Updating of states in operational hydrological models

NASA Astrophysics Data System (ADS)

Bruland, O.; Kolberg, S.; Engeland, K.; Gragne, A. S.; Liston, G.; Sand, K.; Tøfte, L.; Alfredsen, K.

2012-04-01

Operationally the main purpose of hydrological models is to provide runoff forecasts. The quality of the model state and the accuracy of the weather forecast together with the model quality define the runoff forecast quality. Input and model errors accumulate over time and may leave the model in a poor state. Usually model states can be related to observable conditions in the catchment. Updating of these states, knowing their relation to observable catchment conditions, influence directly the forecast quality. Norway is internationally in the forefront in hydropower scheduling both on short and long terms. The inflow forecasts are fundamental to this scheduling. Their quality directly influence the producers profit as they optimize hydropower production to market demand and at the same time minimize spill of water and maximize available hydraulic head. The quality of the inflow forecasts strongly depends on the quality of the models applied and the quality of the information they use. In this project the focus has been to improve the quality of the model states which the forecast is based upon. Runoff and snow storage are two observable quantities that reflect the model state and are used in this project for updating. Generally the methods used can be divided in three groups: The first re-estimates the forcing data in the updating period; the second alters the weights in the forecast ensemble; and the third directly changes the model states. The uncertainty related to the forcing data through the updating period is due to both uncertainty in the actual observation and to how well the gauging stations represent the catchment both in respect to temperatures and precipitation. The project looks at methodologies that automatically re-estimates the forcing data and tests the result against observed response. Model uncertainty is reflected in a joint distribution of model parameters estimated using the Dream algorithm.

Steady-state kinetic modeling constrains cellular resting states and dynamic behavior.

PubMed

Purvis, Jeremy E; Radhakrishnan, Ravi; Diamond, Scott L

2009-03-01

A defining characteristic of living cells is the ability to respond dynamically to external stimuli while maintaining homeostasis under resting conditions. Capturing both of these features in a single kinetic model is difficult because the model must be able to reproduce both behaviors using the same set of molecular components. Here, we show how combining small, well-defined steady-state networks provides an efficient means of constructing large-scale kinetic models that exhibit realistic resting and dynamic behaviors. By requiring each kinetic module to be homeostatic (at steady state under resting conditions), the method proceeds by (i) computing steady-state solutions to a system of ordinary differential equations for each module, (ii) applying principal component analysis to each set of solutions to capture the steady-state solution space of each module network, and (iii) combining optimal search directions from all modules to form a global steady-state space that is searched for accurate simulation of the time-dependent behavior of the whole system upon perturbation. Importantly, this stepwise approach retains the nonlinear rate expressions that govern each reaction in the system and enforces constraints on the range of allowable concentration states for the full-scale model. These constraints not only reduce the computational cost of fitting experimental time-series data but can also provide insight into limitations on system concentrations and architecture. To demonstrate application of the method, we show how small kinetic perturbations in a modular model of platelet P2Y(1) signaling can cause widespread compensatory effects on cellular resting states.

Battery-Charge-State Model

NASA Technical Reports Server (NTRS)

Vivian, H. C.

1985-01-01

Charge-state model for lead/acid batteries proposed as part of effort to make equivalent of fuel gage for battery-powered vehicles. Models based on equations that approximate observable characteristics of battery electrochemistry. Uses linear equations, easier to simulate on computer, and gives smooth transitions between charge, discharge, and recuperation.

Markov State Models of gene regulatory networks.

PubMed

Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L

2017-02-06

Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.

Exploring extensions to multi-state models with multiple unobservable states

USGS Publications Warehouse

Bailey, L.L.; Kendall, W.L.; Church, D.R.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Many biological systems include a portion of the target population that is unobservable during certain life history stages. Transition to and from an unobservable state may be of primary interest in many ecological studies and such movements are easily incorporated into multi-state models. Several authors have investigated properties of open-population multi-state mark-recapture models with unobservable states, and determined the scope and constraints under which parameters are identifiable (or, conversely, are redundant), but only in the context of a single observable and a single unobservable state (Schmidt et al. 2002; Kendall and Nichols 2002; Schaub et al. 2004; Kendall 2004). Some of these constraints can be relaxed if data are collected under a version of the robust design (Kendall and Bjorkland 2001; Kendall and Nichols 2002; Kendall 2004; Bailey et al. 2004), which entails >1 capture period per primary period of interest (e.g., 2 sampling periods within a breeding season). The critical assumption shared by all versions of the robust design is that the state of the individual (e.g. observable or unobservable) remains static for the duration of the primary period (Kendall 2004). In this paper, we extend previous work by relaxing this assumption to allow movement among observable states within primary periods while maintaining static observable or unobservable states. Stated otherwise, both demographic and geographic closure assumptions are relaxed, but all individuals are either observable or unobservable within primary periods. Within these primary periods transitions are possible among multiple observable states, but transitions are not allowed among the corresponding unobservable states. Our motivation for this work is exploring potential differences in population parameters for pond-breeding amphibians, where the quality of habitat surrounding the pond is not spatially uniform. The scenario is an example of a more general case where individuals move

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models.

PubMed

Liu, Ziyue; Cappola, Anne R; Crofford, Leslie J; Guo, Wensheng

2014-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls.

The two-state dimer receptor model: a general model for receptor dimers.

PubMed

Franco, Rafael; Casadó, Vicent; Mallol, Josefa; Ferrada, Carla; Ferré, Sergi; Fuxe, Kjell; Cortés, Antoni; Ciruela, Francisco; Lluis, Carmen; Canela, Enric I

2006-06-01

Nonlinear Scatchard plots are often found for agonist binding to G-protein-coupled receptors. Because there is clear evidence of receptor dimerization, these nonlinear Scatchard plots can reflect cooperativity on agonist binding to the two binding sites in the dimer. According to this, the "two-state dimer receptor model" has been recently derived. In this article, the performance of the model has been analyzed in fitting data of agonist binding to A(1) adenosine receptors, which are an example of receptor displaying concave downward Scatchard plots. Analysis of agonist/antagonist competition data for dopamine D(1) receptors using the two-state dimer receptor model has also been performed. Although fitting to the two-state dimer receptor model was similar to the fitting to the "two-independent-site receptor model", the former is simpler, and a discrimination test selects the two-state dimer receptor model as the best. This model was also very robust in fitting data of estrogen binding to the estrogen receptor, for which Scatchard plots are concave upward. On the one hand, the model would predict the already demonstrated existence of estrogen receptor dimers. On the other hand, the model would predict that concave upward Scatchard plots reflect positive cooperativity, which can be neither predicted nor explained by assuming the existence of two different affinity states. In summary, the two-state dimer receptor model is good for fitting data of binding to dimeric receptors displaying either linear, concave upward, or concave downward Scatchard plots.

Operationalizing resilience using state and transition models

USDA-ARS?s Scientific Manuscript database

In management, restoration, and policy contexts, the notion of resilience can be confusing. Systematic development of conceptual models of ecological state change (state transition models; STMs) can help overcome semantic confusion and promote a mechanistic understanding of resilience. Drawing on ex...

Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models

PubMed Central

Liu, Ziyue; Cappola, Anne R.; Crofford, Leslie J.; Guo, Wensheng

2013-01-01

The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this paper, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with both population-average and subject-specific components. The bivariate model is constructed by concatenating the univariate models based on the hypothesized relationship. Because of the flexible framework of state space form, the resultant models not only can handle complex individual profiles, but also can incorporate complex relationships between two hormones, including both concurrent and feedback relationship. Estimation and inference are based on marginal likelihood and posterior means and variances. Computationally efficient Kalman filtering and smoothing algorithms are used for implementation. Application of the proposed method to a study of chronic fatigue syndrome and fibromyalgia reveals that the relationships between adrenocorticotropic hormone and cortisol in the patient group are weaker than in healthy controls. PMID:24729646

Crowd macro state detection using entropy model

NASA Astrophysics Data System (ADS)

Zhao, Ying; Yuan, Mengqi; Su, Guofeng; Chen, Tao

2015-08-01

In the crowd security research area a primary concern is to identify the macro state of crowd behaviors to prevent disasters and to supervise the crowd behaviors. The entropy is used to describe the macro state of a self-organization system in physics. The entropy change indicates the system macro state change. This paper provides a method to construct crowd behavior microstates and the corresponded probability distribution using the individuals' velocity information (magnitude and direction). Then an entropy model was built up to describe the crowd behavior macro state. Simulation experiments and video detection experiments were conducted. It was verified that in the disordered state, the crowd behavior entropy is close to the theoretical maximum entropy; while in ordered state, the entropy is much lower than half of the theoretical maximum entropy. The crowd behavior macro state sudden change leads to the entropy change. The proposed entropy model is more applicable than the order parameter model in crowd behavior detection. By recognizing the entropy mutation, it is possible to detect the crowd behavior macro state automatically by utilizing cameras. Results will provide data support on crowd emergency prevention and on emergency manual intervention.

Inter-Industry Wage Differentials and the Gender Wage Gap: An Identification Problem.

ERIC Educational Resources Information Center

Horrace, William C.; Oaxaca, Ronald L.

2001-01-01

States that a method for estimating gender wage gaps by industry yields estimates that vary according to arbitrary choice of omitted reference groups. Suggests alternative methods not susceptible to this problem that can be applied to other contexts, such as racial, union/nonunion, and immigrant/native wage differences. (SK)

On rate-state and Coulomb failure models

USGS Publications Warehouse

Gomberg, J.; Beeler, N.; Blanpied, M.

2000-01-01

We examine the predictions of Coulomb failure stress and rate-state frictional models. We study the change in failure time (clock advance) Δt due to stress step perturbations (i.e., coseismic static stress increases) added to "background" stressing at a constant rate (i.e., tectonic loading) at time t0. The predictability of Δt implies a predictable change in seismicity rate r(t)/r0, testable using earthquake catalogs, where r0 is the constant rate resulting from tectonic stressing. Models of r(t)/r0, consistent with general properties of aftershock sequences, must predict an Omori law seismicity decay rate, a sequence duration that is less than a few percent of the mainshock cycle time and a return directly to the background rate. A Coulomb model requires that a fault remains locked during loading, that failure occur instantaneously, and that Δt is independent of t0. These characteristics imply an instantaneous infinite seismicity rate increase of zero duration. Numerical calculations of r(t)/r0 for different state evolution laws show that aftershocks occur on faults extremely close to failure at the mainshock origin time, that these faults must be "Coulomb-like," and that the slip evolution law can be precluded. Real aftershock population characteristics also may constrain rate-state constitutive parameters; a may be lower than laboratory values, the stiffness may be high, and/or normal stress may be lower than lithostatic. We also compare Coulomb and rate-state models theoretically. Rate-state model fault behavior becomes more Coulomb-like as constitutive parameter a decreases relative to parameter b. This is because the slip initially decelerates, representing an initial healing of fault contacts. The deceleration is more pronounced for smaller a, more closely simulating a locked fault. Even when the rate-state Δt has Coulomb characteristics, its magnitude may differ by some constant dependent on b. In this case, a rate-state model behaves like a modified

Model reduction in a subset of the original states

NASA Technical Reports Server (NTRS)

Yae, K. H.; Inman, D. J.

1992-01-01

A model reduction method is investigated to provide a smaller structural dynamic model for subsequent structural control design. A structural dynamic model is assumed to be derived from finite element analysis. It is first converted into the state space form, and is further reduced by the internal balancing method. Through the co-ordinate transformation derived from the states that are deleted during reduction, the reduced model is finally expressed with the states that are members of the original states. Therefore, the states in the final reduced model represent the degrees of freedom of the nodes that are selected by the designer. The procedure provides a more practical implementation of model reduction for applications in which specific nodes, such as sensor and/or actuator attachment points, are to be retained in the reduced model. Thus, it ensures that the reduced model is under the same input and output condition as the original physical model. The procedure is applied to two simple examples and comparisons are made between the full and reduced order models. The method can be applied to a linear, continuous and time-invariant model of structural dynamics with nonproportional viscous damping.

Multi-temperature model derived from state-to-state kinetics for hypersonic entry in Jupiter atmosphere

NASA Astrophysics Data System (ADS)

Colonna, G.; D'Ambrosio, D.; D'Ammando, G.; Pietanza, L. D.; Capitelli, M.

2014-12-01

A state-to-state model of H2/He plasmas coupling the master equations for internal distributions of heavy species with the transport equation for the free electrons has been used as a basis for implementing a multi-temperature kinetic model. In the multi-temperature model internal distributions of heavy particles are Boltzmann, the electron energy distribution function is Maxwell, and the rate coefficients of the elementary processes become a function of local temperatures associated to the relevant equilibrium distributions. The state-to-state and multi-temperature models have been compared in the case of a homogenous recombining plasma, reproducing the conditions met during supersonic expansion though converging-diverging nozzles.

A Model of Solid State Gas Sensors

NASA Astrophysics Data System (ADS)

Woestman, J. T.; Brailsford, A. D.; Shane, M.; Logothetis, E. M.

1997-03-01

Solid state gas sensors are widely used to measure the concentrations of gases such as CO, CH_4, C_3H_6, H_2, C_3H8 and O2 The applications of these sensors range from air-to-fuel ratio control in combustion processes including those in automotive engines and industrial furnaces to leakage detection of inflammable and toxic gases in domestic and industrial environments. As the need increases to accurately measure smaller and smaller concentrations, problems such as poor selectivity, stability and response time limit the use of these sensors. In an effort to overcome some of these limitations, a theoretical model of the transient behavior of solid state gas sensors has been developed. In this presentation, a model for the transient response of an electrochemical gas sensor to gas mixtures containing O2 and one reducing species, such as CO, is discussed. This model accounts for the transport of the reactive species to the sampling electrode, the catalyzed oxidation/reduction reaction of these species and the generation of the resulting electrical signal. The model will be shown to reproduce the results of published steady state models and to agree with experimental steady state and transient data.

Multi-state succession in wetlands: a novel use of state and transition models

USGS Publications Warehouse

Zweig, Christa L.; Kitchens, Wiley M.

2009-01-01

The complexity of ecosystems and mechanisms of succession are often simplified by linear and mathematical models used to understand and predict system behavior. Such models often do not incorporate multivariate, nonlinear feedbacks in pattern and process that include multiple scales of organization inherent within real-world systems. Wetlands are ecosystems with unique, nonlinear patterns of succession due to the regular, but often inconstant, presence of water on the landscape. We develop a general, nonspatial state and transition (S and T) succession conceptual model for wetlands and apply the general framework by creating annotated succession/management models and hypotheses for use in impact analysis on a portion of an imperiled wetland. The S and T models for our study area, Water Conservation Area 3A South (WCA3), Florida, USA, included hydrologic and peat depth values from multivariate analyses and classification and regression trees. We used the freeware Vegetation Dynamics Development Tool as an exploratory application to evaluate our S and T models with different management actions (equal chance [a control condition], deeper conditions, dry conditions, and increased hydrologic range) for three communities: slough, sawgrass (Cladium jamaicense), and wet prairie. Deeper conditions and increased hydrologic range behaved similarly, with the transition of community states to deeper states, particularly for sawgrass and slough. Hydrology is the primary mechanism for multi-state transitions within our study period, and we show both an immediate and lagged effect on vegetation, depending on community state. We consider these S and T succession models as a fraction of the framework for the Everglades. They are hypotheses for use in adaptive management, represent the community response to hydrology, and illustrate which aspects of hydrologic variability are important to community structure. We intend for these models to act as a foundation for further restoration

Using Parameter Constraints to Choose State Structures in Cost-Effectiveness Modelling.

PubMed

Thom, Howard; Jackson, Chris; Welton, Nicky; Sharples, Linda

2017-09-01

This article addresses the choice of state structure in a cost-effectiveness multi-state model. Key model outputs, such as treatment recommendations and prioritisation of future research, may be sensitive to state structure choice. For example, it may be uncertain whether to consider similar disease severities or similar clinical events as the same state or as separate states. Standard statistical methods for comparing models require a common reference dataset but merging states in a model aggregates the data, rendering these methods invalid. We propose a method that involves re-expressing a model with merged states as a model on the larger state space in which particular transition probabilities, costs and utilities are constrained to be equal between states. This produces a model that gives identical estimates of cost effectiveness to the model with merged states, while leaving the data unchanged. The comparison of state structures can be achieved by comparing maximised likelihoods or information criteria between constrained and unconstrained models. We can thus test whether the costs and/or health consequences for a patient in two states are the same, and hence if the states can be merged. We note that different structures can be used for rates, costs and utilities, as appropriate. We illustrate our method with applications to two recent models evaluating the cost effectiveness of prescribing anti-depressant medications by depression severity and the cost effectiveness of diagnostic tests for coronary artery disease. State structures in cost-effectiveness models can be compared using standard methods to compare constrained and unconstrained models.

Occupancy estimation and modeling with multiple states and state uncertainty

USGS Publications Warehouse

Nichols, J.D.; Hines, J.E.; MacKenzie, D.I.; Seamans, M.E.; Gutierrez, R.J.

2007-01-01

The distribution of a species over space is of central interest in ecology, but species occurrence does not provide all of the information needed to characterize either the well-being of a population or the suitability of occupied habitat. Recent methodological development has focused on drawing inferences about species occurrence in the face of imperfect detection. Here we extend those methods by characterizing occupied locations by some additional state variable ( e. g., as producing young or not). Our modeling approach deals with both detection probabilities,1 and uncertainty in state classification. We then use the approach with occupancy and reproductive rate data from California Spotted Owls (Strix occidentalis occidentalis) collected in the central Sierra Nevada during the breeding season of 2004 to illustrate the utility of the modeling approach. Estimates of owl reproductive rate were larger than naive estimates, indicating the importance of appropriately accounting for uncertainty in detection and state classification.

Connecting the Kuramoto Model and the Chimera State

NASA Astrophysics Data System (ADS)

Kotwal, Tejas; Jiang, Xin; Abrams, Daniel M.

2017-12-01

Since its discovery in 2002, the chimera state has frequently been described as a counterintuitive, puzzling phenomenon. The Kuramoto model, in contrast, has become a celebrated paradigm useful for understanding a range of phenomena related to phase transitions, synchronization, and network effects. Here we show that the chimera state can be understood as emerging naturally through a symmetry-breaking bifurcation from the Kuramoto model's partially synchronized state. Our analysis sheds light on recent observations of chimera states in laser arrays, chemical oscillators, and mechanical pendula.

Zero-state Markov switching count-data models: an empirical assessment.

PubMed

Malyshkina, Nataliya V; Mannering, Fred L

2010-01-01

In this study, a two-state Markov switching count-data model is proposed as an alternative to zero-inflated models to account for the preponderance of zeros sometimes observed in transportation count data, such as the number of accidents occurring on a roadway segment over some period of time. For this accident-frequency case, zero-inflated models assume the existence of two states: one of the states is a zero-accident count state, which has accident probabilities that are so low that they cannot be statistically distinguished from zero, and the other state is a normal-count state, in which counts can be non-negative integers that are generated by some counting process, for example, a Poisson or negative binomial. While zero-inflated models have come under some criticism with regard to accident-frequency applications - one fact is undeniable - in many applications they provide a statistically superior fit to the data. The Markov switching approach we propose seeks to overcome some of the criticism associated with the zero-accident state of the zero-inflated model by allowing individual roadway segments to switch between zero and normal-count states over time. An important advantage of this Markov switching approach is that it allows for the direct statistical estimation of the specific roadway-segment state (i.e., zero-accident or normal-count state) whereas traditional zero-inflated models do not. To demonstrate the applicability of this approach, a two-state Markov switching negative binomial model (estimated with Bayesian inference) and standard zero-inflated negative binomial models are estimated using five-year accident frequencies on Indiana interstate highway segments. It is shown that the Markov switching model is a viable alternative and results in a superior statistical fit relative to the zero-inflated models.

An Ab Initio Exciton Model Including Charge-Transfer Excited States.

PubMed

Li, Xin; Parrish, Robert M; Liu, Fang; Kokkila Schumacher, Sara I L; Martínez, Todd J

2017-08-08

The Frenkel exciton model is a useful tool for theoretical studies of multichromophore systems. We recently showed that the exciton model could be used to coarse-grain electronic structure in multichromophoric systems, focusing on singly excited exciton states [ Acc. Chem. Res. 2014 , 47 , 2857 - 2866 ]. However, our previous implementation excluded charge-transfer excited states, which can play an important role in light-harvesting systems and near-infrared optoelectronic materials. Recent studies have also emphasized the significance of charge-transfer in singlet fission, which mediates the coupling between the locally excited states and the multiexcitonic states. In this work, we report on an ab initio exciton model that incorporates charge-transfer excited states and demonstrate that the model provides correct charge-transfer excitation energies and asymptotic behavior. Comparison with TDDFT and EOM-CC2 calculations shows that our exciton model is robust with respect to system size, screening parameter, and different density functionals. Inclusion of charge-transfer excited states makes the exciton model more useful for studies of singly excited states and provides a starting point for future construction of a model that also includes double-exciton states.

Two-nucleon high-spin states, the Bansal-French model and the crude shell model

NASA Astrophysics Data System (ADS)

Chan, Tsan Ung

1987-08-01

Recent data on two-nucleon stretched high-spin states agree well with the crude shell model predictions. For two-neutron high-spin states, the A and T linear dependence of B2n in the Bansal-French model can be deduced from the A and T linear dependence of Bn and the crude shell model. 7-2 states in some Zn and Ge even nuclei might be two-proton states. This hypothesis should be confirmed by two-proton transfer reaction.

Two-nucleon high-spin states, the Bansal-French model and the crude shell model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chan, T.U.

Recent data on two-nucleon stretched high-spin states agree well with the crude shell model predictions. For two-neutron high-spin states, the A and T linear dependence of B/sub 2n/ in the Bansal-French model can be deduced from the A and T linear dependence of B/sub n/ and the crude shell model. 7/sub 2//sup -/ states in some Zn and Ge even nuclei might be two-proton states. This hypothesis should be confirmed by two-proton transfer reaction.

Identified state-space prediction model for aero-optical wavefronts

NASA Astrophysics Data System (ADS)

Faghihi, Azin; Tesch, Jonathan; Gibson, Steve

2013-07-01

A state-space disturbance model and associated prediction filter for aero-optical wavefronts are described. The model is computed by system identification from a sequence of wavefronts measured in an airborne laboratory. Estimates of the statistics and flow velocity of the wavefront data are shown and can be computed from the matrices in the state-space model without returning to the original data. Numerical results compare velocity values and power spectra computed from the identified state-space model with those computed from the aero-optical data.

Valuation of financial models with non-linear state spaces

NASA Astrophysics Data System (ADS)

Webber, Nick

2001-02-01

A common assumption in valuation models for derivative securities is that the underlying state variables take values in a linear state space. We discuss numerical implementation issues in an interest rate model with a simple non-linear state space, formulating and comparing Monte Carlo, finite difference and lattice numerical solution methods. We conclude that, at least in low dimensional spaces, non-linear interest rate models may be viable.

State-space prediction model for chaotic time series

NASA Astrophysics Data System (ADS)

Alparslan, A. K.; Sayar, M.; Atilgan, A. R.

1998-08-01

A simple method for predicting the continuation of scalar chaotic time series ahead in time is proposed. The false nearest neighbors technique in connection with the time-delayed embedding is employed so as to reconstruct the state space. A local forecasting model based upon the time evolution of the topological neighboring in the reconstructed phase space is suggested. A moving root-mean-square error is utilized in order to monitor the error along the prediction horizon. The model is tested for the convection amplitude of the Lorenz model. The results indicate that for approximately 100 cycles of the training data, the prediction follows the actual continuation very closely about six cycles. The proposed model, like other state-space forecasting models, captures the long-term behavior of the system due to the use of spatial neighbors in the state space.

An Ab Initio Exciton Model Including Charge-Transfer Excited States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xin; Parrish, Robert M.; Liu, Fang

Here, the Frenkel exciton model is a useful tool for theoretical studies of multichromophore systems. We recently showed that the exciton model could be used to coarse-grain electronic structure in multichromophoric systems, focusing on singly excited exciton states. However, our previous implementation excluded charge-transfer excited states, which can play an important role in light-harvesting systems and near-infrared optoelectronic materials. Recent studies have also emphasized the significance of charge-transfer in singlet fission, which mediates the coupling between the locally excited states and the multiexcitonic states. In this work, we report on an ab initio exciton model that incorporates charge-transfer excited statesmore » and demonstrate that the model provides correct charge-transfer excitation energies and asymptotic behavior. Comparison with TDDFT and EOM-CC2 calculations shows that our exciton model is robust with respect to system size, screening parameter, and different density functionals. Inclusion of charge-transfer excited states makes the exciton model more useful for studies of singly excited states and provides a starting point for future construction of a model that also includes double-exciton states.« less

An Ab Initio Exciton Model Including Charge-Transfer Excited States

DOE PAGES

Li, Xin; Parrish, Robert M.; Liu, Fang; ...

2017-06-15

Here, the Frenkel exciton model is a useful tool for theoretical studies of multichromophore systems. We recently showed that the exciton model could be used to coarse-grain electronic structure in multichromophoric systems, focusing on singly excited exciton states. However, our previous implementation excluded charge-transfer excited states, which can play an important role in light-harvesting systems and near-infrared optoelectronic materials. Recent studies have also emphasized the significance of charge-transfer in singlet fission, which mediates the coupling between the locally excited states and the multiexcitonic states. In this work, we report on an ab initio exciton model that incorporates charge-transfer excited statesmore » and demonstrate that the model provides correct charge-transfer excitation energies and asymptotic behavior. Comparison with TDDFT and EOM-CC2 calculations shows that our exciton model is robust with respect to system size, screening parameter, and different density functionals. Inclusion of charge-transfer excited states makes the exciton model more useful for studies of singly excited states and provides a starting point for future construction of a model that also includes double-exciton states.« less

Parameter redundancy in discrete state-space and integrated models.

PubMed

Cole, Diana J; McCrea, Rachel S

2016-09-01

Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Model of Mental State Transition Network

NASA Astrophysics Data System (ADS)

Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo

Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

STEADY-STATE MODEL OF SOLAR WIND ELECTRONS REVISITED

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoon, Peter H.; Kim, Sunjung; Choe, G. S., E-mail: yoonp@umd.edu

2015-10-20

In a recent paper, Kim et al. put forth a steady-state model for the solar wind electrons. The model assumed local equilibrium between the halo electrons, characterized by an intermediate energy range, and the whistler-range fluctuations. The basic wave–particle interaction is assumed to be the cyclotron resonance. Similarly, it was assumed that a dynamical steady state is established between the highly energetic superhalo electrons and high-frequency Langmuir fluctuations. Comparisons with the measured solar wind electron velocity distribution function (VDF) during quiet times were also made, and reasonable agreements were obtained. In such a model, however, only the steady-state solution for themore » Fokker–Planck type of electron particle kinetic equation was considered. The present paper complements the previous analysis by considering both the steady-state particle and wave kinetic equations. It is shown that the model halo and superhalo electron VDFs, as well as the assumed wave intensity spectra for the whistler and Langmuir fluctuations, approximately satisfy the quasi-linear wave kinetic equations in an approximate sense, thus further validating the local equilibrium model constructed in the paper by Kim et al.« less

Reuleaux models at St. Petersburg State University

NASA Astrophysics Data System (ADS)

Kuteeva, G. A.; Sinilshchikova, G. A.; Trifonenko, B. V.

2018-05-01

Franz Reuleaux (1829 - 1905) is a famous mechanical engineer, a Professor of the Berlin Royal Technical Academy. He became widely known as an engineer-scientist, a Professor and industrial consultant, education reformer and leader of the technical elite of Germany. He directed the design and manufacture of over 300 models of simple mechanisms. They were sold to many famous universities for pedagogical and scientific purposes. Today, the most complete set is at Cornell University, College of Engineering. In this article we discuss the history, the modern state and our using the Reuleaux models that survived at St. Petersburg State University for educational purposes. We present description of certain models and our electronic resource with these models. We provide the information of similar electronic resources from other universities.

Boundary States and Broken Bulk Symmetries in WAr Minimal Models

NASA Astrophysics Data System (ADS)

Caldeira, Alexandre F.; Wheater, J. F.

We review the free-field formalism for boundary states. The multi-component free-field formalism is then used to study the boundary states of (p',p) rational conformal field theories having a W symmetry of the type Ar. We show how the classification of primary fields for these models is obtained by demanding modular covariance of cylinder amplitudes and that the resulting modular S matrix satisfies all the necessary conditions. Basis states satisfying the boundary conditions are found in the form of coherent states and as expected we find that W violating states can be found for all these models. We construct consistent physical boundary states for all the rank 2 (p + 1,p) models (of which the already known case of the 3-state Potts model is the simplest example) and find that the W violating sector possesses a direct analogue of the Verlinde formula.

Modeling T-cell activation using gene expression profiling and state-space models.

PubMed

Rangel, Claudia; Angus, John; Ghahramani, Zoubin; Lioumi, Maria; Sotheran, Elizabeth; Gaiba, Alessia; Wild, David L; Falciani, Francesco

2004-06-12

We have used state-space models to reverse engineer transcriptional networks from highly replicated gene expression profiling time series data obtained from a well-established model of T-cell activation. State space models are a class of dynamic Bayesian networks that assume that the observed measurements depend on some hidden state variables that evolve according to Markovian dynamics. These hidden variables can capture effects that cannot be measured in a gene expression profiling experiment, e.g. genes that have not been included in the microarray, levels of regulatory proteins, the effects of messenger RNA and protein degradation, etc. Bootstrap confidence intervals are developed for parameters representing 'gene-gene' interactions over time. Our models represent the dynamics of T-cell activation and provide a methodology for the development of rational and experimentally testable hypotheses. Supplementary data and Matlab computer source code will be made available on the web at the URL given below. http://public.kgi.edu/~wild/LDS/index.htm

Modeling solid-state transformations occurring in dissolution testing.

PubMed

Laaksonen, Timo; Aaltonen, Jaakko

2013-04-15

Changes in the solid-state form can occur during dissolution testing of drugs. This can often complicate interpretation of results. Additionally, there can be several mechanisms through which such a change proceeds, e.g. solvent-mediated transformation or crystal growth within the drug material itself. Here, a mathematical model was constructed to study the dissolution testing of a material, which undergoes such changes. The model consisted of two processes: the recrystallization of the drug from a supersaturated liquid state caused by the dissolution of the more soluble solid form and the crystal growth of the stable solid form at the surface of the drug formulation. Comparison to experimental data on theophylline dissolution showed that the results obtained with the model matched real solid-state changes and that it was able to distinguish between cases where the transformation was controlled either by solvent-mediated crystallization or solid-state crystal growth. Copyright © 2013 Elsevier B.V. All rights reserved.

Development of Water Quality Modeling in the United States

EPA Science Inventory

This presentation describes historical trends in water quality model development in the United States, reviews current efforts, and projects promising future directions. Water quality modeling has a relatively long history in the United States. While its origins lie in the work...

Quasi steady-state aerodynamic model development for race vehicle simulations

NASA Astrophysics Data System (ADS)

Mohrfeld-Halterman, J. A.; Uddin, M.

2016-01-01

Presented in this paper is a procedure to develop a high fidelity quasi steady-state aerodynamic model for use in race car vehicle dynamic simulations. Developed to fit quasi steady-state wind tunnel data, the aerodynamic model is regressed against three independent variables: front ground clearance, rear ride height, and yaw angle. An initial dual range model is presented and then further refined to reduce the model complexity while maintaining a high level of predictive accuracy. The model complexity reduction decreases the required amount of wind tunnel data thereby reducing wind tunnel testing time and cost. The quasi steady-state aerodynamic model for the pitch moment degree of freedom is systematically developed in this paper. This same procedure can be extended to the other five aerodynamic degrees of freedom to develop a complete six degree of freedom quasi steady-state aerodynamic model for any vehicle.

Monitoring alert and drowsy states by modeling EEG source nonstationarity

NASA Astrophysics Data System (ADS)

Hsu, Sheng-Hsiou; Jung, Tzyy-Ping

2017-10-01

Objective. As a human brain performs various cognitive functions within ever-changing environments, states of the brain characterized by recorded brain activities such as electroencephalogram (EEG) are inevitably nonstationary. The challenges of analyzing the nonstationary EEG signals include finding neurocognitive sources that underlie different brain states and using EEG data to quantitatively assess the state changes. Approach. This study hypothesizes that brain activities under different states, e.g. levels of alertness, can be modeled as distinct compositions of statistically independent sources using independent component analysis (ICA). This study presents a framework to quantitatively assess the EEG source nonstationarity and estimate levels of alertness. The framework was tested against EEG data collected from 10 subjects performing a sustained-attention task in a driving simulator. Main results. Empirical results illustrate that EEG signals under alert versus drowsy states, indexed by reaction speeds to driving challenges, can be characterized by distinct ICA models. By quantifying the goodness-of-fit of each ICA model to the EEG data using the model deviation index (MDI), we found that MDIs were significantly correlated with the reaction speeds (r  =  -0.390 with alertness models and r  =  0.449 with drowsiness models) and the opposite correlations indicated that the two models accounted for sources in the alert and drowsy states, respectively. Based on the observed source nonstationarity, this study also proposes an online framework using a subject-specific ICA model trained with an initial (alert) state to track the level of alertness. For classification of alert against drowsy states, the proposed online framework achieved an averaged area-under-curve of 0.745 and compared favorably with a classic power-based approach. Significance. This ICA-based framework provides a new way to study changes of brain states and can be applied to

Steady state analysis of Boolean molecular network models via model reduction and computational algebra.

PubMed

Veliz-Cuba, Alan; Aguilar, Boris; Hinkelmann, Franziska; Laubenbacher, Reinhard

2014-06-26

A key problem in the analysis of mathematical models of molecular networks is the determination of their steady states. The present paper addresses this problem for Boolean network models, an increasingly popular modeling paradigm for networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general. This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean network. The code for the algorithm, as well as the test suite of benchmark networks, is available upon request from the corresponding author. The algorithm presented in this paper reliably determines all steady states of sparse Boolean networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate connectivity. The problem for

Steady state analysis of Boolean molecular network models via model reduction and computational algebra

PubMed Central

2014-01-01

Background A key problem in the analysis of mathematical models of molecular networks is the determination of their steady states. The present paper addresses this problem for Boolean network models, an increasingly popular modeling paradigm for networks lacking detailed kinetic information. For small models, the problem can be solved by exhaustive enumeration of all state transitions. But for larger models this is not feasible, since the size of the phase space grows exponentially with the dimension of the network. The dimension of published models is growing to over 100, so that efficient methods for steady state determination are essential. Several methods have been proposed for large networks, some of them heuristic. While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general. Results This paper presents an algorithm that consists of two main parts. The first is a graph theoretic reduction of the wiring diagram of the network, while preserving all information about steady states. The second part formulates the determination of all steady states of a Boolean network as a problem of finding all solutions to a system of polynomial equations over the finite number system with two elements. This problem can be solved with existing computer algebra software. This algorithm compares favorably with several existing algorithms for steady state determination. One advantage is that it is not heuristic or reliant on sampling, but rather determines algorithmically and exactly all steady states of a Boolean network. The code for the algorithm, as well as the test suite of benchmark networks, is available upon request from the corresponding author. Conclusions The algorithm presented in this paper reliably determines all steady states of sparse Boolean networks with up to 1000 nodes. The algorithm is effective at analyzing virtually all published models even those of moderate

2018 Regional, State, and Local Modelers' Workshop

EPA Pesticide Factsheets

The 2018 Regional, State, and Local (RSL) Modelers' Workshop is being held at the EPA's Region 1 Offices in Boston, MA from June 5-7, 2018. This page provides information on the agenda and registration for the RSL Modelers' Workshop.

Dichromatic State Sum Models for Four-Manifolds from Pivotal Functors

NASA Astrophysics Data System (ADS)

Bärenz, Manuel; Barrett, John

2017-11-01

A family of invariants of smooth, oriented four-dimensional manifolds is defined via handle decompositions and the Kirby calculus of framed link diagrams. The invariants are parametrised by a pivotal functor from a spherical fusion category into a ribbon fusion category. A state sum formula for the invariant is constructed via the chain-mail procedure, so a large class of topological state sum models can be expressed as link invariants. Most prominently, the Crane-Yetter state sum over an arbitrary ribbon fusion category is recovered, including the nonmodular case. It is shown that the Crane-Yetter invariant for nonmodular categories is stronger than signature and Euler invariant. A special case is the four-dimensional untwisted Dijkgraaf-Witten model. Derivations of state space dimensions of TQFTs arising from the state sum model agree with recent calculations of ground state degeneracies in Walker-Wang models. Relations to different approaches to quantum gravity such as Cartan geometry and teleparallel gravity are also discussed.

Dichromatic State Sum Models for Four-Manifolds from Pivotal Functors

NASA Astrophysics Data System (ADS)

Bärenz, Manuel; Barrett, John

2018-06-01

A family of invariants of smooth, oriented four-dimensional manifolds is defined via handle decompositions and the Kirby calculus of framed link diagrams. The invariants are parametrised by a pivotal functor from a spherical fusion category into a ribbon fusion category. A state sum formula for the invariant is constructed via the chain-mail procedure, so a large class of topological state sum models can be expressed as link invariants. Most prominently, the Crane-Yetter state sum over an arbitrary ribbon fusion category is recovered, including the nonmodular case. It is shown that the Crane-Yetter invariant for nonmodular categories is stronger than signature and Euler invariant. A special case is the four-dimensional untwisted Dijkgraaf-Witten model. Derivations of state space dimensions of TQFTs arising from the state sum model agree with recent calculations of ground state degeneracies in Walker-Wang models. Relations to different approaches to quantum gravity such as Cartan geometry and teleparallel gravity are also discussed.

Ontology and modeling patterns for state-based behavior representation

NASA Technical Reports Server (NTRS)

Castet, Jean-Francois; Rozek, Matthew L.; Ingham, Michel D.; Rouquette, Nicolas F.; Chung, Seung H.; Kerzhner, Aleksandr A.; Donahue, Kenneth M.; Jenkins, J. Steven; Wagner, David A.; Dvorak, Daniel L.;

Steady-State Ion Beam Modeling with MICHELLE

NASA Astrophysics Data System (ADS)

Petillo, John

2003-10-01

There is a need to efficiently model ion beam physics for ion implantation, chemical vapor deposition, and ion thrusters. Common to all is the need for three-dimensional (3D) simulation of volumetric ion sources, ion acceleration, and optics, with the ability to model charge exchange of the ion beam with a background neutral gas. The two pieces of physics stand out as significant are the modeling of the volumetric source and charge exchange. In the MICHELLE code, the method for modeling the plasma sheath in ion sources assumes that the electron distribution function is a Maxwellian function of electrostatic potential over electron temperature. Charge exchange is the process by which a neutral background gas with a "fast" charged particle streaming through exchanges its electron with the charged particle. An efficient method for capturing this is essential, and the model presented is based on semi-empirical collision cross section functions. This appears to be the first steady-state 3D algorithm of its type to contain multiple generations of charge exchange, work with multiple species and multiple charge state beam/source particles simultaneously, take into account the self-consistent space charge effects, and track the subsequent fast neutral particles. The solution used by MICHELLE is to combine finite element analysis with particle-in-cell (PIC) methods. The basic physics model is based on the equilibrium steady-state application of the electrostatic particle-in-cell (PIC) approximation employing a conformal computational mesh. The foundation stems from the same basic model introduced in codes such as EGUN. Here, Poisson's equation is used to self-consistently include the effects of space charge on the fields, and the relativistic Lorentz equation is used to integrate the particle trajectories through those fields. The presentation will consider the complexity of modeling ion thrusters.

Energy modeling. Volume 2: Inventory and details of state energy models

NASA Astrophysics Data System (ADS)

Melcher, A. G.; Underwood, R. G.; Weber, J. C.; Gist, R. L.; Holman, R. P.; Donald, D. W.

