Eutrophication Modeling Using Variable Chlorophyll Approach
International Nuclear Information System (INIS)
Abdolabadi, H.; Sarang, A.; Ardestani, M.; Mahjoobi, E.
2016-01-01
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.
Bayesian approach to errors-in-variables in regression models
Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad
2017-05-01
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.
A new approach for modelling variability in residential construction projects
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Mehrdad Arashpour
2013-06-01
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.
A new approach for modelling variability in residential construction projects
Directory of Open Access Journals (Sweden)
Mehrdad Arashpour
2013-06-01
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.
How to get rid of W: a latent variables approach to modelling spatially lagged variables
Folmer, H.; Oud, J.
2008-01-01
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
How to get rid of W : a latent variables approach to modelling spatially lagged variables
Folmer, Henk; Oud, Johan
2008-01-01
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
A Novel Approach to model EPIC variable background
Marelli, M.; De Luca, A.; Salvetti, D.; Belfiore, A.
2017-10-01
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.
A Variable Flow Modelling Approach To Military End Strength Planning
2016-12-01
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 these...is 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
Deng, Chenhui; Plan, Elodie L; Karlsson, Mats O
2016-06-01
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.
The Matrix model, a driven state variables approach to non-equilibrium thermodynamics
Jongschaap, R.J.J.
2001-01-01
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
Pek, Jolynn; Losardo, Diane; Bauer, Daniel J.
2011-01-01
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…
DEFF Research Database (Denmark)
Panduro, Toke Emil; Thorsen, Bo Jellesmark
2014-01-01
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...
Clark, Renee M; Besterfield-Sacre, Mary E
2009-03-01
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.
Optimal speech motor control and token-to-token variability: a Bayesian modeling approach.
Patri, Jean-François; Diard, Julien; Perrier, Pascal
2015-12-01
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.
Directory of Open Access Journals (Sweden)
Dirk Temme
2008-12-01
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.
Novel Harmonic Regularization Approach for Variable Selection in Cox’s Proportional Hazards Model
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Ge-Jin Chu
2014-01-01
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.
Study The role of latent variables in lost working days by Structural Equation Modeling Approach
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Meysam Heydari
2016-12-01
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.
Evans, Wiley; Mathis, Jeremy T.; Winsor, Peter; Statscewich, Hank; Whitledge, Terry E.
2013-01-01
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.
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Kori Blankenship
2015-04-01
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.
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H. Bassi
2017-04-01
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.
Thomas E. Dilts; Peter J. Weisberg; Camie M. Dencker; Jeanne C. Chambers
2015-01-01
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...
Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe
2017-09-01
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.
Energy Technology Data Exchange (ETDEWEB)
Yang, Jinzhong, E-mail: jyang4@mdanderson.org [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)
2014-05-01
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
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
Directory of Open Access Journals (Sweden)
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
Christiansen, Bo
2015-04-01
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.
The long-term variability of cosmic ray protons in the heliosphere: A modeling approach
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M.S. Potgieter
2013-05-01
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.
Hu, Yong; Olguin, Hernan; Gutheil, Eva
2017-05-01
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
International Nuclear Information System (INIS)
Morice, Erwan
2014-01-01
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)
Pervin, Lia; Islam, Md Saiful
2015-02-01
The aim of this study was to develop a system dynamics model for computation of yields and to investigate the dependency of yields on some major climatic parameters, i.e. temperature and rainfall, for Beta vulgaris subsp. (sugar beet crops) under future climate change scenarios. A system dynamics model was developed which takes account of the effects of rainfall and temperature on sugar beet yields under limited irrigation conditions. A relationship was also developed between the seasonal evapotranspiration and seasonal growing degree days for sugar beet crops. The proposed model was set to run for the present time period of 1993-2012 and for the future period 2013-2040 for Lethbridge region (Alberta, Canada). The model provides sugar beet yields on a yearly basis which are comparable to the present field data. It was found that the future average yield will be increased at about 14% with respect to the present average yield. The proposed model can help to improve the understanding of soil water conditions and irrigation water requirements of an area under certain climatic conditions and can be used for future prediction of yields for any crops in any region (with the required information to be provided). The developed system dynamics model can be used as a supporting tool for decision making, for improvement of agricultural management practice of any region. © 2014 Society of Chemical Industry.
Regression models for categorical, count, and related variables an applied approach
Hoffmann, John P
2016-01-01
Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes--all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapte...
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Běták Emil
2014-04-01
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.
Henseler, Jorg; Chin, Wynne W.
2010-01-01
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…
Variable importance in latent variable regression models
Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.
2014-01-01
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
Eldridge, Ronald C; Flanders, W Dana; Bostick, Roberd M; Fedirko, Veronika; Gross, Myron; Thyagarajan, Bharat; Goodman, Michael
2017-09-01
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.
Directory of Open Access Journals (Sweden)
E. Pishbahar
2015-05-01
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.
Madonna, Erica; Ginsbourger, David; Martius, Olivia
2018-05-01
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.
International Nuclear Information System (INIS)
Ahmed, Fayez Shakil; Laghrouche, Salah; Mehmood, Adeel; El Bagdouri, Mohammed
2014-01-01
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
Pleban, J. R.; Mackay, D. S.; Aston, T.; Ewers, B. E.; Wienig, C.
2013-12-01
Quantifying the drought tolerance of crop species and genotypes is essential in order to predict how water stress may impact agricultural productivity. As climate models predict an increase in both frequency and severity of drought corresponding plant hydraulic and biochemical models are needed to accurately predict crop drought tolerance. Drought can result in cavitation of xylem conduits and related loss of plant hydraulic conductivity. This study tested the hypothesis that a model incorporating a plants vulnerability to cavitation would best assess drought tolerance in Brassica rapa. Four Brassica genotypes were subjected to drought conditions at a field site in Laramie, WY. Concurrent leaf gas exchange, volumetric soil moisture content and xylem pressure measurements were made during the drought period. Three models were used to access genotype specific drought tolerance. All 3 models rely on the Farquhar biochemical/biophysical model of leaf level photosynthesis, which is integrated into the Terrestrial Regional Ecosystem Exchange Simulator (TREES). The models differ in how TREES applies the environmental driving data and plant physiological mechanisms; specifically how water availability at the site of photosynthesis is derived. Model 1 established leaf water availability from a modeled soil moisture content; Model 2 input soil moisture measurements directly to establish leaf water availability; Model 3 incorporated the Sperry soil-plant transport model, which calculates flows and pressure along the soil-plant water transport pathway to establish leaf water availability. This third model incorporated measured xylem pressures thus constraining leaf water availability via genotype specific vulnerability curves. A multi-model intercomparison was made using a Bayesian approach, which assessed the interaction between uncertainty in model results and data. The three models were further evaluated by assessing model accuracy and complexity via deviance information
Brunwasser, Steven M; Gebretsadik, Tebeb; Gold, Diane R; Turi, Kedir N; Stone, Cosby A; Datta, Soma; Gern, James E; Hartert, Tina V
2018-01-01
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
Rousseaux, C. S.; Gregg, W. W.
2012-01-01
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.
Analysis on inter-annual variability of CO2 exchange in Arctic tundra: a model-data approach
López Blanco, E.; Lund, M.; Christensen, T. R.; Smallman, T. L.; Slevin, D.; Westergaard-Nielsen, A.; Tamstorf, M. P.; Williams, M.
2017-12-01
Arctic ecosystems are exposed to rapid changes triggered by the climate variability, thus there is a growing concern about how the carbon (C) exchange balance will respond to climate change. There is a lack of knowledge about the mechanisms that drive the interactions between photosynthesis and ecological respiration with changes in C stocks in the Arctic tundra across full annual cycles. The reduction of uncertainties can be addressed through process-based modelling efforts. Here, we report the independent predictions of net ecosystem exchange (NEE), gross primary production (GPP) and ecosystem respiration (Reco) calculated from the soil-plant-atmosphere (SPA) model across eight years. The model products are validated with observational data obtained from the Greenland Ecosystem Monitoring (GEM) program in West Greenland tundra (64° N). Overall, the model results explain 71%, 73% and 51% of the variance in NEE, GPP and Reco respectively using data on meteorology and local vegetation and soil structure. The estimated leaf area index (LAI) is able to explain 80% of the plant greenness variation, which was used as a plant phenology proxy. The full annual cumulated NEE during the 2008-2015 period was -0.13 g C m-2 on average (range -30.6 to 34.1 g C m-2), while GPP was -214.6 g C m-2 (-126.2 to -332.8 g C m-2) and Reco was 214.4 g C m-2 (213.9 to 302.2 g C m-2). We found that the model supports the main finding from our previous analysis on flux responses to meteorological variations and biological disturbance. Here, large inter-annual variations in GPP and Reco are also compensatory, and so NEE remains stable across climatically diverse snow-free seasons. Further, we note evidence that leaf maintenance and root growth respiration are highly correlated with GPP (R2 = 0.92 and 0.83, p < 0.001), concluding that these relations likely drive the insensitivity of NEE. Interestingly, the model quantifies the contribution of the larvae outbreak occurred in 2011 in about 27
Illicit drug use and abuse/dependence: modeling of two-stage variables using the CCC approach.
Agrawal, A; Neale, M C; Jacobson, K C; Prescott, C A; Kendler, K S
2005-06-01
Drug use and abuse/dependence are stages of a complex drug habit. Most genetically informative models that are fit to twin data examine drug use and abuse/dependence independent of each other. This poses an interesting question: for a multistage process, how can we partition the factors influencing each stage specifically from the factors that are common to both stages? We used a causal-common-contingent (CCC) model to partition the common and specific influences on drug use and abuse/dependence. Data on use and abuse/dependence of cannabis, cocaine, sedatives, stimulants and any illicit drug was obtained from male and female twin pairs. CCC models were tested individually for each sex and in a sex-equal model. Our results suggest that there is evidence for additive genetic, shared environmental and unique environmental influences that are common to illicit drug use and abuse/dependence. Furthermore, we found substantial evidence for factors that were specific to abuse/dependence. Finally, sexes could be equated for all illicit drugs. The findings of this study emphasize the need for models that can partition the sources of individual differences into common and stage-specific influences.
Fernández-Antelo, Inmaculada; Cuadrado-Gordillo, Isabel
2018-04-01
The controversies that exist regarding the delimitation of the cyberbullying construct demonstrate the need for further research focused on determining the criteria that shape the structure of the perceptions that adolescents have of this phenomenon and on seeking explanations of this behavior. The objectives of this study were to (a) construct possible explanatory models of the perception of cyberbullying from identifying and relating the criteria that form this construct and (b) analyze the influence of previous cyber victimization and cyber aggression experiences in the construction of explanatory models of the perception of cyberbullying. The sample consisted of 2,148 adolescents (49.1% girls; SD = 0.5) aged from 12 to 16 years ( M = 13.9 years; SD = 1.2). The results have shown that previous cyber victimization and cyber aggression experiences lead to major differences in the explanatory models to interpret cyber-abusive behavior as cyberbullying episodes, or as social relationship mechanisms, or as a revenge reaction. We note that the aggressors' explanatory model is based primarily on a strong reciprocal relationship between the imbalance of power and intentionality, that it functions as a link promoting indirect causal relationships of the anonymity and repetition factors with the cyberbullying construct. The victims' perceptual structure is based on three criteria-imbalance of power, intentionality, and publicity-where the key factor in this structure is the intention to harm. These results allow to design more effective measures of prevention and intervention closely tailored to addressing directly the factors that are considered to be predictors of risk.
Coppola, Antonio; Comegna, Alessandro; Dragonetti, Giovanna; Lamaddalena, Nicola; Zdruli, Pandi
2013-04-01
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
International Nuclear Information System (INIS)
Menouar, Salah; Maamache, Mustapha; Choi, Jeong Ryeol
2010-01-01
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.
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.
2017-04-01
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.
Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao
2014-10-07
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: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.
Gait variability: methods, modeling and meaning
Directory of Open Access Journals (Sweden)
Hausdorff Jeffrey M
2005-07-01
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.
Patsahan, O. V.; Patsahan, T. M.; Holovko, M. F.
2018-02-01
We develop a theory based on the method of collective variables to study the vapor-liquid equilibrium of asymmetric ionic fluids confined in a disordered porous matrix. The approach allows us to formulate the perturbation theory using an extension of the scaled particle theory for a description of a reference system presented as a two-component hard-sphere fluid confined in a hard-sphere matrix. Treating an ionic fluid as a size- and charge-asymmetric primitive model (PM) we derive an explicit expression for the relevant chemical potential of a confined ionic system which takes into account the third-order correlations between ions. Using this expression, the phase diagrams for a size-asymmetric PM are calculated for different matrix porosities as well as for different sizes of matrix and fluid particles. It is observed that general trends of the coexistence curves with the matrix porosity are similar to those of simple fluids under disordered confinement, i.e., the coexistence region gets narrower with a decrease of porosity and, simultaneously, the reduced critical temperature Tc* and the critical density ρi,c * become lower. At the same time, our results suggest that an increase in size asymmetry of oppositely charged ions considerably affects the vapor-liquid diagrams leading to a faster decrease of Tc* and ρi,c * and even to a disappearance of the phase transition, especially for the case of small matrix particles.
Control approach development for variable recruitment artificial muscles
Jenkins, Tyler E.; Chapman, Edward M.; Bryant, Matthew
2016-04-01
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.
Schwartzman, Benjamin C.; Wood, Jeffrey J.; Kapp, Steven K.
2016-01-01
The present study aimed to: determine the extent to which the five factor model of personality (FFM) accounts for variability in autism spectrum disorder (ASD) symptomatology in adults, examine differences in average FFM personality traits of adults with and without ASD and identify distinct behavioral phenotypes within ASD. Adults (N = 828;…
Inverse Ising problem in continuous time: A latent variable approach
Donner, Christian; Opper, Manfred
2017-12-01
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.
Variable selection in Logistic regression model with genetic algorithm.
Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi
2018-02-01
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.
Engelhardt, Benjamin; Kschischo, Maik; Fröhlich, Holger
2017-06-01
Ordinary differential equations (ODEs) are a popular approach to quantitatively model molecular networks based on biological knowledge. However, such knowledge is typically restricted. Wrongly modelled biological mechanisms as well as relevant external influence factors that are not included into the model are likely to manifest in major discrepancies between model predictions and experimental data. Finding the exact reasons for such observed discrepancies can be quite challenging in practice. In order to address this issue, we suggest a Bayesian approach to estimate hidden influences in ODE-based models. The method can distinguish between exogenous and endogenous hidden influences. Thus, we can detect wrongly specified as well as missed molecular interactions in the model. We demonstrate the performance of our Bayesian dynamic elastic-net with several ordinary differential equation models from the literature, such as human JAK-STAT signalling, information processing at the erythropoietin receptor, isomerization of liquid α -Pinene, G protein cycling in yeast and UV-B triggered signalling in plants. Moreover, we investigate a set of commonly known network motifs and a gene-regulatory network. Altogether our method supports the modeller in an algorithmic manner to identify possible sources of errors in ODE-based models on the basis of experimental data. © 2017 The Author(s).
Gene variants associated with antisocial behaviour: a latent variable approach.
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
2013-10-01
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
Estimating variability in functional images using a synthetic resampling approach
International Nuclear Information System (INIS)
Maitra, R.; O'Sullivan, F.
1996-01-01
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
Modeling the Variable Heliopause Location
Hensley, Kerry
2018-03-01
In 2012, Voyager 1 zipped across the heliopause. Five and a half years later, Voyager 2 still hasnt followed its twin into interstellar space. Can models of the heliopause location help determine why?How Far to the Heliopause?Artists conception of the heliosphere with the important structures and boundaries labeled. [NASA/Goddard/Walt Feimer]As our solar system travels through the galaxy, the solar outflow pushes against the surrounding interstellar medium, forming a bubble called the heliosphere. The edge of this bubble, the heliopause, is the outermost boundary of our solar system, where the solar wind and the interstellar medium meet. Since the solar outflow is highly variable, the heliopause is constantly moving with the motion driven by changes inthe Sun.NASAs twin Voyager spacecraft were poisedto cross the heliopause after completingtheir tour of the outer planets in the 1980s. In 2012, Voyager 1 registered a sharp increase in the density of interstellar particles, indicating that the spacecraft had passed out of the heliosphere and into the interstellar medium. The slower-moving Voyager 2 was set to pierce the heliopause along a different trajectory, but so far no measurements have shown that the spacecraft has bid farewell to oursolar system.In a recent study, ateam of scientists led by Haruichi Washimi (Kyushu University, Japan and CSPAR, University of Alabama-Huntsville) argues that models of the heliosphere can help explain this behavior. Because the heliopause location is controlled by factors that vary on many spatial and temporal scales, Washimiand collaborators turn to three-dimensional, time-dependent magnetohydrodynamics simulations of the heliosphere. In particular, they investigate how the position of the heliopause along the trajectories of Voyager 1 and Voyager 2 changes over time.Modeled location of the heliopause along the paths of Voyagers 1 (blue) and 2 (orange). Click for a closer look. The red star indicates the location at which Voyager
A variable resolution right TIN approach for gridded oceanographic data
Marks, David; Elmore, Paul; Blain, Cheryl Ann; Bourgeois, Brian; Petry, Frederick; Ferrini, Vicki
2017-12-01
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.
Handbook of latent variable and related models
Lee, Sik-Yum
2011-01-01
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.
Gene Variants Associated with Antisocial Behaviour: A Latent Variable Approach
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.
2013-01-01
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…
Generalized latent variable modeling multilevel, longitudinal, and structural equation models
Skrondal, Anders; Rabe-Hesketh, Sophia
2004-01-01
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.
Spatial variability and parametric uncertainty in performance assessment models
International Nuclear Information System (INIS)
Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo
2011-01-01
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)
Schwartzman, Benjamin C; Wood, Jeffrey J; Kapp, Steven K
2016-01-01
The present study aimed to: determine the extent to which the five factor model of personality (FFM) accounts for variability in autism spectrum disorder (ASD) symptomatology in adults, examine differences in average FFM personality traits of adults with and without ASD and identify distinct behavioral phenotypes within ASD. Adults (N = 828; nASD = 364) completed an online survey with an autism trait questionnaire and an FFM personality questionnaire. FFM facets accounted for 70 % of variance in autism trait scores. Neuroticism positively correlated with autism symptom severity, while extraversion, openness to experience, agreeableness, and conscientiousness negatively correlated with autism symptom severity. Four FFM subtypes emerged within adults with ASD, with three subtypes characterized by high neuroticism and none characterized by lower-than-average neuroticism.
A Core Language for Separate Variability Modeling
DEFF Research Database (Denmark)
Iosif-Lazăr, Alexandru Florin; Wasowski, Andrzej; Schaefer, Ina
2014-01-01
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...
Latent variable models are network models.
Molenaar, Peter C M
2010-06-01
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.
Evaporator modeling - A hybrid approach
International Nuclear Information System (INIS)
Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun
2009-01-01
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
Variable phase approach to potential scattering
Calogero, Francesco
1967-01-01
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank mat
International Nuclear Information System (INIS)
Chiu, Weihsueh A.; Okino, Miles S.; Evans, Marina V.
2009-01-01
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.
Error-in-variables models in calibration
Lira, I.; Grientschnig, D.
2017-12-01
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.
The productivity of mental health care: an instrumental variable approach.
Lu, Mingshan
1999-06-01
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
International Nuclear Information System (INIS)
Shipler, D.B.; Napier, B.A.
1992-07-01
This report details the conceptual approaches to be used in calculating radiation doses to individuals throughout the various periods of operations at the Hanford Site. The report considers the major environmental transport pathways--atmospheric, surface water, and ground water--and projects and appropriate modeling technique for each. The modeling sequence chosen for each pathway depends on the available data on doses, the degree of confidence justified by such existing data, and the level of sophistication deemed appropriate for the particular pathway and time period being considered
In search of control variables : A systems approach
Dalenoort, GJ
1997-01-01
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
Galactic models with variable spiral structure
International Nuclear Information System (INIS)
James, R.A.; Sellwood, J.A.
1978-01-01
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)
Gaussian Mixture Model of Heart Rate Variability
Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario
2012-01-01
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
Confounding of three binary-variables counterfactual model
Liu, Jingwei; Hu, Shuang
2011-01-01
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...
Efficient Business Service Consumption by Customization with Variability Modelling
Directory of Open Access Journals (Sweden)
Michael Stollberg
2010-07-01
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.
Variable selection and model choice in geoadditive regression models.
Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard
2009-06-01
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.
STATUS SOSIAL EKONOMI DAN FERTILITAS: A Latent Variable Approach
Directory of Open Access Journals (Sweden)
Suandi -
2012-11-01
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.
Natural climate variability in a coupled model
International Nuclear Information System (INIS)
Zebiak, S.E.; Cane, M.A.
1990-01-01
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
A Model for Positively Correlated Count Variables
DEFF Research Database (Denmark)
Møller, Jesper; Rubak, Ege Holger
2010-01-01
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....
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
2000-01-01
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
Explicit estimating equations for semiparametric generalized linear latent variable models
Ma, Yanyuan
2010-07-05
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.
MODELS OF TECHNOLOGY ADOPTION: AN INTEGRATIVE APPROACH
Directory of Open Access Journals (Sweden)
Andrei OGREZEANU
2015-06-01
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.
Two-step variable selection in quantile regression models
Directory of Open Access Journals (Sweden)
FAN Yali
2015-06-01
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.
Thermodynamic approach to the inelastic state variable theories
International Nuclear Information System (INIS)
Dashner, P.A.
1978-06-01
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
Variable sound speed in interacting dark energy models
Linton, Mark S.; Pourtsidou, Alkistis; Crittenden, Robert; Maartens, Roy
2018-04-01
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.
Integrated variable projection approach (IVAPA) for parallel magnetic resonance imaging.
Zhang, Qiao; Sheng, Jinhua
2012-10-01
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.
Preliminary Multi-Variable Parametric Cost Model for Space Telescopes
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
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.
Modeling variability in porescale multiphase flow experiments
Ling, Bowen; Bao, Jie; Oostrom, Mart; Battiato, Ilenia; Tartakovsky, Alexandre M.
2017-07-01
Microfluidic devices and porescale numerical models are commonly used to study multiphase flow in biological, geological, and engineered porous materials. In this work, we perform a set of drainage and imbibition experiments in six identical microfluidic cells to study the reproducibility of multiphase flow experiments. We observe significant variations in the experimental results, which are smaller during the drainage stage and larger during the imbibition stage. We demonstrate that these variations are due to sub-porescale geometry differences in microcells (because of manufacturing defects) and variations in the boundary condition (i.e., fluctuations in the injection rate inherent to syringe pumps). Computational simulations are conducted using commercial software STAR-CCM+, both with constant and randomly varying injection rates. Stochastic simulations are able to capture variability in the experiments associated with the varying pump injection rate.
Ensembling Variable Selectors by Stability Selection for the Cox Model
Directory of Open Access Journals (Sweden)
Qing-Yan Yin
2017-01-01
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.
Hidden Markov latent variable models with multivariate longitudinal data.
Song, Xinyuan; Xia, Yemao; Zhu, Hongtu
2017-03-01
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.
Material Modelling - Composite Approach
DEFF Research Database (Denmark)
Nielsen, Lauge Fuglsang
1997-01-01
is successfully justified comparing predicted results with experimental data obtained in the HETEK-project on creep, relaxation, and shrinkage of very young concretes cured at a temperature of T = 20^o C and a relative humidity of RH = 100%. The model is also justified comparing predicted creep, shrinkage......, and internal stresses caused by drying shrinkage with experimental results reported in the literature on the mechanical behavior of mature concretes. It is then concluded that the model presented applied in general with respect to age at loading.From a stress analysis point of view the most important finding...... in this report is that cement paste and concrete behave practically as linear-viscoelastic materials from an age of approximately 10 hours. This is a significant age extension relative to earlier studies in the literature where linear-viscoelastic behavior is only demonstrated from ages of a few days. Thus...
Characteristics of quantum open systems: free random variables approach
International Nuclear Information System (INIS)
Gudowska-Nowak, E.; Papp, G.; Brickmann, J.
1998-01-01
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)
Efficient family-based model checking via variability abstractions
DEFF Research Database (Denmark)
Dimovski, Aleksandar; Al-Sibahi, Ahmad Salim; Brabrand, Claus
2016-01-01
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...
S-variable approach to LMI-based robust control
Ebihara, Yoshio; Arzelier, Denis
2015-01-01
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...
Quantifying uncertainty, variability and likelihood for ordinary differential equation models
LENUS (Irish Health Repository)
Weisse, Andrea Y
2010-10-28
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.
Bayesian modeling of measurement error in predictor variables
Fox, Gerardus J.A.; Glas, Cornelis A.W.
2003-01-01
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
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
2015-01-01
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...
A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses
Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini
2012-01-01
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…
Drag coefficient Variability and Thermospheric models
Moe, Kenneth
Satellite drag coefficients depend upon a variety of factors: The shape of the satellite, its altitude, the eccentricity of its orbit, the temperature and mean molecular mass of the ambient atmosphere, and the time in the sunspot cycle. At altitudes where the mean free path of the atmospheric molecules is large compared to the dimensions of the satellite, the drag coefficients can be determined from the theory of free-molecule flow. The dependence on altitude is caused by the concentration of atomic oxygen which plays an important role by its ability to adsorb on the satellite surface and thereby affect the energy loss of molecules striking the surface. The eccentricity of the orbit determines the satellite velocity at perigee, and therefore the energy of the incident molecules relative to the energy of adsorption of atomic oxygen atoms on the surface. The temperature of the ambient atmosphere determines the extent to which the random thermal motion of the molecules influences the momentum transfer to the satellite. The time in the sunspot cycle affects the ambient temperature as well as the concentration of atomic oxygen at a particular altitude. Tables and graphs will be used to illustrate the variability of drag coefficients. Before there were any measurements of gas-surface interactions in orbit, Izakov and Cook independently made an excellent estimate that the drag coefficient of satellites of compact shape would be 2.2. That numerical value, independent of altitude, was used by Jacchia to construct his model from the early measurements of satellite drag. Consequently, there is an altitude dependent bias in the model. From the sparce orbital experiments that have been done, we know that the molecules which strike satellite surfaces rebound in a diffuse angular distribution with an energy loss given by the energy accommodation coefficient. As more evidence accumulates on the energy loss, more realistic drag coefficients are being calculated. These improved drag
Variability in personality expression across contexts: a social network approach.
Clifton, Allan
2014-04-01
The current research investigated how the contextual expression of personality differs across interpersonal relationships. Two related studies were conducted with college samples (Study 1: N = 52, 38 female; Study 2: N = 111, 72 female). Participants in each study completed a five-factor measure of personality and constructed a social network detailing their 30 most important relationships. Participants used a brief Five-Factor Model scale to rate their personality as they experience it when with each person in their social network. Multiple informants selected from each social network then rated the target participant's personality (Study 1: N = 227, Study 2: N = 777). Contextual personality ratings demonstrated incremental validity beyond standard global self-report in predicting specific informants' perceptions. Variability in these contextualized personality ratings was predicted by the position of the other individuals within the social network. Across both studies, participants reported being more extraverted and neurotic, and less conscientious, with more central members of their social networks. Dyadic social network-based assessments of personality provide incremental validity in understanding personality, revealing dynamic patterns of personality variability unobservable with standard assessment techniques. © 2013 Wiley Periodicals, Inc.
