Egodawatta, Prasanna; Rahman, Ataur; Goonetilleke, Ashantha


    Reliable pollutant build-up prediction plays a critical role in the accuracy of urban stormwater quality modelling outcomes. However, water quality data collection is resource demanding compared to streamflow data monitoring, where a greater quantity of data is generally available. Consequently, available water quality datasets span only relatively short time scales unlike water quantity data. Therefore, the ability to take due consideration of the variability associated with pollutant processes and natural phenomena is constrained. This in turn gives rise to uncertainty in the modelling outcomes as research has shown that pollutant loadings on catchment surfaces and rainfall within an area can vary considerably over space and time scales. Therefore, the assessment of model uncertainty is an essential element of informed decision making in urban stormwater management. This paper presents the application of a range of regression approaches such as ordinary least squares regression, weighted least squares regression and Bayesian weighted least squares regression for the estimation of uncertainty associated with pollutant build-up prediction using limited datasets. The study outcomes confirmed that the use of ordinary least squares regression with fixed model inputs and limited observational data may not provide realistic estimates. The stochastic nature of the dependent and independent variables need to be taken into consideration in pollutant build-up prediction. It was found that the use of the Bayesian approach along with the Monte Carlo simulation technique provides a powerful tool, which attempts to make the best use of the available knowledge in prediction and thereby presents a practical solution to counteract the limitations which are otherwise imposed on water quality modelling. - Highlights: ► Water quality data spans short time scales leading to significant model uncertainty. ► Assessment of uncertainty essential for informed decision making in water

  9. Characterizing the Surface Connectivity of Depressional Wetlands: Linking Remote Sensing and Hydrologic Modeling Approaches (United States)

    Christensen, J.; Evenson, G. R.; Vanderhoof, M.; Wu, Q.; Golden, H. E.; Lane, C.


    Surface connectivity of wetlands in the 700,000 km2 Prairie Pothole Region of North America (PPR) can occur through fill-spill and fill-merge mechanisms, with some wetlands eventually spilling into stream/river systems. These wetland-to-wetland and wetland-to-stream connections vary both spatially and temporally in PPR watersheds and are important to understanding hydrologic and biogeochemical processes in the landscape. To explore how to best characterize spatial and temporal variability in aquatic connectivity, we compared three approaches, 1) hydrological modeling alone, 2) remotely-sensed data alone, and 3) integrating remotely-sensed data into a hydrological model. These approaches were tested in the Pipestem Creek Watershed, North Dakota across a drought to deluge cycle (1990-2011). A Soil and Water Assessment Tool (SWAT) model was modified to include the water storage capacity of individual non-floodplain wetlands identified in the National Wetland Inventory (NWI) dataset. The SWAT-NWI model simulated the water balance and storage of each wetland and the temporal variability of their hydrologic connections between wetlands during the 21-year study period. However, SWAT-NWI only accounted for fill-spill, and did not allow for the expansion and merging of wetlands situated within larger depressions. Alternatively, we assessed the occurrence of fill-merge mechanisms using inundation maps derived from Landsat images on 19 cloud-free days during the 21 years. We found fill-merge mechanisms to be prevalent across the Pipestem watershed during times of deluge. The SWAT-NWI model was then modified to use LiDAR-derived depressions that account for the potential maximum depression extent, including the merging of smaller wetlands. The inundation maps were used to evaluate the ability of the SWAT-depression model to simulate fill-merge dynamics in addition to fill-spill dynamics throughout the study watershed. Ultimately, using remote sensing to inform and validate

  10. Comparisons of Multilevel Modeling and Structural Equation Modeling Approaches to Actor-Partner Interdependence Model. (United States)

    Hong, Sehee; Kim, Soyoung


    There are basically two modeling approaches applicable to analyzing an actor-partner interdependence model: the multilevel modeling (hierarchical linear model) and the structural equation modeling. This article explains how to use these two models in analyzing an actor-partner interdependence model and how these two approaches work differently. As an empirical example, marital conflict data were used to analyze an actor-partner interdependence model. The multilevel modeling and the structural equation modeling produced virtually identical estimates for a basic model. However, the structural equation modeling approach allowed more realistic assumptions on measurement errors and factor loadings, rendering better model fit indices.

  11. Adaptation of endothelial cells to physiologically-modeled, variable shear stress.

    Directory of Open Access Journals (Sweden)