1981-05-01

An inventory of energy models developed by or for state governments is presented, and certain models are discussed in depth. These models address a variety of purposes such as: supply or demand of energy or of certain types of energy; emergency management of energy; and energy economics. Ten models are described. The purpose, use, and history of the model is discussed, and information is given on the outputs, inputs, and mathematical structure of the model. The models include five models dealing with energy demand, one of which is econometric and four of which are econometric-engineering end-use models.

Descriptive Linear modeling of steady-state visual evoked response

NASA Technical Reports Server (NTRS)

Levison, W. H.; Junker, A. M.; Kenner, K.

1986-01-01

A study is being conducted to explore use of the steady state visual-evoke electrocortical response as an indicator of cognitive task loading. Application of linear descriptive modeling to steady state Visual Evoked Response (VER) data is summarized. Two aspects of linear modeling are reviewed: (1) unwrapping the phase-shift portion of the frequency response, and (2) parsimonious characterization of task-loading effects in terms of changes in model parameters. Model-based phase unwrapping appears to be most reliable in applications, such as manual control, where theoretical models are available. Linear descriptive modeling of the VER has not yet been shown to provide consistent and readily interpretable results.

The Impact of State Legislation and Model Policies on Bullying in Schools.

PubMed

Terry, Amanda

2018-04-01

The purpose of this study was to determine the impact of the coverage of state legislation and the expansiveness ratings of state model policies on the state-level prevalence of bullying in schools. The state-level prevalence of bullying in schools was based on cross-sectional data from the 2013 High School Youth Risk Behavior Survey. Multiple regression was conducted to determine whether the coverage of state legislation and the expansiveness rating of a state model policy affected the state-level prevalence of bullying in schools. The purpose and definition category of components in state legislation and the expansiveness rating of a state model policy were statistically significant predictors of the state-level prevalence of bullying in schools. The other 3 categories of components in state legislation-District Policy Development and Review, District Policy Components, and Additional Components-were not statistically significant predictors in the model. Extensive coverage in the purpose and definition category of components in state legislation and a high expansiveness rating of a state model policy may be important in efforts to reduce bullying in schools. Improving these areas may reduce the state-level prevalence of bullying in schools. © 2018, American School Health Association.

Dynamics from a mathematical model of a two-state gas laser

NASA Astrophysics Data System (ADS)

Kleanthous, Antigoni; Hua, Tianshu; Manai, Alexandre; Yawar, Kamran; Van Gorder, Robert A.

2018-05-01

Motivated by recent work in the area, we consider the behavior of solutions to a nonlinear PDE model of a two-state gas laser. We first review the derivation of the two-state gas laser model, before deriving a non-dimensional model given in terms of coupled nonlinear partial differential equations. We then classify the steady states of this system, in order to determine the possible long-time asymptotic solutions to this model, as well as corresponding stability results, showing that the only uniform steady state (the zero motion state) is unstable, while a linear profile in space is stable. We then provide numerical simulations for the full unsteady model. We show for a wide variety of initial conditions that the solutions tend toward the stable linear steady state profiles. We also consider traveling wave solutions, and determine the unique wave speed (in terms of the other model parameters) which allows wave-like solutions to exist. Despite some similarities between the model and the inviscid Burger's equation, the solutions we obtain are much more regular than the solutions to the inviscid Burger's equation, with no evidence of shock formation or loss of regularity.

Sharp Contradiction for Local-Hidden-State Model in Quantum Steering

PubMed Central

Chen, Jing-Ling; Su, Hong-Yi; Xu, Zhen-Peng; Pati, Arun Kumar

2016-01-01

In quantum theory, no-go theorems are important as they rule out the existence of a particular physical model under consideration. For instance, the Greenberger-Horne-Zeilinger (GHZ) theorem serves as a no-go theorem for the nonexistence of local hidden variable models by presenting a full contradiction for the multipartite GHZ states. However, the elegant GHZ argument for Bell’s nonlocality does not go through for bipartite Einstein-Podolsky-Rosen (EPR) state. Recent study on quantum nonlocality has shown that the more precise description of EPR’s original scenario is “steering”, i.e., the nonexistence of local hidden state models. Here, we present a simple GHZ-like contradiction for any bipartite pure entangled state, thus proving a no-go theorem for the nonexistence of local hidden state models in the EPR paradox. This also indicates that the very simple steering paradox presented here is indeed the closest form to the original spirit of the EPR paradox. PMID:27562658

Sharp Contradiction for Local-Hidden-State Model in Quantum Steering

NASA Astrophysics Data System (ADS)

Chen, Jing-Ling; Su, Hong-Yi; Xu, Zhen-Peng; Pati, Arun Kumar

2016-08-01

In quantum theory, no-go theorems are important as they rule out the existence of a particular physical model under consideration. For instance, the Greenberger-Horne-Zeilinger (GHZ) theorem serves as a no-go theorem for the nonexistence of local hidden variable models by presenting a full contradiction for the multipartite GHZ states. However, the elegant GHZ argument for Bell’s nonlocality does not go through for bipartite Einstein-Podolsky-Rosen (EPR) state. Recent study on quantum nonlocality has shown that the more precise description of EPR’s original scenario is “steering”, i.e., the nonexistence of local hidden state models. Here, we present a simple GHZ-like contradiction for any bipartite pure entangled state, thus proving a no-go theorem for the nonexistence of local hidden state models in the EPR paradox. This also indicates that the very simple steering paradox presented here is indeed the closest form to the original spirit of the EPR paradox.

Sharp Contradiction for Local-Hidden-State Model in Quantum Steering.

PubMed

Chen, Jing-Ling; Su, Hong-Yi; Xu, Zhen-Peng; Pati, Arun Kumar

2016-08-26

In quantum theory, no-go theorems are important as they rule out the existence of a particular physical model under consideration. For instance, the Greenberger-Horne-Zeilinger (GHZ) theorem serves as a no-go theorem for the nonexistence of local hidden variable models by presenting a full contradiction for the multipartite GHZ states. However, the elegant GHZ argument for Bell's nonlocality does not go through for bipartite Einstein-Podolsky-Rosen (EPR) state. Recent study on quantum nonlocality has shown that the more precise description of EPR's original scenario is "steering", i.e., the nonexistence of local hidden state models. Here, we present a simple GHZ-like contradiction for any bipartite pure entangled state, thus proving a no-go theorem for the nonexistence of local hidden state models in the EPR paradox. This also indicates that the very simple steering paradox presented here is indeed the closest form to the original spirit of the EPR paradox.

Equation of State of the Two-Dimensional Hubbard Model

NASA Astrophysics Data System (ADS)

Cocchi, Eugenio; Miller, Luke A.; Drewes, Jan H.; Koschorreck, Marco; Pertot, Daniel; Brennecke, Ferdinand; Köhl, Michael

2016-04-01

The subtle interplay between kinetic energy, interactions, and dimensionality challenges our comprehension of strongly correlated physics observed, for example, in the solid state. In this quest, the Hubbard model has emerged as a conceptually simple, yet rich model describing such physics. Here we present an experimental determination of the equation of state of the repulsive two-dimensional Hubbard model over a broad range of interactions 0 ≲U /t ≲20 and temperatures, down to kBT /t =0.63 (2 ) using high-resolution imaging of ultracold fermionic atoms in optical lattices. We show density profiles, compressibilities, and double occupancies over the whole doping range, and, hence, our results constitute benchmarks for state-of-the-art theoretical approaches.

Exotic superconducting states in the extended attractive Hubbard model.

PubMed

Nayak, Swagatam; Kumar, Sanjeev

2018-04-04

We show that the extended attractive Hubbard model on a square lattice allows for a variety of superconducting phases, including exotic mixed-symmetry phases with [Formula: see text] and [Formula: see text] symmetries, and a novel [Formula: see text] state. The calculations are performed within the Hartree-Fock Bardeen-Cooper-Schrieffer framework. The ground states of the mean-field Hamiltonian are obtained via a minimization scheme that relaxes the symmetry constraints on the superconducting solutions, hence allowing for a mixing of s-, p- and d-wave order parameters. The results are obtained within the assumption of uniform-density states. Our results show that extended attractive Hubbard model can serve as an effective model for investigating properties of exotic superconductors.

A state-based probabilistic model for tumor respiratory motion prediction

NASA Astrophysics Data System (ADS)

Kalet, Alan; Sandison, George; Wu, Huanmei; Schmitz, Ruth

2010-12-01

This work proposes a new probabilistic mathematical model for predicting tumor motion and position based on a finite state representation using the natural breathing states of exhale, inhale and end of exhale. Tumor motion was broken down into linear breathing states and sequences of states. Breathing state sequences and the observables representing those sequences were analyzed using a hidden Markov model (HMM) to predict the future sequences and new observables. Velocities and other parameters were clustered using a k-means clustering algorithm to associate each state with a set of observables such that a prediction of state also enables a prediction of tumor velocity. A time average model with predictions based on average past state lengths was also computed. State sequences which are known a priori to fit the data were fed into the HMM algorithm to set a theoretical limit of the predictive power of the model. The effectiveness of the presented probabilistic model has been evaluated for gated radiation therapy based on previously tracked tumor motion in four lung cancer patients. Positional prediction accuracy is compared with actual position in terms of the overall RMS errors. Various system delays, ranging from 33 to 1000 ms, were tested. Previous studies have shown duty cycles for latencies of 33 and 200 ms at around 90% and 80%, respectively, for linear, no prediction, Kalman filter and ANN methods as averaged over multiple patients. At 1000 ms, the previously reported duty cycles range from approximately 62% (ANN) down to 34% (no prediction). Average duty cycle for the HMM method was found to be 100% and 91 ± 3% for 33 and 200 ms latency and around 40% for 1000 ms latency in three out of four breathing motion traces. RMS errors were found to be lower than linear and no prediction methods at latencies of 1000 ms. The results show that for system latencies longer than 400 ms, the time average HMM prediction outperforms linear, no prediction, and the more

Noninformative prior in the quantum statistical model of pure states

NASA Astrophysics Data System (ADS)

Tanaka, Fuyuhiko

2012-06-01

In the present paper, we consider a suitable definition of a noninformative prior on the quantum statistical model of pure states. While the full pure-states model is invariant under unitary rotation and admits the Haar measure, restricted models, which we often see in quantum channel estimation and quantum process tomography, have less symmetry and no compelling rationale for any choice. We adopt a game-theoretic approach that is applicable to classical Bayesian statistics and yields a noninformative prior for a general class of probability distributions. We define the quantum detection game and show that there exist noninformative priors for a general class of a pure-states model. Theoretically, it gives one of the ways that we represent ignorance on the given quantum system with partial information. Practically, our method proposes a default distribution on the model in order to use the Bayesian technique in the quantum-state tomography with a small sample.

Einstein's steady-state theory: an abandoned model of the cosmos

NASA Astrophysics Data System (ADS)

O'Raifeartaigh, Cormac; McCann, Brendan; Nahm, Werner; Mitton, Simon

2014-09-01

We present a translation and analysis of an unpublished manuscript by Albert Einstein in which he attempted to construct a `steady-state' model of the universe. The manuscript, which appears to have been written in early 1931, demonstrates that Einstein once explored a cosmic model in which the mean density of matter in an expanding universe is maintained constant by the continuous formation of matter from empty space. This model is very different to previously known Einsteinian models of the cosmos (both static and dynamic) but anticipates the later steady-state cosmology of Hoyle, Bondi and Gold in some ways. We find that Einstein's steady-state model contains a fundamental flaw and suggest that it was abandoned for this reason. We also suggest that he declined to explore a more sophisticated version because he found such theories rather contrived. The manuscript is of historical interest because it reveals that Einstein debated between steady-state and evolving models of the cosmos decades before a similar debate took place in the cosmological community.

State-to-state modeling of non-equilibrium air nozzle flows

NASA Astrophysics Data System (ADS)

Nagnibeda, E.; Papina, K.; Kunova, O.

2018-05-01

One-dimensional non-equilibrium air flows in nozzles are studied on the basis of the state-to-state description of vibrational-chemical kinetics. Five-component mixture N2/O2/NO/N/O is considered taking into account Zeldovich exchange reactions of NO formation, dissociation, recombination and vibrational energy transitions. The equations for vibrational and chem-ical kinetics in a flow are coupled to the conservation equations of momentum and total energy and solved numerically for different conditions in a nozzle throat. The vibrational distributions of nitrogen and oxygen molecules, number densities of species as well as the gas temperature and flow velocity along a nozzle axis are analysed using the detailed state-to-state flow description and in the frame of the simplified one-temperature thermal equilibrium kinetic model. The comparison of the results showed the influence of non-equilibrium kinetics on macroscopic nozzle flow parameters. In the state-to-state approach, non-Boltzmann vibrational dis-tributions of N2 and O2 molecules with a plateau part at intermediate levels are found. The results are found with the use of the complete and simplified schemes of reactions and the impact of exchange reactions, dissociation and recombination on variation of vibrational level populations, mixture composition, gas velocity and temperature along a nozzle axis is shown.

Uncertainty in a Markov state model with missing states and rates: Application to a room temperature kinetic model obtained using high temperature molecular dynamics.

PubMed

Chatterjee, Abhijit; Bhattacharya, Swati

2015-09-21

Several studies in the past have generated Markov State Models (MSMs), i.e., kinetic models, of biomolecular systems by post-analyzing long standard molecular dynamics (MD) calculations at the temperature of interest and focusing on the maximally ergodic subset of states. Questions related to goodness of these models, namely, importance of the missing states and kinetic pathways, and the time for which the kinetic model is valid, are generally left unanswered. We show that similar questions arise when we generate a room-temperature MSM (denoted MSM-A) for solvated alanine dipeptide using state-constrained MD calculations at higher temperatures and Arrhenius relation — the main advantage of such a procedure being a speed-up of several thousand times over standard MD-based MSM building procedures. Bounds for rate constants calculated using probability theory from state-constrained MD at room temperature help validate MSM-A. However, bounds for pathways possibly missing in MSM-A show that alternate kinetic models exist that produce the same dynamical behaviour at short time scales as MSM-A but diverge later. Even in the worst case scenario, MSM-A is found to be valid longer than the time required to generate it. Concepts introduced here can be straightforwardly extended to other MSM building techniques.

Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

USGS Publications Warehouse

Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin

2014-01-01

Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.

Modeling individual effects in the Cormack-Jolly-Seber Model: A state-space formulation

USGS Publications Warehouse

Royle, J. Andrew

2008-01-01

In population and evolutionary biology, there exists considerable interest in individual heterogeneity in parameters of demographic models for open populations. However, flexible and practical solutions to the development of such models have proven to be elusive. In this article, I provide a state-space formulation of open population capture-recapture models with individual effects. The state-space formulation provides a generic and flexible framework for modeling and inference in models with individual effects, and it yields a practical means of estimation in these complex problems via contemporary methods of Markov chain Monte Carlo. A straightforward implementation can be achieved in the software package WinBUGS. I provide an analysis of a simple model with constant parameter detection and survival probability parameters. A second example is based on data from a 7-year study of European dippers, in which a model with year and individual effects is fitted.

State Machine Modeling of the Space Launch System Solid Rocket Boosters

NASA Technical Reports Server (NTRS)

Harris, Joshua A.; Patterson-Hine, Ann

2013-01-01

The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.

Modeling in the Common Core State Standards

ERIC Educational Resources Information Center

Tam, Kai Chung

2011-01-01

The inclusion of modeling and applications into the mathematics curriculum has proven to be a challenging task over the last fifty years. The Common Core State Standards (CCSS) has made mathematical modeling both one of its Standards for Mathematical Practice and one of its Conceptual Categories. This article discusses the need for mathematical…

Exotic superconducting states in the extended attractive Hubbard model

NASA Astrophysics Data System (ADS)

Nayak, Swagatam; Kumar, Sanjeev

2018-04-01

We show that the extended attractive Hubbard model on a square lattice allows for a variety of superconducting phases, including exotic mixed-symmetry phases with dx^2-y^2 + i [s + s^*] and dx^2-y^2 + px symmetries, and a novel px + i py state. The calculations are performed within the Hartree-Fock Bardeen-Cooper-Schrieffer framework. The ground states of the mean-field Hamiltonian are obtained via a minimization scheme that relaxes the symmetry constraints on the superconducting solutions, hence allowing for a mixing of s-, p- and d-wave order parameters. The results are obtained within the assumption of uniform-density states. Our results show that extended attractive Hubbard model can serve as an effective model for investigating properties of exotic superconductors.

New Equation of State Models for Hydrodynamic Applications

NASA Astrophysics Data System (ADS)

Young, David A.; Barbee, Troy W., III; Rogers, Forrest J.

1997-07-01

Accurate models of the equation of state of matter at high pressures and temperatures are increasingly required for hydrodynamic simulations. We have developed two new approaches to accurate EOS modeling: 1) ab initio phonons from electron band structure theory for condensed matter and 2) the ACTEX dense plasma model for ultrahigh pressure shocks. We have studied the diamond and high pressure phases of carbon with the ab initio model and find good agreement between theory and experiment for shock Hugoniots, isotherms, and isobars. The theory also predicts a comprehensive phase diagram for carbon. For ultrahigh pressure shock states, we have studied the comparison of ACTEX theory with experiments for deuterium, beryllium, polystyrene, water, aluminum, and silicon dioxide. The agreement is good, showing that complex multispecies plasmas are treated adequately by the theory. These models will be useful in improving the numerical EOS tables used by hydrodynamic codes.

Review: Regional groundwater flow modeling in heavily irrigated basins of selected states in the western United States

NASA Astrophysics Data System (ADS)

Rossman, Nathan R.; Zlotnik, Vitaly A.

2013-09-01

Water resources in agriculture-dominated basins of the arid western United States are stressed due to long-term impacts from pumping. A review of 88 regional groundwater-flow modeling applications from seven intensively irrigated western states (Arizona, California, Colorado, Idaho, Kansas, Nebraska and Texas) was conducted to provide hydrogeologists, modelers, water managers, and decision makers insight about past modeling studies that will aid future model development. Groundwater models were classified into three types: resource evaluation models (39 %), which quantify water budgets and act as preliminary models intended to be updated later, or constitute re-calibrations of older models; management/planning models (55 %), used to explore and identify management plans based on the response of the groundwater system to water-development or climate scenarios, sometimes under water-use constraints; and water rights models (7 %), used to make water administration decisions based on model output and to quantify water shortages incurred by water users or climate changes. Results for 27 model characteristics are summarized by state and model type, and important comparisons and contrasts are highlighted. Consideration of modeling uncertainty and the management focus toward sustainability, adaptive management and resilience are discussed, and future modeling recommendations, in light of the reviewed models and other published works, are presented.

Steady state phosphorus mass balance model during hemodialysis based on a pseudo one-compartment kinetic model.

PubMed

Leypoldt, John K; Agar, Baris U; Akonur, Alp; Gellens, Mary E; Culleton, Bruce F

2012-11-01

Mathematical models of phosphorus kinetics and mass balance during hemodialysis are in early development. We describe a theoretical phosphorus steady state mass balance model during hemodialysis based on a novel pseudo one-compartment kinetic model. The steady state mass balance model accounted for net intestinal absorption of phosphorus and phosphorus removal by both dialysis and residual kidney function. Analytical mathematical solutions were derived to describe time-dependent intradialytic and interdialytic serum phosphorus concentrations assuming hemodialysis treatments were performed symmetrically throughout a week. Results from the steady state phosphorus mass balance model are described for thrice weekly hemodialysis treatment prescriptions only. The analysis predicts 1) a minimal impact of dialyzer phosphorus clearance on predialysis serum phosphorus concentration using modern, conventional hemodialysis technology, 2) variability in the postdialysis-to-predialysis phosphorus concentration ratio due to differences in patient-specific phosphorus mobilization, and 3) the importance of treatment time in determining the predialysis serum phosphorus concentration. We conclude that a steady state phosphorus mass balance model can be developed based on a pseudo one-compartment kinetic model and that predictions from this model are consistent with previous clinical observations. The predictions from this mass balance model are theoretical and hypothesis-generating only; additional prospective clinical studies will be required for model confirmation.

On the time to steady state: insights from numerical modeling

NASA Astrophysics Data System (ADS)

Goren, L.; Willett, S.; McCoy, S. W.; Perron, J.

2013-12-01

How fast do fluvial landscapes approach steady state after an application of tectonic or climatic perturbation? While theory and some numerical models predict that the celerity of the advective wave (knickpoint) controls the response time for perturbations, experiments and other landscape evolution models demonstrate that the time to steady state is much longer than the theoretically predicted response time. We posit that the longevity of transient features and the time to steady state are controlled by the stability of the topology and geometry of channel networks. Evolution of a channel network occurs by a combination of discrete capture events and continuous migration of water divides, processes, which are difficult to represent accurately in landscape evolution models. We therefore address the question of the time to steady state using the DAC landscape evolution model that solves accurately for the location of water divides, using a combination of analytical solution for hillslopes and low-order channels together with a numerical solution for higher order channels. DAC also includes an explicit capture criterion. We have tested fundamental predictions from DAC and show that modeled networks reproduce natural network characteristics such as the Hack's exponent and coefficient and the fractal dimension. We define two steady-state criteria: a topographic steady state, defined by global, pointwise steady elevation, and a topological steady state defined as the state in which no further reorganization of the drainage network takes place. Analyzing block uplift simulations, we find that the time to achieve either topographic or topological steady state exceeds by an order of magnitude the theoretical response time of the fluvial network. The longevity of the transient state is the result of the area feedback, by which, migration of a divide changes the local contributing area. This change propagates downstream as a slope adjustment, forcing further divide migrations

Magnetic models for the United States for 1985

USGS Publications Warehouse

Peddie, Norman W.; Zunde, Audronis K.

1990-01-01

New models describing the magnetic field in the United States at the beginning of 1985 and the rate of change expected during the next few years have been developed. The models--which will serve as the basis for a new set of magnetic charts--were derived from several tens of thousands of original field measurements from land, marine, and aerial surveys; from values derived from the MAGSAT-based International Geomagnetic Reference Field; and from recent data from magnetic observatories and repeat stations. , They are in the form of spherical harmonic series that represent the scalar magnetic potential from which all the field components can be derived. The models for the conterminous States and Alaska are of maximum degree and order 4 (24 coefficients each) and the models for Hawaii are of maximum degree and order 2 (8 coefficients each).

Capturing the state transitions of seizure-like events using Hidden Markov models.

PubMed

Guirgis, Mirna; Serletis, Demitre; Carlen, Peter L; Bardakjian, Berj L

2011-01-01

The purpose of this study was to investigate the number of states present in the progression of a seizure-like event (SLE). Of particular interest is to determine if there are more than two clearly defined states, as this would suggest that there is a distinct state preceding an SLE. Whole-intact hippocampus from C57/BL mice was used to model epileptiform activity induced by the perfusion of a low Mg(2+)/high K(+) solution while extracellular field potentials were recorded from CA3 pyramidal neurons. Hidden Markov models (HMM) were used to model the state transitions of the recorded SLEs by incorporating various features of the Hilbert transform into the training algorithm; specifically, 2- and 3-state HMMs were explored. Although the 2-state model was able to distinguish between SLE and nonSLE behavior, it provided no improvements compared to visual inspection alone. However, the 3-state model was able to capture two distinct nonSLE states that visual inspection failed to discriminate. Moreover, by developing an HMM based system a priori knowledge of the state transitions was not required making this an ideal platform for seizure prediction algorithms.

Ground-state and Thermodynamic Properties of an S = 1 Kitaev Model

NASA Astrophysics Data System (ADS)

Koga, Akihisa; Tomishige, Hiroyuki; Nasu, Joji

2018-06-01

We study the ground-state and thermodynamic properties of an S = 1 Kitaev model. We first clarify the existence of global parity symmetry in addition to the local symmetry on each plaquette, which enables us to perform large-scale calculations on up to 24 sites. It is found that the ground state should be singlet, and its energy is estimated as E/N ˜ -0.65J, where J is the Kitaev exchange coupling. We find that the lowest excited state belongs to the same subspace as the ground state, and that the gap decreases monotonically with increasing system size, which suggests that the ground state of the S = 1 Kitaev model is gapless. Using the thermal pure quantum states, we clarify the finite temperature properties characteristic of the Kitaev models with S ≤ 2.

Markov state models and molecular alchemy

NASA Astrophysics Data System (ADS)

Schütte, Christof; Nielsen, Adam; Weber, Marcus

2015-01-01

In recent years, Markov state models (MSMs) have attracted a considerable amount of attention with regard to modelling conformation changes and associated function of biomolecular systems. They have been used successfully, e.g. for peptides including time-resolved spectroscopic experiments, protein function and protein folding , DNA and RNA, and ligand-receptor interaction in drug design and more complicated multivalent scenarios. In this article, a novel reweighting scheme is introduced that allows to construct an MSM for certain molecular system out of an MSM for a similar system. This permits studying how molecular properties on long timescales differ between similar molecular systems without performing full molecular dynamics simulations for each system under consideration. The performance of the reweighting scheme is illustrated for simple test cases, including one where the main wells of the respective energy landscapes are located differently and an alchemical transformation of butane to pentane where the dimension of the state space is changed.

Chimera states in a Hodgkin-Huxley model of thermally sensitive neurons

NASA Astrophysics Data System (ADS)

Glaze, Tera A.; Lewis, Scott; Bahar, Sonya

2016-08-01

Chimera states occur when identically coupled groups of nonlinear oscillators exhibit radically different dynamics, with one group exhibiting synchronized oscillations and the other desynchronized behavior. This dynamical phenomenon has recently been studied in computational models and demonstrated experimentally in mechanical, optical, and chemical systems. The theoretical basis of these states is currently under active investigation. Chimera behavior is of particular relevance in the context of neural synchronization, given the phenomenon of unihemispheric sleep and the recent observation of asymmetric sleep in human patients with sleep apnea. The similarity of neural chimera states to neural "bump" states, which have been suggested as a model for working memory and visual orientation tuning in the cortex, adds to their interest as objects of study. Chimera states have been demonstrated in the FitzHugh-Nagumo model of excitable cells and in the Hindmarsh-Rose neural model. Here, we demonstrate chimera states and chimera-like behaviors in a Hodgkin-Huxley-type model of thermally sensitive neurons both in a system with Abrams-Strogatz (mean field) coupling and in a system with Kuramoto (distance-dependent) coupling. We map the regions of parameter space for which chimera behavior occurs in each of the two coupling schemes.

Practical Application of Model-based Programming and State-based Architecture to Space Missions

NASA Technical Reports Server (NTRS)

Horvath, Gregory; Ingham, Michel; Chung, Seung; Martin, Oliver; Williams, Brian

2006-01-01

A viewgraph presentation to develop models from systems engineers that accomplish mission objectives and manage the health of the system is shown. The topics include: 1) Overview; 2) Motivation; 3) Objective/Vision; 4) Approach; 5) Background: The Mission Data System; 6) Background: State-based Control Architecture System; 7) Background: State Analysis; 8) Overview of State Analysis; 9) Background: MDS Software Frameworks; 10) Background: Model-based Programming; 10) Background: Titan Model-based Executive; 11) Model-based Execution Architecture; 12) Compatibility Analysis of MDS and Titan Architectures; 13) Integrating Model-based Programming and Execution into the Architecture; 14) State Analysis and Modeling; 15) IMU Subsystem State Effects Diagram; 16) Titan Subsystem Model: IMU Health; 17) Integrating Model-based Programming and Execution into the Software IMU; 18) Testing Program; 19) Computationally Tractable State Estimation & Fault Diagnosis; 20) Diagnostic Algorithm Performance; 21) Integration and Test Issues; 22) Demonstrated Benefits; and 23) Next Steps

Recognition of human activity characteristics based on state transitions modeling technique

NASA Astrophysics Data System (ADS)

Elangovan, Vinayak; Shirkhodaie, Amir

2012-06-01

Human Activity Discovery & Recognition (HADR) is a complex, diverse and challenging task but yet an active area of ongoing research in the Department of Defense. By detecting, tracking, and characterizing cohesive Human interactional activity patterns, potential threats can be identified which can significantly improve situation awareness, particularly, in Persistent Surveillance Systems (PSS). Understanding the nature of such dynamic activities, inevitably involves interpretation of a collection of spatiotemporally correlated activities with respect to a known context. In this paper, we present a State Transition model for recognizing the characteristics of human activities with a link to a prior contextbased ontology. Modeling the state transitions between successive evidential events determines the activities' temperament. The proposed state transition model poses six categories of state transitions including: Human state transitions of Object handling, Visibility, Entity-entity relation, Human Postures, Human Kinematics and Distance to Target. The proposed state transition model generates semantic annotations describing the human interactional activities via a technique called Casual Event State Inference (CESI). The proposed approach uses a low cost kinect depth camera for indoor and normal optical camera for outdoor monitoring activities. Experimental results are presented here to demonstrate the effectiveness and efficiency of the proposed technique.

Absorbing multicultural states in the Axelrod model

NASA Astrophysics Data System (ADS)

Vazquez, Federico; Redner, Sidney

2005-03-01

We determine the ultimate fate of a limit of the Axelrod model that consists of a population of leftists, centrists, and rightists. In an elemental interaction between agents, a centrist and a leftist can both become centrists or both become leftists with equal rates (similarly for a centrist and a rightist), but leftists and rightists do not interact. This interaction is applied repeatedly until the system can no longer evolve. The constraint between extremists can lead to a frustrated final state where the system consists of only leftists and rightists. In the mean field limit, we can view the evolution of the system as the motion of a random walk in the 3-dimensional space whose coordinates correspond to the density of each species. We find the exact final state probabilities and the time to reach consensus by solving for the first-passage probability of the random walk to the corresponding absorbing boundaries. The extension to a larger number of states will be discussed. This approach is a first step towards the analytic solution of Axelrod-like models.

A Rigorous Investigation on the Ground State of the Penson-Kolb Model

NASA Astrophysics Data System (ADS)

Yang, Kai-Hua; Tian, Guang-Shan; Han, Ru-Qi

2003-05-01

By using either numerical calculations or analytical methods, such as the bosonization technique, the ground state of the Penson-Kolb model has been previously studied by several groups. Some physicists argued that, as far as the existence of superconductivity in this model is concerned, it is canonically equivalent to the negative-U Hubbard model. However, others did not agree. In the present paper, we shall investigate this model by an independent and rigorous approach. We show that the ground state of the Penson-Kolb model is nondegenerate and has a nonvanishing overlap with the ground state of the negative-U Hubbard model. Furthermore, we also show that the ground states of both the models have the same good quantum numbers and may have superconducting long-range order at the same momentum q = 0. Our results support the equivalence between these models. The project partially supported by the Special Funds for Major State Basic Research Projects (G20000365) and National Natural Science Foundation of China under Grant No. 10174002

Calibration of state and transition models with FVS

Treesearch

Melinda Moeur; Don Vandendriesche

2010-01-01

The Interagency Mapping and Assessment Project (IMAP), a partnership between federal and state agencies, is developing mid-scale vegetation data and state and transition models (STM) for comparing the likely outcomes of alternative management policies on forested landscapes across the Pacific Northwest Region. In an STM, acres within a forested ecosystem transition...

Solvable model for chimera states of coupled oscillators.

PubMed

Abrams, Daniel M; Mirollo, Rennie; Strogatz, Steven H; Wiley, Daniel A

2008-08-22

Networks of identical, symmetrically coupled oscillators can spontaneously split into synchronized and desynchronized subpopulations. Such chimera states were discovered in 2002, but are not well understood theoretically. Here we obtain the first exact results about the stability, dynamics, and bifurcations of chimera states by analyzing a minimal model consisting of two interacting populations of oscillators. Along with a completely synchronous state, the system displays stable chimeras, breathing chimeras, and saddle-node, Hopf, and homoclinic bifurcations of chimeras.

Equivalence and Differences between Structural Equation Modeling and State-Space Modeling Techniques

ERIC Educational Resources Information Center

Chow, Sy-Miin; Ho, Moon-ho R.; Hamaker, Ellen L.; Dolan, Conor V.

2010-01-01

State-space modeling techniques have been compared to structural equation modeling (SEM) techniques in various contexts but their unique strengths have often been overshadowed by their similarities to SEM. In this article, we provide a comprehensive discussion of these 2 approaches' similarities and differences through analytic comparisons and…

Oscillator-like coherent states for the Jaynes-Cummings Model

NASA Technical Reports Server (NTRS)

Berubelauziere, Y.; Hussin, V.; Nieto, Michael M.

1995-01-01

A new way of diagonalizing the Jaynes-Cummings Hamiltonian is proposed, which allows the definition of annihilation operators and coherent states for this model. Mean values and dispersions over these states are computed and interpreted.

State variable modeling of the integrated engine and aircraft dynamics

NASA Astrophysics Data System (ADS)

Rotaru, Constantin; Sprinţu, Iuliana

2014-12-01

This study explores the dynamic characteristics of the combined aircraft-engine system, based on the general theory of the state variables for linear and nonlinear systems, with details leading first to the separate formulation of the longitudinal and the lateral directional state variable models, followed by the merging of the aircraft and engine models into a single state variable model. The linearized equations were expressed in a matrix form and the engine dynamics was included in terms of variation of thrust following a deflection of the throttle. The linear model of the shaft dynamics for a two-spool jet engine was derived by extending the one-spool model. The results include the discussion of the thrust effect upon the aircraft response when the thrust force associated with the engine has a sizable moment arm with respect to the aircraft center of gravity for creating a compensating moment.

Markov state models of protein misfolding

NASA Astrophysics Data System (ADS)

Sirur, Anshul; De Sancho, David; Best, Robert B.

2016-02-01

Markov state models (MSMs) are an extremely useful tool for understanding the conformational dynamics of macromolecules and for analyzing MD simulations in a quantitative fashion. They have been extensively used for peptide and protein folding, for small molecule binding, and for the study of native ensemble dynamics. Here, we adapt the MSM methodology to gain insight into the dynamics of misfolded states. To overcome possible flaws in root-mean-square deviation (RMSD)-based metrics, we introduce a novel discretization approach, based on coarse-grained contact maps. In addition, we extend the MSM methodology to include "sink" states in order to account for the irreversibility (on simulation time scales) of processes like protein misfolding. We apply this method to analyze the mechanism of misfolding of tandem repeats of titin domains, and how it is influenced by confinement in a chaperonin-like cavity.

Predicting landscape vegetation dynamics using state-and-transition simulation models

Treesearch

Colin J. Daniel; Leonardo Frid

2012-01-01

This paper outlines how state-and-transition simulation models (STSMs) can be used to project changes in vegetation over time across a landscape. STSMs are stochastic, empirical simulation models that use an adapted Markov chain approach to predict how vegetation will transition between states over time, typically in response to interactions between succession,...

Methods of Selecting Industries for Depressed Areas--An Introduction to Feasibility Studies. Developing Job Opportunities 2.

ERIC Educational Resources Information Center

Klaassen, Leo H.

This report presents severl alternative methods which may be employed by local authorities in identifying likely prospects for local industrialization, and describes a specialized input-output technique to define inter-industry relations and inter-regional relations of industries. This technique is applied, for illustrative purposes, to three…

Using Markov state models to study self-assembly

NASA Astrophysics Data System (ADS)

Perkett, Matthew R.; Hagan, Michael F.

2014-06-01

Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class of multi-molecular assembly reactions. Distinct structures formed during assembly are distinguished by their undirected graphs, which are defined by strong subunit interactions. Spatial inhomogeneities of free subunits are accounted for using a recently developed Gaussian-based signature. Simplifications to this state identification are also investigated. The feasibility of this approach is demonstrated on two different coarse-grained models for virus self-assembly. We find good agreement between the dynamics predicted by the MSMs and long, unbiased simulations, and that the MSMs can reduce overall simulation time by orders of magnitude.

Two-state model based on the block-localized wave function method

NASA Astrophysics Data System (ADS)

Mo, Yirong

2007-06-01

The block-localized wave function (BLW) method is a variant of ab initio valence bond method but retains the efficiency of molecular orbital methods. It can derive the wave function for a diabatic (resonance) state self-consistently and is available at the Hartree-Fock (HF) and density functional theory (DFT) levels. In this work we present a two-state model based on the BLW method. Although numerous empirical and semiempirical two-state models, such as the Marcus-Hush two-state model, have been proposed to describe a chemical reaction process, the advantage of this BLW-based two-state model is that no empirical parameter is required. Important quantities such as the electronic coupling energy, structural weights of two diabatic states, and excitation energy can be uniquely derived from the energies of two diabatic states and the adiabatic state at the same HF or DFT level. Two simple examples of formamide and thioformamide in the gas phase and aqueous solution were presented and discussed. The solvation of formamide and thioformamide was studied with the combined ab initio quantum mechanical and molecular mechanical Monte Carlo simulations, together with the BLW-DFT calculations and analyses. Due to the favorable solute-solvent electrostatic interaction, the contribution of the ionic resonance structure to the ground state of formamide and thioformamide significantly increases, and for thioformamide the ionic form is even more stable than the covalent form. Thus, thioformamide in aqueous solution is essentially ionic rather than covalent. Although our two-state model in general underestimates the electronic excitation energies, it can predict relative solvatochromic shifts well. For instance, the intense π →π* transition for formamide upon solvation undergoes a redshift of 0.3eV, compared with the experimental data (0.40-0.5eV).

Dynamic elementary mode modelling of non-steady state flux data.

PubMed

Folch-Fortuny, Abel; Teusink, Bas; Hoefsloot, Huub C J; Smilde, Age K; Ferrer, Alberto

2018-06-18

A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling. This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

A Multiyear Model of Influenza Vaccination in the United States.

PubMed

Kamis, Arnold; Zhang, Yuji; Kamis, Tamara

2017-07-28

Vaccinating adults against influenza remains a challenge in the United States. Using data from the Centers for Disease Control and Prevention, we present a model for predicting who receives influenza vaccination in the United States between 2012 and 2014, inclusive. The logistic regression model contains nine predictors: age, pneumococcal vaccination, time since last checkup, highest education level attained, employment, health care coverage, number of personal doctors, smoker status, and annual household income. The model, which classifies correctly 67 percent of the data in 2013, is consistent with models tested on the 2012 and 2014 datasets. Thus, we have a multiyear model to explain and predict influenza vaccination in the United States. The results indicate room for improvement in vaccination rates. We discuss how cognitive biases may underlie reluctance to obtain vaccination. We argue that targeted communications addressing cognitive biases could be useful for effective framing of vaccination messages, thus increasing the vaccination rate. Finally, we discuss limitations of the current study and questions for future research.

Modeling of cortical signals using echo state networks

NASA Astrophysics Data System (ADS)

Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

2009-10-01

Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.

State-and-transition simulation models: a framework for forecasting landscape change

USGS Publications Warehouse

Daniel, Colin; Frid, Leonardo; Sleeter, Benjamin M.; Fortin, Marie-Josée

2016-01-01

SummaryA wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features.We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states.We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years.State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of

Markov state modeling of sliding friction

NASA Astrophysics Data System (ADS)

Pellegrini, F.; Landes, François P.; Laio, A.; Prestipino, S.; Tosatti, E.

2016-11-01

Markov state modeling (MSM) has recently emerged as one of the key techniques for the discovery of collective variables and the analysis of rare events in molecular simulations. In particular in biochemistry this approach is successfully exploited to find the metastable states of complex systems and their evolution in thermal equilibrium, including rare events, such as a protein undergoing folding. The physics of sliding friction and its atomistic simulations under external forces constitute a nonequilibrium field where relevant variables are in principle unknown and where a proper theory describing violent and rare events such as stick slip is still lacking. Here we show that MSM can be extended to the study of nonequilibrium phenomena and in particular friction. The approach is benchmarked on the Frenkel-Kontorova model, used here as a test system whose properties are well established. We demonstrate that the method allows the least prejudiced identification of a minimal basis of natural microscopic variables necessary for the description of the forced dynamics of sliding, through their probabilistic evolution. The steps necessary for the application to realistic frictional systems are highlighted.

