A finite element model updating technique for adjustment of parameters near boundaries
NASA Astrophysics Data System (ADS)
Gwinn, Allen Fort, Jr.
Even though there have been many advances in research related to methods of updating finite element models based on measured normal mode vibration characteristics, there is yet to be a widely accepted method that works reliably with a wide range of problems. This dissertation focuses on the specific class of problems having to do with changes in stiffness near the clamped boundary of plate structures. This class of problems is especially important as it relates to the performance of turbine engine blades, where a change in stiffness at the base of the blade can be indicative of structural damage. The method that is presented herein is a new technique for resolving the differences between the physical structure and the finite element model. It is a semi-iterative technique that incorporates a "physical expansion" of the measured eigenvectors along with appropriate scaling of these expanded eigenvectors into an iterative loop that uses the Engel's model modification method to then calculate adjusted stiffness parameters for the finite element model. Three example problems are presented that use eigenvalues and mass normalized eigenvectors that have been calculated from experimentally obtained accelerometer readings. The test articles that were used were all thin plates with one edge fully clamped. They each had a cantilevered length of 8.5 inches and a width of 4 inches. The three plates differed from one another in thickness from 0.100 inches to 0.188 inches. These dimensions were selected in order to approximate a gas turbine engine blade. The semi-iterative modification technique is shown to do an excellent job of calculating the necessary adjustments to the finite element model so that the analytically determined eigenvalues and eigenvectors for the adjusted model match the corresponding values from the experimental data with good agreement. Furthermore, the semi-iterative method is quite robust. For the examples presented here, the method consistently converged
Goeritz, Marie L.; Marder, Eve
2014-01-01
We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy. PMID:25008414
A Class of Elementary Particle Models Without Any Adjustable Real Parameters
NASA Astrophysics Data System (ADS)
't Hooft, Gerard
2011-12-01
Conventional particle theories such as the Standard Model have a number of freely adjustable coupling constants and mass parameters, depending on the symmetry algebra of the local gauge group and the representations chosen for the spinor and scalar fields. There seems to be no physical principle to determine these parameters as long as they stay within certain domains dictated by the renormalization group. Here however, reasons are given to demand that, when gravity is coupled to the system, local conformal invariance should be a spontaneously broken exact symmetry. The argument has to do with the requirement that black holes obey a complementarity principle relating ingoing observers to outside observers, or equivalently, initial states to final states. This condition fixes all parameters, including masses and the cosmological constant. We suspect that only examples can be found where these are all of order one in Planck units, but the values depend on the algebra chosen. This paper combines findings reported in two previous preprints (G. 't Hooft in arXiv:1009.0669 [gr-qc], 2010; arXiv:1011.0061 [gr-qc], 2010) and puts these in a clearer perspective by shifting the emphasis towards the implications for particle models.
Testing the compatibility of constraints for parameters of a geodetic adjustment model
NASA Astrophysics Data System (ADS)
Lehmann, Rüdiger; Neitzel, Frank
2013-06-01
Geodetic adjustment models are often set up in a way that the model parameters need to fulfil certain constraints. The normalized Lagrange multipliers have been used as a measure of the strength of constraint in such a way that if one of them exceeds in magnitude a certain threshold then the corresponding constraint is likely to be incompatible with the observations and the rest of the constraints. We show that these and similar measures can be deduced as test statistics of a likelihood ratio test of the statistical hypothesis that some constraints are incompatible in the same sense. This has been done before only for special constraints (Teunissen in Optimization and Design of Geodetic Networks, pp. 526-547, 1985). We start from the simplest case, that the full set of constraints is to be tested, and arrive at the advanced case, that each constraint is to be tested individually. Every test is worked out both for a known as well as for an unknown prior variance factor. The corresponding distributions under null and alternative hypotheses are derived. The theory is illustrated by the example of a double levelled line.
Modeling and simulation of M/M/c queuing pharmacy system with adjustable parameters
NASA Astrophysics Data System (ADS)
Rashida, A. R.; Fadzli, Mohammad; Ibrahim, Safwati; Goh, Siti Rohana
2016-02-01
This paper studies a discrete event simulation (DES) as a computer based modelling that imitates a real system of pharmacy unit. M/M/c queuing theo is used to model and analyse the characteristic of queuing system at the pharmacy unit of Hospital Tuanku Fauziah, Kangar in Perlis, Malaysia. The input of this model is based on statistical data collected for 20 working days in June 2014. Currently, patient waiting time of pharmacy unit is more than 15 minutes. The actual operation of the pharmacy unit is a mixed queuing server with M/M/2 queuing model where the pharmacist is referred as the server parameters. DES approach and ProModel simulation software is used to simulate the queuing model and to propose the improvement for queuing system at this pharmacy system. Waiting time for each server is analysed and found out that Counter 3 and 4 has the highest waiting time which is 16.98 and 16.73 minutes. Three scenarios; M/M/3, M/M/4 and M/M/5 are simulated and waiting time for actual queuing model and experimental queuing model are compared. The simulation results show that by adding the server (pharmacist), it will reduce patient waiting time to a reasonable improvement. Almost 50% average patient waiting time is reduced when one pharmacist is added to the counter. However, it is not necessary to fully utilize all counters because eventhough M/M/4 and M/M/5 produced more reduction in patient waiting time, but it is ineffective since Counter 5 is rarely used.
Kautter, John; Pope, Gregory C.
2004-01-01
The authors document the development of the CMS frailty adjustment model, a Medicare payment approach that adjusts payments to a Medicare managed care organization (MCO) according to the functional impairment of its community-residing enrollees. Beginning in 2004, this approach is being applied to certain organizations, such as Program of All-Inclusive Care for the Elderly (PACE), that specialize in providing care to the community-residing frail elderly. In the future, frailty adjustment could be extended to more Medicare managed care organizations. PMID:25372243
Optical phantoms with adjustable subdiffusive scattering parameters.
Krauter, Philipp; Nothelfer, Steffen; Bodenschatz, Nico; Simon, Emanuel; Stocker, Sabrina; Foschum, Florian; Kienle, Alwin
2015-10-01
A new epoxy-resin-based optical phantom system with adjustable subdiffusive scattering parameters is presented along with measurements of the intrinsic absorption, scattering, fluorescence, and refractive index of the matrix material. Both an aluminium oxide powder and a titanium dioxide dispersion were used as scattering agents and we present measurements of their scattering and reduced scattering coefficients. A method is theoretically described for a mixture of both scattering agents to obtain continuously adjustable anisotropy values g between 0.65 and 0.9 and values of the phase function parameter γ in the range of 1.4 to 2.2. Furthermore, we show absorption spectra for a set of pigments that can be added to achieve particular absorption characteristics. By additional analysis of the aging, a fully characterized phantom system is obtained with the novelty of g and γ parameter adjustment. PMID:26473589
Optical phantoms with adjustable subdiffusive scattering parameters
NASA Astrophysics Data System (ADS)
Krauter, Philipp; Nothelfer, Steffen; Bodenschatz, Nico; Simon, Emanuel; Stocker, Sabrina; Foschum, Florian; Kienle, Alwin
2015-10-01
A new epoxy-resin-based optical phantom system with adjustable subdiffusive scattering parameters is presented along with measurements of the intrinsic absorption, scattering, fluorescence, and refractive index of the matrix material. Both an aluminium oxide powder and a titanium dioxide dispersion were used as scattering agents and we present measurements of their scattering and reduced scattering coefficients. A method is theoretically described for a mixture of both scattering agents to obtain continuously adjustable anisotropy values g between 0.65 and 0.9 and values of the phase function parameter γ in the range of 1.4 to 2.2. Furthermore, we show absorption spectra for a set of pigments that can be added to achieve particular absorption characteristics. By additional analysis of the aging, a fully characterized phantom system is obtained with the novelty of g and γ parameter adjustment.
Resonance Parameter Adjustment Based on Integral Experiments
Sobes, Vladimir; Leal, Luiz; Arbanas, Goran; Forget, Benoit
2016-06-02
Our project seeks to allow coupling of differential and integral data evaluation in a continuous-energy framework and to use the generalized linear least-squares (GLLS) methodology in the TSURFER module of the SCALE code package to update the parameters of a resolved resonance region evaluation. We recognize that the GLLS methodology in TSURFER is identical to the mathematical description of a Bayesian update in SAMMY, the SAMINT code was created to use the mathematical machinery of SAMMY to update resolved resonance parameters based on integral data. Traditionally, SAMMY used differential experimental data to adjust nuclear data parameters. Integral experimental data, suchmore » as in the International Criticality Safety Benchmark Experiments Project, remain a tool for validation of completed nuclear data evaluations. SAMINT extracts information from integral benchmarks to aid the nuclear data evaluation process. Later, integral data can be used to resolve any remaining ambiguity between differential data sets, highlight troublesome energy regions, determine key nuclear data parameters for integral benchmark calculations, and improve the nuclear data covariance matrix evaluation. Moreover, SAMINT is not intended to bias nuclear data toward specific integral experiments but should be used to supplement the evaluation of differential experimental data. Using GLLS ensures proper weight is given to the differential data.« less
NASA Technical Reports Server (NTRS)
Reichle, Rolf; Koster, Randal; DeLannoy, Gabrielle; Forman, Barton; Liu, Qing; Mahanama, Sarith; Toure, Ally
2011-01-01
The Modern-Era Retrospective analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides. in addition to atmospheric fields. global estimates of soil moisture, latent heat flux. snow. and runoff for J 979-present. This study introduces a supplemental and improved set of land surface hydrological fields ('MERRA-Land') generated by replaying a revised version of the land component of the MERRA system. Specifically. the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameters in the rainfall interception model, changes that effectively correct for known limitations in the MERRA land surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ERA-Interim reanalysis. MERRA-Land and ERA-Interim root zone soil moisture skills (against in situ observations at 85 US stations) are comparable and significantly greater than that of MERRA. Throughout the northern hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 15 basins in the western US) of MERRA and MERRA-Land is typically higher than that of ERA-Interim. With a few exceptions. the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using '\\-tERRA output for land surface hydrological studies.
Concurrently adjusting interrelated control parameters to achieve optimal engine performance
Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna
2015-12-01
Methods and systems for real-time engine control optimization are provided. A value of an engine performance variable is determined, a value of a first operating condition and a value of a second operating condition of a vehicle engine are detected, and initial values for a first engine control parameter and a second engine control parameter are determined based on the detected first operating condition and the detected second operating condition. The initial values for the first engine control parameter and the second engine control parameter are adjusted based on the determined value of the engine performance variable to cause the engine performance variable to approach a target engine performance variable. In order to cause the engine performance variable to approach the target engine performance variable, adjusting the initial value for the first engine control parameter necessitates a corresponding adjustment of the initial value for the second engine control parameter.
ITRF2008 solution, geodetic parameters and Glacial Isostatic Adjustment
NASA Astrophysics Data System (ADS)
Metivier, L.; Collilieux, X.; Greff-Lefftz, M.; Altamimi, Z.
2011-12-01
Glacial Isostatic Adjustment (GIA) leads to long term crust motion, gravity variation, sea level rise and perturbation of Earth rotation. Recent studies have enlightened unexpected differences between a few recent GIA models mostly due to the way GIA induced rotational feedback is modeled. The validity and quality of these models have been essentially discussed with respect to space gravity observations. Here, we investigate what information the up-to-date International Terrestrial Reference Frame solution, ITRF2008, provides on large scale geodetic observables and by extension on Glacial Isostatic Adjustment (GIA) and recent ice melting processes. We particularly focus on the GNSS network of ITRF2008 solution because of the present day high precision of GNSS technique and because of the good density of the GNSS network. From these data, we infer and study large scale geodetic parameters and their time evolutions, such as Earth oblateness and J2 rate, or secular rotational feedback. We also investigate different GIA and recent ice melting models.
Sensitivity of adjustment to parameter correlations and to response-parameter correlations
Wagschal, J.J.
2011-07-01
The adjusted parameters and response, and their respective posterior uncertainties and correlations, are presented explicitly as functions of all relevant prior correlations for the two parameters, one response case. The dependence of these adjusted entities on the various prior correlations is analyzed and portrayed graphically for various valid correlation combinations on a simple criticality problem. (authors)
Global positioning system (GPS) supported block adjustment with self-calibration parameters
NASA Astrophysics Data System (ADS)
Blankenberg, Leif E.
1994-08-01
Systematic errors in the image coordinates is often a problem when working with high accuracy bundle block adjustment. These errors can be modeled by introducing self calibration parameters in the adjustment. In a traditional block adjustment, a dense network of ground control points are required to ensure a reliable estimate of these parameters. In the case of GPS-supported block adjustment, there is usually very little ground control available, but as shown in this paper, it is still possible to estimate the self calibration parameters. This is because of the stabilizing effect the GPS-determined perspective center coordinates have on the block. In this paper, both simulated data and real block data are used to evaluate the accuracy properties of GPS-supported blocks with additional self calibration parameters. Two different sets of additional parameters are evaluated. In the GPS-supported test blocks, the introduction of self calibration parameters improves the empirical tie point accuracy by 30 - 40%.
40 CFR 86.1833-01 - Adjustable parameters.
Code of Federal Regulations, 2013 CFR
2013-07-01
... 40 Protection of Environment 20 2013-07-01 2013-07-01 false Adjustable parameters. 86.1833-01 Section 86.1833-01 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED) AIR PROGRAMS (CONTINUED) CONTROL OF EMISSIONS FROM NEW AND IN-USE HIGHWAY VEHICLES AND ENGINES (CONTINUED) General Compliance Provisions for Control of Air...
Linking Item Response Model Parameters.
van der Linden, Wim J; Barrett, Michelle D
2016-09-01
With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of test equating scores on different test forms. This paper argues, however, that the use of item response models does not require any test score equating. Instead, it involves the necessity of parameter linking due to a fundamental problem inherent in the formal nature of these models-their general lack of identifiability. More specifically, item response model parameters need to be linked to adjust for the different effects of the identifiability restrictions used in separate item calibrations. Our main theorems characterize the formal nature of these linking functions for monotone, continuous response models, derive their specific shapes for different parameterizations of the 3PL model, and show how to identify them from the parameter values of the common items or persons in different linking designs. PMID:26155754
Dutra, José Diogo L.; Lima, Nathalia B. D.; Freire, Ricardo O.; Simas, Alfredo M.
2015-01-01
We advance the concept that the charge factors of the simple overlap model and the polarizabilities of Judd-Ofelt theory for the luminescence of europium complexes can be effectively and uniquely modeled by perturbation theory on the semiempirical electronic wave function of the complex. With only three adjustable constants, we introduce expressions that relate: (i) the charge factors to electronic densities, and (ii) the polarizabilities to superdelocalizabilities that we derived specifically for this purpose. The three constants are then adjusted iteratively until the calculated intensity parameters, corresponding to the 5D0→7F2 and 5D0→7F4 transitions, converge to the experimentally determined ones. This adjustment yields a single unique set of only three constants per complex and semiempirical model used. From these constants, we then define a binary outcome acceptance attribute for the adjustment, and show that when the adjustment is acceptable, the predicted geometry is, in average, closer to the experimental one. An important consequence is that the terms of the intensity parameters related to dynamic coupling and electric dipole mechanisms will be unique. Hence, the important energy transfer rates will also be unique, leading to a single predicted intensity parameter for the 5D0→7F6 transition. PMID:26329420
Overpaying morbidity adjusters in risk equalization models.
van Kleef, R C; van Vliet, R C J A; van de Ven, W P M M
2016-09-01
Most competitive social health insurance markets include risk equalization to compensate insurers for predictable variation in healthcare expenses. Empirical literature shows that even the most sophisticated risk equalization models-with advanced morbidity adjusters-substantially undercompensate insurers for selected groups of high-risk individuals. In the presence of premium regulation, these undercompensations confront consumers and insurers with incentives for risk selection. An important reason for the undercompensations is that not all information with predictive value regarding healthcare expenses is appropriate for use as a morbidity adjuster. To reduce incentives for selection regarding specific groups we propose overpaying morbidity adjusters that are already included in the risk equalization model. This paper illustrates the idea of overpaying by merging data on morbidity adjusters and healthcare expenses with health survey information, and derives three preconditions for meaningful application. Given these preconditions, we think overpaying may be particularly useful for pharmacy-based cost groups. PMID:26420555
40 CFR 86.1833-01 - Adjustable parameters.
Code of Federal Regulations, 2010 CFR
2010-07-01
... Compliance Provisions for Control of Air Pollution From New and In-Use Light-Duty Vehicles, Light-Duty Trucks... adjustment at the factory, and the inaccessibility is such that the screw cannot be accessed and/or...
40 CFR 94.205 - Prohibited controls, adjustable parameters.
Code of Federal Regulations, 2014 CFR
2014-07-01
... achieved the same percent reduction in NOX emissions from the optimal calibration would be considered to be... must specify in the maintenance instructions how to adjust the engines to achieve emission performance... performance that could be achieved in the absence of emission standards (i.e., the calibration that result...
40 CFR 94.205 - Prohibited controls, adjustable parameters.
Code of Federal Regulations, 2012 CFR
2012-07-01
... achieved the same percent reduction in NOX emissions from the optimal calibration would be considered to be... must specify in the maintenance instructions how to adjust the engines to achieve emission performance... performance that could be achieved in the absence of emission standards (i.e., the calibration that result...
40 CFR 94.205 - Prohibited controls, adjustable parameters.
Code of Federal Regulations, 2011 CFR
2011-07-01
... achieved the same percent reduction in NOX emissions from the optimal calibration would be considered to be... must specify in the maintenance instructions how to adjust the engines to achieve emission performance... performance that could be achieved in the absence of emission standards (i.e., the calibration that result...
40 CFR 94.205 - Prohibited controls, adjustable parameters.
Code of Federal Regulations, 2010 CFR
2010-07-01
... achieved the same percent reduction in NOX emissions from the optimal calibration would be considered to be... must specify in the maintenance instructions how to adjust the engines to achieve emission performance... performance that could be achieved in the absence of emission standards (i.e., the calibration that result...
40 CFR 86.1833-01 - Adjustable parameters.
Code of Federal Regulations, 2012 CFR
2012-07-01
... idle fuel-air mixture parameter on Otto-cycle vehicles; the choke valve action parameter(s) on... bimetal spring, the plate covering the bimetal spring is riveted or welded in place, or held in place with... return to its original shape after the force is removed (plastic or spring steel materials); (D) In...
40 CFR 86.1833-01 - Adjustable parameters.
Code of Federal Regulations, 2014 CFR
2014-07-01
... idle fuel-air mixture parameter on Otto-cycle vehicles; the choke valve action parameter(s) on... bimetal spring, the plate covering the bimetal spring is riveted or welded in place, or held in place with... return to its original shape after the force is removed (plastic or spring steel materials); (D) In...
Adjustment of Sensor Locations During Thermal Property Parameter Estimation
NASA Technical Reports Server (NTRS)
Milos, Frank S.; Marschall, Jochen; Rasky, Daniel J. (Technical Monitor)
1996-01-01
The temperature dependent thermal properties of a material may be evaluated from transient temperature histories using nonlinear parameter estimation techniques. The usual approach is to minimize the sum of the squared errors between measured and calculated temperatures at specific locations in the body. Temperature measurements are usually made with thermocouples and it is customary to take thermocouple locations as known and fixed during parameter estimation computations. In fact, thermocouple locations are never known exactly. Location errors on the order of the thermocouple wire diameter are intrinsic to most common instrumentation procedures (e.g., inserting a thermocouple into a drilled hole) and additional errors can be expected for delicate materials, difficult installations, large thermocouple beads, etc.. Thermocouple location errors are especially significant when estimating thermal properties of low diffusively materials which can sustain large temperature gradients during testing. In the present work, a parameter estimation formulation is presented which allows for the direct inclusion of thermocouple positions into the primary parameter estimation procedure. It is straightforward to set bounds on thermocouple locations which exclude non-physical locations and are consistent with installation tolerances. Furthermore, bounds may be tightened to an extent consistent with any independent verification of thermocouple location, such as x-raying, and so the procedure is entirely consonant with experimental information. A mathematical outline of the procedure is given and its implementation is illustrated through numerical examples characteristic of light-weight, high-temperature ceramic insulation during transient heating. The efficacy and the errors associated with the procedure are discussed.