    Joseph S Uzarski

    Full Text Available Endothelial cell (EC function is mediated by variable hemodynamic shear stress patterns at the vascular wall, where complex shear stress profiles directly correlate with blood flow conditions that vary temporally based on metabolic demand. The interactions of these more complex and variable shear fields with EC have not been represented in hemodynamic flow models. We hypothesized that EC exposed to pulsatile shear stress that changes in magnitude and duration, modeled directly from real-time physiological variations in heart rate, would elicit phenotypic changes as relevant to their critical roles in thrombosis, hemostasis, and inflammation. Here we designed a physiological flow (PF model based on short-term temporal changes in blood flow observed in vivo and compared it to static culture and steady flow (SF at a fixed pulse frequency of 1.3 Hz. Results show significant changes in gene regulation as a function of temporally variable flow, indicating a reduced wound phenotype more representative of quiescence. EC cultured under PF exhibited significantly higher endothelial nitric oxide synthase (eNOS activity (PF: 176.0±11.9 nmol/10(5 EC; SF: 115.0±12.5 nmol/10(5 EC, p = 0.002 and lower TNF-a-induced HL-60 leukocyte adhesion (PF: 37±6 HL-60 cells/mm(2; SF: 111±18 HL-60/mm(2, p = 0.003 than cells cultured under SF which is consistent with a more quiescent anti-inflammatory and anti-thrombotic phenotype. In vitro models have become increasingly adept at mimicking natural physiology and in doing so have clarified the importance of both chemical and physical cues that drive cell function. These data illustrate that the variability in metabolic demand and subsequent changes in perfusion resulting in constantly variable shear stress plays a key role in EC function that has not previously been described.

  12. Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl

    This paper generalizes the results for the Bridge estimator of Huang et al. (2008) to linear random and fixed effects panel data models which are allowed to grow in both dimensions. In particular we show that the Bridge estimator is oracle efficient. It can correctly distinguish between relevant...... and irrelevant variables and the asymptotic distribution of the estimators of the coefficients of the relevant variables is the same as if only these had been included in the model, i.e. as if an oracle had revealed the true model prior to estimation. In the case of more explanatory variables than observations......, we prove that the Marginal Bridge estimator can asymptotically correctly distinguish between relevant and irrelevant explanatory variables. We do this without restricting the dependence between covariates and without assuming sub Gaussianity of the error terms thereby generalizing the results...

  13. Variable Renewable Energy in Long-Term Planning Models: A Multi-Model Perspective

    Energy Technology Data Exchange (ETDEWEB)

    Cole, Wesley J. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Frew, Bethany A. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Mai, Trieu T. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sun, Yinong [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bistline, John [Electric Power Research Inst., Palo Alto, CA (United States); Blanford, Geoffrey [Electric Power Research Inst., Palo Alto, CA (United States); Young, David [Electric Power Research Inst., Palo Alto, CA (United States); Marcy, Cara [Energy Information Administration, Washington, DC (United States); Namovicz, Chris [Energy Information Administration, Washington, DC (United States); Edelman, Risa [Environmental Protection Agency, Washington, DC (United States); Meroney, Bill [Environmental Protection Agency; Sims, Ryan [Environmental Protection Agency; Stenhouse, Jeb [Environmental Protection Agency; Donohoo-Vallett, Paul [U.S. Department of Energy


    Long-term capacity expansion models of the U.S. electricity sector have long been used to inform electric sector stakeholders and decision makers. With the recent surge in variable renewable energy (VRE) generators - primarily wind and solar photovoltaics - the need to appropriately represent VRE generators in these long-term models has increased. VRE generators are especially difficult to represent for a variety of reasons, including their variability, uncertainty, and spatial diversity. To assess current best practices, share methods and data, and identify future research needs for VRE representation in capacity expansion models, four capacity expansion modeling teams from the Electric Power Research Institute, the U.S. Energy Information Administration, the U.S. Environmental Protection Agency, and the National Renewable Energy Laboratory conducted two workshops of VRE modeling for national-scale capacity expansion models. The workshops covered a wide range of VRE topics, including transmission and VRE resource data, VRE capacity value, dispatch and operational modeling, distributed generation, and temporal and spatial resolution. The objectives of the workshops were both to better understand these topics and to improve the representation of VRE across the suite of models. Given these goals, each team incorporated model updates and performed additional analyses between the first and second workshops. This report summarizes the analyses and model 'experiments' that were conducted as part of these workshops as well as the various methods for treating VRE among the four modeling teams. The report also reviews the findings and learnings from the two workshops. We emphasize the areas where there is still need for additional research and development on analysis tools to incorporate VRE into long-term planning and decision-making.

  14. On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio

    Directory of Open Access Journals (Sweden)

    Tatjana Miljkovic


    Full Text Available We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM and mixture-based clustering for an ordered stereotype model (OSM. The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.

  15. Perceptions of ambiguously unpleasant interracial interactions: a structural equation modeling approach. (United States)

    Marino, Teresa L; Negy, Charles; Hammons, Mary E; McKinney, Cliff; Asberg, Kia


    Despite a general consensus in the United States that overtly racist acts are unacceptable, many ambiguous situations in everyday life raise questions of whether racism has influenced a person's behavior in an interracial encounter. The authors of the present study sought to (a) examine simultaneously an array of variables thought to be related to perceived racism and (b) investigate how the contribution of these variables may differ with respect to the asymmetry hypothesis, which suggests that acts of discrimination from a dominant person toward a subordinate person will be viewed as more biased than if the situation were reversed. The authors used a dual structural equation modeling approach. Results indicated that ethnic identity significantly predicted perceived racism. In addition, the extent to which cognitive interpretation style significantly predicted perceived racism depended on the ethnicity of participants involved in the interaction.