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
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.
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
2001-01-01
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.
Generalized Network Psychometrics : Combining Network and Latent Variable Models
Epskamp, S.; Rhemtulla, M.; Borsboom, D.
2017-01-01
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
Predictor variable resolution governs modeled soil types
Soil mapping identifies different soil types by compressing a unique suite of spatial patterns and processes across multiple spatial scales. It can be quite difficult to quantify spatial patterns of soil properties with remotely sensed predictor variables. More specifically, matching the right scale...
Modeling Coast Redwood Variable Retention Management Regimes
John-Pascal Berrill; Kevin O' Hara
2007-01-01
Variable retention is a flexible silvicultural system that provides forest managers with an alternative to clearcutting. While much of the standing volume is removed in one harvesting operation, residual stems are retained to provide structural complexity and wildlife habitat functions, or to accrue volume before removal during subsequent stand entries. The residual...
Feingold, Alan; Tiberio, Stacey S; Capaldi, Deborah M
2014-03-01
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.
Variable Fidelity Aeroelastic Toolkit - Structural Model, Phase I
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...
Multi-wheat-model ensemble responses to interannual climatic variability
DEFF Research Database (Denmark)
Ruane, A C; Hudson, N I; Asseng, S
2016-01-01
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...
ABOUT PSYCHOLOGICAL VARIABLES IN APPLICATION SCORING MODELS
Directory of Open Access Journals (Sweden)
Pablo Rogers
2015-01-01
Full Text Available The purpose of this study is to investigate the contribution of psychological variables and scales suggested by Economic Psychology in predicting individuals’ default. Therefore, a sample of 555 individuals completed a self-completion questionnaire, which was composed of psychological variables and scales. By adopting the methodology of the logistic regression, the following psychological and behavioral characteristics were found associated with the group of individuals in default: a negative dimensions related to money (suffering, inequality and conflict; b high scores on the self-efficacy scale, probably indicating a greater degree of optimism and over-confidence; c buyers classified as compulsive; d individuals who consider it necessary to give gifts to children and friends on special dates, even though many people consider this a luxury; e problems of self-control identified by individuals who drink an average of more than four glasses of alcoholic beverage a day.
Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed
2017-05-01
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.
R Package multiPIM: A Causal Inference Approach to Variable Importance Analysis
Directory of Open Access Journals (Sweden)
Stephan J Ritter
2014-04-01
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.
Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability
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
2016-01-01
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.
Fixed transaction costs and modelling limited dependent variables
Hempenius, A.L.
1994-01-01
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.
Coevolution of variability models and related software artifacts
DEFF Research Database (Denmark)
Passos, Leonardo; Teixeira, Leopoldo; Dinztner, Nicolas
2015-01-01
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...
Vera, José Fernando; de Rooij, Mark; Heiser, Willem J
2014-11-01
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.
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
2016-01-01
. 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...
Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables
Henson, Robert A.; Templin, Jonathan L.; Willse, John T.
2009-01-01
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…
Constructing a justice model based on Sen's capability approach
Yüksel, Sevgi; Yuksel, Sevgi
2008-01-01
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...
Schmidtmann, I; Elsäßer, A; Weinmann, A; Binder, H
2014-12-30
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
Variable Selection for Regression Models of Percentile Flows
Fouad, G.
2017-12-01
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
Variability aware compact model characterization for statistical circuit design optimization
Qiao, Ying; Qian, Kun; Spanos, Costas J.
2012-03-01
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.
Variable amplitude fatigue, modelling and testing
International Nuclear Information System (INIS)
Svensson, Thomas.
1993-01-01
Problems related to metal fatigue modelling and testing are here treated in four different papers. In the first paper different views of the subject are summarised in a literature survey. In the second paper a new model for fatigue life is investigated. Experimental results are established which are promising for further development of the mode. In the third paper a method is presented that generates a stochastic process, suitable to fatigue testing. The process is designed in order to resemble certain fatigue related features in service life processes. In the fourth paper fatigue problems in transport vibrations are treated
Wang, Jun; Wang, Yang; Zeng, Hui
2016-01-01
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.
modelling relationship between rainfall variability and yields
African Journals Online (AJOL)
, S. and ... factors to rice yield. Adebayo and Adebayo (1997) developed double log multiple regression model to predict rice yield in Adamawa State, Nigeria. The general form of .... the second are the crop yield/values for millet and sorghum ...
An automated approach for finding variable-constant pairing bugs
DEFF Research Database (Denmark)
Lawall, Julia; Lo, David
2010-01-01
program-analysis and data-mining based approach to identify the uses of named constants and to identify anomalies in these uses. We have applied our approach to a recent version of the Linux kernel and have found a number of bugs affecting both correctness and software maintenance. Many of these bugs...... have been validated by the Linux developers....
Guyon, Hervé; Falissard, Bruno; Kop, Jean-Luc
2017-01-01
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
Linear latent variable models: the lava-package
DEFF Research Database (Denmark)
Holst, Klaus Kähler; Budtz-Jørgensen, Esben
2013-01-01
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...
The Structure of Character Strengths: Variable- and Person-Centered Approaches
Directory of Open Access Journals (Sweden)
Małgorzata Najderska
2018-02-01
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.
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
2007-01-01
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
HEDR modeling approach: Revision 1
International Nuclear Information System (INIS)
Shipler, D.B.; Napier, B.A.
1994-05-01
This report is a revision of the previous Hanford Environmental Dose Reconstruction (HEDR) Project modeling approach report. This revised report describes the methods used in performing scoping studies and estimating final radiation doses to real and representative individuals who lived in the vicinity of the Hanford Site. The scoping studies and dose estimates pertain to various environmental pathways during various periods of time. The original report discussed the concepts under consideration in 1991. The methods for estimating dose have been refined as understanding of existing data, the scope of pathways, and the magnitudes of dose estimates were evaluated through scoping studies
An algebraic geometric approach to separation of variables
Schöbel, Konrad
2015-01-01
Konrad Schöbel aims to lay the foundations for a consequent algebraic geometric treatment of variable separation, which is one of the oldest and most powerful methods to construct exact solutions for the fundamental equations in classical and quantum physics. The present work reveals a surprising algebraic geometric structure behind the famous list of separation coordinates, bringing together a great range of mathematics and mathematical physics, from the late 19th century theory of separation of variables to modern moduli space theory, Stasheff polytopes and operads. "I am particularly impressed by his mastery of a variety of techniques and his ability to show clearly how they interact to produce his results.” (Jim Stasheff) Contents The Foundation: The Algebraic Integrability Conditions The Proof of Concept: A Complete Solution for the 3-Sphere The Generalisation: A Solution for Spheres of Arbitrary Dimension The Perspectives: Applications and Generalisations Target Groups Scientists in the fie...
Variable system: An alternative approach for the analysis of mediated moderation.
Kwan, Joyce Lok Yin; Chan, Wai
2018-06-01
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).
A geometric model for magnetizable bodies with internal variables
Directory of Open Access Journals (Sweden)
Restuccia, L
2005-11-01
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.
Variable-Structure Control of a Model Glider Airplane
Waszak, Martin R.; Anderson, Mark R.
2008-01-01
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.
Fox, Eric W; Hill, Ryan A; Leibowitz, Scott G; Olsen, Anthony R; Thornbrugh, Darren J; Weber, Marc H
2017-07-01
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
Geometrical approach to fluid models
International Nuclear Information System (INIS)
Kuvshinov, B.N.; Schep, T.J.
1997-01-01
Differential geometry based upon the Cartan calculus of differential forms is applied to investigate invariant properties of equations that describe the motion of continuous media. The main feature of this approach is that physical quantities are treated as geometrical objects. The geometrical notion of invariance is introduced in terms of Lie derivatives and a general procedure for the construction of local and integral fluid invariants is presented. The solutions of the equations for invariant fields can be written in terms of Lagrange variables. A generalization of the Hamiltonian formalism for finite-dimensional systems to continuous media is proposed. Analogously to finite-dimensional systems, Hamiltonian fluids are introduced as systems that annihilate an exact two-form. It is shown that Euler and ideal, charged fluids satisfy this local definition of a Hamiltonian structure. A new class of scalar invariants of Hamiltonian fluids is constructed that generalizes the invariants that are related with gauge transformations and with symmetries (Noether). copyright 1997 American Institute of Physics
Verification of models for ballistic movement time and endpoint variability.
Lin, Ray F; Drury, Colin G
2013-01-01
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.
Interdecadal variability in a global coupled model
International Nuclear Information System (INIS)
Storch, J.S. von.
1994-01-01
Interdecadal variations are studied in a 325-year simulation performed by a coupled atmosphere - ocean general circulation model. The patterns obtained in this study may be considered as characteristic patterns for interdecadal variations. 1. The atmosphere: Interdecadal variations have no preferred time scales, but reveal well-organized spatial structures. They appear as two modes, one is related with variations of the tropical easterlies and the other with the Southern Hemisphere westerlies. Both have red spectra. The amplitude of the associated wind anomalies is largest in the upper troposphere. The associated temperature anomalies are in thermal-wind balance with the zonal winds and are out-of-phase between the troposphere and the lower stratosphere. 2. The Pacific Ocean: The dominant mode in the Pacific appears to be wind-driven in the midlatitudes and is related to air-sea interaction processes during one stage of the oscillation in the tropics. Anomalies of this mode propagate westward in the tropics and the northward (southwestward) in the North (South) Pacific on a time scale of about 10 to 20 years. (orig.)
Influences of variables on ship collision probability in a Bayesian belief network model
International Nuclear Information System (INIS)
Hänninen, Maria; Kujala, Pentti
2012-01-01
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.
Using structural equation modeling to investigate relationships among ecological variables
Malaeb, Z.A.; Kevin, Summers J.; Pugesek, B.H.
2000-01-01
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
Razafindrakoto, Hoby
2015-04-22
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
Razafindrakoto, Hoby; Mai, Paul Martin; Genton, Marc G.; Zhang, Ling; Thingbaijam, Kiran Kumar
2015-01-01
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
Multiple Imputation of Predictor Variables Using Generalized Additive Models
de Jong, Roel; van Buuren, Stef; Spiess, Martin
2016-01-01
The sensitivity of multiple imputation methods to deviations from their distributional assumptions is investigated using simulations, where the parameters of scientific interest are the coefficients of a linear regression model, and values in predictor variables are missing at random. The
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 ...
MEANINGFUL VARIABILITY: A SOCIOLINGUISTICALLY-GROUNDED APPROACH TO VARIATION IN OPTIMALITY THEORY
Directory of Open Access Journals (Sweden)
Juan Antonio Cutillas Espinosa
2004-12-01
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.
A variable-order fractal derivative model for anomalous diffusion
Directory of Open Access Journals (Sweden)
Liu Xiaoting
2017-01-01
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.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina
2012-08-03
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.
A Non-Gaussian Spatial Generalized Linear Latent Variable Model
Irincheeva, Irina; Cantoni, Eva; Genton, Marc G.
2012-01-01
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.
Predictive and Descriptive CoMFA Models: The Effect of Variable Selection.
Sepehri, Bakhtyar; Omidikia, Nematollah; Kompany-Zareh, Mohsen; Ghavami, Raouf
2018-01-01
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 epub@benthamscience.org.
New variable separation approach: application to nonlinear diffusion equations
International Nuclear Information System (INIS)
Zhang Shunli; Lou, S Y; Qu Changzheng
2003-01-01
The concept of the derivative-dependent functional separable solution (DDFSS), as a generalization to the functional separable solution, is proposed. As an application, it is used to discuss the generalized nonlinear diffusion equations based on the generalized conditional symmetry approach. As a consequence, a complete list of canonical forms for such equations which admit the DDFSS is obtained and some exact solutions to the resulting equations are described
A thermodynamic approach to fatigue damage accumulation under variable loading
International Nuclear Information System (INIS)
Naderi, M.; Khonsari, M.M.
2010-01-01
We put forward a general procedure for assessment of damage evolution based on the concept of entropy production. The procedure is applicable to both constant- and variable amplitude loading. The results of a series of bending fatigue tests under both two-stage and three-stage loadings are reported to investigate the validity of the proposed methodology. Also presented are the results of experiments involving bending, torsion, and tension-compression fatigue tests with Al 6061-T6 and SS 304 specimens. It is shown that, within the range of parameters tested, the evolution of fatigue damage for these materials in terms of entropy production is independent of load, frequency, size, loading sequence and loading history. Furthermore, entropy production fractions of individual amplitudes sums to unity.
Jin, Zhonghai; Wielicki, Bruce A.; Loukachine, Constantin; Charlock, Thomas P.; Young, David; Noeel, Stefan
2011-01-01
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.
Preliminary Multi-Variable Cost Model for Space Telescopes
Stahl, H. Philip; Hendrichs, Todd
2010-01-01
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.
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.
2012-01-01
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....
Variable Width Riparian Model Enhances Landscape and Watershed Condition
Abood, S. A.; Spencer, L.
2017-12-01
Riparian areas are ecotones that represent about 1% of USFS administered landscape and contribute to numerous valuable ecosystem functions such as wildlife habitat, stream water quality and flows, bank stability and protection against erosion, and values related to diversity, aesthetics and recreation. Riparian zones capture the transitional area between terrestrial and aquatic ecosystems with specific vegetation and soil characteristics which provide critical values/functions and are very responsive to changes in land management activities and uses. Two staff areas at the US Forest Service have coordinated on a two phase project to support the National Forests in their planning revision efforts and to address rangeland riparian business needs at the Forest Plan and Allotment Management Plan levels. The first part of the project will include a national fine scale (USGS HUC-12 digits watersheds) inventory of riparian areas on National Forest Service lands in western United States with riparian land cover, utilizing GIS capabilities and open source geospatial data. The second part of the project will include the application of riparian land cover change and assessment based on selected indicators to assess and monitor riparian areas on annual/5-year cycle basis.This approach recognizes the dynamic and transitional nature of riparian areas by accounting for hydrologic, geomorphic and vegetation data as inputs into the delineation process. The results suggest that incorporating functional variable width riparian mapping within watershed management planning can improve riparian protection and restoration. The application of Riparian Buffer Delineation Model (RBDM) approach can provide the agency Watershed Condition Framework (WCF) with observed riparian area condition on an annual basis and on multiple scales. The use of this model to map moderate to low gradient systems of sufficient width in conjunction with an understanding of the influence of distinctive landscape
International Nuclear Information System (INIS)
Malmberg, T.
1993-09-01
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
A model for AGN variability on multiple time-scales
Sartori, Lia F.; Schawinski, Kevin; Trakhtenbrot, Benny; Caplar, Neven; Treister, Ezequiel; Koss, Michael J.; Urry, C. Megan; Zhang, C. E.
2018-05-01
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.
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)
1995-07-01
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.
DEFF Research Database (Denmark)
Köster, Fritz; Hinrichsen, H.H.; St. John, Michael
2001-01-01
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...
Loss given default models incorporating macroeconomic variables for credit cards
Crook, J.; Bellotti, T.
2012-01-01
Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to m...
Stereotype Threat and College Academic Performance: A Latent Variables Approach*
Owens, Jayanti; Massey, Douglas S.
2013-01-01
Stereotype threat theory has gained experimental and survey-based support in helping explain the academic underperformance of minority students at selective colleges and universities. Stereotype threat theory states that minority students underperform because of pressures created by negative stereotypes about their racial group. Past survey-based studies, however, are characterized by methodological inefficiencies and potential biases: key theoretical constructs have only been measured using summed indicators and predicted relationships modeled using ordinary least squares. Using the National Longitudinal Survey of Freshman, this study overcomes previous methodological shortcomings by developing a latent construct model of stereotype threat. Theoretical constructs and equations are estimated simultaneously from multiple indicators, yielding a more reliable, valid, and parsimonious test of key propositions. Findings additionally support the view that social stigma can indeed have strong negative effects on the academic performance of pejoratively stereotyped racial-minority group members, not only in laboratory settings, but also in the real world. PMID:23950616
A Variational Approach to the Modeling of MIMO Systems
Directory of Open Access Journals (Sweden)
Jraifi A
2007-01-01
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.
Interacting ghost dark energy models with variable G and Λ
Sadeghi, J.; Khurshudyan, M.; Movsisyan, A.; Farahani, H.
2013-12-01
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.
Variable selection for mixture and promotion time cure rate models.
Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng
2016-11-16
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.
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.
Simple model for crop photosynthesis in terms of weather variables ...
African Journals Online (AJOL)
A theoretical mathematical model for describing crop photosynthetic rate in terms of the weather variables and crop characteristics is proposed. The model utilizes a series of efficiency parameters, each of which reflect the fraction of possible photosynthetic rate permitted by the different weather elements or crop architecture.
Model for expressing leaf photosynthesis in terms of weather variables
African Journals Online (AJOL)
A theoretical mathematical model for describing photosynthesis in individual leaves in terms of weather variables is proposed. The model utilizes a series of efficiency parameters, each of which reflect the fraction of potential photosynthetic rate permitted by the different environmental elements. These parameters are useful ...
Modeling and Simulation of Variable Mass, Flexible Structures
Tobbe, Patrick A.; Matras, Alex L.; Wilson, Heath E.
2009-01-01
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
Fernandes, José Antonio
2013-02-01
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.
An Alternative Approach to the Variable Housing Allowance Program
1987-01-01
Chamber of Commerce Researchers Association. "Inter-City Cost of Living Indicators." Indianapolis, Fourth Quarter, 1985. Deaton, Angus S. "The Analysis of Consumer Demand in the United Kingdom, 1900-1970." Econometrica, 42, No. 2 (1974), pp. 341-367. 9 Models and Projections of Demand in Past-War Britain. London: Chapman and Hall, 1975. Haworth, C. T. and D. W. Rasmussen. "Determinants of Metropolitan Cost of Living Variations." Southern Economic Journal, 40, No. 2 (1973), pp. 183-192. Stone,
Directory of Open Access Journals (Sweden)
Murat Selim Çepni
2017-03-01
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
A Synergetic Approach to Describe the Stability and Variability of Motor Behavior
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.
Modelling the effects of spatial variability on radionuclide migration
International Nuclear Information System (INIS)
1998-01-01
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.)
From Transition Systems to Variability Models and from Lifted Model Checking Back to UPPAAL
DEFF Research Database (Denmark)
Dimovski, Aleksandar; Wasowski, Andrzej
2017-01-01
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...
Internal variability of a 3-D ocean model
Directory of Open Access Journals (Sweden)
Bjarne Büchmann
2016-11-01
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
Understanding and forecasting polar stratospheric variability with statistical models
Directory of Open Access Journals (Sweden)
C. Blume
2012-07-01
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.
Fatigue Crack Propagation Under Variable Amplitude Loading Analyses Based on Plastic Energy Approach
Directory of Open Access Journals (Sweden)
Sofiane Maachou
2014-04-01
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.
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
Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.
2017-01-01
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
Mediterranean climate modelling: variability and climate change scenarios
International Nuclear Information System (INIS)
Somot, S.
2005-12-01
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)
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)
2017-11-01
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.
Modelling food-web mediated effects of hydrological variability and environmental flows.
Robson, Barbara J; Lester, Rebecca E; Baldwin, Darren S; Bond, Nicholas R; Drouart, Romain; Rolls, Robert J; Ryder, Darren S; Thompson, Ross M
2017-11-01
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.
A Bayesian approach to model uncertainty
International Nuclear Information System (INIS)
Buslik, A.
1994-01-01
A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given
Modeling Turbulent Combustion for Variable Prandtl and Schmidt Number
Hassan, H. A.
2004-01-01
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.
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....
Stage-by-Stage and Parallel Flow Path Compressor Modeling for a Variable Cycle Engine
Kopasakis, George; Connolly, Joseph W.; Cheng, Larry
2015-01-01
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.
Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling
Raykov, Tenko; Marcoulides, George A.
2012-01-01
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…
Modeling Time-Dependent Association in Longitudinal Data: A Lag as Moderator Approach
Selig, James P.; Preacher, Kristopher J.; Little, Todd D.
2012-01-01
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…
Ocean carbon and heat variability in an Earth System Model
Thomas, J. L.; Waugh, D.; Gnanadesikan, A.
2016-12-01
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.
Sinkov, Nikolai A; Johnston, Brandon M; Sandercock, P Mark L; Harynuk, James J
2011-07-04
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.
DEFF Research Database (Denmark)
Sun, Xiangqing; Vengoechea, Jaime; Elston, Robert
2012-01-01
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....
System Behavior Models: A Survey of Approaches
2016-06-01
OF FIGURES Spiral Model .................................................................................................3 Figure 1. Approaches in... spiral model was chosen for researching and structuring this thesis, shown in Figure 1. This approach allowed multiple iterations of source material...applications and refining through iteration. 3 Spiral Model Figure 1. D. SCOPE The research is limited to a literature review, limited
Classification criteria of syndromes by latent variable models
DEFF Research Database (Denmark)
Petersen, Janne
2010-01-01
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...
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)
2008-02-15
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.)
Automatic Welding Control Using a State Variable Model.
1979-06-01
A-A10 610 NAVEAL POSTGRADUATE SCH4O.M CEAY CA0/ 13/ SAUTOMATIC WELDING CONTROL USING A STATE VARIABLE MODEL.W()JUN 79 W V "my UNCLASSIFIED...taverse Drive Unit // Jbint Path /Fixed Track 34 (servomotor positioning). Additional controls of heave (vertical), roll (angular rotation about the
Viscous cosmological models with a variable cosmological term ...
African Journals Online (AJOL)
Einstein's field equations for a Friedmann-Lamaitre Robertson-Walker universe filled with a dissipative fluid with a variable cosmological term L described by full Israel-Stewart theory are considered. General solutions to the field equations for the flat case have been obtained. The solution corresponds to the dust free model ...
Appraisal and Reliability of Variable Engagement Model Prediction ...
African Journals Online (AJOL)
The variable engagement model based on the stress - crack opening displacement relationship and, which describes the behaviour of randomly oriented steel fibres composite subjected to uniaxial tension has been evaluated so as to determine the safety indices associated when the fibres are subjected to pullout and with ...
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 ...
Ciffroy, Philippe; Charlatchka, Rayna; Ferreira, Daniel; Marang, Laura
2013-07-01
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.
Oscillating shells: A model for a variable cosmic object
Nunez, Dario
1997-01-01
A model for a possible variable cosmic object is presented. The model consists of a massive shell surrounding a compact object. The gravitational and self-gravitational forces tend to collapse the shell, but the internal tangential stresses oppose the collapse. The combined action of the two types of forces is studied and several cases are presented. In particular, we investigate the spherically symmetric case in which the shell oscillates radially around a central compact object.
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)
2008-10-15
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.
Stratified flows with variable density: mathematical modelling and numerical challenges.
Murillo, Javier; Navas-Montilla, Adrian
2017-04-01
Stratified flows appear in a wide variety of fundamental problems in hydrological and geophysical sciences. They may involve from hyperconcentrated floods carrying sediment causing collapse, landslides and debris flows, to suspended material in turbidity currents where turbulence is a key process. Also, in stratified flows variable horizontal density is present. Depending on the case, density varies according to the volumetric concentration of different components or species that can represent transported or suspended materials or soluble substances. Multilayer approaches based on the shallow water equations provide suitable models but are not free from difficulties when moving to the numerical resolution of the governing equations. Considering the variety of temporal and spatial scales, transfer of mass and energy among layers may strongly differ from one case to another. As a consequence, in order to provide accurate solutions, very high order methods of proved quality are demanded. Under these complex scenarios it is necessary to observe that the numerical solution provides the expected order of accuracy but also converges to the physically based solution, which is not an easy task. To this purpose, this work will focus in the use of Energy balanced augmented solvers, in particular, the Augmented Roe Flux ADER scheme. References: J. Murillo , P. García-Navarro, Wave Riemann description of friction terms in unsteady shallow flows: Application to water and mud/debris floods. J. Comput. Phys. 231 (2012) 1963-2001. J. Murillo B. Latorre, P. García-Navarro. A Riemann solver for unsteady computation of 2D shallow flows with variable density. J. Comput. Phys.231 (2012) 4775-4807. A. Navas-Montilla, J. Murillo, Energy balanced numerical schemes with very high order. The Augmented Roe Flux ADER scheme. Application to the shallow water equations, J. Comput. Phys. 290 (2015) 188-218. A. Navas-Montilla, J. Murillo, Asymptotically and exactly energy balanced augmented flux
Directory of Open Access Journals (Sweden)
Liu Yufeng
2011-01-01
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
On the Integrity of Online Testing for Introductory Statistics Courses: A Latent Variable Approach
Directory of Open Access Journals (Sweden)
Alan Fask
2015-04-01
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
A dual model approach to ground water recovery trench design
International Nuclear Information System (INIS)
Clodfelter, C.L.; Crouch, M.S.
1992-01-01
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
Sparse modeling of spatial environmental variables associated with asthma.
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
2015-02-01
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.
Learning Actions Models: Qualitative Approach
DEFF Research Database (Denmark)
Bolander, Thomas; Gierasimczuk, Nina
2015-01-01
In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite ident...
Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach
DEFF Research Database (Denmark)
Asgharian, Hossein; Hou, Ai Jun; Javed, Farrukh
2013-01-01
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...
Joint Bayesian variable and graph selection for regression models with network-structured predictors
Peterson, C. B.; Stingo, F. C.; Vannucci, M.
2015-01-01
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
Analysis models for variables associated with breastfeeding duration
Directory of Open Access Journals (Sweden)
Edson Theodoro dos S. Neto
2013-09-01
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.
Energy Technology Data Exchange (ETDEWEB)
Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory
2010-01-01
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.
Dondeynaz, C.; Lopez-Puga, J.; Carmona-Moreno, C.
2012-04-01
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
Fenicia, Fabrizio; Kavetski, Dmitri; Savenije, Hubert H. G.; Pfister, Laurent
2016-02-01
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
Environmental versus demographic variability in stochastic predator–prey models
International Nuclear Information System (INIS)
Dobramysl, U; Täuber, U C
2013-01-01
In contrast to the neutral population cycles of the deterministic mean-field Lotka–Volterra rate equations, including spatial structure and stochastic noise in models for predator–prey interactions yields complex spatio-temporal structures associated with long-lived erratic population oscillations. Environmental variability in the form of quenched spatial randomness in the predation rates results in more localized activity patches. Our previous study showed that population fluctuations in rare favorable regions in turn cause a remarkable increase in the asymptotic densities of both predators and prey. Very intriguing features are found when variable interaction rates are affixed to individual particles rather than lattice sites. Stochastic dynamics with demographic variability in conjunction with inheritable predation efficiencies generate non-trivial time evolution for the predation rate distributions, yet with overall essentially neutral optimization. (paper)
Pre-quantum mechanics. Introduction to models with hidden variables
International Nuclear Information System (INIS)
Grea, J.