Lai, Zhi-Hui; Leng, Yong-Gang
2015-01-01
A two-dimensional Duffing oscillator which can produce stochastic resonance (SR) is studied in this paper. We introduce its SR mechanism and present a generalized parameter-adjusted SR (GPASR) model of this oscillator for the necessity of parameter adjustments. The Kramers rate is chosen as the theoretical basis to establish a judgmental function for judging the occurrence of SR in this model; and to analyze and summarize the parameter-adjusted rules under unmatched signal amplitude, frequency, and/or noise-intensity. Furthermore, we propose the weak-signal detection approach based on this GPASR model. Finally, we employ two practical examples to demonstrate the feasibility of the proposed approach in practical engineering application. PMID:26343671
Estimation of Data Uncertainty Adjustment Parameters for Multivariate Earth Rotation Series
NASA Technical Reports Server (NTRS)
Sung, Li-yu; Steppe, J. Alan
1994-01-01
We have developed a maximum likelihood method to estimate a set of data uncertainty adjustment parameters, iccluding scaling factors and additive variances and covariances, for multivariate Earth rotation series.
Saturation-power enhancement of a free-electron laser amplifier through parameters adjustment
NASA Astrophysics Data System (ADS)
Ji, Yu-Pin; Xu, Y.-G.; Wang, S.-J.; Xu, J.-Y.; Liu, X.-X.; Zhang, S.-C.
2015-06-01
Saturation-power enhancement of a free-electron laser (FEL) amplifier by using tapered wiggler amplitude is based on the postponement of the saturation length of the uniform wiggler. In this paper, we qualitatively and quantitatively demonstrate that the saturation-power enhancement can be approached by means of the parameters adjustment, which is comparable to that by using a tapered wiggler. Compared to the method by tapering the wiggler amplitude, the method of parameters adjustment substantially shortens the saturation length, which is favorable to cutting down the manufacture and operation costs of the device.
Bailit, Jennifer L.; Grobman, William A.; Rice, Madeline Murguia; Spong, Catherine Y.; Wapner, Ronald J.; Varner, Michael W.; Thorp, John M.; Leveno, Kenneth J.; Caritis, Steve N.; Shubert, Phillip J.; Tita, Alan T. N.; Saade, George; Sorokin, Yoram; Rouse, Dwight J.; Blackwell, Sean C.; Tolosa, Jorge E.; Van Dorsten, J. Peter
2014-01-01
Objective Regulatory bodies and insurers evaluate hospital quality using obstetrical outcomes, however meaningful comparisons should take pre-existing patient characteristics into account. Furthermore, if risk-adjusted outcomes are consistent within a hospital, fewer measures and resources would be needed to assess obstetrical quality. Our objective was to establish risk-adjusted models for five obstetric outcomes and assess hospital performance across these outcomes. Study Design A cohort study of 115,502 women and their neonates born in 25 hospitals in the United States between March 2008 and February 2011. Hospitals were ranked according to their unadjusted and risk-adjusted frequency of venous thromboembolism, postpartum hemorrhage, peripartum infection, severe perineal laceration, and a composite neonatal adverse outcome. Correlations between hospital risk-adjusted outcome frequencies were assessed. Results Venous thromboembolism occurred too infrequently (0.03%, 95% CI 0.02% – 0.04%) for meaningful assessment. Other outcomes occurred frequently enough for assessment (postpartum hemorrhage 2.29% (95% CI 2.20–2.38), peripartum infection 5.06% (95% CI 4.93–5.19), severe perineal laceration at spontaneous vaginal delivery 2.16% (95% CI 2.06–2.27), neonatal composite 2.73% (95% CI 2.63–2.84)). Although there was high concordance between unadjusted and adjusted hospital rankings, several individual hospitals had an adjusted rank that was substantially different (as much as 12 rank tiers) than their unadjusted rank. None of the correlations between hospital adjusted outcome frequencies was significant. For example, the hospital with the lowest adjusted frequency of peripartum infection had the highest adjusted frequency of severe perineal laceration. Conclusions Evaluations based on a single risk-adjusted outcome cannot be generalized to overall hospital obstetric performance. PMID:23891630
Parameter extraction and transistor models
NASA Technical Reports Server (NTRS)
Rykken, Charles; Meiser, Verena; Turner, Greg; Wang, QI
1985-01-01
Using specified mathematical models of the MOSFET device, the optimal values of the model-dependent parameters were extracted from data provided by the Jet Propulsion Laboratory (JPL). Three MOSFET models, all one-dimensional were used. One of the models took into account diffusion (as well as convection) currents. The sensitivity of the models was assessed for variations of the parameters from their optimal values. Lines of future inquiry are suggested on the basis of the behavior of the devices, of the limitations of the proposed models, and of the complexity of the required numerical investigations.
Weissman, Oren; Domniz, Noam; Petashnick, Yoel R.; Gilboa, Dalia; Raviv, Tal; Barzilai, Liran; Farber, Nimrod; Harats, Moti; Winkler, Eyal; Haik, Josef
2015-01-01
Background: Burn victims experience immense physical and mental hardship during their process of rehabilitation and regaining functionality. We examined different objective burn-related factors as well as psychological ones, in the form of personality traits that may affect the rehabilitation process and its outcome. Objective: To assess the influence and correlation of specific personality traits and objective injury-related parameters on the adjustment of burn victims post-injury. Methods: Sixty-two male patients admitted to our burn unit due to burn injuries were compared with 36 healthy male individuals by use of questionnaires to assess each group’s psychological adjustment parameters. Multivariate and hierarchical regression analysis was conducted to identify differences between the groups. Results: A significant negative correlation was found between the objective burn injury severity (e.g., total body surface area and burn depth) and the adjustment of burn victims (p < 0.05, p < 0.001, Table 3). Moreover, patients more severely injured tend to be more neurotic (p < 0.001), and less extroverted and agreeable (p < 0.01, Table 4). Conclusion: Extroverted burn victims tend to adjust better to their post-injury life while the neurotic patients tend to have difficulties adjusting. This finding may suggest new tools for early identification of maladjustment-prone patients and therefore provide them with better psychological support in a more dedicated manner. PMID:25874193
An interface model for dosage adjustment connects hematotoxicity to pharmacokinetics.
Meille, C; Iliadis, A; Barbolosi, D; Frances, N; Freyer, G
2008-12-01
When modeling is required to describe pharmacokinetics and pharmacodynamics simultaneously, it is difficult to link time-concentration profiles and drug effects. When patients are under chemotherapy, despite the huge amount of blood monitoring numerations, there is a lack of exposure variables to describe hematotoxicity linked with the circulating drug blood levels. We developed an interface model that transforms circulating pharmacokinetic concentrations to adequate exposures, destined to be inputs of the pharmacodynamic process. The model is materialized by a nonlinear differential equation involving three parameters. The relevance of the interface model for dosage adjustment is illustrated by numerous simulations. In particular, the interface model is incorporated into a complex system including pharmacokinetics and neutropenia induced by docetaxel and by cisplatin. Emphasis is placed on the sensitivity of neutropenia with respect to the variations of the drug amount. This complex system including pharmacokinetic, interface, and pharmacodynamic hematotoxicity models is an interesting tool for analysis of hematotoxicity induced by anticancer agents. The model could be a new basis for further improvements aimed at incorporating new experimental features. PMID:19107581
Estimation of pharmacokinetic model parameters.
Timcenko, A; Reich, D L; Trunfio, G
1995-01-01
This paper addresses the problem of estimating the depth of anesthesia in clinical practice where many drugs are used in combination. The aim of the project is to use pharmacokinetically-derived data to predict episodes of light anesthesia. The weighted linear combination of anesthetic drug concentrations was computed using a stochastic pharmacokinetic model. The clinical definition of light anesthesia was based on the hemodynamic consequences of autonomic nervous system responses to surgical stimuli. A rule-based expert system was used to review anesthesia records to determine instances of light anesthesia using hemodynamic criteria. It was assumed that light anesthesia was a direct consequence of the weighted linear combination of drug concentrations in the patient's body that decreased below a certain threshold. We augmented traditional two-compartment models with a stochastic component of anesthetics' concentrations to compensate for interpatient pharmacokinetic and pharmacodynamic variability. A cohort of 532 clinical anesthesia cases was examined and parameters of two compartment pharmacokinetic models for 6 intravenously administered anesthetic drugs (fentanyl, thiopenthal, morphine, propofol, midazolam, ketamine) were estimated, as well as the parameters for 2 inhalational anesthetics (N2O and isoflurane). These parameters were then prospectively applied to 22 cases that were not used for parameter estimation, and the predictive ability of the pharmacokinetic model was determined. The goal of the study is the development of a pharmacokinetic model that will be useful in predicting light anesthesia in the clinically relevant circumstance where many drugs are used concurrently. PMID:8563327
Covariate-Adjusted Linear Mixed Effects Model with an Application to Longitudinal Data
Nguyen, Danh V.; Şentürk, Damla; Carroll, Raymond J.
2009-01-01
Linear mixed effects (LME) models are useful for longitudinal data/repeated measurements. We propose a new class of covariate-adjusted LME models for longitudinal data that nonparametrically adjusts for a normalizing covariate. The proposed approach involves fitting a parametric LME model to the data after adjusting for the nonparametric effects of a baseline confounding covariate. In particular, the effect of the observable covariate on the response and predictors of the LME model is modeled nonparametrically via smooth unknown functions. In addition to covariate-adjusted estimation of fixed/population parameters and random effects, an estimation procedure for the variance components is also developed. Numerical properties of the proposed estimators are investigated with simulation studies. The consistency and convergence rates of the proposed estimators are also established. An application to a longitudinal data set on calcium absorption, accounting for baseline distortion from body mass index, illustrates the proposed methodology. PMID:19266053
Cognitive Models of Risky Choice: Parameter Stability and Predictive Accuracy of Prospect Theory
ERIC Educational Resources Information Center
Glockner, Andreas; Pachur, Thorsten
2012-01-01
In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are…
Parameter identification in continuum models
NASA Technical Reports Server (NTRS)
Banks, H. T.; Crowley, J. M.
1983-01-01
Approximation techniques for use in numerical schemes for estimating spatially varying coefficients in continuum models such as those for Euler-Bernoulli beams are discussed. The techniques are based on quintic spline state approximations and cubic spline parameter approximations. Both theoretical and numerical results are presented.
Parameter estimation for transformer modeling
NASA Astrophysics Data System (ADS)
Cho, Sung Don
Large Power transformers, an aging and vulnerable part of our energy infrastructure, are at choke points in the grid and are key to reliability and security. Damage or destruction due to vandalism, misoperation, or other unexpected events is of great concern, given replacement costs upward of $2M and lead time of 12 months. Transient overvoltages can cause great damage and there is much interest in improving computer simulation models to correctly predict and avoid the consequences. EMTP (the Electromagnetic Transients Program) has been developed for computer simulation of power system transients. Component models for most equipment have been developed and benchmarked. Power transformers would appear to be simple. However, due to their nonlinear and frequency-dependent behaviors, they can be one of the most complex system components to model. It is imperative that the applied models be appropriate for the range of frequencies and excitation levels that the system experiences. Thus, transformer modeling is not a mature field and newer improved models must be made available. In this work, improved topologically-correct duality-based models are developed for three-phase autotransformers having five-legged, three-legged, and shell-form cores. The main problem in the implementation of detailed models is the lack of complete and reliable data, as no international standard suggests how to measure and calculate parameters. Therefore, parameter estimation methods are developed here to determine the parameters of a given model in cases where available information is incomplete. The transformer nameplate data is required and relative physical dimensions of the core are estimated. The models include a separate representation of each segment of the core, including hysteresis of the core, lambda-i saturation characteristic, capacitive effects, and frequency dependency of winding resistance and core loss. Steady-state excitation, and de-energization and re-energization transients
Block adjustment of Chang'E-1 images based on rational function model
NASA Astrophysics Data System (ADS)
Liu, Bin; Liu, Yiliang; Di, Kaichang; Sun, Xiliang
2014-05-01
Chang'E-1(CE-1) is the first lunar orbiter of China's lunar exploration program. The CCD camera carried by CE-1 has acquired stereo images covering the entire lunar surface. Block adjustment and 3D mapping using CE-1 images are of great importance for morphological and other scientific research of the Moon. Traditional block adjustment based on rigorous sensor model is complicated due to a large number of parameters and possible correlations among them. To tackle this problem, this paper presents a block adjustment method using Rational Function Model (RFM). The RFM parameters are generated based on rigorous sensor model using virtual grid of control points. Afterwards, the RFM based block adjustment solves the refinement parameters through a least squares solution. Experimental results using CE-1 images located in Sinus Irdium show that the RFM can fit the rigorous sensor model with a high precision of 1% pixel level. Through the RFM-based block adjustment, the back-projection residuals in image space can be reduced from around 1.5 pixels to sub-pixel., indicating that RFM can replace rigorous sensor model for geometric processing of lunar images.
Storm Water Management Model Climate Adjustment Tool (SWMM-CAT)
The US EPA’s newest tool, the Stormwater Management Model (SWMM) – Climate Adjustment Tool (CAT) is meant to help municipal stormwater utilities better address potential climate change impacts affecting their operations. SWMM, first released in 1971, models hydrology and hydrauli...
Unified Model for Academic Competence, Social Adjustment, and Psychopathology.
ERIC Educational Resources Information Center
Schaefer, Earl S.; And Others
A unified conceptual model is needed to integrate the extensive research on (1) social competence and adaptive behavior, (2) converging conceptualizations of social adjustment and psychopathology, and (3) emerging concepts and measures of academic competence. To develop such a model, a study was conducted in which teacher ratings were collected on…
Effects of alignment of adjustable collimator on dosimetric parameters of a telecobalt machine.
Sahani, G; Sharma, S D; Dash Sharma, P K; Sharma, D N; Hussain, S A
2013-04-01
The objectives of this study was to investigate the most appropriate hinge point for the alignment of adjustable collimators/trimmer bars in a telecobalt machine for obtaining acceptable dosimetric parameters of a telecobalt machine. Variations of relative output of the telecobalt machine with selection of different hinge points were also investigated. MCNP code was used for the present the study. A water phantom of dimension 50 x 50 x 40 cm(3) having voxels each of volume 0.72 cm(3) was used in our study for generating beam profiles and depth dose curves. When hinge points are selected at the periphery of the source bottom and the source top, flatness, symmetry and penumbra were found to be well within the recommended tolerance limits whereas values are far beyond with the hinge points selected at the centre of source bottom or the source top. Moreover, it was observed that the relative output of a telecobalt machine with hinge points at centre of the source bottom and the source top are appreciably lower than that of at periphery of source bottom particularly for smaller field sizes. This effect is due to the blockage of the part of the source volume in the radiation field. Therefore, hinge point for the alignment of adjustable collimators/trimmer bars should be selected either at periphery of source bottom or the source top for obtaining clinically acceptable flatness, symmetry and penumbra. However, selecting hinge point at the periphery of the source bottom for the alignment of the adjustable collimators/trimmer bars would be more appropriate as height of the source will vary depending on activity of the source used in the source capsule for given specific activity. PMID:23098284
Initial experience in operation of furnace burners with adjustable flame parameters
Garzanov, A.L.; Dolmatov, V.L.; Saifullin, N.R.
1995-07-01
The designs of burners currently used in tube furnaces (CP, FGM, GMG, GIK, GNF, etc.) do not have any provision for adjusting the heat-transfer characteristics of the flame, since the gas and air feed systems in these burners do not allow any variation of the parameters of mixture formation, even though this process is critical in determining the length, shape, and luminosity of the flame and also the furnace operating conditions: efficiency, excess air coefficient, flue gas temperature at the bridgewall, and other indexes. In order to provide the controlling the heat-transfer characteristics of the flame, the Elektrogorsk Scientific-Research Center (ENITs), on the assignment of the Novo-Ufa Petroleum Refinery, developed a burner with diffusion regulation of the flame. The gas nozzle of the burner is made up of two coaxial gas chambers 1 and 2, with independent feed of gas from a common line through two supply lines.
NASA Astrophysics Data System (ADS)
Wang, Pengbo; Gao, Yuan; Chen, Xiao; Li, Ting
2016-03-01
Low-level light therapy (LLLT) has been clinically applied. Recently, more and more cases are reported with positive therapeutic effect by using transcranial light emitting diodes (LEDs) illumination. Here, we developed a LLLT helmet for treating brain injuries based on LED arrays. We designed the LED arrays in circle shape and assembled them in multilayered 3D printed helmet with water-cooling module. The LED arrays can be adjust to touch the head of subjects. A control circuit was developed to drive and control the illumination of the LLLT helmet. The software portion provides the control of on and off of each LED arrays, the setup of illumination parameters, and 3D distribution of LLLT light dose in human subject according to the illumination setups. This LLLT light dose distribution was computed by a Monte Carlo model for voxelized media and the Visible Chinese Human head dataset and displayed in 3D view at the background of head anatomical structure. The performance of the whole system was fully tested. One stroke patient was recruited in the preliminary LLLT experiment and the following neuropsychological testing showed obvious improvement in memory and executive functioning. This clinical case suggested the potential of this Illumination-parameter adjustable and illuminationdistribution visible LED helmet as a reliable, noninvasive, and effective tool in treating brain injuries.
Using Bibliotherapy to Help Children Adjust to Changing Role Models.
ERIC Educational Resources Information Center
Pardeck, John T.; Pardeck, Jean A.
One technique for helping children adjust to changing role models is bibliotherapy--the use of children's books to facilitate identification with and exploration of sex role behavior. Confronted with change in various social systems, particularly the family, children are faced with conflicts concerning their sex role development. The process…
Catastrophe, Chaos, and Complexity Models and Psychosocial Adjustment to Disability.
ERIC Educational Resources Information Center
Parker, Randall M.; Schaller, James; Hansmann, Sandra
2003-01-01
Rehabilitation professionals may unknowingly rely on stereotypes and specious beliefs when dealing with people with disabilities, despite the formulation of theories that suggest new models of the adjustment process. Suggests that Catastrophe, Chaos, and Complexity Theories hold considerable promise in this regard. This article reviews these…
Multi-objective parameter optimization of common land model using adaptive surrogate modelling
NASA Astrophysics Data System (ADS)
Gong, W.; Duan, Q.; Li, J.; Wang, C.; Di, Z.; Dai, Y.; Ye, A.; Miao, C.
2014-06-01
Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20-100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) regional LSMs are expensive to run, while conventional multi-objective optimization methods needs a huge number of model runs (typically 105~106). It makes parameter optimization computationally prohibitive. An uncertainty qualification framework was developed to meet the aforementioned challenges: (1) use parameter screening to reduce the number of adjustable parameters; (2) use surrogate models to emulate the response of dynamic models to the variation of adjustable parameters; (3) use an adaptive strategy to promote the efficiency of surrogate modeling based optimization; (4) use a weighting function to transfer multi-objective optimization to single objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column case study of a land surface model - Common Land Model (CoLM) and evaluate the effectiveness and efficiency of the proposed framework. The result indicated that this framework can achieve optimal parameter set using totally 411 model runs, and worth to be extended to other large complex dynamic models, such as regional land surface models, atmospheric models and climate models.