Mathematical model for Dengue with three states of infection

NASA Astrophysics Data System (ADS)

Hincapie, Doracelly; Ospina, Juan

2012-06-01

A mathematical model for dengue with three states of infection is proposed and analyzed. The model consists in a system of differential equations. The three states of infection are respectively asymptomatic, partially asymptomatic and fully asymptomatic. The model is analyzed using computer algebra software, specifically Maple, and the corresponding basic reproductive number and the epidemic threshold are computed. The resulting basic reproductive number is an algebraic synthesis of all epidemic parameters and it makes clear the possible control measures. The microscopic structure of the epidemic parameters is established using the quantum theory of the interactions between the atoms and radiation. In such approximation, the human individual is represented by an atom and the mosquitoes are represented by radiation. The force of infection from the mosquitoes to the humans is considered as the transition probability from the fundamental state of atom to excited states. The combination of computer algebra software and quantum theory provides a very complete formula for the basic reproductive number and the possible control measures tending to stop the propagation of the disease. It is claimed that such result may be important in military medicine and the proposed method can be applied to other vector-borne diseases.

Transitions in state public health law: comparative analysis of state public health law reform following the Turning Point Model State Public Health Act.

PubMed

Meier, Benjamin Mason; Hodge, James G; Gebbie, Kristine M

2009-03-01

Given the public health importance of law modernization, we undertook a comparative analysis of policy efforts in 4 states (Alaska, South Carolina, Wisconsin, and Nebraska) that have considered public health law reform based on the Turning Point Model State Public Health Act. Through national legislative tracking and state case studies, we investigated how the Turning Point Act's model legal language has been considered for incorporation into state law and analyzed key facilitating and inhibiting factors for public health law reform. Our findings provide the practice community with a research base to facilitate further law reform and inform future scholarship on the role of law as a determinant of the public's health.

Using Markov state models to study self-assembly

PubMed Central

Perkett, Matthew R.; Hagan, Michael F.

2014-01-01

Markov state models (MSMs) have been demonstrated to be a powerful method for computationally studying intramolecular processes such as protein folding and macromolecular conformational changes. In this article, we present a new approach to construct MSMs that is applicable to modeling a broad class of multi-molecular assembly reactions. Distinct structures formed during assembly are distinguished by their undirected graphs, which are defined by strong subunit interactions. Spatial inhomogeneities of free subunits are accounted for using a recently developed Gaussian-based signature. Simplifications to this state identification are also investigated. The feasibility of this approach is demonstrated on two different coarse-grained models for virus self-assembly. We find good agreement between the dynamics predicted by the MSMs and long, unbiased simulations, and that the MSMs can reduce overall simulation time by orders of magnitude. PMID:24907984

State-space reduction and equivalence class sampling for a molecular self-assembly model.

PubMed

Packwood, Daniel M; Han, Patrick; Hitosugi, Taro

2016-07-01

Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

Ground-state properties of the three-band Hubbard model

NASA Astrophysics Data System (ADS)

Zhang, Shiwei; Vitali, Ettore; Chiciak, Adam; Shi, Hao

The three-band Hubbard model proposed by Emery describes the CuO2 plane in cuprate superconductors by retaining both Cu and O orbitals in a minimal sense. Applying the latest developments in the auxiliary-field quantum Monte Carlo (AFQMC) method, we investigate ground-state properties of this model at half-filling and when lightly (under-)doped. The AFQMC uses generalized Hartree-Fock (GHF) trial wave functions to control the sign problem. A self-consistent constraint is applied. We also determine the unrestricted Hartree-Fock (UHF) and GHF ground states and compare their predictions with those from AFQMC. Similarities and differences between the three-band model and one-band Hubbard model will be discussed. Supported by NSF, and the Simons Foundation. Computing is carried out at the Extreme Science and Engineering Discovery Environment(XSEDE).

Model for Vortex Ring State Influence on Rotorcraft Flight Dynamics

NASA Technical Reports Server (NTRS)

Johnson, Wayne

2005-01-01

The influence of vortex ring state (VRS) on rotorcraft flight dynamics is investigated, specifically the vertical velocity drop of helicopters and the roll-off of tiltrotors encountering VRS. The available wind tunnel and flight test data for rotors in vortex ring state are reviewed. Test data for axial flow, non-axial flow, two rotors, unsteadiness, and vortex ring state boundaries are described and discussed. Based on the available measured data, a VRS model is developed. The VRS model is a parametric extension of momentum theory for calculation of the mean inflow of a rotor, hence suitable for simple calculations and real-time simulations. This inflow model is primarily defined in terms of the stability boundary of the aircraft motion. Calculations of helicopter response during VRS encounter were performed, and good correlation is shown with the vertical velocity drop measured in flight tests. Calculations of tiltrotor response during VRS encounter were performed, showing the roll-off behavior characteristic of tiltrotors. Hence it is possible, using a model of the mean inflow of an isolated rotor, to explain the basic behavior of both helicopters and tiltrotors in vortex ring state.

An improved state-parameter analysis of ecosystem models using data assimilation

USGS Publications Warehouse

Chen, M.; Liu, S.; Tieszen, L.L.; Hollinger, D.Y.

2008-01-01

Much of the effort spent in developing data assimilation methods for carbon dynamics analysis has focused on estimating optimal values for either model parameters or state variables. The main weakness of estimating parameter values alone (i.e., without considering state variables) is that all errors from input, output, and model structure are attributed to model parameter uncertainties. On the other hand, the accuracy of estimating state variables may be lowered if the temporal evolution of parameter values is not incorporated. This research develops a smoothed ensemble Kalman filter (SEnKF) by combining ensemble Kalman filter with kernel smoothing technique. SEnKF has following characteristics: (1) to estimate simultaneously the model states and parameters through concatenating unknown parameters and state variables into a joint state vector; (2) to mitigate dramatic, sudden changes of parameter values in parameter sampling and parameter evolution process, and control narrowing of parameter variance which results in filter divergence through adjusting smoothing factor in kernel smoothing algorithm; (3) to assimilate recursively data into the model and thus detect possible time variation of parameters; and (4) to address properly various sources of uncertainties stemming from input, output and parameter uncertainties. The SEnKF is tested by assimilating observed fluxes of carbon dioxide and environmental driving factor data from an AmeriFlux forest station located near Howland, Maine, USA, into a partition eddy flux model. Our analysis demonstrates that model parameters, such as light use efficiency, respiration coefficients, minimum and optimum temperatures for photosynthetic activity, and others, are highly constrained by eddy flux data at daily-to-seasonal time scales. The SEnKF stabilizes parameter values quickly regardless of the initial values of the parameters. Potential ecosystem light use efficiency demonstrates a strong seasonality. Results show that the

Dynamic models for problems of species occurrence with multiple states

USGS Publications Warehouse

MacKenzie, D.I.; Nichols, J.D.; Seamans, M.E.; Gutierrez, R.J.

2009-01-01

Recent extensions of occupancy modeling have focused not only on the distribution of species over space, but also on additional state variables (e.g., reproducing or not, with or without disease organisms, relative abundance categories) that provide extra information about occupied sites. These biologist-driven extensions are characterized by ambiguity in both species presence and correct state classification, caused by imperfect detection. We first show the relationships between independently published approaches to the modeling of multistate occupancy. We then extend the pattern-based modeling to the case of sampling over multiple seasons or years in order to estimate state transition probabilities associated with system dynamics. The methodology and its potential for addressing relevant ecological questions are demonstrated using both maximum likelihood (occupancy and successful reproduction dynamics of California Spotted Owl) and Markov chain Monte Carlo estimation approaches (changes in relative abundance of green frogs in Maryland). Just as multistate capture?recapture modeling has revolutionized the study of individual marked animals, we believe that multistate occupancy modeling will dramatically increase our ability to address interesting questions about ecological processes underlying population-level dynamics.

Transitions in State Public Health Law: Comparative Analysis of State Public Health Law Reform Following the Turning Point Model State Public Health Act

PubMed Central

Meier, Benjamin Mason; Gebbie, Kristine M.

2009-01-01

Given the public health importance of law modernization, we undertook a comparative analysis of policy efforts in 4 states (Alaska, South Carolina, Wisconsin, and Nebraska) that have considered public health law reform based on the Turning Point Model State Public Health Act. Through national legislative tracking and state case studies, we investigated how the Turning Point Act's model legal language has been considered for incorporation into state law and analyzed key facilitating and inhibiting factors for public health law reform. Our findings provide the practice community with a research base to facilitate further law reform and inform future scholarship on the role of law as a determinant of the public's health. PMID:19150900

State resolved vibrational relaxation modeling for strongly nonequilibrium flows

NASA Astrophysics Data System (ADS)

Boyd, Iain D.; Josyula, Eswar

2011-05-01

Vibrational relaxation is an important physical process in hypersonic flows. Activation of the vibrational mode affects the fundamental thermodynamic properties and finite rate relaxation can reduce the degree of dissociation of a gas. Low fidelity models of vibrational activation employ a relaxation time to capture the process at a macroscopic level. High fidelity, state-resolved models have been developed for use in continuum gas dynamics simulations based on computational fluid dynamics (CFD). By comparison, such models are not as common for use with the direct simulation Monte Carlo (DSMC) method. In this study, a high fidelity, state-resolved vibrational relaxation model is developed for the DSMC technique. The model is based on the forced harmonic oscillator approach in which multi-quantum transitions may become dominant at high temperature. Results obtained for integrated rate coefficients from the DSMC model are consistent with the corresponding CFD model. Comparison of relaxation results obtained with the high-fidelity DSMC model shows significantly less excitation of upper vibrational levels in comparison to the standard, lower fidelity DSMC vibrational relaxation model. Application of the new DSMC model to a Mach 7 normal shock wave in carbon monoxide provides better agreement with experimental measurements than the standard DSMC relaxation model.

Research on Turbofan Engine Model above Idle State Based on NARX Modeling Approach

NASA Astrophysics Data System (ADS)

Yu, Bing; Shu, Wenjun

2017-03-01

The nonlinear model for turbofan engine above idle state based on NARX is studied. Above all, the data sets for the JT9D engine from existing model are obtained via simulation. Then, a nonlinear modeling scheme based on NARX is proposed and several models with different parameters are built according to the former data sets. Finally, the simulations have been taken to verify the precise and dynamic performance the models, the results show that the NARX model can well reflect the dynamics characteristic of the turbofan engine with high accuracy.

A five states survivability model for missions with ground-to-air threats

NASA Astrophysics Data System (ADS)

Erlandsson, Tina; Niklasson, Lars

2013-05-01

Fighter pilots are exposed to the risk of getting hit by enemy fire when flying missions with ground-to-air threats. A tactical support system including a survivability model could aid the pilot to assess and handle this risk. The survivability model presented here is a Markov model with five states; Undetected, Detected, Tracked, Engaged and Hit. The output from the model is the probabilities that the aircraft is in these states during the mission. The enemy's threat systems are represented with sensor and weapon areas and the transitions between the states depend on whether or not the aircraft is within any of these areas. Contrary to previous work, the model can capture the behaviors that the enemy's sensor systems communicate and that the risk of getting hit depends on the enemy's knowledge regarding the aircraft's kinematics. The paper includes a discussion regarding the interpretation of the states and the factors that influence the transitions between the states. Further developments are also identified for using the model to aid fighter pilots and operators of unmanned aerial vehicles with planning and evaluating missions as well as analyzing the situation during flight.

The Total Quality Management Model Department of Personnel State of Colorado,

DTIC Science & Technology

A panel of three members will present the Total Quality Management model recently designed for the Department of Personnel, State of Colorado. This model was selected to increase work quality and productivity of the Department and to exemplify Governor Romer’s commitment to quality work within state government.

Recent Trends in Collective Bargaining. International Management Seminar (Castelfusano, September 21-24, 1971.) International Seminars 1971-1. Final Report.

ERIC Educational Resources Information Center

Candau, Pierre

In this monograph, the author summarizes the findings of a seminar on collective negotiation that was conducted by representatives of several countries. The report (1) examines the ideas contributed that concern the roles of the different social partners; (2) discusses the structure of collective bargaining at the international, interindustry,…

Set-free Markov state model building

NASA Astrophysics Data System (ADS)

Weber, Marcus; Fackeldey, Konstantin; Schütte, Christof

2017-03-01

Molecular dynamics (MD) simulations face challenging problems since the time scales of interest often are much longer than what is possible to simulate; and even if sufficiently long simulations are possible the complex nature of the resulting simulation data makes interpretation difficult. Markov State Models (MSMs) help to overcome these problems by making experimentally relevant time scales accessible via coarse grained representations that also allow for convenient interpretation. However, standard set-based MSMs exhibit some caveats limiting their approximation quality and statistical significance. One of the main caveats results from the fact that typical MD trajectories repeatedly re-cross the boundary between the sets used to build the MSM which causes statistical bias in estimating the transition probabilities between these sets. In this article, we present a set-free approach to MSM building utilizing smooth overlapping ansatz functions instead of sets and an adaptive refinement approach. This kind of meshless discretization helps to overcome the recrossing problem and yields an adaptive refinement procedure that allows us to improve the quality of the model while exploring state space and inserting new ansatz functions into the MSM.

Numerical analysis of the chimera states in the multilayered network model

NASA Astrophysics Data System (ADS)

Goremyko, Mikhail V.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Ghosh, Dibakar; Bera, Bidesh K.; Dana, Syamal K.; Hramov, Alexander E.

2017-03-01

We numerically study the interaction between the ensembles of the Hindmarsh-Rose (HR) neuron systems, arranged in the multilayer network model. We have shown that the fully identical layers, demonstrated individually different chimera due to the initial mismatch, come to the identical chimera state with the increase of inter-layer coupling. Within the multilayer model we also consider the case, when the one layer demonstrates chimera state, while another layer exhibits coherent or incoherent dynamics. It has been shown that the interactions chimera-coherent state and chimera-incoherent state leads to the both excitation of chimera as from the ensemble of fully coherent or incoherent oscillators, and suppression of initially stable chimera state

Almost certain escape from black holes in final state projection models.

PubMed

Lloyd, Seth

2006-02-17

Recent models of the black-hole final state suggest that quantum information can escape from a black hole by a process akin to teleportation. These models rely on a controversial process called final-state projection. This Letter discusses the self-consistency of the final-state projection hypothesis and investigates escape from black holes for arbitrary final states and for generic interactions between matter and Hawking radiation. Quantum information escapes with fidelity approximately = (8/3pi)2: only half a bit of quantum information is lost on average, independent of the number of bits that escape from the hole.

A two-state hysteresis model from high-dimensional friction

PubMed Central

Biswas, Saurabh; Chatterjee, Anindya

2015-01-01

In prior work (Biswas & Chatterjee 2014 Proc. R. Soc. A 470, 20130817 (doi:10.1098/rspa.2013.0817)), we developed a six-state hysteresis model from a high-dimensional frictional system. Here, we use a more intuitively appealing frictional system that resembles one studied earlier by Iwan. The basis functions now have simple analytical description. The number of states required decreases further, from six to the theoretical minimum of two. The number of fitted parameters is reduced by an order of magnitude, to just six. An explicit and faster numerical solution method is developed. Parameter fitting to match different specified hysteresis loops is demonstrated. In summary, a new two-state model of hysteresis is presented that is ready for practical implementation. Essential Matlab code is provided. PMID:26587279

A pairwise maximum entropy model accurately describes resting-state human brain networks

PubMed Central

Watanabe, Takamitsu; Hirose, Satoshi; Wada, Hiroyuki; Imai, Yoshio; Machida, Toru; Shirouzu, Ichiro; Konishi, Seiki; Miyashita, Yasushi; Masuda, Naoki

2013-01-01

The resting-state human brain networks underlie fundamental cognitive functions and consist of complex interactions among brain regions. However, the level of complexity of the resting-state networks has not been quantified, which has prevented comprehensive descriptions of the brain activity as an integrative system. Here, we address this issue by demonstrating that a pairwise maximum entropy model, which takes into account region-specific activity rates and pairwise interactions, can be robustly and accurately fitted to resting-state human brain activities obtained by functional magnetic resonance imaging. Furthermore, to validate the approximation of the resting-state networks by the pairwise maximum entropy model, we show that the functional interactions estimated by the pairwise maximum entropy model reflect anatomical connexions more accurately than the conventional functional connectivity method. These findings indicate that a relatively simple statistical model not only captures the structure of the resting-state networks but also provides a possible method to derive physiological information about various large-scale brain networks. PMID:23340410

Model Checking - My 27-Year Quest to Overcome the State Explosion Problem

NASA Technical Reports Server (NTRS)

Clarke, Ed

2009-01-01

Model Checking is an automatic verification technique for state-transition systems that are finite=state or that have finite-state abstractions. In the early 1980 s in a series of joint papers with my graduate students E.A. Emerson and A.P. Sistla, we proposed that Model Checking could be used for verifying concurrent systems and gave algorithms for this purpose. At roughly the same time, Joseph Sifakis and his student J.P. Queille at the University of Grenoble independently developed a similar technique. Model Checking has been used successfully to reason about computer hardware and communication protocols and is beginning to be used for verifying computer software. Specifications are written in temporal logic, which is particularly valuable for expressing concurrency properties. An intelligent, exhaustive search is used to determine if the specification is true or not. If the specification is not true, the Model Checker will produce a counterexample execution trace that shows why the specification does not hold. This feature is extremely useful for finding obscure errors in complex systems. The main disadvantage of Model Checking is the state-explosion problem, which can occur if the system under verification has many processes or complex data structures. Although the state-explosion problem is inevitable in worst case, over the past 27 years considerable progress has been made on the problem for certain classes of state-transition systems that occur often in practice. In this talk, I will describe what Model Checking is, how it works, and the main techniques that have been developed for combating the state explosion problem.

The memory state heuristic: A formal model based on repeated recognition judgments.

PubMed

Castela, Marta; Erdfelder, Edgar

2017-02-01

The recognition heuristic (RH) theory predicts that, in comparative judgment tasks, if one object is recognized and the other is not, the recognized one is chosen. The memory-state heuristic (MSH) extends the RH by assuming that choices are not affected by recognition judgments per se, but by the memory states underlying these judgments (i.e., recognition certainty, uncertainty, or rejection certainty). Specifically, the larger the discrepancy between memory states, the larger the probability of choosing the object in the higher state. The typical RH paradigm does not allow estimation of the underlying memory states because it is unknown whether the objects were previously experienced or not. Therefore, we extended the paradigm by repeating the recognition task twice. In line with high threshold models of recognition, we assumed that inconsistent recognition judgments result from uncertainty whereas consistent judgments most likely result from memory certainty. In Experiment 1, we fitted 2 nested multinomial models to the data: an MSH model that formalizes the relation between memory states and binary choices explicitly and an approximate model that ignores the (unlikely) possibility of consistent guesses. Both models provided converging results. As predicted, reliance on recognition increased with the discrepancy in the underlying memory states. In Experiment 2, we replicated these results and found support for choice consistency predictions of the MSH. Additionally, recognition and choice latencies were in agreement with the MSH in both experiments. Finally, we validated critical parameters of our MSH model through a cross-validation method and a third experiment. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Chiral helimagnetic state in a Kondo lattice model with the Dzyaloshinskii-Moriya interaction

NASA Astrophysics Data System (ADS)

Okumura, Shun; Kato, Yasuyuki; Motome, Yukitoshi

2018-05-01

Monoaxial chiral magnets can form a noncollinear twisted spin structure called the chiral helimagnetic state. We study magnetic properties of such a chiral helimagnetic state, with emphasis on the effect of itinerant electrons. Modeling a monoaxial chiral helimagnet by a one-dimensional Kondo lattice model with the Dzyaloshinskii-Moriya interaction, we perform a variational calculation to elucidate the stable spin configuration in the ground state. We obtain a chiral helimagnetic state as a candidate for the ground state, whose helical pitch is modulated by the model parameters: the Kondo coupling, the Dzyaloshinski-Moriya interaction, and electron filling.

Genetic Algorithm-Based Model Order Reduction of Aeroservoelastic Systems with Consistant States

NASA Technical Reports Server (NTRS)

Zhu, Jin; Wang, Yi; Pant, Kapil; Suh, Peter M.; Brenner, Martin J.

2017-01-01

This paper presents a model order reduction framework to construct linear parameter-varying reduced-order models of flexible aircraft for aeroservoelasticity analysis and control synthesis in broad two-dimensional flight parameter space. Genetic algorithms are used to automatically determine physical states for reduction and to generate reduced-order models at grid points within parameter space while minimizing the trial-and-error process. In addition, balanced truncation for unstable systems is used in conjunction with the congruence transformation technique to achieve locally optimal realization and weak fulfillment of state consistency across the entire parameter space. Therefore, aeroservoelasticity reduced-order models at any flight condition can be obtained simply through model interpolation. The methodology is applied to the pitch-plant model of the X-56A Multi-Use Technology Testbed currently being tested at NASA Armstrong Flight Research Center for flutter suppression and gust load alleviation. The present studies indicate that the reduced-order model with more than 12× reduction in the number of states relative to the original model is able to accurately predict system response among all input-output channels. The genetic-algorithm-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The interpolated aeroservoelasticity reduced order models exhibit smooth pole transition and continuously varying gains along a set of prescribed flight conditions, which verifies consistent state representation obtained by congruence transformation. The present model order reduction framework can be used by control engineers for robust aeroservoelasticity controller synthesis and novel vehicle design.

Modeling species occurrence dynamics with multiple states and imperfect detection

USGS Publications Warehouse

MacKenzie, D.I.; Nichols, J.D.; Seamans, M.E.; Gutierrez, R.J.

2009-01-01

Recent extensions of occupancy modeling have focused not only on the distribution of species over space, but also on additional state variables (e.g., reproducing or not, with or without disease organisms, relative abundance categories) that provide extra information about occupied sites. These biologist-driven extensions are characterized by ambiguity in both species presence and correct state classification, caused by imperfect detection. We first show the relationships between independently published approaches to the modeling of multistate occupancy. We then extend the pattern-based modeling to the case of sampling over multiple seasons or years in order to estimate state transition probabilities associated with system dynamics. The methodology and its potential for addressing relevant ecological questions are demonstrated using both maximum likelihood (occupancy and successful reproduction dynamics of California Spotted Owl) and Markov chain Monte Carlo estimation approaches (changes in relative abundance of green frogs in Maryland). Just as multistate capture-recapture modeling has revolutionized the study of individual marked animals, we believe that multistate occupancy modeling will dramatically increase our ability to address interesting questions about ecological processes underlying population-level dynamics. ?? 2009 by the Ecological Society of America.

Impact of state-specific flowfield modeling on atomic nitrogen radiation

NASA Astrophysics Data System (ADS)

Johnston, Christopher O.; Panesi, Marco

2018-01-01

A hypersonic flowfield model that treats electronic levels of the dominant afterbody radiator N as individual species is presented. This model allows electron-ion recombination rate and two-temperature modeling improvements, the latter which are shown to decrease afterbody radiative heating by up to 30%. This decrease is primarily due to the addition of the electron-impact excitation energy-exchange term to the energy equation governing the vibrational-electronic electron temperature. This model also allows the validity of the often applied quasi-steady-state (QSS) approximation to be assessed. The QSS approximation is shown to fail throughout most of the afterbody region for lower electronic states, although this impacts the radiative intensity reaching the surface by less than 15%. By computing the electronic-state populations of N within the flowfield solver, instead of through the QSS approximation in the radiation solver, the coupling of nonlocal radiative transition rates to the species continuity equations becomes feasible. Implementation of this higher-fidelity level of coupling between the flowfield and radiation solvers is shown to increase the afterbody radiation by up to 50% relative to the conventional model.

Modeling the Dynamics of Disease States in Depression

PubMed Central

Demic, Selver; Cheng, Sen

2014-01-01

Major depressive disorder (MDD) is a common and costly disorder associated with considerable morbidity, disability, and risk for suicide. The disorder is clinically and etiologically heterogeneous. Despite intense research efforts, the response rates of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood. Here we use computational modeling to advance our understanding of MDD. First, we propose a systematic and comprehensive definition of disease states, which is based on a type of mathematical model called a finite-state machine. Second, we propose a dynamical systems model for the progression, or dynamics, of MDD. The model is abstract and combines several major factors (mechanisms) that influence the dynamics of MDD. We study under what conditions the model can account for the occurrence and recurrence of depressive episodes and how we can model the effects of antidepressant treatments and cognitive behavioral therapy within the same dynamical systems model through changing a small subset of parameters. Our computational modeling suggests several predictions about MDD. Patients who suffer from depression can be divided into two sub-populations: a high-risk sub-population that has a high risk of developing chronic depression and a low-risk sub-population, in which patients develop depression stochastically with low probability. The success of antidepressant treatment is stochastic, leading to widely different times-to-remission in otherwise identical patients. While the specific details of our model might be subjected to criticism and revisions, our approach shows the potential power of computationally modeling depression and the need for different type of quantitative data for understanding depression. PMID:25330102

Identification of linear system models and state estimators for controls

NASA Technical Reports Server (NTRS)

Chen, Chung-Wen

1992-01-01

The following paper is presented in viewgraph format and covers topics including: (1) linear state feedback control system; (2) Kalman filter state estimation; (3) relation between residual and stochastic part of output; (4) obtaining Kalman filter gain; (5) state estimation under unknown system model and unknown noises; and (6) relationship between filter Markov parameters and system Markov parameters.

Mathematical modeling of a Ti:sapphire solid-state laser

NASA Technical Reports Server (NTRS)

Swetits, John J.

1987-01-01

The project initiated a study of a mathematical model of a tunable Ti:sapphire solid-state laser. A general mathematical model was developed for the purpose of identifying design parameters which will optimize the system, and serve as a useful predictor of the system's behavior.

The New York State Bird Conservation Area (BCA) Program: A Model for the United States

Treesearch

M. F. Burger; D. J. Adams; T. Post; L. Sommers; B. Swift

2005-01-01

The New York State Bird Conservation Area (BCA) Program, modeled after the National Audubon Society?s Important Bird Areas Program, is based on legislation signed by Governor Pataki in 1997. New York is the first state in the nation to enact such a program. The BCA Program seeks to provide a comprehensive, ecosystem approach to conserving birds and their habitats on...

A mathematical model of liver metabolism: from steady state to dynamic

NASA Astrophysics Data System (ADS)

Calvetti, D.; Kuceyeski, A.; Somersalo, E.

2008-07-01

The increase in Type 2 diabetes and other metabolic disorders has led to an intense focus on the areas of research related to metabolism. Because the liver is essential in regulating metabolite concentrations that maintain life, it is especially important to have good knowledge of the functions within this organ. In silico mathematical models that can adequately describe metabolite concentrations, flux and transport rates in the liver in vivo can be a useful predictive tool. Fully dynamic models, which contain expressions for Michaelis-Menten reaction kinetics can be utilized to investigate different metabolic states, for example exercise, fed or starved state. In this paper we describe a two compartment (blood and tissue) spatially lumped liver metabolism model. First, we use Bayesian Flux Balance Analysis (BFBA) to estimate the values of flux and transport rates at steady state, which agree closely with values from the literature. These values are then used to find a set of Michaelis-Menten parameters and initial concentrations which identify a dynamic model that can be used for exploring different metabolic states. In particular, we investigate the effect of doubling the concentration of lactate entering the system via the hepatic artery and portal vein. This change in lactate concentration forces the system to a new steady state, where glucose production is increased.

Oscillations in Spurious States of the Associative Memory Model with Synaptic Depression

NASA Astrophysics Data System (ADS)

Murata, Shin; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

2014-12-01

The associative memory model is a typical neural network model that can store discretely distributed fixed-point attractors as memory patterns. When the network stores the memory patterns extensively, however, the model has other attractors besides the memory patterns. These attractors are called spurious memories. Both spurious states and memory states are in equilibrium, so there is little difference between their dynamics. Recent physiological experiments have shown that the short-term dynamic synapse called synaptic depression decreases its efficacy of transmission to postsynaptic neurons according to the activities of presynaptic neurons. Previous studies revealed that synaptic depression destabilizes the memory states when the number of memory patterns is finite. However, it is very difficult to study the dynamical properties of the spurious states if the number of memory patterns is proportional to the number of neurons. We investigate the effect of synaptic depression on spurious states by Monte Carlo simulation. The results demonstrate that synaptic depression does not affect the memory states but mainly destabilizes the spurious states and induces periodic oscillations.

Directions of management for the development of fuel-and-energy complex as the key driver of the social-and-economic development of regions

NASA Astrophysics Data System (ADS)

Mottaeva, Asiiat

2017-10-01

The article is dedicated to the problems of the participation of the energy enterprises in the social-and-economic development of the regions and municipalities. The complex of mechanisms of the implementation of the Energy strategy in the form of strategic initiatives of the development of the energy industry representing the complex inter-industry state-private long-term projects is presented in the article. The author considers the development of the energy industry to be the key driver of the social-and-economic development of regions. The author proves, that the increase in competitiveness of Russian energy, geographical and grocery diversification of export and improvement of quality of export products might allow to solve some problems of the development of national economy.

Modulation of Soil Initial State on WRF Model Performance Over China

NASA Astrophysics Data System (ADS)

Xue, Haile; Jin, Qinjian; Yi, Bingqi; Mullendore, Gretchen L.; Zheng, Xiaohui; Jin, Hongchun

2017-11-01

The soil state (e.g., temperature and moisture) in a mesoscale numerical prediction model is typically initialized by reanalysis or analysis data that may be subject to large bias. Such bias may lead to unrealistic land-atmosphere interactions. This study shows that the Climate Forecast System Reanalysis (CFSR) dramatically underestimates soil temperature and overestimates soil moisture over most parts of China in the first (0-10 cm) and second (10-25 cm) soil layers compared to in situ observations in July 2013. A correction based on the global optimal dual kriging is employed to correct CFSR bias in soil temperature and moisture using in situ observations. To investigate the impacts of the corrected soil state on model forecasts, two numerical model simulations—a control run with CFSR soil state and a disturbed run with the corrected soil state—were conducted using the Weather Research and Forecasting model. All the simulations are initiated 4 times per day and run 48 h. Model results show that the corrected soil state, for example, warmer and drier surface over the most parts of China, can enhance evaporation over wet regions, which changes the overlying atmospheric temperature and moisture. The changes of the lifting condensation level, level of free convection, and water transport due to corrected soil state favor precipitation over wet regions, while prohibiting precipitation over dry regions. Moreover, diagnoses indicate that the remote moisture flux convergence plays a dominant role in the precipitation changes over the wet regions.

A critical state model for mudrock behavior at high stress levels

NASA Astrophysics Data System (ADS)

Heidari, M.; Nikolinakou, M. A.; Flemings, P. B.

2016-12-01

Recent experimental work has documented that the compression behavior, friction angle, and lateral stress ratio (k0) of mudrocks vary over the stress range of 1 to 100 MPa. We integrate these observations into a critical state model. The internal friction angle and the slope of the compression curve are key parameters in a mudrock critical state model. Published models assume that these parameters do not depend on the stress level, and hence predict lateral stress and normalized strength ratios that do not change with the stress level. However, recent experimental data on resedimented mudrock samples from Eugene Island, Gulf of Mexico, demonstrate that all these parameters vary considerably with the stress level (Casey and Germaine, 2013; Casey et al., 2015). To represent these variations, we develop an enhanced critical state model that uses a stress-level-dependent friction angle and a curvilinear compression curve. We show that this enhanced model predicts the observed variations of the lateral stress and strength ratios. The successful performance of our model indicates that the critical state theory developed for soil can predict mudrock nonlinear behavior at high stress levels and thus can be used in modeling geologic systems. Casey, B., Germaine, J., 2013. Stress Dependence of Shear Strength in Fine-Grained Soils and Correlations with Liquid Limit. J. Geotech. Geoenviron. Eng. 139, 1709-1717. Casey, B., Germaine, J., Flemings, P.B., Fahy, B.P., 2015. Estimating horizontal stresses for mudrocks under one-dimensional compression. Mar. Pet. Geol. 65, 178-186.

Remaining lifetime modeling using State-of-Health estimation

NASA Astrophysics Data System (ADS)

Beganovic, Nejra; Söffker, Dirk

2017-08-01

Technical systems and system's components undergo gradual degradation over time. Continuous degradation occurred in system is reflected in decreased system's reliability and unavoidably lead to a system failure. Therefore, continuous evaluation of State-of-Health (SoH) is inevitable to provide at least predefined lifetime of the system defined by manufacturer, or even better, to extend the lifetime given by manufacturer. However, precondition for lifetime extension is accurate estimation of SoH as well as the estimation and prediction of Remaining Useful Lifetime (RUL). For this purpose, lifetime models describing the relation between system/component degradation and consumed lifetime have to be established. In this contribution modeling and selection of suitable lifetime models from database based on current SoH conditions are discussed. Main contribution of this paper is the development of new modeling strategies capable to describe complex relations between measurable system variables, related system degradation, and RUL. Two approaches with accompanying advantages and disadvantages are introduced and compared. Both approaches are capable to model stochastic aging processes of a system by simultaneous adaption of RUL models to current SoH. The first approach requires a priori knowledge about aging processes in the system and accurate estimation of SoH. An estimation of SoH here is conditioned by tracking actual accumulated damage into the system, so that particular model parameters are defined according to a priori known assumptions about system's aging. Prediction accuracy in this case is highly dependent on accurate estimation of SoH but includes high number of degrees of freedom. The second approach in this contribution does not require a priori knowledge about system's aging as particular model parameters are defined in accordance to multi-objective optimization procedure. Prediction accuracy of this model does not highly depend on estimated SoH. This model

State-Dependence of the Climate Sensitivity in Earth System Models of Intermediate Complexity

NASA Astrophysics Data System (ADS)

Pfister, Patrik L.; Stocker, Thomas F.

2017-10-01

Growing evidence from general circulation models (GCMs) indicates that the equilibrium climate sensitivity (ECS) depends on the magnitude of forcing, which is commonly referred to as state-dependence. We present a comprehensive assessment of ECS state-dependence in Earth system models of intermediate complexity (EMICs) by analyzing millennial simulations with sustained 2×CO2 and 4×CO2 forcings. We compare different extrapolation methods and show that ECS is smaller in the higher-forcing scenario in 12 out of 15 EMICs, in contrast to the opposite behavior reported from GCMs. In one such EMIC, the Bern3D-LPX model, this state-dependence is mainly due to the weakening sea ice-albedo feedback in the Southern Ocean, which depends on model configuration. Due to ocean-mixing adjustments, state-dependence is only detected hundreds of years after the abrupt forcing, highlighting the need for long model integrations. Adjustments to feedback parametrizations of EMICs may be necessary if GCM intercomparisons confirm an opposite state-dependence.

Active State Model for Autonomous Systems

NASA Technical Reports Server (NTRS)

Park, Han; Chien, Steve; Zak, Michail; James, Mark; Mackey, Ryan; Fisher, Forest

2003-01-01

The concept of the active state model (ASM) is an architecture for the development of advanced integrated fault-detection-and-isolation (FDI) systems for robotic land vehicles, pilotless aircraft, exploratory spacecraft, or other complex engineering systems that will be capable of autonomous operation. An FDI system based on the ASM concept would not only provide traditional diagnostic capabilities, but also integrate the FDI system under a unified framework and provide mechanism for sharing of information between FDI subsystems to fully assess the overall health of the system. The ASM concept begins with definitions borrowed from psychology, wherein a system is regarded as active when it possesses self-image, self-awareness, and an ability to make decisions itself, such that it is able to perform purposeful motions and other transitions with some degree of autonomy from the environment. For an engineering system, self-image would manifest itself as the ability to determine nominal values of sensor data by use of a mathematical model of itself, and selfawareness would manifest itself as the ability to relate sensor data to their nominal values. The ASM for such a system may start with the closed-loop control dynamics that describe the evolution of state variables. As soon as this model was supplemented with nominal values of sensor data, it would possess self-image. The ability to process the current sensor data and compare them with the nominal values would represent self-awareness. On the basis of self-image and self-awareness, the ASM provides the capability for self-identification, detection of abnormalities, and self-diagnosis.

State-based Marketplaces using 'clearinghouse' plan management models are associated with lower premiums.

PubMed

Krinn, Kelly; Karaca-Mandic, Pinar; Blewett, Lynn A

2015-01-01

The state-based and federally facilitated health insurance Marketplaces, or exchanges, enrolled more than eight million people during the first open enrollment period, which ended March 31, 2014. There is significant variation in how states have designed and implemented their Marketplaces. We examined how premiums varied with states' involvement in the Marketplaces through governance, plan management authority, and strategy during the first year that the exchanges have been open. State-based Marketplaces using "clearinghouse" plan management models had significantly lower adjusted average premiums for all plans within each metal level compared to state-based Marketplaces using "active purchaser" models and the federally facilitated and partnership Marketplaces. Clearinghouse management models are those in which all health plans that meet published criteria are accepted. Active purchaser models are those in which states negotiate premiums, provider networks, number of plans, and benefits. Our baseline estimates provide valuable benchmarks for evaluating future performance of states' involvement in governance, plan management, and regulatory authority of the insurance Marketplaces. Project HOPE—The People-to-People Health Foundation, Inc.

Dynamical initial-state model for relativistic heavy-ion collisions

NASA Astrophysics Data System (ADS)

Shen, Chun; Schenke, Björn

2018-02-01

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy-ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a fluctuating time depending on sampled final rapidities. Energy is deposited in space time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directly from the initial-state model, including net-baryon rapidity distributions, two-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. We also present the implementation of the model with 3+1-dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial-state model at proper times greater than the initial time for the hydrodynamic simulation.

Dynamical initial-state model for relativistic heavy-ion collisions

DOE PAGES

Shen, Chun; Schenke, Bjorn

2018-02-15

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a uctuating time depending on sampled final rapidities. Energy is deposited in space-time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directlymore » from the initial state model, including net-baryon rapidity distributions, 2-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. Here, we also present the implementation of the model with 3+1 dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial state model at proper times greater than the initial time for the hydrodynamic simulation.« less

Dynamical initial-state model for relativistic heavy-ion collisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shen, Chun; Schenke, Bjorn

We present a fully three-dimensional model providing initial conditions for energy and net-baryon density distributions in heavy ion collisions at arbitrary collision energy. The model includes the dynamical deceleration of participating nucleons or valence quarks, depending on the implementation. The duration of the deceleration continues until the string spanned between colliding participants is assumed to thermalize, which is either after a fixed proper time, or a uctuating time depending on sampled final rapidities. Energy is deposited in space-time along the string, which in general will span a range of space-time rapidities and proper times. We study various observables obtained directlymore » from the initial state model, including net-baryon rapidity distributions, 2-particle rapidity correlations, as well as the rapidity decorrelation of the transverse geometry. Their dependence on the model implementation and parameter values is investigated. Here, we also present the implementation of the model with 3+1 dimensional hydrodynamics, which involves the addition of source terms that deposit energy and net-baryon densities produced by the initial state model at proper times greater than the initial time for the hydrodynamic simulation.« less

Bayesian state space models for dynamic genetic network construction across multiple tissues.

PubMed

Liang, Yulan; Kelemen, Arpad

2016-08-01

Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

Construction of ground-state preserving sparse lattice models for predictive materials simulations

NASA Astrophysics Data System (ADS)

Huang, Wenxuan; Urban, Alexander; Rong, Ziqin; Ding, Zhiwei; Luo, Chuan; Ceder, Gerbrand

2017-08-01

First-principles based cluster expansion models are the dominant approach in ab initio thermodynamics of crystalline mixtures enabling the prediction of phase diagrams and novel ground states. However, despite recent advances, the construction of accurate models still requires a careful and time-consuming manual parameter tuning process for ground-state preservation, since this property is not guaranteed by default. In this paper, we present a systematic and mathematically sound method to obtain cluster expansion models that are guaranteed to preserve the ground states of their reference data. The method builds on the recently introduced compressive sensing paradigm for cluster expansion and employs quadratic programming to impose constraints on the model parameters. The robustness of our methodology is illustrated for two lithium transition metal oxides with relevance for Li-ion battery cathodes, i.e., Li2xFe2(1-x)O2 and Li2xTi2(1-x)O2, for which the construction of cluster expansion models with compressive sensing alone has proven to be challenging. We demonstrate that our method not only guarantees ground-state preservation on the set of reference structures used for the model construction, but also show that out-of-sample ground-state preservation up to relatively large supercell size is achievable through a rapidly converging iterative refinement. This method provides a general tool for building robust, compressed and constrained physical models with predictive power.