1976-01-01
Within the context of formalism of hidden variable type, the author considers the models used to describe mechanical systems before the introduction of the quantum model. An account is given of the characteristics of the theoretical models and their relationships with experimental methodology. The models of analytical, pre-ergodic, stochastic and thermodynamic mechanics are studied in succession. At each stage the physical hypothesis is enunciated by postulate corresponding to the type of description of the reality of the model. Starting from this postulate, the physical propositions which are meaningful for the model under consideration are defined and their logical structure is indicated. It is then found that on passing from one level of description to another, one can obtain successively Boolean lattices embedded in lattices of continuous geometric type, which are themselves embedded in Boolean lattices. It is therefore possible to envisage a more detailed description than that given by the quantum lattice and to construct it by analogy. (Auth.)
An Atmospheric Variability Model for Venus Aerobraking Missions
Tolson, Robert T.; Prince, Jill L. H.; Konopliv, Alexander A.
2013-01-01
Aerobraking has proven to be an enabling technology for planetary missions to Mars and has been proposed to enable low cost missions to Venus. Aerobraking saves a significant amount of propulsion fuel mass by exploiting atmospheric drag to reduce the eccentricity of the initial orbit. The solar arrays have been used as the primary drag surface and only minor modifications have been made in the vehicle design to accommodate the relatively modest aerothermal loads. However, if atmospheric density is highly variable from orbit to orbit, the mission must either accept higher aerothermal risk, a slower pace for aerobraking, or a tighter corridor likely with increased propulsive cost. Hence, knowledge of atmospheric variability is of great interest for the design of aerobraking missions. The first planetary aerobraking was at Venus during the Magellan mission. After the primary Magellan science mission was completed, aerobraking was used to provide a more circular orbit to enhance gravity field recovery. Magellan aerobraking took place between local solar times of 1100 and 1800 hrs, and it was found that the Venusian atmospheric density during the aerobraking phase had less than 10% 1 sigma orbit to orbit variability. On the other hand, at some latitudes and seasons, Martian variability can be as high as 40% 1 sigmaFrom both the MGN and PVO mission it was known that the atmosphere, above aerobraking altitudes, showed greater variability at night, but this variability was never quantified in a systematic manner. This paper proposes a model for atmospheric variability that can be used for aerobraking mission design until more complete data sets become available.
Variable Pitch Approach for Performance Improving of Straight-Bladed VAWT at Rated Tip Speed Ratio
Directory of Open Access Journals (Sweden)
Zhenzhou Zhao
2018-06-01
Full Text Available This paper presents a new variable pitch (VP approach to increase the peak power coefficient of the straight-bladed vertical-axis wind turbine (VAWT, by widening the azimuthal angle band of the blade with the highest aerodynamic torque, instead of increasing the highest torque. The new VP-approach provides a curve of pitch angle designed for the blade operating at the rated tip speed ratio (TSR corresponding to the peak power coefficient of the fixed pitch (FP-VAWT. The effects of the new approach are exploited by using the double multiple stream tubes (DMST model and Prandtl’s mathematics to evaluate the blade tip loss. The research describes the effects from six aspects, including the lift, drag, angle of attack (AoA, resultant velocity, torque, and power output, through a comparison between VP-VAWTs and FP-VAWTs working at four TSRs: 4, 4.5, 5, and 5.5. Compared with the FP-blade, the VP-blade has a wider azimuthal zone with the maximum AoA, lift, drag, and torque in the upwind half-cycle, and yields the two new larger maximum values in the downwind half-cycle. The power distribution in the swept area of the turbine changes from an arched shape of the FP-VAWT into the rectangular shape of the VP-VAWT. The new VP-approach markedly widens the highest-performance zone of the blade in a revolution, and ultimately achieves an 18.9% growth of the peak power coefficient of the VAWT at the optimum TSR. Besides achieving this growth, the new pitching method will enhance the performance at TSRs that are higher than current optimal values, and an increase of torque is also generated.
Global energy modeling - A biophysical approach
Energy Technology Data Exchange (ETDEWEB)
Dale, Michael
2010-09-15
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.
Tracy, Allison J.; Erkut, Sumru; Porche, Michelle V.; Kim, Jo; Charmaraman, Linda; Grossman, Jennifer M.; Ceder, Ineke; Garcia, Heidie Vazquez
2010-01-01
In this article, we operationalize identification of mixed racial and ethnic ancestry among adolescents as a latent variable to (a) account for measurement uncertainty, and (b) compare alternative wording formats for racial and ethnic self-categorization in surveys. Two latent variable models were fit to multiple mixed-ancestry indicator data from…
Mangado, Nerea; Piella, Gemma; Noailly, Jérôme; Pons-Prats, Jordi; Ballester, Miguel Ángel González
2016-01-01
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.
Energy Technology Data Exchange (ETDEWEB)
Penny, Robert D., E-mail: robert.d.penny@leidos.com [Leidos Inc., 10260 Campus Point Road, San Diego, CA (United States); Crowley, Tanya M.; Gardner, Barbara M.; Mandell, Myron J.; Guo, Yanlin; Haas, Eric B.; Knize, Duane J.; Kuharski, Robert A.; Ranta, Dale; Shyffer, Ryan [Leidos Inc., 10260 Campus Point Road, San Diego, CA (United States); Labov, Simon; Nelson, Karl; Seilhan, Brandon [Lawrence Livermore National Laboratory, Livermore, CA (United States); Valentine, John D. [Lawrence Berkeley National Laboratory, Berkeley, CA (United States)
2015-06-01
A novel approach and algorithm have been developed to rapidly detect and localize both moving and static radiological/nuclear (R/N) sources from an airborne platform. Current aerial systems with radiological sensors are limited in their ability to compensate for variable naturally occurring radioactive material (NORM) background. The proposed approach suppresses the effects of NORM background by incorporating additional information to segment the survey area into regions over which the background is likely to be uniform. The method produces pixelated Source Activity Maps (SAMs) of both target and background radionuclide activity over the survey area. The task of producing the SAMs requires (1) the development of a forward model which describes the transformation of radionuclide activity to detector measurements and (2) the solution of the associated inverse problem. The inverse problem is ill-posed as there are typically fewer measurements than unknowns. In addition the measurements are subject to Poisson statistical noise. The Maximum-Likelihood Expectation-Maximization (MLEM) algorithm is used to solve the inverse problem as it is well suited for under-determined problems corrupted by Poisson noise. A priori terrain information is incorporated to segment the reconstruction space into regions within which we constrain NORM background activity to be uniform. Descriptions of the algorithm and examples of performance with and without segmentation on simulated data are presented.
Speech-discrimination scores modeled as a binomial variable.
Thornton, A R; Raffin, M J
1978-09-01
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.
A Unified Approach to Modeling and Programming
DEFF Research Database (Denmark)
Madsen, Ole Lehrmann; Møller-Pedersen, Birger
2010-01-01
of this paper is to go back to the future and get inspiration from SIMULA and propose a unied approach. In addition to reintroducing the contributions of SIMULA and the Scandinavian approach to object-oriented programming, we do this by discussing a number of issues in modeling and programming and argue3 why we......SIMULA was a language for modeling and programming and provided a unied approach to modeling and programming in contrast to methodologies based on structured analysis and design. The current development seems to be going in the direction of separation of modeling and programming. The goal...
Predictive modeling and reducing cyclic variability in autoignition engines
Hellstrom, Erik; Stefanopoulou, Anna; Jiang, Li; Larimore, Jacob
2016-08-30
Methods and systems are provided for controlling a vehicle engine to reduce cycle-to-cycle combustion variation. A predictive model is applied to predict cycle-to-cycle combustion behavior of an engine based on observed engine performance variables. Conditions are identified, based on the predicted cycle-to-cycle combustion behavior, that indicate high cycle-to-cycle combustion variation. Corrective measures are then applied to prevent the predicted high cycle-to-cycle combustion variation.
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
2012-01-01
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.
Muller, Benjamin J.; Cade, Brian S.; Schwarzkoph, Lin
2018-01-01
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.
Peltola, Olli; Raivonen, Maarit; Li, Xuefei; Vesala, Timo
2018-02-01
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.
Structural identifiability of cyclic graphical models of biological networks with latent variables.
Wang, Yulin; Lu, Na; Miao, Hongyu
2016-06-13
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
Are revised models better models? A skill score assessment of regional interannual variability
Sperber, Kenneth R.; Participating AMIP Modelling Groups
1999-05-01
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.
Connolly, Joseph W.; Friedlander, David; Kopasakis, George
2015-01-01
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.
Santos, José António; Galante-Oliveira, Susana; Barroso, Carlos
2011-03-01
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.
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.
1993-01-01
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
Quantifying intrinsic and extrinsic variability in stochastic gene expression models.
Singh, Abhyudai; Soltani, Mohammad
2013-01-01
Genetically identical cell populations exhibit considerable intercellular variation in the level of a given protein or mRNA. Both intrinsic and extrinsic sources of noise drive this variability in gene expression. More specifically, extrinsic noise is the expression variability that arises from cell-to-cell differences in cell-specific factors such as enzyme levels, cell size and cell cycle stage. In contrast, intrinsic noise is the expression variability that is not accounted for by extrinsic noise, and typically arises from the inherent stochastic nature of biochemical processes. Two-color reporter experiments are employed to decompose expression variability into its intrinsic and extrinsic noise components. Analytical formulas for intrinsic and extrinsic noise are derived for a class of stochastic gene expression models, where variations in cell-specific factors cause fluctuations in model parameters, in particular, transcription and/or translation rate fluctuations. Assuming mRNA production occurs in random bursts, transcription rate is represented by either the burst frequency (how often the bursts occur) or the burst size (number of mRNAs produced in each burst). Our analysis shows that fluctuations in the transcription burst frequency enhance extrinsic noise but do not affect the intrinsic noise. On the contrary, fluctuations in the transcription burst size or mRNA translation rate dramatically increase both intrinsic and extrinsic noise components. Interestingly, simultaneous fluctuations in transcription and translation rates arising from randomness in ATP abundance can decrease intrinsic noise measured in a two-color reporter assay. Finally, we discuss how these formulas can be combined with single-cell gene expression data from two-color reporter experiments for estimating model parameters.
Directory of Open Access Journals (Sweden)
Marc A. Rosen
2012-08-01
Full Text Available The temperature response in the soil surrounding multiple boreholes is evaluated analytically and numerically. The assumption of constant heat flux along the borehole wall is examined by coupling the problem to the heat transfer problem inside the borehole and presenting a model with variable heat flux along the borehole length. In the analytical approach, a line source of heat with a finite length is used to model the conduction of heat in the soil surrounding the boreholes. In the numerical method, a finite volume method in a three dimensional meshed domain is used. In order to determine the heat flux boundary condition, the analytical quasi-three-dimensional solution to the heat transfer problem of the U-tube configuration inside the borehole is used. This solution takes into account the variation in heating strength along the borehole length due to the temperature variation of the fluid running in the U-tube. Thus, critical depths at which thermal interaction occurs can be determined. Finally, in order to examine the validity of the numerical method, a comparison is made with the results of line source method.
Modeling key processes causing climate change and variability
Energy Technology Data Exchange (ETDEWEB)
Henriksson, S.
2013-09-01
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
Multiple Model Approaches to Modelling and Control,
DEFF Research Database (Denmark)
on the ease with which prior knowledge can be incorporated. It is interesting to note that researchers in Control Theory, Neural Networks,Statistics, Artificial Intelligence and Fuzzy Logic have more or less independently developed very similar modelling methods, calling them Local ModelNetworks, Operating......, and allows direct incorporation of high-level and qualitative plant knowledge into themodel. These advantages have proven to be very appealing for industrial applications, and the practical, intuitively appealing nature of the framework isdemonstrated in chapters describing applications of local methods...... to problems in the process industries, biomedical applications and autonomoussystems. The successful application of the ideas to demanding problems is already encouraging, but creative development of the basic framework isneeded to better allow the integration of human knowledge with automated learning...
Geochemical Modeling Of F Area Seepage Basin Composition And Variability
International Nuclear Information System (INIS)
Millings, M.; Denham, M.; Looney, B.
2012-01-01
From the 1950s through 1989, the F Area Seepage Basins at the Savannah River Site (SRS) received low level radioactive wastes resulting from processing nuclear materials. Discharges of process wastes to the F Area Seepage Basins followed by subsequent mixing processes within the basins and eventual infiltration into the subsurface resulted in contamination of the underlying vadose zone and downgradient groundwater. For simulating contaminant behavior and subsurface transport, a quantitative understanding of the interrelated discharge-mixing-infiltration system along with the resulting chemistry of fluids entering the subsurface is needed. An example of this need emerged as the F Area Seepage Basins was selected as a key case study demonstration site for the Advanced Simulation Capability for Environmental Management (ASCEM) Program. This modeling evaluation explored the importance of the wide variability in bulk wastewater chemistry as it propagated through the basins. The results are intended to generally improve and refine the conceptualization of infiltration of chemical wastes from seepage basins receiving variable waste streams and to specifically support the ASCEM case study model for the F Area Seepage Basins. Specific goals of this work included: (1) develop a technically-based 'charge-balanced' nominal source term chemistry for water infiltrating into the subsurface during basin operations, (2) estimate the nature of short term and long term variability in infiltrating water to support scenario development for uncertainty quantification (i.e., UQ analysis), (3) identify key geochemical factors that control overall basin water chemistry and the projected variability/stability, and (4) link wastewater chemistry to the subsurface based on monitoring well data. Results from this study provide data and understanding that can be used in further modeling efforts of the F Area groundwater plume. As identified in this study, key geochemical factors affecting basin
Modelling the Spatial Isotope Variability of Precipitation in Syria
Energy Technology Data Exchange (ETDEWEB)
Kattan, Z.; Kattaa, B. [Department of Geology, Atomic Energy Commission of Syria (AECS), Damascus (Syrian Arab Republic)
2013-07-15
Attempts were made to model the spatial variability of environmental isotope ({sup 18}O, {sup 2}H and {sup 3}H) compositions of precipitation in syria. Rainfall samples periodically collected on a monthly basis from 16 different stations were used for processing and demonstrating the spatial distributions of these isotopes, together with those of deuterium excess (d) values. Mathematically, the modelling process was based on applying simple polynomial models that take into consideration the effects of major geographic factors (Lon.E., Lat.N., and altitude). The modelling results of spatial distribution of stable isotopes ({sup 18}O and {sup 2}H) were generally good, as shown from the high correlation coefficients (R{sup 2} = 0.7-0.8), calculated between the observed and predicted values. In the case of deuterium excess and tritium distributions, the results were most likely approximates (R{sup 2} = 0.5-0.6). Improving the simulation of spatial isotope variability probably requires the incorporation of other local meteorological factors, such as relative air humidity, precipitation amount and vapour pressure, which are supposed to play an important role in such an arid country. (author)
Uncertainty and variability in computational and mathematical models of cardiac physiology.
Mirams, Gary R; Pathmanathan, Pras; Gray, Richard A; Challenor, Peter; Clayton, Richard H
2016-12-01
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
Seychelles Dome variability in a high resolution ocean model
Nyadjro, E. S.; Jensen, T.; Richman, J. G.; Shriver, J. F.
2016-02-01
The Seychelles-Chagos Thermocline Ridge (SCTR; 5ºS-10ºS, 50ºE-80ºE) in the tropical Southwest Indian Ocean (SWIO) has been recognized as a region of prominence with regards to climate variability in the Indian Ocean. Convective activities in this region have regional consequences as it affect socio-economic livelihood of the people especially in the countries along the Indian Ocean rim. The SCTR is characterized by a quasi-permanent upwelling that is often associated with thermocline shoaling. This upwelling affects sea surface temperature (SST) variability. We present results on the variability and dynamics of the SCTR as simulated by the 1/12º high resolution HYbrid Coordinate Ocean Model (HYCOM). It is observed that locally, wind stress affects SST via Ekman pumping of cooler subsurface waters, mixing and anomalous zonal advection. Remotely, wind stress curl in the eastern equatorial Indian Ocean generates westward-propagating Rossby waves that impacts the depth of the thermocline which in turn impacts SST variability in the SCTR region. The variability of the contributions of these processes, especially with regard to the Indian Ocean Dipole (IOD) are further examined. In a typical positive IOD (PIOD) year, the net vertical velocity in the SCTR is negative year-round as easterlies along the region are intensified leading to a strong positive curl. This vertical velocity is caused mainly by anomalous local Ekman downwelling (with peak during September-November), a direct opposite to the climatology scenario when local Ekman pumping is positive (upwelling favorable) year-round. The anomalous remote contribution to the vertical velocity changes is minimal especially during the developing and peak stages of PIOD events. In a typical negative IOD (NIOD) year, anomalous vertical velocity is positive almost year-round with peaks in May and October. The remote contribution is positive, in contrast to the climatology and most of the PIOD years.
Commeau, Natalie; Cornu, Marie; Albert, Isabelle; Denis, Jean-Baptiste; Parent, Eric
2012-03-01
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.
Shared Variable Oriented Parallel Precompiler for SPMD Model
Institute of Scientific and Technical Information of China (English)
无
1995-01-01
For the moment,commercial parallel computer systems with distributed memory architecture are usually provided with parallel FORTRAN or parallel C compliers,which are just traditional sequential FORTRAN or C compilers expanded with communication statements.Programmers suffer from writing parallel programs with communication statements. The Shared Variable Oriented Parallel Precompiler (SVOPP) proposed in this paper can automatically generate appropriate communication statements based on shared variables for SPMD(Single Program Multiple Data) computation model and greatly ease the parallel programming with high communication efficiency.The core function of parallel C precompiler has been successfully verified on a transputer-based parallel computer.Its prominent performance shows that SVOPP is probably a break-through in parallel programming technique.
Geospatial models of climatological variables distribution over Colombian territory
International Nuclear Information System (INIS)
Baron Leguizamon, Alicia
2003-01-01
Diverse studies have dealt on the existing relation between the variables temperature about the air and precipitation with the altitude; nevertheless they have been precise analyses or by regions, but no of them has gotten to constitute itself in a tool that reproduces the space distribution, of the temperature or the precipitation, taking into account orography and allowing to obtain from her data on these variables in a certain place. Cradle in the raised relation and from the multi-annual monthly information of the temperature of the air and the precipitation, it was calculated the vertical gradients of temperature and the related the precipitation to the altitude. After it, with base in the data of altitude provided by the DEM, one calculated the values of temperature and precipitation, and those values were interpolated to generate geospatial models monthly
Current approaches to gene regulatory network modelling
Directory of Open Access Journals (Sweden)
Brazma Alvis
2007-09-01
Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
An introduction to latent variable growth curve modeling concepts, issues, and application
Duncan, Terry E; Strycker, Lisa A
2013-01-01
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...
Initial CGE Model Results Summary Exogenous and Endogenous Variables Tests
Energy Technology Data Exchange (ETDEWEB)
Edwards, Brian Keith [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Boero, Riccardo [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Rivera, Michael Kelly [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-08-07
The following discussion presents initial results of tests of the most recent version of the National Infrastructure Simulation and Analysis Center Dynamic Computable General Equilibrium (CGE) model developed by Los Alamos National Laboratory (LANL). The intent of this is to test and assess the model’s behavioral properties. The test evaluated whether the predicted impacts are reasonable from a qualitative perspective. This issue is whether the predicted change, be it an increase or decrease in other model variables, is consistent with prior economic intuition and expectations about the predicted change. One of the purposes of this effort is to determine whether model changes are needed in order to improve its behavior qualitatively and quantitatively.
Towards multi-resolution global climate modeling with ECHAM6-FESOM. Part II: climate variability
Rackow, T.; Goessling, H. F.; Jung, T.; Sidorenko, D.; Semmler, T.; Barbi, D.; Handorf, D.
2018-04-01
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
Emerging adulthood features and criteria for adulthood : Variable- and person-centered approaches
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
Preßler, Anna-Lena; Könen, Tanja; Hasselhorn, Marcus; Krajewski, Kristin
2014-01-01
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…
Kaushanskaya, Margarita; Park, Ji Sook; Gangopadhyay, Ishanti; Davidson, Meghan M.; Weismer, Susan Ellis
2017-01-01
Purpose: We aimed to outline the latent variables approach for measuring nonverbal executive function (EF) skills in school-age children, and to examine the relationship between nonverbal EF skills and language performance in this age group. Method: Seventy-one typically developing children, ages 8 through 11, participated in the study. Three EF…
2003-03-01
within the Automated Cost Estimating Integrated Tools ( ACEIT ) software suite (version 5.x). With this capability, one can set cost targets or time...not allow the user to vary more than one decision variable. This limitation of the ACEIT approach thus hinders a holistic view when attempting to
Cadier, Mathilde; Gorgues, Thomas; Sourisseau, Marc; Edwards, Christopher A.; Aumont, Olivier; Marié, Louis; Memery, Laurent
2017-01-01
Understanding the dynamic interplay between physical, biogeochemical and biological processes represents a key challenge in oceanography, particularly in shelf seas where complex hydrodynamics are likely to drive nutrient distribution and niche partitioning of phytoplankton communities. The Iroise Sea includes a tidal front called the 'Ushant Front' that undergoes a pronounced seasonal cycle, with a marked signal during the summer. These characteristics as well as relatively good observational sampling make it a region of choice to study processes impacting phytoplankton dynamics. This innovative modeling study employs a phytoplankton-diversity model, coupled to a regional circulation model to explore mechanisms that alter biogeography of phytoplankton in this highly dynamic environment. Phytoplankton assemblages are mainly influenced by the depth of the mixed layer on a seasonal time scale. Indeed, solar incident irradiance is a limiting resource for phototrophic growth and small phytoplankton cells are advantaged over larger cells. This phenomenon is particularly relevant when vertical mixing is intense, such as during winter and early spring. Relaxation of wind-induced mixing in April causes an improvement of irradiance experienced by cells across the whole study area. This leads, in late spring, to a competitive advantage of larger functional groups such as diatoms as long as the nutrient supply is sufficient. This dominance of large, fast-growing autotrophic cells is also maintained during summer in the productive tidally-mixed shelf waters. In the oligotrophic surface layer of the western part of the Iroise Sea, small cells coexist in a greater proportion with large, nutrient limited cells. The productive Ushant tidal front's region (1800 mgC·m- 2·d- 1 between August and September) is also characterized by a high degree of coexistence between three functional groups (diatoms, micro/nano-flagellates and small eukaryotes/cyanobacteria). Consistent with
Distributed simulation a model driven engineering approach
Topçu, Okan; Oğuztüzün, Halit; Yilmaz, Levent
2016-01-01
Backed by substantive case studies, the novel approach to software engineering for distributed simulation outlined in this text demonstrates the potent synergies between model-driven techniques, simulation, intelligent agents, and computer systems development.
Variable slip wind generator modeling for real-time simulation
Energy Technology Data Exchange (ETDEWEB)
Gagnon, R.; Brochu, J.; Turmel, G. [Hydro-Quebec, Varennes, PQ (Canada). IREQ
2006-07-01
A model of a wind turbine using a variable slip wound-rotor induction machine was presented. The model was created as part of a library of generic wind generator models intended for wind integration studies. The stator winding of the wind generator was connected directly to the grid and the rotor was driven by the turbine through a drive train. The variable resistors was synthesized by an external resistor in parallel with a diode rectifier. A forced-commutated power electronic device (IGBT) was connected to the wound rotor by slip rings and brushes. Simulations were conducted in a Matlab/Simulink environment using SimPowerSystems blocks to model power systems elements and Simulink blocks to model the turbine, control system and drive train. Detailed descriptions of the turbine, the drive train and the control system were provided. The model's implementation in the simulator was also described. A case study demonstrating the real-time simulation of a wind generator connected at the distribution level of a power system was presented. Results of the case study were then compared with results obtained from the SimPowerSystems off-line simulation. Results showed good agreement between the waveforms, demonstrating the conformity of the real-time and the off-line simulations. The capability of Hypersim for real-time simulation of wind turbines with power electronic converters in a distribution network was demonstrated. It was concluded that hardware-in-the-loop (HIL) simulation of wind turbine controllers for wind integration studies in power systems is now feasible. 5 refs., 1 tab., 6 figs.
Service creation: a model-based approach
Quartel, Dick; van Sinderen, Marten J.; Ferreira Pires, Luis
1999-01-01
This paper presents a model-based approach to support service creation. In this approach, services are assumed to be created from (available) software components. The creation process may involve multiple design steps in which the requested service is repeatedly decomposed into more detailed
Mazumder, Sonal; Pavurala, Naresh; Manda, Prashanth; Xu, Xiaoming; Cruz, Celia N; Krishnaiah, Yellela S R
2017-07-15
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.
Ding, Xuan; He, Minxia; Kulkarni, Rajesh; Patel, Nita; Zhang, Xiaoyu
2013-08-01
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.
ltm: An R Package for Latent Variable Modeling and Item Response Analysis
Directory of Open Access Journals (Sweden)
Dimitris Rizopoulos
2006-11-01
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.
Models of galaxies - The modal approach
International Nuclear Information System (INIS)
Lin, C.C.; Lowe, S.A.
1990-01-01
The general viability of the modal approach to the spiral structure in normal spirals and the barlike structure in certain barred spirals is discussed. The usefulness of the modal approach in the construction of models of such galaxies is examined, emphasizing the adoption of a model appropriate to observational data for both the spiral structure of a galaxy and its basic mass distribution. 44 refs
International Nuclear Information System (INIS)
Sun, Huaiwei; Zhao, Na; Zeng, Xiaofan; Yan, Dong
2015-01-01
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
Modeling Source Water TOC Using Hydroclimate Variables and Local Polynomial Regression.
Samson, Carleigh C; Rajagopalan, Balaji; Summers, R Scott
2016-04-19
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.
Estimation and variable selection for generalized additive partial linear models
Wang, Li
2011-08-01
We study generalized additive partial linear models, proposing the use of polynomial spline smoothing for estimation of nonparametric functions, and deriving quasi-likelihood based estimators for the linear parameters. We establish asymptotic normality for the estimators of the parametric components. The procedure avoids solving large systems of equations as in kernel-based procedures and thus results in gains in computational simplicity. We further develop a class of variable selection procedures for the linear parameters by employing a nonconcave penalized quasi-likelihood, which is shown to have an asymptotic oracle property. Monte Carlo simulations and an empirical example are presented for illustration. © Institute of Mathematical Statistics, 2011.
Context Tree Estimation in Variable Length Hidden Markov Models
Dumont, Thierry
2011-01-01
We address the issue of context tree estimation in variable length hidden Markov models. We propose an estimator of the context tree of the hidden Markov process which needs no prior upper bound on the depth of the context tree. We prove that the estimator is strongly consistent. This uses information-theoretic mixture inequalities in the spirit of Finesso and Lorenzo(Consistent estimation of the order for Markov and hidden Markov chains(1990)) and E.Gassiat and S.Boucheron (Optimal error exp...