Achieving high bit rate logical stochastic resonance in a bistable system by adjusting parameters
NASA Astrophysics Data System (ADS)
Yang, Ding-Xin; Gu, Feng-Shou; Feng, Guo-Jin; Yang, Yong-Min; Ball, Andrew
2015-11-01
The phenomenon of logical stochastic resonance (LSR) in a nonlinear bistable system is demonstrated by numerical simulations and experiments. However, the bit rates of the logical signals are relatively low and not suitable for practical applications. First, we examine the responses of the bistable system with fixed parameters to different bit rate logic input signals, showing that an arbitrary high bit rate LSR in a bistable system cannot be achieved. Then, a normalized transform of the LSR bistable system is introduced through a kind of variable substitution. Based on the transform, it is found that LSR for arbitrary high bit rate logic signals in a bistable system can be achieved by adjusting the parameters of the system, setting bias value and amplifying the amplitudes of logic input signals and noise properly. Finally, the desired OR and AND logic outputs to high bit rate logic inputs in a bistable system are obtained by numerical simulations. The study might provide higher feasibility of LSR in practical engineering applications. Project supported by the National Natural Science Foundation of China (Grant No. 51379526).
ERIC Educational Resources Information Center
Pakenham, Kenneth I.; Samios, Christina; Sofronoff, Kate
2005-01-01
The present study examined the applicability of the double ABCX model of family adjustment in explaining maternal adjustment to caring for a child diagnosed with Asperger syndrome. Forty-seven mothers completed questionnaires at a university clinic while their children were participating in an anxiety intervention. The children were aged between…
Parameter estimation for distributed parameter models of complex, flexible structures
NASA Technical Reports Server (NTRS)
Taylor, Lawrence W., Jr.
1991-01-01
Distributed parameter modeling of structural dynamics has been limited to simple spacecraft configurations because of the difficulty of handling several distributed parameter systems linked at their boundaries. Although there is other computer software able to generate such models or complex, flexible spacecraft, unfortunately, neither is suitable for parameter estimation. Because of this limitation the computer software PDEMOD is being developed for the express purposes of modeling, control system analysis, parameter estimation and structure optimization. PDEMOD is capable of modeling complex, flexible spacecraft which consist of a three-dimensional network of flexible beams and rigid bodies. Each beam has bending (Bernoulli-Euler or Timoshenko) in two directions, torsion, and elongation degrees of freedom. The rigid bodies can be attached to the beam ends at any angle or body location. PDEMOD is also capable of performing parameter estimation based on matching experimental modal frequencies and static deflection test data. The underlying formulation and the results of using this approach for test data of the Mini-MAST truss will be discussed. The resulting accuracy of the parameter estimates when using such limited data can impact significantly the instrumentation requirements for on-orbit tests.
Coercively Adjusted Auto Regression Model for Forecasting in Epilepsy EEG
Kim, Sun-Hee; Faloutsos, Christos; Yang, Hyung-Jeong
2013-01-01
Recently, data with complex characteristics such as epilepsy electroencephalography (EEG) time series has emerged. Epilepsy EEG data has special characteristics including nonlinearity, nonnormality, and nonperiodicity. Therefore, it is important to find a suitable forecasting method that covers these special characteristics. In this paper, we propose a coercively adjusted autoregression (CA-AR) method that forecasts future values from a multivariable epilepsy EEG time series. We use the technique of random coefficients, which forcefully adjusts the coefficients with −1 and 1. The fractal dimension is used to determine the order of the CA-AR model. We applied the CA-AR method reflecting special characteristics of data to forecast the future value of epilepsy EEG data. Experimental results show that when compared to previous methods, the proposed method can forecast faster and accurately. PMID:23710252
Huang, Lam O.; Infante-Rivard, Claire; Labbe, Aurélie
2016-01-01
Transmission of the two parental alleles to offspring deviating from the Mendelian ratio is termed Transmission Ratio Distortion (TRD), occurs throughout gametic and embryonic development. TRD has been well-studied in animals, but remains largely unknown in humans. The Transmission Disequilibrium Test (TDT) was first proposed to test for association and linkage in case-trios (affected offspring and parents); adjusting for TRD using control-trios was recommended. However, the TDT does not provide risk parameter estimates for different genetic models. A loglinear model was later proposed to provide child and maternal relative risk (RR) estimates of disease, assuming Mendelian transmission. Results from our simulation study showed that case-trios RR estimates using this model are biased in the presence of TRD; power and Type 1 error are compromised. We propose an extended loglinear model adjusting for TRD. Under this extended model, RR estimates, power and Type 1 error are correctly restored. We applied this model to an intrauterine growth restriction dataset, and showed consistent results with a previous approach that adjusted for TRD using control-trios. Our findings suggested the need to adjust for TRD in avoiding spurious results. Documenting TRD in the population is therefore essential for the correct interpretation of genetic association studies.
NASA Astrophysics Data System (ADS)
Mizukami, Naoki; Clark, Martyn; Newman, Andrew; Wood, Andy
2016-04-01
Estimation of spatially distributed parameters is one of the biggest challenges in hydrologic modeling over a large spatial domain. This problem arises from methodological challenges such as the transfer of calibrated parameters to ungauged locations. Consequently, many current large scale hydrologic assessments rely on spatially inconsistent parameter fields showing patchwork patterns resulting from individual basin calibration or spatially constant parameters resulting from the adoption of default or a-priori estimates. In this study we apply the Multi-scale Parameter Regionalization (MPR) framework (Samaniego et al., 2010) to generate spatially continuous and optimized parameter fields for the Variable Infiltration Capacity (VIC) model over the contiguous United States(CONUS). The MPR method uses transfer functions that relate geophysical attributes (e.g., soil) to model parameters (e.g., parameters that describe the storage and transmission of water) at the native resolution of the geophysical attribute data and then scale to the model spatial resolution with several scaling functions, e.g., arithmetic mean, harmonic mean, and geometric mean. Model parameter adjustments are made by calibrating the parameters of the transfer function rather than the model parameters themselves. In this presentation, we first discuss conceptual challenges in a "model agnostic" continental-domain application of the MPR approach. We describe development of transfer functions for the soil parameters, and discuss challenges associated with extending MPR for VIC to multiple models. Next, we discuss the "computational shortcut" of headwater basin calibration where we estimate the parameters for only 500 headwater basins rather than conducting simulations for every grid box across the entire domain. We first performed individual basin calibration to obtain a benchmark of the maximum achievable performance in each basin, and examined their transferability to the other basins. We then
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J.; Maity, Arnab
2013-01-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data. PMID:24363476
Parameter Estimation of Partial Differential Equation Models.
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab
2013-01-01
Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data. PMID:24363476
Understanding Parameter Invariance in Unidimensional IRT Models
ERIC Educational Resources Information Center
Rupp, Andre A.; Zumbo, Bruno D.
2006-01-01
One theoretical feature that makes item response theory (IRT) models those of choice for many psychometric data analysts is parameter invariance, the equality of item and examinee parameters from different examinee populations or measurement conditions. In this article, using the well-known fact that item and examinee parameters are identical only…
Model parameter updating using Bayesian networks
Treml, C. A.; Ross, Timothy J.
2004-01-01
This paper outlines a model parameter updating technique for a new method of model validation using a modified model reference adaptive control (MRAC) framework with Bayesian Networks (BNs). The model parameter updating within this method is generic in the sense that the model/simulation to be validated is treated as a black box. It must have updateable parameters to which its outputs are sensitive, and those outputs must have metrics that can be compared to that of the model reference, i.e., experimental data. Furthermore, no assumptions are made about the statistics of the model parameter uncertainty, only upper and lower bounds need to be specified. This method is designed for situations where a model is not intended to predict a complete point-by-point time domain description of the item/system behavior; rather, there are specific points, features, or events of interest that need to be predicted. These specific points are compared to the model reference derived from actual experimental data. The logic for updating the model parameters to match the model reference is formed via a BN. The nodes of this BN consist of updateable model input parameters and the specific output values or features of interest. Each time the model is executed, the input/output pairs are used to adapt the conditional probabilities of the BN. Each iteration further refines the inferred model parameters to produce the desired model output. After parameter updating is complete and model inputs are inferred, reliabilities for the model output are supplied. Finally, this method is applied to a simulation of a resonance control cooling system for a prototype coupled cavity linac. The results are compared to experimental data.
Global Model Analysis by Parameter Space Partitioning
ERIC Educational Resources Information Center
Pitt, Mark A.; Kim, Woojae; Navarro, Daniel J.; Myung, Jay I.
2006-01-01
To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g.,…
Measurement of the angular-motion parameters of a base by a dynamically adjustable gyroscope
NASA Astrophysics Data System (ADS)
Zbrutskii, A. V.
1986-04-01
The paper examines the dynamics and errors of a balanced dynamically adjustable gyroscope as a sensor of the angular deviations and angular velocities of the base. Attention is given to measurements made under conditions of uniform and uniformly accelerated rotation of the base.
Lithium-ion Open Circuit Voltage (OCV) curve modelling and its ageing adjustment
NASA Astrophysics Data System (ADS)
Lavigne, L.; Sabatier, J.; Francisco, J. Mbala; Guillemard, F.; Noury, A.
2016-08-01
This paper is a contribution to lithium-ion batteries modelling taking into account aging effects. It first analyses the impact of aging on electrode stoichiometry and then on lithium-ion cell Open Circuit Voltage (OCV) curve. Through some hypotheses and an appropriate definition of the cell state of charge, it shows that each electrode equilibrium potential, but also the whole cell equilibrium potential can be modelled by a polynomial that requires only one adjustment parameter during aging. An adjustment algorithm, based on the idea that for two fixed OCVs, the state of charge between these two equilibrium states is unique for a given aging level, is then proposed. Its efficiency is evaluated on a battery pack constituted of four cells.
RECURSIVE PARAMETER ESTIMATION OF HYDROLOGIC MODELS
Proposed is a nonlinear filtering approach to recursive parameter estimation of conceptual watershed response models in state-space form. he conceptual model state is augmented by the vector of free parameters which are to be estimated from input-output data, and the extended Kal...
Multi-objective parameter optimization of common land model using adaptive surrogate modeling
NASA Astrophysics Data System (ADS)
Gong, W.; Duan, Q.; Li, J.; Wang, C.; Di, Z.; Dai, Y.; Ye, A.; Miao, C.
2015-05-01
Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ~105-106). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM - the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regional-scale LSMs, atmospheric models and climate models.
NASA Astrophysics Data System (ADS)
Müller, Marc F.; Thompson, Sally E.
2013-10-01
Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Sparse ground-based gauge networks do not provide a robust basis for interpolation, and the reliability of remote sensing products, although improving, is still imperfect. Current techniques to estimate precipitation rely on combining these different kinds of measurements to correct the bias in the satellite observations. We propose a novel procedure that, unlike existing techniques, (i) allows correcting the possibly confounding effects of different sources of errors in satellite estimates, (ii) explicitly accounts for the spatial heterogeneity of the biases and (iii) allows the use of non overlapping historical observations. The proposed method spatially aggregates and interpolates gauge data at the satellite grid resolution by focusing on parameters that describe the frequency and intensity of the rainfall observed at the gauges. The resulting gridded parameters can then be used to adjust the probability density function of satellite rainfall observations at each grid cell, accounting for spatial heterogeneity. Unlike alternate methods, we explicitly adjust biases on rainfall frequency in addition to its intensity. Adjusted rainfall distributions can then readily be applied as input in stochastic rainfall generators or frequency domain hydrological models. Finally, we also provide a procedure to use them to correct remotely sensed rainfall time series. We apply the method to adjust the distributions of daily rainfall observed by the TRMM satellite in Nepal, which exemplifies the challenges associated with a sparse gauge network and large biases due to complex topography. In a cross-validation analysis on daily rainfall from TRMM 3B42 v6, we find that using a small subset of the available gauges, the proposed method outperforms local rainfall estimations using the complete network of available gauges to directly interpolate local rainfall or correct TRMM by adjusting
Disaster Hits Home: A Model of Displaced Family Adjustment after Hurricane Katrina
ERIC Educational Resources Information Center
Peek, Lori; Morrissey, Bridget; Marlatt, Holly
2011-01-01
The authors explored individual and family adjustment processes among parents (n = 30) and children (n = 55) who were displaced to Colorado after Hurricane Katrina. Drawing on in-depth interviews with 23 families, this article offers an inductive model of displaced family adjustment. Four stages of family adjustment are presented in the model: (a)…
ERIC Educational Resources Information Center
Tay, Louis; Drasgow, Fritz
2012-01-01
Two Monte Carlo simulation studies investigated the effectiveness of the mean adjusted X[superscript 2]/df statistic proposed by Drasgow and colleagues and, because of problems with the method, a new approach for assessing the goodness of fit of an item response theory model was developed. It has been previously recommended that mean adjusted…
Parameter Invariance in the Rasch Model.
ERIC Educational Resources Information Center
Davison, Mark L.; Chen, Tsuey-Hwa
This paper explores a logistic regression procedure for estimating item parameters in the Rasch model and testing the hypothesis of item parameter invariance across several groups/populations. Rather than using item responses directly, the procedure relies on "pseudo-paired comparisons" (PC) statistics defined over all possible pairs of items.…
NASA Astrophysics Data System (ADS)
Brix, H.; Menemenlis, D.; Hill, C.; Dutkiewicz, S.; Jahn, O.; Wang, D.; Bowman, K.; Zhang, H.
2015-11-01
The NASA Carbon Monitoring System (CMS) Flux Project aims to attribute changes in the atmospheric accumulation of carbon dioxide to spatially resolved fluxes by utilizing the full suite of NASA data, models, and assimilation capabilities. For the oceanic part of this project, we introduce ECCO2-Darwin, a new ocean biogeochemistry general circulation model based on combining the following pre-existing components: (i) a full-depth, eddying, global-ocean configuration of the Massachusetts Institute of Technology general circulation model (MITgcm), (ii) an adjoint-method-based estimate of ocean circulation from the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) project, (iii) the MIT ecosystem model "Darwin", and (iv) a marine carbon chemistry model. Air-sea gas exchange coefficients and initial conditions of dissolved inorganic carbon, alkalinity, and oxygen are adjusted using a Green's Functions approach in order to optimize modeled air-sea CO2 fluxes. Data constraints include observations of carbon dioxide partial pressure (pCO2) for 2009-2010, global air-sea CO2 flux estimates, and the seasonal cycle of the Takahashi et al. (2009) Atlas. The model sensitivity experiments (or Green's Functions) include simulations that start from different initial conditions as well as experiments that perturb air-sea gas exchange parameters and the ratio of particulate inorganic to organic carbon. The Green's Functions approach yields a linear combination of these sensitivity experiments that minimizes model-data differences. The resulting initial conditions and gas exchange coefficients are then used to integrate the ECCO2-Darwin model forward. Despite the small number (six) of control parameters, the adjusted simulation is significantly closer to the data constraints (37% cost function reduction, i.e., reduction in the model-data difference, relative to the baseline simulation) and to independent observations (e.g., alkalinity). The adjusted air-sea gas
Li, Li; Brumback, Babette A; Weppelmann, Thomas A; Morris, J Glenn; Ali, Afsar
2016-08-15
Motivated by an investigation of the effect of surface water temperature on the presence of Vibrio cholerae in water samples collected from different fixed surface water monitoring sites in Haiti in different months, we investigated methods to adjust for unmeasured confounding due to either of the two crossed factors site and month. In the process, we extended previous methods that adjust for unmeasured confounding due to one nesting factor (such as site, which nests the water samples from different months) to the case of two crossed factors. First, we developed a conditional pseudolikelihood estimator that eliminates fixed effects for the levels of each of the crossed factors from the estimating equation. Using the theory of U-Statistics for independent but non-identically distributed vectors, we show that our estimator is consistent and asymptotically normal, but that its variance depends on the nuisance parameters and thus cannot be easily estimated. Consequently, we apply our estimator in conjunction with a permutation test, and we investigate use of the pigeonhole bootstrap and the jackknife for constructing confidence intervals. We also incorporate our estimator into a diagnostic test for a logistic mixed model with crossed random effects and no unmeasured confounding. For comparison, we investigate between-within models extended to two crossed factors. These generalized linear mixed models include covariate means for each level of each factor in order to adjust for the unmeasured confounding. We conduct simulation studies, and we apply the methods to the Haitian data. Copyright © 2016 John Wiley & Sons, Ltd. PMID:26892025
Exploiting intrinsic fluctuations to identify model parameters.
Zimmer, Christoph; Sahle, Sven; Pahle, Jürgen
2015-04-01
Parameterisation of kinetic models plays a central role in computational systems biology. Besides the lack of experimental data of high enough quality, some of the biggest challenges here are identification issues. Model parameters can be structurally non-identifiable because of functional relationships. Noise in measured data is usually considered to be a nuisance for parameter estimation. However, it turns out that intrinsic fluctuations in particle numbers can make parameters identifiable that were previously non-identifiable. The authors present a method to identify model parameters that are structurally non-identifiable in a deterministic framework. The method takes time course recordings of biochemical systems in steady state or transient state as input. Often a functional relationship between parameters presents itself by a one-dimensional manifold in parameter space containing parameter sets of optimal goodness. Although the system's behaviour cannot be distinguished on this manifold in a deterministic framework it might be distinguishable in a stochastic modelling framework. Their method exploits this by using an objective function that includes a measure for fluctuations in particle numbers. They show on three example models, immigration-death, gene expression and Epo-EpoReceptor interaction, that this resolves the non-identifiability even in the case of measurement noise with known amplitude. The method is applied to partially observed recordings of biochemical systems with measurement noise. It is simple to implement and it is usually very fast to compute. This optimisation can be realised in a classical or Bayesian fashion. PMID:26672148
Parameter Estimation for Groundwater Models under Uncertain Irrigation Data.
Demissie, Yonas; Valocchi, Albert; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen
2015-01-01
The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes. PMID:25040235
Robust parameter estimation method for bilinear model
NASA Astrophysics Data System (ADS)
Ismail, Mohd Isfahani; Ali, Hazlina; Yahaya, Sharipah Soaad S.
2015-12-01
This paper proposed the method of parameter estimation for bilinear model, especially on BL(1,0,1,1) model without and with the presence of additive outlier (AO). In this study, the estimated parameters for BL(1,0,1,1) model are using nonlinear least squares (LS) method and also through robust approaches. The LS method employs the Newton-Raphson (NR) iterative procedure in estimating the parameters of bilinear model, but, using LS in estimating the parameters can be affected with the occurrence of outliers. As a solution, this study proposed robust approaches in dealing with the problem of outliers specifically on AO in BL(1,0,1,1) model. In robust estimation method, for improvement, we proposed to modify the NR procedure with robust scale estimators. We introduced two robust scale estimators namely median absolute deviation (MADn) and Tn in linear autoregressive model, AR(1) that be adequate and suitable for bilinear BL(1,0,1,1) model. We used the estimated parameter value in AR(1) model as an initial value in estimating the parameter values of BL(1,0,1,1) model. The investigation of the performance of LS and robust estimation methods in estimating the coefficients of BL(1,0,1,1) model is carried out through simulation study. The achievement of performance for both methods will be assessed in terms of bias values. Numerical results present that, the robust estimation method performs better than LS method in estimating the parameters without and with the presence of AO.