Practical extension of a Lake States tree height model

Treesearch

Don C. Bragg

2008-01-01

By adapting data from national and state champion lists and the predictions of an existing height model, an exponential function was developed to improvetree height estimation. As a case study, comparisons between the original and redesigned model were made with eastern white pine (Pinus strobus L.). Forexample, the heights...

General Method for Constructing Local Hidden Variable Models for Entangled Quantum States

NASA Astrophysics Data System (ADS)

Cavalcanti, D.; Guerini, L.; Rabelo, R.; Skrzypczyk, P.

2016-11-01

Entanglement allows for the nonlocality of quantum theory, which is the resource behind device-independent quantum information protocols. However, not all entangled quantum states display nonlocality. A central question is to determine the precise relation between entanglement and nonlocality. Here we present the first general test to decide whether a quantum state is local, and show that the test can be implemented by semidefinite programing. This method can be applied to any given state and for the construction of new examples of states with local hidden variable models for both projective and general measurements. As applications, we provide a lower-bound estimate of the fraction of two-qubit local entangled states and present new explicit examples of such states, including those that arise from physical noise models, Bell-diagonal states, and noisy Greenberger-Horne-Zeilinger and W states.

Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification.

PubMed

Harrington, S; Reeder, T W

2017-02-01

The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have shown that the standard procedure of comparing the fit of the BiSSE model to constant-rate birth-death models often inappropriately favours the BiSSE model when diversification rates vary in a state-independent fashion. The newly developed HiSSE model enables researchers to identify state-dependent diversification rates while accounting for state-independent diversification at the same time. The HiSSE model also allows researchers to test state-dependent models against appropriate state-independent null models that have the same number of parameters as the state-dependent models being tested. We reanalyse two data sets that originally used BiSSE to reconstruct ancestral states within squamate reptiles and reached surprising conclusions regarding the evolution of toepads within Gekkota and viviparity across Squamata. We used this new method to demonstrate that there are many shifts in diversification rates across squamates. We then fit various HiSSE submodels and null models to the state and phylogenetic data and reconstructed states under these models. We found that there is no single, consistent signal for state-dependent diversification associated with toepads in gekkotans or viviparity across all squamates. Our reconstructions show limited support for the recently proposed hypotheses that toepads evolved multiple times independently in Gekkota and that transitions from viviparity to oviparity are common in Squamata. Our results highlight the importance of considering an adequate pool of models and null models when estimating diversification rate parameters and reconstructing ancestral states. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Formal Analysis of Self-Efficacy in Job Interviewee’s Mental State Model

NASA Astrophysics Data System (ADS)

Ajoge, N. S.; Aziz, A. A.; Yusof, S. A. Mohd

2017-08-01

This paper presents a formal analysis approach for self-efficacy model of interviewee’s mental state during a job interview session. Self-efficacy is a construct that has been hypothesised to combine with motivation and interviewee anxiety to define state influence of interviewees. The conceptual model was built based on psychological theories and models related to self-efficacy. A number of well-known relations between events and the course of self-efficacy are summarized from the literature and it is shown that the proposed model exhibits those patterns. In addition, this formal model has been mathematically analysed to find out which stable situations exist. Finally, it is pointed out how this model can be used in a software agent or robot-based platform. Such platform can provide an interview coaching approach where support to the user is provided based on their individual metal state during interview sessions.

Prevalence of Prostate Cancer Clinical States and Mortality in the United States: Estimates Using a Dynamic Progression Model

PubMed Central

Scher, Howard I.; Solo, Kirk; Valant, Jason; Todd, Mary B.; Mehra, Maneesha

2015-01-01

Objective To identify patient populations most in need of treatment across the prostate cancer disease continuum, we developed a novel dynamic transition model based on risk of disease progression and mortality. Design and Outcome Measurements We modeled the flow of patient populations through eight prostate cancer clinical states (PCCS) that are characterized by the status of the primary tumor, presence of metastases, prior and current treatment, and testosterone levels. Simulations used published US incidence rates for each year from 1990. Progression and mortality rates were derived from published clinical trials, meta-analyses, and observational studies. Model outputs included the incidence, prevalence, and mortality for each PCCS. The impact of novel treatments was modeled in three distinct scenarios: metastatic castration-resistant prostate cancer (mCRPC), non-metastatic CRPC (nmCRPC), or both. Results and Limitations The model estimated the prevalence of prostate cancer as 2,219,280 in the US in 2009 and 3,072,480 in 2020, and incidence of mCRPC as 36,100 and 42,970, respectively. All-cause mortality in prostate cancer was estimated at 168,290 in 2009 and 219,360 in 2020, with 20.5% and 19.5% of these deaths, respectively, occurring in men with mCRPC. The majority (86%) of incidence flow into mCRPC states was from the nmCRPC clinical state. In the scenario with novel interventions for nmCRPC states, the progression to mCRPC is reduced, thus decreasing mCRPC incidence by 12% in 2020, with a sustained decline in mCRPC mortality. A limitation of the model is that it does not estimate prostate cancer—specific mortality. Conclusion The model informs clinical trial design for prostate cancer by quantifying outcomes in PCCS, and demonstrates the impact of an effective therapy applied in an earlier clinical state of nmCRPC on the incidence of mCRPC morbidity and subsequent mortality. PMID:26460686

A Self-Organizing Incremental Spatiotemporal Associative Memory Networks Model for Problems with Hidden State

PubMed Central

2016-01-01

Identifying the hidden state is important for solving problems with hidden state. We prove any deterministic partially observable Markov decision processes (POMDP) can be represented by a minimal, looping hidden state transition model and propose a heuristic state transition model constructing algorithm. A new spatiotemporal associative memory network (STAMN) is proposed to realize the minimal, looping hidden state transition model. STAMN utilizes the neuroactivity decay to realize the short-term memory, connection weights between different nodes to represent long-term memory, presynaptic potentials, and synchronized activation mechanism to complete identifying and recalling simultaneously. Finally, we give the empirical illustrations of the STAMN and compare the performance of the STAMN model with that of other methods. PMID:27891146

A 2-categorical state sum model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baratin, Aristide, E-mail: abaratin@uwaterloo.ca; Freidel, Laurent, E-mail: lfreidel@perimeterinstitute.ca

It has long been argued that higher categories provide the proper algebraic structure underlying state sum invariants of 4-manifolds. This idea has been refined recently, by proposing to use 2-groups and their representations as specific examples of 2-categories. The challenge has been to make these proposals fully explicit. Here, we give a concrete realization of this program. Building upon our earlier work with Baez and Wise on the representation theory of 2-groups, we construct a four-dimensional state sum model based on a categorified version of the Euclidean group. We define and explicitly compute the simplex weights, which may be viewedmore » a categorified analogue of Racah-Wigner 6j-symbols. These weights solve a hexagon equation that encodes the formal invariance of the state sum under the Pachner moves of the triangulation. This result unravels the combinatorial formulation of the Feynman amplitudes of quantum field theory on flat spacetime proposed in A. Baratin and L. Freidel [Classical Quantum Gravity 24, 2027–2060 (2007)] which was shown to lead after gauge-fixing to Korepanov’s invariant of 4-manifolds.« less

An optimal state estimation model of sensory integration in human postural balance

NASA Astrophysics Data System (ADS)

Kuo, Arthur D.

2005-09-01

We propose a model for human postural balance, combining state feedback control with optimal state estimation. State estimation uses an internal model of body and sensor dynamics to process sensor information and determine body orientation. Three sensory modalities are modeled: joint proprioception, vestibular organs in the inner ear, and vision. These are mated with a two degree-of-freedom model of body dynamics in the sagittal plane. Linear quadratic optimal control is used to design state feedback and estimation gains. Nine free parameters define the control objective and the signal-to-noise ratios of the sensors. The model predicts statistical properties of human sway in terms of covariance of ankle and hip motion. These predictions are compared with normal human responses to alterations in sensory conditions. With a single parameter set, the model successfully reproduces the general nature of postural motion as a function of sensory environment. Parameter variations reveal that the model is highly robust under normal sensory conditions, but not when two or more sensors are inaccurate. This behavior is similar to that of normal human subjects. We propose that age-related sensory changes may be modeled with decreased signal-to-noise ratios, and compare the model's behavior with degraded sensors against experimental measurements from older adults. We also examine removal of the model's vestibular sense, which leads to instability similar to that observed in bilateral vestibular loss subjects. The model may be useful for predicting which sensors are most critical for balance, and how much they can deteriorate before posture becomes unstable.

Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy

DOE PAGES

Rosewater, David; Ferreira, Summer; Schoenwald, David; ...

2018-01-25

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This study presents three advances in BESS state-of-charge forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational datamore » is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. Finally, the proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.« less

Battery Energy Storage State-of-Charge Forecasting: Models, Optimization, and Accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosewater, David; Ferreira, Summer; Schoenwald, David

Battery energy storage systems (BESS) are a critical technology for integrating high penetration renewable power on an intelligent electrical grid. As limited energy restricts the steady-state operational state-of-charge (SoC) of storage systems, SoC forecasting models are used to determine feasible charge and discharge schedules that supply grid services. Smart grid controllers use SoC forecasts to optimize BESS schedules to make grid operation more efficient and resilient. This study presents three advances in BESS state-of-charge forecasting. First, two forecasting models are reformulated to be conducive to parameter optimization. Second, a new method for selecting optimal parameter values based on operational datamore » is presented. Last, a new framework for quantifying model accuracy is developed that enables a comparison between models, systems, and parameter selection methods. The accuracies achieved by both models, on two example battery systems, with each method of parameter selection are then compared in detail. The results of this analysis suggest variation in the suitability of these models for different battery types and applications. Finally, the proposed model formulations, optimization methods, and accuracy assessment framework can be used to improve the accuracy of SoC forecasts enabling better control over BESS charge/discharge schedules.« less

A model of the normal and null states of pulsars

NASA Astrophysics Data System (ADS)

Jones, P. B.

1981-12-01

A solvable three-dimensional polar cap model of pair creation and charged particle acceleration has been derived. There are no free parameters of significance apart from the polar surface magnetic flux density. The parameter determining the acceleration potential difference has been obtained by calculation of elementary nuclear and electromagnetic processes. Solutions of the model exist for both normal and null states of a pulsar, and the instability in the normal state leading to the normal to null transition has been identified. The predicted necessary condition for the transition is entirely consistent with observation.

A model of the normal and null states of pulsars

NASA Astrophysics Data System (ADS)

Jones, P. B.

A solvable three dimensional polar cap model of pair creation and charged particle acceleration is derived. There are no free parameters of significance apart from the polar surface magnetic flux density. The parameter CO determining the acceleration potential difference was obtained by calculation of elementary nuclear and electromagnetic processes. Solutions of the model exist for both normal and null states of a pulsar, and the instability in the normal state leading to the normal to null transition is identified. The predicted necessary condition for the transition is entirely consistent with observation.

Steady-state and dynamic models for particle engulfment during solidification

NASA Astrophysics Data System (ADS)

Tao, Yutao; Yeckel, Andrew; Derby, Jeffrey J.

2016-06-01

Steady-state and dynamic models are developed to study the physical mechanisms that determine the pushing or engulfment of a solid particle at a moving solid-liquid interface. The mathematical model formulation rigorously accounts for energy and momentum conservation, while faithfully representing the interfacial phenomena affecting solidification phase change and particle motion. A numerical solution approach is developed using the Galerkin finite element method and elliptic mesh generation in an arbitrary Lagrangian-Eulerian implementation, thus allowing for a rigorous representation of forces and dynamics previously inaccessible by approaches using analytical approximations. We demonstrate that this model accurately computes the solidification interface shape while simultaneously resolving thin fluid layers around the particle that arise from premelting during particle engulfment. We reinterpret the significance of premelting via the definition an unambiguous critical velocity for engulfment from steady-state analysis and bifurcation theory. We also explore the complicated transient behaviors that underlie the steady states of this system and posit the significance of dynamical behavior on engulfment events for many systems. We critically examine the onset of engulfment by comparing our computational predictions to those obtained using the analytical model of Rempel and Worster [29]. We assert that, while the accurate calculation of van der Waals repulsive forces remains an open issue, the computational model developed here provides a clear benefit over prior models for computing particle drag forces and other phenomena needed for the faithful simulation of particle engulfment.

Magnon edge states in the hardcore- Bose-Hubbard model.

PubMed

Owerre, S A

2016-11-02

Quantum Monte Carlo (QMC) simulation has uncovered nonzero Berry curvature and bosonic edge states in the hardcore-Bose-Hubbard model on the gapped honeycomb lattice. The competition between the chemical potential and staggered onsite potential leads to an interesting quantum phase diagram comprising the superfluid phase, Mott insulator, and charge density wave insulator. In this paper, we present a semiclassical perspective of this system by mapping to a spin-1/2 quantum XY model. We give an explicit analytical origin of the quantum phase diagram, the Berry curvatures, and the edge states using semiclassical approximations. We find very good agreement between the semiclassical analyses and the QMC results. Our results show that the topological properties of the hardcore-Bose-Hubbard model are the same as those of magnon in the corresponding quantum spin system. Our results are applicable to systems of ultracold bosonic atoms trapped in honeycomb optical lattices.

Modeling activated states of GPCRs: the rhodopsin template.

PubMed

Niv, Masha Y; Skrabanek, Lucy; Filizola, Marta; Weinstein, Harel

2006-01-01

Activation of G Protein-Coupled Receptors (GPCRs) is an allosteric mechanism triggered by ligand binding and resulting in conformational changes transduced by the transmembrane domain. Models of the activated forms of GPCRs have become increasingly necessary for the development of a clear understanding of signal propagation into the cell. Experimental evidence points to a multiplicity of conformations related to the activation of the receptor, rendered important physiologically by the suggestion that different conformations may be responsible for coupling to different signaling pathways. In contrast to the inactive state of rhodopsin (RHO) for which several high quality X-ray structures are available, the structure-related information for the active states of rhodopsin and all other GPCRs is indirect. We have collected and stored such information in a repository we maintain for activation-specific structural data available for rhodopsin-like GPCRs, http://www.physiology.med.cornell.edu/GPCRactivation/gpcrindex.html . Using these data as structural constraints, we have applied Simulated Annealing Molecular Dynamics to construct a number of different active state models of RHO starting from the known inactive structure. The common features of the models indicate that TM3 and TM5 play an important role in activation, in addition to the well-established rearrangement of TM6. Some of the structural changes observed in these models occur in regions that were not involved in the constraints, and have not been previously tested experimentally; they emerge as interesting candidates for further experimental exploration of the conformational space of activated GPCRs. We show that none of the normal modes calculated from the inactive structure has a dominant contribution along the path of conformational rearrangement from inactive to the active forms of RHO in the models. This result may differentiate rhodopsin from other GPCRs, and the reasons for this difference are

Modeling Pilot State in Next Generation Aircraft Alert Systems

NASA Technical Reports Server (NTRS)

Carlin, Alan S.; Alexander, Amy L.; Schurr, Nathan

2011-01-01

The Next Generation Air Transportation System will introduce new, advanced sensor technologies into the cockpit that must convey a large number of potentially complex alerts. Our work focuses on the challenges associated with prioritizing aircraft sensor alerts in a quick and efficient manner, essentially determining when and how to alert the pilot This "alert decision" becomes very difficult in NextGen due to the following challenges: 1) the increasing number of potential hazards, 2) the uncertainty associated with the state of potential hazards as well as pilot slate , and 3) the limited time to make safely-critical decisions. In this paper, we focus on pilot state and present a model for anticipating duration and quality of pilot behavior, for use in a larger system which issues aircraft alerts. We estimate pilot workload, which we model as being dependent on factors including mental effort, task demands. and task performance. We perform a mathematically rigorous analysis of the model and resulting alerting plans. We simulate the model in software and present simulated results with respect to manipulation of the pilot measures.

Path Flow Estimation Using Time Varying Coefficient State Space Model

NASA Astrophysics Data System (ADS)

Jou, Yow-Jen; Lan, Chien-Lun

2009-08-01

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

Stochastic spectral projection of electrochemical thermal model for lithium-ion cell state estimation

NASA Astrophysics Data System (ADS)

Tagade, Piyush; Hariharan, Krishnan S.; Kolake, Subramanya Mayya; Song, Taewon; Oh, Dukjin

2017-03-01

A novel approach for integrating a pseudo-two dimensional electrochemical thermal (P2D-ECT) model and data assimilation algorithm is presented for lithium-ion cell state estimation. This approach refrains from making any simplifications in the P2D-ECT model while making it amenable for online state estimation. Though deterministic, uncertainty in the initial states induces stochasticity in the P2D-ECT model. This stochasticity is resolved by spectrally projecting the stochastic P2D-ECT model on a set of orthogonal multivariate Hermite polynomials. Volume averaging in the stochastic dimensions is proposed for efficient numerical solution of the resultant model. A state estimation framework is developed using a transformation of the orthogonal basis to assimilate the measurables with this system of equations. Effectiveness of the proposed method is first demonstrated by assimilating the cell voltage and temperature data generated using a synthetic test bed. This validated method is used with the experimentally observed cell voltage and temperature data for state estimation at different operating conditions and drive cycle protocols. The results show increased prediction accuracy when the data is assimilated every 30s. High accuracy of the estimated states is exploited to infer temperature dependent behavior of the lithium-ion cell.

State and Parameter Estimation for a Coupled Ocean--Atmosphere Model

NASA Astrophysics Data System (ADS)

Ghil, M.; Kondrashov, D.; Sun, C.

2006-12-01

The El-Nino/Southern-Oscillation (ENSO) dominates interannual climate variability and plays, therefore, a key role in seasonal-to-interannual prediction. Much is known by now about the main physical mechanisms that give rise to and modulate ENSO, but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean--atmosphere model of ENSO. The coupled model consists of an upper-ocean, reduced-gravity model of the Tropical Pacific and a steady-state atmospheric response to the sea surface temperature (SST). The model errors are assumed to be mainly in the atmospheric wind stress, and assimilated data are equatorial Pacific SSTs. Model behavior is very sensitive to two key parameters: (i) μ, the ocean-atmosphere coupling coefficient between SST and wind stress anomalies; and (ii) δs, the surface-layer coefficient. Previous work has shown that δs determines the period of the model's self-sustained oscillation, while μ measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Estimation of these parameters is tested first on synthetic data and allows us to recover the delayed-oscillator mode starting from model parameter values that correspond to the westward-propagating case. Assimilation of SST data from the NCEP-NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean--atmosphere GCMs will be discussed.

Inter-model Diversity of ENSO simulation and its relation to basic states

NASA Astrophysics Data System (ADS)

Kug, J. S.; Ham, Y. G.

2016-12-01

In this study, a new methodology is developed to improve the climate simulation of state-of-the-art coupledglobal climate models (GCMs), by a postprocessing based on the intermodel diversity. Based on the closeconnection between the interannual variability and climatological states, the distinctive relation between theintermodel diversity of the interannual variability and that of the basic state is found. Based on this relation,the simulated interannual variabilities can be improved, by correcting their climatological bias. To test thismethodology, the dominant intermodel difference in precipitation responses during El Niño-SouthernOscillation (ENSO) is investigated, and its relationship with climatological state. It is found that the dominantintermodel diversity of the ENSO precipitation in phase 5 of the Coupled Model Intercomparison Project(CMIP5) is associated with the zonal shift of the positive precipitation center during El Niño. This dominantintermodel difference is significantly correlated with the basic states. The models with wetter (dryer) climatologythan the climatology of the multimodel ensemble (MME) over the central Pacific tend to shift positiveENSO precipitation anomalies to the east (west). Based on the model's systematic errors in atmosphericENSO response and bias, the models with better climatological state tend to simulate more realistic atmosphericENSO responses.Therefore, the statistical method to correct the ENSO response mostly improves the ENSO response. Afterthe statistical correction, simulating quality of theMMEENSO precipitation is distinctively improved. Theseresults provide a possibility that the present methodology can be also applied to improving climate projectionand seasonal climate prediction.

Optimizing Likelihood Models for Particle Trajectory Segmentation in Multi-State Systems.

PubMed

Young, Dylan Christopher; Scrimgeour, Jan

2018-06-19

Particle tracking offers significant insight into the molecular mechanics that govern the behav- ior of living cells. The analysis of molecular trajectories that transition between different motive states, such as diffusive, driven and tethered modes, is of considerable importance, with even single trajectories containing significant amounts of information about a molecule's environment and its interactions with cellular structures. Hidden Markov models (HMM) have been widely adopted to perform the segmentation of such complex tracks. In this paper, we show that extensive analysis of hidden Markov model outputs using data derived from multi-state Brownian dynamics simulations can be used both for the optimization of the likelihood models used to describe the states of the system and for characterization of the technique's failure mechanisms. This analysis was made pos- sible by the implementation of parallelized adaptive direct search algorithm on a Nvidia graphics processing unit. This approach provides critical information for the visualization of HMM failure and successful design of particle tracking experiments where trajectories contain multiple mobile states. © 2018 IOP Publishing Ltd.

Modeling the Effects of Irrigation on Land Surface Fluxes and States over the Conterminous United States: Sensitivity to Input Data and Model Parameters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leng, Guoyong; Huang, Maoyi; Tang, Qiuhong

2013-09-16

Previous studies on irrigation impacts on land surface fluxes/states were mainly conducted as sensitivity experiments, with limited analysis of uncertainties from the input data and model irrigation schemes used. In this study, we calibrated and evaluated the performance of irrigation water use simulated by the Community Land Model version 4 (CLM4) against observations from agriculture census. We investigated the impacts of irrigation on land surface fluxes and states over the conterminous United States (CONUS) and explored possible directions of improvement. Specifically, we found large uncertainty in the irrigation area data from two widely used sources and CLM4 tended to producemore » unrealistically large temporal variations of irrigation demand for applications at the water resources region scale over CONUS. At seasonal to interannual time scales, the effects of irrigation on surface energy partitioning appeared to be large and persistent, and more pronounced in dry than wet years. Even with model calibration to yield overall good agreement with the irrigation amounts from the National Agricultural Statistics Service (NASS), differences between the two irrigation area datasets still dominate the differences in the interannual variability of land surface response to irrigation. Our results suggest that irrigation amount simulated by CLM4 can be improved by (1) calibrating model parameter values to account for regional differences in irrigation demand and (2) accurate representation of the spatial distribution and intensity of irrigated areas.« less

A descriptive model of resting-state networks using Markov chains.

PubMed

Xie, H; Pal, R; Mitra, S

2016-08-01

Resting-state functional connectivity (RSFC) studies considering pairwise linear correlations have attracted great interests while the underlying functional network structure still remains poorly understood. To further our understanding of RSFC, this paper presents an analysis of the resting-state networks (RSNs) based on the steady-state distributions and provides a novel angle to investigate the RSFC of multiple functional nodes. This paper evaluates the consistency of two networks based on the Hellinger distance between the steady-state distributions of the inferred Markov chain models. The results show that generated steady-state distributions of default mode network have higher consistency across subjects than random nodes from various RSNs.

Ground states of baryoleptonic Q-balls in supersymmetric models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shoemaker, Ian M.; Kusenko, Alexander

2008-10-01

In supersymmetric generalizations of the standard model, all stable Q-balls are associated with some flat directions. We show that, if the flat direction has both the baryon number and the lepton number, the scalar field inside the Q-ball can deviate slightly from the flat direction in the ground state. We identify the true ground states of such nontopological solitons, including the electrically neutral and electrically charged Q-balls.

Finite state modeling of aeroelastic systems

NASA Technical Reports Server (NTRS)

Vepa, R.

1977-01-01

A general theory of finite state modeling of aerodynamic loads on thin airfoils and lifting surfaces performing completely arbitrary, small, time-dependent motions in an airstream is developed and presented. The nature of the behavior of the unsteady airloads in the frequency domain is explained, using as raw materials any of the unsteady linearized theories that have been mechanized for simple harmonic oscillations. Each desired aerodynamic transfer function is approximated by means of an appropriate Pade approximant, that is, a rational function of finite degree polynomials in the Laplace transform variable. The modeling technique is applied to several two dimensional and three dimensional airfoils. Circular, elliptic, rectangular and tapered planforms are considered as examples. Identical functions are also obtained for control surfaces for two and three dimensional airfoils.

A framework model for water-sharing among co-basin states of a river basin

NASA Astrophysics Data System (ADS)

Garg, N. K.; Azad, Shambhu

2018-05-01

A new framework model is presented in this study for sharing of water in a river basin using certain governing variables, in an effort to enhance the objectivity for a reasonable and equitable allocation of water among co-basin states. The governing variables were normalised to reduce the governing variables of different co-basin states of a river basin on same scale. In the absence of objective methods for evaluating the weights to be assigned to co-basin states for water allocation, a framework was conceptualised and formulated to determine the normalised weighting factors of different co-basin states as a function of the governing variables. The water allocation to any co-basin state had been assumed to be proportional to its struggle for equity, which in turn was assumed to be a function of the normalised discontent, satisfaction, and weighting factors of each co-basin state. System dynamics was used effectively to represent and solve the proposed model formulation. The proposed model was successfully applied to the Vamsadhara river basin located in the South-Eastern part of India, and a sensitivity analysis of the proposed model parameters was carried out to prove its robustness in terms of the proposed model convergence and validity over the broad spectrum values of the proposed model parameters. The solution converged quickly to a final allocation of 1444 million cubic metre (MCM) in the case of the Odisha co-basin state, and to 1067 MCM for the Andhra Pradesh co-basin state. The sensitivity analysis showed that the proposed model's allocation varied from 1584 MCM to 1336 MCM for Odisha state and from 927 to 1175 MCM for Andhra, depending upon the importance weights given to the governing variables for the calculation of the weighting factors. Thus, the proposed model was found to be very flexible to explore various policy options to arrive at a decision in a water sharing problem. It can therefore be effectively applied to any trans-boundary problem where

A new class of enhanced kinetic sampling methods for building Markov state models

NASA Astrophysics Data System (ADS)

Bhoutekar, Arti; Ghosh, Susmita; Bhattacharya, Swati; Chatterjee, Abhijit

2017-10-01

Markov state models (MSMs) and other related kinetic network models are frequently used to study the long-timescale dynamical behavior of biomolecular and materials systems. MSMs are often constructed bottom-up using brute-force molecular dynamics (MD) simulations when the model contains a large number of states and kinetic pathways that are not known a priori. However, the resulting network generally encompasses only parts of the configurational space, and regardless of any additional MD performed, several states and pathways will still remain missing. This implies that the duration for which the MSM can faithfully capture the true dynamics, which we term as the validity time for the MSM, is always finite and unfortunately much shorter than the MD time invested to construct the model. A general framework that relates the kinetic uncertainty in the model to the validity time, missing states and pathways, network topology, and statistical sampling is presented. Performing additional calculations for frequently-sampled states/pathways may not alter the MSM validity time. A new class of enhanced kinetic sampling techniques is introduced that aims at targeting rare states/pathways that contribute most to the uncertainty so that the validity time is boosted in an effective manner. Examples including straightforward 1D energy landscapes, lattice models, and biomolecular systems are provided to illustrate the application of the method. Developments presented here will be of interest to the kinetic Monte Carlo community as well.

Quantum transverse-field Ising model on an infinite tree from matrix product states

NASA Astrophysics Data System (ADS)

Nagaj, Daniel; Farhi, Edward; Goldstone, Jeffrey; Shor, Peter; Sylvester, Igor

2008-06-01

We give a generalization to an infinite tree geometry of Vidal’s infinite time-evolving block decimation (iTEBD) algorithm [G. Vidal, Phys. Rev. Lett. 98, 070201 (2007)] for simulating an infinite line of quantum spins. We numerically investigate the quantum Ising model in a transverse field on the Bethe lattice using the matrix product state ansatz. We observe a second order phase transition, with certain key differences from the transverse field Ising model on an infinite spin chain. We also investigate a transverse field Ising model with a specific longitudinal field. When the transverse field is turned off, this model has a highly degenerate ground state as opposed to the pure Ising model whose ground state is only doubly degenerate.

Functional connectivity dynamics: modeling the switching behavior of the resting state.

PubMed

Hansen, Enrique C A; Battaglia, Demian; Spiegler, Andreas; Deco, Gustavo; Jirsa, Viktor K

2015-01-15

Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, epilepsy, and traumatic brain injury. Simulations of whole-brain mean-field computational models with realistic connectivity determined by tractography studies enable us to reproduce with accuracy aspects of average FC in the resting state. Most computational studies, however, did not address the prominent non-stationarity in resting state FC, which may result in large intra- and inter-subject variability and thus preclude an accurate individual predictability. Here we show that this non-stationarity reveals a rich structure, characterized by rapid transitions switching between a few discrete FC states. We also show that computational models optimized to fit time-averaged FC do not reproduce these spontaneous state transitions and, thus, are not qualitatively superior to simplified linear stochastic models, which account for the effects of structure alone. We then demonstrate that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks. Because of the noise-driven exploration of this repertoire, the dynamics of FC qualitatively change now and display non-stationary switching similar to empirical resting state recordings (Functional Connectivity Dynamics (FCD)). Thus FCD bear promise to serve as a better biomarker of resting state neural activity and of its pathologic alterations. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Measurement of the Equation of State of the Two-Dimensional Hubbard Model

NASA Astrophysics Data System (ADS)

Miller, Luke; Cocchi, Eugenio; Drewes, Jan; Koschorreck, Marco; Pertot, Daniel; Brennecke, Ferdinand; Koehl, Michael

2016-05-01

The subtle interplay between kinetic energy, interactions and dimensionality challenges our comprehension of strongly-correlated physics observed, for example, in the solid state. In this quest, the Hubbard model has emerged as a conceptually simple, yet rich model describing such physics. Here we present an experimental determination of the equation of state of the repulsive two-dimensional Hubbard model over a broad range of interactions, 0 <= U / t <= 20 , and temperatures, down to kB T / t = 0 . 63(2) using high-resolution imaging of ultracold fermionic atoms in optical lattices. We show density profiles, compressibilities and double occupancies over the whole doping range, and hence our results constitute benchmarks for state-of-the-art theoretical approaches.

Down-state model of the voltage-sensing domain of a potassium channel.

PubMed

Schow, Eric V; Freites, J Alfredo; Gogna, Karun; White, Stephen H; Tobias, Douglas J

2010-06-16

Voltage-sensing domains (VSDs) of voltage-gated potassium (Kv) channels undergo a series of conformational changes upon membrane depolarization, from a down state when the channel is at rest to an up state, all of which lead to the opening of the channel pore. The crystal structures reported to date reveal the pore in an open state and the VSDs in an up state. To gain insights into the structure of the down state, we used a set of experiment-based restraints to generate a model of the down state of the KvAP VSD using molecular-dynamics simulations of the VSD in a lipid bilayer in excess water. The equilibrated VSD configuration is consistent with the biotin-avidin accessibility and internal salt-bridge data used to generate it, and with additional biotin-avidin accessibility data. In the model, both the S3b and S4 segments are displaced approximately 10 A toward the intracellular side with respect to the up-state configuration, but they do not move as a rigid body. Arginine side chains that carry the majority of the gating charge also make large excursions between the up and down states. In both states, arginines interact with water and participate in salt bridges with acidic residues and lipid phosphate groups. An important feature that emerges from the down-state model is that the N-terminal half of the S4 segment adopts a 3(10)-helical conformation, which appears to be necessary to satisfy a complex salt-bridge network. (c) 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Down-State Model of the Voltage-Sensing Domain of a Potassium Channel

PubMed Central

Schow, Eric V.; Freites, J. Alfredo; Gogna, Karun; White, Stephen H.; Tobias, Douglas J.

2010-01-01

Abstract Voltage-sensing domains (VSDs) of voltage-gated potassium (Kv) channels undergo a series of conformational changes upon membrane depolarization, from a down state when the channel is at rest to an up state, all of which lead to the opening of the channel pore. The crystal structures reported to date reveal the pore in an open state and the VSDs in an up state. To gain insights into the structure of the down state, we used a set of experiment-based restraints to generate a model of the down state of the KvAP VSD using molecular-dynamics simulations of the VSD in a lipid bilayer in excess water. The equilibrated VSD configuration is consistent with the biotin-avidin accessibility and internal salt-bridge data used to generate it, and with additional biotin-avidin accessibility data. In the model, both the S3b and S4 segments are displaced ∼10 Å toward the intracellular side with respect to the up-state configuration, but they do not move as a rigid body. Arginine side chains that carry the majority of the gating charge also make large excursions between the up and down states. In both states, arginines interact with water and participate in salt bridges with acidic residues and lipid phosphate groups. An important feature that emerges from the down-state model is that the N-terminal half of the S4 segment adopts a 310-helical conformation, which appears to be necessary to satisfy a complex salt-bridge network. PMID:20550898

Chimera-like states in a neuronal network model of the cat brain

NASA Astrophysics Data System (ADS)

Santos, M. S.; Szezech, J. D.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.; Viana, R. L.; Kurths, J.

2017-08-01

Neuronal systems have been modeled by complex networks in different description levels. Recently, it has been verified that networks can simultaneously exhibit one coherent and other incoherent domain, known as chimera states. In this work, we study the existence of chimera states in a network considering the connectivity matrix based on the cat cerebral cortex. The cerebral cortex of the cat can be separated in 65 cortical areas organised into the four cognitive regions: visual, auditory, somatosensory-motor and frontolimbic. We consider a network where the local dynamics is given by the Hindmarsh-Rose model. The Hindmarsh-Rose equations are a well known model of neuronal activity that has been considered to simulate membrane potential in neuron. Here, we analyse under which conditions chimera states are present, as well as the affects induced by intensity of coupling on them. We observe the existence of chimera states in that incoherent structure can be composed of desynchronised spikes or desynchronised bursts. Moreover, we find that chimera states with desynchronised bursts are more robust to neuronal noise than with desynchronised spikes.

Systematic assignment of Feshbach resonances via an asymptotic bound state model

NASA Astrophysics Data System (ADS)

Goosen, Maikel; Kokkelmans, Servaas

2008-05-01

We present an Asymptotic Bound state Model (ABM), which is useful to predict Feshbach resonances. The model utilizes asymptotic properties of the interaction potentials to represent coupled molecular wavefunctions. The bound states of this system give rise to Feshbach resonances, localized at the magnetic fields of intersection of these bound states with the scattering threshold. This model was very successful to assign measured Feshbach resonances in an ultra cold mixture of ^6Li and ^40K atomsootnotetextE. Wille, F.M. Spiegelhalder, G. Kerner, D. Naik, A. Trenkwalder, G. Hendl, F. Schreck, R. Grimm, T.G. Tiecke, J.T.M. Walraven, S.J.J.M.F. Kokkelmans, E. Tiesinga, P.S. Julienne, arXiv:0711.2916. For this system, the accuracy of the determined scattering lengths is comparable to full coupled channels results. However, it was not possible to predict the width of the resonances. We discuss how an incorporation of threshold effects will improve the model, and we apply it to a mixture of ^87Rb and ^133Cs atoms, where recently Feshbach resonances have been measured.

Adaptive behaviour and multiple equilibrium states in a predator-prey model.

PubMed

Pimenov, Alexander; Kelly, Thomas C; Korobeinikov, Andrei; O'Callaghan, Michael J A; Rachinskii, Dmitrii

2015-05-01

There is evidence that multiple stable equilibrium states are possible in real-life ecological systems. Phenomenological mathematical models which exhibit such properties can be constructed rather straightforwardly. For instance, for a predator-prey system this result can be achieved through the use of non-monotonic functional response for the predator. However, while formal formulation of such a model is not a problem, the biological justification for such functional responses and models is usually inconclusive. In this note, we explore a conjecture that a multitude of equilibrium states can be caused by an adaptation of animal behaviour to changes of environmental conditions. In order to verify this hypothesis, we consider a simple predator-prey model, which is a straightforward extension of the classic Lotka-Volterra predator-prey model. In this model, we made an intuitively transparent assumption that the prey can change a mode of behaviour in response to the pressure of predation, choosing either "safe" of "risky" (or "business as usual") behaviour. In order to avoid a situation where one of the modes gives an absolute advantage, we introduce the concept of the "cost of a policy" into the model. A simple conceptual two-dimensional predator-prey model, which is minimal with this property, and is not relying on odd functional responses, higher dimensionality or behaviour change for the predator, exhibits two stable co-existing equilibrium states with basins of attraction separated by a separatrix of a saddle point. Copyright © 2015 Elsevier Inc. All rights reserved.

COMPUTER MODEL TECHNOLOGY TRANSFER IN THE UNITED STATES

EPA Science Inventory

Computer-based mathematical models for urban water resources planning, management and design are widely used by engineers and planners in both the public and private sectors. In the United States, the majority of the users are in the private (consulting) sector, yet most of the m...

Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)

NASA Astrophysics Data System (ADS)

Santos, Léonard; Thirel, Guillaume; Perrin, Charles

2018-04-01

In many conceptual rainfall-runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called operator splitting. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall-runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall-runoff models and make the resolution of the representation difficult, are first replaced by a so-called Nash cascade and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

Mesoscale modeling of strain induced solid state amorphization in crystalline materials

NASA Astrophysics Data System (ADS)

Lei, Lei

Solid state amorphization, and in particular crystalline to amorphous transformation, can be observed in metallic alloys, semiconductors, intermetallics, minerals, and also molecular crystals when they undergo irradiation, hydrogen gas dissolution, thermal interdiffusion, mechanical alloying, or mechanical milling. Although the amorphization mechanisms may be different, the transformation occurs due to the high level of disorder introduced into the material. Milling induced solid state amorphization is proposed to be the result of accumulation of crystal defects, specifically dislocations, as the material is subjected to large deformations during the high energy process. Thus, understanding the deformation mechanisms of crystalline materials will be the first step in studying solid state amorphization in crystalline materials, which not only has scientific contributions, but also technical consequences. A phase field dislocation dynamics (PFDD) approach is employed in this work to simulate plastic deformation of molecular crystals. This PFDD model has the advantage of tracking all of the dislocations in a material simultaneously. The model takes into account the elastic interaction between dislocations, the lattice resistance to dislocation motion, and the elastic interaction of dislocations with an external stress field. The PFDD model is employed to describe the deformation of molecular crystals with pharmaceutical applications, namely, single crystal sucrose, acetaminophen, gamma-indomethacin, and aspirin. Stress-strain curves are produced that result in expected anisotropic material response due to the activation of different slip systems and yield stresses that agree well with those from experiments. The PFDD model is coupled to a phase transformation model to study the relation between plastic deformation and the solid state amorphization of crystals that undergo milling. This model predicts the amorphous volume fraction in excellent agreement with

Mean state dependence of ENSO diversity resulting from an intermediate coupled model

NASA Astrophysics Data System (ADS)

Xie, Ruihuang; Jin, Fei-Fei; Mu, Mu

2016-04-01

ENSO diversity is referred to the event-to-event differences in the amplitude, longitudinal location of maximum sea surface temperature (SST) anomalies and evolutional mechanisms, as manifested in both observation data and climate model simulations. Previous studies argued that westerly wind burst (WWB) has strong influence on ENSO diversity. Here, we bring evidences, from a modified intermediate complexity Zebiak-Cane (ZC) coupled model, to illustrate that the ENSO diversity is also determined by the mean states. Stabilities of the linearized ZC model reveal that the mean state with weak (strong) wind stress and deep (shallow) thermocline prefers ENSO variation in the equitorial eastern (central) Pacific with a four-year (two-year) period. Weak wind stress and deep thermocline make the thermocline (TH) feedback the dominant contribution to the growth of ENSO SST anomalies, whereas the opposite mean state favors the zonal advective (ZA) feedback. Different leading dynamical SST-controller makes ENSO display its diversity. In a mean state that resembles the recent climate in the tropical Pacific, the four-year and two-year ENSO variations coexist with similar growth rate. Even without WWB forcing, the nonlinear integration results with adjusted parameters in this special mean state also present at least two types of El Niño, in which the maximum warming rates are contributed by either TH or ZA feedback. The consistency between linear and nonlinear model results indicates that the ENSO diversity is dependent on the mean states.