Remote sensing of the Canadian Arctic: Modelling biophysical variables
Liu, Nanfeng
It is anticipated that Arctic vegetation will respond in a variety of ways to altered temperature and precipitation patterns expected with climate change, including changes in phenology, productivity, biomass, cover and net ecosystem exchange. Remote sensing provides data and data processing methodologies for monitoring and assessing Arctic vegetation over large areas. The goal of this research was to explore the potential of hyperspectral and high spatial resolution multispectral remote sensing data for modelling two important Arctic biophysical variables: Percent Vegetation Cover (PVC) and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). A series of field experiments were conducted to collect PVC and fAPAR at three Canadian Arctic sites: (1) Sabine Peninsula, Melville Island, NU; (2) Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, NU; and (3) Apex River Watershed (ARW), Baffin Island, NU. Linear relationships between biophysical variables and Vegetation Indices (VIs) were examined at different spatial scales using field spectra (for the Sabine Peninsula site) and high spatial resolution satellite data (for the CBAWO and ARW sites). At the Sabine Peninsula site, hyperspectral VIs exhibited a better performance for modelling PVC than multispectral VIs due to their capacity for sampling fine spectral features. The optimal hyperspectral bands were located at important spectral features observed in Arctic vegetation spectra, including leaf pigment absorption in the red wavelengths and at the red-edge, leaf water absorption in the near infrared, and leaf cellulose and lignin absorption in the shortwave infrared. At the CBAWO and ARW sites, field PVC and fAPAR exhibited strong correlations (R2 > 0.70) with the NDVI (Normalized Difference Vegetation Index) derived from high-resolution WorldView-2 data. Similarly, high spatial resolution satellite-derived fAPAR was correlated to MODIS fAPAR (R2 = 0.68), with a systematic
Classification criteria of syndromes by latent variable models
DEFF Research Database (Denmark)
Petersen, Janne
2010-01-01
, although this is often desired. I have proposed a new method for predicting class membership that, in contrast to methods based on posterior probabilities of class membership, yields consistent estimates when regressed on explanatory variables in a subsequent analysis. There are four different basic models...... analyses. Part 1: HALS engages different phenotypic changes of peripheral lipoatrophy and central lipohypertrophy. There are several different definitions of HALS and no consensus on the number of phenotypes. Many of the definitions consist of counting fulfilled criteria on markers and do not include...
Modeling intraindividual variability with repeated measures data methods and applications
Hershberger, Scott L
2013-01-01
This book examines how individuals behave across time and to what degree that behavior changes, fluctuates, or remains stable.It features the most current methods on modeling repeated measures data as reported by a distinguished group of experts in the field. The goal is to make the latest techniques used to assess intraindividual variability accessible to a wide range of researchers. Each chapter is written in a ""user-friendly"" style such that even the ""novice"" data analyst can easily apply the techniques.Each chapter features:a minimum discussion of mathematical detail;an empirical examp
Variable recruitment fluidic artificial muscles: modeling and experiments
International Nuclear Information System (INIS)
Bryant, Matthew; Meller, Michael A; Garcia, Ephrahim
2014-01-01
We investigate taking advantage of the lightweight, compliant nature of fluidic artificial muscles to create variable recruitment actuators in the form of artificial muscle bundles. Several actuator elements at different diameter scales are packaged to act as a single actuator device. The actuator elements of the bundle can be connected to the fluidic control circuit so that different groups of actuator elements, much like individual muscle fibers, can be activated independently depending on the required force output and motion. This novel actuation concept allows us to save energy by effectively impedance matching the active size of the actuators on the fly based on the instantaneous required load. This design also allows a single bundled actuator to operate in substantially different force regimes, which could be valuable for robots that need to perform a wide variety of tasks and interact safely with humans. This paper proposes, models and analyzes the actuation efficiency of this actuator concept. The analysis shows that variable recruitment operation can create an actuator that reduces throttling valve losses to operate more efficiently over a broader range of its force–strain operating space. We also present preliminary results of the design, fabrication and experimental characterization of three such bioinspired variable recruitment actuator prototypes. (paper)
Uncertainty importance measure for models with correlated normal variables
International Nuclear Information System (INIS)
Hao, Wenrui; Lu, Zhenzhou; Wei, Pengfei
2013-01-01
In order to explore the contributions by correlated input variables to the variance of the model output, the contribution decomposition of the correlated input variables based on Mara's definition is investigated in detail. By taking the quadratic polynomial output without cross term as an illustration, the solution of the contribution decomposition is derived analytically using the statistical inference theory. After the correction of the analytical solution is validated by the numerical examples, they are employed to two engineering examples to show their wide application. The derived analytical solutions can directly be used to recognize the contributions by the correlated input variables in case of the quadratic or linear polynomial output without cross term, and the analytical inference method can be extended to the case of higher order polynomial output. Additionally, the origins of the interaction contribution of the correlated inputs are analyzed, and the comparisons of the existing contribution indices are completed, on which the engineer can select the suitable indices to know the necessary information. At last, the degeneration of the correlated inputs to the uncorrelated ones and some computational issues are discussed in concept
Consumer preference models: fuzzy theory approach
Turksen, I. B.; Wilson, I. A.
1993-12-01
Consumer preference models are widely used in new product design, marketing management, pricing and market segmentation. The purpose of this article is to develop and test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation) and how much to make (market share prediction).
Cadier, Mathilde; Sourisseau, Marc; Gorgues, Thomas; Edwards, Christopher A.; Memery, Laurent
2017-05-01
Tidal front ecosystems are especially dynamic environments usually characterized by high phytoplankton biomass and high primary production. However, the description of functional microbial diversity occurring in these regions remains only partially documented. In this article, we use a numerical model, simulating a large number of phytoplankton phenotypes to explore the three-dimensional spatial patterns of phytoplankton abundance and diversity in the Iroise Sea (western Brittany). Our results suggest that, in boreal summer, a seasonally marked tidal front shapes the phytoplankton species richness. A diversity maximum is found in the surface mixed layer located slightly west of the tidal front (i.e., not strictly co-localized with high biomass concentrations) which separates tidally mixed from stratified waters. Differences in phenotypic composition between sub-regions with distinct hydrodynamic regimes (defined by vertical mixing, nutrients gradients and light penetration) are discussed. Local growth and/or physical transport of phytoplankton phenotypes are shown to explain our simulated diversity distribution. We find that a large fraction (64%) of phenotypes present during the considered period of September are ubiquitous, found in the frontal area and on both sides of the front (i.e., over the full simulated domain). The frontal area does not exhibit significant differences between its community composition and that of either the well-mixed region or an offshore Deep Chlorophyll Maximum (DCM). Only three phenotypes (out of 77) specifically grow locally and are found at substantial concentration only in the surface diversity maximum. Thus, this diversity maximum is composed of a combination of ubiquitous phenotypes with specific picoplankton deriving from offshore, stratified waters (including specific phenotypes from both the surface and the DCM) and imported through physical transport, completed by a few local phenotypes. These results are discussed in light
Garibaldi, Jonathan M; Zhou, Shang-Ming; Wang, Xiao-Ying; John, Robert I; Ellis, Ian O
2012-06-01
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.
Retention and Curve Number Variability in a Small Agricultural Catchment: The Probabilistic Approach
Directory of Open Access Journals (Sweden)
Kazimierz Banasik
2014-04-01
Full Text Available The variability of the curve number (CN and the retention parameter (S of the Soil Conservation Service (SCS-CN method in a small agricultural, lowland watershed (23.4 km2 to the gauging station in central Poland has been assessed using the probabilistic approach: distribution fitting and confidence intervals (CIs. Empirical CNs and Ss were computed directly from recorded rainfall depths and direct runoff volumes. Two measures of the goodness of fit were used as selection criteria in the identification of the parent distribution function. The measures specified the generalized extreme value (GEV, normal and general logistic (GLO distributions for 100-CN and GLO, lognormal and GEV distributions for S. The characteristics estimated from theoretical distribution (median, quantiles were compared to the tabulated CN and to the antecedent runoff conditions of Hawkins and Hjelmfelt. The distribution fitting for the whole sample revealed a good agreement between the tabulated CN and the median and between the antecedent runoff conditions (ARCs of Hawkins and Hjelmfelt, which certified a good calibration of the model. However, the division of the CN sample due to heavy and moderate rainfall depths revealed a serious inconsistency between the parameters mentioned. This analysis proves that the application of the SCS-CN method should rely on deep insight into the probabilistic properties of CN and S.
A first approach to calculate BIOCLIM variables and climate zones for Antarctica
Wagner, Monika; Trutschnig, Wolfgang; Bathke, Arne C.; Ruprecht, Ulrike
2018-02-01
For testing the hypothesis that macroclimatological factors determine the occurrence, biodiversity, and species specificity of both symbiotic partners of Antarctic lecideoid lichens, we present a first approach for the computation of the full set of 19 BIOCLIM variables, as available at http://www.worldclim.org/ for all regions of the world with exception of Antarctica. Annual mean temperature (Bio 1) and annual precipitation (Bio 12) were chosen to define climate zones of the Antarctic continent and adjacent islands as required for ecological niche modeling (ENM). The zones are based on data for the years 2009-2015 which was obtained from the Antarctic Mesoscale Prediction System (AMPS) database of the Ohio State University. For both temperature and precipitation, two separate zonings were specified; temperature values were divided into 12 zones (named 1 to 12) and precipitation values into five (named A to E). By combining these two partitions, we defined climate zonings where each geographical point can be uniquely assigned to exactly one zone, which allows an immediate explicit interpretation. The soundness of the newly calculated climate zones was tested by comparison with already published data, which used only three zones defined on climate information from the literature. The newly defined climate zones result in a more precise assignment of species distribution to the single habitats. This study provides the basis for a more detailed continental-wide ENM using a comprehensive dataset of lichen specimens which are located within 21 different climate regions.
Fletcher, Jason M
2015-07-01
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.
Modelling the diurnal variability of SST and its vertical extent
DEFF Research Database (Denmark)
Karagali, Ioanna; Høyer, Jacob L.; Donlon, Craig J.
2014-01-01
of the water column where most of the heat is absorbed and where the exchange of heat and momentum with the atmosphere occurs. During day-time and under favourable conditions of low winds and high insolation, diurnal warming of the upper layer poses challenges for validating and calibrating satellite sensors......Sea Surface Temperature (SST) is a key variable in air-sea interactions, partly controlling the oceanic uptake of CO2 and the heat exchange between the ocean and the atmosphere, amongst others. Satellite SSTs are representative of skin and sub-skin temperature, i.e. in the upper millimetres...... and merging SST time series. When radiometer signals, typically from satellites, are validated with in situ measurements from drifting and moored buoys a general mismatch is found, associated with the different reference depth of each type of measurement. A generally preferred approach to bridge the gap...
Atzori, A S; Tedeschi, L O; Cannas, A
2013-05-01
The economic efficiency of dairy farms is the main goal of farmers. The objective of this work was to use routinely available information at the dairy farm level to develop an index of profitability to rank dairy farms and to assist the decision-making process of farmers to increase the economic efficiency of the entire system. A stochastic modeling approach was used to study the relationships between inputs and profitability (i.e., income over feed cost; IOFC) of dairy cattle farms. The IOFC was calculated as: milk revenue + value of male calves + culling revenue - herd feed costs. Two databases were created. The first one was a development database, which was created from technical and economic variables collected in 135 dairy farms. The second one was a synthetic database (sDB) created from 5,000 synthetic dairy farms using the Monte Carlo technique and based on the characteristics of the development database data. The sDB was used to develop a ranking index as follows: (1) principal component analysis (PCA), excluding IOFC, was used to identify principal components (sPC); and (2) coefficient estimates of a multiple regression of the IOFC on the sPC were obtained. Then, the eigenvectors of the sPC were used to compute the principal component values for the original 135 dairy farms that were used with the multiple regression coefficient estimates to predict IOFC (dRI; ranking index from development database). The dRI was used to rank the original 135 dairy farms. The PCA explained 77.6% of the sDB variability and 4 sPC were selected. The sPC were associated with herd profile, milk quality and payment, poor management, and reproduction based on the significant variables of the sPC. The mean IOFC in the sDB was 0.1377 ± 0.0162 euros per liter of milk (€/L). The dRI explained 81% of the variability of the IOFC calculated for the 135 original farms. When the number of farms below and above 1 standard deviation (SD) of the dRI were calculated, we found that 21
Energy Technology Data Exchange (ETDEWEB)
Ghasemi, Jahan B.; Zolfonoun, Ehsan [Toosi University of Technology, Tehran (Korea, Republic of)
2012-05-15
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.
International Nuclear Information System (INIS)
Ghasemi, Jahan B.; Zolfonoun, Ehsan
2012-01-01
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
Viscous dark energy models with variable G and Λ
International Nuclear Information System (INIS)
Arbab, Arbab I.
2008-01-01
We consider a cosmological model with bulk viscosity η and variable cosmological A ∝ ρ -α , alpha = const and gravitational G constants. The model exhibits many interesting cosmological features. Inflation proceeds due to the presence of bulk viscosity and dark energy without requiring the equation of state p=-ρ. During the inflationary era the energy density ρ does not remain constant, as in the de-Sitter type. Moreover, the cosmological and gravitational constants increase exponentially with time, whereas the energy density and viscosity decrease exponentially with time. The rate of mass creation during inflation is found to be very huge suggesting that all matter in the universe is created during inflation. (author)
Constrained variability of modeled T:ET ratio across biomes
Fatichi, Simone; Pappas, Christoforos
2017-07-01
A large variability (35-90%) in the ratio of transpiration to total evapotranspiration (referred here as T:ET) across biomes or even at the global scale has been documented by a number of studies carried out with different methodologies. Previous empirical results also suggest that T:ET does not covary with mean precipitation and has a positive dependence on leaf area index (LAI). Here we use a mechanistic ecohydrological model, with a refined process-based description of evaporation from the soil surface, to investigate the variability of T:ET across biomes. Numerical results reveal a more constrained range and higher mean of T:ET (70 ± 9%, mean ± standard deviation) when compared to observation-based estimates. T:ET is confirmed to be independent from mean precipitation, while it is found to be correlated with LAI seasonally but uncorrelated across multiple sites. Larger LAI increases evaporation from interception but diminishes ground evaporation with the two effects largely compensating each other. These results offer mechanistic model-based evidence to the ongoing research about the patterns of T:ET and the factors influencing its magnitude across biomes.
Multi-infill strategy for kriging models used in variable fidelity optimization
Directory of Open Access Journals (Sweden)
Chao SONG
2018-03-01
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
Modeling the variability of shapes of a human placenta.
Yampolsky, M; Salafia, C M; Shlakhter, O; Haas, D; Eucker, B; Thorp, J
2008-09-01
Placentas are generally round/oval in shape, but "irregular" shapes are common. In the Collaborative Perinatal Project data, irregular shapes were associated with lower birth weight for placental weight, suggesting variably shaped placentas have altered function. (I) Using a 3D one-parameter model of placental vascular growth based on Diffusion Limited Aggregation (an accepted model for generating highly branched fractals), models were run with a branching density growth parameter either fixed or perturbed at either 5-7% or 50% of model growth. (II) In a data set with detailed measures of 1207 placental perimeters, radial standard deviations of placental shapes were calculated from the umbilical cord insertion, and from the centroid of the shape (a biologically arbitrary point). These two were compared to the difference between the observed scaling exponent and the Kleiber scaling exponent (0.75), considered optimal for vascular fractal transport systems. Spearman's rank correlation considered pcentroid) was associated with differences from the Kleiber exponent (p=0.006). A dynamical DLA model recapitulates multilobate and "star" placental shapes via changing fractal branching density. We suggest that (1) irregular placental outlines reflect deformation of the underlying placental fractal vascular network, (2) such irregularities in placental outline indicate sub-optimal branching structure of the vascular tree, and (3) this accounts for the lower birth weight observed in non-round/oval placentas in the Collaborative Perinatal Project.
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Multiscale approach to equilibrating model polymer melts
DEFF Research Database (Denmark)
Svaneborg, Carsten; Ali Karimi-Varzaneh, Hossein; Hojdis, Nils
2016-01-01
We present an effective and simple multiscale method for equilibrating Kremer Grest model polymer melts of varying stiffness. In our approach, we progressively equilibrate the melt structure above the tube scale, inside the tube and finally at the monomeric scale. We make use of models designed...
DEFF Research Database (Denmark)
Köster, Fritz; Hinrichsen, H.H.; St. John, Michael
2001-01-01
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...
REDUCING PROCESS VARIABILITY BY USING DMAIC MODEL: A CASE STUDY IN BANGLADESH
Directory of Open Access Journals (Sweden)
Ripon Kumar Chakrabortty
2013-03-01
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.
Barnett, Tony; Fournié, Guillaume; Gupta, Sunetra; Seeley, Janet
2015-01-01
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.
Schauberger, Bernhard; Rolinski, Susanne; Müller, Christoph
2016-12-01
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.
Multi-scale climate modelling over Southern Africa using a variable-resolution global model
CSIR Research Space (South Africa)
Engelbrecht, FA
2011-12-01
Full Text Available -mail: fengelbrecht@csir.co.za 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...
A Quantitative Approach to Variables Affecting Production of Short Films in Turkey
Directory of Open Access Journals (Sweden)
Vedat Akman
2011-08-01
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.
Pradeep, Krishna; Poiroux, Thierry; Scheer, Patrick; Juge, André; Gouget, Gilles; Ghibaudo, Gérard
2018-07-01
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.
Application of various FLD modelling approaches
Banabic, D.; Aretz, H.; Paraianu, L.; Jurco, P.
2005-07-01
This paper focuses on a comparison between different modelling approaches to predict the forming limit diagram (FLD) for sheet metal forming under a linear strain path using the recently introduced orthotropic yield criterion BBC2003 (Banabic D et al 2005 Int. J. Plasticity 21 493-512). The FLD models considered here are a finite element based approach, the well known Marciniak-Kuczynski model, the modified maximum force criterion according to Hora et al (1996 Proc. Numisheet'96 Conf. (Dearborn/Michigan) pp 252-6), Swift's diffuse (Swift H W 1952 J. Mech. Phys. Solids 1 1-18) and Hill's classical localized necking approach (Hill R 1952 J. Mech. Phys. Solids 1 19-30). The FLD of an AA5182-O aluminium sheet alloy has been determined experimentally in order to quantify the predictive capabilities of the models mentioned above.
Transient modelling of a natural circulation loop under variable pressure
International Nuclear Information System (INIS)
Vianna, Andre L.B.; Faccini, Jose L.H.; Su, Jian; Instituto de Engenharia Nuclear
2017-01-01
The objective of the present work is to model the transient operation of a natural circulation loop, which is one-tenth scale in height to a typical Passive Residual Heat Removal system (PRHR) of an Advanced Pressurized Water Nuclear Reactor and was designed to meet the single and two-phase flow similarity criteria to it. The loop consists of a core barrel with electrically heated rods, upper and lower plena interconnected by hot and cold pipe legs to a seven-tube shell heat exchanger of countercurrent design, and an expansion tank with a descending tube. A long transient characterized the loop operation, during which a phenomenon of self-pressurization, without self-regulation of the pressure, was experimentally observed. This represented a unique situation, named natural circulation under variable pressure (NCVP). The self-pressurization was originated in the air trapped in the expansion tank and compressed by the loop water dilatation, as it heated up during each experiment. The mathematical model, initially oriented to the single-phase flow, included the heat capacity of the structure and employed a cubic polynomial approximation for the density, in the buoyancy term calculation. The heater was modelled taking into account the different heat capacities of the heating elements and the heater walls. The heat exchanger was modelled considering the coolant heating, during the heat exchanging process. The self-pressurization was modelled as an isentropic compression of a perfect gas. The whole model was computationally implemented via a set of finite difference equations. The corresponding computational algorithm of solution was of the explicit, marching type, as for the time discretization, in an upwind scheme, regarding the space discretization. The computational program was implemented in MATLAB. Several experiments were carried out in the natural circulation loop, having the coolant flow rate and the heating power as control parameters. The variables used in the
Transient modelling of a natural circulation loop under variable pressure
Energy Technology Data Exchange (ETDEWEB)
Vianna, Andre L.B.; Faccini, Jose L.H.; Su, Jian, E-mail: avianna@nuclear.ufrj.br, E-mail: sujian@nuclear.ufrj.br, E-mail: faccini@ien.gov.br [Coordenacao de Pos-Graduacao e Pesquisa de Engenharia (COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear; Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil). Lab. de Termo-Hidraulica Experimental
2017-07-01
The objective of the present work is to model the transient operation of a natural circulation loop, which is one-tenth scale in height to a typical Passive Residual Heat Removal system (PRHR) of an Advanced Pressurized Water Nuclear Reactor and was designed to meet the single and two-phase flow similarity criteria to it. The loop consists of a core barrel with electrically heated rods, upper and lower plena interconnected by hot and cold pipe legs to a seven-tube shell heat exchanger of countercurrent design, and an expansion tank with a descending tube. A long transient characterized the loop operation, during which a phenomenon of self-pressurization, without self-regulation of the pressure, was experimentally observed. This represented a unique situation, named natural circulation under variable pressure (NCVP). The self-pressurization was originated in the air trapped in the expansion tank and compressed by the loop water dilatation, as it heated up during each experiment. The mathematical model, initially oriented to the single-phase flow, included the heat capacity of the structure and employed a cubic polynomial approximation for the density, in the buoyancy term calculation. The heater was modelled taking into account the different heat capacities of the heating elements and the heater walls. The heat exchanger was modelled considering the coolant heating, during the heat exchanging process. The self-pressurization was modelled as an isentropic compression of a perfect gas. The whole model was computationally implemented via a set of finite difference equations. The corresponding computational algorithm of solution was of the explicit, marching type, as for the time discretization, in an upwind scheme, regarding the space discretization. The computational program was implemented in MATLAB. Several experiments were carried out in the natural circulation loop, having the coolant flow rate and the heating power as control parameters. The variables used in the
Risk Modelling for Passages in Approach Channel
Directory of Open Access Journals (Sweden)
Leszek Smolarek
2013-01-01
Full Text Available Methods of multivariate statistics, stochastic processes, and simulation methods are used to identify and assess the risk measures. This paper presents the use of generalized linear models and Markov models to study risks to ships along the approach channel. These models combined with simulation testing are used to determine the time required for continuous monitoring of endangered objects or period at which the level of risk should be verified.
Zhaohua Dai; Carl C. Trettin; Changsheng Li; Ge Sun; Devendra M. Amatya; Harbin Li
2013-01-01
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...
Reduced modeling of signal transduction – a modular approach
Directory of Open Access Journals (Sweden)
Ederer Michael
2007-09-01
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
Energy Technology Data Exchange (ETDEWEB)
Deque, M.; Somot, S. [Meteo-France, Centre National de Recherches Meteorologiques, CNRS/GAME, Toulouse Cedex 01 (France); Sanchez-Gomez, E. [Cerfacs/CNRS, SUC URA1875, Toulouse Cedex 01 (France); Goodess, C.M. [University of East Anglia, Climatic Research Unit, Norwich (United Kingdom); Jacob, D. [Max Planck Institute for Meteorology, Hamburg (Germany); Lenderink, G. [KNMI, Postbus 201, De Bilt (Netherlands); Christensen, O.B. [Danish Meteorological Institute, Copenhagen Oe (Denmark)
2012-03-15
Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021-2050 and the 1961-1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM x GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021-2050 response which shows a similar pattern to the one obtained for 2071-2100 in PRUDENCE. The uncertainty
Resolving structural variability in network models and the brain.
Directory of Open Access Journals (Sweden)
Florian Klimm
2014-03-01
Full Text Available Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling--in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity do not in general simultaneously display a second (e.g., hierarchy. This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful
Fernandes, José Antonio; Lozano, Jose A.; Iñ za, Iñ aki; Irigoien, Xabier; Pé rez, Aritz; Rodrí guez, Juan Diego
2013-01-01
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
On the explaining-away phenomenon in multivariate latent variable models.
van Rijn, Peter; Rijmen, Frank
2015-02-01
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.
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)
2016-05-11
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.
Set-Theoretic Approach to Maturity Models
DEFF Research Database (Denmark)
Lasrado, Lester Allan
Despite being widely accepted and applied, maturity models in Information Systems (IS) have been criticized for the lack of theoretical grounding, methodological rigor, empirical validations, and ignorance of multiple and non-linear paths to maturity. This PhD thesis focuses on addressing...... these criticisms by incorporating recent developments in configuration theory, in particular application of set-theoretic approaches. The aim is to show the potential of employing a set-theoretic approach for maturity model research and empirically demonstrating equifinal paths to maturity. Specifically...... methodological guidelines consisting of detailed procedures to systematically apply set theoretic approaches for maturity model research and provides demonstrations of it application on three datasets. The thesis is a collection of six research papers that are written in a sequential manner. The first paper...
Total Variability Modeling using Source-specific Priors
DEFF Research Database (Denmark)
Shepstone, Sven Ewan; Lee, Kong Aik; Li, Haizhou
2016-01-01
sequence of an utterance. In both cases the prior for the latent variable is assumed to be non-informative, since for homogeneous datasets there is no gain in generality in using an informative prior. This work shows in the heterogeneous case, that using informative priors for com- puting the posterior......, can lead to favorable results. We focus on modeling the priors using minimum divergence criterion or fac- tor analysis techniques. Tests on the NIST 2008 and 2010 Speaker Recognition Evaluation (SRE) dataset show that our proposed method beats four baselines: For i-vector extraction using an already...... trained matrix, for the short2-short3 task in SRE’08, five out of eight female and four out of eight male common conditions, were improved. For the core-extended task in SRE’10, four out of nine female and six out of nine male common conditions were improved. When incorporating prior information...
Garcia-Diaz, Antón; Leborán, Víctor; Fdez-Vidal, Xosé R; Pardo, Xosé M
2012-06-12
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.
Mathematical Modeling Approaches in Plant Metabolomics.
Fürtauer, Lisa; Weiszmann, Jakob; Weckwerth, Wolfram; Nägele, Thomas
2018-01-01
The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.
Dom, Nazri Che; Hassan, A Abu; Latif, Z Abd; Ismail, Rodziah
2013-01-01
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.
SLS Navigation Model-Based Design Approach
Oliver, T. Emerson; Anzalone, Evan; Geohagan, Kevin; Bernard, Bill; Park, Thomas
2018-01-01
The SLS Program chose to implement a Model-based Design and Model-based Requirements approach for managing component design information and system requirements. This approach differs from previous large-scale design efforts at Marshall Space Flight Center where design documentation alone conveyed information required for vehicle design and analysis and where extensive requirements sets were used to scope and constrain the design. The SLS Navigation Team has been responsible for the Program-controlled Design Math Models (DMMs) which describe and represent the performance of the Inertial Navigation System (INS) and the Rate Gyro Assemblies (RGAs) used by Guidance, Navigation, and Controls (GN&C). The SLS Navigation Team is also responsible for the navigation algorithms. The navigation algorithms are delivered for implementation on the flight hardware as a DMM. For the SLS Block 1-B design, the additional GPS Receiver hardware is managed as a DMM at the vehicle design level. This paper provides a discussion of the processes and methods used to engineer, design, and coordinate engineering trades and performance assessments using SLS practices as applied to the GN&C system, with a particular focus on the Navigation components. These include composing system requirements, requirements verification, model development, model verification and validation, and modeling and analysis approaches. The Model-based Design and Requirements approach does not reduce the effort associated with the design process versus previous processes used at Marshall Space Flight Center. Instead, the approach takes advantage of overlap between the requirements development and management process, and the design and analysis process by efficiently combining the control (i.e. the requirement) and the design mechanisms. The design mechanism is the representation of the component behavior and performance in design and analysis tools. The focus in the early design process shifts from the development and
DEFF Research Database (Denmark)
Østergaard, Jacob; Kramer, Mark A.; Eden, Uri T.