NASA Astrophysics Data System (ADS)
Barsai, Gabor
provides a path to fuse data from lidar, GIS and digital multispectral images and reconstructing the precise 3-D scene model, without human intervention, regardless of the type of data or features in the data. The data are initially registered to each other using GPS/INS initial positional values, then conjugate features are found in the datasets to refine the registration. The novelty of the research is that no conjugate points are necessary in the various datasets, and registration is performed without human intervention. The proposed system uses the original lidar and GIS data and finds edges of buildings with the help of the digital images, utilizing the exterior orientation parameters to project the lidar points onto the edge extracted image/map. These edge points are then utilized to orient and locate the datasets, in a correct position with respect to each other.
Autonomous Parameter Adjustment for SSVEP-Based BCIs with a Novel BCI Wizard
Gembler, Felix; Stawicki, Piotr; Volosyak, Ivan
2015-01-01
Brain-Computer Interfaces (BCIs) transfer human brain activities into computer commands and enable a communication channel without requiring movement. Among other BCI approaches, steady-state visual evoked potential (SSVEP)-based BCIs have the potential to become accurate, assistive technologies for persons with severe disabilities. Those systems require customization of different kinds of parameters (e.g., stimulation frequencies). Calibration usually requires selecting predefined parameters by experienced/trained personnel, though in real-life scenarios an interface allowing people with no experience in programming to set up the BCI would be desirable. Another occurring problem regarding BCI performance is BCI illiteracy (also called BCI deficiency). Many articles reported that BCI control could not be achieved by a non-negligible number of users. In order to bypass those problems we developed a SSVEP-BCI wizard, a system that automatically determines user-dependent key-parameters to customize SSVEP-based BCI systems. This wizard was tested and evaluated with 61 healthy subjects. All subjects were asked to spell the phrase “RHINE WAAL UNIVERSITY” with a spelling application after key parameters were determined by the wizard. Results show that all subjects were able to control the spelling application. A mean (SD) accuracy of 97.14 (3.73)% was reached (all subjects reached an accuracy above 85% and 25 subjects even reached 100% accuracy). PMID:26733788
Models and parameters for environmental radiological assessments
Miller, C W
1984-01-01
This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base. (ACR)
Revised Parameters for the AMOEBA Polarizable Atomic Multipole Water Model.
Laury, Marie L; Wang, Lee-Ping; Pande, Vijay S; Head-Gordon, Teresa; Ponder, Jay W
2015-07-23
A set of improved parameters for the AMOEBA polarizable atomic multipole water model is developed. An automated procedure, ForceBalance, is used to adjust model parameters to enforce agreement with ab initio-derived results for water clusters and experimental data for a variety of liquid phase properties across a broad temperature range. The values reported here for the new AMOEBA14 water model represent a substantial improvement over the previous AMOEBA03 model. The AMOEBA14 model accurately predicts the temperature of maximum density and qualitatively matches the experimental density curve across temperatures from 249 to 373 K. Excellent agreement is observed for the AMOEBA14 model in comparison to experimental properties as a function of temperature, including the second virial coefficient, enthalpy of vaporization, isothermal compressibility, thermal expansion coefficient, and dielectric constant. The viscosity, self-diffusion constant, and surface tension are also well reproduced. In comparison to high-level ab initio results for clusters of 2-20 water molecules, the AMOEBA14 model yields results similar to AMOEBA03 and the direct polarization iAMOEBA models. With advances in computing power, calibration data, and optimization techniques, we recommend the use of the AMOEBA14 water model for future studies employing a polarizable water model. PMID:25683601
Dolphins Adjust Species-Specific Frequency Parameters to Compensate for Increasing Background Noise
Papale, Elena; Gamba, Marco; Perez-Gil, Monica; Martin, Vidal Martel; Giacoma, Cristina
2015-01-01
An increase in ocean noise levels could interfere with acoustic communication of marine mammals. In this study we explored the effects of anthropogenic and natural noise on the acoustic properties of a dolphin communication signal, the whistle. A towed array with four elements was used to record environmental background noise and whistles of short-beaked common-, Atlantic spotted- and striped-dolphins in the Canaries archipelago. Four frequency parameters were measured from each whistle, while Sound Pressure Levels (SPL) of the background noise were measured at the central frequencies of seven one-third octave bands, from 5 to 20 kHz. Results show that dolphins increase the whistles’ frequency parameters with lower variability in the presence of anthropogenic noise, and increase the end frequency of their whistles when confronted with increasing natural noise. This study provides the first evidence that the synergy among SPLs has a role in shaping the whistles' structure of these three species, with respect to both natural and anthropogenic noise. PMID:25853825
Identification of the Jiles-Atherton model parameters using random and deterministic searches
NASA Astrophysics Data System (ADS)
Del Moral Hernandez, Emilio; S. Muranaka, Carlos; Cardoso, José R.
2000-01-01
The five parameters of the Jiles-Atherton (J-A) model are identified using a simple program based on the Matlab platform which identifies the J-A parameters automatically from experimental B- H hysteresis curves of magnetic cores. This computational tool is based on adaptive adjustment of the J-A model parameters and conjugates its parametric non-linear coupled differential equations with techniques of simulated annealing.
Wanninkhof, R.
2003-05-21
As part of the global synthesis effort sponsored by the Global Carbon Cycle project of the National Oceanic and Atmospheric Administration (NOAA) and U.S. Department of Energy, a comprehensive comparison was performed of inorganic carbon parameters measured on oceanographic surveys carried out under auspices of the Joint Global Ocean Flux Study and related programs. Many of the cruises were performed as part of the World Hydrographic Program of the World Ocean Circulation Experiment and the NOAA Ocean-Atmosphere Carbon Exchange Study. Total dissolved inorganic carbon (DIC), total alkalinity (TAlk), fugacity of CO{sub 2}, and pH data from twenty-three cruises were checked to determine whether there were systematic offsets of these parameters between cruises. The focus was on the DIC and TAlk state variables. Data quality and offsets of DIC and TAlk were determined by using several different techniques. One approach was based on crossover analyses, where the deep-water concentrations of DIC and TAlk were compared for stations on different cruises that were within 100 km of each other. Regional comparisons were also made by using a multiple-parameter linear regression technique in which DIC or TAlk was regressed against hydrographic and nutrient parameters. When offsets of greater than 4 {micro}mol/kg were observed for DIC and/or 6 {micro}mol/kg were observed for TAlk, the data taken on the cruise were closely scrutinized to determine whether the offsets were systematic. Based on these analyses, the DIC data and TAlk data of three cruises were deemed of insufficient quality to be included in the comprehensive basinwide data set. For several of the cruises, small adjustments in TAlk were recommended for consistency with other cruises in the region. After these adjustments were incorporated, the inorganic carbon data from all cruises along with hydrographic, chlorofluorocarbon, and nutrient data were combined as a research quality product for the scientific community.
Analysis of Modeling Parameters on Threaded Screws.
Vigil, Miquela S.; Brake, Matthew Robert; Vangoethem, Douglas
2015-06-01
Assembled mechanical systems often contain a large number of bolted connections. These bolted connections (joints) are integral aspects of the load path for structural dynamics, and, consequently, are paramount for calculating a structure's stiffness and energy dissipation prop- erties. However, analysts have not found the optimal method to model appropriately these bolted joints. The complexity of the screw geometry cause issues when generating a mesh of the model. This paper will explore different approaches to model a screw-substrate connec- tion. Model parameters such as mesh continuity, node alignment, wedge angles, and thread to body element size ratios are examined. The results of this study will give analysts a better understanding of the influences of these parameters and will aide in finding the optimal method to model bolted connections.
EMG/ECG Acquisition System with Online Adjustable Parameters Using ZigBee Wireless Technology
NASA Astrophysics Data System (ADS)
Kobayashi, Hiroyuki
This paper deals with a novel wireless bio-signal acquisition system employing ZigBee wireless technology, which consists of mainly two components, that is, intelligent electrode and data acquisition host. The former is the main topic of this paper. It is put on a subject's body to amplify bio-signal such as EMG or ECG and stream its data at upto 2 ksps. One of the most remarkable feature of the intelligent electrode is that it can change its own parameters including both digital and analog ones on-line. The author describes its design first, then introduces a small, light and low cost implementation of the intelligent electrode named as “VAMPIRE-BAT.” And he show some experimental results to confirm its usability and to estimate its practical performances.
Parameter Estimation of Spacecraft Fuel Slosh Model
NASA Technical Reports Server (NTRS)
Gangadharan, Sathya; Sudermann, James; Marlowe, Andrea; Njengam Charles
2004-01-01
Fuel slosh in the upper stages of a spinning spacecraft during launch has been a long standing concern for the success of a space mission. Energy loss through the movement of the liquid fuel in the fuel tank affects the gyroscopic stability of the spacecraft and leads to nutation (wobble) which can cause devastating control issues. The rate at which nutation develops (defined by Nutation Time Constant (NTC can be tedious to calculate and largely inaccurate if done during the early stages of spacecraft design. Pure analytical means of predicting the influence of onboard liquids have generally failed. A strong need exists to identify and model the conditions of resonance between nutation motion and liquid modes and to understand the general characteristics of the liquid motion that causes the problem in spinning spacecraft. A 3-D computerized model of the fuel slosh that accounts for any resonant modes found in the experimental testing will allow for increased accuracy in the overall modeling process. Development of a more accurate model of the fuel slosh currently lies in a more generalized 3-D computerized model incorporating masses, springs and dampers. Parameters describing the model include the inertia tensor of the fuel, spring constants, and damper coefficients. Refinement and understanding the effects of these parameters allow for a more accurate simulation of fuel slosh. The current research will focus on developing models of different complexity and estimating the model parameters that will ultimately provide a more realistic prediction of Nutation Time Constant obtained through simulation.
Obtaining diverse behaviors in a climate model without the use of flux adjustment
NASA Astrophysics Data System (ADS)
Yamazaki, K.; Rowlands, D. J.; Williamson, D.; Allen, M.
2011-12-01
Efforts have been made in past research to attain a wide range of atmosphere and ocean model behaviors by perturbing the model physics of Global Climate Models. However, obtaining a large spread of behaviors of the ocean model has so far been unsuccessful. In an ongoing project within RAPID-WATCH, physical parameters of HadCM3 have been perturbed within plausible ranges across the Latin-hypercube to generate a 10,000 member ensemble, which have been running on the distributed computing platform of climateprediction.net. In this work we resample and run a second, 20,000 member ensemble of model variants that have been identified not to drift significantly away from a realistic initial base state, a key step since we are not using flux adjustment. To this end, they are conditioned on the diagnosed fluxes from the first ensemble by statistical methods to sample regions of parameter space that are predicted to exhibit low top-of-atmosphere (TOA) flux imbalance. Specifically, we constrain the distribution of outgoing longwave radiation (OLR) and reflected shortwave radiation (RSR) by laying an uncertainty ellipse at the 99% significance level, using the error analysis from Tett et al. (2011), over the standard configuration. In addition, parameters are sampled to generate a wide spread in estimated climate sensitivities, informed by results from a separate, coupled atmosphere-thermodynamic ocean coupled model ensemble. The results from the conditioned ensemble show that its members have successfully attained the distribution of OLR and RSR very similar to those predicted, while exhibiting a wide range of behaviors in both the atmosphere and the ocean. The spread of estimated effective climate sensitivity with the balanced TOA fluxes shows that the range of sensitivities of the conditioned ensemble is substantially smaller than that obtained with flux adjustment, but still as large or larger than the range in an ensemble of opportunity. This confirms that flux adjustment
Choy, Yun Ho; Mahboob, Alam; Cho, Chung Il; Choi, Jae Gwan; Choi, Im Soo; Choi, Tae Jeong; Cho, Kwang Hyun; Park, Byoung Ho
2015-01-01
The objective of this study was to compare the effects of body weight growth adjustment methods on genetic parameters of body growth and tissue among three pig breeds. Data collected on 101,820 Landrace, 281,411 Yorkshire, and 78,068 Duroc pigs, born in Korean swine breeder farms since 2000, were analyzed. Records included body weights on test day and amplitude (A)-mode ultrasound carcass measures of backfat thickness (BF), eye muscle area (EMA), and retail cut percentage (RCP). Days to 90 kg body weight (DAYS90), through an adjustment of the age based on the body weight at the test day, were obtained. Ultrasound measures were also pre-adjusted (ABF, EMA, AEMA, ARCP) based on their test day measures. The (co)variance components were obtained with 3 multi-trait animal models using the REMLF90 software package. Model I included DAYS90 and ultrasound traits, whereas model II and III accounted DAYS90 and pre-adjusted ultrasound traits. Fixed factors were sex (sex) and contemporary groups (herd-year-month of birth) for all traits among the models. Additionally, model I and II considered a linear covariate of final weight on the ultrasound measure traits. Heritability (h2) estimates for DAYS90, BF, EMA, and RCP ranged from 0.36 to 0.42, 0.34 to 0.43, 0.20 to 0.22, and 0.39 to 0.45, respectively, among the models. The h2 estimates of DAYS90 from model II and III were also somewhat similar. The h2 for ABF, AEMA, and ARCP were 0.35 to 0.44, 0.20 to 0.25, and 0.41 to 0.46, respectively. Our heritability estimates varied mostly among the breeds. The genetic correlations (rG) were moderately negative between DAYS90 and BF (−0.29 to −0.38), and between DAYS90 and EMA (−0.16 to −0.26). BF had strong rG with RCP (−0.87 to −0.93). Moderately positive rG existed between DAYS90 and RCP (0.20 to 0.28) and between EMA and RCP (0.35 to 0.44) among the breeds. For DAYS90, model II and III, its correlations with ABF, AEMA, and ARCP were mostly low or negligible except the r
Choy, Yun Ho; Mahboob, Alam; Cho, Chung Il; Choi, Jae Gwan; Choi, Im Soo; Choi, Tae Jeong; Cho, Kwang Hyun; Park, Byoung Ho
2015-12-01
The objective of this study was to compare the effects of body weight growth adjustment methods on genetic parameters of body growth and tissue among three pig breeds. Data collected on 101,820 Landrace, 281,411 Yorkshire, and 78,068 Duroc pigs, born in Korean swine breeder farms since 2000, were analyzed. Records included body weights on test day and amplitude (A)-mode ultrasound carcass measures of backfat thickness (BF), eye muscle area (EMA), and retail cut percentage (RCP). Days to 90 kg body weight (DAYS90), through an adjustment of the age based on the body weight at the test day, were obtained. Ultrasound measures were also pre-adjusted (ABF, EMA, AEMA, ARCP) based on their test day measures. The (co)variance components were obtained with 3 multi-trait animal models using the REMLF90 software package. Model I included DAYS90 and ultrasound traits, whereas model II and III accounted DAYS90 and pre-adjusted ultrasound traits. Fixed factors were sex (sex) and contemporary groups (herd-year-month of birth) for all traits among the models. Additionally, model I and II considered a linear covariate of final weight on the ultrasound measure traits. Heritability (h(2)) estimates for DAYS90, BF, EMA, and RCP ranged from 0.36 to 0.42, 0.34 to 0.43, 0.20 to 0.22, and 0.39 to 0.45, respectively, among the models. The h(2) estimates of DAYS90 from model II and III were also somewhat similar. The h(2) for ABF, AEMA, and ARCP were 0.35 to 0.44, 0.20 to 0.25, and 0.41 to 0.46, respectively. Our heritability estimates varied mostly among the breeds. The genetic correlations (rG) were moderately negative between DAYS90 and BF (-0.29 to -0.38), and between DAYS90 and EMA (-0.16 to -0.26). BF had strong rG with RCP (-0.87 to -0.93). Moderately positive rG existed between DAYS90 and RCP (0.20 to 0.28) and between EMA and RCP (0.35 to 0.44) among the breeds. For DAYS90, model II and III, its correlations with ABF, AEMA, and ARCP were mostly low or negligible except the r
Blind estimation of compartmental model parameters.
Di Bella, E V; Clackdoyle, R; Gullberg, G T
1999-03-01
Computation of physiologically relevant kinetic parameters from dynamic PET or SPECT imaging requires knowledge of the blood input function. This work is concerned with developing methods to accurately estimate these kinetic parameters blindly; that is, without use of a directly measured blood input function. Instead, only measurements of the output functions--the tissue time-activity curves--are used. The blind estimation method employed here minimizes a set of cross-relation equations, from which the blood term has been factored out, to determine compartmental model parameters. The method was tested with simulated data appropriate for dynamic SPECT cardiac perfusion imaging with 99mTc-teboroxime and for dynamic PET cerebral blood flow imaging with 15O water. The simulations did not model the tomographic process. Noise levels typical of the respective modalities were employed. From three to eight different regions were simulated, each with different time-activity curves. The time-activity curve (24 or 70 time points) for each region was simulated with a compartment model. The simulation used a biexponential blood input function and washin rates between 0.2 and 1.3 min(-1) and washout rates between 0.2 and 1.0 min(-1). The system of equations was solved numerically and included constraints to bound the range of possible solutions. From the cardiac simulations, washin was determined to within a scale factor of the true washin parameters with less than 6% bias and 12% variability. 99mTc-teboroxime washout results had less than 5% bias, but variability ranged from 14% to 43%. The cerebral blood flow washin parameters were determined with less than 5% bias and 4% variability. The washout parameters were determined with less than 4% bias, but had 15-30% variability. Since washin is often the parameter of most use in clinical studies, the blind estimation approach may eliminate the current necessity of measuring the input function when performing certain dynamic studies
Improving the transferability of hydrological model parameters under changing conditions
NASA Astrophysics Data System (ADS)
Huang, Yingchun; Bárdossy, András
2014-05-01
Hydrological models are widely utilized to describe catchment behaviors with observed hydro-meteorological data. Hydrological process may be considered as non-stationary under the changing climate and land use conditions. An applicable hydrological model should be able to capture the essential features of the target catchment and therefore be transferable to different conditions. At present, many model applications based on the stationary assumptions are not sufficient for predicting further changes or time variability. The aim of this study is to explore new model calibration methods in order to improve the transferability of model parameters. To cope with the instability of model parameters calibrated on catchments in non-stationary conditions, we investigate the idea of simultaneously calibration on streamflow records for the period with dissimilar climate characteristics. In additional, a weather based weighting function is implemented to adjust the calibration period to future trends. For regions with limited data and ungauged basins, the common calibration was applied by using information from similar catchments. Result shows the model performance and transfer quantity could be well improved via common calibration. This model calibration approach will be used to enhance regional water management and flood forecasting capabilities.