Wanted dead or alive: A state-space mark-recapture-recovery model incorporating multiple recovery types and state uncertainty

USGS Publications Warehouse

Hostetter, Nathan; Gardner, Beth; Evans, Allen F.; Cramer, Bradley M.; Payton, Quinn; Collis, Ken; Roby, Daniel D.

2017-01-01

We developed a state-space mark-recapture-recovery model that incorporates multiple recovery types and state uncertainty to estimate survival of an anadromous fish species. We apply the model to a dataset of out-migrating juvenile steelhead trout (Oncorhynchus mykiss) tagged with passive integrated transponders, recaptured during outmigration, and recovered on bird colonies in the Columbia River basin (2008-2014). Recoveries on bird colonies are often ignored in survival studies because the river reach of mortality is often unknown, which we model as a form of state uncertainty. Median outmigration survival from release to the lower river (river kilometer 729 to 75) ranged from 0.27 to 0.35, depending on year. Recovery probabilities were frequently >0.20 in the first river reach following tagging, indicating that one out of five fish that died in that reach was recovered on a bird colony. Integrating dead recovery data provided increased parameter precision, estimation of where birds consumed fish, and survival estimates across larger spatial scales. More generally, these modeling approaches provide a flexible framework to integrate multiple sources of tag recovery data into mark-recapture studies.

Granger causality for state-space models

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Seth, Anil K.

2015-04-01

Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

Cluster state generation in one-dimensional Kitaev honeycomb model via shortcut to adiabaticity

NASA Astrophysics Data System (ADS)

Kyaw, Thi Ha; Kwek, Leong-Chuan

2018-04-01

We propose a mean to obtain computationally useful resource states also known as cluster states, for measurement-based quantum computation, via transitionless quantum driving algorithm. The idea is to cool the system to its unique ground state and tune some control parameters to arrive at computationally useful resource state, which is in one of the degenerate ground states. Even though there is set of conserved quantities already present in the model Hamiltonian, which prevents the instantaneous state to go to any other eigenstate subspaces, one cannot quench the control parameters to get the desired state. In that case, the state will not evolve. With involvement of the shortcut Hamiltonian, we obtain cluster states in fast-forward manner. We elaborate our proposal in the one-dimensional Kitaev honeycomb model, and show that the auxiliary Hamiltonian needed for the counterdiabatic driving is of M-body interaction.

A rule-based approach to model checking of UML state machines

NASA Astrophysics Data System (ADS)

Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

2016-12-01

In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

The Impact of State Legislation and Model Policies on Bullying in Schools

ERIC Educational Resources Information Center

Terry, Amanda

2018-01-01

Background: The purpose of this study was to determine the impact of the coverage of state legislation and the expansiveness ratings of state model policies on the state-level prevalence of bullying in schools. Methods: The state-level prevalence of bullying in schools was based on cross-sectional data from the 2013 High School Youth Risk Behavior…

Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models.

PubMed

Cai, Qing; Lee, Jaeyoung; Eluru, Naveen; Abdel-Aty, Mohamed

2016-08-01

This study attempts to explore the viability of dual-state models (i.e., zero-inflated and hurdle models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency analysis. Additionally, spatial spillover effects are explored in the models by employing exogenous variables from neighboring zones. The dual-state models such as zero-inflated negative binomial and hurdle negative binomial models (with and without spatial effects) are compared with the conventional single-state model (i.e., negative binomial). The model comparison for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency. Copyright © 2016 Elsevier Ltd. All rights reserved.

Gibbs Energy Modeling of Digenite and Adjacent Solid-State Phases

NASA Astrophysics Data System (ADS)

Waldner, Peter

2017-08-01

All sulfur potential and phase diagram data available in the literature for solid-state equilibria related to digenite have been assessed. Thorough thermodynamic analysis at 1 bar total pressure has been performed. A three-sublattice approach has been developed to model the Gibbs energy of digenite as a function of composition and temperature using the compound energy formalism. The Gibbs energies of the adjacent solid-state phases covelitte and high-temperature chalcocite are also modeled treating both sulfides as stoichiometric compounds. The novel model for digenite offers new interpretation of experimental data, may contribute from a thermodynamic point of view to the elucidation of the role of copper species within the crystal structure and allows extrapolation to composition regimes richer in copper than stoichiometric digenite Cu2S. Preliminary predictions into the ternary Cu-Fe-S system at 1273 K (1000 °C) using the Gibbs energy model of digenite for calculating its iron solubility are promising.

Impact of the Equation of State in Models for Surfactant Spreading Experiments

NASA Astrophysics Data System (ADS)

Levy, Rachel

2014-11-01

Pulmonary surfactant spreading models often rely on an equation of state relating surfactant concentration to surface tension. Mathematically, these models have been analyzed with simple functional relationships. However, to model an experiment with a given fluid and surfactant, a physically meaningful equation of state can be derived from experimentally obtained isotherms. We discuss the comparison between model and experiment for NBD-PC lipid (surfactant) spreading on glycerol for an empirically-determined equation of state, and compare those results to simulations with traditionally employed functional forms. In particular we compare the timescales by tracking the leading edge of surfactant, the central fluid height and dynamics of the Marangoni ridge. We consider both outward spreading of a disk-shaped region of surfactant and the hole-closure problem in which a disk-shaped surfactant-free region self-heals. Support from NSF-DMS-FRG 0968154, RCSA-CCS-19788, and HHMI.

Approximate ground states of the random-field Potts model from graph cuts

NASA Astrophysics Data System (ADS)

Kumar, Manoj; Kumar, Ravinder; Weigel, Martin; Banerjee, Varsha; Janke, Wolfhard; Puri, Sanjay

2018-05-01

While the ground-state problem for the random-field Ising model is polynomial, and can be solved using a number of well-known algorithms for maximum flow or graph cut, the analog random-field Potts model corresponds to a multiterminal flow problem that is known to be NP-hard. Hence an efficient exact algorithm is very unlikely to exist. As we show here, it is nevertheless possible to use an embedding of binary degrees of freedom into the Potts spins in combination with graph-cut methods to solve the corresponding ground-state problem approximately in polynomial time. We benchmark this heuristic algorithm using a set of quasiexact ground states found for small systems from long parallel tempering runs. For a not-too-large number q of Potts states, the method based on graph cuts finds the same solutions in a fraction of the time. We employ the new technique to analyze the breakup length of the random-field Potts model in two dimensions.

An Examination of State Funding Models Regarding Virtual Schools for Public Elementary and Secondary Education in the United States

ERIC Educational Resources Information Center

Stedrak, Luke J.

2012-01-01

This study contains an analysis of virtual schools, public policy, and funding in the United States. The purpose of this study was to determine what public policies and legislation were in place regarding the funding models of virtual education on a state by state basis. Furthermore, this study addressed how allocations were being made by state…

Forecasting the impact of transport improvements on commuting and residential choice

NASA Astrophysics Data System (ADS)

Elhorst, J. Paul; Oosterhaven, Jan

2006-03-01

This paper develops a probabilistic, competing-destinations, assignment model that predicts changes in the spatial pattern of the working population as a result of transport improvements. The choice of residence is explained by a new non-parametric model, which represents an alternative to the popular multinominal logit model. Travel times between zones are approximated by a normal distribution function with different mean and variance for each pair of zones, whereas previous models only use average travel times. The model’s forecast error of the spatial distribution of the Dutch working population is 7% when tested on 1998 base-year data. To incorporate endogenous changes in its causal variables, an almost ideal demand system is estimated to explain the choice of transport mode, and a new economic geography inter-industry model (RAEM) is estimated to explain the spatial distribution of employment. In the application, the model is used to forecast the impact of six mutually exclusive Dutch core-periphery railway proposals in the projection year 2020.

Analytical thermal model for end-pumped solid-state lasers

NASA Astrophysics Data System (ADS)

Cini, L.; Mackenzie, J. I.

2017-12-01

Fundamentally power-limited by thermal effects, the design challenge for end-pumped "bulk" solid-state lasers depends upon knowledge of the temperature gradients within the gain medium. We have developed analytical expressions that can be used to model the temperature distribution and thermal-lens power in end-pumped solid-state lasers. Enabled by the inclusion of a temperature-dependent thermal conductivity, applicable from cryogenic to elevated temperatures, typical pumping distributions are explored and the results compared with accepted models. Key insights are gained through these analytical expressions, such as the dependence of the peak temperature rise in function of the boundary thermal conductance to the heat sink. Our generalized expressions provide simple and time-efficient tools for parametric optimization of the heat distribution in the gain medium based upon the material and pumping constraints.

Equity Access Plans: A Regulatory and Educational State Response Model.

ERIC Educational Resources Information Center

DeLisle, James

1984-01-01

Introduces the basic notion of equity access plans as property-based solutions to the cash flow needs of elderly homeowners and then proposes a normative response model that states can adopt to help manage the risk exposures. The recommended model incorporates regulatory, information dissemination, and educational elements. (BH)

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

Hall viscosity and geometric response in the Chern-Simons matrix model of the Laughlin states

NASA Astrophysics Data System (ADS)

Lapa, Matthew F.; Hughes, Taylor L.

2018-05-01

We study geometric aspects of the Laughlin fractional quantum Hall (FQH) states using a description of these states in terms of a matrix quantum mechanics model known as the Chern-Simons matrix model (CSMM). This model was proposed by Polychronakos as a regularization of the noncommutative Chern-Simons theory description of the Laughlin states proposed earlier by Susskind. Both models can be understood as describing the electrons in a FQH state as forming a noncommutative fluid, i.e., a fluid occupying a noncommutative space. Here, we revisit the CSMM in light of recent work on geometric response in the FQH effect, with the goal of determining whether the CSMM captures this aspect of the physics of the Laughlin states. For this model, we compute the Hall viscosity, Hall conductance in a nonuniform electric field, and the Hall viscosity in the presence of anisotropy (or intrinsic geometry). Our calculations show that the CSMM captures the guiding center contribution to the known values of these quantities in the Laughlin states, but lacks the Landau orbit contribution. The interesting correlations in a Laughlin state are contained entirely in the guiding center part of the state/wave function, and so we conclude that the CSMM accurately describes the most important aspects of the physics of the Laughlin FQH states, including the Hall viscosity and other geometric properties of these states, which are of current interest.

Automatic Welding Control Using a State Variable Model.

DTIC Science & Technology

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

Steady-state solutions of a diffusive energy-balance climate model and their stability

NASA Technical Reports Server (NTRS)

Ghil, M.

1975-01-01

A diffusive energy-balance climate model, governed by a nonlinear parabolic partial differential equation, was studied. Three positive steady-state solutions of this equation are found; they correspond to three possible climates of our planet: an interglacial (nearly identical to the present climate), a glacial, and a completely ice-covered earth. Models similar to the main one are considered, and the number of their steady states was determined. All the models have albedo continuously varying with latitude and temperature, and entirely diffusive horizontal heat transfer. The stability under small perturbations of the main model's climates was investigated. A stability criterion is derived, and its application shows that the present climate and the deep freeze are stable, whereas the model's glacial is unstable. The dependence was examined of the number of steady states and of their stability on the average solar radiation.

Computerized power supply analysis: State equation generation and terminal models

NASA Technical Reports Server (NTRS)

Garrett, S. J.

1978-01-01

To aid engineers that design power supply systems two analysis tools that can be used with the state equation analysis package were developed. These tools include integration routines that start with the description of a power supply in state equation form and yield analytical results. The first tool uses a computer program that works with the SUPER SCEPTRE circuit analysis program and prints the state equation for an electrical network. The state equations developed automatically by the computer program are used to develop an algorithm for reducing the number of state variables required to describe an electrical network. In this way a second tool is obtained in which the order of the network is reduced and a simpler terminal model is obtained.

Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

NASA Astrophysics Data System (ADS)

Propp, Karsten; Marinescu, Monica; Auger, Daniel J.; O'Neill, Laura; Fotouhi, Abbas; Somasundaram, Karthik; Offer, Gregory J.; Minton, Geraint; Longo, Stefano; Wild, Mark; Knap, Vaclav

2016-10-01

Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a 'behavioural' interpretation of the ECN model; as Li-S exhibits a 'steep' open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV.

Modeling the human invader in the United States

USGS Publications Warehouse

Stohlgren, Thomas J.; Jarnevich, Catherine S.; Giri, Chandra P.

2010-01-01

Modern biogeographers recognize that humans are seen as constituents of ecosystems, drivers of significant change, and perhaps, the most invasive species on earth. We found it instructive to model humans as invasive organisms with the same environmental factors. We present a preliminary model of the spread of modern humans in the conterminous United States between 1992 and 2001 based on a subset of National Land Cover Data (NLCD), a time series LANDSAT product. We relied on the commonly used Maxent model, a species-environmental matching model, to map urbanization. Results: Urban areas represented 5.1% of the lower 48 states in 2001, an increase of 7.5% (18,112 km2) in the nine year period. At this rate, an area the size of Massachusetts is converted to urban land use every ten years. We used accepted models commonly used for mapping plant and animal distributions and found that climatic and environmental factors can strongly predict our spread (i.e., the conversion of forests, shrub/grass, and wetland areas into urban areas), with a 92.5% success rate (Area Under the Curve). Adding a roads layer in the model improved predictions to a 95.5% success rate. 8.8% of the 1-km2> cells in the conterminous U.S. now have a major road in them. In 2001, 0.8% of 1-km2 > cells in the U.S. had an urbanness value of > 800, (>89% of a 1-km2> cell is urban), while we predict that 24.5% of 1-km2> cells in the conterminous U.S. will be > 800 eventually. Main conclusion: Humans have a highly predictable pattern of urbanization based on climatic and topographic variables. Conservation strategies may benefit from that predictability.

Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States

PubMed Central

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam

2010-01-01

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500

Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial.

PubMed

Williams, Claire; Lewsey, James D; Briggs, Andrew H; Mackay, Daniel F

2017-05-01

This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients' history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results-namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.

A Bayesian model for estimating multi-state disease progression.

PubMed

Shen, Shiwen; Han, Simon X; Petousis, Panayiotis; Weiss, Robert E; Meng, Frank; Bui, Alex A T; Hsu, William

2017-02-01

A growing number of individuals who are considered at high risk of cancer are now routinely undergoing population screening. However, noted harms such as radiation exposure, overdiagnosis, and overtreatment underscore the need for better temporal models that predict who should be screened and at what frequency. The mean sojourn time (MST), an average duration period when a tumor can be detected by imaging but with no observable clinical symptoms, is a critical variable for formulating screening policy. Estimation of MST has been long studied using continuous Markov model (CMM) with Maximum likelihood estimation (MLE). However, a lot of traditional methods assume no observation error of the imaging data, which is unlikely and can bias the estimation of the MST. In addition, the MLE may not be stably estimated when data is sparse. Addressing these shortcomings, we present a probabilistic modeling approach for periodic cancer screening data. We first model the cancer state transition using a three state CMM model, while simultaneously considering observation error. We then jointly estimate the MST and observation error within a Bayesian framework. We also consider the inclusion of covariates to estimate individualized rates of disease progression. Our approach is demonstrated on participants who underwent chest x-ray screening in the National Lung Screening Trial (NLST) and validated using posterior predictive p-values and Pearson's chi-square test. Our model demonstrates more accurate and sensible estimates of MST in comparison to MLE. Copyright © 2016 Elsevier Ltd. All rights reserved.

Individual tree-diameter growth model for the Northeastern United States

Treesearch

Richard M. Teck; Donald E. Hilt

1991-01-01

Describes a distance-independent individual-tree diameter growth model for the Northeastern United States. Diameter growth is predicted in two steps using a two parameter, sigmoidal growth function modified by a one parameter exponential decay function with species-specific coefficients. Coefficients are presented for 28 species groups. The model accounts for...

State-level minimum wage and heart disease death rates in the United States, 1980-2015: A novel application of marginal structural modeling.

PubMed

Van Dyke, Miriam E; Komro, Kelli A; Shah, Monica P; Livingston, Melvin D; Kramer, Michael R

2018-07-01

Despite substantial declines since the 1960's, heart disease remains the leading cause of death in the United States (US) and geographic disparities in heart disease mortality have grown. State-level socioeconomic factors might be important contributors to geographic differences in heart disease mortality. This study examined the association between state-level minimum wage increases above the federal minimum wage and heart disease death rates from 1980 to 2015 among 'working age' individuals aged 35-64 years in the US. Annual, inflation-adjusted state and federal minimum wage data were extracted from legal databases and annual state-level heart disease death rates were obtained from CDC Wonder. Although most minimum wage and health studies to date use conventional regression models, we employed marginal structural models to account for possible time-varying confounding. Quasi-experimental, marginal structural models accounting for state, year, and state × year fixed effects estimated the association between increases in the state-level minimum wage above the federal minimum wage and heart disease death rates. In models of 'working age' adults (35-64 years old), a $1 increase in the state-level minimum wage above the federal minimum wage was on average associated with ~6 fewer heart disease deaths per 100,000 (95% CI: -10.4, -1.99), or a state-level heart disease death rate that was 3.5% lower per year. In contrast, for older adults (65+ years old) a $1 increase was on average associated with a 1.1% lower state-level heart disease death rate per year (b = -28.9 per 100,000, 95% CI: -71.1, 13.3). State-level economic policies are important targets for population health research. Copyright © 2018 Elsevier Inc. All rights reserved.

Effect of Clustering Algorithm on Establishing Markov State Model for Molecular Dynamics Simulations.

PubMed

Li, Yan; Dong, Zigang

2016-06-27

Recently, the Markov state model has been applied for kinetic analysis of molecular dynamics simulations. However, discretization of the conformational space remains a primary challenge in model building, and it is not clear how the space decomposition by distinct clustering strategies exerts influence on the model output. In this work, different clustering algorithms are employed to partition the conformational space sampled in opening and closing of fatty acid binding protein 4 as well as inactivation and activation of the epidermal growth factor receptor. Various classifications are achieved, and Markov models are set up accordingly. On the basis of the models, the total net flux and transition rate are calculated between two distinct states. Our results indicate that geometric and kinetic clustering perform equally well. The construction and outcome of Markov models are heavily dependent on the data traits. Compared to other methods, a combination of Bayesian and hierarchical clustering is feasible in identification of metastable states.

A TWO-STATE MIXED HIDDEN MARKOV MODEL FOR RISKY TEENAGE DRIVING BEHAVIOR

PubMed Central

Jackson, John C.; Albert, Paul S.; Zhang, Zhiwei

2016-01-01

This paper proposes a joint model for longitudinal binary and count outcomes. We apply the model to a unique longitudinal study of teen driving where risky driving behavior and the occurrence of crashes or near crashes are measured prospectively over the first 18 months of licensure. Of scientific interest is relating the two processes and predicting crash and near crash outcomes. We propose a two-state mixed hidden Markov model whereby the hidden state characterizes the mean for the joint longitudinal crash/near crash outcomes and elevated g-force events which are a proxy for risky driving. Heterogeneity is introduced in both the conditional model for the count outcomes and the hidden process using a shared random effect. An estimation procedure is presented using the forward–backward algorithm along with adaptive Gaussian quadrature to perform numerical integration. The estimation procedure readily yields hidden state probabilities as well as providing for a broad class of predictors. PMID:27766124

Ground-State of the Bose-Hubbard Model

NASA Astrophysics Data System (ADS)

Mancini, J. D.; Fessatidis, V.; Bowen, S. P.; Murawski, R. K.; Maly, J.

The Bose-Hubbard Model represents a s simple theoretical model to describe the physics of interacting Boson systems. In particular it has proved to be an effective description of a number of physical systems such as arrays of Josephson arrays as well as dilute alkali gases in optical lattices. Here we wish to study the ground-state of this system using two disparate but related moments calculational schemes: the Lanczos (tridiagonal) method as well as a Generalized moments approach. The Hamiltonian to be studied is given by (in second-quantized notation): H = - t ∑ < i , j > bi†bj +U/2 ∑ inini - 1 - μ ∑ ini . Here i is summed over all lattice sites, and < i , j > denotes summation over all neighbhoring sites i and j, while bi† and bi are bosonic creation and annihilation operators. ni = bi†bi gives the number of particles on site i. Parameter t is the hopping amplitude, describing mobility of bosons in the lattice. Parameter U describes the on-site interaction, repulsive, if U > 0 , and attractive for U < 0 . μ is the chemical potential. Both the ground-state energy and energy gap are evaluated as a function of t, U and μ.

Developing a Fundamental Model for an Integrated GPS/INS State Estimation System with Kalman Filtering

NASA Technical Reports Server (NTRS)

Canfield, Stephen

1999-01-01

This work will demonstrate the integration of sensor and system dynamic data and their appropriate models using an optimal filter to create a robust, adaptable, easily reconfigurable state (motion) estimation system. This state estimation system will clearly show the application of fundamental modeling and filtering techniques. These techniques are presented at a general, first principles level, that can easily be adapted to specific applications. An example of such an application is demonstrated through the development of an integrated GPS/INS navigation system. This system acquires both global position data and inertial body data, to provide optimal estimates of current position and attitude states. The optimal states are estimated using a Kalman filter. The state estimation system will include appropriate error models for the measurement hardware. The results of this work will lead to the development of a "black-box" state estimation system that supplies current motion information (position and attitude states) that can be used to carry out guidance and control strategies. This black-box state estimation system is developed independent of the vehicle dynamics and therefore is directly applicable to a variety of vehicles. Issues in system modeling and application of Kalman filtering techniques are investigated and presented. These issues include linearized models of equations of state, models of the measurement sensors, and appropriate application and parameter setting (tuning) of the Kalman filter. The general model and subsequent algorithm is developed in Matlab for numerical testing. The results of this system are demonstrated through application to data from the X-33 Michael's 9A8 mission and are presented in plots and simple animations.

The 2014 United States National Seismic Hazard Model

USGS Publications Warehouse

Petersen, Mark D.; Moschetti, Morgan P.; Powers, Peter; Mueller, Charles; Haller, Kathleen; Frankel, Arthur; Zeng, Yuehua; Rezaeian, Sanaz; Harmsen, Stephen; Boyd, Oliver; Field, Edward; Chen, Rui; Rukstales, Kenneth S.; Luco, Nicolas; Wheeler, Russell; Williams, Robert; Olsen, Anna H.

2015-01-01

New seismic hazard maps have been developed for the conterminous United States using the latest data, models, and methods available for assessing earthquake hazard. The hazard models incorporate new information on earthquake rupture behavior observed in recent earthquakes; fault studies that use both geologic and geodetic strain rate data; earthquake catalogs through 2012 that include new assessments of locations and magnitudes; earthquake adaptive smoothing models that more fully account for the spatial clustering of earthquakes; and 22 ground motion models, some of which consider more than double the shaking data applied previously. Alternative input models account for larger earthquakes, more complicated ruptures, and more varied ground shaking estimates than assumed in earlier models. The ground motions, for levels applied in building codes, differ from the previous version by less than ±10% over 60% of the country, but can differ by ±50% in localized areas. The models are incorporated in insurance rates, risk assessments, and as input into the U.S. building code provisions for earthquake ground shaking.

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models

PubMed Central

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization. PMID:27243005

Customized Steady-State Constraints for Parameter Estimation in Non-Linear Ordinary Differential Equation Models.

PubMed

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

Ordinary differential equation models have become a wide-spread approach to analyze dynamical systems and understand underlying mechanisms. Model parameters are often unknown and have to be estimated from experimental data, e.g., by maximum-likelihood estimation. In particular, models of biological systems contain a large number of parameters. To reduce the dimensionality of the parameter space, steady-state information is incorporated in the parameter estimation process. For non-linear models, analytical steady-state calculation typically leads to higher-order polynomial equations for which no closed-form solutions can be obtained. This can be circumvented by solving the steady-state equations for kinetic parameters, which results in a linear equation system with comparatively simple solutions. At the same time multiplicity of steady-state solutions is avoided, which otherwise is problematic for optimization. When solved for kinetic parameters, however, steady-state constraints tend to become negative for particular model specifications, thus, generating new types of optimization problems. Here, we present an algorithm based on graph theory that derives non-negative, analytical steady-state expressions by stepwise removal of cyclic dependencies between dynamical variables. The algorithm avoids multiple steady-state solutions by construction. We show that our method is applicable to most common classes of biochemical reaction networks containing inhibition terms, mass-action and Hill-type kinetic equations. Comparing the performance of parameter estimation for different analytical and numerical methods of incorporating steady-state information, we show that our approach is especially well-tailored to guarantee a high success rate of optimization.

An Investigation of State-Space Model Fidelity for SSME Data

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2008-01-01

In previous studies, a variety of unsupervised anomaly detection techniques for anomaly detection were applied to SSME (Space Shuttle Main Engine) data. The observed results indicated that the identification of certain anomalies were specific to the algorithmic method under consideration. This is the reason why one of the follow-on goals of these previous investigations was to build an architecture to support the best capabilities of all algorithms. We appeal to that goal here by investigating a cascade, serial architecture for the best performing and most suitable candidates from previous studies. As a precursor to a formal ROC (Receiver Operating Characteristic) curve analysis for validation of resulting anomaly detection algorithms, our primary focus here is to investigate the model fidelity as measured by variants of the AIC (Akaike Information Criterion) for state-space based models. We show that placing constraints on a state-space model during or after the training of the model introduces a modest level of suboptimality. Furthermore, we compare the fidelity of all candidate models including those embodying the cascade, serial architecture. We make recommendations on the most suitable candidates for application to subsequent anomaly detection studies as measured by AIC-based criteria.

State-and-transition prototype model of riparian vegetation downstream of Glen Canyon Dam, Arizona

USGS Publications Warehouse

Ralston, Barbara E.; Starfield, Anthony M.; Black, Ronald S.; Van Lonkhuyzen, Robert A.

2014-01-01

Facing an altered riparian plant community dominated by nonnative species, resource managers are increasingly interested in understanding how to manage and promote healthy riparian habitats in which native species dominate. For regulated rivers, managing flows is one tool resource managers consider to achieve these goals. Among many factors that can influence riparian community composition, hydrology is a primary forcing variable. Frame-based models, used successfully in grassland systems, provide an opportunity for stakeholders concerned with riparian systems to evaluate potential riparian vegetation responses to alternative flows. Frame-based, state-and-transition models of riparian vegetation for reattachment bars, separation bars, and the channel margin found on the Colorado River downstream of Glen Canyon Dam were constructed using information from the literature. Frame-based models can be simple spreadsheet models (created in Microsoft® Excel) or developed further with programming languages (for example, C-sharp). The models described here include seven community states and five dam operations that cause transitions between states. Each model divides operations into growing (April–September) and non-growing seasons (October–March) and incorporates upper and lower bar models, using stage elevation as a division. The inputs (operations) can be used by stakeholders to evaluate flows that may promote dynamic riparian vegetation states, or identify those flow options that may promote less desirable states (for example, Tamarisk [Tamarix sp.] temporarily flooded shrubland). This prototype model, although simple, can still elicit discussion about operational options and vegetation response.

Population decoding of motor cortical activity using a generalized linear model with hidden states.

PubMed

Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam

2010-06-15

Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

WastePlan model implementation for New York State. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Visalli, J.R.; Blackman, D.A.

1995-07-01

WastePlan is a computer software tool that models solid waste quantities, costs, and other parameters on a regional basis. The software was developed by the Tellus Institute, a nonprofit research and consulting firm. The project`s objective was to provide local solid waste management planners in New York State responsible to develop and implement comprehensive solid waste management plans authorized by the Solid Waste Management Act of 1988, with a WastePlan model specifically tailored to fit the demographic and other characteristics of New York State and to provide training and technical support to the users. Two-day workshops were held in 1992more » to introduce planners to the existing versions; subsequently, extensive changes were made to the model and a second set of two-day workshops were held in 1993 to introduce planners to the enhanced version of WastePlan. Following user evaluations, WastePlan was further modified to allow users to model systems using a simplified version, and to incorporate report forms required by New York State. A post-project survey of trainees revealed limited regular use of software. Possible reasons include lack of synchronicity with NYSDEC planning process; lack of computer literacy and aptitude among trainees; hardware limitations; software user-friendliness; and the work environment of the trainees. A number of recommendations are made to encourage use of WastePlan by local solid waste management planners.« less

Ground-state energies of the nonlinear sigma model and the Heisenberg spin chains

NASA Technical Reports Server (NTRS)

Zhang, Shoucheng; Schulz, H. J.; Ziman, Timothy

1989-01-01

A theorem on the O(3) nonlinear sigma model with the topological theta term is proved, which states that the ground-state energy at theta = pi is always higher than the ground-state energy at theta = 0, for the same value of the coupling constant g. Provided that the nonlinear sigma model gives the correct description for the Heisenberg spin chains in the large-s limit, this theorem makes a definite prediction relating the ground-state energies of the half-integer and the integer spin chains. The ground-state energies obtained from the exact Bethe ansatz solution for the spin-1/2 chain and the numerical diagonalization on the spin-1, spin-3/2, and spin-2 chains support this prediction.

Intrinsic states in the sdg interacting boson model

NASA Astrophysics Data System (ADS)

Yoshinaga, N.

1986-08-01

We give the intrinsic states explicitly in the boson representation in the framework of the sdg interacting boson model. Although they are only valid in the large- N limit, they are useful to estimate various physical quantities in well deformed nuclei. One can compare these results with those predicted in the IBM1 or in the IBM2.

Models for Selecting Chief State School Officers. Policy Memo Series, No. 1.

ERIC Educational Resources Information Center

Sanchez, Karen L. Van Til; Hall, Gayle C.

The process of selecting a chief state school officer (CSSO) can be a significant means of allocating policymaking power in state educational governance. This paper examines the role of the chief state school officer and explains how that role is influenced by the selection process. Four selection models are described, along with the advantages…

The "intoxication state of consciousness": a model for alcohol and drug abuse.

PubMed

Galanter, M

1976-06-01

The author describes a model of intoxicant use based on altered states of consciousness and reviews his own and others' research on marijuana to illustrate the utility of this model, which is derived from both introspective reports and observed data. The relationship of social behavior and cognitive functioning to the "intoxication state of consciousness" is discussed. This state of consciousness may have an adaptive value in engendering and stabilizing social cohesion. Possible treatment implications include cognitive labeling of cues that precipitate episodes of abuse, training for moderated drug use while patients are intoxicated, and providing abusers with altered consciousness through other means, such as meditation.

Using thermal stress to model aspects of disease states.

PubMed

Wilson, Thad E; Klabunde, Richard E; Monahan, Kevin D

2014-07-01

Exposure to acute heat or cold stress elicits numerous physiological responses aimed at maintaining body temperatures. Interestingly, many of the physiological responses, mediated by the cardiovascular and autonomic nervous systems, resemble aspects of, or responses to, certain disease states. The purpose of this Perspective is to highlight some of these areas in order to explore how they may help us better understand the pathophysiology underlying aspects of certain disease states. The benefits of using this human thermal stress approach are that (1) no adjustments for inherent comparative differences in animals are needed, (2) non-medicated healthy humans with no underlying co-morbidities can be studied in place of complex patients, and (3) more mechanistic perturbations can be safely employed without endangering potentially vulnerable populations. Cold stress can be used to induce stable elevations in blood pressure. Cold stress may also be used to model conditions where increases in myocardial oxygen demand are not met by anticipated increases in coronary blood flow, as occurs in older adults. Lower-body negative pressure has the capacity to model aspects of shock, and the further addition of heat stress improves and expands this model because passive-heat exposure lowers systemic vascular resistance at a time when central blood volume and left-ventricular filling pressure are reduced. Heat stress can model aspects of heat syncope and orthostatic intolerance as heat stress decreases cerebral blood flow and alters the Frank-Starling mechanism resulting in larger decreases in stroke volume for a given change in left-ventricular filling pressure. Combined, thermal perturbations may provide in vivo paradigms that can be employed to gain insights into pathophysiological aspects of certain disease states. Copyright © 2014 Elsevier Ltd. All rights reserved.

AERIS : State-of-the-Practice Scan of Environmental Models

DOT National Transportation Integrated Search

2011-06-24

This report has been developed under the Track 1 effort of Phase 1 of the AERIS program and presents the findings of the state-of-the-practice scan of environmental models to estimate environmental impacts (emissions, fuel consumption, etc.) due to c...

Structure of the low-lying positive parity states in the proton-neutron symplectic model

NASA Astrophysics Data System (ADS)

Ganev, H. G.

2018-05-01

The proton-neutron symplectic model with Sp(12, R) dynamical symmetry is applied for the simultaneous description of the microscopic structure of the low-lying states of the ground state, γ and β bands in 166 Er. For this purpose, the model Hamiltonian is diagonalized in the space of stretched states by exploiting the SUp (3) ⊗ SUn (3) symmetry-adapted basis. The theoretical predictions are compared with experiment and some other microscopic collective models, like the one-component Sp(6, R) symplectic and pseudo-SU(3) models. A good description of the energy levels of the three bands under consideration, as well as the enhanced intraband B(E2) transition strengths between the states of the ground and γ bands is obtained without the use of effective charges. The results show the presence of a good SU(3) dynamical symmetry. It is also shown that, in contrast to the Sp(6, R) case, the lowest excited bands, e.g., the β and γ bands, naturally appear together with the ground state band within a single Sp(12, R) irreducible representation.

State-and-transition model archetypes: a global taxonomy of rangeland change

USDA-ARS?s Scientific Manuscript database

State and transition models (STMs) synthesize science-based and local knowledge to formally represent the dynamics of rangeland and other ecosystems. Mental models or concepts of ecosystem dynamics implicitly underlie all management decisions in rangelands and thus how people influence rangeland sus...

Turbulence Modeling Effects on the Prediction of Equilibrium States of Buoyant Shear Flows

NASA Technical Reports Server (NTRS)

Zhao, C. Y.; So, R. M. C.; Gatski, T. B.

2001-01-01

The effects of turbulence modeling on the prediction of equilibrium states of turbulent buoyant shear flows were investigated. The velocity field models used include a two-equation closure, a Reynolds-stress closure assuming two different pressure-strain models and three different dissipation rate tensor models. As for the thermal field closure models, two different pressure-scrambling models and nine different temperature variance dissipation rate, Epsilon(0) equations were considered. The emphasis of this paper is focused on the effects of the Epsilon(0)-equation, of the dissipation rate models, of the pressure-strain models and of the pressure-scrambling models on the prediction of the approach to equilibrium turbulence. Equilibrium turbulence is defined by the time rate (if change of the scaled Reynolds stress anisotropic tensor and heat flux vector becoming zero. These conditions lead to the equilibrium state parameters. Calculations show that the Epsilon(0)-equation has a significant effect on the prediction of the approach to equilibrium turbulence. For a particular Epsilon(0)-equation, all velocity closure models considered give an equilibrium state if anisotropic dissipation is accounted for in one form or another in the dissipation rate tensor or in the Epsilon(0)-equation. It is further found that the models considered for the pressure-strain tensor and the pressure-scrambling vector have little or no effect on the prediction of the approach to equilibrium turbulence.

Modeling of the blood rheology in steady-state shear flows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Apostolidis, Alex J.; Beris, Antony N., E-mail: beris@udel.edu

We undertake here a systematic study of the rheology of blood in steady-state shear flows. As blood is a complex fluid, the first question that we try to answer is whether, even in steady-state shear flows, we can model it as a rheologically simple fluid, i.e., we can describe its behavior through a constitutive model that involves only local kinematic quantities. Having answered that question positively, we then probe as to which non-Newtonian model best fits available shear stress vs shear-rate literature data. We show that under physiological conditions blood is typically viscoplastic, i.e., it exhibits a yield stress thatmore » acts as a minimum threshold for flow. We further show that the Casson model emerges naturally as the best approximation, at least for low and moderate shear-rates. We then develop systematically a parametric dependence of the rheological parameters entering the Casson model on key physiological quantities, such as the red blood cell volume fraction (hematocrit). For the yield stress, we base our description on its critical, percolation-originated nature. Thus, we first determine onset conditions, i.e., the critical threshold value that the hematocrit has to have in order for yield stress to appear. It is shown that this is a function of the concentration of a key red blood cell binding protein, fibrinogen. Then, we establish a parametric dependence as a function of the fibrinogen and the square of the difference of the hematocrit from its critical onset value. Similarly, we provide an expression for the Casson viscosity, in terms of the hematocrit and the temperature. A successful validation of the proposed formula is performed against additional experimental literature data. The proposed expression is anticipated to be useful not only for steady-state blood flow modeling but also as providing the starting point for transient shear, or more general flow modeling.« less

Low-lying Photoexcited States of a One-Dimensional Ionic Extended Hubbard Model

NASA Astrophysics Data System (ADS)

Yokoi, Kota; Maeshima, Nobuya; Hino, Ken-ichi

2017-10-01

We investigate the properties of low-lying photoexcited states of a one-dimensional (1D) ionic extended Hubbard model at half-filling. Numerical analysis by using the full and Lanczos diagonalization methods shows that, in the ionic phase, there exist low-lying photoexcited states below the charge transfer gap. As a result of comparison with numerical data for the 1D antiferromagnetic (AF) Heisenberg model, it was found that, for a small alternating potential Δ, these low-lying photoexcited states are spin excitations, which is consistent with a previous analytical study [Katsura et al., Phys. Rev. Lett. 103, 177402 (2009)]. As Δ increases, the spectral intensity of the 1D ionic extended Hubbard model rapidly deviates from that of the 1D AF Heisenberg model and it is clarified that this deviation is due to the neutral-ionic domain wall, an elementary excitation near the neutral-ionic transition point.

Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

PubMed Central

Sun, Xiaodian; Jin, Li; Xiong, Momiao

2008-01-01

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286

Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models

PubMed Central

Ou, Jinli; Xie, Li; Jin, Changfeng; Li, Xiang; Zhu, Dajiang; Jiang, Rongxin; Chen, Yaowu

2014-01-01

Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain’s functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain’s functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84 % of PTSD patients and 86 % of NC subjects are successfully classified via multiple HMMs using majority voting. PMID:25331991

Evaluation of Aerosol Mixing State Classes in the GISS Modele-matrix Climate Model Using Single-particle Mass Spectrometry Measurements

NASA Technical Reports Server (NTRS)

Bauer, Susanne E.; Ault, Andrew; Prather, Kimberly A.

2013-01-01

Aerosol particles in the atmosphere are composed of multiple chemical species. The aerosol mixing state, which describes how chemical species are mixed at the single-particle level, provides critical information on microphysical characteristics that determine the interaction of aerosols with the climate system. The evaluation of mixing state has become the next challenge. This study uses aerosol time-of-flight mass spectrometry (ATOFMS) data and compares the results to those of the Goddard Institute for Space Studies modelE-MATRIX (Multiconfiguration Aerosol TRacker of mIXing state) model, a global climate model that includes a detailed aerosol microphysical scheme. We use data from field campaigns that examine a variety of air mass regimens (urban, rural, and maritime). At all locations, polluted areas in California (Riverside, La Jolla, and Long Beach), a remote location in the Sierra Nevada Mountains (Sugar Pine) and observations from Jeju (South Korea), the majority of aerosol species are internally mixed. Coarse aerosol particles, those above 1 micron, are typically aged, such as coated dust or reacted sea-salt particles. Particles below 1 micron contain large fractions of organic material, internally-mixed with sulfate and black carbon, and few external mixtures. We conclude that observations taken over multiple weeks characterize typical air mass types at a given location well; however, due to the instrumentation, we could not evaluate mass budgets. These results represent the first detailed comparison of single-particle mixing states in a global climate model with real-time single-particle mass spectrometry data, an important step in improving the representation of mixing state in global climate models.