  16. Separation of variables in anisotropic models: anisotropic Rabi and elliptic Gaudin model in an external magnetic field (United States)

    Skrypnyk, T.


    We study the problem of separation of variables for classical integrable Hamiltonian systems governed by non-skew-symmetric non-dynamical so(3)\\otimes so(3) -valued elliptic r-matrices with spectral parameters. We consider several examples of such models, and perform separation of variables for classical anisotropic one- and two-spin Gaudin-type models in an external magnetic field, and for Jaynes-Cummings-Dicke-type models without the rotating wave approximation.

  17. Sensitivity analysis on uncertainty variables affecting the NPP's LUEC with probabilistic approach

    International Nuclear Information System (INIS)

    Nuryanti; Akhmad Hidayatno; Erlinda Muslim


    One thing that is quite crucial to be reviewed prior to any investment decision on the nuclear power plant (NPP) project is the calculation of project economic, including calculation of Levelized Unit Electricity Cost (LUEC). Infrastructure projects such as NPP’s project are vulnerable to a number of uncertainty variables. Information on the uncertainty variables which makes LUEC’s value quite sensitive due to the changes of them is necessary in order the cost overrun can be avoided. Therefore this study aimed to do the sensitivity analysis on variables that affect LUEC with probabilistic approaches. This analysis was done by using Monte Carlo technique that simulate the relationship between the uncertainty variables and visible impact on LUEC. The sensitivity analysis result shows the significant changes on LUEC value of AP1000 and OPR due to the sensitivity of investment cost and capacity factors. While LUEC changes due to sensitivity of U 3 O 8 ’s price looks not quite significant. (author)

  18. Modelling the effects of spatial variability on radionuclide migration

    International Nuclear Information System (INIS)


    The NEA workshop reflect the present status in national waste management program, specifically in spatial variability and performance assessment of geologic disposal sites for deed repository system the four sessions were: Spatial Variability: Its Definition and Significance to Performance Assessment and Site Characterisation; Experience with the Modelling of Radionuclide Migration in the Presence of Spatial Variability in Various Geological Environments; New Areas for Investigation: Two Personal Views; What is Wanted and What is Feasible: Views and Future Plans in Selected Waste Management Organisations. The 26 papers presented on the four oral sessions and on the poster session have been abstracted and indexed individually for the INIS database. (R.P.)

  19. JuPOETs: a constrained multiobjective optimization approach to estimate biochemical model ensembles in the Julia programming language. (United States)

    Bassen, David M; Vilkhovoy, Michael; Minot, Mason; Butcher, Jonathan T; Varner, Jeffrey D


    Ensemble modeling is a promising approach for obtaining robust predictions and coarse grained population behavior in deterministic mathematical models. Ensemble approaches address model uncertainty by using parameter or model families instead of single best-fit parameters or fixed model structures. Parameter ensembles can be selected based upon simulation error, along with other criteria such as diversity or steady-state performance. Simulations using parameter ensembles can estimate confidence intervals on model variables, and robustly constrain model predictions, despite having many poorly constrained parameters. In this software note, we present a multiobjective based technique to estimate parameter or models ensembles, the Pareto Optimal Ensemble Technique in the Julia programming language (JuPOETs). JuPOETs integrates simulated annealing with Pareto optimality to estimate ensembles on or near the optimal tradeoff surface between competing training objectives. We demonstrate JuPOETs on a suite of multiobjective problems, including test functions with parameter bounds and system constraints as well as for the identification of a proof-of-concept biochemical model with four conflicting training objectives. JuPOETs identified optimal or near optimal solutions approximately six-fold faster than a corresponding implementation in Octave for the suite of test functions. For the proof-of-concept biochemical model, JuPOETs produced an ensemble of parameters that gave both the mean of the training data for conflicting data sets, while simultaneously estimating parameter sets that performed well on each of the individual objective functions. JuPOETs is a promising approach for the estimation of parameter and model ensembles using multiobjective optimization. JuPOETs can be adapted to solve many problem types, including mixed binary and continuous variable types, bilevel optimization problems and constrained problems without altering the base algorithm. JuPOETs is open

  20. AeroPropulsoServoElasticity: Dynamic Modeling of the Variable Cycle Propulsion System (United States)

    Kopasakis, George


    This presentation was made at the 2012 Fundamental Aeronautics Program Technical Conference and it covers research work for the Dynamic Modeling of the Variable cycle Propulsion System that was done under the Supersonics Project, in the area of AeroPropulsoServoElasticity. The presentation covers the objective for the propulsion system dynamic modeling work, followed by the work that has been done so far to model the variable Cycle Engine, modeling of the inlet, the nozzle, the modeling that has been done to model the affects of flow distortion, and finally presenting some concluding remarks and future plans.

  1. Ocean carbon and heat variability in an Earth System Model (United States)

    Thomas, J. L.; Waugh, D.; Gnanadesikan, A.