2018-01-01
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...
Stochastic approaches to inflation model building
International Nuclear Information System (INIS)
Ramirez, Erandy; Liddle, Andrew R.
2005-01-01
While inflation gives an appealing explanation of observed cosmological data, there are a wide range of different inflation models, providing differing predictions for the initial perturbations. Typically models are motivated either by fundamental physics considerations or by simplicity. An alternative is to generate large numbers of models via a random generation process, such as the flow equations approach. The flow equations approach is known to predict a definite structure to the observational predictions. In this paper, we first demonstrate a more efficient implementation of the flow equations exploiting an analytic solution found by Liddle (2003). We then consider alternative stochastic methods of generating large numbers of inflation models, with the aim of testing whether the structures generated by the flow equations are robust. We find that while typically there remains some concentration of points in the observable plane under the different methods, there is significant variation in the predictions amongst the methods considered
Model validation: a systemic and systematic approach
International Nuclear Information System (INIS)
Sheng, G.; Elzas, M.S.; Cronhjort, B.T.
1993-01-01
The term 'validation' is used ubiquitously in association with the modelling activities of numerous disciplines including social, political natural, physical sciences, and engineering. There is however, a wide range of definitions which give rise to very different interpretations of what activities the process involves. Analyses of results from the present large international effort in modelling radioactive waste disposal systems illustrate the urgent need to develop a common approach to model validation. Some possible explanations are offered to account for the present state of affairs. The methodology developed treats model validation and code verification in a systematic fashion. In fact, this approach may be regarded as a comprehensive framework to assess the adequacy of any simulation study. (author)
Energy Technology Data Exchange (ETDEWEB)
Senechal, U.; Breitkopf, C. [Technische Univ. Dresden (Germany). Inst. fuer Energietechnik
2011-07-01
Temporal analysis of products (TAP) is a valuable tool for characterization of porous catalytic structures. Established TAP-modeling requires a spatially constant diffusion coefficient and neglect convective flows, which is only valid in Knudsen diffusion regime. Therefore in experiments, the number of molecules per pulse must be chosen accordingly. New approaches for variable process conditions are highly required. Thus, a new theoretical model is developed for estimating the number of molecules per pulse to meet these requirements under any conditions and at any time. The void volume is calculated as the biggest sphere fitting between three pellets. The total number of pulsed molecules is assumed to fill the first void volume at the inlet immediately. Molecule numbers from these calculations can be understood as maximum possible molecules at any time in the reactor to be in Knudsen diffusion regime, i.e., above the Knudsen number of 2. Moreover, a new methodology for generating a full three-dimensional geometrical representation of beds is presented and used for numerical simulations to investigate spatial effects. Based on a freely available open-source game physics engine library (BULLET), beds of arbitrary-sized pellets can be generated and transformed to CFD-usable geometry. In CFD-software (ANSYS CFX registered) a transient diffusive transport equation with time-dependent inlet boundary conditions is solved. Three different pellet diameters were investigated with 1e18 molecules per pulse, which is higher than the limit from the theoretical calculation. Spatial and temporal distributions of transported species show regions inside the reactor, where non-Knudsen conditions exist. From this results, the distance from inlet can be calculated where the theoretical pressure limit (Knudsen number equals 2) is obtained, i.e., from this point to the end of the reactor Knudsen regime can be assumed. Due to linear dependency of pressure and concentration (assuming ideal
Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C
2017-08-22
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
Modelling carbon and nitrogen turnover in variably saturated soils
Batlle-Aguilar, J.; Brovelli, A.; Porporato, A.; Barry, D. A.
2009-04-01
Natural ecosystems provide services such as ameliorating the impacts of deleterious human activities on both surface and groundwater. For example, several studies have shown that a healthy riparian ecosystem can reduce the nutrient loading of agricultural wastewater, thus protecting the receiving surface water body. As a result, in order to develop better protection strategies and/or restore natural conditions, there is a growing interest in understanding ecosystem functioning, including feedbacks and nonlinearities. Biogeochemical transformations in soils are heavily influenced by microbial decomposition of soil organic matter. Carbon and nutrient cycles are in turn strongly sensitive to environmental conditions, and primarily to soil moisture and temperature. These two physical variables affect the reaction rates of almost all soil biogeochemical transformations, including microbial and fungal activity, nutrient uptake and release from plants, etc. Soil water saturation and temperature are not constants, but vary both in space and time, thus further complicating the picture. In order to interpret field experiments and elucidate the different mechanisms taking place, numerical tools are beneficial. In this work we developed a 3D numerical reactive-transport model as an aid in the investigation the complex physical, chemical and biological interactions occurring in soils. The new code couples the USGS models (MODFLOW 2000-VSF, MT3DMS and PHREEQC) using an operator-splitting algorithm, and is a further development an existing reactive/density-dependent flow model PHWAT. The model was tested using simplified test cases. Following verification, a process-based biogeochemical reaction network describing the turnover of carbon and nitrogen in soils was implemented. Using this tool, we investigated the coupled effect of moisture content and temperature fluctuations on nitrogen and organic matter cycling in the riparian zone, in order to help understand the relative
Examples of EOS Variables as compared to the UMM-Var Data Model
Cantrell, Simon; Lynnes, Chris
2016-01-01
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.
Modelling of Station of Pumping by Variable Speed
Directory of Open Access Journals (Sweden)
Benretem A.
2016-05-01
Full Text Available An increased energetic efficiency will make it possible to decrease the factory operating costs and hence to increase productivity. The centrifugal pumps are largely used because of their relatively simple operation and of their purchase price. One analyses thorough requirements imposed by the pumping plants is decisive. It is important to keep in mind the fact that the pumps consume approximately 20% of energy in the world. They constitute the possibility for the most significant efficiency improvement. They can reach their maximum effectiveness only with one pressure and a given flow. The approach suggested makes it possible to adapt with accuracy and effectiveness of system output of the industrial process requirements. The variable speed drive is one of best and effective techniques studied to reach this objective. The appearance of this technique comes only after the evolution obtained in the field of power electronics systems precisely static inverters as well as the efforts made by the researchers in the field of electric drive systems. This work suggested is the result of an in-depth study on the effectiveness of this new technique applied for the centrifugal pumps.
Harrigan, George G; Harrison, Jay M
2012-01-01
New transgenic (GM) crops are subjected to extensive safety assessments that include compositional comparisons with conventional counterparts as a cornerstone of the process. The influence of germplasm, location, environment, and agronomic treatments on compositional variability is, however, often obscured in these pair-wise comparisons. Furthermore, classical statistical significance testing can often provide an incomplete and over-simplified summary of highly responsive variables such as crop composition. In order to more clearly describe the influence of the numerous sources of compositional variation we present an introduction to two alternative but complementary approaches to data analysis and interpretation. These include i) exploratory data analysis (EDA) with its emphasis on visualization and graphics-based approaches and ii) Bayesian statistical methodology that provides easily interpretable and meaningful evaluations of data in terms of probability distributions. The EDA case-studies include analyses of herbicide-tolerant GM soybean and insect-protected GM maize and soybean. Bayesian approaches are presented in an analysis of herbicide-tolerant GM soybean. Advantages of these approaches over classical frequentist significance testing include the more direct interpretation of results in terms of probabilities pertaining to quantities of interest and no confusion over the application of corrections for multiple comparisons. It is concluded that a standardized framework for these methodologies could provide specific advantages through enhanced clarity of presentation and interpretation in comparative assessments of crop composition.
Niederdrenk, L.; Sein, D.; Mikolajewicz, U.
2013-12-01
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
Unified models of interactions with gauge-invariant variables
International Nuclear Information System (INIS)
Zet, Gheorghe
2000-01-01
A model of gauge theory is formulated in terms of gauge-invariant variables over a 4-dimensional space-time. Namely, we define a metric tensor g μν ( μ , ν = 0,1,2,3) starting with the components F μν a and F μν a tilde of the tensor associated to the Yang-Mills fields and its dual: g μν = 1/(3Δ 1/3 ) (ε abc F μα a F αβ b tilde F βν c ). Here Δ is a scale factor which can be chosen of a convenient form so that the theory may be self-dual or not. The components g μν are interpreted as new gauge-invariant variables. The model is applied to the case when the gauge group is SU(2). For the space-time we choose two different manifolds: (i) the space-time is R x S 3 , where R is the real line and S 3 is the three-dimensional sphere; (ii) the space-time is endowed with axial symmetry. We calculate the components g μν of the new metric for the two cases in terms of SU(2) gauge potentials. Imposing the supplementary condition that the new metric coincides with the initial metric of the space-time, we obtain the field equations (of the first order in derivatives) for the gauge fields. In addition, we determine the scale factor Δ which is introduced in the definition of g μν to ensure the property of self-duality for our SU(2) gauge theory, namely, 1/(2√g)(ε αβστ g μα g νβ F στ a = F μν a , g = det (g μν ). In the case (i) we show that the space-time R x S 3 is not compatible with a self-dual SU(2) gauge theory, but in the case (ii) the condition of self-duality is satisfied. The model developed in our work can be considered as a possible way to unification of general relativity and Yang-Mills theories. This means that the gauge theory can be formulated in the close analogy with the general relativity, i.e. the Yang-Mills equations are equivalent to Einstein equations with the right-hand side of a simple form. (authors)
White dwarf models of supernovae and cataclysmic variables
International Nuclear Information System (INIS)
Nomoto, K.; Hashimoto, M.
1986-01-01
If the accreting white dwarf increases its mass to the Chandrasekhar mass, it will either explode as a Type I supernova or collapse to form a neutron star. In fact, there is a good agreement between the exploding white dwarf model for Type I supernovae and observations. We describe various types of evolution of accreting white dwarfs as a function of binary parameters (i.e,. composition, mass, and age of the white dwarf, its companion star, and mass accretion rate), and discuss the conditions for the precursors of exploding or collapsing white dwarfs, and their relevance to cataclysmic variables. Particular attention is given to helium star cataclysmics which might be the precursors of some Type I supernovae or ultrashort period x-ray binaries. Finally we present new evolutionary calculations using the updated nuclear reaction rates for the formation of O+Ne+Mg white dwarfs, and discuss the composition structure and their relevance to the model for neon novae. 61 refs., 14 figs
Multidecadal Variability in Surface Albedo Feedback Across CMIP5 Models
Schneider, Adam; Flanner, Mark; Perket, Justin
2018-02-01
Previous studies quantify surface albedo feedback (SAF) in climate change, but few assess its variability on decadal time scales. Using the Coupled Model Intercomparison Project Version 5 (CMIP5) multimodel ensemble data set, we calculate time evolving SAF in multiple decades from surface albedo and temperature linear regressions. Results are meaningful when temperature change exceeds 0.5 K. Decadal-scale SAF is strongly correlated with century-scale SAF during the 21st century. Throughout the 21st century, multimodel ensemble mean SAF increases from 0.37 to 0.42 W m-2 K-1. These results suggest that models' mean decadal-scale SAFs are good estimates of their century-scale SAFs if there is at least 0.5 K temperature change. Persistent SAF into the late 21st century indicates ongoing capacity for Arctic albedo decline despite there being less sea ice. If the CMIP5 multimodel ensemble results are representative of the Earth, we cannot expect decreasing Arctic sea ice extent to suppress SAF in the 21st century.
Energy Technology Data Exchange (ETDEWEB)
Bjoerndal, Endre; Bjoerndal, Mette; Camanho, Ana
2008-07-01
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
A Conceptual Modeling Approach for OLAP Personalization
Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan
Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.
Variational approach to chiral quark models
Energy Technology Data Exchange (ETDEWEB)
Futami, Yasuhiko; Odajima, Yasuhiko; Suzuki, Akira
1987-03-01
A variational approach is applied to a chiral quark model to test the validity of the perturbative treatment of the pion-quark interaction based on the chiral symmetry principle. It is indispensably related to the chiral symmetry breaking radius if the pion-quark interaction can be regarded as a perturbation.
A variational approach to chiral quark models
International Nuclear Information System (INIS)
Futami, Yasuhiko; Odajima, Yasuhiko; Suzuki, Akira.
1987-01-01
A variational approach is applied to a chiral quark model to test the validity of the perturbative treatment of the pion-quark interaction based on the chiral symmetry principle. It is indispensably related to the chiral symmetry breaking radius if the pion-quark interaction can be regarded as a perturbation. (author)
Validation of an employee satisfaction model: A structural equation model approach
Directory of Open Access Journals (Sweden)
Ophillia Ledimo
2015-01-01
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.
Directory of Open Access Journals (Sweden)
Gu NY
2008-12-01
Full Text Available There are limited studies on quantifying the impact of patient satisfaction with pharmacist consultation on patient medication adherence. Objectives: The objective of this study is to evaluate the effect of patient satisfaction with pharmacist consultation services on medication adherence in a large managed care organization. Methods: We analyzed data from a patient satisfaction survey of 6,916 patients who had used pharmacist consultation services in Kaiser Permanente Southern California from 1993 to 1996. We compared treating patient satisfaction as exogenous, in a single-equation probit model, with a bivariate probit model where patient satisfaction was treated as endogenous. Different sets of instrumental variables were employed, including measures of patients' emotional well-being and patients' propensity to fill their prescriptions at a non-Kaiser Permanente (KP pharmacy. The Smith-Blundell test was used to test whether patient satisfaction was endogenous. Over-identification tests were used to test the validity of the instrumental variables. The Staiger-Stock weak instrument test was used to evaluate the explanatory power of the instrumental variables. Results: All tests indicated that the instrumental variables method was valid and the instrumental variables used have significant explanatory power. The single equation probit model indicated that the effect of patient satisfaction with pharmacist consultation was significant (p<0.010. However, the bivariate probit models revealed that the marginal effect of pharmacist consultation on medication adherence was significantly greater than the single equation probit. The effect increased from 7% to 30% (p<0.010 after controlling for endogeneity bias. Conclusion: After appropriate adjustment for endogeneity bias, patients satisfied with their pharmacy services are substantially more likely to adhere to their medication. The results have important policy implications given the increasing focus
Energy Technology Data Exchange (ETDEWEB)
Fedorov, Alexey V. [Yale Univ., New Haven, CT (United States)
2015-01-14
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.
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling
Amatulli, Giuseppe; Domisch, Sami; Tuanmu, Mao-Ning; Parmentier, Benoit; Ranipeta, Ajay; Malczyk, Jeremy; Jetz, Walter
2018-03-01
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 http://www.earthenv.org/topography.
A novel approach to modeling atmospheric convection
Goodman, A.
2016-12-01
The inadequate representation of clouds continues to be a large source of uncertainty in the projections from global climate models (GCMs). With continuous advances in computational power, however, the ability for GCMs to explicitly resolve cumulus convection will soon be realized. For this purpose, Jung and Arakawa (2008) proposed the Vector Vorticity Model (VVM), in which vorticity is the predicted variable instead of momentum. This has the advantage of eliminating the pressure gradient force within the framework of an anelastic system. However, the VVM was designed for use on a planar quadrilateral grid, making it unsuitable for implementation in global models discretized on the sphere. Here we have proposed a modification to the VVM where instead the curl of the horizontal vorticity is the primary predicted variable. This allows us to maintain the benefits of the original VVM while working within the constraints of a non-quadrilateral mesh. We found that our proposed model produced results from a warm bubble simulation that were consistent with the VVM. Further improvements that can be made to the VVM are also discussed.
Energy Technology Data Exchange (ETDEWEB)
Eugène, Sarah, E-mail: Sarah.Eugene@inria.fr; Doumic, Marie, E-mail: Philippe.Robert@inria.fr, E-mail: Marie.Doumic@inria.fr [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: W.F.Xue@kent.ac.uk [School of Biosciences, University of Kent, Canterbury, Kent CT2 7NJ (United Kingdom); Robert, Philippe, E-mail: Philippe.Robert@inria.fr [INRIA de Paris, 2 Rue Simone Iff, CS 42112, 75589 Paris Cedex 12 (France)
2016-05-07
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.
Approaches for developing a sizing method for stand-alone PV systems with variable demand
Energy Technology Data Exchange (ETDEWEB)
Posadillo, R. [Grupo de Investigacion en Energias y Recursos Renovables, Dpto. de Fisica Aplicada, E.P.S., Universidad de Cordoba, Avda. Menendez Pidal s/n, 14004 Cordoba (Spain); Lopez Luque, R. [Grupo de Investigacion de Fisica para las Energias y Recursos Renovables, Dpto. de Fisica Aplicada. Edificio C2 Campus de Rabanales, 14071 Cordoba (Spain)
2008-05-15
Accurate sizing is one of the most important aspects to take into consideration when designing a stand-alone photovoltaic system (SAPV). Various methods, which differ in terms of their simplicity or reliability, have been developed for this purpose. Analytical methods, which seek functional relationships between variables of interest to the sizing problem, are one of these approaches. A series of rational considerations are presented in this paper with the aim of shedding light upon the basic principles and results of various sizing methods proposed by different authors. These considerations set the basis for a new analytical method that has been designed for systems with variable monthly energy demands. Following previous approaches, the method proposed is based on the concept of loss of load probability (LLP) - a parameter that is used to characterize system design. The method includes information on the standard deviation of loss of load probability ({sigma}{sub LLP}) and on two new parameters: annual number of system failures (f) and standard deviation of annual number of failures ({sigma}{sub f}). The method proves useful for sizing a PV system in a reliable manner and serves to explain the discrepancies found in the research on systems with LLP<10{sup -2}. We demonstrate that reliability depends not only on the sizing variables and on the distribution function of solar radiation, but on the minimum value as well, which in a given location and with a monthly average clearness index, achieves total solar radiation on the receiver surface. (author)
A Set Theoretical Approach to Maturity Models
DEFF Research Database (Denmark)
Lasrado, Lester; Vatrapu, Ravi; Andersen, Kim Normann
2016-01-01
characterized by equifinality, multiple conjunctural causation, and case diversity. We prescribe methodological guidelines consisting of a six-step procedure to systematically apply set theoretic methods to conceptualize, develop, and empirically derive maturity models and provide a demonstration......Maturity Model research in IS has been criticized for the lack of theoretical grounding, methodological rigor, empirical validations, and ignorance of multiple and non-linear paths to maturity. To address these criticisms, this paper proposes a novel set-theoretical approach to maturity models...
International Nuclear Information System (INIS)
Zarzo, Manuel; Marti, Pau
2011-01-01
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
A hybrid modeling approach for option pricing
Hajizadeh, Ehsan; Seifi, Abbas
2011-11-01
The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.
BN-FLEMOps pluvial - A probabilistic multi-variable loss estimation model for pluvial floods
Roezer, V.; Kreibich, H.; Schroeter, K.; Doss-Gollin, J.; Lall, U.; Merz, B.
2017-12-01
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
Heat transfer modeling an inductive approach
Sidebotham, George
2015-01-01
This innovative text emphasizes a "less-is-more" approach to modeling complicated systems such as heat transfer by treating them first as "1-node lumped models" that yield simple closed-form solutions. The author develops numerical techniques for students to obtain more detail, but also trains them to use the techniques only when simpler approaches fail. Covering all essential methods offered in traditional texts, but with a different order, Professor Sidebotham stresses inductive thinking and problem solving as well as a constructive understanding of modern, computer-based practice. Readers learn to develop their own code in the context of the material, rather than just how to use packaged software, offering a deeper, intrinsic grasp behind models of heat transfer. Developed from over twenty-five years of lecture notes to teach students of mechanical and chemical engineering at The Cooper Union for the Advancement of Science and Art, the book is ideal for students and practitioners across engineering discipl...
Quantifying natural delta variability using a multiple-point geostatistics prior uncertainty model
Scheidt, Céline; Fernandes, Anjali M.; Paola, Chris; Caers, Jef
2016-10-01
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.
Sensitivity analysis on uncertainty variables affecting the NPP's LUEC with probabilistic approach
International Nuclear Information System (INIS)
Nuryanti; Akhmad Hidayatno; Erlinda Muslim
2013-01-01
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)
Tao, F.; Rötter, R.
2013-12-01
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
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
2017-12-01
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
Nonperturbative approach to the attractive Hubbard model
International Nuclear Information System (INIS)
Allen, S.; Tremblay, A.-M. S.
2001-01-01
A nonperturbative approach to the single-band attractive Hubbard model is presented in the general context of functional-derivative approaches to many-body theories. As in previous work on the repulsive model, the first step is based on a local-field-type ansatz, on enforcement of the Pauli principle and a number of crucial sumrules. The Mermin-Wagner theorem in two dimensions is automatically satisfied. At this level, two-particle self-consistency has been achieved. In the second step of the approximation, an improved expression for the self-energy is obtained by using the results of the first step in an exact expression for the self-energy, where the high- and low-frequency behaviors appear separately. The result is a cooperon-like formula. The required vertex corrections are included in this self-energy expression, as required by the absence of a Migdal theorem for this problem. Other approaches to the attractive Hubbard model are critically compared. Physical consequences of the present approach and agreement with Monte Carlo simulations are demonstrated in the accompanying paper (following this one)
Directory of Open Access Journals (Sweden)
Günther Klonner
Full Text Available The search for traits that make alien species invasive has mostly concentrated on comparing successful invaders and different comparison groups with respect to average trait values. By contrast, little attention has been paid to trait variability among invaders. Here, we combine an analysis of trait differences between invasive and non-invasive species with a comparison of multidimensional trait variability within these two species groups. We collected data on biological and distributional traits for 1402 species of the native, non-woody vascular plant flora of Austria. We then compared the subsets of species recorded and not recorded as invasive aliens anywhere in the world, respectively, first, with respect to the sampled traits using univariate and multiple regression models; and, second, with respect to their multidimensional trait diversity by calculating functional richness and dispersion metrics. Attributes related to competitiveness (strategy type, nitrogen indicator value, habitat use (agricultural and ruderal habitats, occurrence under the montane belt, and propagule pressure (frequency were most closely associated with invasiveness. However, even the best multiple model, including interactions, only explained a moderate fraction of the differences in invasive success. In addition, multidimensional variability in trait space was even larger among invasive than among non-invasive species. This pronounced variability suggests that invasive success has a considerable idiosyncratic component and is probably highly context specific. We conclude that basing risk assessment protocols on species trait profiles will probably face hardly reducible uncertainties.
A variable capacitance based modeling and power capability predicting method for ultracapacitor
Liu, Chang; Wang, Yujie; Chen, Zonghai; Ling, Qiang
2018-01-01
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.
McCarty, James; Parrinello, Michele
2017-11-01
In this paper, we combine two powerful computational techniques, well-tempered metadynamics and time-lagged independent component analysis. The aim is to develop a new tool for studying rare events and exploring complex free energy landscapes. Metadynamics is a well-established and widely used enhanced sampling method whose efficiency depends on an appropriate choice of collective variables. Often the initial choice is not optimal leading to slow convergence. However by analyzing the dynamics generated in one such run with a time-lagged independent component analysis and the techniques recently developed in the area of conformational dynamics, we obtain much more efficient collective variables that are also better capable of illuminating the physics of the system. We demonstrate the power of this approach in two paradigmatic examples.
Quasirelativistic quark model in quasipotential approach
Matveev, V A; Savrin, V I; Sissakian, A N
2002-01-01
The relativistic particles interaction is described within the frames of quasipotential approach. The presentation is based on the so called covariant simultaneous formulation of the quantum field theory, where by the theory is considered on the spatial-like three-dimensional hypersurface in the Minkowski space. Special attention is paid to the methods of plotting various quasipotentials as well as to the applications of the quasipotential approach to describing the characteristics of the relativistic particles interaction in the quark models, namely: the hadrons elastic scattering amplitudes, the mass spectra and widths mesons decays, the cross sections of the deep inelastic leptons scattering on the hadrons
A multiscale modeling approach for biomolecular systems
Energy Technology Data Exchange (ETDEWEB)
Bowling, Alan, E-mail: bowling@uta.edu; Haghshenas-Jaryani, Mahdi, E-mail: mahdi.haghshenasjaryani@mavs.uta.edu [The University of Texas at Arlington, Department of Mechanical and Aerospace Engineering (United States)
2015-04-15
This paper presents a new multiscale molecular dynamic model for investigating the effects of external interactions, such as contact and impact, during stepping and docking of motor proteins and other biomolecular systems. The model retains the mass properties ensuring that the result satisfies Newton’s second law. This idea is presented using a simple particle model to facilitate discussion of the rigid body model; however, the particle model does provide insights into particle dynamics at the nanoscale. The resulting three-dimensional model predicts a significant decrease in the effect of the random forces associated with Brownian motion. This conclusion runs contrary to the widely accepted notion that the motor protein’s movements are primarily the result of thermal effects. This work focuses on the mechanical aspects of protein locomotion; the effect ATP hydrolysis is estimated as internal forces acting on the mechanical model. In addition, the proposed model can be numerically integrated in a reasonable amount of time. Herein, the differences between the motion predicted by the old and new modeling approaches are compared using a simplified model of myosin V.
Blums, Angela
The present study examines instructional approaches and cognitive factors involved in elementary school children's thinking and learning the Control of Variables Strategy (CVS), a critical aspect of scientific reasoning. Previous research has identified several features related to effective instruction of CVS, including using a guided learning approach, the use of self-reflective questions, and learning in individual and group contexts. The current study examined the roles of procedural and conceptual instruction in learning CVS and investigated the role of executive function in the learning process. Additionally, this study examined how learning to identify variables is a part of the CVS process. In two studies (individual and classroom experiments), 139 third, fourth, and fifth grade students participated in hands-on and paper and pencil CVS learning activities and, in each study, were assigned to either a procedural instruction, conceptual instruction, or control (no instruction) group. Participants also completed a series of executive function tasks. The study was carried out with two parts--Study 1 used an individual context and Study 2 was carried out in a group setting. Results indicated that procedural and conceptual instruction were more effective than no instruction, and the ability to identify variables was identified as a key piece to the CVS process. Executive function predicted ability to identify variables and predicted success on CVS tasks. Developmental differences were present, in that older children outperformed younger children on CVS tasks, and that conceptual instruction was slightly more effective for older children. Some differences between individual and group instruction were found, with those in the individual context showing some advantage over the those in the group setting in learning CVS concepts. Conceptual implications about scientific thinking and practical implications in science education are discussed.
A new approach for developing adjoint models
Farrell, P. E.; Funke, S. W.