Kinematic Cosmology & a new ``Steady State'' Model of Continued Creation

NASA Astrophysics Data System (ADS)

Wegener, Mogens

2006-03-01

Only a new "steady state" model justifies the observations of fully mature galaxies at ever increasing distances. The basic idea behind the world model presented here, which is a synthesis of the cosmologies of Parmenides and Herakleitos, is that the invariant structure of the infinite contents of a universe in flux may be depicted as a finite hyperbolic pseudo-sphere.

Two States Mapping Based Time Series Neural Network Model for Compensation Prediction Residual Error

NASA Astrophysics Data System (ADS)

Jung, Insung; Koo, Lockjo; Wang, Gi-Nam

2008-11-01

The objective of this paper was to design a model of human bio signal data prediction system for decreasing of prediction error using two states mapping based time series neural network BP (back-propagation) model. Normally, a lot of the industry has been applied neural network model by training them in a supervised manner with the error back-propagation algorithm for time series prediction systems. However, it still has got a residual error between real value and prediction result. Therefore, we designed two states of neural network model for compensation residual error which is possible to use in the prevention of sudden death and metabolic syndrome disease such as hypertension disease and obesity. We determined that most of the simulation cases were satisfied by the two states mapping based time series prediction model. In particular, small sample size of times series were more accurate than the standard MLP model.

Automatic sleep classification using a data-driven topic model reveals latent sleep states.

PubMed

Koch, Henriette; Christensen, Julie A E; Frandsen, Rune; Zoetmulder, Marielle; Arvastson, Lars; Christensen, Soren R; Jennum, Poul; Sorensen, Helge B D

2014-09-30

The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. To meet the criticism and reveal the latent sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group. The model was optimized using 50 subjects and validated on 76 subjects. The optimized sleep model used six topics, and the topic probabilities changed smoothly during transitions. According to the manual scorings, the model scored an overall subject-specific accuracy of 68.3 ± 7.44 (% μ ± σ) and group specific accuracies of 69.0 ± 4.62 (control), 70.1 ± 5.10 (PLM), 67.2 ± 8.30 (iRBD) and 67.7 ± 9.07 (PD). Statistics of the latent sleep state content showed accordances to the sleep stages defined in the golden standard. However, this study indicates that sleep contains six diverse latent sleep states and that state transitions are continuous processes. The model is generally applicable and may contribute to the research in neurodegenerative diseases and sleep disorders. Copyright © 2014 Elsevier B.V. All rights reserved.

Dynamic 8-state ICSAR rumor propagation model considering official rumor refutation

NASA Astrophysics Data System (ADS)

Zhang, Nan; Huang, Hong; Su, Boni; Zhao, Jinlong; Zhang, Bo

2014-12-01

With the rapid development of information networks, negative impacts of rumor propagation become more serious. Nowadays, knowing the mechanisms of rumor propagation and having an efficient official rumor refutation plan play very important roles in reducing losses and ensuring social safety. In this paper we first develop the dynamic 8-state ICSAR (Ignorance, Information Carrier, Information Spreader, Information Advocate, Removal) rumor propagation model to study the mechanism of rumor propagation. Eight influencing factors including information attraction, objective identification of rumors, subjective identification of people, the degree of trust of information media, spread probability, reinforcement coefficient, block value and expert effects which are related to rumor propagation were analyzed. Next, considering these factors and mechanisms of rumor propagation and refutation, the dynamic 8-state ICSAR rumor propagation model is verified by the SIR epidemic model, computer simulation and actual data. Thirdly, through quantitative sensitivity analysis, the detailed function of each influencing factor was studied and shown in the figure directly. According to these mechanisms, we could understand how to block a rumor in a very efficient way and which methods should be chosen in different situations. The ICSAR model can divide people into 8 states and analyze rumor and anti-rumor dissemination in an accurate way. Furthermore, official rumor refutation is considered in rumor propagation. The models and the results are essential for improving the efficiency of rumor refutation and making emergency plans, which help to reduce the possibility of losses in disasters and rumor propagation.

Solvable four-state Landau-Zener model of two interacting qubits with path interference

DOE PAGES

Sinitsyn, Nikolai A.

2015-11-30

In this paper, I identify a nontrivial four-state Landau-Zener model for which transition probabilities between any pair of diabatic states can be determined analytically and exactly. The model describes an experimentally accessible system of two interacting qubits, such as a localized state in a Dirac material with both valley and spin degrees of freedom or a singly charged quantum dot (QD) molecule with spin orbit coupling. Application of the linearly time-dependent magnetic field induces a sequence of quantum level crossings with possibility of interference of different trajectories in a semiclassical picture. I argue that this system satisfies the criteria ofmore » integrability in the multistate Landau-Zener theory, which allows one to derive explicit exact analytical expressions for the transition probability matrix. Finally, I also argue that this model is likely a special case of a larger class of solvable systems, and present a six-state generalization as an example.« less

Observational constraints on cosmological models with Chaplygin gas and quadratic equation of state

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sharov, G.S., E-mail: german.sharov@mail.ru

Observational manifestations of accelerated expansion of the universe, in particular, recent data for Type Ia supernovae, baryon acoustic oscillations, for the Hubble parameter H ( z ) and cosmic microwave background constraints are described with different cosmological models. We compare the ΛCDM, the models with generalized and modified Chaplygin gas and the model with quadratic equation of state. For these models we estimate optimal model parameters and their permissible errors with different approaches to calculation of sound horizon scale r {sub s} ( z {sub d} ). Among the considered models the best value of χ{sup 2} is achieved formore » the model with quadratic equation of state, but it has 2 additional parameters in comparison with the ΛCDM and therefore is not favored by the Akaike information criterion.« less

Analysis of membrane fusion as a two-state sequential process: evaluation of the stalk model.

PubMed

Weinreb, Gabriel; Lentz, Barry R

2007-06-01

We propose a model that accounts for the time courses of PEG-induced fusion of membrane vesicles of varying lipid compositions and sizes. The model assumes that fusion proceeds from an initial, aggregated vesicle state ((A) membrane contact) through two sequential intermediate states (I(1) and I(2)) and then on to a fusion pore state (FP). Using this model, we interpreted data on the fusion of seven different vesicle systems. We found that the initial aggregated state involved no lipid or content mixing but did produce leakage. The final state (FP) was not leaky. Lipid mixing normally dominated the first intermediate state (I(1)), but content mixing signal was also observed in this state for most systems. The second intermediate state (I(2)) exhibited both lipid and content mixing signals and leakage, and was sometimes the only leaky state. In some systems, the first and second intermediates were indistinguishable and converted directly to the FP state. Having also tested a parallel, two-intermediate model subject to different assumptions about the nature of the intermediates, we conclude that a sequential, two-intermediate model is the simplest model sufficient to describe PEG-mediated fusion in all vesicle systems studied. We conclude as well that a fusion intermediate "state" should not be thought of as a fixed structure (e.g., "stalk" or "transmembrane contact") of uniform properties. Rather, a fusion "state" describes an ensemble of similar structures that can have different mechanical properties. Thus, a "state" can have varying probabilities of having a given functional property such as content mixing, lipid mixing, or leakage. Our data show that the content mixing signal may occur through two processes, one correlated and one not correlated with leakage. Finally, we consider the implications of our results in terms of the "modified stalk" hypothesis for the mechanism of lipid pore formation. We conclude that our results not only support this hypothesis but

One size does not fit all: Adapting mark-recapture and occupancy models for state uncertainty

USGS Publications Warehouse

Kendall, W.L.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Multistate capture?recapture models continue to be employed with greater frequency to test hypotheses about metapopulation dynamics and life history, and more recently disease dynamics. In recent years efforts have begun to adjust these models for cases where there is uncertainty about an animal?s state upon capture. These efforts can be categorized into models that permit misclassification between two states to occur in either direction or one direction, where state is certain for a subset of individuals or is always uncertain, and where estimation is based on one sampling occasion per period of interest or multiple sampling occasions per period. State uncertainty also arises in modeling patch occupancy dynamics. I consider several case studies involving bird and marine mammal studies that illustrate how misclassified states can arise, and outline model structures for properly utilizing the data that are produced. In each case misclassification occurs in only one direction (thus there is a subset of individuals or patches where state is known with certainty), and there are multiple sampling occasions per period of interest. For the cases involving capture?recapture data I allude to a general model structure that could include each example as a special case. However, this collection of cases also illustrates how difficult it is to develop a model structure that can be directly useful for answering every ecological question of interest and account for every type of data from the field.

Computer Modeling of Transportation-Generated Air Pollution : State-of-the-Art Survey, II

DOT National Transportation Integrated Search

1978-06-01

THE STATE-OF-THE-ART IS SURVEYED IN AIR POLLUTION MODELLING WITH PARTICULAR EMPHASIS ON THE MODELING OF DISPERSION FROM TRANSPORTATION SOURCES. MODELS WHICH HAVE ACTUALLY BEEN IMPLEMENTED ARE STRESSED AND THE COMPUTATIONAL ASPECTS OF THESE MODELS ARE...

Computer Modeling of Transportation-Generated Air Pollution : State-of-the-Art Survey, II

DOT National Transportation Integrated Search

1978-06-01

The state-of-the-art is surveyed in air pollution modeling with particular emphasis on the modeling of dispersion from transportation sources. Models which have actually been implemented are stressed and the computational aspects of these models are ...

Computer Modeling of Transportation-Generated Air Pollution - a State-of-the-Art Survey

DOT National Transportation Integrated Search

1972-06-01

THE STATE-OF-THE-ART IS SURVEYED IN AIR POLLUTION MODELLING WITH PARTICULAR EMPHASIS ON THE MODELING OF DISPERSION FROM TRANSPORTATION SOURCES. MODELS WHICH HAVE ACTUALLY BEEN IMPLEMENTED ARE STRESSED AND THE COMPUTATIONAL ASPECTS OF THESE MODELS ARE...

A nonlinear model for myogenic regulation of blood flow to bone: equilibrium states and stability characteristics.

PubMed

Harrigan, T P

1996-01-01

A simple compartmental model for myogenic regulation of interstitial pressure in bone is developed, and the interaction between changes in interstitial pressure and changes in arterial and venous resistance is studied. The arterial resistance is modeled by a myogenic model that depends on transmural pressure, and the venous resistance is modeled by using a vascular waterfall. Two series capacitances model blood storage in the vascular system and interstitial fluid storage in the extravascular space. The static results mimic the observed effect that vasodilators work less well in bone than do vasoconstrictors. The static results also show that the model gives constant flow rates over a limited range of arterial pressure. The dynamic model shows unstable behavior at small values of bony capacitance and at high enough myogenic gain. At low myogenic gain, only a single equilibrium state is present, but a high enough myogenic gain, two new equilibrium states appear. At additional increases in gain, one of the two new states merges with and then separates from the original state, and the original state becomes a saddle point. The appearance of the new states and the transition of the original state to a saddle point do not depend on the bony capacitance, and these results are relevant to general fluid compartments. Numerical integration of the rate equations confirms the stability calculations and shows limit cycling behavior in several situations. The relevance of this model to circulation in bone and to other compartments is discussed.

States-of-Mind Model: Cognitive Balance in the Treatment of Agoraphobia.

ERIC Educational Resources Information Center

Schwartz, Robert M.; Michelson, Larry

1987-01-01

Used states-of-mind model to track therapeutic changes in cognitive balance of 39 agoraphobics. Descriptive and statistical analyses from an outcome study of graduated exposure, relaxation training, and paradoxical intention supported the model. Agoraphobics evinced negative dialogue at pretreatment, positive dialogue at mid and posttreatment, and…

Discrete State Change Model of Manufacturing Quality to Aid Assembly Process Design

NASA Astrophysics Data System (ADS)

Koga, Tsuyoshi; Aoyama, Kazuhiro

This paper proposes a representation model of the quality state change in an assembly process that can be used in a computer-aided process design system. In order to formalize the state change of the manufacturing quality in the assembly process, the functions, operations, and quality changes in the assembly process are represented as a network model that can simulate discrete events. This paper also develops a design method for the assembly process. The design method calculates the space of quality state change and outputs a better assembly process (better operations and better sequences) that can be used to obtain the intended quality state of the final product. A computational redesigning algorithm of the assembly process that considers the manufacturing quality is developed. The proposed method can be used to design an improved manufacturing process by simulating the quality state change. A prototype system for planning an assembly process is implemented and applied to the design of an auto-breaker assembly process. The result of the design example indicates that the proposed assembly process planning method outputs a better manufacturing scenario based on the simulation of the quality state change.

Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Conyers, Howard J.; Mavris, Dimitri N.

2014-01-01

This paper introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio and number of control surfaces. A doublet lattice approach is taken to compute generalized forces. A rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. Although, all parameters can be easily modified if desired.The focus of this paper is on tool presentation, verification and validation. This process is carried out in stages throughout the paper. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool. Therefore the flutter speed and frequency for a clamped plate are computed using V-g and V-f analysis. The computational results are compared to a previously published computational analysis and wind tunnel results for the same structure. Finally a case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to V-g and V-f analysis. This also includes the analysis of the model in response to a 1-cos gust.

Bistability and State Transition of a Delay Differential Equation Model of Neutrophil Dynamics

NASA Astrophysics Data System (ADS)

Ma, Suqi; Zhu, Kaiyi; Lei, Jinzhi

This paper studies the existence of bistable states and control strategies to induce state transitions of a delay differential equation model of neutrophil dynamics. We seek the conditions that a stable steady state and an oscillatory state coexist in the neutrophil dynamical system. Physiologically, stable steady state represents the healthy state, while oscillatory state is usually associated with diseases such as cyclical neutropenia. We study the control strategies to induce the transitions from the disease state to the healthy state by introducing temporal perturbations to system parameters. This study is valuable in designing clinical protocols for the treatment of cyclical neutropenia.

An evaluation of behavior inferences from Bayesian state-space models: A case study with the Pacific walrus

USGS Publications Warehouse

Beatty, William; Jay, Chadwick V.; Fischbach, Anthony S.

2016-01-01

State-space models offer researchers an objective approach to modeling complex animal location data sets, and state-space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state-space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two-state discrete-time continuous-space Bayesian state-space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state-space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two-state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state-space models, and reconcile these parameters with the study species and its expected behaviors.

A probabilistic tornado wind hazard model for the continental United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hossain, Q; Kimball, J; Mensing, R

A probabilistic tornado wind hazard model for the continental United States (CONUS) is described. The model incorporates both aleatory (random) and epistemic uncertainties associated with quantifying the tornado wind hazard parameters. The temporal occurrences of tornadoes within the continental United States (CONUS) is assumed to be a Poisson process. A spatial distribution of tornado touchdown locations is developed empirically based on the observed historical events within the CONUS. The hazard model is an aerial probability model that takes into consideration the size and orientation of the facility, the length and width of the tornado damage area (idealized as a rectanglemore » and dependent on the tornado intensity scale), wind speed variation within the damage area, tornado intensity classification errors (i.e.,errors in assigning a Fujita intensity scale based on surveyed damage), and the tornado path direction. Epistemic uncertainties in describing the distributions of the aleatory variables are accounted for by using more than one distribution model to describe aleatory variations. The epistemic uncertainties are based on inputs from a panel of experts. A computer program, TORNADO, has been developed incorporating this model; features of this program are also presented.« less

Efficient Ensemble State-Parameters Estimation Techniques in Ocean Ecosystem Models: Application to the North Atlantic

NASA Astrophysics Data System (ADS)

El Gharamti, M.; Bethke, I.; Tjiputra, J.; Bertino, L.

2016-02-01

Given the recent strong international focus on developing new data assimilation systems for biological models, we present in this comparative study the application of newly developed state-parameters estimation tools to an ocean ecosystem model. It is quite known that the available physical models are still too simple compared to the complexity of the ocean biology. Furthermore, various biological parameters remain poorly unknown and hence wrong specifications of such parameters can lead to large model errors. Standard joint state-parameters augmentation technique using the ensemble Kalman filter (Stochastic EnKF) has been extensively tested in many geophysical applications. Some of these assimilation studies reported that jointly updating the state and the parameters might introduce significant inconsistency especially for strongly nonlinear models. This is usually the case for ecosystem models particularly during the period of the spring bloom. A better handling of the estimation problem is often carried out by separating the update of the state and the parameters using the so-called Dual EnKF. The dual filter is computationally more expensive than the Joint EnKF but is expected to perform more accurately. Using a similar separation strategy, we propose a new EnKF estimation algorithm in which we apply a one-step-ahead smoothing to the state. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. Unlike the classical filtering path, the new scheme starts with an update step and later a model propagation step is performed. We test the performance of the new smoothing-based schemes against the standard EnKF in a one-dimensional configuration of the Norwegian Earth System Model (NorESM) in the North Atlantic. We use nutrients profile (up to 2000 m deep) data and surface partial CO2 measurements from Mike weather station (66o N, 2o E) to estimate

Digital soil mapping as a tool for quantifying state-and-transition models

USDA-ARS?s Scientific Manuscript database

Ecological sites and associated state-and-transition models (STMs) are rapidly becoming important land management tools in rangeland systems in the US and around the world. Descriptions of states and transitions are largely developed from expert knowledge and generally accepted species and community...

Application of wheat yield model to United States and India. [Great Plains

NASA Technical Reports Server (NTRS)

Feyerherm, A. M. (Principal Investigator)

1977-01-01

The author has identified the following significant results. The wheat yield model was applied to the major wheat-growing areas of the US and India. In the US Great Plains, estimates from the winter and spring wheat models agreed closely with USDA-SRS values in years with the lowest yields, but underestimated in years with the highest yields. Application to the Eastern Plains and Northwest indicated the importance of cultural factors, as well as meteorological ones in the model. It also demonstrated that the model could be used, in conjunction with USDA-SRRS estimates, to estimate yield losses due to factors not included in the model, particularly diseases and freezes. A fixed crop calendar for India was built from a limited amount of available plot data from that country. Application of the yield model gave measurable evidence that yield variation from state to state was due to different mixes of levels of meteorological and cultural factors.

How Kinetics within the Unfolded State Affects Protein Folding: an Analysis Based on Markov State Models and an Ultra-Long MD Trajectory

PubMed Central

Deng, Nan-jie; Dai, Wei

2013-01-01

Understanding how kinetics in the unfolded state affects protein folding is a fundamentally important yet less well-understood issue. Here we employ three different models to analyze the unfolded landscape and folding kinetics of the miniprotein Trp-cage. The first is a 208 μs explicit solvent molecular dynamics (MD) simulation from D. E. Shaw Research containing tens of folding events. The second is a Markov state model (MSM-MD) constructed from the same ultra-long MD simulation; MSM-MD can be used to generate thousands of folding events. The third is a Markov state model built from temperature replica exchange MD simulations in implicit solvent (MSM-REMD). All the models exhibit multiple folding pathways, and there is a good correspondence between the folding pathways from direct MD and those computed from the MSMs. The unfolded populations interconvert rapidly between extended and collapsed conformations on time scales ≤ 40 ns, compared with the folding time of ≈ 5 μs. The folding rates are independent of where the folding is initiated from within the unfolded ensemble. About 90 % of the unfolded states are sampled within the first 40 μs of the ultra-long MD trajectory, which on average explores ~27 % of the unfolded state ensemble between consecutive folding events. We clustered the folding pathways according to structural similarity into “tubes”, and kinetically partitioned the unfolded state into populations that fold along different tubes. From our analysis of the simulations and a simple kinetic model, we find that when the mixing within the unfolded state is comparable to or faster than folding, the folding waiting times for all the folding tubes are similar and the folding kinetics is essentially single exponential despite the presence of heterogeneous folding paths with non-uniform barriers. When the mixing is much slower than folding, different unfolded populations fold independently leading to non-exponential kinetics. A kinetic partition of

Arbitrary Steady-State Solutions with the K-epsilon Model

NASA Technical Reports Server (NTRS)

Rumsey, Christopher L.; Pettersson Reif, B. A.; Gatski, Thomas B.

2006-01-01

Widely-used forms of the K-epsilon turbulence model are shown to yield arbitrary steady-state converged solutions that are highly dependent on numerical considerations such as initial conditions and solution procedure. These solutions contain pseudo-laminar regions of varying size. By applying a nullcline analysis to the equation set, it is possible to clearly demonstrate the reasons for the anomalous behavior. In summary, the degenerate solution acts as a stable fixed point under certain conditions, causing the numerical method to converge there. The analysis also suggests a methodology for preventing the anomalous behavior in steady-state computations.

User's instructions for the 41-node thermoregulatory model (steady state version)

NASA Technical Reports Server (NTRS)

Leonard, J. I.

1974-01-01

A user's guide for the steady-state thermoregulatory model is presented. The model was modified to provide conversational interaction on a remote terminal, greater flexibility for parameter estimation, increased efficiency of convergence, greater choice of output variable and more realistic equations for respiratory and skin diffusion water losses.

Technical Assistance Model for Long-Term Systems Change: Three State Examples

ERIC Educational Resources Information Center

Kasprzak, Christina; Hurth, Joicey; Lucas, Anne; Marshall, Jacqueline; Terrell, Adriane; Jones, Elizabeth

2010-01-01

The National Early Childhood Technical Assistance Center (NECTAC) Technical Assistance (TA) Model for Long-Term Systems Change (LTSC) is grounded in conceptual frameworks in the literature on systems change and systems thinking. The NECTAC conceptual framework uses a logic model approach to change developed specifically for states' infant and…

Two-polariton bound states in the Jaynes-Cummings-Hubbard model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Max T. C.; Law, C. K.

2011-05-15

We examine the eigenstates of the one-dimensional Jaynes-Cummings-Hubbard model in the two-excitation subspace. We discover that two-excitation bound states emerge when the ratio of vacuum Rabi frequency to the tunneling rate between cavities exceeds a critical value. We determine the critical value as a function of the quasimomentum quantum number, and indicate that the bound states carry a strong correlation in which the two polaritons appear to be spatially confined together.

Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

NASA Astrophysics Data System (ADS)

Abellán-Nebot, J. V.; Liu, J.; Romero, F.

2009-11-01

The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

Molecular Modeling and Computational Chemistry at Humboldt State University.

ERIC Educational Resources Information Center

Paselk, Richard A.; Zoellner, Robert W.

2002-01-01

Describes a molecular modeling and computational chemistry (MM&CC) facility for undergraduate instruction and research at Humboldt State University. This facility complex allows the introduction of MM&CC throughout the chemistry curriculum with tailored experiments in general, organic, and inorganic courses as well as a new molecular modeling…

Search for the standard model Higgs boson in tau final states.

PubMed

Abazov, V M; Abbott, B; Abolins, M; Acharya, B S; Adams, M; Adams, T; Aguilo, E; Ahsan, M; Alexeev, G D; Alkhazov, G; Alton, A; Alverson, G; Alves, G A; Ancu, L S; Andeen, T; Anzelc, M S; Aoki, M; Arnoud, Y; Arov, M; Arthaud, M; Askew, A; Asman, B; Atramentov, O; Avila, C; Backusmayes, J; Badaud, F; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, S; Barberis, E; Barfuss, A-F; Bargassa, P; Baringer, P; Barreto, J; Bartlett, J F; Bassler, U; Bauer, D; Beale, S; Bean, A; Begalli, M; Begel, M; Belanger-Champagne, C; Bellantoni, L; Bellavance, A; Benitez, J A; Beri, S B; Bernardi, G; Bernhard, R; Bertram, I; Besançon, M; Beuselinck, R; Bezzubov, V A; Bhat, P C; Bhatnagar, V; Blazey, G; Blessing, S; Bloom, K; Boehnlein, A; Boline, D; Bolton, T A; Boos, E E; Borissov, G; Bose, T; Brandt, A; Brock, R; Brooijmans, G; Bross, A; Brown, D; Bu, X B; Buchholz, D; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Burnett, T H; Buszello, C P; Calfayan, P; Calpas, B; Calvet, S; Cammin, J; Carrasco-Lizarraga, M A; Carrera, E; Carvalho, W; Casey, B C K; Castilla-Valdez, H; Chakrabarti, S; Chakraborty, D; Chan, K M; Chandra, A; Cheu, E; Cho, D K; Choi, S; Choudhary, B; Christoudias, T; Cihangir, S; Claes, D; Clutter, J; Cooke, M; Cooper, W E; Corcoran, M; Couderc, F; Cousinou, M-C; Crépé-Renaudin, S; Cuplov, V; Cutts, D; Cwiok, M; Das, A; Davies, G; De, K; de Jong, S J; De La Cruz-Burelo, E; DeVaughan, K; Déliot, F; Demarteau, M; Demina, R; Denisov, D; Denisov, S P; Desai, S; Diehl, H T; Diesburg, M; Dominguez, A; Dorland, T; Dubey, A; Dudko, L V; Duflot, L; Duggan, D; Duperrin, A; Dutt, S; Dyshkant, A; Eads, M; Edmunds, D; Ellison, J; Elvira, V D; Enari, Y; Eno, S; Ermolov, P; Escalier, M; Evans, H; Evdokimov, A; Evdokimov, V N; Facini, G; Ferapontov, A V; Ferbel, T; Fiedler, F; Filthaut, F; Fisher, W; Fisk, H E; Fortner, M; Fox, H; Fu, S; Fuess, S; Gadfort, T; Galea, C F; Garcia-Bellido, A; Gavrilov, V; Gay, P; Geist, W; Geng, W; Gerber, C E; Gershtein, Y; Gillberg, D; Ginther, G; Gómez, B; Goussiou, A; Grannis, P D; Greder, S; Greenlee, H; Greenwood, Z D; Gregores, E M; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grünendahl, S; Grünewald, M W; Guo, F; Guo, J; Gutierrez, G; Gutierrez, P; Haas, A; Hadley, N J; Haefner, P; Hagopian, S; Haley, J; Hall, I; Hall, R E; Han, L; Harder, K; Harel, A; Hauptman, J M; Hays, J; Hebbeker, T; Hedin, D; Hegeman, J G; Heinson, A P; Heintz, U; Hensel, C; Heredia-De La Cruz, I; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hoang, T; Hobbs, J D; Hoeneisen, B; Hohlfeld, M; Hossain, S; Houben, P; Hu, Y; Hubacek, Z; Huske, N; Hynek, V; Iashvili, I; Illingworth, R; Ito, A S; Jabeen, S; Jaffré, M; Jain, S; Jakobs, K; Jamin, D; Jarvis, C; Jesik, R; Johns, K; Johnson, C; Johnson, M; Johnston, D; Jonckheere, A; Jonsson, P; Juste, A; Kajfasz, E; Karmanov, D; Kasper, P A; Katsanos, I; Kaushik, V; Kehoe, R; Kermiche, S; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y N; Khatidze, D; Kim, T J; Kirby, M H; Kirsch, M; Klima, B; Kohli, J M; Konrath, J-P; Kozelov, A V; Kraus, J; Kuhl, T; Kumar, A; Kupco, A; Kurca, T; Kuzmin, V A; Kvita, J; Lacroix, F; Lam, D; Lammers, S; Landsberg, G; Lebrun, P; Lee, W M; Leflat, A; Lellouch, J; Li, J; Li, L; Li, Q Z; Lietti, S M; Lim, J K; Lincoln, D; Linnemann, J; Lipaev, V V; Lipton, R; Liu, Y; Liu, Z; Lobodenko, A; Lokajicek, M; Love, P; Lubatti, H J; Luna-Garcia, R; Lyon, A L; Maciel, A K A; Mackin, D; Mättig, P; Magerkurth, A; Mal, P K; Malbouisson, H B; Malik, S; Malyshev, V L; Maravin, Y; Martin, B; McCarthy, R; McGivern, C L; Meijer, M M; Melnitchouk, A; Mendoza, L; Menezes, D; Mercadante, P G; Merkin, M; Merritt, K W; Meyer, A; Meyer, J; Mitrevski, J; Mommsen, R K; Mondal, N K; Moore, R W; Moulik, T; Muanza, G S; Mulhearn, M; Mundal, O; Mundim, L; Nagy, E; Naimuddin, M; Narain, M; Neal, H A; Negret, J P; Neustroev, P; Nilsen, H; Nogima, H; Novaes, S F; Nunnemann, T; Obrant, G; Ochando, C; Onoprienko, D; Orduna, J; Oshima, N; Osman, N; Osta, J; Otec, R; Otero Y Garzón, G J; Owen, M; Padilla, M; Padley, P; Pangilinan, M; Parashar, N; Park, S-J; Park, S K; Parsons, J; Partridge, R; Parua, N; Patwa, A; Pawloski, G; Penning, B; Perfilov, M; Peters, K; Peters, Y; Pétroff, P; Piegaia, R; Piper, J; Pleier, M-A; Podesta-Lerma, P L M; Podstavkov, V M; Pogorelov, Y; Pol, M-E; Polozov, P; Popov, A V; Potter, C; Prado da Silva, W L; Protopopescu, S; Qian, J; Quadt, A; Quinn, B; Rakitine, A; Rangel, M S; Ranjan, K; Ratoff, P N; Renkel, P; Rich, P; Rijssenbeek, M; Ripp-Baudot, I; Rizatdinova, F; Robinson, S; Rodrigues, R F; Rominsky, M; Royon, C; Rubinov, P; Ruchti, R; Safronov, G; Sajot, G; Sánchez-Hernández, A; Sanders, M P; Sanghi, B; Savage, G; Sawyer, L; Scanlon, T; Schaile, D; Schamberger, R D; Scheglov, Y; Schellman, H; Schliephake, T; Schlobohm, S; Schwanenberger, C; Schwienhorst, R; Sekaric, J; Severini, H; Shabalina, E; Shamim, M; Shary, V; Shchukin, A A; Shivpuri, R K; Siccardi, V; Simak, V; Sirotenko, V; Skubic, P; Slattery, P; Smirnov, D; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Sonnenschein, L; Sopczak, A; Sosebee, M; Soustruznik, K; Spurlock, B; Stark, J; Stolin, V; Stoyanova, D A; Strandberg, J; Strandberg, S; Strang, M A; Strauss, E; Strauss, M; Ströhmer, R; Strom, D; Stutte, L; Sumowidagdo, S; Svoisky, P; Takahashi, M; Tanasijczuk, A; Taylor, W; Tiller, B; Tissandier, F; Titov, M; Tokmenin, V V; Torchiani, I; Tsybychev, D; Tuchming, B; Tully, C; Tuts, P M; Unalan, R; Uvarov, L; Uvarov, S; Uzunyan, S; Vachon, B; van den Berg, P J; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vasilyev, I A; Verdier, P; Vertogradov, L S; Verzocchi, M; Vilanova, D; Vint, P; Vokac, P; Voutilainen, M; Wagner, R; Wahl, H D; Wang, M H L S; Warchol, J; Watts, G; Wayne, M; Weber, G; Weber, M; Welty-Rieger, L; Wenger, A; Wetstein, M; White, A; Wicke, D; Williams, M R J; Wilson, G W; Wimpenny, S J; Wobisch, M; Wood, D R; Wyatt, T R; Xie, Y; Xu, C; Yacoob, S; Yamada, R; Yang, W-C; Yasuda, T; Yatsunenko, Y A; Ye, Z; Yin, H; Yip, K; Yoo, H D; Youn, S W; Yu, J; Zeitnitz, C; Zelitch, S; Zhao, T; Zhou, B; Zhu, J; Zielinski, M; Zieminska, D; Zivkovic, L; Zutshi, V; Zverev, E G

2009-06-26

We present a search for the standard model Higgs boson using hadronically decaying tau leptons, in 1 fb(-1) of data collected with the D0 detector at the Fermilab Tevatron pp collider. We select two final states: tau+/- plus missing transverse energy and b jets, and tau+ tau- plus jets. These final states are sensitive to a combination of associated W/Z boson plus Higgs boson, vector boson fusion, and gluon-gluon fusion production processes. The observed ratio of the combined limit on the Higgs production cross section at the 95% C.L. to the standard model expectation is 29 for a Higgs boson mass of 115 GeV.

Get Over It! A Multilevel Threshold Autoregressive Model for State-Dependent Affect Regulation.

PubMed

De Haan-Rietdijk, Silvia; Gottman, John M; Bergeman, Cindy S; Hamaker, Ellen L

2016-03-01

Intensive longitudinal data provide rich information, which is best captured when specialized models are used in the analysis. One of these models is the multilevel autoregressive model, which psychologists have applied successfully to study affect regulation as well as alcohol use. A limitation of this model is that the autoregressive parameter is treated as a fixed, trait-like property of a person. We argue that the autoregressive parameter may be state-dependent, for example, if the strength of affect regulation depends on the intensity of affect experienced. To allow such intra-individual variation, we propose a multilevel threshold autoregressive model. Using simulations, we show that this model can be used to detect state-dependent regulation with adequate power and Type I error. The potential of the new modeling approach is illustrated with two empirical applications that extend the basic model to address additional substantive research questions.

Principal dynamic mode analysis of neural mass model for the identification of epileptic states

NASA Astrophysics Data System (ADS)

Cao, Yuzhen; Jin, Liu; Su, Fei; Wang, Jiang; Deng, Bin

2016-11-01

The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.

A Bayesian state-space formulation of dynamic occupancy models

USGS Publications Warehouse

Royle, J. Andrew; Kery, M.

2007-01-01

Species occurrence and its dynamic components, extinction and colonization probabilities, are focal quantities in biogeography and metapopulation biology, and for species conservation assessments. It has been increasingly appreciated that these parameters must be estimated separately from detection probability to avoid the biases induced by nondetection error. Hence, there is now considerable theoretical and practical interest in dynamic occupancy models that contain explicit representations of metapopulation dynamics such as extinction, colonization, and turnover as well as growth rates. We describe a hierarchical parameterization of these models that is analogous to the state-space formulation of models in time series, where the model is represented by two components, one for the partially observable occupancy process and another for the observations conditional on that process. This parameterization naturally allows estimation of all parameters of the conventional approach to occupancy models, but in addition, yields great flexibility and extensibility, e.g., to modeling heterogeneity or latent structure in model parameters. We also highlight the important distinction between population and finite sample inference; the latter yields much more precise estimates for the particular sample at hand. Finite sample estimates can easily be obtained using the state-space representation of the model but are difficult to obtain under the conventional approach of likelihood-based estimation. We use R and Win BUGS to apply the model to two examples. In a standard analysis for the European Crossbill in a large Swiss monitoring program, we fit a model with year-specific parameters. Estimates of the dynamic parameters varied greatly among years, highlighting the irruptive population dynamics of that species. In the second example, we analyze route occupancy of Cerulean Warblers in the North American Breeding Bird Survey (BBS) using a model allowing for site

Modeling the Effects of Nanopatterned Surfaces on Wetting States of Droplets

NASA Astrophysics Data System (ADS)

Xiao, Ke; Zhao, Yanping; Ouyang, Gang; Li, Xinlei

2017-04-01

An analytic thermodynamic model has been established to quantitatively investigate the wetting states of droplets on nanopatterned surfaces. Based on the calculations for the free energies of droplets with the Wenzel state and the Cassie-Baxter state, it is found that the size and shape of nanostructured surfaces play crucial roles in wetting states. In detail, for nanohole-patterned surfaces, the deep and thin nanoholes lead to the Cassie-Baxter state, and contrarily, the shallow and thick nanoholes result in the Wenzel state. However, the droplets have the Wenzel state on the patterned surfaces with small height and radii nanopillars and have the Cassie-Baxter state when the height and radii of nanopillars are large. Furthermore, the intuitive phase diagrams of the wetting states of the droplet in the space of surface geometrical parameters are obtained. The theoretical results are in good agreement with the experimental observations and reveal physical mechanisms involved in the effects of nanopatterned surfaces on wetting states, which implies that these studies may provide useful guidance to the conscious design of patterned surfaces to control the wetting states of droplets.

Developing a PLC-friendly state machine model: lessons learned

NASA Astrophysics Data System (ADS)

Pessemier, Wim; Deconinck, Geert; Raskin, Gert; Saey, Philippe; Van Winckel, Hans

2014-07-01

Modern Programmable Logic Controllers (PLCs) have become an attractive platform for controlling real-time aspects of astronomical telescopes and instruments due to their increased versatility, performance and standardization. Likewise, vendor-neutral middleware technologies such as OPC Unified Architecture (OPC UA) have recently demonstrated that they can greatly facilitate the integration of these industrial platforms into the overall control system. Many practical questions arise, however, when building multi-tiered control systems that consist of PLCs for low level control, and conventional software and platforms for higher level control. How should the PLC software be structured, so that it can rely on well-known programming paradigms on the one hand, and be mapped to a well-organized OPC UA interface on the other hand? Which programming languages of the IEC 61131-3 standard closely match the problem domains of the abstraction levels within this structure? How can the recent additions to the standard (such as the support for namespaces and object-oriented extensions) facilitate a model based development approach? To what degree can our applications already take advantage of the more advanced parts of the OPC UA standard, such as the high expressiveness of the semantic modeling language that it defines, or the support for events, aggregation of data, automatic discovery, ... ? What are the timing and concurrency problems to be expected for the higher level tiers of the control system due to the cyclic execution of control and communication tasks by the PLCs? We try to answer these questions by demonstrating a semantic state machine model that can readily be implemented using IEC 61131 and OPC UA. One that does not aim to capture all possible states of a system, but rather one that attempts to organize the course-grained structure and behaviour of a system. In this paper we focus on the intricacies of this seemingly simple task, and on the lessons that we

Ordered states in the Kitaev-Heisenberg model: From 1D chains to 2D honeycomb.

PubMed

Agrapidis, Cliò Efthimia; van den Brink, Jeroen; Nishimoto, Satoshi

2018-01-29

We study the ground state of the 1D Kitaev-Heisenberg (KH) model using the density-matrix renormalization group and Lanczos exact diagonalization methods. We obtain a rich ground-state phase diagram as a function of the ratio between Heisenberg (J = cosϕ) and Kitaev (K = sinϕ) interactions. Depending on the ratio, the system exhibits four long-range ordered states: ferromagnetic-z, ferromagnetic-xy, staggered-xy, Néel-z, and two liquid states: Tomonaga-Luttinger liquid and spiral-xy. The two Kitaev points [Formula: see text] and [Formula: see text] are singular. The ϕ-dependent phase diagram is similar to that for the 2D honeycomb-lattice KH model. Remarkably, all the ordered states of the honeycomb-lattice KH model can be interpreted in terms of the coupled KH chains. We also discuss the magnetic structure of the K-intercalated RuCl 3 , a potential Kitaev material, in the framework of the 1D KH model. Furthermore, we demonstrate that the low-lying excitations of the 1D KH Hamiltonian can be explained within the combination of the known six-vertex model and spin-wave theory.

DUAL STATE-PARAMETER UPDATING SCHEME ON A CONCEPTUAL HYDROLOGIC MODEL USING SEQUENTIAL MONTE CARLO FILTERS

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin

Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.

Language Model Combination and Adaptation Using Weighted Finite State Transducers

NASA Technical Reports Server (NTRS)

Liu, X.; Gales, M. J. F.; Hieronymus, J. L.; Woodland, P. C.

2010-01-01

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaption may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences

Model for Steady-State Combustion of Unimodal Composite Solid Propellants.