    Ocean carbon and heat content are very important for regulating global climate. Furthermore, due to lack of observations and dependence on parameterizations, there has been little consensus in the modeling community on the magnitude of realistic ocean carbon and heat content variability, particularly in the Southern Ocean. We assess the differences between global oceanic heat and carbon content variability in GFDL ESM2Mc using a 500-year, pre-industrial control simulation. The global carbon and heat content are directly out of phase with each other; however, in the Southern Ocean the heat and carbon content are in phase. The global heat mutli-decadal variability is primarily explained by variability in the tropics and mid-latitudes, while the variability in global carbon content is primarily explained by Southern Ocean variability. In order to test the robustness of this relationship, we use three additional pre-industrial control simulations using different mesoscale mixing parameterizations. Three pre-industrial control simulations are conducted with the along-isopycnal diffusion coefficient (Aredi) set to constant values of 400, 800 (control) and 2400 m2 s-1. These values for Aredi are within the range of parameter settings commonly used in modeling groups. Finally, one pre-industrial control simulation is conducted where the minimum in the Gent-McWilliams parameterization closure scheme (AGM) increased to 600 m2 s-1. We find that the different simulations have very different multi-decadal variability, especially in the Weddell Sea where the characteristics of deep convection are drastically changed. While the temporal frequency and amplitude global heat and carbon content changes significantly, the overall spatial pattern of variability remains unchanged between the simulations.

  2. Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking. (United States)

    Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C


    The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative variables. Many variable-reduction methods are based on so-called predictor-variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the variable-reduction process. Recently, a new predictive-property-ranked variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward variable elimination method applied on the predictive-property-ranked variables. The variable number is first reduced, with constant PLS1 model complexity A, until A variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of variables. In this study for three data sets the utility and effectiveness of six individual and nine combined predictor-variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a predictor variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are

  3. Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses. (United States)

    Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming


    Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

  4. Inter-annual variability of the atmospheric carbon dioxide concentrations as simulated with global terrestrial biosphere models and an atmospheric transport model

    Energy Technology Data Exchange (ETDEWEB)

    Fujita, Daisuke; Saeki, Tazu; Nakazawa, Takakiyo [Tohoku Univ., Sendai (Japan). Center for Atmospheric and Oceanic Studies; Ishizawa, Misa; Maksyutov, Shamil [Inst. for Global Change Research, Yokohama (Japan). Frontier Research System for Global Change; Thornton, Peter E. [National Center for Atmospheric Research, Boulder, CO (United States). Climate and Global Dynamics Div.


    Seasonal and inter-annual variations of atmospheric CO{sub 2} for the period from 1961 to 1997 have been simulated using a global tracer transport model driven by a new version of the Biome BioGeochemical Cycle model (Biome-BGC). Biome-BGC was forced by daily temperature and precipitation from the NCEP reanalysis dataset, and the calculated monthly-averaged CO{sub 2} fluxes were used as input to the global transport model. Results from an inter-comparison with the Carnegie-Ames-Stanford Approach model (CASA) and the Simulation model of Carbon CYCLE in Land Ecosystems (Sim-CYCLE) model are also reported. The phase of the seasonal cycle in the Northern Hemisphere was reproduced generally well by Biome-BGC, although the amplitude was smaller compared to the observations and to the other biosphere models. The CO{sub 2} time series simulated by Biome-BGC were compared to the global CO{sub 2} concentration anomalies from the observations at Mauna Loa and the South Pole. The modeled concentration anomalies matched the phase of the inter-annual variations in the atmospheric CO{sub 2} observations; however, the modeled amplitude was lower than the observed value in several cases. The result suggests that a significant part of the inter-annual variability in the global carbon cycle can be accounted for by the terrestrial biosphere models. Simulations performed with another climate-based model, Sim-CYCLE, produced a larger amplitude of inter-annual variability in atmospheric CO{sub 2}, making the amplitude closer to the observed range, but with a more visible phase mismatch in a number of time periods. This may indicate the need to increase the Biome-BGC model sensitivity to seasonal and inter-annual changes in temperature and precipitation.

  5. Inter-annual variability of the atmospheric carbon dioxide concentrations as simulated with global terrestrial biosphere models and an atmospheric transport model

    International Nuclear Information System (INIS)

    Fujita, Daisuke; Saeki, Tazu; Nakazawa, Takakiyo; Ishizawa, Misa; Maksyutov, Shamil; Thornton, Peter E.