2011-12-01
Many data assimilation algorithms rely on the availability of gradients of misfit functionals, which can be efficiently computed with adjoint models. However, the development of an adjoint model for a complex geophysical code is generally very difficult. Algorithmic differentiation (AD, also called automatic differentiation) offers one strategy for simplifying this task: it takes the abstraction that a model is a sequence of primitive instructions, each of which may be differentiated in turn. While extremely successful, this low-level abstraction runs into time-consuming difficulties when applied to the whole codebase of a model, such as differentiating through linear solves, model I/O, calls to external libraries, language features that are unsupported by the AD tool, and the use of multiple programming languages. While these difficulties can be overcome, it requires a large amount of technical expertise and an intimate familiarity with both the AD tool and the model. An alternative to applying the AD tool to the whole codebase is to assemble the discrete adjoint equations and use these to compute the necessary gradients. With this approach, the AD tool must be applied to the nonlinear assembly operators, which are typically small, self-contained units of the codebase. The disadvantage of this approach is that the assembly of the discrete adjoint equations is still very difficult to perform correctly, especially for complex multiphysics models that perform temporal integration; as it stands, this approach is as difficult and time-consuming as applying AD to the whole model. In this work, we have developed a library which greatly simplifies and automates the alternate approach of assembling the discrete adjoint equations. We propose a complementary, higher-level abstraction to that of AD: that a model is a sequence of linear solves. The developer annotates model source code with library calls that build a 'tape' of the operators involved and their dependencies, and
VAM2D: Variably saturated analysis model in two dimensions
International Nuclear Information System (INIS)
Huyakorn, P.S.; Kool, J.B.; Wu, Y.S.
1991-10-01
This report documents a two-dimensional finite element model, VAM2D, developed to simulate water flow and solute transport in variably saturated porous media. Both flow and transport simulation can be handled concurrently or sequentially. The formulation of the governing equations and the numerical procedures used in the code are presented. The flow equation is approximated using the Galerkin finite element method. Nonlinear soil moisture characteristics and atmospheric boundary conditions (e.g., infiltration, evaporation and seepage face), are treated using Picard and Newton-Raphson iterations. Hysteresis effects and anisotropy in the unsaturated hydraulic conductivity can be taken into account if needed. The contaminant transport simulation can account for advection, hydrodynamic dispersion, linear equilibrium sorption, and first-order degradation. Transport of a single component or a multi-component decay chain can be handled. The transport equation is approximated using an upstream weighted residual method. Several test problems are presented to verify the code and demonstrate its utility. These problems range from simple one-dimensional to complex two-dimensional and axisymmetric problems. This document has been produced as a user's manual. It contains detailed information on the code structure along with instructions for input data preparation and sample input and printed output for selected test problems. Also included are instructions for job set up and restarting procedures. 44 refs., 54 figs., 24 tabs
Modeling Variable Phanerozoic Oxygen Effects on Physiology and Evolution.
Graham, Jeffrey B; Jew, Corey J; Wegner, Nicholas C
2016-01-01
Geochemical approximation of Earth's atmospheric O2 level over geologic time prompts hypotheses linking hyper- and hypoxic atmospheres to transformative events in the evolutionary history of the biosphere. Such correlations, however, remain problematic due to the relative imprecision of the timing and scope of oxygen change and the looseness of its overlay on the chronology of key biotic events such as radiations, evolutionary innovation, and extinctions. There are nevertheless general attributions of atmospheric oxygen concentration to key evolutionary changes among groups having a primary dependence upon oxygen diffusion for respiration. These include the occurrence of Devonian hypoxia and the accentuation of air-breathing dependence leading to the origin of vertebrate terrestriality, the occurrence of Carboniferous-Permian hyperoxia and the major radiation of early tetrapods and the origins of insect flight and gigantism, and the Mid-Late Permian oxygen decline accompanying the Permian extinction. However, because of variability between and error within different atmospheric models, there is little basis for postulating correlations outside the Late Paleozoic. Other problems arising in the correlation of paleo-oxygen with significant biological events include tendencies to ignore the role of blood pigment affinity modulation in maintaining homeostasis, the slow rates of O2 change that would have allowed for adaptation, and significant respiratory and circulatory modifications that can and do occur without changes in atmospheric oxygen. The purpose of this paper is thus to refocus thinking about basic questions central to the biological and physiological implications of O2 change over geological time.
Stochastic transport models for mixing in variable-density turbulence
Bakosi, J.; Ristorcelli, J. R.
2011-11-01
In variable-density (VD) turbulent mixing, where very-different- density materials coexist, the density fluctuations can be an order of magnitude larger than their mean. Density fluctuations are non-negligible in the inertia terms of the Navier-Stokes equation which has both quadratic and cubic nonlinearities. Very different mixing rates of different materials give rise to large differential accelerations and some fundamentally new physics that is not seen in constant-density turbulence. In VD flows material mixing is active in a sense far stronger than that applied in the Boussinesq approximation of buoyantly-driven flows: the mass fraction fluctuations are coupled to each other and to the fluid momentum. Statistical modeling of VD mixing requires accounting for basic constraints that are not important in the small-density-fluctuation passive-scalar-mixing approximation: the unit-sum of mass fractions, bounded sample space, and the highly skewed nature of the probability densities become essential. We derive a transport equation for the joint probability of mass fractions, equivalent to a system of stochastic differential equations, that is consistent with VD mixing in multi-component turbulence and consistently reduces to passive scalar mixing in constant-density flows.
Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T
2013-07-02
Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.
Virtuous organization: A structural equation modeling approach
Directory of Open Access Journals (Sweden)
Majid Zamahani
2013-02-01
Full Text Available For years, the idea of virtue was unfavorable among researchers and virtues were traditionally considered as culture-specific, relativistic and they were supposed to be associated with social conservatism, religious or moral dogmatism, and scientific irrelevance. Virtue and virtuousness have been recently considered seriously among organizational researchers. The proposed study of this paper examines the relationships between leadership, organizational culture, human resource, structure and processes, care for community and virtuous organization. Structural equation modeling is employed to investigate the effects of each variable on other components. The data used in this study consists of questionnaire responses from employees in Payam e Noor University in Yazd province. A total of 250 questionnaires were sent out and a total of 211 valid responses were received. Our results have revealed that all the five variables have positive and significant impacts on virtuous organization. Among the five variables, organizational culture has the most direct impact (0.80 and human resource has the most total impact (0.844 on virtuous organization.
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
2013-01-01
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.
Polynomial Chaos Expansion Approach to Interest Rate Models
Directory of Open Access Journals (Sweden)
Luca Di Persio
2015-01-01
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.
Thomas, Andrew C.; Chai, F.; Townsend, D. W.; Xue, H.
2002-01-01
The goals of this project were to acquire, process, QC, archive and analyze SeaWiFS chlorophyll fields over the Gulf of Maine and Scotia Shelf region. The focus of the analysis effort was to calculate and quantify seasonality and interannual. variability of SeaWiFS-measured phytoplankton biomass in the study area and compare these to physical forcing and hydrography. An additional focus within this effort was on regional differences within the heterogeneous biophysical regions of the Gulf of Maine / Scotia Shelf. Overall goals were approached through the combined use of SeaWiFS and AVHRR data and the development of a coupled biology-physical numerical model.
A variable resolution nonhydrostatic global atmospheric semi-implicit semi-Lagrangian model
Pouliot, George Antoine
2000-10-01
-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.
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)
2017-11-01
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.
Estimating net present value variability for deterministic models
van Groenendaal, W.J.H.
1995-01-01
For decision makers the variability in the net present value (NPV) of an investment project is an indication of the project's risk. So-called risk analysis is one way to estimate this variability. However, risk analysis requires knowledge about the stochastic character of the inputs. For large,
Hong, Sehee; Kim, Soyoung
2018-01-01
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.
Evolutionary modeling-based approach for model errors correction
Directory of Open Access Journals (Sweden)
S. Q. Wan
2012-08-01
Full Text Available The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963 equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data."
On the basis of the intelligent features of evolutionary modeling (EM, including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.
Parameter Estimation of Structural Equation Modeling Using Bayesian Approach
Directory of Open Access Journals (Sweden)
Dewi Kurnia Sari
2016-05-01
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.
Interfacial Fluid Mechanics A Mathematical Modeling Approach
Ajaev, Vladimir S
2012-01-01
Interfacial Fluid Mechanics: A Mathematical Modeling Approach provides an introduction to mathematical models of viscous flow used in rapidly developing fields of microfluidics and microscale heat transfer. The basic physical effects are first introduced in the context of simple configurations and their relative importance in typical microscale applications is discussed. Then,several configurations of importance to microfluidics, most notably thin films/droplets on substrates and confined bubbles, are discussed in detail. Topics from current research on electrokinetic phenomena, liquid flow near structured solid surfaces, evaporation/condensation, and surfactant phenomena are discussed in the later chapters. This book also: Discusses mathematical models in the context of actual applications such as electrowetting Includes unique material on fluid flow near structured surfaces and phase change phenomena Shows readers how to solve modeling problems related to microscale multiphase flows Interfacial Fluid Me...
A new modelling approach for zooplankton behaviour
Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.
We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.
Continuum modeling an approach through practical examples
Muntean, Adrian
2015-01-01
This book develops continuum modeling skills and approaches the topic from three sides: (1) derivation of global integral laws together with the associated local differential equations, (2) design of constitutive laws and (3) modeling boundary processes. The focus of this presentation lies on many practical examples covering aspects such as coupled flow, diffusion and reaction in porous media or microwave heating of a pizza, as well as traffic issues in bacterial colonies and energy harvesting from geothermal wells. The target audience comprises primarily graduate students in pure and applied mathematics as well as working practitioners in engineering who are faced by nonstandard rheological topics like those typically arising in the food industry.
Global Environmental Change: An integrated modelling approach
International Nuclear Information System (INIS)
Den Elzen, M.
1993-01-01
Two major global environmental problems are dealt with: climate change and stratospheric ozone depletion (and their mutual interactions), briefly surveyed in part 1. In Part 2 a brief description of the integrated modelling framework IMAGE 1.6 is given. Some specific parts of the model are described in more detail in other Chapters, e.g. the carbon cycle model, the atmospheric chemistry model, the halocarbon model, and the UV-B impact model. In Part 3 an uncertainty analysis of climate change and stratospheric ozone depletion is presented (Chapter 4). Chapter 5 briefly reviews the social and economic uncertainties implied by future greenhouse gas emissions. Chapters 6 and 7 describe a model and sensitivity analysis pertaining to the scientific uncertainties and/or lacunae in the sources and sinks of methane and carbon dioxide, and their biogeochemical feedback processes. Chapter 8 presents an uncertainty and sensitivity analysis of the carbon cycle model, the halocarbon model, and the IMAGE model 1.6 as a whole. Part 4 presents the risk assessment methodology as applied to the problems of climate change and stratospheric ozone depletion more specifically. In Chapter 10, this methodology is used as a means with which to asses current ozone policy and a wide range of halocarbon policies. Chapter 11 presents and evaluates the simulated globally-averaged temperature and sea level rise (indicators) for the IPCC-1990 and 1992 scenarios, concluding with a Low Risk scenario, which would meet the climate targets. Chapter 12 discusses the impact of sea level rise on the frequency of the Dutch coastal defence system (indicator) for the IPCC-1990 scenarios. Chapter 13 presents projections of mortality rates due to stratospheric ozone depletion based on model simulations employing the UV-B chain model for a number of halocarbon policies. Chapter 14 presents an approach for allocating future emissions of CO 2 among regions. (Abstract Truncated)
An analytical approach to separate climate and human contributions to basin streamflow variability
Li, Changbin; Wang, Liuming; Wanrui, Wang; Qi, Jiaguo; Linshan, Yang; Zhang, Yuan; Lei, Wu; Cui, Xia; Wang, Peng
2018-04-01
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.
Jordan, Pascal; Shedden-Mora, Meike C; Löwe, Bernd
To obtain predictors of suicidal ideation, which can also be used for an indirect assessment of suicidal ideation (SI). To create a classifier for SI based on variables of the Patient Health Questionnaire (PHQ) and sociodemographic variables, and to obtain an upper bound on the best possible performance of a predictor based on those variables. From a consecutive sample of 9025 primary care patients, 6805 eligible patients (60% female; mean age = 51.5 years) participated. Advanced methods of machine learning were used to derive the prediction equation. Various classifiers were applied and the area under the curve (AUC) was computed as a performance measure. Classifiers based on methods of machine learning outperformed ordinary regression methods and achieved AUCs around 0.87. The key variables in the prediction equation comprised four items - namely feelings of depression/hopelessness, low self-esteem, worrying, and severe sleep disturbances. The generalized anxiety disorder scale (GAD-7) and the somatic symptom subscale (PHQ-15) did not enhance prediction substantially. In predicting suicidal ideation researchers should refrain from using ordinary regression tools. The relevant information is primarily captured by the depression subscale and should be incorporated in a nonlinear model. For clinical practice, a classification tree using only four items of the whole PHQ may be advocated. Copyright © 2018 Elsevier Inc. All rights reserved.
Bhaskar, Ankush; Ramesh, Durbha Sai; Vichare, Geeta; Koganti, Triven; Gurubaran, S.
2017-12-01
Identification and quantification of possible drivers of recent global temperature variability remains a challenging task. This important issue is addressed adopting a non-parametric information theory technique, the Transfer Entropy and its normalized variant. It distinctly quantifies actual information exchanged along with the directional flow of information between any two variables with no bearing on their common history or inputs, unlike correlation, mutual information etc. Measurements of greenhouse gases: CO2, CH4 and N2O; volcanic aerosols; solar activity: UV radiation, total solar irradiance ( TSI) and cosmic ray flux ( CR); El Niño Southern Oscillation ( ENSO) and Global Mean Temperature Anomaly ( GMTA) made during 1984-2005 are utilized to distinguish driving and responding signals of global temperature variability. Estimates of their relative contributions reveal that CO2 ({˜ } 24 %), CH4 ({˜ } 19 %) and volcanic aerosols ({˜ }23 %) are the primary contributors to the observed variations in GMTA. While, UV ({˜ } 9 %) and ENSO ({˜ } 12 %) act as secondary drivers of variations in the GMTA, the remaining play a marginal role in the observed recent global temperature variability. Interestingly, ENSO and GMTA mutually drive each other at varied time lags. This study assists future modelling efforts in climate science.
Kipnis, Victor; Freedman, Laurence S; Carroll, Raymond J; Midthune, Douglas
2016-03-01
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.
DEFF Research Database (Denmark)
Ditlevsen, Susanne; Christensen, Ulla; Lynch, John
2005-01-01
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...
First, Matthew R.; Robbins-Wamsley, Stephanie H.; Riley, Scott C.; Drake, Lisa A.
2018-03-01
Variable fluorescence fluorometry, an analytical approach that estimates the fluorescence yield of chlorophyll a (F0, a proximal measure of algal concentration) and photochemical yield (FV/FM, an indicator of the physiological status of algae) was evaluated as a means to rapidly assess photoautotrophs. Specifically, it was used to gauge the efficacy of ballast water treatment designed to reduce the transport and delivery of potentially invasive organisms. A phytoflagellate, Tetraselmis spp. (10-12 μm) and mixed communities of ambient protists were examined in both laboratory experiments and large-scale field trials simulating 5-d hold times in mock ballast tanks. In laboratory incubations, ambient organisms held in the dark exhibited declining F0 and FV/FM measurements relative to organisms held under lighted conditions. In field experiments, increases and decreases in F0 and FV/FM over the tank hold time corresponded to those of microscope counts of organisms in two of three trials. In the third trial, concentrations of organisms ≥ 10 and protists) increased while F0 and FV/FM decreased. Rapid and sensitive, variable fluorescence fluorometry is appropriate for detecting changes in organism concentrations and physiological status in samples dominated by microalgae. Changes in the heterotrophic community, which may become more prevalent in light-limited ballast tanks, would not be detected via variable fluorescence fluorometry, however.
Grzegożek, W.; Dobaj, K.; Kot, A.
2016-09-01
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.
Ockey, Gary
2011-01-01
Drawing on current theories in personality, second-language (L2) oral ability, and psychometrics, this study investigates the extent to which self-consciousness and assertiveness are explanatory variables of L2 oral ability. Three hundred sixty first-year Japanese university students who were studying English as a foreign language participated in…
Sperber, K. R.; Palmer, T. N.
1996-11-01
The interannual variability of rainfall over the Indian subcontinent, the African Sahel, and the Nordeste region of Brazil have been evaluated in 32 models for the period 1979-88 as part of the Atmospheric Model Intercomparison Project (AMIP). The interannual variations of Nordeste rainfall are the most readily captured, owing to the intimate link with Pacific and Atlantic sea surface temperatures. The precipitation variations over India and the Sahel are less well simulated. Additionally, an Indian monsoon wind shear index was calculated for each model. Evaluation of the interannual variability of a wind shear index over the summer monsoon region indicates that the models exhibit greater fidelity in capturing the large-scale dynamic fluctuations than the regional-scale rainfall variations. A rainfall/SST teleconnection quality control was used to objectively stratify model performance. Skill scores improved for those models that qualitatively simulated the observed rainfall/El Niño- Southern Oscillation SST correlation pattern. This subset of models also had a rainfall climatology that was in better agreement with observations, indicating a link between systematic model error and the ability to simulate interannual variations.A suite of six European Centre for Medium-Range Weather Forecasts (ECMWF) AMIP runs (differing only in their initial conditions) have also been examined. As observed, all-India rainfall was enhanced in 1988 relative to 1987 in each of these realizations. All-India rainfall variability during other years showed little or no predictability, possibly due to internal chaotic dynamics associated with intraseasonal monsoon fluctuations and/or unpredictable land surface process interactions. The interannual variations of Nordeste rainfall were best represented. The State University of New York at Albany/National Center for Atmospheric Research Genesis model was run in five initial condition realizations. In this model, the Nordeste rainfall
Directory of Open Access Journals (Sweden)
Delphine eSarafian
2013-07-01
Full Text Available Limitations of current methods: The assessment of human variability in various compartments of daily energy expenditure (EE under standardized conditions is well defined at rest (as basal metabolic rate and thermic effect of feeding, and currently under validation for assessing the energy cost of low-intensity dynamic work. However, because physical activities of daily life consist of a combination of both dynamic and isometric work, there is also a need to develop standardized tests for assessing human variability in the energy cost of low-intensity isometric work.Experimental objectives: Development of an approach to study human variability in isometric thermogenesis by incorporating a protocol of intermittent leg press exercise of varying low-intensity isometric loads with measurements of EE by indirect calorimetry. Results: EE was measured in the seated position with the subject at rest or while intermittently pressing both legs against a press-platform at 5 low-intensity isometric loads (+5, +10, + 15, +20 and +25 kg force, each consisting of a succession of 8 cycles of press (30 s and rest (30 s. EE, integrated over each 8-min period of the intermittent leg press exercise, was found to increase linearly across the 5 isometric loads with a correlation coefficient (r > 0.9 for each individual. The slope of this EE-Load relationship, which provides the energy cost of this standardized isometric exercise expressed per kg force applied intermittently (30 s in every min, was found to show good repeatability when assessed in subjects who repeated the same experimental protocol on 3 separate days: its low intra-individual coefficient of variation (CV of ~ 10% contrasted with its much higher inter-individual CV of 35%; the latter being mass-independent but partly explained by height. Conclusion: This standardized approach to study isometric thermogenesis opens up a new avenue for research in EE phenotyping and metabolic predisposition to obesity
Modeling the Power Variability of Core Speed Scaling on Homogeneous Multicore Systems
Directory of Open Access Journals (Sweden)
Zhihui Du
2017-01-01
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%.
A distributed approach for parameters estimation in System Biology models
International Nuclear Information System (INIS)
Mosca, E.; Merelli, I.; Alfieri, R.; Milanesi, L.
2009-01-01
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.
International energy market dynamics: a modelling approach. Tome 1
International Nuclear Information System (INIS)
Nachet, S.
1996-01-01
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
International energy market dynamics: a modelling approach. Tome 2
International Nuclear Information System (INIS)
Nachet, S.
1996-01-01
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
Bauer, Daniel J.; Curran, Patrick J.
2004-01-01
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,…
Selecting candidate predictor variables for the modelling of post ...
African Journals Online (AJOL)
Objectives: The objective of this project was to determine the variables most likely to be associated with post- .... (as defined subjectively by the research team) in global .... ed on their lack of knowledge of wealth scoring tools. ... HIV serology.
Modelling for Fuel Optimal Control of a Variable Compression Engine
Nilsson, Ylva
2007-01-01
Variable compression engines are a mean to meet the demand on lower fuel consumption. A high compression ratio results in high engine efficiency, but also increases the knock tendency. On conventional engines with fixed compression ratio, knock is avoided by retarding the ignition angle. The variable compression engine offers an extra dimension in knock control, since both ignition angle and compression ratio can be adjusted. The central question is thus for what combination of compression ra...
Exact solutions to a nonlinear dispersive model with variable coefficients
International Nuclear Information System (INIS)
Yin Jun; Lai Shaoyong; Qing Yin
2009-01-01
A mathematical technique based on an auxiliary differential equation and the symbolic computation system Maple is employed to investigate a prototypical and nonlinear K(n, n) equation with variable coefficients. The exact solutions to the equation are constructed analytically under various circumstances. It is shown that the variable coefficients and the exponent appearing in the equation determine the quantitative change in the physical structures of the solutions.
Modeling and designing of variable-period and variable-pole-number undulator
Directory of Open Access Journals (Sweden)
I. Davidyuk
2016-02-01
Full Text Available The concept of permanent-magnet variable-period undulator (VPU was proposed several years ago and has found few implementations so far. The VPUs have some advantages as compared with conventional undulators, e.g., a wider range of radiation wavelength tuning and the option to increase the number of poles for shorter periods. Both these advantages will be realized in the VPU under development now at Budker INP. In this paper, we present the results of 2D and 3D magnetic field simulations and discuss some design features of this VPU.
Using Enthalpy as a Prognostic Variable in Atmospheric Modelling with Variable Composition
2016-04-14
Sela, personal communication, 2005). These terms are also routinely neglected in models. In models with a limited number of gaseous tracers, such as...so-called energy- exchange term (second term on the left- hand side) in Equation (5). The finite-difference schemes in existing atmospheric models have...equation for the sum of enthalpy and kinetic energy of horizontal motion is solved. This eliminates the energy- exchange term and automatically
A Novel Approach to Implement Takagi-Sugeno Fuzzy Models.
Chang, Chia-Wen; Tao, Chin-Wang
2017-09-01
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.
Alternative models developed for estimating acute systemic toxicity are generally evaluated using in vivo LD50 values. However, in vivo acute systemic toxicity studies can produce variable results, even when conducted according to accepted test guidelines. This variability can ma...
Wang, Xingsheng; Reid, Dave; Wang, Liping; Millar, Campbell; Burenkov, Alex; Evanschitzky, Peter; Baer, Eberhard; Lorenz, Juergen; Asenov, Asen
2016-01-01
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...
Merging Digital Surface Models Implementing Bayesian Approaches
Sadeq, H.; Drummond, J.; Li, Z.
2016-06-01
In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES
Directory of Open Access Journals (Sweden)
H. Sadeq
2016-06-01
Full Text Available In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades. It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies.
Savitsky, Terrance; Vannucci, Marina; Sha, Naijun
2011-02-01
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.
Directory of Open Access Journals (Sweden)
Ernest Kissi
2018-03-01
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.
Model predictive control approach for a CPAP-device
Directory of Open Access Journals (Sweden)
Scheel Mathias
2017-09-01
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.
Hu, Jiexiang; Zhou, Qi; Jiang, Ping; Shao, Xinyu; Xie, Tingli
2018-01-01
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.
McNally, Kevin; Loizou, George D
2015-01-01
The risk assessment of environmental chemicals and drugs is undergoing a paradigm shift in approach which seeks the full replacement of animal testing with high throughput, mechanistic, in vitro systems. This new approach will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated by in vitro, in silico, and in chemico systems can be integrated and utilized for quantitative in vitro-to-in vivo extrapolation (QIVIVE), ultimately to the human population level. Physiologically based pharmacokinetic (PBPK) models are ideally suited to this and are needed to translate in vitro concentration- response relationships to an exposure or dose, route and duration regime in human populations. Thus, a realistic description of the variation in the physiology of the human population being modeled is critical. Whilst various studies in the past decade have made progress in describing human variability, the algorithms are typically coded in computer programs and as such are unsuitable for reverse dosimetry. In this report we overcome this limitation by developing a hierarchical statistical model using standard probability distributions for the specification of a virtual US and UK human population. The work draws on information from both population databases and cadaver studies.
Directory of Open Access Journals (Sweden)
Kevin eMcNally
2015-10-01
Full Text Available The risk assessment of environmental chemicals and drugs is undergoing a paradigm shift in approach which seeks the full replacement of animal testing with high throughput, mechanistic, in vitro systems. This new approach will be reliant on the measurement in vitro, of concentration-dependent responses where prolonged excessive perturbations of specific biochemical pathways are likely to lead to adverse health effects in an intact organism. Such an approach requires a framework, into which disparate data generated by in vitro, in silico and in chemico systems can be integrated and utilised for quantitative in vitro-to-in vivo extrapolation (QIVIVE, ultimately to the human population level. Physiologically based pharmacokinetic (PBPK models are ideally suited to this and are needed to translate in vitro concentration- response relationships to an exposure or dose, route and duration regime in human populations. Thus a realistic description of the variation in the physiology of the human population being modelled is critical. Whilst various studies in the past decade have made progress in describing human variability, the algorithms are typically coded in computer programs and as such are unsuitable for reverse dosimetry. In this report we overcome this limitation by developing a hierarchical statistical model using standard probability distributions for the specification of a virtual US and UK human population. The work draws on information from both population databases and cadaver studies.
What variables are important in predicting bovine viral diarrhea virus? A random forest approach.
Machado, Gustavo; Mendoza, Mariana Recamonde; Corbellini, Luis Gustavo
2015-07-24
Bovine viral diarrhea virus (BVDV) causes one of the most economically important diseases in cattle, and the virus is found worldwide. A better understanding of the disease associated factors is a crucial step towards the definition of strategies for control and eradication. In this study we trained a random forest (RF) prediction model and performed variable importance analysis to identify factors associated with BVDV occurrence. In addition, we assessed the influence of features selection on RF performance and evaluated its predictive power relative to other popular classifiers and to logistic regression. We found that RF classification model resulted in an average error rate of 32.03% for the negative class (negative for BVDV) and 36.78% for the positive class (positive for BVDV).The RF model presented area under the ROC curve equal to 0.702. Variable importance analysis revealed that important predictors of BVDV occurrence were: a) who inseminates the animals, b) number of neighboring farms that have cattle and c) rectal palpation performed routinely. Our results suggest that the use of machine learning algorithms, especially RF, is a promising methodology for the analysis of cross-sectional studies, presenting a satisfactory predictive power and the ability to identify predictors that represent potential risk factors for BVDV investigation. We examined classical predictors and found some new and hard to control practices that may lead to the spread of this disease within and among farms, mainly regarding poor or neglected reproduction management, which should be considered for disease control and eradication.