DTIC Science & Technology

1978-01-01

Research and Technology Div.do= * 5390 Cherokee Avenue Alexandria, Virginia 22314 Cw* Contract F49620-78-C-0016 Air Force Office of Scientific Research ...owmaretgli w SW MODEL FOR STEADY-STATE COMBUSTION OF UNIMODAL COMPOSITE SOLID PROPELLANTS* Dr. Merrill K. Kingk* Atlantic Research Corporation...this country today) for pre- model, all flames are considered to occur in flame sheets at discrete distances from the * Research sponsored by the Air

A 3-states magnetic model of binary decisions in sociophysics

NASA Astrophysics Data System (ADS)

Fernandez, Miguel A.; Korutcheva, Elka; de la Rubia, F. Javier

2016-11-01

We study a diluted Blume-Capel model of 3-states sites as an attempt to understand how some social processes as cooperation or organization happen. For this aim, we study the effect of the complex network topology on the equilibrium properties of the model, by focusing on three different substrates: random graph, Watts-Strogatz and Newman substrates. Our computer simulations are in good agreement with the corresponding analytical results.

Constrained model predictive control, state estimation and coordination

NASA Astrophysics Data System (ADS)

Yan, Jun

In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to

Linear modeling of steady-state behavioral dynamics.

PubMed Central

Palya, William L; Walter, Donald; Kessel, Robert; Lucke, Robert

2002-01-01

The observed steady-state behavioral dynamics supported by unsignaled periods of reinforcement within repeating 2,000-s trials were modeled with a linear transfer function. These experiments employed improved schedule forms and analytical methods to improve the precision of the measured transfer function, compared to previous work. The refinements include both the use of multiple reinforcement periods that improve spectral coverage and averaging of independently determined transfer functions. A linear analysis was then used to predict behavior observed for three different test schedules. The fidelity of these predictions was determined. PMID:11831782

Individual-tree probability of survival model for the Northeastern United States

Treesearch

Richard M. Teck; Donald E. Hilt

1990-01-01

Describes a distance-independent individual-free probability of survival model for the Northeastern United States. Survival is predicted using a sixparameter logistic function with species-specific coefficients. Coefficients are presented for 28 species groups. The model accounts for variability in annual survival due to species, tree size, site quality, and the tree...

Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Conyers, Howard J.; Mavris, Dimitri N.

2015-01-01

This paper introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing-edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio, and number of control surfaces. Using this information, the generalized forces are computed using the doublet-lattice method. Using Roger's approximation, a rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. All parameters can be easily modified if desired. The focus of this paper is on tool presentation, verification, and validation. These processes are carried out in stages throughout the paper. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool, therefore, the flutter speed and frequency for a clamped plate are computed using damping-versus-velocity and frequency-versus-velocity analysis. The computational results are compared to a previously published computational analysis and wind-tunnel results for the same structure. A case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to damping-versus-velocity and frequency-versus-velocity analysis, including the analysis of the model in response to a 1-cos gust.

Rapid State Space Modeling Tool for Rectangular Wing Aeroservoelastic Studies

NASA Technical Reports Server (NTRS)

Suh, Peter M.; Conyers, Howard Jason; Mavris, Dimitri N.

2015-01-01

This report introduces a modeling and simulation tool for aeroservoelastic analysis of rectangular wings with trailing-edge control surfaces. The inputs to the code are planform design parameters such as wing span, aspect ratio, and number of control surfaces. Using this information, the generalized forces are computed using the doublet-lattice method. Using Roger's approximation, a rational function approximation is computed. The output, computed in a few seconds, is a state space aeroservoelastic model which can be used for analysis and control design. The tool is fully parameterized with default information so there is little required interaction with the model developer. All parameters can be easily modified if desired. The focus of this report is on tool presentation, verification, and validation. These processes are carried out in stages throughout the report. The rational function approximation is verified against computed generalized forces for a plate model. A model composed of finite element plates is compared to a modal analysis from commercial software and an independently conducted experimental ground vibration test analysis. Aeroservoelastic analysis is the ultimate goal of this tool, therefore, the flutter speed and frequency for a clamped plate are computed using damping-versus-velocity and frequency-versus-velocity analysis. The computational results are compared to a previously published computational analysis and wind-tunnel results for the same structure. A case study of a generic wing model with a single control surface is presented. Verification of the state space model is presented in comparison to damping-versus-velocity and frequency-versus-velocity analysis, including the analysis of the model in response to a 1-cos gust.

Multi-scale chromatin state annotation using a hierarchical hidden Markov model

NASA Astrophysics Data System (ADS)

Marco, Eugenio; Meuleman, Wouter; Huang, Jialiang; Glass, Kimberly; Pinello, Luca; Wang, Jianrong; Kellis, Manolis; Yuan, Guo-Cheng

2017-04-01

Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.

Current Pressure Transducer Application of Model-based Prognostics Using Steady State Conditions

NASA Technical Reports Server (NTRS)

Teubert, Christopher; Daigle, Matthew J.

2014-01-01

Prognostics is the process of predicting a system's future states, health degradation/wear, and remaining useful life (RUL). This information plays an important role in preventing failure, reducing downtime, scheduling maintenance, and improving system utility. Prognostics relies heavily on wear estimation. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, describes how this is used to predict future states, and presents a case study of a current/pressure (I/P) Transducer. I/P Transducer nominal and off-nominal behaviors are characterized using a physics-based model, and validated against expected and observed component behavior. This model is used to map observed steady-state responses to corresponding fault parameter values in the form of a lookup table. This method was chosen because of its fast, efficient nature, and its ability to be applied to both linear and non-linear systems. Using measurements of the steady state output, and the lookup table, wear is estimated. A regression is used to estimate the wear propagation parameter and characterize the damage progression function, which are used to predict future states and the remaining useful life of the system.

Anatomy of an Error: A Bidirectional State Model of Task Engagement/Disengagement and Attention-Related Errors

ERIC Educational Resources Information Center

Cheyne, J. Allan; Solman, Grayden J. F.; Carriere, Jonathan S. A.; Smilek, Daniel

2009-01-01

We present arguments and evidence for a three-state attentional model of task engagement/disengagement. The model postulates three states of mind-wandering: occurrent task inattention, generic task inattention, and response disengagement. We hypothesize that all three states are both causes and consequences of task performance outcomes and apply…

Anatomy of an error: a bidirectional state model of task engagement/disengagement and attention-related errors.

PubMed

Allan Cheyne, J; Solman, Grayden J F; Carriere, Jonathan S A; Smilek, Daniel

2009-04-01

We present arguments and evidence for a three-state attentional model of task engagement/disengagement. The model postulates three states of mind-wandering: occurrent task inattention, generic task inattention, and response disengagement. We hypothesize that all three states are both causes and consequences of task performance outcomes and apply across a variety of experimental and real-world tasks. We apply this model to the analysis of a widely used GO/NOGO task, the Sustained Attention to Response Task (SART). We identify three performance characteristics of the SART that map onto the three states of the model: RT variability, anticipations, and omissions. Predictions based on the model are tested, and largely corroborated, via regression and lag-sequential analyses of both successful and unsuccessful withholding on NOGO trials as well as self-reported mind-wandering and everyday cognitive errors. The results revealed theoretically consistent temporal associations among the state indicators and between these and SART errors as well as with self-report measures. Lag analysis was consistent with the hypotheses that temporal transitions among states are often extremely abrupt and that the association between mind-wandering and performance is bidirectional. The bidirectional effects suggest that errors constitute important occasions for reactive mind-wandering. The model also enables concrete phenomenological, behavioral, and physiological predictions for future research.

A non-coaxial critical state soil model and its application to simple shear simulations

NASA Astrophysics Data System (ADS)

Yang, Yunming; Yu, H. S.

2006-11-01

The yield vertex non-coaxial theory is implemented into a critical state soil model, CASM (Int. J. Numer. Anal. Meth. Geomech. 1998; 22:621-653) to investigate the non-coaxial influences on the stress-strain simulations of real soil behaviour in the presence of principal stress rotations. The CASM is a unified clay and sand model, developed based on the soil critical state concept and the state parameter concept. Without loss of simplicity, it is capable of simulating the behaviour of sands and clays within a wide range of densities. The non-coaxial CASM is employed to simulate the simple shear responses of Erksak sand and Weald clay under different densities and initial stress states. Dependence of the soil behaviour on the Lode angle and different plastic flow rules in the deviatoric plane are also considered in the study of non-coaxial influences. All the predictions indicate that the use of the non-coaxial model makes the orientations of the principal stress and the principal strain rate different during the early stage of shearing, and they approach the same ultimate values with an increase in loading. These ultimate orientations are dependent on the density of soils, and independent of their initial stress states. The use of the non-coaxial model also softens the shear stress evolutions, compared with the coaxial model. It is also found that the ultimate shear strengths by using the coaxial and non-coaxial models are dependent on the plastic flow rules in the deviatoric plane. Copyright

Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India.

PubMed

Kumar, Rajesh; Dash, Chinmaya; Rani, Khushbu

2017-09-01

Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover ( p  < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024-1.042)], maximum humidity [IRR-1.016 (1.013-1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231-1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974-0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.

Do state-and-transition models derived from vegetation succession also represent avian succession in restored mine pits?

PubMed

Craig, Michael D; Stokes, Vicki L; Fontaine, Joseph B; Hardy, Giles E StJ; Grigg, Andrew H; Hobbs, Richard J

2015-10-01

State-and-transition models are increasingly used as a tool to inform management of post-disturbance succession and effective conservation of biodiversity in production landscapes. However, if they are to do this effectively, they need to represent faunal, as well as vegetation, succession. We assessed the congruence between vegetation and avian succession by sampling avian communities in each state of a state-and-transition model used to inform management of post-mining restoration in a production landscape in southwestern Australia. While avian communities differed significantly among states classified as on a desirable successional pathway, they did not differ between desirable and deviated states of the same post-mining age. Overall, we concluded there was poor congruence between vegetation and avian succession in this state-and-transition model. We identified four factors that likely contributed to this lack of congruence, which were that long-term monitoring of succession in restored mine pits was not used to update and improve models, states were not defined based on ecological processes and thresholds, states were not defined by criteria that were important in structuring the avian community, and states were not based on criteria that related to values in the reference community. We believe that consideration of these four factors in the development of state-and-transition models should improve their ability to accurately represent faunal, as well as vegetation, succession. Developing state-and-transition models that better incorporate patterns of faunal succession should improve the ability to manage post-disturbance succession across a range of ecosystems for biodiversity conservation.

Modeling SF-6D Hong Kong standard gamble health state preference data using a nonparametric Bayesian method.

PubMed

Kharroubi, Samer A; Brazier, John E; McGhee, Sarah

2013-01-01

This article reports on the findings from applying a recently described approach to modeling health state valuation data and the impact of the respondent characteristics on health state valuations. The approach applies a nonparametric model to estimate a Bayesian six-dimensional health state short form (derived from short-form 36 health survey) health state valuation algorithm. A sample of 197 states defined by the six-dimensional health state short form (derived from short-form 36 health survey)has been valued by a representative sample of the Hong Kong general population by using standard gamble. The article reports the application of the nonparametric model and compares it to the original model estimated by using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics than observed in the survey sample and that it allows for the impact of respondent characteristics to vary by health state (while ensuring that full health passes through unity). The results suggest an important age effect with sex, having some effect, but the remaining covariates having no discernible effect. The nonparametric Bayesian model is argued to be more theoretically appropriate than previously used parametric models. Furthermore, it is more flexible to take into account the impact of covariates. Copyright © 2013, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.

State Higher Education Funding Models: An Assessment of Current and Emerging Approaches

ERIC Educational Resources Information Center

Layzell, Daniel T.

2007-01-01

This article provides an assessment of the current and emerging approaches used by state governments in allocating funding for higher education institutions and programs. It reviews a number of desired characteristics or outcomes for state higher education funding models, including equity, adequacy, stability, and flexibility. Although there is…

Ab initio state-specific N2 + O dissociation and exchange modeling for molecular simulations

NASA Astrophysics Data System (ADS)

Luo, Han; Kulakhmetov, Marat; Alexeenko, Alina

2017-02-01

Quasi-classical trajectory (QCT) calculations are used in this work to calculate state-specific N2(X1Σ ) +O(3P ) →2 N(4S ) +O(3P ) dissociation and N2(X1Σ ) +O(3P ) →NO(X2Π ) +N(4S ) exchange cross sections and rates based on the 13A″ and 13A' ab initio potential energy surface by Gamallo et al. [J. Chem. Phys. 119, 2545-2556 (2003)]. The calculations consider translational energies up to 23 eV and temperatures between 1000 K and 20 000 K. Vibrational favoring is observed for dissociation reaction at the whole range of collision energies and for exchange reaction around the dissociation limit. For the same collision energy, cross sections for v = 30 are 4 to 6 times larger than those for the ground state. The exchange reaction has an effective activation energy that is dependent on the initial rovibrational level, which is different from dissociation reaction. In addition, the exchange cross sections have a maximum when the total collision energy (TCE) approaches dissociation energy. The calculations are used to generate compact QCT-derived state-specific dissociation (QCT-SSD) and QCT-derived state-specific exchange (QCT-SSE) models, which describe over 1 × 106 cross sections with about 150 model parameters. The models can be used directly within direct simulation Monte Carlo and computational fluid dynamics simulations. Rate constants predicted by the new models are compared to the experimental measurements, direct QCT calculations and predictions by other models that include: TCE model, Bose-Candler QCT-based exchange model, Macheret-Fridman dissociation model, Macheret's exchange model, and Park's two-temperature model. The new models match QCT-calculated and experimental rates within 30% under nonequilibrium conditions while other models under predict by over an order of magnitude under vibrationally-cold conditions.

Steady-State Modeling of Modular Multilevel Converter Under Unbalanced Grid Conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shi, Xiaojie M.; Wang, Zhiqiang; Liu, Bo

This paper presents a steady-state model of MMC for the second-order phase voltage ripple prediction under unbalanced conditions, taking the impact of negative-sequence current control into account. From the steady-state model, a circular relationship is found among current and voltage quantities, which can be used to evaluate the magnitudes and initial phase angles of different circulating current components. Moreover, in order to calculate the circulating current in a point-to-point MMC-based HVdc system under unbalanced grid conditions, the derivation of equivalent dc impedance of an MMC is discussed as well. According to the dc impedance model, an MMC inverter can bemore » represented as a series connected R-L-C branch, with its equivalent resistance and capacitance directly related to the circulating current control parameters. Experimental results from a scaled-down three-phase MMC system under an emulated single-line-to-ground fault are provided to support the theoretical analysis and derived model. In conclusion, this new models provides an insight into the impact of different control schemes on the fault characteristics and improves the understanding of the operation of MMC under unbalanced conditions.« less

Steady-State Modeling of Modular Multilevel Converter Under Unbalanced Grid Conditions

DOE PAGES

Shi, Xiaojie M.; Wang, Zhiqiang; Liu, Bo; ...

2016-11-16

This paper presents a steady-state model of MMC for the second-order phase voltage ripple prediction under unbalanced conditions, taking the impact of negative-sequence current control into account. From the steady-state model, a circular relationship is found among current and voltage quantities, which can be used to evaluate the magnitudes and initial phase angles of different circulating current components. Moreover, in order to calculate the circulating current in a point-to-point MMC-based HVdc system under unbalanced grid conditions, the derivation of equivalent dc impedance of an MMC is discussed as well. According to the dc impedance model, an MMC inverter can bemore » represented as a series connected R-L-C branch, with its equivalent resistance and capacitance directly related to the circulating current control parameters. Experimental results from a scaled-down three-phase MMC system under an emulated single-line-to-ground fault are provided to support the theoretical analysis and derived model. In conclusion, this new models provides an insight into the impact of different control schemes on the fault characteristics and improves the understanding of the operation of MMC under unbalanced conditions.« less

A new state space model for the NASA/JPL 70-meter antenna servo controls

NASA Technical Reports Server (NTRS)

Hill, R. E.

1987-01-01

A control axis referenced model of the NASA/JPL 70-m antenna structure is combined with the dynamic equations of servo components to produce a comprehansive state variable (matrix) model of the coupled system. An interactive Fortran program for generating the linear system model and computing its salient parameters is described. Results are produced in a state variable, block diagram, and in factored transfer function forms to facilitate design and analysis by classical as well as modern control methods.

Floquet resonant states and validity of the Floquet-Magnus expansion in the periodically driven Friedrichs models

NASA Astrophysics Data System (ADS)

Mori, Takashi

2015-02-01

The Floquet eigenvalue problem is analyzed for periodically driven Friedrichs models on discrete and continuous space. In the high-frequency regime, there exists a Floquet bound state consistent with the Floquet-Magnus expansion in the discrete Friedrichs model, while it is not the case in the continuous model. In the latter case, however, the bound state predicted by the Floquet-Magnus expansion appears as a metastable state whose lifetime diverges in the limit of large frequencies. We obtain the lifetime by evaluating the imaginary part of the quasienergy of the Floquet resonant state. In the low-frequency regime, there is no Floquet bound state and instead the Floquet resonant state with exponentially small imaginary part of the quasienergy appears, which is understood as the quantum tunneling in the energy space.

Nonadiabatic excited-state molecular dynamics: modeling photophysics in organic conjugated materials.

PubMed

Nelson, Tammie; Fernandez-Alberti, Sebastian; Roitberg, Adrian E; Tretiak, Sergei

2014-04-15

To design functional photoactive materials for a variety of technological applications, researchers need to understand their electronic properties in detail and have ways to control their photoinduced pathways. When excited by photons of light, organic conjugated materials (OCMs) show dynamics that are often characterized by large nonadiabatic (NA) couplings between multiple excited states through a breakdown of the Born-Oppenheimer (BO) approximation. Following photoexcitation, various nonradiative intraband relaxation pathways can lead to a number of complex processes. Therefore, computational simulation of nonadiabatic molecular dynamics is an indispensable tool for understanding complex photoinduced processes such as internal conversion, energy transfer, charge separation, and spatial localization of excitons. Over the years, we have developed a nonadiabatic excited-state molecular dynamics (NA-ESMD) framework that efficiently and accurately describes photoinduced phenomena in extended conjugated molecular systems. We use the fewest-switches surface hopping (FSSH) algorithm to treat quantum transitions among multiple adiabatic excited state potential energy surfaces (PESs). Extended molecular systems often contain hundreds of atoms and involve large densities of excited states that participate in the photoinduced dynamics. We can achieve an accurate description of the multiple excited states using the configuration interaction single (CIS) formalism with a semiempirical model Hamiltonian. Analytical techniques allow the trajectory to be propagated "on the fly" using the complete set of NA coupling terms and remove computational bottlenecks in the evaluation of excited-state gradients and NA couplings. Furthermore, the use of state-specific gradients for propagation of nuclei on the native excited-state PES eliminates the need for simplifications such as the classical path approximation (CPA), which only uses ground-state gradients. Thus, the NA-ESMD methodology

Finite element dynamic analysis of soft tissues using state-space model.

PubMed

Iorga, Lucian N; Shan, Baoxiang; Pelegri, Assimina A

2009-04-01

A finite element (FE) model is employed to investigate the dynamic response of soft tissues under external excitations, particularly corresponding to the case of harmonic motion imaging. A solid 3D mixed 'u-p' element S8P0 is implemented to capture the near-incompressibility inherent in soft tissues. Two important aspects in structural modelling of these tissues are studied; these are the influence of viscous damping on the dynamic response and, following FE-modelling, a developed state-space formulation that valuates the efficiency of several order reduction methods. It is illustrated that the order of the mathematical model can be significantly reduced, while preserving the accuracy of the observed system dynamics. Thus, the reduced-order state-space representation of soft tissues for general dynamic analysis significantly reduces the computational cost and provides a unitary framework for the 'forward' simulation and 'inverse' estimation of soft tissues. Moreover, the results suggest that damping in soft-tissue is significant, effectively cancelling the contribution of all but the first few vibration modes.

A magnetic model for low/hard state of black hole binaries

NASA Astrophysics Data System (ADS)

Wang, Ding-Xiong

2015-08-01

A magnetic model for low/hard state (LHS) of black hole X-ray binaries (BHXBs), H1743-322 and GX 339-4, is proposed based on the transportation of magnetic field from a companion into an accretion disc around a black hole (BH). This model consists of a truncated thin disc with an inner advection-dominated accretion flow (ADAF). The spectral profiles of the sources are fitted in agreement with the data observed at four different dates corresponding to the rising stage of the LHS. In addition, the association of the LHS with quasi-steady jet is modelled based on the transportation of magnetic field, where the Blandford-Znajek (BZ) and Blandford-Payne (BP) processes are invoked to drive the jets from BH and inner ADAF. It turns out that the steep radio-X-ray correlations observed in H1743-322 and GX 339-4 can be interpreted based on our model. It is suggested that large-scale magnetic field can be regarded as the second parameter for governing the state transitions in some BHXBs.

Reconstructing the hidden states in time course data of stochastic models.

PubMed

Zimmer, Christoph

2015-11-01

Parameter estimation is central for analyzing models in Systems Biology. The relevance of stochastic modeling in the field is increasing. Therefore, the need for tailored parameter estimation techniques is increasing as well. Challenges for parameter estimation are partial observability, measurement noise, and the computational complexity arising from the dimension of the parameter space. This article extends the multiple shooting for stochastic systems' method, developed for inference in intrinsic stochastic systems. The treatment of extrinsic noise and the estimation of the unobserved states is improved, by taking into account the correlation between unobserved and observed species. This article demonstrates the power of the method on different scenarios of a Lotka-Volterra model, including cases in which the prey population dies out or explodes, and a Calcium oscillation system. Besides showing how the new extension improves the accuracy of the parameter estimates, this article analyzes the accuracy of the state estimates. In contrast to previous approaches, the new approach is well able to estimate states and parameters for all the scenarios. As it does not need stochastic simulations, it is of the same order of speed as conventional least squares parameter estimation methods with respect to computational time. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Experimental evidence and modelling of drought induced alternative stable soil moisture states

NASA Astrophysics Data System (ADS)

Robinson, David; Jones, Scott; Lebron, Inma; Reinsch, Sabine; Dominguez, Maria; Smith, Andrew; Marshal, Miles; Emmett, Bridget

2017-04-01

The theory of alternative stable states in ecosystems is well established in ecology; however, evidence from manipulation experiments supporting the theory is limited. Developing the evidence base is important because it has profound implications for ecosystem management. Here we show evidence of the existence of alternative stable soil moisture states induced by drought in an upland wet heath. We used a long-term (15 yrs) climate change manipulation experiment with moderate sustained drought, which reduced the ability of the soil to retain soil moisture by degrading the soil structure, reducing moisture retention. Moreover, natural intense droughts superimposed themselves on the experiment, causing an unexpected additional alternative soil moisture state to develop, both for the drought manipulation and control plots; this impaired the soil from rewetting in winter. Our results show the coexistence of three stable states. Using modelling with the Hydrus 1D software package we are able to show the circumstances under which shifts in soil moisture states are likely to occur. Given the new understanding it presents a challenge of how to incorporate feedbacks, particularly related to soil structure, into soil flow and transport models?

Parallel State Space Construction for a Model Checking Based on Maximality Semantics

NASA Astrophysics Data System (ADS)

El Abidine Bouneb, Zine; Saīdouni, Djamel Eddine

2009-03-01

The main limiting factor of the model checker integrated in the concurrency verification environment FOCOVE [1, 2], which use the maximality based labeled transition system (noted MLTS) as a true concurrency model[3, 4], is currently the amount of available physical memory. Many techniques have been developed to reduce the size of a state space. An interesting technique among them is the alpha equivalence reduction. Distributed memory execution environment offers yet another choice. The main contribution of the paper is to show that the parallel state space construction algorithm proposed in [5], which is based on interleaving semantics using LTS as semantic model, may be adapted easily to the distributed implementation of the alpha equivalence reduction for the maximality based labeled transition systems.

Calculation of density of states for modeling photoemission using method of moments

NASA Astrophysics Data System (ADS)

Finkenstadt, Daniel; Lambrakos, Samuel G.; Jensen, Kevin L.; Shabaev, Andrew; Moody, Nathan A.

2017-09-01

Modeling photoemission using the Moments Approach (akin to Spicer's "Three Step Model") is often presumed to follow simple models for the prediction of two critical properties of photocathodes: the yield or "Quantum Efficiency" (QE), and the intrinsic spreading of the beam or "emittance" ɛnrms. The simple models, however, tend to obscure properties of electrons in materials, the understanding of which is necessary for a proper prediction of a semiconductor or metal's QE and ɛnrms. This structure is characterized by localized resonance features as well as a universal trend at high energy. Presented in this study is a prototype analysis concerning the density of states (DOS) factor D(E) for Copper in bulk to replace the simple three-dimensional form of D(E) = (m/π2 h3)p2mE currently used in the Moments approach. This analysis demonstrates that excited state spectra of atoms, molecules and solids based on density-functional theory can be adapted as useful information for practical applications, as well as providing theoretical interpretation of density-of-states structure, e.g., qualitatively good descriptions of optical transitions in matter, in addition to DFT's utility in providing the optical constants and material parameters also required in the Moments Approach.

Integrating Ecosystem Carbon Dynamics into State-and-Transition Simulation Models of Land Use/Land Cover Change

NASA Astrophysics Data System (ADS)

Sleeter, B. M.; Daniel, C.; Frid, L.; Fortin, M. J.

2016-12-01

State-and-transition simulation models (STSMs) provide a general approach for incorporating uncertainty into forecasts of landscape change. Using a Monte Carlo approach, STSMs generate spatially-explicit projections of the state of a landscape based upon probabilistic transitions defined between states. While STSMs are based on the basic principles of Markov chains, they have additional properties that make them applicable to a wide range of questions and types of landscapes. A current limitation of STSMs is that they are only able to track the fate of discrete state variables, such as land use/land cover (LULC) classes. There are some landscape modelling questions, however, for which continuous state variables - for example carbon biomass - are also required. Here we present a new approach for integrating continuous state variables into spatially-explicit STSMs. Specifically we allow any number of continuous state variables to be defined for each spatial cell in our simulations; the value of each continuous variable is then simulated forward in discrete time as a stochastic process based upon defined rates of change between variables. These rates can be defined as a function of the realized states and transitions of each cell in the STSM, thus providing a connection between the continuous variables and the dynamics of the landscape. We demonstrate this new approach by (1) developing a simple IPCC Tier 3 compliant model of ecosystem carbon biomass, where the continuous state variables are defined as terrestrial carbon biomass pools and the rates of change as carbon fluxes between pools, and (2) integrating this carbon model with an existing LULC change model for the state of Hawaii, USA.

Integrated driver modelling considering state transition feature for individual adaptation of driver assistance systems

NASA Astrophysics Data System (ADS)

Raksincharoensak, Pongsathorn; Khaisongkram, Wathanyoo; Nagai, Masao; Shimosaka, Masamichi; Mori, Taketoshi; Sato, Tomomasa

2010-12-01

This paper describes the modelling of naturalistic driving behaviour in real-world traffic scenarios, based on driving data collected via an experimental automobile equipped with a continuous sensing drive recorder. This paper focuses on the longitudinal driving situations which are classified into five categories - car following, braking, free following, decelerating and stopping - and are referred to as driving states. Here, the model is assumed to be represented by a state flow diagram. Statistical machine learning of driver-vehicle-environment system model based on driving database is conducted by a discriminative modelling approach called boosting sequential labelling method.

Projecting state-level air pollutant emissions using an integrated assessment model: GCAM-USA

EPA Science Inventory

The Global Change Assessment Model (GCAM) is an integrated assessment model that links representations of the economy, energy sector, land use, and climate within an integrated modeling environment. GCAM-USA, which is an extension of GCAM, provides U.S. state-level resolution wit...

Observations and Modelling of Alternative Tree Cover States of the Boreal Ecosystem

NASA Astrophysics Data System (ADS)

Abis, B.; Brovkin, V.

2017-12-01

Recently, multimodality of the tree cover distribution of the boreal forests has been detected, revealing the existence of three alternative vegetation modes. Identifying which are the regions with a potential for alternative tree cover states, and assessing which are the main factors underlying their existence, is important to project future change of natural vegetation cover and its effect on climate.Through the use of generalised additive models and phase-space analysis, we study the link between tree cover distribution and eight globally-observed environmental factors, such as rainfall, temperature, and permafrost distribution. Using a classification based on these factors, we show the location of areas with potentially alternative tree cover states under the same environmental conditions in the boreal region. Furthermore, to explain the multimodality found in the data and the asymmetry between North America and Eurasia, we study a conceptual model based on tree species competition, and use it to simulate the sensitivity of tree cover to changes in environmental factors.We find that the link between individual environmental variables and tree cover differs regionally. Nonetheless, environmental conditions uniquely determine the vegetation state among the three dominant modes in ˜95% of the cases. On the other hand, areas with potentially alternative tree cover states encompass ˜1.1 million km2, and correspond to possible transition zones with a reduced resilience to disturbances. Employing our conceptual model, we show that multimodality can be explained through competition between tree species with different adaptations to environmental factors and disturbances. Moreover, the model is able to reproduce the asymmetry in tree species distribution between Eurasia and North America. Finally, we find that changes in permafrost could be associated with bifurcation points of the model, corroborating the importance of permafrost in a changing climate.

Modelling hard and soft states of Cygnus X-1 with propagating mass accretion rate fluctuations

NASA Astrophysics Data System (ADS)

Rapisarda, S.; Ingram, A.; van der Klis, M.

2017-12-01

We present a timing analysis of three Rossi X-ray Timing Explorer observations of the black hole binary Cygnus X-1 with the propagating mass accretion rate fluctuations model PROPFLUC. The model simultaneously predicts power spectra, time lags and coherence of the variability as a function of energy. The observations cover the soft and hard states of the source, and the transition between the two. We find good agreement between model predictions and data in the hard and soft states. Our analysis suggests that in the soft state the fluctuations propagate in an optically thin hot flow extending up to large radii above and below a stable optically thick disc. In the hard state, our results are consistent with a truncated disc geometry, where the hot flow extends radially inside the inner radius of the disc. In the transition from soft to hard state, the characteristics of the rapid variability are too complex to be successfully described with PROPFLUC. The surface density profile of the hot flow predicted by our model and the lack of quasi-periodic oscillations in the soft and hard states suggest that the spin of the black hole is aligned with the inner accretion disc and therefore probably with the rotational axis of the binary system.

Probabilistic choice models in health-state valuation research: background, theories, assumptions and applications.

PubMed

Arons, Alexander M M; Krabbe, Paul F M

2013-02-01

Interest is rising in measuring subjective health outcomes, such as treatment outcomes that are not directly quantifiable (functional disability, symptoms, complaints, side effects and health-related quality of life). Health economists in particular have applied probabilistic choice models in the area of health evaluation. They increasingly use discrete choice models based on random utility theory to derive values for healthcare goods or services. Recent attempts have been made to use discrete choice models as an alternative method to derive values for health states. In this article, various probabilistic choice models are described according to their underlying theory. A historical overview traces their development and applications in diverse fields. The discussion highlights some theoretical and technical aspects of the choice models and their similarity and dissimilarity. The objective of the article is to elucidate the position of each model and their applications for health-state valuation.

Optimal Tree Increment Models for the Northeastern United States

Treesearch

Don C. Bragg

2005-01-01

I used the potential relative increment (PRI) methodology to develop optimal tree diameter growth models for the Northeastern United States. Thirty species from the Eastwide Forest Inventory Database yielded 69,676 individuals, which were then reduced to fast-growing subsets for PRI analysis. For instance, only 14 individuals from the greater than 6,300-tree eastern...

Electronic states of the Cu 3O 1217- model cluster

NASA Astrophysics Data System (ADS)

Chen, Xue-an; Chen, Zhi-fang; Heng, Fu; Tang, Youqi; Ye, Xue-qi; Zhu, Min-hui

1991-03-01

The Fenske-Hall molecular orbital calculations were performed on the model cluster Cu 3O 1217-. The calculated results revealed that the major contribution to the electronic states near the Fermi level comes from the orbitals of Cu 3d and O 2p, with dominantly oxygen p character, and the oxidation beyond the Cu 2+ state does not lead to Cu 3+ but to O - state. There exists the strong covalent bonding between copper and neighboring oxygen ions, especially between the chain Cu(1) and bridge O(4) ions. The slight displacement of O(4) along the c-axis toward Cu(2) can result in a decrease in the HOMO-LUMO gap and a strengthening of the chain-plane coupling.

Wavelet modeling and prediction of the stability of states: the Roman Empire and the European Union

NASA Astrophysics Data System (ADS)

Yaroshenko, Tatyana Y.; Krysko, Dmitri V.; Dobriyan, Vitalii; Zhigalov, Maksim V.; Vos, Hendrik; Vandenabeele, Peter; Krysko, Vadim A.

2015-09-01

How can the stability of a state be quantitatively determined and its future stability predicted? The rise and collapse of empires and states is very complex, and it is exceedingly difficult to understand and predict it. Existing theories are usually formulated as verbal models and, consequently, do not yield sharply defined, quantitative prediction that can be unambiguously validated with data. Here we describe a model that determines whether the state is in a stable or chaotic condition and predicts its future condition. The central model, which we test, is that growth and collapse of states is reflected by the changes of their territories, populations and budgets. The model was simulated within the historical societies of the Roman Empire (400 BC to 400 AD) and the European Union (1957-2007) by using wavelets and analysis of the sign change of the spectrum of Lyapunov exponents. The model matches well with the historical events. During wars and crises, the state becomes unstable; this is reflected in the wavelet analysis by a significant increase in the frequency ω (t) and wavelet coefficients W (ω, t) and the sign of the largest Lyapunov exponent becomes positive, indicating chaos. We successfully reconstructed and forecasted time series in the Roman Empire and the European Union by applying artificial neural network. The proposed model helps to quantitatively determine and forecast the stability of a state.

General three-state model with biased population replacement: Analytical solution and application to language dynamics

NASA Astrophysics Data System (ADS)

Colaiori, Francesca; Castellano, Claudio; Cuskley, Christine F.; Loreto, Vittorio; Pugliese, Martina; Tria, Francesca

2015-01-01

Empirical evidence shows that the rate of irregular usage of English verbs exhibits discontinuity as a function of their frequency: the most frequent verbs tend to be totally irregular. We aim to qualitatively understand the origin of this feature by studying simple agent-based models of language dynamics, where each agent adopts an inflectional state for a verb and may change it upon interaction with other agents. At the same time, agents are replaced at some rate by new agents adopting the regular form. In models with only two inflectional states (regular and irregular), we observe that either all verbs regularize irrespective of their frequency, or a continuous transition occurs between a low-frequency state, where the lemma becomes fully regular, and a high-frequency one, where both forms coexist. Introducing a third (mixed) state, wherein agents may use either form, we find that a third, qualitatively different behavior may emerge, namely, a discontinuous transition in frequency. We introduce and solve analytically a very general class of three-state models that allows us to fully understand these behaviors in a unified framework. Realistic sets of interaction rules, including the well-known naming game (NG) model, result in a discontinuous transition, in agreement with recent empirical findings. We also point out that the distinction between speaker and hearer in the interaction has no effect on the collective behavior. The results for the general three-state model, although discussed in terms of language dynamics, are widely applicable.

Control of discrete event systems modeled as hierarchical state machines

NASA Technical Reports Server (NTRS)

Brave, Y.; Heymann, M.

1991-01-01

The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

Aeroservoelastic modeling and applications using minimum-state approximations of the unsteady aerodynamics

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Karpel, Mordechay

1989-01-01

Various control analysis, design, and simulation techniques for aeroelastic applications require the equations of motion to be cast in a linear time-invariant state-space form. Unsteady aerodynamics forces have to be approximated as rational functions of the Laplace variable in order to put them in this framework. For the minimum-state method, the number of denominator roots in the rational approximation. Results are shown of applying various approximation enhancements (including optimization, frequency dependent weighting of the tabular data, and constraint selection) with the minimum-state formulation to the active flexible wing wind-tunnel model. The results demonstrate that good models can be developed which have an order of magnitude fewer augmenting aerodynamic equations more than traditional approaches. This reduction facilitates the design of lower order control systems, analysis of control system performance, and near real-time simulation of aeroservoelastic phenomena.

State Faith-Based Education in Israel: A Messianic-Nationalist Model of Interpretation

ERIC Educational Resources Information Center

Silberman-Keller, Diana

2005-01-01

This article analyzes the traces and configuration of three syntagmatic interpretation models in the educational text produced by Israel's state religious educational system: the "modus", the "hermetic" and the "gnostic" models, which together add up to a fourth, and unique one: "the messianic nationalist"…

Cooperative Effects in Models of Steady-State Transport across Membranes

PubMed Central

Hill, Terrell L.; Chen, Yi-Der

1971-01-01

Several different one-site, two-site, and multisite models of steady-state ion transport across a membrane are investigated. The basic features, including cooperative interactions between channels, are the same as in earlier papers in this series. In particular, the present paper represents a considerable elaboration of part III. The models might apply to artificial or possibly to biological membranes, but particular applications must await further elucidation of the molecular structure and operation of these membranes. PMID:5132496

Model-independent confirmation of the Z ( 4430 ) - state

DOE PAGES

Aaij, R.; Adeva, B.; Adinolfi, M.; ...

2015-12-29

Here, the decay B 0→ψ(2S)K +π - is analyzed using 3 fb -1 of pp collision data collected with the LHCb detector. A model-independent description of the ψ(2S)π mass spectrum is obtained, using as input the Kπ mass spectrum and angular distribution derived directly from data, without requiring a theoretical description of resonance shapes or their interference. The hypothesis that the ψ(2S)π mass spectrum can be described in terms of Kπ reflections alone is rejected with more than 8σ significance. This provides confirmation, in a model-independent way, of the need for an additional resonant component in the mass region ofmore » the Z(4430) - exotic state.« less

Analytical ground state for the Jaynes-Cummings model with ultrastrong coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang Yuanwei; Institute of Theoretical Physics, Shanxi University, Taiyuan 030006; Chen Gang

2011-06-15

We present a generalized variational method to analytically obtain the ground-state properties of the Jaynes-Cummings model with the ultrastrong coupling. An explicit expression for the ground-state energy, which agrees well with the numerical simulation in a wide range of the experimental parameters, is given. In particular, the introduced method can successfully solve this Jaynes-Cummings model with the positive detuning (the atomic resonant level is larger than the photon frequency), which cannot be treated in the adiabatical approximation and the generalized rotating-wave approximation. Finally, we also demonstrate analytically how to control the mean photon number by means of the current experimentalmore » parameters including the photon frequency, the coupling strength, and especially the atomic resonant level.« less

Steady State Model for Solar Coronal Loops

NASA Astrophysics Data System (ADS)

Sugiyama, L.; Asgari-Targhi, M.

2017-12-01

Solar coronal loops on the surface of the sun provide background magnetic and plasma structures for the release of a significant amount of the sun's energy, through energetic solar flares and coronal mass ejections and more gradual processes. Understanding their steady states is the first step in understanding loop dynamics. A consistent MHD steady state model, for a curved magnetic flux rope that contains plasma, has been developed[1] for simple coronal loops with both ends anchored in the photosphere. Plasma pressure or current makes the loop unstable to expansion in major radius and must be balanced by external forces, such as the solar gravity. The MHD momentum equation has a well defined small parameter ordering in the loop inverse aspect ratio ɛ=a/Ro (minor/major radius). Different types of common coronal loops fall in different parameter regimes, determined by the relative values of the plasma beta β=po/(Bo2/2μo), the MHD gravity parameter Ĝ≡ga/vA2 (the gravitational acceleration g normalized to the minor radius a and shear Alfvén velocity vA), and ɛ. The largest possible gravity, Ĝ ɛ1β, corresponds to the largest loops because it reduces the plasma density at the top of the loop exponentially compared to its lower ends, reducing the downward gravitational force -ρĜ there. The thin loops that are ubiquitous in solar active regions have ``high'' beta, β ɛ1, for ɛ≃0.02, and fit the predicted model scalings. The thicker loops that can give rise to flares and CMEs have ``low'' beta, β ɛ2. Cool loops, such as solar filaments outside active regions, that have a central pressure lower than that of the surrounding corona would have the strongest stability against radial expansion. The model raises a number of questions about the connection of loops to the photosphere and the force-free nature of the magnetic field there. [1] L. Sugiyama, M. Asgari-Targhi, Phys. Plasmas 24, 022904 (2017).