Glacial isostatic adjustment using GNSS permanent stations and GIA modelling tools
NASA Astrophysics Data System (ADS)
Kollo, Karin; Spada, Giorgio; Vermeer, Martin
2013-04-01
Glacial Isostatic Adjustment (GIA) affects the Earth's mantle in areas which were once ice covered and the process is still ongoing. In this contribution we focus on GIA processes in Fennoscandian and North American uplift regions. In this contribution we use horizontal and vertical uplift rates from Global Navigation Satellite System (GNSS) permanent stations. For Fennoscandia the BIFROST dataset (Lidberg, 2010) and North America the dataset from Sella, 2007 were used respectively. We perform GIA modelling with the SELEN program (Spada and Stocchi, 2007) and we vary ice model parameters in space in order to find ice model which suits best with uplift values obtained from GNSS time series analysis. In the GIA modelling, the ice models ICE-5G (Peltier, 2004) and the ice model denoted as ANU05 ((Fleming and Lambeck, 2004) and references therein) were used. As reference, the velocity field from GNSS permanent station time series was used for both target areas. Firstly the sensitivity to the harmonic degree was tested in order to reduce the computation time. In the test, nominal viscosity values and pre-defined lithosphere thicknesses models were used, varying maximum harmonic degree values. Main criteria for choosing the suitable harmonic degree was chi-square fit - if the error measure does not differ more than 10%, then one might use as well lower harmonic degree value. From this test, maximum harmonic degree of 72 was chosen to perform calculations, as the larger value did not significantly modify the results obtained, as well the computational time for observations was kept reasonable. Secondly the GIA computations were performed to find the model, which could fit with highest probability to the GNSS-based velocity field in the target areas. In order to find best fitting Earth viscosity parameters, different viscosity profiles for the Earth models were tested and their impact on horizontal and vertical velocity rates from GIA modelling was studied. For every
NASA Astrophysics Data System (ADS)
Barsai, Gabor
Creating accurate, current digital maps and 3-D scenes is a high priority in today's fast changing environment. The nation's maps are in a constant state of revision, with many alterations or new additions each day. Digital maps have become quite common. Google maps, Mapquest and others are examples. These also have 3-D viewing capability. Many details are now included, such as the height of low bridges, in the attribute data for the objects displayed on digital maps and scenes. To expedite the updating of these datasets, they should be created autonomously, without human intervention, from data streams. Though systems exist that attain fast, or even real-time performance mapping and reconstruction, they are typically restricted to creating sketches from the data stream, and not accurate maps or scenes. The ever increasing amount of image data available from private companies, governments and the internet, suggest the development of an automated system is of utmost importance. The proposed framework can create 3-D views autonomously; which extends the functionality of digital mapping. The first step to creating 3-D views is to reconstruct the scene of the area to be mapped. To reconstruct a scene from heterogeneous sources, the data has to be registered: either to each other or, preferably, to a general, absolute coordinate system. Registering an image is based on the reconstruction of the geometric relationship of the image to the coordinate system at the time of imaging. Registration is the process of determining the geometric transformation parameters of a dataset in one coordinate system, the source, with respect to the other coordinate system, the target. The advantages of fusing these datasets by registration manifests itself by the data contained in the complementary information that different modality datasets have. The complementary characteristics of these systems can be fully utilized only after successful registration of the photogrammetric and
Observation model and parameter partials for the JPL VLBI parameter estimation software MODEST, 1996
NASA Astrophysics Data System (ADS)
Sovers, O. J.; Jacobs, Christopher S.
1996-08-01
al., as well as a larger selection of recent models for the diurnal and semidiurnal bands. The ever-lengthening time base of VLBI results will soon require modeling the rotation of the Galaxy. Effects of such long period motion of the Earth against the background of extragalactic sources are now included in the MODEST model. More details are given concerning antenna subreflector focussing. Amendments to the tropospheric modeling include the option to model the latitude dependence of the Earth's curvature and gravity in the Lanyi mapping function, as well as to set the mapping function adjustable parameters via a standard gobble atmosphere model. The Ifadis and NMF mapping functions, and the capability of estimating azimuthal troposphere gradients are also available. Finally, a number of m nor misprints in Revision 5 are corrected.
Principal Component Analysis of breast DCE-MRI Adjusted with a Model Based Method
Eyal, Erez.; Badikhi, Daria; Furman-Haran, Edna; Kelcz, Fredrick; Kirshenbaum, Kevin J.; Degani, Hadassa
2010-01-01
Purpose To investigate a fast, objective and standardized method for analyzing breast DCE-MRI applying principal component analysis (PCA) adjusted with a model based method. Materials and Methods 3D gradient-echo dynamic contrast-enhanced breast images of 31 malignant and 38 benign lesions, recorded on a 1.5 Tesla scanner were retrospectively analyzed by PCA and by the model based three-time-point (3TP) method. Results Intensity scaled (IS) and enhancement scaled (ES) datasets were reduced by PCA yielding a 1st IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors that captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions enabling calculation of a general rotated eigenvector base. ROC curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the 1st rotated eigenvector to detect lesions (AUC>0.97) and of the 2nd rotated eigenvector to differentiate malignancy from benignancy (AUC=0.87). Conclusion PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI. PMID:19856419
NASA Astrophysics Data System (ADS)
Mizukami, N.; Clark, M. P.; Nijssen, B.; Sampson, K. M.; Newman, A. J.; Samaniego, L. E.
2014-12-01
Parameter estimation is one of the biggest challenges in hydrologic modeling, particularly over large spatial scales. Model uncertainty as a result of parameter values can be as large as that from other sources such as the choice of hydrologic model or the choice of model forcing data. Thus far, parameter estimation has been performed in an inconsistent manner across the model domain, e.g., using patchy calibration or spatially constant parameters. This can produce artifacts in the spatial variability of model outputs, e.g., discontinuity of simulated hydrologic fields, difficulty with spatially consistent parameter adjustments, and so on. We implement a framework that is suitable for use across multiple model physics options to map between geophysical attributes (i.e., soil, vegetation) and model parameters that describe the storage and transmission of water and energy. Specifically, we apply the transfer functions that transform geophysical attributes into model parameters and apply these transfer functions at the native resolution of the geophysical attribute data rather than at the resolution of the model application. The model parameters are then aggregated to the spatial scale of the model simulation with several scaling functions - arithmetic mean, harmonic mean, geometric mean. Model parameter adjustments are made by calibrating the parameters of the transfer function rather than the model parameters themselves.We demonstrate this general parameter estimation approach using a continental scale VIC implementation at a 12km resolution. The VIC soil parameters were generated by a set of transfer functions developed with nation-wide STATSGO soil data. The VIC model with new soil parameters is forced with Maurer et al. 2002 climate dataset (1979-2008) and the simulation results are compared with the previous simulations with parameters used in past studies as well as observed streamflows at selected basins.
Parameter Estimation for Viscoplastic Material Modeling
NASA Technical Reports Server (NTRS)
Saleeb, Atef F.; Gendy, Atef S.; Wilt, Thomas E.
1997-01-01
A key ingredient in the design of engineering components and structures under general thermomechanical loading is the use of mathematical constitutive models (e.g. in finite element analysis) capable of accurate representation of short and long term stress/deformation responses. In addition to the ever-increasing complexity of recent viscoplastic models of this type, they often also require a large number of material constants to describe a host of (anticipated) physical phenomena and complicated deformation mechanisms. In turn, the experimental characterization of these material parameters constitutes the major factor in the successful and effective utilization of any given constitutive model; i.e., the problem of constitutive parameter estimation from experimental measurements.
Testing Linear Models for Ability Parameters in Item Response Models
ERIC Educational Resources Information Center
Glas, Cees A. W.; Hendrawan, Irene
2005-01-01
Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum likelihood framework. They are explicitly formulated…
Art, T; Votion, D; McEntee, K; Amory, H; Linden, A; Close, R; Lekeux, P
1994-01-01
Two randomly distributed groups of thoroughbred horses were compared during a 12-week period for their cardio-respiratory and metabolic adjustment to strenuous exercise, training and detraining. The horses were trained following the same standardized schedule and were regularly investigated using standardized treadmill exercise tests (SET) of increasing speed. After the first SET and during the whole experimental period, a group of 6 horses received a probiotic (Bioracing) once a day while a group of 5 horses received a placebo. All other conditions were similar for both groups. During each SET, the oxygen uptake, carbon dioxide output, tidal volume (inspired volume), respiratory rate and expired minute volume were obtained using 2 ultrasonic pneumotachographs and a mass spectrometer. All the parameters were the mean of the values calculated during the last 20 s of the SET. Heart rate was continuously measured with a polar horse tester. Venous blood was sampled before and after the test and analyzed for various biochemical parameters. In both groups, training induced significant modification in most of the cardio-respiratory parameters, ie peak oxygen uptake, peak carbon dioxide output, respiratory exchange ratio, ventilation/min to oxygen-uptake ratio and oxygen-uptake to heart-rate ratio. After the 3-week detraining period, most of the values were again similar to the pre-training values in both groups. However, the training-induced modifications of most of the cardio-respiratory parameters occurred earlier and were proportionally greater in the probiotic-treated group than in the control. The respiratory coefficient decreased in the control but not in the treated group. All other parameters changed similarly in both groups. This suggests that Bioracing could modify the physiological effects of training by improving some aerobic metabolic capacities for carbohydrate utilization, but that this effect occurs only during training and not during periods of physical
A whale better adjusts the biosonar to ordered rather than to random changes in the echo parameters.
Supin, Alexander Ya; Nachtigall, Paul E; Breese, Marlee
2012-09-01
A false killer whale's (Pseudorca crassidens) sonar clicks and auditory evoked potentials (AEPs) were recorded during echolocation with simulated echoes in two series of experiments. In the first, both the echo delay and transfer factor (which is the dB-ratio of the echo sound-pressure level to emitted pulse source level) were varied randomly from trial to trial until enough data were collected (random presentation). In the second, a combination of the echo delay and transfer factor was kept constant until enough data were collected (ordered presentation). The mean click level decreased with shortening the delay and increasing the transfer factor, more at the ordered presentation rather than at the random presentation. AEPs to the self-heard emitted clicks decreased with shortening the delay and increasing the echo level equally in both series. AEPs to echoes increased with increasing the echo level, little dependent on the echo delay at random presentations but much more dependent on delay with ordered presentations. So some adjustment of the whale's biosonar was possible without prior information about the echo parameters; however, the availability of prior information about echoes provided additional whale capabilities to adjust both the transmitting and receiving parts of the biosonar. PMID:22978908
ERIC Educational Resources Information Center
Fitzpatrick, Anne R.; And Others
1996-01-01
One-parameter (1PPC) and two-parameter partial credit (2PPC) models were compared using real and simulated data with constructed response items present. Results suggest that the more flexible three-parameter logistic-2PPC model combination produces better model fit than the combination of the one-parameter logistic and the 1PPC models. (SLD)
Modelling spin Hamiltonian parameters of molecular nanomagnets.
Gupta, Tulika; Rajaraman, Gopalan
2016-07-12
Molecular nanomagnets encompass a wide range of coordination complexes possessing several potential applications. A formidable challenge in realizing these potential applications lies in controlling the magnetic properties of these clusters. Microscopic spin Hamiltonian (SH) parameters describe the magnetic properties of these clusters, and viable ways to control these SH parameters are highly desirable. Computational tools play a proactive role in this area, where SH parameters such as isotropic exchange interaction (J), anisotropic exchange interaction (Jx, Jy, Jz), double exchange interaction (B), zero-field splitting parameters (D, E) and g-tensors can be computed reliably using X-ray structures. In this feature article, we have attempted to provide a holistic view of the modelling of these SH parameters of molecular magnets. The determination of J includes various class of molecules, from di- and polynuclear Mn complexes to the {3d-Gd}, {Gd-Gd} and {Gd-2p} class of complexes. The estimation of anisotropic exchange coupling includes the exchange between an isotropic metal ion and an orbitally degenerate 3d/4d/5d metal ion. The double-exchange section contains some illustrative examples of mixed valance systems, and the section on the estimation of zfs parameters covers some mononuclear transition metal complexes possessing very large axial zfs parameters. The section on the computation of g-anisotropy exclusively covers studies on mononuclear Dy(III) and Er(III) single-ion magnets. The examples depicted in this article clearly illustrate that computational tools not only aid in interpreting and rationalizing the observed magnetic properties but possess the potential to predict new generation MNMs. PMID:27366794
Constant-parameter capture-recapture models
Brownie, C.; Hines, J.E.; Nichols, J.D.
1986-01-01
Jolly (1982, Biometrics 38, 301-321) presented modifications of the Jolly-Seber model for capture-recapture data, which assume constant survival and/or capture rates. Where appropriate, because of the reduced number of parameters, these models lead to more efficient estimators than the Jolly-Seber model. The tests to compare models given by Jolly do not make complete use of the data, and we present here the appropriate modifications, and also indicate how to carry out goodness-of-fit tests which utilize individual capture history information. We also describe analogous models for the case where young and adult animals are tagged. The availability of computer programs to perform the analysis is noted, and examples are given using output from these programs.
Modeling fluvial incision and transient landscape evolution: Influence of dynamic channel adjustment
NASA Astrophysics Data System (ADS)
Attal, M.; Tucker, G. E.; Whittaker, A. C.; Cowie, P. A.; Roberts, G. P.
2008-09-01
Channel geometry exerts a fundamental control on fluvial processes. Recent work has shown that bedrock channel width depends on a number of parameters, including channel slope, and is not solely a function of drainage area as is commonly assumed. The present work represents the first attempt to investigate the consequences of dynamic, gradient-sensitive channel adjustment for drainage-basin evolution. We use the Channel-Hillslope Integrated Landscape Development (CHILD) model to analyze the response of a catchment to a given tectonic perturbation, using, as a template, the topography of a well-documented catchment in the footwall of an active normal fault in the Apennines (Italy) that is known to be undergoing a transient response to tectonic forcing. We show that the observed transient response can be reproduced to first order with a simple detachment-limited fluvial incision law. Transient landscape is characterized by gentler gradients and a shorter response time when dynamic channel adjustment is allowed. The differences in predicted channel geometry between the static case (width dependent solely on upstream area) and dynamic case (width dependent on both drainage area and channel slope) lead to contrasting landscape morphologies when integrated at the scale of a whole catchment, particularly in presence of strong tilting and/or pronounced slip-rate acceleration. Our results emphasize the importance of channel width in controlling fluvial processes and landscape evolution. They stress the need for using a dynamic hydraulic scaling law when modeling landscape evolution, particularly when the relative uplift field is nonuniform.
NASA Astrophysics Data System (ADS)
Li, J.; Duan, Q. Y.; Gong, W.; Ye, A.; Dai, Y.; Miao, C.; Di, Z.; Tong, C.; Sun, Y.
2013-08-01
Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of an LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2-8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e., sensitive parameters labeled as insensitive) or type II errors (i.e., insensitive parameters labeled as sensitive). Finally, we evaluated and confirmed the screening results for their consistency with the physical interpretation of the model parameters.
NASA Astrophysics Data System (ADS)
Li, J. D.; Duan, Q. Y.; Gong, W.; Ye, A. Z.; Dai, Y. J.; Miao, C. Y.; Di, Z. H.; Tong, C.; Sun, Y. W.
2013-02-01
Proper specification of model parameters is critical to the performance of land surface models (LSMs). Due to high dimensionality and parameter interaction, estimating parameters of a LSM is a challenging task. Sensitivity analysis (SA) is a tool that can screen out the most influential parameters on model outputs. In this study, we conducted parameter screening for six output fluxes for the Common Land Model: sensible heat, latent heat, upward longwave radiation, net radiation, soil temperature and soil moisture. A total of 40 adjustable parameters were considered. Five qualitative SA methods, including local, sum-of-trees, multivariate adaptive regression splines, delta test and Morris methods, were compared. The proper sampling design and sufficient sample size necessary to effectively screen out the sensitive parameters were examined. We found that there are 2-8 sensitive parameters, depending on the output type, and about 400 samples are adequate to reliably identify the most sensitive parameters. We also employed a revised Sobol' sensitivity method to quantify the importance of all parameters. The total effects of the parameters were used to assess the contribution of each parameter to the total variances of the model outputs. The results confirmed that global SA methods can generally identify the most sensitive parameters effectively, while local SA methods result in type I errors (i.e. sensitive parameters labeled as insensitive) or type II errors (i.e. insensitive parameters labeled as sensitive). Finally, we evaluated and confirmed the screening results for their consistence with the physical interpretation of the model parameters.
NASA Astrophysics Data System (ADS)
Belousov, V. I.; Ezhela, V. V.; Kuyanov, Yu. V.; Tkachenko, N. P.
2015-12-01
The experience of using the dynamic atlas of the experimental data and mathematical models of their description in the problems of adjusting parametric models of observable values depending on kinematic variables is presented. The functional possibilities of an image of a large number of experimental data and the models describing them are shown by examples of data and models of observable values determined by the amplitudes of elastic scattering of hadrons. The Internet implementation of an interactive tool DaMoScope and its interface with the experimental data and codes of adjusted parametric models with the parameters of the best description of data are schematically shown. The DaMoScope codes are freely available.
Belousov, V. I.; Ezhela, V. V.; Kuyanov, Yu. V. Tkachenko, N. P.
2015-12-15
The experience of using the dynamic atlas of the experimental data and mathematical models of their description in the problems of adjusting parametric models of observable values depending on kinematic variables is presented. The functional possibilities of an image of a large number of experimental data and the models describing them are shown by examples of data and models of observable values determined by the amplitudes of elastic scattering of hadrons. The Internet implementation of an interactive tool DaMoScope and its interface with the experimental data and codes of adjusted parametric models with the parameters of the best description of data are schematically shown. The DaMoScope codes are freely available.
Parameter optimization in S-system models
Vilela, Marco; Chou, I-Chun; Vinga, Susana; Vasconcelos, Ana Tereza R; Voit, Eberhard O; Almeida, Jonas S
2008-01-01
Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well. PMID:18416837
Kendall, W.L.; Hines, J.E.; Nichols, J.D.
2003-01-01
Matrix population models are important tools for research and management of populations. Estimating the parameters of these models is an important step in applying them to real populations. Multistate capture-recapture methods have provided a useful means for estimating survival and parameters of transition between locations or life history states but have mostly relied on the assumption that the state occupied by each detected animal is known with certainty. Nevertheless, in some cases animals can be misclassified. Using multiple capture sessions within each period of interest, we developed a method that adjusts estimates of transition probabilities for bias due to misclassification. We applied this method to 10 years of sighting data for a population of Florida manatees (Trichechus manatus latirostris) in order to estimate the annual probability of transition from nonbreeding to breeding status. Some sighted females were unequivocally classified as breeders because they were clearly accompanied by a first-year calf. The remainder were classified, sometimes erroneously, as nonbreeders because an attendant first-year calf was not observed or was classified as more than one year old. We estimated a conditional breeding probability of 0.31 + 0.04 (estimate + 1 SE) when we ignored misclassification bias, and 0.61 + 0.09 when we accounted for misclassification.
Moose models with vanishing S parameter
Casalbuoni, R.; De Curtis, S.; Dominici, D.
2004-09-01
In the linear moose framework, which naturally emerges in deconstruction models, we show that there is a unique solution for the vanishing of the S parameter at the lowest order in the weak interactions. We consider an effective gauge theory based on K SU(2) gauge groups, K+1 chiral fields, and electroweak groups SU(2){sub L} and U(1){sub Y} at the ends of the chain of the moose. S vanishes when a link in the moose chain is cut. As a consequence one has to introduce a dynamical nonlocal field connecting the two ends of the moose. Then the model acquires an additional custodial symmetry which protects this result. We examine also the possibility of a strong suppression of S through an exponential behavior of the link couplings as suggested by the Randall Sundrum metric.
Model parameters for simulation of physiological lipids.
Hills, Ronald D; McGlinchey, Nicholas
2016-05-01
Coarse grain simulation of proteins in their physiological membrane environment can offer insight across timescales, but requires a comprehensive force field. Parameters are explored for multicomponent bilayers composed of unsaturated lipids DOPC and DOPE, mixed-chain saturation POPC and POPE, and anionic lipids found in bacteria: POPG and cardiolipin. A nonbond representation obtained from multiscale force matching is adapted for these lipids and combined with an improved bonding description of cholesterol. Equilibrating the area per lipid yields robust bilayer simulations and properties for common lipid mixtures with the exception of pure DOPE, which has a known tendency to form nonlamellar phase. The models maintain consistency with an existing lipid-protein interaction model, making the force field of general utility for studying membrane proteins in physiologically representative bilayers. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26864972
A New Climate Adjustment Tool: An update to EPA’s Storm Water Management Model
The US EPA’s newest tool, the Stormwater Management Model (SWMM) – Climate Adjustment Tool (CAT) is meant to help municipal stormwater utilities better address potential climate change impacts affecting their operations.