Seeley, Saren H; Yanez, Betina; Stanton, Annette L; Hoyt, Michael A
2017-08-01
Expressing and understanding one's own emotional responses to negative events, particularly those that challenge the attainment of important life goals, is thought to confer physiological benefit. Individual preferences and/or abilities in approaching emotions might condition the efficacy of interventions designed to encourage written emotional processing (EP). This study examines the physiological impact (as indexed by heart rate variability (HRV)) of an emotional processing writing (EPW) task as well as the moderating influence of a dispositional preference for coping through emotional approach (EP and emotional expression (EE)), in response to a laboratory stress task designed to challenge an important life goal. Participants (n = 98) were randomly assigned to either EPW or fact control writing (FCW) following the stress task. Regression analyses revealed a significant dispositional EP by condition interaction, such that high EP participants in the EPW condition demonstrated higher HRV after writing compared to low EP participants. No significant main effects of condition or EE coping were observed. These findings suggest that EPW interventions may be best suited for those with preference or ability to process emotions related to a stressor or might require adaptation for those who less often cope through emotional approach.
Guzik, Przemyslaw; Piekos, Caroline; Pierog, Olivia; Fenech, Naiman; Krauze, Tomasz; Piskorski, Jaroslaw; Wykretowicz, Andrzej
2018-05-01
We compared classic ECG-derived versus a mobile approach to heart rate variability (HRV) measurement. 29 young adult healthy volunteers underwent a simultaneous recording of heart rate using an ECG and a chest heart rate monitor at supine rest, during mental stress and active standing. Mean RR interval, Standard Deviation of Normal-to-Normal (SDNN) of RR intervals, and Root Mean Square of the Successive Differences (RMSSD) between RR intervals were computed in 168 pairs of 5-minute epochs by in-house software on a PC (only sinus beats) and by mobile application "ELITEHRV" on a smartphone (no beat type identification). ECG analysis showed that 33.9% of the recordings contained at least one non-sinus beat or artefact, the mobile app did not report this. The mean RR intervals were significantly longer (p = 0.0378), while SDNN (p = 0.0001) and RMSSD (p = 0.0199) were smaller for the mobile approach. Measures of identical HRV parameters by ECG-based and mobile approaches are not equivalent. Copyright © 2018 Elsevier B.V. All rights reserved.
Analysis of optically variable devices using a photometric light-field approach
Soukup, Daniel; Å tolc, Svorad; Huber-Mörk, Reinhold
2015-03-01
Diffractive Optically Variable Image Devices (DOVIDs), sometimes loosely referred to as holograms, are popular security features for protecting banknotes, ID cards, or other security documents. Inspection, authentication, as well as forensic analysis of these security features are still demanding tasks requiring special hardware tools and expert knowledge. Existing equipment for such analyses is based either on a microscopic analysis of the grating structure or a point-wise projection and recording of the diffraction patterns. We investigated approaches for an examination of DOVID security features based on sampling the Bidirectional Reflectance Distribution Function (BRDF) of DOVIDs using photometric stereo- and light-field-based methods. Our approach is demonstrated on the practical task of automated discrimination between genuine and counterfeited DOVIDs on banknotes. For this purpose, we propose a tailored feature descriptor which is robust against several expected sources of inaccuracy but still specific enough for the given task. The suggested approach is analyzed from both theoretical as well as practical viewpoints and w.r.t. analysis based on photometric stereo and light fields. We show that especially the photometric method provides a reliable and robust tool for revealing DOVID behavior and authenticity.
Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.
Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui
2017-07-15
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
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
2014-01-01
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
A nationwide modelling approach to decommissioning - 16182
International Nuclear Information System (INIS)
Kelly, Bernard; Lowe, Andy; Mort, Paul
2009-01-01
In this paper we describe a proposed UK national approach to modelling decommissioning. For the first time, we shall have an insight into optimizing the safety and efficiency of a national decommissioning strategy. To do this we use the General Case Integrated Waste Algorithm (GIA), a universal model of decommissioning nuclear plant, power plant, waste arisings and the associated knowledge capture. The model scales from individual items of plant through cells, groups of cells, buildings, whole sites and then on up to a national scale. We describe the national vision for GIA which can be broken down into three levels: 1) the capture of the chronological order of activities that an experienced decommissioner would use to decommission any nuclear facility anywhere in the world - this is Level 1 of GIA; 2) the construction of an Operational Research (OR) model based on Level 1 to allow rapid what if scenarios to be tested quickly (Level 2); 3) the construction of a state of the art knowledge capture capability that allows future generations to learn from our current decommissioning experience (Level 3). We show the progress to date in developing GIA in levels 1 and 2. As part of level 1, GIA has assisted in the development of an IMechE professional decommissioning qualification. Furthermore, we describe GIA as the basis of a UK-Owned database of decommissioning norms for such things as costs, productivity, durations etc. From level 2, we report on a pilot study that has successfully tested the basic principles for the OR numerical simulation of the algorithm. We then highlight the advantages of applying the OR modelling approach nationally. In essence, a series of 'what if...' scenarios can be tested that will improve the safety and efficiency of decommissioning. (authors)
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)
2017-08-01
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
Economies of scale in the Korean district heating system: A variable cost function approach
International Nuclear Information System (INIS)
Park, Sun-Young; Lee, Kyoung-Sil; Yoo, Seung-Hoon
2016-01-01
This paper aims to investigate the cost efficiency of South Korea’s district heating (DH) system by using a variable cost function and cost-share equation. We employ a seemingly unrelated regression model, with quarterly time-series data from the Korea District Heating Corporation (KDHC)—a public utility that covers about 59% of the DH system market in South Korea—over the 1987–2011 period. The explanatory variables are price of labor, price of material, capital cost, and production level. The results indicate that economies of scale are present and statistically significant. Thus, expansion of its DH business would allow KDHC to obtain substantial economies of scale. According to our forecasts vis-à-vis scale economies, the KDHC will enjoy cost efficiency for some time yet. To ensure a socially efficient supply of DH, it is recommended that the KDHC expand its business proactively. With regard to informing policy or regulations, our empirical results could play a significant role in decision-making processes. - Highlights: • We examine economies of scale in the South Korean district heating sector. • We focus on Korea District Heating Corporation (KDHC), a public utility. • We estimate a translog cost function, using a variable cost function. • We found economies of scale to be present and statistically significant. • KDHC will enjoy cost efficiency and expanding its supply is socially efficient.
Modeling in transport phenomena a conceptual approach
Tosun, Ismail
2007-01-01
Modeling in Transport Phenomena, Second Edition presents and clearly explains with example problems the basic concepts and their applications to fluid flow, heat transfer, mass transfer, chemical reaction engineering and thermodynamics. A balanced approach is presented between analysis and synthesis, students will understand how to use the solution in engineering analysis. Systematic derivations of the equations and the physical significance of each term are given in detail, for students to easily understand and follow up the material. There is a strong incentive in science and engineering to
Nuclear physics for applications. A model approach
International Nuclear Information System (INIS)
Prussin, S.G.
2007-01-01
Written by a researcher and teacher with experience at top institutes in the US and Europe, this textbook provides advanced undergraduates minoring in physics with working knowledge of the principles of nuclear physics. Simplifying models and approaches reveal the essence of the principles involved, with the mathematical and quantum mechanical background integrated in the text where it is needed and not relegated to the appendices. The practicality of the book is enhanced by numerous end-of-chapter problems and solutions available on the Wiley homepage. (orig.)
Ensembles modeling approach to study Climate Change impacts on Wheat
Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart
2017-04-01
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.
Pedagogic process modeling: Humanistic-integrative approach
Directory of Open Access Journals (Sweden)
Boritko Nikolaj M.
2007-01-01
Full Text Available The paper deals with some current problems of modeling the dynamics of the subject-features development of the individual. The term "process" is considered in the context of the humanistic-integrative approach, in which the principles of self education are regarded as criteria for efficient pedagogic activity. Four basic characteristics of the pedagogic process are pointed out: intentionality reflects logicality and regularity of the development of the process; discreteness (stageability in dicates qualitative stages through which the pedagogic phenomenon passes; nonlinearity explains the crisis character of pedagogic processes and reveals inner factors of self-development; situationality requires a selection of pedagogic conditions in accordance with the inner factors, which would enable steering the pedagogic process. Offered are two steps for singling out a particular stage and the algorithm for developing an integrative model for it. The suggested conclusions might be of use for further theoretic research, analyses of educational practices and for realistic predicting of pedagogical phenomena. .
Variable speed wind turbine control by discrete-time sliding mode approach.
Torchani, Borhen; Sellami, Anis; Garcia, Germain
2016-05-01
The aim of this paper is to propose a new design variable speed wind turbine control by discrete-time sliding mode approach. This methodology is designed for linear saturated system. The saturation constraint is reported on inputs vector. To this end, the back stepping design procedure is followed to construct a suitable sliding manifold that guarantees the attainment of a stabilization control objective. It is well known that the mechanisms are investigated in term of the most proposed assumptions to deal with the damping, shaft stiffness and inertia effect of the gear. The objectives are to synthesize robust controllers that maximize the energy extracted from wind, while reducing mechanical loads and rotor speed tracking combined with an electromagnetic torque. Simulation results of the proposed scheme are presented. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Some issues in the loop variable approach to open strings and an extension to closed strings
International Nuclear Information System (INIS)
Sathiapalan, B.
1994-01-01
Some issues in the loop variable renormalization group approach to gauge-invariant equations for the free fields of the open string are discussed. It had been shown in an earlier paper that this leads to a simple form of the gauge transformation law. We discuss in some detail some of the curious features encountered there. The theory looks a little like a massless theory in one higher dimension that can be dimensionally reduced to give a massive theory. We discuss the origin of some constraints that are needed for gauge invariance and also for reducing the set of fields to that of standard string theory. The mechanism of gauge invariance and the connection with the Virasoro algebra is a little different from the usual story and is discussed. It is also shown that these results can be extended in a straightforward manner to closed strings. (orig.)
A state variable approach to the BESSY II local beam-position-feedback system
International Nuclear Information System (INIS)
Gilpatrick, J.D.; Khan, S.; Kraemer, D.
1996-01-01
At the BESSY II facility, stability of the electron beam position and angle near insertion devices (IDs) is of utmost importance. Disturbances due to ground motion could result in unwanted broad-bandwidth beam-jitter which decreases the electron (and resultant photon) beam's effective brightness. Therefore, feedback techniques must be used. Operating over a frequency range of 100-Hz, a local feedback system will correct these beam-trajectory errors using the four bumps around IDs. This paper reviews how the state-variable feedback approach can be applied to real-time correction of these beam position and angle errors. A frequency-domain solution showing beam jitter reduction is presented. Finally, this paper reports results of a beam-feedback test at BESSY I
International Nuclear Information System (INIS)
Carvajal Escobar Yesid; Munoz, Flor Matilde
2007-01-01
The project this centred in the revision of the state of the art of the ocean-atmospheric phenomena that you affect the Colombian hydrology especially The Phenomenon Enos that causes a socioeconomic impact of first order in our country, it has not been sufficiently studied; therefore it is important to approach the thematic one, including the variable macroclimates associated to the Enos in the analyses of water planning. The analyses include revision of statistical techniques of analysis of consistency of hydrological data with the objective of conforming a database of monthly flow of the river reliable and homogeneous Cauca. Statistical methods are used (Analysis of data multivariante) specifically The analysis of principal components to involve them in the development of models of prediction of flows monthly means in the river Cauca involving the Lineal focus as they are the model autoregressive AR, ARX and Armax and the focus non lineal Net Artificial Network.
A Latent-Variable Causal Model of Faculty Reputational Ratings.
King, Suzanne; Wolfle, Lee M.
A reanalysis was conducted of Saunier's research (1985) on sources of variation in the National Research Council (NRC) reputational ratings of university faculty. Saunier conducted a stepwise regression analysis using 12 predictor variables. Due to problems with multicollinearity and because of the atheoretical nature of stepwise regression,…
Instrumental variables estimation under a structural Cox model
DEFF Research Database (Denmark)
Martinussen, Torben; Nørbo Sørensen, Ditte; Vansteelandt, Stijn
2017-01-01
Instrumental variable (IV) analysis is an increasingly popular tool for inferring the effect of an exposure on an outcome, as witnessed by the growing number of IV applications in epidemiology, for instance. The majority of IV analyses of time-to-event endpoints are, however, dominated by heurist...
Variability of four-dimensional computed tomography patient models
Sonke, Jan-Jakob; Lebesque, Joos; van Herk, Marcel
2008-01-01
PURPOSE: To quantify the interfractional variability in lung tumor trajectory and mean position during the course of radiation therapy. METHODS AND MATERIALS: Repeat four-dimensional (4D) cone-beam computed tomography (CBCT) scans (median, nine scans/patient) routinely acquired during the course of
A hybrid approach to fault diagnosis of roller bearings under variable speed conditions
Wang, Yanxue; Yang, Lin; Xiang, Jiawei; Yang, Jianwei; He, Shuilong
2017-12-01
Rolling element bearings are one of the main elements in rotating machines, whose failure may lead to a fatal breakdown and significant economic losses. Conventional vibration-based diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speeds. This constraint limits the bearing diagnosis to the industrial application significantly. A hybrid approach to fault diagnosis of roller bearings under variable speed conditions is proposed in this work, based on computed order tracking (COT) and variational mode decomposition (VMD)-based time frequency representation (VTFR). COT is utilized to resample the non-stationary vibration signal in the angular domain, while VMD is used to decompose the resampled signal into a number of band-limited intrinsic mode functions (BLIMFs). A VTFR is then constructed based on the estimated instantaneous frequency and instantaneous amplitude of each BLIMF. Moreover, the Gini index and time-frequency kurtosis are both proposed to quantitatively measure the sparsity and concentration measurement of time-frequency representation, respectively. The effectiveness of the VTFR for extracting nonlinear components has been verified by a bat signal. Results of this numerical simulation also show the sparsity and concentration of the VTFR are better than those of short-time Fourier transform, continuous wavelet transform, Hilbert-Huang transform and Wigner-Ville distribution techniques. Several experimental results have further demonstrated that the proposed method can well detect bearing faults under variable speed conditions.
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
2014-01-01
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
Ferrari, G.; Kozarski, M.; Gu, Y. J.; De Lazzari, C.; Di Molfetta, A.; Palko, K. J.; Zielinski, K.; Gorczynska, K.; Darowski, M.; Rakhorst, G.
2008-01-01
Purpose: Application of a comprehensive, user-friendly, digital computer circulatory model to estimate hemodynamic and ventricular variables. Methods: The closed-loop lumped parameter circulatory model represents the circulation at the level of large vessels. A variable elastance model reproduces
Fraser, Stuart A.; Wood, Nathan J.; Johnston, David A.; Leonard, Graham S.; Greening, Paul D.; Rossetto, Tiziana
2014-01-01
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.
Stochastic Fractional Programming Approach to a Mean and Variance Model of a Transportation Problem
Directory of Open Access Journals (Sweden)
V. Charles
2011-01-01
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.
The place of quantitative energy models in a prospective approach
International Nuclear Information System (INIS)
Taverdet-Popiolek, N.
2009-01-01
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)
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
2017-11-03
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.
Static models, recursive estimators and the zero-variance approach
Rubino, Gerardo
2016-01-07
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.
Ordered LOGIT Model approach for the determination of financial distress.
Kinay, B
2010-01-01
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.
A novel approach to pipeline tensioner modeling
Energy Technology Data Exchange (ETDEWEB)
O' Grady, Robert; Ilie, Daniel; Lane, Michael [MCS Software Division, Galway (Ireland)
2009-07-01
As subsea pipeline developments continue to move into deep and ultra-deep water locations, there is an increasing need for the accurate prediction of expected pipeline fatigue life. A significant factor that must be considered as part of this process is the fatigue damage sustained by the pipeline during installation. The magnitude of this installation-related damage is governed by a number of different agents, one of which is the dynamic behavior of the tensioner systems during pipe-laying operations. There are a variety of traditional finite element methods for representing dynamic tensioner behavior. These existing methods, while basic in nature, have been proven to provide adequate forecasts in terms of the dynamic variation in typical installation parameters such as top tension and sagbend/overbend strain. However due to the simplicity of these current approaches, some of them tend to over-estimate the frequency of tensioner pay out/in under dynamic loading. This excessive level of pay out/in motion results in the prediction of additional stress cycles at certain roller beds, which in turn leads to the prediction of unrealistic fatigue damage to the pipeline. This unwarranted fatigue damage then equates to an over-conservative value for the accumulated damage experienced by a pipeline weld during installation, and so leads to a reduction in the estimated fatigue life for the pipeline. This paper describes a novel approach to tensioner modeling which allows for greater control over the velocity of dynamic tensioner pay out/in and so provides a more accurate estimation of fatigue damage experienced by the pipeline during installation. The paper reports on a case study, as outlined in the proceeding section, in which a comparison is made between results from this new tensioner model and from a more conventional approach. The comparison considers typical installation parameters as well as an in-depth look at the predicted fatigue damage for the two methods
Yuan, Ke-Hai; Tian, Yubin; Yanagihara, Hirokazu
2015-06-01
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.
A Structural Equation Approach to Models with Spatial Dependence
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
A structural equation approach to models with spatial dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
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
A Structural Equation Approach to Models with Spatial Dependence
Oud, J.H.L.; Folmer, H.
2008-01-01
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
Modelling and Multi-Variable Control of Refrigeration Systems
DEFF Research Database (Denmark)
Larsen, Lars Finn Slot; Holm, J. R.
2003-01-01
In this paper a dynamic model of a 1:1 refrigeration system is presented. The main modelling effort has been concentrated on a lumped parameter model of a shell and tube condenser. The model has shown good resemblance with experimental data from a test rig, regarding as well the static as the dyn......In this paper a dynamic model of a 1:1 refrigeration system is presented. The main modelling effort has been concentrated on a lumped parameter model of a shell and tube condenser. The model has shown good resemblance with experimental data from a test rig, regarding as well the static...... as the dynamic behavior. Based on this model the effects of the cross couplings has been examined. The influence of the cross couplings on the achievable control performance has been investigated. A MIMO controller is designed and the performance is compared with the control performance achieved by using...
Mobile phone use while driving: a hybrid modeling approach.
Márquez, Luis; Cantillo, Víctor; Arellana, Julián
2015-05-01
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.
Development of a plug-in for Variability Modeling in Software Product Lines
Directory of Open Access Journals (Sweden)
María Lucía López-Araujo
2012-03-01
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
An Object-Based Approach to Evaluation of Climate Variability Projections and Predictions
Ammann, C. M.; Brown, B.; Kalb, C. P.; Bullock, R.
2017-12-01
Evaluations of the performance of earth system model predictions and projections are of critical importance to enhance usefulness of these products. Such evaluations need to address specific concerns depending on the system and decisions of interest; hence, evaluation tools must be tailored to inform about specific issues. Traditional approaches that summarize grid-based comparisons of analyses and models, or between current and future climate, often do not reveal important information about the models' performance (e.g., spatial or temporal displacements; the reason behind a poor score) and are unable to accommodate these specific information needs. For example, summary statistics such as the correlation coefficient or the mean-squared error provide minimal information to developers, users, and decision makers regarding what is "right" and "wrong" with a model. New spatial and temporal-spatial object-based tools from the field of weather forecast verification (where comparisons typically focus on much finer temporal and spatial scales) have been adapted to more completely answer some of the important earth system model evaluation questions. In particular, the Method for Object-based Diagnostic Evaluation (MODE) tool and its temporal (three-dimensional) extension (MODE-TD) have been adapted for these evaluations. More specifically, these tools can be used to address spatial and temporal displacements in projections of El Nino-related precipitation and/or temperature anomalies, ITCZ-associated precipitation areas, atmospheric rivers, seasonal sea-ice extent, and other features of interest. Examples of several applications of these tools in a climate context will be presented, using output of the CESM large ensemble. In general, these tools provide diagnostic information about model performance - accounting for spatial, temporal, and intensity differences - that cannot be achieved using traditional (scalar) model comparison approaches. Thus, they can provide more
Linear mixed-effects modeling approach to FMRI group analysis.
Chen, Gang; Saad, Ziad S; Britton, Jennifer C; Pine, Daniel S; Cox, Robert W
2013-06-01
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
Lunt, Mark
2015-07-01
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: journals.permissions@oup.com.
Muñoz, Joel; Alcaide, Daniel; Ocaña, Jordi
2016-05-30
The 2010 US Food and Drug Administration and European Medicines Agency regulatory approaches to establish bioequivalence in highly variable drugs are both based on linearly scaling the bioequivalence limits, both take a 'scaled average bioequivalence' approach. The present paper corroborates previous work suggesting that none of them adequately controls type I error or consumer's risk, so they result in invalid test procedures in the neighbourhood of a within-subject coefficient of variation osf 30% for the reference (R) formulation. The problem is particularly serious in the US Food and Drug Administration regulation, but it is also appreciable in the European Medicines Agency one. For the partially replicated TRR/RTR/RRT and the replicated TRTR/RTRT crossover designs, we quantify these type I error problems by means of a simulation study, discuss their possible causes and propose straightforward improvements on both regulatory procedures that improve their type I error control while maintaining an adequate power. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
Gravity wave control on ESF day-to-day variability: An empirical approach
Aswathy, R. P.; Manju, G.
2017-06-01
irregularities lie below and above the curve. The model is validated with data from the years 2001 (high solar activity), 2004 (moderate solar activity), and 1995 (low solar activity) which have not been used in the model development. Presently, the model is developed for autumnal equinox season, but the model development will be undertaken for other seasons also in a future work so that the seasonal variability is also incorporated. This model thus holds the potential to be developed into a full-fledged model which can predict occurrence of nocturnal ionospheric irregularities. Globally, concerted efforts are underway to predict these ionospheric irregularities. Hence, this study is extremely important from the point of view of predicting communication and navigation outages.
Directory of Open Access Journals (Sweden)
Nerea Mangado
2016-11-01
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.
Igne, Benoit; Shi, Zhenqi; Drennen, James K; Anderson, Carl A
2014-02-01
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.
Lloret-Linares, Célia; Daali, Youssef; Chevret, Sylvie; Nieto, Isabelle; Molière, Fanny; Courtet, Philippe; Galtier, Florence; Richieri, Raphaëlle-Marie; Morange, Sophie; Llorca, Pierre-Michel; El-Hage, Wissam; Desmidt, Thomas; Haesebaert, Frédéric; Vignaud, Philippe; Holtzmann, Jerôme; Cracowski, Jean-Luc; Leboyer, Marion; Yrondi, Antoine; Calvas, Fabienne; Yon, Liova; Le Corvoisier, Philippe; Doumy, Olivier; Heron, Kyle; Montange, Damien; Davani, Siamak; Déglon, Julien; Besson, Marie; Desmeules, Jules; Haffen, Emmanuel; Bellivier, Frank
2017-11-07
It is well known that the standard doses of a given drug may not have equivalent effects in all patients. To date, the management of depression remains mainly empirical and often poorly evaluated. The development of a personalized medicine in psychiatry may reduce treatment failure, intolerance or resistance, and hence the burden and costs of mood depressive disorders. The Geneva Cocktail Phenotypic approach presents several advantages including the "in vivo" measure of different cytochromes and transporter P-gp activities, their simultaneous determination in a single test, avoiding the influence of variability over time on phenotyping results, the administration of low dose substrates, a limited sampling strategy with an analytical method developed on DBS analysis. The goal of this project is to explore the relationship between the activity of drug-metabolizing enzymes (DME), assessed by a phenotypic approach, and the concentrations of Venlafaxine (VLX) + O-demethyl-venlafaxine (ODV), the efficacy and tolerance of VLX. This study is a multicentre prospective non-randomized open trial. Eligible patients present a major depressive episode, MADRS over or equal to 20, treatment with VLX regardless of the dose during at least 4 weeks. The Phenotype Visit includes VLX and ODV concentration measurement. Following the oral absorption of low doses of omeprazole, midazolam, dextromethorphan, and fexofenadine, drug metabolizing enzymes activity is assessed by specific metabolite/probe concentration ratios from a sample taken 2 h after cocktail administration for CYP2C19, CYP3A4, CYP2D6; and by the determination of the limited area under the curve from the capillary blood samples taken 2-3 and 6 h after cocktail administration for CYP2C19 and P-gp. Two follow-up visits will take place between 25 and 40 days and 50-70 days after inclusion. They include assessment of efficacy, tolerance and observance. Eleven french centres are involved in recruitment, expected to be
Electrochemo-hydrodynamics modeling approach for a uranium electrowinning cell
Energy Technology Data Exchange (ETDEWEB)
Kim, K.R.; Paek, S.; Ahn, D.H., E-mail: krkim1@kaeri.re.kr, E-mail: swpaek@kaeri.re.kr, E-mail: dhahn2@kaeri.re.kr [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Park, J.Y.; Hwang, I.S., E-mail: d486916@snu.ac.kr, E-mail: hisline@snu.ac.kr [Department of Nuclear Engineering, Seoul National University (Korea, Republic of)
2011-07-01
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)
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.
2011-01-01
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)
Modeling variably saturated subsurface solute transport with MODFLOW-UZF and MT3DMS
Morway, Eric D.; Niswonger, Richard G.; Langevin, Christian D.; Bailey, Ryan T.; Healy, Richard W.
2013-01-01
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.
A MIMIC approach to modeling the underground economy in Taiwan
Wang, David Han-Min; Lin, Jer-Yan; Yu, Tiffany Hui-Kuang
2006-11-01
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.
Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.
2018-05-01
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
A multi-model approach to X-ray pulsars
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Schönherr G.
2014-01-01
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
Approaches and models of intercultural education
Directory of Open Access Journals (Sweden)
Iván Manuel Sánchez Fontalvo
2013-10-01
Full Text Available Needed to be aware of the need to build an intercultural society, awareness must be assumed in all social spheres, where stands the role play education. A role of transcendental, since it must promote educational spaces to form people with virtues and powers that allow them to live together / as in multicultural contexts and social diversities (sometimes uneven in an increasingly globalized and interconnected world, and foster the development of feelings of civic belonging shared before the neighborhood, city, region and country, allowing them concern and critical judgement to marginalization, poverty, misery and inequitable distribution of wealth, causes of structural violence, but at the same time, wanting to work for the welfare and transformation of these scenarios. Since these budgets, it is important to know the approaches and models of intercultural education that have been developed so far, analysing their impact on the contexts educational where apply.
Transport modeling: An artificial immune system approach
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Teodorović Dušan
2006-01-01
Full Text Available This paper describes an artificial immune system approach (AIS to modeling time-dependent (dynamic, real time transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies for different antigens (different traffic "scenarios". This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.
System approach to modeling of industrial technologies
Toropov, V. S.; Toropov, E. S.
2018-03-01
The authors presented a system of methods for modeling and improving industrial technologies. The system consists of information and software. The information part is structured information about industrial technologies. The structure has its template. The template has several essential categories used to improve the technological process and eliminate weaknesses in the process chain. The base category is the physical effect that takes place when the technical process proceeds. The programming part of the system can apply various methods of creative search to the content stored in the information part of the system. These methods pay particular attention to energy transformations in the technological process. The system application will allow us to systematize the approach to improving technologies and obtaining new technical solutions.