    Seasonal and inter-annual variations of atmospheric CO 2 for the period from 1961 to 1997 have been simulated using a global tracer transport model driven by a new version of the Biome BioGeochemical Cycle model (Biome-BGC). Biome-BGC was forced by daily temperature and precipitation from the NCEP reanalysis dataset, and the calculated monthly-averaged CO 2 fluxes were used as input to the global transport model. Results from an inter-comparison with the Carnegie-Ames-Stanford Approach model (CASA) and the Simulation model of Carbon CYCLE in Land Ecosystems (Sim-CYCLE) model are also reported. The phase of the seasonal cycle in the Northern Hemisphere was reproduced generally well by Biome-BGC, although the amplitude was smaller compared to the observations and to the other biosphere models. The CO 2 time series simulated by Biome-BGC were compared to the global CO 2 concentration anomalies from the observations at Mauna Loa and the South Pole. The modeled concentration anomalies matched the phase of the inter-annual variations in the atmospheric CO 2 observations; however, the modeled amplitude was lower than the observed value in several cases. The result suggests that a significant part of the inter-annual variability in the global carbon cycle can be accounted for by the terrestrial biosphere models. Simulations performed with another climate-based model, Sim-CYCLE, produced a larger amplitude of inter-annual variability in atmospheric CO 2 , making the amplitude closer to the observed range, but with a more visible phase mismatch in a number of time periods. This may indicate the need to increase the Biome-BGC model sensitivity to seasonal and inter-annual changes in temperature and precipitation

  6. Generating synthetic wave climates for coastal modelling: a linear mixed modelling approach (United States)

    Thomas, C.; Lark, R. M.


    (spherical) model, it cuts off at a temporal range. Having fitted the model, multiple realisations were generated; the random effects were simulated by specifying a covariance matrix for the simulated values, with the estimated parameters. The Cholesky factorisation of the covariance matrix was computed and realizations of the random component of the model generated by pre-multiplying a vector of iid standard Gaussian variables by the lower triangular factor. The resulting random variate was added to the mean value computed from the fixed effects, and the result back-transformed to the original scale of the measurement. Realistic simulations result from approach described above. Background exploratory data analysis was undertaken on 20-day sets of 30-minute buoy data, selected from days 5-24 of months January, April, July, October, 2011, to elucidate daily to weekly variations, and to keep numerical analysis tractable computationally. Work remains to be undertaken to develop suitable models for synthetic directional data. We suggest that the general principles of the method will have applications in other geomorphological modelling endeavours requiring time series of stochastically variable environmental parameters.

  7. A Multi-Model Approach for System Diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Bækgaard, Mikkel Ask Buur


    A multi-model approach for system diagnosis is presented in this paper. The relation with fault diagnosis as well as performance validation is considered. The approach is based on testing a number of pre-described models and find which one is the best. It is based on an active approach......,i.e. an auxiliary input to the system is applied. The multi-model approach is applied on a wind turbine system....

  8. Effect of climate variables on cocoa black pod incidence in Sabah using ARIMAX model (United States)

    Ling Sheng Chang, Albert; Ramba, Haya; Mohd. Jaaffar, Ahmad Kamil; Kim Phin, Chong; Chong Mun, Ho


    Cocoa black pod disease is one of the major diseases affecting the cocoa production in Malaysia and also around the world. Studies have shown that the climate variables have influenced the cocoa black pod disease incidence and it is important to quantify the black pod disease variation due to the effect of climate variables. Application of time series analysis especially auto-regressive moving average (ARIMA) model has been widely used in economics study and can be used to quantify the effect of climate variables on black pod incidence to forecast the right time to control the incidence. However, ARIMA model does not capture some turning points in cocoa black pod incidence. In order to improve forecasting performance, other explanatory variables such as climate variables should be included into ARIMA model as ARIMAX model. Therefore, this paper is to study the effect of climate variables on the cocoa black pod disease incidence using ARIMAX model. The findings of the study showed ARIMAX model using MA(1) and relative humidity at lag 7 days, RHt - 7 gave better R square value compared to ARIMA model using MA(1) which could be used to forecast the black pod incidence to assist the farmers determine timely application of fungicide spraying and culture practices to control the black pod incidence.

  9. Ensembles modeling approach to study Climate Change impacts on Wheat (United States)

    Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart


    Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

  10. a modified intervention model for gross domestic product variable

    African Journals Online (AJOL)

    observations on a variable that have been measured at ... assumption that successive values in the data file ... these interventions, one may try to evaluate the effect of ... generalized series by comparing the distinct periods. A ... the process of checking for adequacy of the model based .... As a result, the model's forecast will.

  11. Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

    Directory of Open Access Journals (Sweden)

    Ernest Kissi


    Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

  12. Changes in Southern Hemisphere circulation variability in climate change modelling experiments

    International Nuclear Information System (INIS)