Development of a winter wheat adjustable crop calendar model
NASA Technical Reports Server (NTRS)
Baker, J. R. (Principal Investigator)
1978-01-01
The author has identified the following significant results. After parameter estimation, tests were conducted with variances from the fits, and on independent data. From these tests, it was generally concluded that exponential functions have little advantage over polynomials. Precipitation was not found to significantly affect the fits. The Robertson's triquadratic form, in general use for spring wheat, was found to show promise for winter wheat, but special techniques and care were required for its use. In most instances, equations with nonlinear effects were found to yield erratic results when utilized with daily environmental values as independent variables.
Procedures for adjusting regional regression models of urban-runoff quality using local data
Hoos, Anne B.; Lizarraga, Joy S.
1996-01-01
Statistical operations termed model-adjustment procedures can be used to incorporate local data into existing regression modes to improve the predication of urban-runoff quality. Each procedure is a form of regression analysis in which the local data base is used as a calibration data set; the resulting adjusted regression models can then be used to predict storm-runoff quality at unmonitored sites. Statistical tests of the calibration data set guide selection among proposed procedures.
Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna
2014-10-28
Methods and systems for engine control optimization are provided. A first and a second operating condition of a vehicle engine are detected. An initial value is identified for a first and a second engine control parameter corresponding to a combination of the detected operating conditions according to a first and a second engine map look-up table. The initial values for the engine control parameters are adjusted based on a detected engine performance variable to cause the engine performance variable to approach a target value. A first and a second sensitivity of the engine performance variable are determined in response to changes in the engine control parameters. The first engine map look-up table is adjusted when the first sensitivity is greater than a threshold, and the second engine map look-up table is adjusted when the second sensitivity is greater than a threshold.
Modeling of an Adjustable Beam Solid State Light Project
NASA Technical Reports Server (NTRS)
Clark, Toni
2015-01-01
This proposal is for the development of a computational model of a prototype variable beam light source using optical modeling software, Zemax Optics Studio. The variable beam light source would be designed to generate flood, spot, and directional beam patterns, while maintaining the same average power usage. The optical model would demonstrate the possibility of such a light source and its ability to address several issues: commonality of design, human task variability, and light source design process improvements. An adaptive lighting solution that utilizes the same electronics footprint and power constraints while addressing variability of lighting needed for the range of exploration tasks can save costs and allow for the development of common avionics for lighting controls.
Modelling of snow avalanche dynamics: influence of model parameters
NASA Astrophysics Data System (ADS)
Bozhinskiy, A. N.
The three-parameter hydraulic model of snow avalanche dynamics including the coefficients of dry and turbulent friction and the coefficient of new-snow-mass entrainment was investigated. The 'Domestic' avalanche site in Elbrus region, Caucasus, Russia, was chosen as the model avalanche range. According to the model, the fixed avalanche run-out can be achieved with various combinations of model parameters. At the fixed value of the coefficient of entrainment me, we have a curve on a plane of the coefficients of dry and turbulent friction. It was found that the family of curves (me is a parameter) are crossed at the single point. The value of the coefficient of turbulent friction at the cross-point remained practically constant for the maximum and average avalanche run-outs. The conclusions obtained are confirmed by the results of modelling for six arbitrarily chosen avalanche sites: three in the Khibiny mountains, Kola Peninsula, Russia, two in the Elbrus region and one idealized site with an exponential longitudinal profile. The dependences of run-out on the coefficient of dry friction are constructed for all the investigated avalanche sites. The results are important for the statistical simulation of avalanche dynamics since they suggest the possibility of using only one random model parameter, namely, the coefficient of dry friction, in the model. The histograms and distribution functions of the coefficient of dry friction are constructed and presented for avalanche sites Nos 22 and 43 (Khibiny mountains) and 'Domestic', with the available series of field data.
Circumplex and Spherical Models for Child School Adjustment and Competence.
ERIC Educational Resources Information Center
Schaefer, Earl S.; Edgerton, Marianna
The goal of this study is to broaden the scope of a conceptual model for child behavior by analyzing constructs relevant to cognition, conation, and affect. Two samples were drawn from school populations. For the first sample, 28 teachers from 8 rural, suburban, and urban schools rated 193 kindergarten children. Each teacher rated up to eight…
A General Linear Model Approach to Adjusting the Cumulative GPA.
ERIC Educational Resources Information Center
Young, John W.
A general linear model (GLM), using least-squares techniques, was used to develop a criterion measure to replace freshman year grade point average (GPA) in college admission predictive validity studies. Problems with the use of GPA include those associated with the combination of grades from different courses and disciplines into a single measure,…
Development of a charge adjustment model for cardiac catheterization.
Brennan, Andrew; Gauvreau, Kimberlee; Connor, Jean; O'Connell, Cheryl; David, Sthuthi; Almodovar, Melvin; DiNardo, James; Banka, Puja; Mayer, John E; Marshall, Audrey C; Bergersen, Lisa
2015-02-01
A methodology that would allow for comparison of charges across institutions has not been developed for catheterization in congenital heart disease. A single institution catheterization database with prospectively collected case characteristics was linked to hospital charges related and limited to an episode of care in the catheterization laboratory for fiscal years 2008-2010. Catheterization charge categories (CCC) were developed to group types of catheterization procedures using a combination of empiric data and expert consensus. A multivariable model with outcome charges was created using CCC and additional patient and procedural characteristics. In 3 fiscal years, 3,839 cases were available for analysis. Forty catheterization procedure types were categorized into 7 CCC yielding a grouper variable with an R (2) explanatory value of 72.6%. In the final CCC, the largest proportion of cases was in CCC 2 (34%), which included diagnostic cases without intervention. Biopsy cases were isolated in CCC 1 (12%), and percutaneous pulmonary valve placement alone made up CCC 7 (2%). The final model included CCC, number of interventions, and cardiac diagnosis (R (2) = 74.2%). Additionally, current financial metrics such as APR-DRG severity of illness and case mix index demonstrated a lack of correlation with CCC. We have developed a catheterization procedure type financial grouper that accounts for the diverse case population encountered in catheterization for congenital heart disease. CCC and our multivariable model could be used to understand financial characteristics of a population at a single point in time, longitudinally, and to compare populations. PMID:25113520
Multiscale modeling of failure in composites under model parameter uncertainty
NASA Astrophysics Data System (ADS)
Bogdanor, Michael J.; Oskay, Caglar; Clay, Stephen B.
2015-09-01
This manuscript presents a multiscale stochastic failure modeling approach for fiber reinforced composites. A homogenization based reduced-order multiscale computational model is employed to predict the progressive damage accumulation and failure in the composite. Uncertainty in the composite response is modeled at the scale of the microstructure by considering the constituent material (i.e., matrix and fiber) parameters governing the evolution of damage as random variables. Through the use of the multiscale model, randomness at the constituent scale is propagated to the scale of the composite laminate. The probability distributions of the underlying material parameters are calibrated from unidirectional composite experiments using a Bayesian statistical approach. The calibrated multiscale model is exercised to predict the ultimate tensile strength of quasi-isotropic open-hole composite specimens at various loading rates. The effect of random spatial distribution of constituent material properties on the composite response is investigated.
Comparison of the Properties of Regression and Categorical Risk-Adjustment Models
Averill, Richard F.; Muldoon, John H.; Hughes, John S.
2016-01-01
Clinical risk-adjustment, the ability to standardize the comparison of individuals with different health needs, is based upon 2 main alternative approaches: regression models and clinical categorical models. In this article, we examine the impact of the differences in the way these models are constructed on end user applications. PMID:26945302
ERIC Educational Resources Information Center
Olejnik, Stephen; Mills, Jamie; Keselman, Harvey
2000-01-01
Evaluated the use of Mallow's C(p) and Wherry's adjusted R squared (R. Wherry, 1931) statistics to select a final model from a pool of model solutions using computer generated data. Neither statistic identified the underlying regression model any better than, and usually less well than, the stepwise selection method, which itself was poor for…
Fixing the c Parameter in the Three-Parameter Logistic Model
ERIC Educational Resources Information Center
Han, Kyung T.
2012-01-01
For several decades, the "three-parameter logistic model" (3PLM) has been the dominant choice for practitioners in the field of educational measurement for modeling examinees' response data from multiple-choice (MC) items. Past studies, however, have pointed out that the c-parameter of 3PLM should not be interpreted as a guessing parameter. This…
Milly, P.C.D.; Dunne, K.A.
2011-01-01
Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median 211%) caused by the hydrologic model's apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen-Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climatechange impacts on water. Copyright ?? 2011, Paper 15-001; 35,952 words, 3 Figures, 0 Animations, 1 Tables.
Milly, Paul C.; Dunne, Krista A.
2011-01-01
Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median -11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.
Estimating parameters of hidden Markov models based on marked individuals: use of robust design data
Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun
2012-01-01
Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).
Kendall, William L; White, Gary C; Hines, James E; Langtimm, Catherine A; Yoshizaki, Jun
2012-04-01
Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last 20 years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected-value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We have also implemented these models in program MARK. This general framework could also be used by practitioners to consider constrained models of particular interest, or to model the relationship between within-primary-period parameters (e.g., state structure) and between-primary-period parameters (e.g., state transition probabilities). PMID:22690641
A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops
NASA Astrophysics Data System (ADS)
Lu, Hai-liang; Wen, Xi-shan; Lan, Lei; An, Yun-zhu; Li, Xiao-ping
2015-01-01
A self-adaptive genetic algorithm for estimating Jiles-Atherton (JA) magnetic hysteresis model parameters is presented. The fitness function is established based on the distances between equidistant key points of normalized hysteresis loops. Linearity function and logarithm function are both adopted to code the five parameters of JA model. Roulette wheel selection is used and the selection pressure is adjusted adaptively by deducting a proportional which depends on current generation common value. The Crossover operator is established by combining arithmetic crossover and multipoint crossover. Nonuniform mutation is improved by adjusting the mutation ratio adaptively. The algorithm is used to estimate the parameters of one kind of silicon-steel sheet's hysteresis loops, and the results are in good agreement with published data.
Richardson, David B.; Laurier, Dominique; Schubauer-Berigan, Mary K.; Tchetgen, Eric Tchetgen; Cole, Stephen R.
2014-01-01
Workers' smoking histories are not measured in many occupational cohort studies. Here we discuss the use of negative control outcomes to detect and adjust for confounding in analyses that lack information on smoking. We clarify the assumptions necessary to detect confounding by smoking and the additional assumptions necessary to indirectly adjust for such bias. We illustrate these methods using data from 2 studies of radiation and lung cancer: the Colorado Plateau cohort study (1950–2005) of underground uranium miners (in which smoking was measured) and a French cohort study (1950–2004) of nuclear industry workers (in which smoking was unmeasured). A cause-specific relative hazards model is proposed for estimation of indirectly adjusted associations. Among the miners, the proposed method suggests no confounding by smoking of the association between radon and lung cancer—a conclusion supported by adjustment for measured smoking. Among the nuclear workers, the proposed method suggests substantial confounding by smoking of the association between radiation and lung cancer. Indirect adjustment for confounding by smoking resulted in an 18% decrease in the adjusted estimated hazard ratio, yet this cannot be verified because smoking was unmeasured. Assumptions underlying this method are described, and a cause-specific proportional hazards model that allows easy implementation using standard software is presented. PMID:25245043
Attar-Schwartz, Shalhevet
2015-09-01
Warm and emotionally close relationships with parents and grandparents have been found in previous studies to be linked with better adolescent adjustment. The present study, informed by Family Systems Theory and Intergenerational Solidarity Theory, uses a moderated mediation model analyzing the contribution of the dynamics of these intergenerational relationships to adolescent adjustment. Specifically, it examines the mediating role of emotional closeness to the closest grandparent in the relationship between emotional closeness to a parent (the offspring of the closest grandparent) and adolescent adjustment difficulties. The model also examines the moderating role of emotional closeness to parents in the relationship between emotional closeness to grandparents and adjustment difficulties. The study was based on a sample of 1,405 Jewish Israeli secondary school students (ages 12-18) who completed a structured questionnaire. It was found that emotional closeness to the closest grandparent was more strongly associated with reduced adjustment difficulties among adolescents with higher levels of emotional closeness to their parents. In addition, adolescent adjustment and emotional closeness to parents was partially mediated by emotional closeness to grandparents. Examining the family conditions under which adolescents' relationships with grandparents is stronger and more beneficial for them can help elucidate variations in grandparent-grandchild ties and expand our understanding of the mechanisms that shape child outcomes. PMID:26237053
Modeling Quality-Adjusted Life Expectancy Loss Resulting from Tobacco Use in the United States
ERIC Educational Resources Information Center
Kaplan, Robert M.; Anderson, John P.; Kaplan, Cameron M.
2007-01-01
Purpose: To describe the development of a model for estimating the effects of tobacco use upon Quality Adjusted Life Years (QALYs) and to estimate the impact of tobacco use on health outcomes for the United States (US) population using the model. Method: We obtained estimates of tobacco consumption from 6 years of the National Health Interview…
Evaluation of the Stress Adjustment and Adaptation Model among Families Reporting Economic Pressure
ERIC Educational Resources Information Center
Vandsburger, Etty; Biggerstaff, Marilyn A.
2004-01-01
This research evaluates the Stress Adjustment and Adaptation Model (double ABCX model) examining the effects resiliency resources on family functioning when families experience economic pressure. Families (N = 128) with incomes at or below the poverty line from a rural area of a southern state completed measures of perceived economic pressure,…
A Model of Divorce Adjustment for Use in Family Service Agencies.
ERIC Educational Resources Information Center
Faust, Ruth Griffith
1987-01-01
Presents a combined educationally and therapeutically oriented model of treatment to (1) control and lessen disruptive experiences associated with divorce; (2) enable individuals to improve their skill in coping with adjustment reactions to divorce; and (3) modify the pressures and response of single parenthood. Describes the model's four-session…
ERIC Educational Resources Information Center
Nettles, Saundra Murray; Caughy, Margaret O'Brien; O'Campo, Patricia J.
2008-01-01
Examining recent research on neighborhood influences on child development, this review focuses on social influences on school adjustment in the early elementary years. A model to guide community research and intervention is presented. The components of the model of integrated processes are neighborhoods and their effects on academic outcomes and…
Risk adjustment of Medicare capitation payments using the CMS-HCC model.
Pope, Gregory C; Kautter, John; Ellis, Randall P; Ash, Arlene S; Ayanian, John Z; Lezzoni, Lisa I; Ingber, Melvin J; Levy, Jesse M; Robst, John
2004-01-01
This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees. We explain the model's principles, elements, organization, calibration, and performance. Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed. PMID:15493448
Community Influences on Adjustment in First Grade: An Examination of an Integrated Process Model
ERIC Educational Resources Information Center
Caughy, Margaret O'Brien; Nettles, Saundra M.; O'Campo, Patricia J.
2007-01-01
We examined the impact of neighborhood characteristics both directly and indirectly as mediated by parent coaching and the parent/child affective relationship on behavioral and school adjustment in a sample of urban dwelling first graders. We used structural equations modeling to assess model fit and estimate direct, indirect, and total effects of…
Transfer function modeling of damping mechanisms in distributed parameter models
NASA Technical Reports Server (NTRS)
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
Glacial isostatic adjustment model with composite 3-D Earth rheology for Fennoscandia
NASA Astrophysics Data System (ADS)
van der Wal, Wouter; Barnhoorn, Auke; Stocchi, Paolo; Gradmann, Sofie; Wu, Patrick; Drury, Martyn; Vermeersen, Bert
2013-07-01
Models for glacial isostatic adjustment (GIA) can provide constraints on rheology of the mantle if past ice thickness variations are assumed to be known. The Pleistocene ice loading histories that are used to obtain such constraints are based on an a priori 1-D mantle viscosity profile that assumes a single deformation mechanism for mantle rocks. Such a simplified viscosity profile makes it hard to compare the inferred mantle rheology to inferences from seismology and laboratory experiments. It is unknown what constraints GIA observations can provide on more realistic mantle rheology with an ice history that is not based on an a priori mantle viscosity profile. This paper investigates a model for GIA with a new ice history for Fennoscandia that is constrained by palaeoclimate proxies and glacial sediments. Diffusion and dislocation creep flow law data are taken from a compilation of laboratory measurements on olivine. Upper-mantle temperature data sets down to 400 km depth are derived from surface heatflow measurements, a petrochemical model for Fennoscandia and seismic velocity anomalies. Creep parameters below 400 km are taken from an earlier study and are only varying with depth. The olivine grain size and water content (a wet state, or a dry state) are used as free parameters. The solid Earth response is computed with a global spherical 3-D finite-element model for an incompressible, self-gravitating Earth. We compare predictions to sea level data and GPS uplift rates in Fennoscandia. The objective is to see if the mantle rheology and the ice model is consistent with GIA observations. We also test if the inclusion of dislocation creep gives any improvements over predictions with diffusion creep only, and whether the laterally varying temperatures result in an improved fit compared to a widely used 1-D viscosity profile (VM2). We find that sea level data can be explained with our ice model and with information on mantle rheology from laboratory experiments
Some aspects of application of the two parameter SEU model
Miroshkin, V.V.; Tverskoy, M.G.
1995-12-01
Influence of the projectile type, pion production in nucleon-nucleon interaction inside nucleus and direction of the beam incidence on SEU cross section for INTEL 2164A microcircuit in framework of the two parameter model is investigated. Model parameters for devices, investigated recently are reported. Optimum proton energies for determination of model parameters are proposed.
Plumb, John M.; Moffitt, Christine M.
2015-01-01
Researchers have cautioned against the borrowing of consumption and growth parameters from other species and life stages in bioenergetics growth models. In particular, the function that dictates temperature dependence in maximum consumption (Cmax) within the Wisconsin bioenergetics model for Chinook Salmon Oncorhynchus tshawytscha produces estimates that are lower than those measured in published laboratory feeding trials. We used published and unpublished data from laboratory feeding trials with subyearling Chinook Salmon from three stocks (Snake, Nechako, and Big Qualicum rivers) to estimate and adjust the model parameters for temperature dependence in Cmax. The data included growth measures in fish ranging from 1.5 to 7.2 g that were held at temperatures from 14°C to 26°C. Parameters for temperature dependence in Cmax were estimated based on relative differences in food consumption, and bootstrapping techniques were then used to estimate the error about the parameters. We found that at temperatures between 17°C and 25°C, the current parameter values did not match the observed data, indicating that Cmax should be shifted by about 4°C relative to the current implementation under the bioenergetics model. We conclude that the adjusted parameters for Cmax should produce more accurate predictions from the bioenergetics model for subyearling Chinook Salmon.
Sample Size and Item Parameter Estimation Precision When Utilizing the One-Parameter "Rasch" Model
ERIC Educational Resources Information Center
Custer, Michael
2015-01-01
This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across…
Contact angle adjustment in equation-of-state-based pseudopotential model
NASA Astrophysics Data System (ADS)
Hu, Anjie; Li, Longjian; Uddin, Rizwan; Liu, Dong
2016-05-01
The single component pseudopotential lattice Boltzmann model has been widely applied in multiphase simulation due to its simplicity and stability. In many studies, it has been claimed that this model can be stable for density ratios larger than 1000. However, the application of the model is still limited to small density ratios when the contact angle is considered. The reason is that the original contact angle adjustment method influences the stability of the model. Moreover, simulation results in the present work show that, by applying the original contact angle adjustment method, the density distribution near the wall is artificially changed, and the contact angle is dependent on the surface tension. Hence, it is very inconvenient to apply this method with a fixed contact angle, and the accuracy of the model cannot be guaranteed. To solve these problems, a contact angle adjustment method based on the geometry analysis is proposed and numerically compared with the original method. Simulation results show that, with our contact angle adjustment method, the stability of the model is highly improved when the density ratio is relatively large, and it is independent of the surface tension.