Latent variable modeling%建立隐性变量模型
Institute of Scientific and Technical Information of China (English)
蔡力
2012-01-01
@@ 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.
Torque Modeling and Control of a Variable Compression Engine
Bergström, Andreas
2003-01-01
The SAAB variable compression engine is a new engine concept that enables the fuel consumption to be radically cut by varying the compression ratio. A challenge with this new engine concept is that the compression ratio has a direct influence on the output torque, which means that a change in compression ratio also leads to a change in the torque. A torque change may be felt as a jerk in the movement of the car, and this is an undesirable effect since the driver has no control over the compre...
Design and Modeling of a Variable Heat Rejection Radiator
Miller, Jennifer R.; Birur, Gajanana C.; Ganapathi, Gani B.; Sunada, Eric T.; Berisford, Daniel F.; Stephan, Ryan
2011-01-01
Variable Heat Rejection Radiator technology needed for future NASA human rated & robotic missions Primary objective is to enable a single loop architecture for human-rated missions (1) Radiators are typically sized for maximum heat load in the warmest continuous environment resulting in a large panel area (2) Large radiator area results in fluid being susceptible to freezing at low load in cold environment and typically results in a two-loop system (3) Dual loop architecture is approximately 18% heavier than single loop architecture (based on Orion thermal control system mass) (4) Single loop architecture requires adaptability to varying environments and heat loads
Mechanical disequilibria in two-phase flow models: approaches by relaxation and by a reduced model
International Nuclear Information System (INIS)
Labois, M.
2008-10-01
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)
Models for turbulent flows with variable density and combustion
International Nuclear Information System (INIS)
Jones, W.P.
1980-01-01
Models for transport processes and combustion in turbulent flows are outlined with emphasis on the situation where the fuel and air are injected separately. Attention is restricted to relatively simple flames. The flows investigated are high Reynolds number, single-phase, turbulent high-temperature flames in which radiative heat transfer can be considered negligible. Attention is given to the lower order closure models, algebraic stress and flux models, the k-epsilon turbulence model, the diffusion flame approximation, and finite rate reaction mechanisms
Energy Technology Data Exchange (ETDEWEB)
Ihme, Matthias; See, Yee Chee [Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 (United States)
2010-10-15
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)
Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason P.; McCabe, Matthew
2013-01-01
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.
Balzan, Mario V
2012-01-01
Relatively little information is available on environmental associations and the conservation of Odonata in the Maltese Islands. Aquatic habitats are normally spatio-temporally restricted, often located within predominantly rural landscapes, and are thereby susceptible to farmland water management practices, which may create additional pressure on water resources. This study investigates how odonate assemblage structure and diversity are associated with habitat variables of local breeding habitats and the surrounding agricultural landscapes. Standardized survey methodology for adult Odonata involved periodical counts over selected water-bodies (valley systems, semi-natural ponds, constructed agricultural reservoirs). Habitat variables relating to the type of water body, the floristic and physiognomic characteristics of vegetation, and the composition of the surrounding landscape, were studied and analyzed through a multivariate approach. Overall, odonate diversity was associated with a range of factors across multiple spatial scales, and was found to vary with time. Lentic water-bodies are probably of high conservation value, given that larval stages were mainly associated with this habitat category, and that all species were recorded in the adult stage in this habitat type. Comparatively, lentic and lotic seminatural waterbodies were more diverse than agricultural reservoirs and brackish habitats. Overall, different odonate groups were associated with different vegetation life-forms and height categories. The presence of the great reed, Arundo donax L., an invasive alien species that forms dense stands along several water-bodies within the Islands, seems to influence the abundance and/or occurrence of a number of species. At the landscape scale, roads and other ecologically disturbed ground, surface water-bodies, and landscape diversity were associated with particular components of the odonate assemblages. Findings from this study have several implications for the
Directory of Open Access Journals (Sweden)
Lorena Olmos-Pérez
2017-05-01
Full Text Available The high mortality of cephalopod early stages is the main bottleneck to grow them from paralarvae to adults in culture conditions, probably because the inadequacy of the diet that results in malnutrition. Since visual analysis of digestive tract contents of paralarvae provides little evidence of diet composition, the use of molecular tools, particularly next generation sequencing (NGS platforms, offers an alternative to understand prey preferences and nutrient requirements of wild paralarvae. In this work, we aimed to determine the diet of paralarvae of the loliginid squid Alloteuthis media and to enhance the knowledge of the diet of recently hatched Octopus vulgaris paralarvae collected in different areas and seasons in an upwelling area (NW Spain. DNA from the dissected digestive glands of 32 A. media and 64 O. vulgaris paralarvae was amplified with universal primers for the mitochondrial gene COI, and specific primers targeting the mitochondrial gene 16S gene of arthropods and the mitochondrial gene 16S of Chordata. Following high-throughput DNA sequencing with the MiSeq run (Illumina, up to 4,124,464 reads were obtained and 234,090 reads of prey were successfully identified in 96.87 and 81.25% of octopus and squid paralarvae, respectively. Overall, we identified 122 Molecular Taxonomic Units (MOTUs belonging to several taxa of decapods, copepods, euphausiids, amphipods, echinoderms, molluscs, and hydroids. Redundancy analysis (RDA showed seasonal and spatial variability in the diet of O. vulgaris and spatial variability in A. media diet. General Additive Models (GAM of the most frequently detected prey families of O. vulgaris revealed seasonal variability of the presence of copepods (family Paracalanidae and ophiuroids (family Euryalidae, spatial variability in presence of crabs (family Pilumnidae and preference in small individual octopus paralarvae for cladocerans (family Sididae and ophiuroids. No statistically significant variation in
International Nuclear Information System (INIS)
Chen, C.-K.; Liao, T.-L.; Yan, J.-J.
2009-01-01
On the basis of variable structure control (VSC), an active queue management (AQM) controller is presented for a class of TCP communication networks. In the TCP/IP networks, the packet drop probability is limited between 0 and 1. Therefore, we modeled TCP/AQM as a rate-based non-linear system with a saturated input. The objective of the VSC-based AQM controller is to achieve the desired queue size and to guarantee the asymptotic stability of the closed-loop TCP non-linear system with saturated input. The performance and effectiveness of the proposed control law are then validated for different network scenarios through numerical simulations in both MATLAB and Network Simulator-2 (NS-2). Both sets of simulation results have confirmed that the proposed scheme outperforms other AQM schemes.
Energy Technology Data Exchange (ETDEWEB)
Chen, C.-K. [Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan (China); Liao, T.-L. [Department of Engineering Science, National Cheng Kung University, Tainan 701, Taiwan (China)], E-mail: tlliao@mail.ncku.edu; Yan, J.-J. [Department of Computer and Communication, Shu-Te University, Kaohsiung 824, Taiwan (China)
2009-04-15
On the basis of variable structure control (VSC), an active queue management (AQM) controller is presented for a class of TCP communication networks. In the TCP/IP networks, the packet drop probability is limited between 0 and 1. Therefore, we modeled TCP/AQM as a rate-based non-linear system with a saturated input. The objective of the VSC-based AQM controller is to achieve the desired queue size and to guarantee the asymptotic stability of the closed-loop TCP non-linear system with saturated input. The performance and effectiveness of the proposed control law are then validated for different network scenarios through numerical simulations in both MATLAB and Network Simulator-2 (NS-2). Both sets of simulation results have confirmed that the proposed scheme outperforms other AQM schemes.
A coupled approach for the three-dimensional simulation of pipe leakage in variably saturated soil
Peche, Aaron; Graf, Thomas; Fuchs, Lothar; Neuweiler, Insa
2017-12-01
In urban water pipe networks, pipe leakage may lead to subsurface contamination or to reduced waste water treatment efficiency. The quantification of pipe leakage is challenging due to inaccessibility and unknown hydraulic properties of the soil. A novel physically-based model for three-dimensional numerical simulation of pipe leakage in variably saturated soil is presented. We describe the newly implemented coupling between the pipe flow simulator HYSTEM-EXTRAN and the groundwater flow simulator OpenGeoSys and its validation. We further describe a novel upscaling of leakage using transfer functions derived from numerical simulations. This upscaling enables the simulation of numerous pipe defects with the benefit of reduced computation times. Finally, we investigate the response of leakage to different time-dependent pipe flow events and conclude that larger pipe flow volume and duration lead to larger leakage while the peak position in time has a small effect on leakage.
A Bézier-Spline-based Model for the Simulation of Hysteresis in Variably Saturated Soil
Cremer, Clemens; Peche, Aaron; Thiele, Luisa-Bianca; Graf, Thomas; Neuweiler, Insa
2017-04-01
Most transient variably saturated flow models neglect hysteresis in the p_c-S-relationship (Beven, 2012). Such models tend to inadequately represent matrix potential and saturation distribution. Thereby, when simulating flow and transport processes, fluid and solute fluxes might be overestimated (Russo et al., 1989). In this study, we present a simple, computationally efficient and easily applicable model that enables to adequately describe hysteresis in the p_c-S-relationship for variably saturated flow. This model can be seen as an extension to the existing play-type model (Beliaev and Hassanizadeh, 2001), where scanning curves are simplified as vertical lines between main imbibition and main drainage curve. In our model, we use continuous linear and Bézier-Spline-based functions. We show the successful validation of the model by numerically reproducing a physical experiment by Gillham, Klute and Heermann (1976) describing primary drainage and imbibition in a vertical soil column. With a deviation of 3%, the simple Bézier-Spline-based model performs significantly better that the play-type approach, which deviates by 30% from the experimental results. Finally, we discuss the realization of physical experiments in order to extend the model to secondary scanning curves and in order to determine scanning curve steepness. {Literature} Beven, K.J. (2012). Rainfall-Runoff-Modelling: The Primer. John Wiley and Sons. Russo, D., Jury, W. A., & Butters, G. L. (1989). Numerical analysis of solute transport during transient irrigation: 1. The effect of hysteresis and profile heterogeneity. Water Resources Research, 25(10), 2109-2118. https://doi.org/10.1029/WR025i010p02109. Beliaev, A.Y. & Hassanizadeh, S.M. (2001). A Theoretical Model of Hysteresis and Dynamic Effects in the Capillary Relation for Two-phase Flow in Porous Media. Transport in Porous Media 43: 487. doi:10.1023/A:1010736108256. Gillham, R., Klute, A., & Heermann, D. (1976). Hydraulic properties of a porous
The biobehavioral family model: testing social support as an additional exogenous variable.
Woods, Sarah B; Priest, Jacob B; Roush, Tara
2014-12-01
This study tests the inclusion of social support as a distinct exogenous variable in the Biobehavioral Family Model (BBFM). The BBFM is a biopsychosocial approach to health that proposes that biobehavioral reactivity (anxiety and depression) mediates the relationship between family emotional climate and disease activity. Data for this study included married, English-speaking adult participants (n = 1,321; 55% female; M age = 45.2 years) from the National Comorbidity Survey Replication, a nationally representative epidemiological study of the frequency of mental disorders in the United States. Participants reported their demographics, marital functioning, social support from friends and relatives, anxiety and depression (biobehavioral reactivity), number of chronic health conditions, and number of prescription medications. Confirmatory factor analyses supported the items used in the measures of negative marital interactions, social support, and biobehavioral reactivity, as well as the use of negative marital interactions, friends' social support, and relatives' social support as distinct factors in the model. Structural equation modeling indicated a good fit of the data to the hypothesized model (χ(2) = 846.04, p = .000, SRMR = .039, CFI = .924, TLI = .914, RMSEA = .043). Negative marital interactions predicted biobehavioral reactivity (β = .38, p social support, inversely (β = -.16, p social support as a predicting factor in the model. © 2014 Family Process Institute.
Patterns and Variability in Global Ocean Chlorophyll: Satellite Observations and Modeling
Gregg, Watson
2004-01-01
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.
Directory of Open Access Journals (Sweden)
A Moameni
2011-02-01
Full Text Available Abstract In Iran, the experimental plots under fertilizer trials are managed in such a way that the whole plot area uniformly receives agricultural inputs. This could lead to biased research results and hence to suppressing of the efforts made by the researchers. This research was conducted in a selected site belonging to the Gonbad Agricultural Research Station, located in the semiarid region, northeastern Iran. The aim was to characterize the short-range spatial variability of the inherent and management-depended soil properties and to determine if this variation is large and can be managed at practical scales. The soils were sampled using a grid 55 m apart. In total, 100 composite soil samples were collected from topsoil (0-30 cm and were analyzed for calcium carbonate equivalent, organic carbon, clay, available phosphorus, available potassium, iron, copper, zinc and manganese. Descriptive statistics were applied to check data trends. Geostatistical analysis was applied to variography, model fitting and contour mapping. Sampling at 55 m made it possible to split the area of the selected experimental plot into relatively uniform areas that allow application of agricultural inputs with variable rates. Keywords: Short-range soil variability, Within-field soil variability, Interpolation, Precision agriculture, Geostatistics
International Nuclear Information System (INIS)
Knight, Jennifer L.; Zhou, Zhiyong; Gallicchio, Emilio; Himmel, Daniel M.; Friesner, Richard A.; Arnold, Eddy; Levy, Ronald M.
2008-01-01
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
Latent variable models an introduction to factor, path, and structural equation analysis
Loehlin, John C
2004-01-01
This fourth edition introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. The book is intended for advanced students and researchers in the areas of social, educational, clinical, ind
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 datasets there is limited guidance on variable selection methods for RF modeling. Typically, e...
Separation of uncertainty and interindividual variability in human exposure modeling.
Ragas, A.M.J.; Brouwer, F.P.E.; Buchner, F.L.; Hendriks, H.W.; Huijbregts, M.A.J.
2009-01-01
The NORMTOX model predicts the lifetime-averaged exposure to contaminants through multiple environmental media, that is, food, air, soil, drinking and surface water. The model was developed to test the coherence of Dutch environmental quality objectives (EQOs). A set of EQOs is called coherent if
Mediating Variables in a Transtheoretical Model Dietary Intervention Program
Di Noia, Jennifer; Prochaska, James O.
2010-01-01
This study identified mediators of a Transtheoretical Model (TTM) intervention to increase fruit and vegetable consumption among economically disadvantaged African American adolescents (N = 549). Single-and multiple-mediator models were used to determine whether pros, cons, self-efficacy, and stages of change satisfied four conclusions necessary…
Interannual modes of variability of Southern Hemisphere atmospheric circulation in CMIP3 models
International Nuclear Information System (INIS)
Grainger, S; Frederiksen, C S; Zheng, X
2010-01-01
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.
A Systematic Approach to Modelling Change Processes in Construction Projects
Directory of Open Access Journals (Sweden)
Ibrahim Motawa
2012-11-01
Full Text Available Modelling change processes within construction projects isessential to implement changes efficiently. Incomplete informationon the project variables at the early stages of projects leads toinadequate knowledge of future states and imprecision arisingfrom ambiguity in project parameters. This lack of knowledge isconsidered among the main source of changes in construction.Change identification and evaluation, in addition to predictingits impacts on project parameters, can help in minimising thedisruptive effects of changes. This paper presents a systematicapproach to modelling change process within construction projectsthat helps improve change identification and evaluation. Theapproach represents the key decisions required to implementchanges. The requirements of an effective change processare presented first. The variables defined for efficient changeassessment and diagnosis are then presented. Assessmentof construction changes requires an analysis for the projectcharacteristics that lead to change and also analysis of therelationship between the change causes and effects. The paperconcludes that, at the early stages of a project, projects with a highlikelihood of change occurrence should have a control mechanismover the project characteristics that have high influence on theproject. It also concludes, for the relationship between changecauses and effects, the multiple causes of change should bemodelled in a way to enable evaluating the change effects moreaccurately. The proposed approach is the framework for tacklingsuch conclusions and can be used for evaluating change casesdepending on the available information at the early stages ofconstruction projects.
ECOMOD - An ecological approach to radioecological modelling
International Nuclear Information System (INIS)
Sazykina, Tatiana G.
2000-01-01
A unified methodology is proposed to simulate the dynamic processes of radionuclide migration in aquatic food chains in parallel with their stable analogue elements. The distinguishing feature of the unified radioecological/ecological approach is the description of radionuclide migration along with dynamic equations for the ecosystem. The ability of the methodology to predict the results of radioecological experiments is demonstrated by an example of radionuclide (iron group) accumulation by a laboratory culture of the algae Platymonas viridis. Based on the unified methodology, the 'ECOMOD' radioecological model was developed to simulate dynamic radioecological processes in aquatic ecosystems. It comprises three basic modules, which are operated as a set of inter-related programs. The 'ECOSYSTEM' module solves non-linear ecological equations, describing the biomass dynamics of essential ecosystem components. The 'RADIONUCLIDE DISTRIBUTION' module calculates the radionuclide distribution in abiotic and biotic components of the aquatic ecosystem. The 'DOSE ASSESSMENT' module calculates doses to aquatic biota and doses to man from aquatic food chains. The application of the ECOMOD model to reconstruct the radionuclide distribution in the Chernobyl Cooling Pond ecosystem in the early period after the accident shows good agreement with observations
Modelling Approach In Islamic Architectural Designs
Directory of Open Access Journals (Sweden)
Suhaimi Salleh
2014-06-01
Full Text Available Architectural designs contribute as one of the main factors that should be considered in minimizing negative impacts in planning and structural development in buildings such as in mosques. In this paper, the ergonomics perspective is revisited which hence focuses on the conditional factors involving organisational, psychological, social and population as a whole. This paper tries to highlight the functional and architectural integration with ecstatic elements in the form of decorative and ornamental outlay as well as incorporating the building structure such as wall, domes and gates. This paper further focuses the mathematical aspects of the architectural designs such as polar equations and the golden ratio. These designs are modelled into mathematical equations of various forms, while the golden ratio in mosque is verified using two techniques namely, the geometric construction and the numerical method. The exemplary designs are taken from theSabah Bandaraya Mosque in Likas, Kota Kinabalu and the Sarawak State Mosque in Kuching,while the Universiti Malaysia Sabah Mosque is used for the Golden Ratio. Results show thatIslamic architectural buildings and designs have long had mathematical concepts and techniques underlying its foundation, hence, a modelling approach is needed to rejuvenate these Islamic designs.
Static models, recursive estimators and the zero-variance approach
Rubino, Gerardo
2016-01-01
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
Comparison of various modelling approaches applied to cholera case data
CSIR Research Space (South Africa)
Van Den Bergh, F
2008-06-01
Full Text Available cross-wavelet technique, which is used to compute lead times for co-varying variables, and suggests transformations that enhance co-varying behaviour. Several statistical modelling techniques, including generalised linear models, ARIMA time series...
Modeling Scramjet Flows with Variable Turbulent Prandtl and Schmidt Numbers
Xiao, X.; Hassan, H. A.; Baurle, R. A.
2006-01-01
A complete turbulence model, where the turbulent Prandtl and Schmidt numbers are calculated as part of the solution and where averages involving chemical source terms are modeled, is presented. The ability of avoiding the use of assumed or evolution Probability Distribution Functions (PDF's) results in a highly efficient algorithm for reacting flows. The predictions of the model are compared with two sets of experiments involving supersonic mixing and one involving supersonic combustion. The results demonstrate the need for consideration of turbulence/chemistry interactions in supersonic combustion. In general, good agreement with experiment is indicated.
Directory of Open Access Journals (Sweden)
Osadolor Ebhuoma
2016-06-01
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
Ebhuoma, Osadolor; Gebreslasie, Michael
2016-06-14
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
modelling of hydropower reservoir variables for energy generation
African Journals Online (AJOL)
Osondu
the River Niger (Kainji and Jebba dams) in Nigeria for energy generation using multilayer ... coefficient showed that the networks are reliable for modeling energy generation as a function of ... water, like wind and sun, is a renewable resource.
Importance of predictor variables for models of chemical function
U.S. Environmental Protection Agency — Importance of random forest predictors for all classification models of chemical function. This dataset is associated with the following publication: Isaacs , K., M....
modelling of hydropower reservoir variables for energy generation
African Journals Online (AJOL)
Osondu
the River Niger (Kainji and Jebba dams) in Nigeria for energy generation using multilayer ... coefficient showed that the networks are reliable for modeling energy generation as a function of ... through turbines and electric generator system.
Energy Technology Data Exchange (ETDEWEB)
Heyen, H. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Gewaesserphysik
1998-12-31
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.)
Data and Dynamics Driven Approaches for Modelling and Forecasting the Red Sea Chlorophyll
Dreano, Denis
2017-05-31
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.
Directory of Open Access Journals (Sweden)
Auditya Purwandini Sutarto
2010-06-01
Full Text Available The widespread implementation of advanced and complex systems requires predominantly operators’ cognitive functions and less importance of human manual control. On the other hand, most operators perform their cognitive functions below their peak cognitive capacity level due to fatigue, stress, and boredom. Thus, there is a need to improve their cognitive functions during work. The goal of this paper is to present a psychophysiology training approach derived from cardiovascular response named heart rate variability (HRV biofeedback. Description of resonant frequency biofeedback - a specific HRV training protocol - is discussed as well as its supported researches for the performance enhancement. HRV biofeedback training works by teaching people to recognize their involuntary HRV and to control patterns of this physiological response. The training is directed to increase HRV amplitude that promotes autonomic nervous system balance. This balance is associated with improved physiological functioning as well as psychological benefits. Most individuals can learn HRV biofeedback training easily which involves slowing the breathing rate (around six breaths/min to each individual’s resonant frequency at which the amplitude of HRV is maximized. Maximal control over HRV can be obtained in most people after approximately four sessions of training. Recent studies have demonstrated the effectiveness of HRV biofeedback to the improvement of some cognitive functions in both simulated and real industrial operators.
Directory of Open Access Journals (Sweden)
Julie Baussand
2009-09-01
Full Text Available Communication between distant sites often defines the biological role of a protein: amino acid long-range interactions are as important in binding specificity, allosteric regulation and conformational change as residues directly contacting the substrate. The maintaining of functional and structural coupling of long-range interacting residues requires coevolution of these residues. Networks of interaction between coevolved residues can be reconstructed, and from the networks, one can possibly derive insights into functional mechanisms for the protein family. We propose a combinatorial method for mapping conserved networks of amino acid interactions in a protein which is based on the analysis of a set of aligned sequences, the associated distance tree and the combinatorics of its subtrees. The degree of coevolution of all pairs of coevolved residues is identified numerically, and networks are reconstructed with a dedicated clustering algorithm. The method drops the constraints on high sequence divergence limiting the range of applicability of the statistical approaches previously proposed. We apply the method to four protein families where we show an accurate detection of functional networks and the possibility to treat sets of protein sequences of variable divergence.
Directory of Open Access Journals (Sweden)
João C. O. Marra
2016-01-01
Full Text Available Vibratory phenomena have always surrounded human life. The need for more knowledge and domain of such phenomena increases more and more, especially in the modern society where the human-machine integration becomes closer day after day. In that context, this work deals with the development and practical implementation of a hybrid (passive-active/adaptive vibration control system over a metallic beam excited by a broadband signal and under variable temperature, between 5 and 35°C. Since temperature variations affect directly and considerably the performance of the passive control system, composed of a viscoelastic dynamic vibration neutralizer (also called a viscoelastic dynamic vibration absorber, the associative strategy of using an active-adaptive vibration control system (based on a feedforward approach with the use of the FXLMS algorithm working together with the passive one has shown to be a good option to compensate the neutralizer loss of performance and generally maintain the extended overall level of vibration control. As an additional gain, the association of both vibration control systems (passive and active-adaptive has improved the attenuation of vibration levels. Some key steps matured over years of research on this experimental setup are presented in this paper.
Dynamic modeling of fixed-bed adsorption of flue gas using a variable mass transfer model
International Nuclear Information System (INIS)
Park, Jehun; Lee, Jae W.
2016-01-01
This study introduces a dynamic mass transfer model for the fixed-bed adsorption of a flue gas. The derivation of the variable mass transfer coefficient is based on pore diffusion theory and it is a function of effective porosity, temperature, and pressure as well as the adsorbate composition. Adsorption experiments were done at four different pressures (1.8, 5, 10 and 20 bars) and three different temperatures (30, 50 and 70 .deg. C) with zeolite 13X as the adsorbent. To explain the equilibrium adsorption capacity, the Langmuir-Freundlich isotherm model was adopted, and the parameters of the isotherm equation were fitted to the experimental data for a wide range of pressures and temperatures. Then, dynamic simulations were performed using the system equations for material and energy balance with the equilibrium adsorption isotherm data. The optimal mass transfer and heat transfer coefficients were determined after iterative calculations. As a result, the dynamic variable mass transfer model can estimate the adsorption rate for a wide range of concentrations and precisely simulate the fixed-bed adsorption process of a flue gas mixture of carbon dioxide and nitrogen.
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
A quality risk management model approach for cell therapy manufacturing.
Lopez, Fabio; Di Bartolo, Chiara; Piazza, Tommaso; Passannanti, Antonino; Gerlach, Jörg C; Gridelli, Bruno; Triolo, Fabio
2010-12-01
International regulatory authorities view risk management as an essential production need for the development of innovative, somatic cell-based therapies in regenerative medicine. The available risk management guidelines, however, provide little guidance on specific risk analysis approaches and procedures applicable in clinical cell therapy manufacturing. This raises a number of problems. Cell manufacturing is a poorly automated process, prone to operator-introduced variations, and affected by heterogeneity of the processed organs/tissues and lot-dependent variability of reagent (e.g., collagenase) efficiency. In this study, the principal challenges faced in a cell-based product manufacturing context (i.e., high dependence on human intervention and absence of reference standards for acceptable risk levels) are identified and addressed, and a risk management model approach applicable to manufacturing of cells for clinical use is described for the first time. The use of the heuristic and pseudo-quantitative failure mode and effect analysis/failure mode and critical effect analysis risk analysis technique associated with direct estimation of severity, occurrence, and detection is, in this specific context, as effective as, but more efficient than, the analytic hierarchy process. Moreover, a severity/occurrence matrix and Pareto analysis can be successfully adopted to identify priority failure modes on which to act to mitigate risks. The application of this approach to clinical cell therapy manufacturing in regenerative medicine is also discussed. © 2010 Society for Risk Analysis.
Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses.
Liu, Ruijie; Holik, Aliaksei Z; Su, Shian; Jansz, Natasha; Chen, Kelan; Leong, Huei San; Blewitt, Marnie E; Asselin-Labat, Marie-Liesse; Smyth, Gordon K; Ritchie, Matthew E
2015-09-03
Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean-variance relationship of the log-counts-per-million using 'voom'. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source 'limma' package. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Energy Technology Data Exchange (ETDEWEB)
Rollin, Bertrand [Los Alamos National Laboratory; Andrews, Malcolm J [Los Alamos National Laboratory
2010-01-01
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.
The necessity of connection structures in neural models of variable binding.
van der Velde, Frank; de Kamps, Marc
2015-08-01
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.