    Grainger, Simon; Frederiksen, Carsten; Zheng, Xiaogu


    Full text: The seasonal mean of a climate variable can be considered as a statistical random variable, consisting of a signal and noise components (Madden 1976). The noise component consists of internal intraseasonal variability, and is not predictable on time-scales of a season or more ahead. The signal consists of slowly varying external and internal variability, and is potentially predictable on seasonal time-scales. The method of Zheng and Frederiksen (2004) has been applied to monthly time series of 500hPa Geopotential height from models submitted to the Coupled Model Intercomparison Project (CMIP3) experiment to obtain covariance matrices of the intraseasonal and slow components of covariability for summer and winter. The Empirical Orthogonal Functions (EOFs) of the intraseasonal and slow covariance matrices for the second half of the 20th century are compared with those observed by Frederiksen and Zheng (2007). The leading EOF in summer and winter for both the intraseasonal and slow components of covariability is the Southern Annular Mode (see, e.g. Kiladis and Mo 1998). This is generally reproduced by the CMIP3 models, although with different variance amounts. The observed secondary intraseasonal covariability modes of wave 4 patterns in summer and wave 3 or blocking in winter are also generally seen in the models, although the actual spatial pattern is different. For the slow covariabilty, the models are less successful in reproducing the two observed ENSO modes, with generally only one of them being represented among the leading EOFs. However, most models reproduce the observed South Pacific wave pattern. The intraseasonal and slow covariances matrices of 500hPa geopotential height under three climate change scenarios are also analysed and compared with those found for the second half of the 20th century. Through aggregating the results from a number of CMIP3 models, a consensus estimate of the changes in Southern Hemisphere variability, and their

  13. Modeling key processes causing climate change and variability

    Energy Technology Data Exchange (ETDEWEB)

    Henriksson, S.


    Greenhouse gas warming, internal climate variability and aerosol climate effects are studied and the importance to understand these key processes and being able to separate their influence on the climate is discussed. Aerosol-climate model ECHAM5-HAM and the COSMOS millennium model consisting of atmospheric, ocean and carbon cycle and land-use models are applied and results compared to measurements. Topics at focus are climate sensitivity, quasiperiodic variability with a period of 50-80 years and variability at other timescales, climate effects due to aerosols over India and climate effects of northern hemisphere mid- and high-latitude volcanic eruptions. The main findings of this work are (1) pointing out the remaining challenges in reducing climate sensitivity uncertainty from observational evidence, (2) estimates for the amplitude of a 50-80 year quasiperiodic oscillation in global mean temperature ranging from 0.03 K to 0.17 K and for its phase progression as well as the synchronising effect of external forcing, (3) identifying a power law shape S(f) {proportional_to} f-{alpha} for the spectrum of global mean temperature with {alpha} {approx} 0.8 between multidecadal and El Nino timescales with a smaller exponent in modelled climate without external forcing, (4) separating aerosol properties and climate effects in India by season and location (5) the more efficient dispersion of secondary sulfate aerosols than primary carbonaceous aerosols in the simulations, (6) an increase in monsoon rainfall in northern India due to aerosol light absorption and a probably larger decrease due to aerosol dimming effects and (7) an estimate of mean maximum cooling of 0.19 K due to larger northern hemisphere mid- and high-latitude volcanic eruptions. The results could be applied or useful in better isolating the human-caused climate change signal, in studying the processes further and in more detail, in decadal climate prediction, in model evaluation and in emission policy

  14. Latent variable models are network models. (United States)

    Molenaar, Peter C M


    Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.


    Directory of Open Access Journals (Sweden)

    Paula Medina Maçaira

    Full Text Available ABSTRACT The Brazilian electricity energy matrix is essentially formed by hydraulic sources which currently account for 70% of the installed capacity. One of the most important characteristics of a generation system with hydro predominance is the strong dependence on the inflow regimes. Nowadays, the Brazilian power sector uses the PAR(p model to generate scenarios for hydrological inflows. This approach does not consider any exogenous information that may affect hydrological regimes. The main objective of this paper is to infer on the influence of climatic events in water inflows as a way to improve the model’s performance. The proposed model is called “causal PAR(p” and considers exogenous variables, such as El Niño and Sunspots, to generate scenarios for some Brazilian reservoirs. The result shows that the error measures decrease approximately 3%. This improvement indicates that the inclusion of climate variables to model and simulate the inflows time series is a valid exercise and should be taken into consideration.

  16. a Latent Variable Path Analysis Model of Secondary Physics Enrollments in New York State. (United States)

    Sobolewski, Stanley John

    The Percentage of Enrollment in Physics (PEP) at the secondary level nationally has been approximately 20% for the past few decades. For a more scientifically literate citizenry as well as specialists to continue scientific research and development, it is desirable that more students enroll in physics. Some of the predictor variables for physics enrollment and physics achievement that have been identified previously includes a community's socioeconomic status, the availability of physics, the sex of the student, the curriculum, as well as teacher and student data. This study isolated and identified predictor variables for PEP of secondary schools in New York. Data gathered by the State Education Department for the 1990-1991 school year was used. The source of this data included surveys completed by teachers and administrators on student characteristics and school facilities. A data analysis similar to that done by Bryant (1974) was conducted to determine if the relationships between a set of predictor variables related to physics enrollment had changed in the past 20 years. Variables which were isolated included: community, facilities, teacher experience, number of type of science courses, school size and school science facilities. When these variables were isolated, latent variable path diagrams were proposed and verified by the Linear Structural Relations computer modeling program (LISREL). These diagrams differed from those developed by Bryant in that there were more manifest variables used which included achievement scores in the form of Regents exam results. Two criterion variables were used, percentage of students enrolled in physics (PEP) and percent of students enrolled passing the Regents physics exam (PPP). The first model treated school and community level variables as exogenous while the second model treated only the community level variables as exogenous. The goodness of fit indices for the models was 0.77 for the first model and 0.83 for the second