Modeling Fluvial Incision and Transient Landscape Evolution: Influence of Dynamic Channel Adjustment
NASA Astrophysics Data System (ADS)
Attal, M.; Tucker, G. E.; Cowie, P. A.; Whittaker, A. C.; Roberts, G. P.
2007-12-01
Channel geometry exerts a fundamental control on fluvial processes. Recent work has shown that bedrock channel width (W) depends on a number of parameters, including channel slope, and is not only a function of drainage area (A) as is commonly assumed. The present work represents the first attempt to investigate the consequences, for landscape evolution, of using a static expression of channel width (W ~ A0.5) versus a relationship that allows channels to dynamically adjust to changes in slope. We consider different models for the evolution of the channel geometry, including constant width-to-depth ratio (after Finnegan et al., Geology, v. 33, no. 3, 2005), and width-to-depth ratio varying as a function of slope (after Whittaker et al., Geology, v. 35, no. 2, 2007). We use the Channel-Hillslope Integrated Landscape Development (CHILD) model to analyze the response of a catchment to a given tectonic disturbance. The topography of a catchment in the footwall of an active normal fault in the Apennines (Italy) is used as a template for the study. We show that, for this catchment, the transient response can be fairly well reproduced using a simple detachment-limited fluvial incision law. We also show that, depending on the relationship used to express channel width, initial steady-state topographies differ, as do transient channel width, slope, and the response time of the fluvial system. These differences lead to contrasting landscape morphologies when integrated at the scale of a whole catchment. Our results emphasize the importance of channel width in controlling fluvial processes and landscape evolution. They stress the need for using a dynamic hydraulic scaling law when modeling landscape evolution, particularly when the uplift field is non-uniform.
Cummings, E. Mark; Merrilees, Christine E.; Schermerhorn, Alice C.; Goeke-Morey, Marcie C.; Shirlow, Peter; Cairns, Ed
2013-01-01
Relations between political violence and child adjustment are matters of international concern. Past research demonstrates the significance of community, family and child psychological processes in child adjustment, supporting study of inter-relations between multiple social ecological factors and child adjustment in contexts of political violence. Testing a social ecological model, 300 mothers and their children (M= 12.28 years, SD = 1.77) from Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland completed measures of community discord, family relations, and children’s regulatory processes (i.e., emotional security) and outcomes. Historical political violence in neighborhoods based on objective records (i.e., politically motivated deaths) were related to family members’ reports of current sectarian and non-sectarian antisocial behavior. Interparental conflict and parental monitoring and children’s emotional security about both the community and family contributed to explanatory pathways for relations between sectarian antisocial behavior in communities and children’s adjustment problems. The discussion evaluates support for social ecological models for relations between political violence and child adjustment and its implications for understanding relations in other parts of the world. PMID:20423550
Cummings, E Mark; Merrilees, Christine E; Schermerhorn, Alice C; Goeke-Morey, Marcie C; Shirlow, Peter; Cairns, Ed
2010-05-01
Relations between political violence and child adjustment are matters of international concern. Past research demonstrates the significance of community, family, and child psychological processes in child adjustment, supporting study of interrelations between multiple social ecological factors and child adjustment in contexts of political violence. Testing a social ecological model, 300 mothers and their children (M = 12.28 years, SD = 1.77) from Catholic and Protestant working class neighborhoods in Belfast, Northern Ireland, completed measures of community discord, family relations, and children's regulatory processes (i.e., emotional security) and outcomes. Historical political violence in neighborhoods based on objective records (i.e., politically motivated deaths) were related to family members' reports of current sectarian antisocial behavior and nonsectarian antisocial behavior. Interparental conflict and parental monitoring and children's emotional security about both the community and family contributed to explanatory pathways for relations between sectarian antisocial behavior in communities and children's adjustment problems. The discussion evaluates support for social ecological models for relations between political violence and child adjustment and its implications for understanding relations in other parts of the world. PMID:20423550
Model atmospheres and fundamental stellar parameters
NASA Astrophysics Data System (ADS)
Plez, B.
2013-11-01
I start by illustrating the need for precise and accurate fundamental stellar parameters through there examples: lithium abundances in metal-poor stars, the derivation of stellar ages from isochrones, and the chemical composition of planet-hosting stars. I present widely used methods (infrared flux method, spectroscopy) in the determination of T_{eff}, and log g. I comment upon difficulties encountered with the determination of stellar parameters of red supergiant stars, and I discuss the impact of non-LTE and 3D hydrodynamical effects.
Parameter Estimates in Differential Equation Models for Chemical Kinetics
ERIC Educational Resources Information Center
Winkel, Brian
2011-01-01
We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…
ERIC Educational Resources Information Center
Bernard, Lori L.; Guarnaccia, Charles A.
2003-01-01
Purpose: Caregiver bereavement adjustment literature suggests opposite models of impact of role strain on bereavement adjustment after care-recipient death--a Complicated Grief Model and a Relief Model. This study tests these competing models for husband and adult-daughter caregivers of breast cancer hospice patients. Design and Methods: This…
Kiang, Lisa; Witkow, Melissa R; Thompson, Taylor L
2016-07-01
The model minority image is a common and pervasive stereotype that Asian American adolescents must navigate. Using multiwave data from 159 adolescents from Asian American backgrounds (mean age at initial recruitment = 15.03, SD = .92; 60 % female; 74 % US-born), the current study targeted unexplored aspects of the model minority experience in conjunction with more traditionally measured experiences of negative discrimination. When examining normative changes, perceptions of model minority stereotyping increased over the high school years while perceptions of discrimination decreased. Both experiences were not associated with each other, suggesting independent forms of social interactions. Model minority stereotyping generally promoted academic and socioemotional adjustment, whereas discrimination hindered outcomes. Moreover, in terms of academic adjustment, the model minority stereotype appears to protect against the detrimental effect of discrimination. Implications of the complex duality of adolescents' social interactions are discussed. PMID:26251100
A Four-Part Model of Autonomy during Emerging Adulthood: Associations with Adjustment
ERIC Educational Resources Information Center
Lamborn, Susie D.; Groh, Kelly
2009-01-01
We found support for a four-part model of autonomy that links connectedness, separation, detachment, and agency to adjustment during emerging adulthood. Based on self-report surveys of 285 American college students, expected associations among the autonomy variables were found. In addition, agency, as measured by self-reliance, predicted lower…
A Threshold Model of Social Support, Adjustment, and Distress after Breast Cancer Treatment
ERIC Educational Resources Information Center
Mallinckrodt, Brent; Armer, Jane M.; Heppner, P. Paul
2012-01-01
This study examined a threshold model that proposes that social support exhibits a curvilinear association with adjustment and distress, such that support in excess of a critical threshold level has decreasing incremental benefits. Women diagnosed with a first occurrence of breast cancer (N = 154) completed survey measures of perceived support…
Glacial isostatic adjustment on 3-D Earth models: a finite-volume formulation
NASA Astrophysics Data System (ADS)
Latychev, Konstantin; Mitrovica, Jerry X.; Tromp, Jeroen; Tamisiea, Mark E.; Komatitsch, Dimitri; Christara, Christina C.
2005-05-01
We describe and present results from a finite-volume (FV) parallel computer code for forward modelling the Maxwell viscoelastic response of a 3-D, self-gravitating, elastically compressible Earth to an arbitrary surface load. We implement a conservative, control volume discretization of the governing equations using a tetrahedral grid in Cartesian geometry and a low-order, linear interpolation. The basic starting grid honours all major radial discontinuities in the Preliminary Reference Earth Model (PREM), and the models are permitted arbitrary spatial variations in viscosity and elastic parameters. These variations may be either continuous or discontinuous at a set of grid nodes forming a 3-D surface within the (regional or global) modelling domain. In the second part of the paper, we adopt the FV methodology and a spherically symmetric Earth model to generate a suite of predictions sampling a broad class of glacial isostatic adjustment (GIA) data types (3-D crustal motions, long-wavelength gravity anomalies). These calculations, based on either a simple disc load history or a global Late Pleistocene ice load reconstruction (ICE-3G), are benchmarked against predictions generated using the traditional normal-mode approach to GIA. The detailed comparison provides a guide for future analyses (e.g. what grid resolution is required to obtain a specific accuracy?) and it indicates that discrepancies in predictions of 3-D crustal velocities less than 0.1 mm yr-1 are generally obtainable for global grids with ~3 × 106 nodes; however, grids of higher resolution are required to predict large-amplitude (>1 cm yr-1) radial velocities in zones of peak post-glacial uplift (e.g. James bay) to the same level of absolute accuracy. We conclude the paper with a first application of the new formulation to a 3-D problem. Specifically, we consider the impact of mantle viscosity heterogeneity on predictions of present-day 3-D crustal motions in North America. In these tests, the
Physiological Parameters Database for PBPK Modeling (External Review Draft)
EPA released for public comment a physiological parameters database (created using Microsoft ACCESS) intended to be used in PBPK modeling. The database contains physiological parameter values for humans from early childhood through senescence. It also contains similar data for an...
DINA Model and Parameter Estimation: A Didactic
ERIC Educational Resources Information Center
de la Torre, Jimmy
2009-01-01
Cognitive and skills diagnosis models are psychometric models that have immense potential to provide rich information relevant for instruction and learning. However, wider applications of these models have been hampered by their novelty and the lack of commercially available software that can be used to analyze data from this psychometric…
Parameter recovery and model selection in mixed Rasch models.
Preinerstorfer, David; Formann, Anton K
2012-05-01
This study examines the precision of conditional maximum likelihood estimates and the quality of model selection methods based on information criteria (AIC and BIC) in mixed Rasch models. The design of the Monte Carlo simulation study included four test lengths (10, 15, 25, 40), three sample sizes (500, 1000, 2500), two simulated mixture conditions (one and two groups), and population homogeneity (equally sized subgroups) or heterogeneity (one subgroup three times larger than the other). The results show that both increasing sample size and increasing number of items lead to higher accuracy; medium-range parameters were estimated more precisely than extreme ones; and the accuracy was higher in homogeneous populations. The minimum-BIC method leads to almost perfect results and is more reliable than AIC-based model selection. The results are compared to findings by Li, Cohen, Kim, and Cho (2009) and practical guidelines are provided. PMID:21675964
NASA Astrophysics Data System (ADS)
Naipal, V.; Reick, C.; Pongratz, J.; Van Oost, K.
2015-03-01
Large uncertainties exist in estimated rates and the extent of soil erosion by surface runoff on a global scale, and this limits our understanding of the global impact that soil erosion might have on agriculture and climate. The Revised Universal Soil Loss Equation (RUSLE) model is due to its simple structure and empirical basis a frequently used tool in estimating average annual soil erosion rates at regional to global scales. However, large spatial scale applications often rely on coarse data input, which is not compatible with the local scale at which the model is parameterized. This study aimed at providing the first steps in improving the global applicability of the RUSLE model in order to derive more accurate global soil erosion rates. We adjusted the topographical and rainfall erosivity factors of the RUSLE model and compared the resulting soil erosion rates to extensive empirical databases on soil erosion from the USA and Europe. Adjusting the topographical factor required scaling of slope according to the fractal method, which resulted in improved topographical detail in a coarse resolution global digital elevation model. Applying the linear multiple regression method to adjust rainfall erosivity for various climate zones resulted in values that are in good comparison with high resolution erosivity data for different regions. However, this method needs to be extended to tropical climates, for which erosivity is biased due to the lack of high resolution erosivity data. After applying the adjusted and the unadjusted versions of the RUSLE model on a global scale we find that the adjusted RUSLE model not only shows a global higher mean soil erosion rate but also more variability in the soil erosion rates. Comparison to empirical datasets of the USA and Europe shows that the adjusted RUSLE model is able to decrease the very high erosion rates in hilly regions that are observed in the unadjusted RUSLE model results. Although there are still some regional
An improved bundle adjustment model and algorithm with novel block matrix partition method
NASA Astrophysics Data System (ADS)
Xia, Zemin; Li, Zhongwei; Zhong, Kai
2014-11-01
Sparse bundle adjustment is widely applied in computer vision and photogrammetry. However, existing implementation is based on the model of n 3D points projecting onto m different camera imaging planes at m positions, which can't be applied to commonly monocular, binocular or trinocular imaging systems. A novel design and implementation of bundle adjustment algorithm is proposed in this paper, which is based on n 3D points projecting onto the same camera imaging plane at m positions .To improve the performance of the algorithm, a novel sparse block matrix partition method is proposed. Experiments show that the improved bundle adjustment is effective, robust and has a better tolerance to pixel coordinates error.
Distributed parameter modeling of repeated truss structures
NASA Technical Reports Server (NTRS)
Wang, Han-Ching
1994-01-01
A new approach to find homogeneous models for beam-like repeated flexible structures is proposed which conceptually involves two steps. The first step involves the approximation of 3-D non-homogeneous model by a 1-D periodic beam model. The structure is modeled as a 3-D non-homogeneous continuum. The displacement field is approximated by Taylor series expansion. Then, the cross sectional mass and stiffness matrices are obtained by energy equivalence using their additive properties. Due to the repeated nature of the flexible bodies, the mass, and stiffness matrices are also periodic. This procedure is systematic and requires less dynamics detail. The first step involves the homogenization from a 1-D periodic beam model to a 1-D homogeneous beam model. The periodic beam model is homogenized into an equivalent homogeneous beam model using the additive property of compliance along the generic axis. The major departure from previous approaches in literature is using compliance instead of stiffness in homogenization. An obvious justification is that the stiffness is additive at each cross section but not along the generic axis. The homogenized model preserves many properties of the original periodic model.
NASA Astrophysics Data System (ADS)
Franz, K.; Hogue, T.; Barco, J.
2007-12-01
Identification of appropriate parameter sets for simulation of streamflow in ungauged basins has become a significant challenge for both operational and research hydrologists. This is especially difficult in the case of conceptual models, when model parameters typically must be "calibrated" or adjusted to match streamflow conditions in specific systems (i.e. some of the parameters are not directly observable). This paper addresses the performance and uncertainty associated with transferring conceptual rainfall-runoff model parameters between basins within large-scale ecoregions. We use the National Weather Service's (NWS) operational hydrologic model, the SACramento Soil Moisture Accounting (SAC-SMA) model. A Multi-Step Automatic Calibration Scheme (MACS), using the Shuffle Complex Evolution (SCE), is used to optimize SAC-SMA parameters for a group of watersheds with extensive hydrologic records from the Model Parameter Estimation Experiment (MOPEX) database. We then explore "hydroclimatic" relationships between basins to facilitate regionalization of parameters for an established ecoregion in the southeastern United States. The impact of regionalized parameters is evaluated via standard model performance statistics as well as through generation of hindcasts and probabilistic verification procedures to evaluate streamflow forecast skill. Preliminary results show climatology ("climate neighbor") to be a better indicator of transferability than physical similarities or proximity ("nearest neighbor"). The mean and median of all the parameters within the ecoregion are the poorest choice for the ungauged basin. The choice of regionalized parameter set affected the skill of the ensemble streamflow hindcasts, however, all parameter sets show little skill in forecasts after five weeks (i.e. climatology is as good an indicator of future streamflows). In addition, the optimum parameter set changed seasonally, with the "nearest neighbor" showing the highest skill in the
NASA Astrophysics Data System (ADS)
Naipal, V.; Reick, C.; Pongratz, J.; Van Oost, K.
2015-09-01
Large uncertainties exist in estimated rates and the extent of soil erosion by surface runoff on a global scale. This limits our understanding of the global impact that soil erosion might have on agriculture and climate. The Revised Universal Soil Loss Equation (RUSLE) model is, due to its simple structure and empirical basis, a frequently used tool in estimating average annual soil erosion rates at regional to global scales. However, large spatial-scale applications often rely on coarse data input, which is not compatible with the local scale on which the model is parameterized. Our study aims at providing the first steps in improving the global applicability of the RUSLE model in order to derive more accurate global soil erosion rates. We adjusted the topographical and rainfall erosivity factors of the RUSLE model and compared the resulting erosion rates to extensive empirical databases from the USA and Europe. By scaling the slope according to the fractal method to adjust the topographical factor, we managed to improve the topographical detail in a coarse resolution global digital elevation model. Applying the linear multiple regression method to adjust rainfall erosivity for various climate zones resulted in values that compared well to high resolution erosivity data for different regions. However, this method needs to be extended to tropical climates, for which erosivity is biased due to the lack of high resolution erosivity data. After applying the adjusted and the unadjusted versions of the RUSLE model on a global scale we find that the adjusted version shows a global higher mean erosion rate and more variability in the erosion rates. Comparison to empirical data sets of the USA and Europe shows that the adjusted RUSLE model is able to decrease the very high erosion rates in hilly regions that are observed in the unadjusted RUSLE model results. Although there are still some regional differences with the empirical databases, the results indicate that the
Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; Liu, Ying
2015-12-04
Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalized linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.
Bao, Jie; Hou, Zhangshuan; Huang, Maoyi; Liu, Ying
2015-12-04
Here, effective sensitivity analysis approaches are needed to identify important parameters or factors and their uncertainties in complex Earth system models composed of multi-phase multi-component phenomena and multiple biogeophysical-biogeochemical processes. In this study, the impacts of 10 hydrologic parameters in the Community Land Model on simulations of runoff and latent heat flux are evaluated using data from a watershed. Different metrics, including residual statistics, the Nash-Sutcliffe coefficient, and log mean square error, are used as alternative measures of the deviations between the simulated and field observed values. Four sensitivity analysis (SA) approaches, including analysis of variance based on the generalizedmore » linear model, generalized cross validation based on the multivariate adaptive regression splines model, standardized regression coefficients based on a linear regression model, and analysis of variance based on support vector machine, are investigated. Results suggest that these approaches show consistent measurement of the impacts of major hydrologic parameters on response variables, but with differences in the relative contributions, particularly for the secondary parameters. The convergence behaviors of the SA with respect to the number of sampling points are also examined with different combinations of input parameter sets and output response variables and their alternative metrics. This study helps identify the optimal SA approach, provides guidance for the calibration of the Community Land Model parameters to improve the model simulations of land surface fluxes, and approximates the magnitudes to be adjusted in the parameter values during parametric model optimization.« less
Validating Mechanistic Sorption Model Parameters and Processes for Reactive Transport in Alluvium
Zavarin, M; Roberts, S K; Rose, T P; Phinney, D L
2002-05-02
The laboratory batch and flow-through experiments presented in this report provide a basis for validating the mechanistic surface complexation and ion exchange model we use in our hydrologic source term (HST) simulations. Batch sorption experiments were used to examine the effect of solution composition on sorption. Flow-through experiments provided for an analysis of the transport behavior of sorbing elements and tracers which includes dispersion and fluid accessibility effects. Analysis of downstream flow-through column fluids allowed for evaluation of weakly-sorbing element transport. Secondary Ion Mass Spectrometry (SIMS) analysis of the core after completion of the flow-through experiments permitted the evaluation of transport of strongly sorbing elements. A comparison between these data and model predictions provides additional constraints to our model and improves our confidence in near-field HST model parameters. In general, cesium, strontium, samarium, europium, neptunium, and uranium behavior could be accurately predicted using our mechanistic approach but only after some adjustment was made to the model parameters. The required adjustments included a reduction in strontium affinity for smectite, an increase in cesium affinity for smectite and illite, a reduction in iron oxide and calcite reactive surface area, and a change in clinoptilolite reaction constants to reflect a more recently published set of data. In general, these adjustments are justifiable because they fall within a range consistent with our understanding of the parameter uncertainties. These modeling results suggest that the uncertainty in the sorption model parameters must be accounted for to validate the mechanistic approach. The uncertainties in predicting the sorptive behavior of U-1a and UE-5n alluvium also suggest that these uncertainties must be propagated to nearfield HST and large-scale corrective action unit (CAU) models.