Emergent Chiral Spin State in the Mott Phase of a Bosonic Kane-Mele-Hubbard Model

NASA Astrophysics Data System (ADS)

Plekhanov, Kirill; Vasić, Ivana; Petrescu, Alexandru; Nirwan, Rajbir; Roux, Guillaume; Hofstetter, Walter; Le Hur, Karyn

2018-04-01

Recently, the frustrated X Y model for spins 1 /2 on the honeycomb lattice has attracted a lot of attention in relation with the possibility to realize a chiral spin liquid state. This model is relevant to the physics of some quantum magnets. Using the flexibility of ultracold atom setups, we propose an alternative way to realize this model through the Mott regime of the bosonic Kane-Mele-Hubbard model. The phase diagram of this model is derived using bosonic dynamical mean-field theory. Focusing on the Mott phase, we investigate its magnetic properties as a function of frustration. We do find an emergent chiral spin state in the intermediate frustration regime. Using exact diagonalization we study more closely the physics of the effective frustrated X Y model and the properties of the chiral spin state. This gapped phase displays a chiral order, breaking time-reversal and parity symmetry, but is not topologically ordered (the Chern number is zero).

LACIE: Wheat yield models for the United States, revision A

NASA Technical Reports Server (NTRS)

1977-01-01

For abstract, see volume 1 N77-30577. The enclosed maps indicate the areal coverage of the various models for spring (durum and other spring) and winter wheat. The given regions are the combination of several climatic divisions and many times comprise an entire state.

Road traffic accidents prediction modelling: An analysis of Anambra State, Nigeria.

PubMed

Ihueze, Chukwutoo C; Onwurah, Uchendu O

2018-03-01

One of the major problems in the world today is the rate of road traffic crashes and deaths on our roads. Majority of these deaths occur in low-and-middle income countries including Nigeria. This study analyzed road traffic crashes in Anambra State, Nigeria with the intention of developing accurate predictive models for forecasting crash frequency in the State using autoregressive integrated moving average (ARIMA) and autoregressive integrated moving average with explanatory variables (ARIMAX) modelling techniques. The result showed that ARIMAX model outperformed the ARIMA (1,1,1) model generated when their performances were compared using the lower Bayesian information criterion, mean absolute percentage error, root mean square error; and higher coefficient of determination (R-Squared) as accuracy measures. The findings of this study reveal that incorporating human, vehicle and environmental related factors in time series analysis of crash dataset produces a more robust predictive model than solely using aggregated crash count. This study contributes to the body of knowledge on road traffic safety and provides an approach to forecasting using many human, vehicle and environmental factors. The recommendations made in this study if applied will help in reducing the number of road traffic crashes in Nigeria. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modeled historical land use and land cover for the conterminous United States

USGS Publications Warehouse

Sohl, Terry L.; Reker, Ryan R.; Bouchard, Michelle A.; Sayler, Kristi L.; Dornbierer, Jordan; Wika, Steve; Quenzer, Robert; Friesz, Aaron M.

2016-01-01

The landscape of the conterminous United States has changed dramatically over the last 200 years, with agricultural land use, urban expansion, forestry, and other anthropogenic activities altering land cover across vast swaths of the country. While land use and land cover (LULC) models have been developed to model potential future LULC change, few efforts have focused on recreating historical landscapes. Researchers at the US Geological Survey have used a wide range of historical data sources and a spatially explicit modeling framework to model spatially explicit historical LULC change in the conterminous United States from 1992 back to 1938. Annual LULC maps were produced at 250-m resolution, with 14 LULC classes. Assessment of model results showed good agreement with trends and spatial patterns in historical data sources such as the Census of Agriculture and historical housing density data, although comparison with historical data is complicated by definitional and methodological differences. The completion of this dataset allows researchers to assess historical LULC impacts on a range of ecological processes.

Symmetry-breaking dynamics of the finite-size Lipkin-Meshkov-Glick model near ground state

NASA Astrophysics Data System (ADS)

Huang, Yi; Li, Tongcang; Yin, Zhang-qi

2018-01-01

We study the dynamics of the Lipkin-Meshkov-Glick (LMG) model with a finite number of spins. In the thermodynamic limit, the ground state of the LMG model with an isotropic Hamiltonian in the broken phase breaks to a mean-field ground state with a certain direction. However, when the spin number N is finite, the exact ground state is always unique and is not given by a classical mean-field ground state. Here, we prove that when N is large but finite, through a tiny external perturbation, a localized state which is close to a mean-field ground state can be prepared, which mimics spontaneous symmetry breaking. Also, we find the localized in-plane spin polarization oscillates with two different frequencies ˜O (1 /N ) , and the lifetime of the localized state is long enough to exhibit this oscillation. We numerically test the analytical results and find that they agree very well with each other. Finally, we link the phenomena to quantum time crystals and time quasicrystals.

Spatial perspectives in state-and-transition models: A missing link to land management?

USDA-ARS?s Scientific Manuscript database

Conceptual models of alternative states and thresholds are based largely on observations of ecosystem processes at a few points in space. Because the distribution of alternative states in spatially-structured ecosystems is the result of variations in pattern-process interactions at different scales,...

Generating Models of Infinite-State Communication Protocols Using Regular Inference with Abstraction

NASA Astrophysics Data System (ADS)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

In order to facilitate model-based verification and validation, effort is underway to develop techniques for generating models of communication system components from observations of their external behavior. Most previous such work has employed regular inference techniques which generate modest-size finite-state models. They typically suppress parameters of messages, although these have a significant impact on control flow in many communication protocols. We present a framework, which adapts regular inference to include data parameters in messages and states for generating components with large or infinite message alphabets. A main idea is to adapt the framework of predicate abstraction, successfully used in formal verification. Since we are in a black-box setting, the abstraction must be supplied externally, using information about how the component manages data parameters. We have implemented our techniques by connecting the LearnLib tool for regular inference with the protocol simulator ns-2, and generated a model of the SIP component as implemented in ns-2.

A comparative computational study of coarse-grained and all-atom water models in shock Hugoniot states

NASA Astrophysics Data System (ADS)

Min, Sa Hoon; Berkowitz, Max L.

2018-04-01

We performed molecular dynamics simulations to study how well some of the water models used in simulations describe shocked states. Water in our simulations was described using three different models. One was an often-used all-atom TIP4P/2005 model, while the other two were coarse-grained models used with the MARTINI force field: non-polarizable and polarizable MARTINI water. The all-atom model provided results in good agreement with Hugoniot curves (for data on pressure versus specific volume or, equivalently, on shock wave velocity versus "piston" velocity) describing shocked states in the whole range of pressures (up to 11 GPa) under study. If simulations of shocked states of water using coarse-grained models were performed for short time periods, we observed that data obtained for shocked states at low pressure were fairly accurate compared to experimental Hugoniot curves. Polarizable MARTINI water still provided a good description of Hugoniot curves for pressures up to 11 GPa, while the results for the non-polarizable MARTINI water substantially deviated from the Hugoniot curves. We also calculated the temperature of the Hugoniot states and observed that for TIP4P/2005 water, they were consistent with those from theoretical calculations, while both coarse-grained models predicted much higher temperatures. These high temperatures for MARTINI water can be explained by the loss of degrees of freedom due to coarse-graining procedure.

SYMPLECTIC INVARIANTS AND FLOWERS' CLASSIFICATION OF SHELL MODEL STATES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Helmers, K.

1961-01-01

Flowers has given a classification of shell model states in j-j coupling for a fixed number of nucleons in a shell with respect to a symplectic group. The relation between these classifications for the various nucleon numbers is studied and is found to be governed by another symplectic group, the transformations of which in general change the nucleon number. (auth)

Weakly coupled map lattice models for multicellular patterning and collective normalization of abnormal single-cell states

NASA Astrophysics Data System (ADS)

García-Morales, Vladimir; Manzanares, José A.; Mafe, Salvador

2017-04-01

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according to local rules that are modulated by a parameter κ . This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate external agents. The emergent spatiotemporal maps of single-cell states should be of significance for positional information processes as well as for intercellular communication in tumorigenesis, where the collective normalization of abnormal single-cell states by a predominantly normal neighborhood may be crucial.

Weakly coupled map lattice models for multicellular patterning and collective normalization of abnormal single-cell states.

PubMed

García-Morales, Vladimir; Manzanares, José A; Mafe, Salvador

2017-04-01

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according to local rules that are modulated by a parameter κ. This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate external agents. The emergent spatiotemporal maps of single-cell states should be of significance for positional information processes as well as for intercellular communication in tumorigenesis, where the collective normalization of abnormal single-cell states by a predominantly normal neighborhood may be crucial.

Stated choice for transportation demand management models : using a disaggregate truth set to study predictive validity

DOT National Transportation Integrated Search

1997-01-01

Discrete choice models have expanded the ability of transportation planners to forecast future trends. Where new services or policies are proposed, the stated-choice approach can provide an objective basis for forecasts. Stated-choice models are subj...

Modelling of different enzyme productions by solid-state fermentation on several agro-industrial residues.

PubMed

Diaz, Ana Belen; Blandino, Ana; Webb, Colin; Caro, Ildefonso

2016-11-01

A simple kinetic model, with only three fitting parameters, for several enzyme productions in Petri dishes by solid-state fermentation is proposed in this paper, which may be a valuable tool for simulation of this type of processes. Basically, the model is able to predict temporal fungal enzyme production by solid-state fermentation on complex substrates, maximum enzyme activity expected and time at which these maxima are reached. In this work, several fermentations in solid state were performed in Petri dishes, using four filamentous fungi grown on different agro-industrial residues, measuring xylanase, exo-polygalacturonase, cellulose and laccase activities over time. Regression coefficients after fitting experimental data to the proposed model turned out to be quite high in all cases. In fact, these results are very interesting considering, on the one hand, the simplicity of the model and, on the other hand, that enzyme activities correspond to different enzymes, produced by different fungi on different substrates.

An optical flow-based state-space model of the vocal folds.

PubMed

Granados, Alba; Brunskog, Jonas

2017-06-01

High-speed movies of the vocal fold vibration are valuable data to reveal vocal fold features for voice pathology diagnosis. This work presents a suitable Bayesian model and a purely theoretical discussion for further development of a framework for continuum biomechanical features estimation. A linear and Gaussian nonstationary state-space model is proposed and thoroughly discussed. The evolution model is based on a self-sustained three-dimensional finite element model of the vocal folds, and the observation model involves a dense optical flow algorithm. The results show that the method is able to capture different deformation patterns between the computed optical flow and the finite element deformation, controlled by the choice of the model tissue parameters.

Integrating continuous stocks and flows into state-and-transition simulation models of landscape change

USGS Publications Warehouse

Daniel, Colin J.; Sleeter, Benjamin M.; Frid, Leonardo; Fortin, Marie-Josée

2018-01-01

State-and-transition simulation models (STSMs) provide a general framework for forecasting landscape dynamics, including projections of both vegetation and land-use/land-cover (LULC) change. The STSM method divides a landscape into spatially-referenced cells and then simulates the state of each cell forward in time, as a discrete-time stochastic process using a Monte Carlo approach, in response to any number of possible transitions. A current limitation of the STSM method, however, is that all of the state variables must be discrete.Here we present a new approach for extending a STSM, in order to account for continuous state variables, called a state-and-transition simulation model with stocks and flows (STSM-SF). The STSM-SF method allows for any number of continuous stocks to be defined for every spatial cell in the STSM, along with a suite of continuous flows specifying the rates at which stock levels change over time. The change in the level of each stock is then simulated forward in time, for each spatial cell, as a discrete-time stochastic process. The method differs from the traditional systems dynamics approach to stock-flow modelling in that the stocks and flows can be spatially-explicit, and the flows can be expressed as a function of the STSM states and transitions.We demonstrate the STSM-SF method by integrating a spatially-explicit carbon (C) budget model with a STSM of LULC change for the state of Hawai'i, USA. In this example, continuous stocks are pools of terrestrial C, while the flows are the possible fluxes of C between these pools. Importantly, several of these C fluxes are triggered by corresponding LULC transitions in the STSM. Model outputs include changes in the spatial and temporal distribution of C pools and fluxes across the landscape in response to projected future changes in LULC over the next 50 years.The new STSM-SF method allows both discrete and continuous state variables to be integrated into a STSM, including interactions between

Low-lying 1/2- hidden strange pentaquark states in the constituent quark model

NASA Astrophysics Data System (ADS)

Li, Hui; Wu, Zong-Xiu; An, Chun-Sheng; Chen, Hong

2017-12-01

We investigate the spectrum of the low-lying 1/2- hidden strange pentaquark states, employing the constituent quark model, and looking at two ways within that model of mediating the hyperfine interaction between quarks - Goldstone boson exchange and one gluon exchange. Numerical results show that the lowest 1/2- hidden strange pentaquark state in the Goldstone boson exchange model lies at ˜1570 MeV, so this pentaquark configuration may form a notable component in S 11(1535) if the Goldstone boson exchange model is applied. This is consistent with the prediction that S 11(1535) couples very strongly to strangeness channels. Supported by National Natural Science Foundation of China (11675131, 11645002), Chongqing Natural Science Foundation (cstc2015jcyjA00032) and Fundamental Research Funds for the Central Universities (SWU115020)

State Education Governance Structures: 2017 Update. 50-State Review

ERIC Educational Resources Information Center

Railey, Hunter

2017-01-01

This 50-State Review provides an overview of governance structures in the states, as well as implications for practice, deep dives into four governance models and examples of other governance models. One appendix, State Education Governance Models by State, is included.

Hurricane Loss Estimation Models: Opportunities for Improving the State of the Art.

NASA Astrophysics Data System (ADS)

Watson, Charles C., Jr.; Johnson, Mark E.

2004-11-01

The results of hurricane loss models are used regularly for multibillion dollar decisions in the insurance and financial services industries. These models are proprietary, and this “black box” nature hinders analysis. The proprietary models produce a wide range of results, often producing loss costs that differ by a ratio of three to one or more. In a study for the state of North Carolina, 324 combinations of loss models were analyzed, based on a combination of nine wind models, four surface friction models, and nine damage models drawn from the published literature in insurance, engineering, and meteorology. These combinations were tested against reported losses from Hurricanes Hugo and Andrew as reported by a major insurance company, as well as storm total losses for additional storms. Annual loss costs were then computed using these 324 combinations of models for both North Carolina and Florida, and compared with publicly available proprietary model results in Florida. The wide range of resulting loss costs for open, scientifically defensible models that perform well against observed losses mirrors the wide range of loss costs computed by the proprietary models currently in use. This outcome may be discouraging for governmental and corporate decision makers relying on this data for policy and investment guidance (due to the high variability across model results), but it also provides guidance for the efforts of future investigations to improve loss models. Although hurricane loss models are true multidisciplinary efforts, involving meteorology, engineering, statistics, and actuarial sciences, the field of meteorology offers the most promising opportunities for improvement of the state of the art.

The economic impact of NASA R and D spending: Executive summary

NASA Technical Reports Server (NTRS)

Evans, M. K.

1976-01-01

An evaluation of the economic impact of NASA research and development programs is made. The methodology and the results revolve around the interrelationships existing between the demand and supply effects of increased research and development spending, in particular, NASA research and development spending. The INFORUM Inter-Industry Forecasing Model is used to measure the short-run economic impact of alternative levels of NASA expenditures for 1975. An aggregate production function approach is used to develop the data series necessary to measure the impact of NASA research and development spending, and other determinants of technological progress, on the rate of growth in productivity of the U. S. economy. The measured relationship between NASA research and development spending and technological progress is simulated in the Chase Macroeconometric Model to measure the immediate, intermediate, and long-run economic impact of increased NASA research and development spending over a sustained period.

Specificity in Transition State Binding: The Pauling Model Revisited

PubMed Central

Amyes, Tina L.; Richard, John P.

2013-01-01

Linus Pauling proposed that the large rate accelerations for enzymes are due to the high specificity of the protein catalyst for binding the reaction transition state. The observation that stable analogs of the transition states for enzymatic reactions often act as tight-binding binding inhibitors provided early support for this simple and elegant proposal. We review experimental results which support the proposal that Pauling’s model provides a satisfactory explanation for the rate accelerations for many heterolytic enzymatic reactions through high energy reaction intermediates, such as proton transfer and decarboxylation. Specificity in transition state binding is obtained when the total intrinsic binding energy of the substrate is significantly larger than the binding energy observed at the Michaelis complex. The results of recent studies to characterize the specificity in binding of the enolate oxygen at the transition state for the 1,3-isomerization reaction catalyzed by ketosteroid isomerase are reviewed. Interactions between pig heart succinyl-CoA:3-oxoacid coenzyme A transferase (SCOT) and the nonreacting portions of CoA are responsible for a rate increase of 3 × 1012-fold, which is close to the estimated total 5 × 1013-fold enzymatic rate acceleration. Studies that partition the interactions between SCOT and CoA into their contributing parts are reviewed. Interactions of the protein with the substrate phosphodianion group provide a ca. 12 kcal/mol stabilization of the transition state for the reactions catalyzed by triosephosphate isomerase, orotidine 5′-monophosphate decarboxylase and α-glycerol phosphate dehydrogenase. The interactions of these enzymes with the substrate piece phosphite dianion provide a 6 – 8 kcal/mol stabilization of the transition state for reaction of the appropriate truncated substrate. Enzyme activation by phosphite dianion reflects the higher dianion affinity for binding to the enzyme-transition state complex compared

Measuring up to the Model: A Ranking of State Charter Public School Laws. Eighth Edition

ERIC Educational Resources Information Center

Ziebarth, Todd; Palmer, Louann Bierlein; Schultz, Emily

2017-01-01

This eighth edition of "Measuring up to the Model: A Ranking of State Charter School Laws" presents the latest activity in charter public school legislation across the country. This year's rankings are the first that measure each state's charter school law against the National Alliance's revised model charter school law, "A New…

A New Critical State Model for Geomechanical Behavior of Methane Hydrate-Bearing Sands

NASA Astrophysics Data System (ADS)

Lin, J. S.; Xing, P.; Rutqvist, J.; Seol, Y.; Choi, J. H.

2014-12-01

Methane hydrate bearing sands behave like sands once the hydrate has dissociated, but could exhibit a substantial increase in the shear strength, stiffness and dilatancy as the degree of hydrate saturation increases. A new critical state model was developed that incorporates the spatially mobilized plane (SMP) concept, which has been proven effective in modeling mechanical behavior of sands. While this new model was built on the basic constructs of the critical state model, important enhancements were introduced. The model adopted the t-stress concept, which defined the normal and shear stress on the SMP, in describing the plastic behavior of the soil. In this connection the versatile Matsuoka-Nakai yield criterion was also employed, which defined the general three dimensional yield behavior. The resulting constitutive law was associated in the t-stress space, but became non-associated in the conventional p-q stress space as it should be for sands. The model also introduced a generalized degree of hydrate saturation concept that was modified from the pioneering work of the Cambridge group. The model gives stress change when the sands are subjected to straining, and/or to hydrate saturation changes. The performance of the model has been found satisfactory using data from laboratory triaxial tests on reconstituted samples and core samples taken from Nankai Trough, Japan. The model has been implemented into FLAC3D. A coupling example with the multiphase flow code, TOUGH+, is presented which simulates the mechanical behavior of a sample when the surrounding temperature has been raised, and the hydrate undergoes state change and no longer resides in the stability zone.

A Linear Programming Model to Optimize Various Objective Functions of a Foundation Type State Support Program.

ERIC Educational Resources Information Center

Matzke, Orville R.

The purpose of this study was to formulate a linear programming model to simulate a foundation type support program and to apply this model to a state support program for the public elementary and secondary school districts in the State of Iowa. The model was successful in producing optimal solutions to five objective functions proposed for…

Finite element implementation of state variable-based viscoplasticity models

NASA Technical Reports Server (NTRS)

Iskovitz, I.; Chang, T. Y. P.; Saleeb, A. F.

1991-01-01

The implementation of state variable-based viscoplasticity models is made in a general purpose finite element code for structural applications of metals deformed at elevated temperatures. Two constitutive models, Walker's and Robinson's models, are studied in conjunction with two implicit integration methods: the trapezoidal rule with Newton-Raphson iterations and an asymptotic integration algorithm. A comparison is made between the two integration methods, and the latter method appears to be computationally more appealing in terms of numerical accuracy and CPU time. However, in order to make the asymptotic algorithm robust, it is necessary to include a self adaptive scheme with subincremental step control and error checking of the Jacobian matrix at the integration points. Three examples are given to illustrate the numerical aspects of the integration methods tested.

Emergent low-energy bound states in the two-orbital Hubbard model

NASA Astrophysics Data System (ADS)

Núñez-Fernández, Y.; Kotliar, G.; Hallberg, K.

2018-03-01

A repulsive Coulomb interaction between electrons in different orbitals in correlated materials can give rise to bound quasiparticle states. We study the nonhybridized two-orbital Hubbard model with intra- (inter)orbital interaction U (U12) and different bandwidths using an improved dynamical mean-field theory numerical technique which leads to reliable spectra on the real energy axis directly at zero temperature. We find that a finite density of states at the Fermi energy in one band is correlated with the emergence of well-defined quasiparticle states at excited energies Δ =U -U12 in the other band. These excitations are interband holon-doublon bound states. At the symmetric point U =U12 , the quasiparticle peaks are located at the Fermi energy, leading to a simultaneous and continuous Mott transition settling a long-standing controversy.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

2015-11-01

Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network

Ground-state phase diagram in the Kugel-Khomskii model with finite spin-orbit interactions

NASA Astrophysics Data System (ADS)

Koga, Akihisa; Nakauchi, Shiryu; Nasu, Joji

2018-05-01

We study ground-state properties in the Kugel-Khomskii model on the two-dimensional honeycomb lattice. Using the cluster mean-field approximations, we deal with the exchange and spin-orbit couplings on an equal footing. We then discuss the stability of the ferromagnetically ordered states against the nonmagnetic state, which is adiabatically connected to the quantum spin liquid state realized in a strong spin-orbit coupling limit.

Equations of state for explosive detonation products: The PANDA model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kerley, G.I.

1994-05-01

This paper discusses a thermochemical model for calculating equations of state (EOS) for the detonation products of explosives. This model, which was first presented at the Eighth Detonation Symposium, is available in the PANDA code and is referred to here as ``the Panda model``. The basic features of the PANDA model are as follows. (1) Statistical-mechanical theories are used to construct EOS tables for each of the chemical species that are to be allowed in the detonation products. (2) The ideal mixing model is used to compute the thermodynamic functions for a mixture of these species, and the composition ofmore » the system is determined from assumption of chemical equilibrium. (3) For hydrocode calculations, the detonation product EOS are used in tabular form, together with a reactive burn model that allows description of shock-induced initiation and growth or failure as well as ideal detonation wave propagation. This model has been implemented in the three-dimensional Eulerian code, CTH.« less

Maximum efficiency of state-space models of nanoscale energy conversion devices

NASA Astrophysics Data System (ADS)

Einax, Mario; Nitzan, Abraham

2016-07-01

The performance of nano-scale energy conversion devices is studied in the framework of state-space models where a device is described by a graph comprising states and transitions between them represented by nodes and links, respectively. Particular segments of this network represent input (driving) and output processes whose properly chosen flux ratio provides the energy conversion efficiency. Simple cyclical graphs yield Carnot efficiency for the maximum conversion yield. We give general proof that opening a link that separate between the two driving segments always leads to reduced efficiency. We illustrate these general result with simple models of a thermoelectric nanodevice and an organic photovoltaic cell. In the latter an intersecting link of the above type corresponds to non-radiative carriers recombination and the reduced maximum efficiency is manifested as a smaller open-circuit voltage.

Maximum efficiency of state-space models of nanoscale energy conversion devices.

PubMed

Einax, Mario; Nitzan, Abraham

2016-07-07

The performance of nano-scale energy conversion devices is studied in the framework of state-space models where a device is described by a graph comprising states and transitions between them represented by nodes and links, respectively. Particular segments of this network represent input (driving) and output processes whose properly chosen flux ratio provides the energy conversion efficiency. Simple cyclical graphs yield Carnot efficiency for the maximum conversion yield. We give general proof that opening a link that separate between the two driving segments always leads to reduced efficiency. We illustrate these general result with simple models of a thermoelectric nanodevice and an organic photovoltaic cell. In the latter an intersecting link of the above type corresponds to non-radiative carriers recombination and the reduced maximum efficiency is manifested as a smaller open-circuit voltage.

Majority rule has transition ratio 4 on Yule trees under a 2-state symmetric model.

PubMed

Mossel, Elchanan; Steel, Mike

2014-11-07

Inferring the ancestral state at the root of a phylogenetic tree from states observed at the leaves is a problem arising in evolutionary biology. The simplest technique - majority rule - estimates the root state by the most frequently occurring state at the leaves. Alternative methods - such as maximum parsimony - explicitly take the tree structure into account. Since either method can outperform the other on particular trees, it is useful to consider the accuracy of the methods on trees generated under some evolutionary null model, such as a Yule pure-birth model. In this short note, we answer a recently posed question concerning the performance of majority rule on Yule trees under a symmetric 2-state Markovian substitution model of character state change. We show that majority rule is accurate precisely when the ratio of the birth (speciation) rate of the Yule process to the substitution rate exceeds the value 4. By contrast, maximum parsimony has been shown to be accurate only when this ratio is at least 6. Our proof relies on a second moment calculation, coupling, and a novel application of a reflection principle. Copyright © 2014 Elsevier Ltd. All rights reserved.

An evaluation of 20th century climate for the Southeastern United States as simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models

USGS Publications Warehouse

David E. Rupp,

2016-05-05

The 20th century climate for the Southeastern United States and surrounding areas as simulated by global climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) was evaluated. A suite of statistics that characterize various aspects of the regional climate was calculated from both model simulations and observation-based datasets. CMIP5 global climate models were ranked by their ability to reproduce the observed climate. Differences in the performance of the models between regions of the United States (the Southeastern and Northwestern United States) warrant a regional-scale assessment of CMIP5 models.

Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

PubMed

Aprasoff, Jonathan; Donchin, Opher

2012-04-01

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

Maximum profile likelihood estimation of differential equation parameters through model based smoothing state estimates.

PubMed

Campbell, D A; Chkrebtii, O

2013-12-01

Statistical inference for biochemical models often faces a variety of characteristic challenges. In this paper we examine state and parameter estimation for the JAK-STAT intracellular signalling mechanism, which exemplifies the implementation intricacies common in many biochemical inference problems. We introduce an extension to the Generalized Smoothing approach for estimating delay differential equation models, addressing selection of complexity parameters, choice of the basis system, and appropriate optimization strategies. Motivated by the JAK-STAT system, we further extend the generalized smoothing approach to consider a nonlinear observation process with additional unknown parameters, and highlight how the approach handles unobserved states and unevenly spaced observations. The methodology developed is generally applicable to problems of estimation for differential equation models with delays, unobserved states, nonlinear observation processes, and partially observed histories. Crown Copyright © 2013. Published by Elsevier Inc. All rights reserved.

Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation

PubMed Central

2017-01-01

The thalamus plays a critical role in the genesis of thalamocortical oscillations, yet the underlying mechanisms remain elusive. To understand whether the isolated thalamus can generate multiple distinct oscillations, we developed a biophysical thalamic model to test the hypothesis that generation of and transition between distinct thalamic oscillations can be explained as a function of neuromodulation by acetylcholine (ACh) and norepinephrine (NE) and afferent synaptic excitation. Indeed, the model exhibited four distinct thalamic rhythms (delta, sleep spindle, alpha and gamma oscillations) that span the physiological states corresponding to different arousal levels from deep sleep to focused attention. Our simulation results indicate that generation of these distinct thalamic oscillations is a result of both intrinsic oscillatory cellular properties and specific network connectivity patterns. We then systematically varied the ACh/NE and input levels to generate a complete map of the different oscillatory states and their transitions. Lastly, we applied periodic stimulation to the thalamic network and found that entrainment of thalamic oscillations is highly state-dependent. Our results support the hypothesis that ACh/NE modulation and afferent excitation define thalamic oscillatory states and their response to brain stimulation. Our model proposes a broader and more central role of the thalamus in the genesis of multiple distinct thalamo-cortical rhythms than previously assumed. PMID:29073146

Building hierarchical models of avian distributions for the State of Georgia

USGS Publications Warehouse

Howell, J.E.; Peterson, J.T.; Conroy, M.J.

2008-01-01

To predict the distributions of breeding birds in the state of Georgia, USA, we built hierarchical models consisting of 4 levels of nested mapping units of decreasing area: 90,000 ha, 3,600 ha, 144 ha, and 5.76 ha. We used the Partners in Flight database of point counts to generate presence and absence data at locations across the state of Georgia for 9 avian species: Acadian flycatcher (Empidonax virescens), brownheaded nuthatch (Sitta pusilla), Carolina wren (Thryothorus ludovicianus), indigo bunting (Passerina cyanea), northern cardinal (Cardinalis cardinalis), prairie warbler (Dendroica discolor), yellow-billed cuckoo (Coccyxus americanus), white-eyed vireo (Vireo griseus), and wood thrush (Hylocichla mustelina). At each location, we estimated hierarchical-level-specific habitat measurements using the Georgia GAP Analysis18 class land cover and other Geographic Information System sources. We created candidate, species-specific occupancy models based on previously reported relationships, and fit these using Markov chain Monte Carlo procedures implemented in OpenBugs. We then created a confidence model set for each species based on Akaike's Information Criterion. We found hierarchical habitat relationships for all species. Three-fold cross-validation estimates of model accuracy indicated an average overall correct classification rate of 60.5%. Comparisons with existing Georgia GAP Analysis models indicated that our models were more accurate overall. Our results provide guidance to wildlife scientists and managers seeking predict avian occurrence as a function of local and landscape-level habitat attributes.

Classification of Multiple Seizure-Like States in Three Different Rodent Models of Epileptogenesis.

PubMed

Guirgis, Mirna; Serletis, Demitre; Zhang, Jane; Florez, Carlos; Dian, Joshua A; Carlen, Peter L; Bardakjian, Berj L

2014-01-01

Epilepsy is a dynamical disease and its effects are evident in over fifty million people worldwide. This study focused on objective classification of the multiple states involved in the brain's epileptiform activity. Four datasets from three different rodent hippocampal preparations were explored, wherein seizure-like-events (SLE) were induced by the perfusion of a low - Mg(2+) /high-K(+) solution or 4-Aminopyridine. Local field potentials were recorded from CA3 pyramidal neurons and interneurons and modeled as Markov processes. Specifically, hidden Markov models (HMM) were used to determine the nature of the states present. Properties of the Hilbert transform were used to construct the feature spaces for HMM training. By sequentially applying the HMM training algorithm, multiple states were identified both in episodes of SLE and nonSLE activity. Specifically, preSLE and postSLE states were differentiated and multiple inner SLE states were identified. This was accomplished using features extracted from the lower frequencies (1-4 Hz, 4-8 Hz) alongside those of both the low- (40-100 Hz) and high-gamma (100-200 Hz) of the recorded electrical activity. The learning paradigm of this HMM-based system eliminates the inherent bias associated with other learning algorithms that depend on predetermined state segmentation and renders it an appropriate candidate for SLE classification.

Steady-states for shear flows of a liquid-crystal model: Multiplicity, stability, and hysteresis

NASA Astrophysics Data System (ADS)

Dorn, Tim; Liu, Weishi

In this work, we study shear flows of a fluid layer between two solid blocks via a liquid-crystal type model proposed in [C.H.A. Cheng, L.H. Kellogg, S. Shkoller, D.L. Turcotte, A liquid-crystal model for friction, Proc. Natl. Acad. Sci. USA 21 (2007) 1-5] for an understanding of frictions. A characterization on the existence and multiplicity of steady-states is provided. Stability issue of the steady-states is examined mainly focusing on bifurcations of zero eigenvalues. The stability result suggests that this simple model exhibits hysteresis, and it is supported by a numerical simulation.

Model of Laser-Induced Temperature Changes in Solid-State Optical Refrigerators

DTIC Science & Technology

2010-01-01

Model of laser-induced temperature changes in solid-state optical refrigerators W. M. Patterson,1,a M. Sheik- Bahae ,1 R. I . Epstein,2 and M. P...Applications of Laser Cooling of Sol- ids, edited by R. I . Epstein and M. Sheik- Bahae Wiley, Weinheim, 2009. 2W. Koechner, Solid-State Laser...2004. 19D. V. Seletskiy, S. D. Melgaard, S. Bigotta, A. Di Lieto, M. Tonelli, R. I . Epstein, and M. Sheik- Bahae , in Conference on Lasers and Electro

High fidelity quasi steady-state aerodynamic model effects on race vehicle performance predictions using multi-body simulation

NASA Astrophysics Data System (ADS)

Mohrfeld-Halterman, J. A.; Uddin, M.

2016-07-01

We described in this paper the development of a high fidelity vehicle aerodynamic model to fit wind tunnel test data over a wide range of vehicle orientations. We also present a comparison between the effects of this proposed model and a conventional quasi steady-state aerodynamic model on race vehicle simulation results. This is done by implementing both of these models independently in multi-body quasi steady-state simulations to determine the effects of the high fidelity aerodynamic model on race vehicle performance metrics. The quasi steady state vehicle simulation is developed with a multi-body NASCAR Truck vehicle model, and simulations are conducted for three different types of NASCAR race tracks, a short track, a one and a half mile intermediate track, and a higher speed, two mile intermediate race track. For each track simulation, the effects of the aerodynamic model on handling, maximum corner speed, and drive force metrics are analysed. The accuracy of the high-fidelity model is shown to reduce the aerodynamic model error relative to the conventional aerodynamic model, and the increased accuracy of the high fidelity aerodynamic model is found to have realisable effects on the performance metric predictions on the intermediate tracks resulting from the quasi steady-state simulation.

Three-state combinatorial switch models as applied to the binding of oxygen by human hemoglobin.

PubMed

Straume, M; Johnson, M L

1988-02-23

We have generated a series of all 6561 unique, discrete three-state combinatorial switch models to describe the partitioning of the cooperative oxygen-binding free change among the 10 variously ligated forms of human hemoglobin tetramers. These models were inspired by the experimental observation of Smith and Ackers that the cooperative free energy of the intersubunit contact regions of the 10 possible ligated forms of human hemoglobin tetramers can be represented by a particular distribution of three distinct energy levels [Smith, F. R., & Ackers, G. K. (1985) Proc. Natl. Acad. Sci. U.S.A. 82, 5347-5351]. A statistical thermodynamic formulation accounting for both dimer-tetramer equilibria and ligand binding properties of hemoglobin solutions as a function of oxygen and protein concentrations was utilized to exhaustively test these thermodynamic models. In this series of models each of the 10 ligated forms of the hemoglobin tetramer can exist in one, and only one, of three possible energy levels; i.e., each ligated form was assumed to be associated with a discrete energy state. This series of models includes all possible ways that the 10 ligation states of hemoglobin can be distributed into three distinct cooperative energy levels. The mathematical models, as presented here, do not permit equilibria between energy states to exist for any of the 10 unique ligated forms of hemoglobin tetramers. These models were analyzed by nonlinear least-squares estimation of the free energy parameters characteristic of this statistical thermodynamic development.(ABSTRACT TRUNCATED AT 250 WORDS)

The ground state of the Frenkel-Kontorova model

NASA Astrophysics Data System (ADS)

Babushkin, A. Yu.; Abkaryan, A. K.; Dobronets, B. S.; Krasikov, V. S.; Filonov, A. N.

2016-09-01

The continual approximation of the ground state of the discrete Frenkel-Kontorova model is tested using a symmetric algorithm of numerical simulation. A "kaleidoscope effect" is found, which means that the curves representing the dependences of the relative extension of an N-atom chain vary periodically with increasing N. Stairs of structural transitions for N ≫ 1 are analyzed by the channel selection method with the approximation N = ∞. Images of commensurable and incommensurable structures are constructed. The commensurable-incommensurable phase transitions are stepwise.

Modeling Gas-Particle Partitioning of SOA: Effects of Aerosol Physical State and RH

NASA Astrophysics Data System (ADS)

Zuend, A.; Seinfeld, J.

2011-12-01

Aged tropospheric aerosol particles contain mixtures of inorganic salts, acids, water, and a large variety of organic compounds. In liquid aerosol particles non-ideal mixing of all species determines whether the condensed phase undergoes liquid-liquid phase separation or whether it is stable in a single mixed phase, and whether it contains solid salts in equilibrium with their saturated solution. The extended thermodynamic model AIOMFAC is able to predict such phase states by representing the variety of organic components using functional groups within a group-contribution concept. The number and composition of different condensed phases impacts the diversity of reaction media for multiphase chemistry and the gas-particle partitioning of semivolatile species. Recent studies show that under certain conditions biogenic and other organic-rich particles can be present in a highly viscous, semisolid or amorphous solid physical state, with consequences regarding reaction kinetics and mass transfer limitations. We present results of new gas-particle partitioning computations for aerosol chamber data using a model based on AIOMFAC activity coefficients and state-of-the-art vapor pressure estimation methods. Different environmental conditions in terms of temperature, relative humidity (RH), salt content, amount of precursor VOCs, and physical state of the particles are considered. We show how modifications of absorptive and adsorptive gas-particle mass transfer affects the total aerosol mass in the calculations and how the results of these modeling approaches compare to data of aerosol chamber experiments, such as alpha-pinene oxidation SOA. For a condensed phase in a mixed liquid state containing ammonium sulfate, the model predicts liquid-liquid phase separation up to high RH in case of, on average, moderately hydrophilic organic compounds, such as first generation oxidation products of alpha-pinene. The computations also reveal that treating liquid phases as ideal

Emergent low-energy bound states in the two-orbital Hubbard model

DOE PAGES

Nunez-Fernandez, Y.; Kotliar, G.; Hallberg, K.

2018-03-30

A repulsive Coulomb interaction between electrons in different orbitals in correlated materials can give rise to bound quasiparticle states. We study the nonhybridized two-orbital Hubbard model with intra- (inter)orbital interaction U (U 12) and different bandwidths using an improved dynamical mean-field theory numerical technique which leads to reliable spectra on the real energy axis directly at zero temperature. We find that a finite density of states at the Fermi energy in one band is correlated with the emergence of well-defined quasiparticle states at excited energies Δ = U - U 12 in the other band. These excitations are interband holon-doublonmore » bound states. At the symmetric point U = U 12, the quasiparticle peaks are located at the Fermi energy, leading to a simultaneous and continuous Mott transition settling a long-standing controversy.« less

State estimation of stochastic non-linear hybrid dynamic system using an interacting multiple model algorithm.

PubMed

Elenchezhiyan, M; Prakash, J

2015-09-01

In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

PubMed Central

Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-01-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit. PMID:29765629

Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network

NASA Astrophysics Data System (ADS)

Li, Zhiqiang; Xu, Tingxue; Gu, Junyuan; Dong, Qi; Fu, Linyu

2018-04-01

This paper presents a quantitative reliability modelling and analysis method for multi-state elements based on a combination of the Markov process and a dynamic Bayesian network (DBN), taking perfect repair, imperfect repair and condition-based maintenance (CBM) into consideration. The Markov models of elements without repair and under CBM are established, and an absorbing set is introduced to determine the reliability of the repairable element. According to the state-transition relations between the states determined by the Markov process, a DBN model is built. In addition, its parameters for series and parallel systems, namely, conditional probability tables, can be calculated by referring to the conditional degradation probabilities. Finally, the power of a control unit in a failure model is used as an example. A dynamic fault tree (DFT) is translated into a Bayesian network model, and subsequently extended to a DBN. The results show the state probabilities of an element and the system without repair, with perfect and imperfect repair, and under CBM, with an absorbing set plotted by differential equations and verified. Through referring forward, the reliability value of the control unit is determined in different kinds of modes. Finally, weak nodes are noted in the control unit.