  17. Mechanistic Physiologically Based Pharmacokinetic (PBPK) Model of the Heart Accounting for Inter-Individual Variability: Development and Performance Verification. (United States)

    Tylutki, Zofia; Mendyk, Aleksander; Polak, Sebastian


    Modern model-based approaches to cardiac safety and efficacy assessment require accurate drug concentration-effect relationship establishment. Thus, knowledge of the active concentration of drugs in heart tissue is desirable along with inter-subject variability influence estimation. To that end, we developed a mechanistic physiologically based pharmacokinetic model of the heart. The models were described with literature-derived parameters and written in R, v.3.4.0. Five parameters were estimated. The model was fitted to amitriptyline and nortriptyline concentrations after an intravenous infusion of amitriptyline. The cardiac model consisted of 5 compartments representing the pericardial fluid, heart extracellular water, and epicardial intracellular, midmyocardial intracellular, and endocardial intracellular fluids. Drug cardiac metabolism, passive diffusion, active efflux, and uptake were included in the model as mechanisms involved in the drug disposition within the heart. The model accounted for inter-individual variability. The estimates of optimized parameters were within physiological ranges. The model performance was verified by simulating 5 clinical studies of amitriptyline intravenous infusion, and the simulated pharmacokinetic profiles agreed with clinical data. The results support the model feasibility. The proposed structure can be tested with the goal of improving the patient-specific model-based cardiac safety assessment and offers a framework for predicting cardiac concentrations of various xenobiotics. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  18. Understanding Solar Cycle Variability

    Energy Technology Data Exchange (ETDEWEB)

    Cameron, R. H.; Schüssler, M., E-mail: [Max-Planck-Institut für Sonnensystemforschung, Justus-von-Liebig-Weg 3, D-37077 Göttingen (Germany)


    The level of solar magnetic activity, as exemplified by the number of sunspots and by energetic events in the corona, varies on a wide range of timescales. Most prominent is the 11-year solar cycle, which is significantly modulated on longer timescales. Drawing from dynamo theory, together with the empirical results of past solar activity and similar phenomena for solar-like stars, we show that the variability of the solar cycle can be essentially understood in terms of a weakly nonlinear limit cycle affected by random noise. In contrast to ad hoc “toy models” for the solar cycle, this leads to a generic normal-form model, whose parameters are all constrained by observations. The model reproduces the characteristics of the variable solar activity on timescales between decades and millennia, including the occurrence and statistics of extended periods of very low activity (grand minima). Comparison with results obtained with a Babcock–Leighton-type dynamo model confirm the validity of the normal-mode approach.

  19. Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach. (United States)

    Mohammadi, Tayeb; Kheiri, Soleiman; Sedehi, Morteza


    Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral. The data was analyzed using the Bayesian approach applying noninformative priors at the presence and absence of covariates. Estimating the parameters of the model, that is, correlation, zero-inflation parameter, and regression coefficients, was done through MCMC simulation. Eventually double-Poisson model, bivariate Poisson model, and bivariate zero-inflated Poisson model were fitted on the data and were compared using the deviance information criteria (DIC). The results showed that the bivariate zero-inflated Poisson regression model fitted the data better than the other models.

  20. An analytical approach to separate climate and human contributions to basin streamflow variability (United States)

    Li, Changbin; Wang, Liuming; Wanrui, Wang; Qi, Jiaguo; Linshan, Yang; Zhang, Yuan; Lei, Wu; Cui, Xia; Wang, Peng


    Climate variability and anthropogenic regulations are two interwoven factors in the ecohydrologic system across large basins. Understanding the roles that these two factors play under various hydrologic conditions is of great significance for basin hydrology and sustainable water utilization. In this study, we present an analytical approach based on coupling water balance method and Budyko hypothesis to derive effectiveness coefficients (ECs) of climate change, as a way to disentangle contributions of it and human activities to the variability of river discharges under different hydro-transitional situations. The climate dominated streamflow change (ΔQc) by EC approach was compared with those deduced by the elasticity method and sensitivity index. The results suggest that the EC approach is valid and applicable for hydrologic study at large basin scale. Analyses of various scenarios revealed that contributions of climate change and human activities to river discharge variation differed among the regions of the study area. Over the past several decades, climate change dominated hydro-transitions from dry to wet, while human activities played key roles in the reduction of streamflow during wet to dry periods. Remarkable decline of discharge in upstream was mainly due to human interventions, although climate contributed more to runoff increasing during dry periods in the semi-arid downstream. Induced effectiveness on streamflow changes indicated a contribution ratio of 49% for climate and 51% for human activities at the basin scale from 1956 to 2015. The mathematic derivation based simple approach, together with the case example of temporal segmentation and spatial zoning, could help people understand variation of river discharge with more details at a large basin scale under the background of climate change and human regulations.

  1. Variability of concrete properties: experimental characterisation and probabilistic modelling for calcium leaching

    International Nuclear Information System (INIS)

    De Larrard, Th.


    Evaluating structures durability requires taking into account the variability of material properties. The thesis has two main aspects: on the one hand, an experimental campaign aimed at quantifying the variability of many indicators of concrete behaviour; on the other hand, a simple numerical model for calcium leaching is developed in order to implement probabilistic methods so as to estimate the lifetime of structures such as those related to radioactive waste disposal. The experimental campaign consisted in following up two real building sites, and quantifying the variability of these indicators, studying their correlation, and characterising the random fields variability for the considered variables (especially the correlation length). To draw any conclusion from the accelerated leaching tests with ammonium nitrate by overcoming the effects of temperature, an inverse analysis tool based on the theory of artificial neural networks was developed. Simple numerical tools are presented to investigate the propagation of variability in durability issues, quantify the influence of this variability on the lifespan of structures and explain the variability of the input parameters of the numerical model and the physical measurable quantities of the material. (author)

  2. The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities (United States)

    Bauer, Daniel J.; Curran, Patrick J.


    Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model,…

  3. A Novel Approach to Implement Takagi-Sugeno Fuzzy Models. (United States)

    Chang, Chia-Wen; Tao, Chin-Wang


    This paper proposes new algorithms based on the fuzzy c-regressing model algorithm for Takagi-Sugeno (T-S) fuzzy modeling of the complex nonlinear systems. A fuzzy c-regression state model (FCRSM) algorithm is a T-S fuzzy model in which the functional antecedent and the state-space-model-type consequent are considered with the available input-output data. The antecedent and consequent forms of the proposed FCRSM consists mainly of two advantages: one is that the FCRSM has low computation load due to only one input variable is considered in the antecedent part; another is that the unknown system can be modeled to not only the polynomial form but also the state-space form. Moreover, the FCRSM can be extended to FCRSM-ND and FCRSM-Free algorithms. An algorithm FCRSM-ND is presented to find the T-S fuzzy state-space model of the nonlinear system when the input-output data cannot be precollected and an assumed effective controller is available. In the practical applications, the mathematical model of controller may be hard to be obtained. In this case, an online tuning algorithm, FCRSM-FREE, is designed such that the parameters of a T-S fuzzy controller and the T-S fuzzy state model of an unknown system can be online tuned simultaneously. Four numerical simulations are given to demonstrate the effectiveness of the proposed approach.

  4. Cognitive Preconditions of Early Reading and Spelling: A Latent-Variable Approach with Longitudinal Data (United States)

    Preßler, Anna-Lena; Könen, Tanja; Hasselhorn, Marcus; Krajewski, Kristin


    The aim of the present study was to empirically disentangle the interdependencies of the impact of nonverbal intelligence, working memory capacities, and phonological processing skills on early reading decoding and spelling within a latent variable approach. In a sample of 127 children, these cognitive preconditions were assessed before the onset…

  5. Hospital distribution in a metropolitan city: assessment by a geographical information system grid modelling approach

    Directory of Open Access Journals (Sweden)

    Kwang-Soo Lee


    Full Text Available Grid models were used to assess urban hospital distribution in Seoul, the capital of South Korea. A geographical information system (GIS based analytical model was developed and applied to assess the situation in a metropolitan area with a population exceeding 10 million. Secondary data for this analysis were obtained from multiple sources: the Korean Statistical Information Service, the Korean Hospital Association and the Statistical Geographical Information System. A grid of cells measuring 1 × 1 km was superimposed on the city map and a set of variables related to population, economy, mobility and housing were identified and measured for each cell. Socio-demographic variables were included to reflect the characteristics of each area. Analytical models were then developed using GIS software with the number of hospitals as the dependent variable. Applying multiple linear regression and geographically weighted regression models, three factors (highway and major arterial road areas; number of subway entrances; and row house areas were statistically significant in explaining the variance of hospital distribution for each cell. The overall results show that GIS is a useful tool for analysing and understanding location strategies. This approach appears a useful source of information for decision-makers concerned with the distribution of hospitals and other health care centres in a city.

  6. The Propagation of Movement Variability in Time: A Methodological Approach for Discrete Movements with Multiple Degrees of Freedom (United States)

    Krüger, Melanie; Straube, Andreas; Eggert, Thomas


    In recent years, theory-building in motor neuroscience and our understanding of the synergistic control of the redundant human motor system has significantly profited from the emergence of a range of different mathematical approaches to analyze the structure of movement variability. Approaches such as the Uncontrolled Manifold method or the Noise-Tolerance-Covariance decomposition method allow to detect and interpret changes in movement coordination due to e.g., learning, external task constraints or disease, by analyzing the structure of within-subject, inter-trial movement variability. Whereas, for cyclical movements (e.g., locomotion), mathematical approaches exist to investigate the propagation of movement variability in time (e.g., time series analysis), similar approaches are missing for discrete, goal-directed movements, such as reaching. Here, we propose canonical correlation analysis as a suitable method to analyze the propagation of within-subject variability across different time points during the execution of discrete movements. While similar analyses h