Estimation Methods for One-Parameter Testlet Models
ERIC Educational Resources Information Center
Jiao, Hong; Wang, Shudong; He, Wei
2013-01-01
This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE)…
Equating Parameter Estimates from the Generalized Graded Unfolding Model.
ERIC Educational Resources Information Center
Roberts, James S.
Three common methods for equating parameter estimates from binary item response theory models are extended to the generalized grading unfolding model (GGUM). The GGUM is an item response model in which single-peaked, nonmonotonic expected value functions are implemented for polytomous responses. GGUM parameter estimates are equated using extended…
An improved state-parameter analysis of ecosystem models using data assimilation
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
Parameter estimation and error analysis in environmental modeling and computation
NASA Technical Reports Server (NTRS)
Kalmaz, E. E.
1986-01-01
A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.
Automatic Parameters Identification of Groundwater Model using Expert System
NASA Astrophysics Data System (ADS)
Tsai, P. J.; Chen, Y.; Chang, L.
2011-12-01
Conventionally, parameters identification of groundwater model can be classified into manual parameters identification and automatic parameters identification using optimization method. Parameter searching in manual parameters identification requires heavily interaction with the modeler. Therefore, the identified parameters value is interpretable by the modeler. However, manual method is a complicated and time-consuming work and requires groundwater modeling practice and parameters identification experiences to performing the task. Optimization-based identification is more efficient and convenient comparing to the manual one. Nevertheless, the parameters search in the optimization approach can not directly interactive with modeler and one can only examine the final results. Moreover, because of the simplification of the optimization model, the parameters value obtained by optimization-based identification may not be feasible in reality. In light of previous discussion, this study integrates a rule-based expert system and a groundwater simulation model, MODFLOW 2000, to develop an automatic groundwater parameters identification system. The hydraulic conductivity and specific yield are the parameters to be calibrated in the system. Since the parameter value is automatic searched according the rules that are specified by modeler, it is efficient and the identified parameters value is more interpretable than that by optimized based approach. Beside, since the rules are easy to modify and adding, the system is flexible and can accumulate the expertise experiences. Several hypothesized cases were used to examine the system validity and capability. The result shows a good agreement between the identified and given parameter values and also demonstrates a great potential for extending the system to a fully function and practical field application system.
Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James
2015-03-25
Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.
Medeiros, Stephen; Hagen, Scott; Weishampel, John; Angelo, James
2015-03-25
Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer tomore » true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.« less
Karr, Jonathan R; Williams, Alex H; Zucker, Jeremy D; Raue, Andreas; Steiert, Bernhard; Timmer, Jens; Kreutz, Clemens; Wilkinson, Simon; Allgood, Brandon A; Bot, Brian M; Hoff, Bruce R; Kellen, Michael R; Covert, Markus W; Stolovitzky, Gustavo A; Meyer, Pablo
2015-05-01
Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation. PMID:26020786
Parameter estimation of hydrologic models using data assimilation
NASA Astrophysics Data System (ADS)
Kaheil, Y. H.
2005-12-01
The uncertainties associated with the modeling of hydrologic systems sometimes demand that data should be incorporated in an on-line fashion in order to understand the behavior of the system. This paper represents a Bayesian strategy to estimate parameters for hydrologic models in an iterative mode. The paper presents a modified technique called localized Bayesian recursive estimation (LoBaRE) that efficiently identifies the optimum parameter region, avoiding convergence to a single best parameter set. The LoBaRE methodology is tested for parameter estimation for two different types of models: a support vector machine (SVM) model for predicting soil moisture, and the Sacramento Soil Moisture Accounting (SAC-SMA) model for estimating streamflow. The SAC-SMA model has 13 parameters that must be determined. The SVM model has three parameters. Bayesian inference is used to estimate the best parameter set in an iterative fashion. This is done by narrowing the sampling space by imposing uncertainty bounds on the posterior best parameter set and/or updating the "parent" bounds based on their fitness. The new approach results in fast convergence towards the optimal parameter set using minimum training/calibration data and evaluation of fewer parameter sets. The efficacy of the localized methodology is also compared with the previously used Bayesian recursive estimation (BaRE) algorithm.
Distributed parameter modeling for the control of flexible spacecraft
NASA Technical Reports Server (NTRS)
Taylor, Lawrence W., Jr.
1990-01-01
The use of FEMs of spacecraft structural dynamics is a common practice, but it has a number of shortcomings. Distributed-parameter models offer an alternative, but present both advantages and difficulties. First, the model order does not have to be reduced prior to the inclusion of control system dynamics. This advantage eliminates the risk involved with model 'order reduction'. Second, distributed parameter models inherently involve fewer parameters, thereby enabling more accurate parameter estimation using experimental data. Third, it is possible to include the damping in the basic model, thereby increasing the accuracy of the structural damping. The difficulty in generating distributed parameter models of complex spacecraft configurations has been greatly alleviated by the use of PDEMOD, BUNVIS-RG, or DISTEL. PDEMOD is being developed for simultaneously modeling structural dynamics and control system dynamics.
An appraisal-based coping model of attachment and adjustment to arthritis.
Sirois, Fuschia M; Gick, Mary L
2016-05-01
Guided by pain-related attachment models and coping theory, we used structural equation modeling to test an appraisal-based coping model of how insecure attachment was linked to arthritis adjustment in a sample of 365 people with arthritis. The structural equation modeling analyses revealed indirect and direct associations of anxious and avoidant attachment with greater appraisals of disease-related threat, less perceived social support to deal with this threat, and less coping efficacy. There was evidence of reappraisal processes for avoidant but not anxious attachment. Findings highlight the importance of considering attachment style when assessing how people cope with the daily challenges of arthritis. PMID:24984717
Cassidy, Adam R
2016-01-01
The objective of this study was to establish latent executive function (EF) and psychosocial adjustment factor structure, to examine associations between EF and psychosocial adjustment, and to explore potential development differences in EF-psychosocial adjustment associations in healthy children and adolescents. Using data from the multisite National Institutes of Health (NIH) magnetic resonance imaging (MRI) Study of Normal Brain Development, the current investigation examined latent associations between theoretically and empirically derived EF factors and emotional and behavioral adjustment measures in a large, nationally representative sample of children and adolescents (7-18 years old; N = 352). Confirmatory factor analysis (CFA) was the primary method of data analysis. CFA results revealed that, in the whole sample, the proposed five-factor model (Working Memory, Shifting, Verbal Fluency, Externalizing, and Internalizing) provided a close fit to the data, χ(2)(66) = 114.48, p < .001; RMSEA = .046; NNFI = .973; CFI = .980. Significant negative associations were demonstrated between Externalizing and both Working Memory and Verbal Fluency (p < .01) factors. A series of increasingly restrictive tests led to the rejection of the hypothesis of invariance, thereby precluding formal statistical examination of age-related differences in latent EF-psychosocial adjustment associations. Findings indicate that childhood EF skills are best conceptualized as a constellation of interconnected yet distinguishable cognitive self-regulatory skills. Individual differences in certain domains of EF track meaningfully and in expected directions with emotional and behavioral adjustment indices. Externalizing behaviors, in particular, are associated with latent Working Memory and Verbal Fluency factors. PMID:25569593
Isolating parameter sensitivity in reach scale transient storage modeling
NASA Astrophysics Data System (ADS)
Schmadel, Noah M.; Neilson, Bethany T.; Heavilin, Justin E.; Wörman, Anders
2016-03-01
Parameter sensitivity analyses, although necessary to assess identifiability, may not lead to an increased understanding or accurate representation of transient storage processes when associated parameter sensitivities are muted. Reducing the number of uncertain calibration parameters through field-based measurements may allow for more realistic representations and improved predictive capabilities of reach scale stream solute transport. Using a two-zone transient storage model, we examined the spatial detail necessary to set parameters describing hydraulic characteristics and isolate the sensitivity of the parameters associated with transient storage processes. We represented uncertain parameter distributions as triangular fuzzy numbers and used closed form statistical moment solutions to express parameter sensitivity thus avoiding copious model simulations. These solutions also allowed for the direct incorporation of different levels of spatial information regarding hydraulic characteristics. To establish a baseline for comparison, we performed a sensitivity analysis considering all model parameters as uncertain. Next, we set hydraulic parameters as the reach averages, leaving the transient storage parameters as uncertain, and repeated the analysis. Lastly, we incorporated high resolution hydraulic information assessed from aerial imagery to examine whether more spatial detail was necessary to isolate the sensitivity of transient storage parameters. We found that a reach-average hydraulic representation, as opposed to using detailed spatial information, was sufficient to highlight transient storage parameter sensitivity and provide more information regarding the potential identifiability of these parameters.
Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.
ERIC Educational Resources Information Center
Kim, Seock-Ho; Cohen, Allan S.
The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…
Essa, Mohamed; Sayed, Tarek
2015-11-01
Several studies have investigated the relationship between field-measured conflicts and the conflicts obtained from micro-simulation models using the Surrogate Safety Assessment Model (SSAM). Results from recent studies have shown that while reasonable correlation between simulated and real traffic conflicts can be obtained especially after proper calibration, more work is still needed to confirm that simulated conflicts provide safety measures beyond what can be expected from exposure. As well, the results have emphasized that using micro-simulation model to evaluate safety without proper model calibration should be avoided. The calibration process adjusts relevant simulation parameters to maximize the correlation between field-measured and simulated conflicts. The main objective of this study is to investigate the transferability of calibrated parameters of the traffic simulation model (VISSIM) for safety analysis between different sites. The main purpose is to examine whether the calibrated parameters, when applied to other sites, give reasonable results in terms of the correlation between the field-measured and the simulated conflicts. Eighty-three hours of video data from two signalized intersections in Surrey, BC were used in this study. Automated video-based computer vision techniques were used to extract vehicle trajectories and identify field-measured rear-end conflicts. Calibrated VISSIM parameters obtained from the first intersection which maximized the correlation between simulated and field-observed conflicts were used to estimate traffic conflicts at the second intersection and to compare the results to parameters optimized specifically for the second intersection. The results show that the VISSIM parameters are generally transferable between the two locations as the transferred parameters provided better correlation between simulated and field-measured conflicts than using the default VISSIM parameters. Of the six VISSIM parameters identified as
Karr, Jonathan R.; Williams, Alex H.; Zucker, Jeremy D.; Raue, Andreas; Steiert, Bernhard; Timmer, Jens; Kreutz, Clemens; Wilkinson, Simon; Allgood, Brandon A.; Bot, Brian M.; Hoff, Bruce R.; Kellen, Michael R.; Covert, Markus W.; Stolovitzky, Gustavo A.; Meyer, Pablo
2015-01-01
Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM) 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model’s structure and in silico “experimental” data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation. PMID:26020786
Wang, Chao Yang; Luo, Gang; Jiang, Fangming; Carnes, Brian; Chen, Ken Shuang
2010-05-01
Current computational models for proton exchange membrane fuel cells (PEMFCs) include a large number of parameters such as boundary conditions, material properties, and numerous parameters used in sub-models for membrane transport, two-phase flow and electrochemistry. In order to successfully use a computational PEMFC model in design and optimization, it is important to identify critical parameters under a wide variety of operating conditions, such as relative humidity, current load, temperature, etc. Moreover, when experimental data is available in the form of polarization curves or local distribution of current and reactant/product species (e.g., O2, H2O concentrations), critical parameters can be estimated in order to enable the model to better fit the data. Sensitivity analysis and parameter estimation are typically performed using manual adjustment of parameters, which is also common in parameter studies. We present work to demonstrate a systematic approach based on using a widely available toolkit developed at Sandia called DAKOTA that supports many kinds of design studies, such as sensitivity analysis as well as optimization and uncertainty quantification. In the present work, we couple a multidimensional PEMFC model (which is being developed, tested and later validated in a joint effort by a team from Penn State Univ. and Sandia National Laboratories) with DAKOTA through the mapping of model parameters to system responses. Using this interface, we demonstrate the efficiency of performing simple parameter studies as well as identifying critical parameters using sensitivity analysis. Finally, we show examples of optimization and parameter estimation using the automated capability in DAKOTA.
NASA Astrophysics Data System (ADS)
Mai, Juliane; Cuntz, Matthias; Zink, Matthias; Thober, Stephan; Kumar, Rohini; Schäfer, David; Schrön, Martin; Craven, John; Rakovec, Oldrich; Spieler, Diana; Prykhodko, Vladyslav; Dalmasso, Giovanni; Musuuza, Jude; Langenberg, Ben; Attinger, Sabine; Samaniego, Luis
2016-04-01
Environmental models tend to require increasing computational time and resources as physical process descriptions are improved or new descriptions are incorporated. Many-query applications such as sensitivity analysis or model calibration usually require a large number of model evaluations leading to high computational demand. This often limits the feasibility of rigorous analyses. Here we present a fully automated sequential screening method that selects only informative parameters for a given model output. The method requires a number of model evaluations that is approximately 10 times the number of model parameters. It was tested using the mesoscale hydrologic model mHM in three hydrologically unique European river catchments. It identified around 20 informative parameters out of 52, with different informative parameters in each catchment. The screening method was evaluated with subsequent analyses using all 52 as well as only the informative parameters. Subsequent Sobol's global sensitivity analysis led to almost identical results yet required 40% fewer model evaluations after screening. mHM was calibrated with all and with only informative parameters in the three catchments. Model performances for daily discharge were equally high in both cases with Nash-Sutcliffe efficiencies above 0.82. Calibration using only the informative parameters needed just one third of the number of model evaluations. The universality of the sequential screening method was demonstrated using several general test functions from the literature. We therefore recommend the use of the computationally inexpensive sequential screening method prior to rigorous analyses on complex environmental models.
NASA Astrophysics Data System (ADS)
Cuntz, Matthias; Mai, Juliane; Zink, Matthias; Thober, Stephan; Kumar, Rohini; Schäfer, David; Schrön, Martin; Craven, John; Rakovec, Oldrich; Spieler, Diana; Prykhodko, Vladyslav; Dalmasso, Giovanni; Musuuza, Jude; Langenberg, Ben; Attinger, Sabine; Samaniego, Luis
2015-08-01
Environmental models tend to require increasing computational time and resources as physical process descriptions are improved or new descriptions are incorporated. Many-query applications such as sensitivity analysis or model calibration usually require a large number of model evaluations leading to high computational demand. This often limits the feasibility of rigorous analyses. Here we present a fully automated sequential screening method that selects only informative parameters for a given model output. The method requires a number of model evaluations that is approximately 10 times the number of model parameters. It was tested using the mesoscale hydrologic model mHM in three hydrologically unique European river catchments. It identified around 20 informative parameters out of 52, with different informative parameters in each catchment. The screening method was evaluated with subsequent analyses using all 52 as well as only the informative parameters. Subsequent Sobol's global sensitivity analysis led to almost identical results yet required 40% fewer model evaluations after screening. mHM was calibrated with all and with only informative parameters in the three catchments. Model performances for daily discharge were equally high in both cases with Nash-Sutcliffe efficiencies above 0.82. Calibration using only the informative parameters needed just one third of the number of model evaluations. The universality of the sequential screening method was demonstrated using several general test functions from the literature. We therefore recommend the use of the computationally inexpensive sequential screening method prior to rigorous analyses on complex environmental models.
Agricultural and Environmental Input Parameters for the Biosphere Model
Kaylie Rasmuson; Kurt Rautenstrauch
2003-06-20
This analysis is one of nine technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. It documents input parameters for the biosphere model, and supports the use of the model to develop Biosphere Dose Conversion Factors (BDCF). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in the biosphere Technical Work Plan (TWP, BSC 2003a). It should be noted that some documents identified in Figure 1-1 may be under development and therefore not available at the time this document is issued. The ''Biosphere Model Report'' (BSC 2003b) describes the ERMYN and its input parameters. This analysis report, ANL-MGR-MD-000006, ''Agricultural and Environmental Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. This report defines and justifies values for twelve parameters required in the biosphere model. These parameters are related to use of contaminated groundwater to grow crops. The parameter values recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN.
Kautter, John; Pope, Gregory C; Ingber, Melvin; Freeman, Sara; Patterson, Lindsey; Cohen, Michael; Keenan, Patricia
2014-01-01
Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula. PMID:25360387
Kautter, John; Pope, Gregory C; Ingber, Melvin; Freeman, Sara; Patterson, Lindsey; Cohen, Michael; Keenan, Patricia
2014-01-01
Beginning in 2014, individuals and small businesses are able to purchase private health insurance through competitive Marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the second of three in this issue of the Review that describe the Department of Health and Human Services (HHS) risk adjustment methodology and focuses on the risk adjustment model. In our first companion article, we discuss the key issues and choices in developing the methodology. In this article, we present the risk adjustment model, which is named the HHS-Hierarchical Condition Categories (HHS-HCC) risk adjustment model. We first summarize the HHS-HCC diagnostic classification, which is the key element of the risk adjustment model. Then the data and methods, results, and evaluation of the risk adjustment model are presented. Fifteen separate models are developed. For each age group (adult, child, and infant), a model is developed for each cost sharing level (platinum, gold, silver, and bronze metal levels, as well as catastrophic plans). Evaluation of the risk adjustment models shows good predictive accuracy, both for individuals and for groups. Lastly, this article provides examples of how the model output is used to calculate risk scores, which are an input into the risk transfer formula. Our third companion paper describes the risk transfer formula. PMID:25360387
A simulation of water pollution model parameter estimation
NASA Technical Reports Server (NTRS)
Kibler, J. F.
1976-01-01
A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.
On Interpreting the Parameters for Any Item Response Model
ERIC Educational Resources Information Center
Thissen, David
2009-01-01
Maris and Bechger's article is an exercise in technical virtuosity and provides much to be learned by students of psychometrics. In this commentary, the author begins with making two observations. The first is that the title, "On Interpreting the Model Parameters for the Three Parameter Logistic Model," belies the generality of parts of Maris and…
Exploring the interdependencies between parameters in a material model.
Silling, Stewart Andrew; Fermen-Coker, Muge
2014-01-01
A method is investigated to reduce the number of numerical parameters in a material model for a solid. The basis of the method is to detect interdependencies between parameters within a class of materials of interest. The method is demonstrated for a set of material property data for iron and steel using the Johnson-Cook plasticity model.
ERIC Educational Resources Information Center
Siman-Tov, Ayelet; Kaniel, Shlomo
2011-01-01
The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…
Brownian motion model with stochastic parameters for asset prices
NASA Astrophysics Data System (ADS)
Ching, Soo Huei; Hin, Pooi Ah
2013-09-01
The Brownian motion model may not be a completely realistic model for asset prices because in real asset prices the drift μ and volatility σ may change over time. Presently we consider a model in which the parameter x = (μ,σ) is such that its value x (t + Δt) at a short time Δt ahead of the present time t depends on the value of the asset price at time t + Δt as well as the present parameter value x(t) and m-1 other parameter values before time t via a conditional distribution. The Malaysian stock prices are used to compare the performance of the Brownian motion model with fixed parameter with that of the model with stochastic parameter.
Estimation of Accumulation Parameters for Urban Runoff Quality Modeling
NASA Astrophysics Data System (ADS)
Alley, William M.; Smith, Peter E.
1981-12-01
Many recently developed watershed models utilize accumulation and washoff equations to simulate the quality of runofffrom urban impervious areas. These models often have been calibrated by trial and error and with little understanding of model sensitivity to the various parameters. Methodologies for estimating best fit values of the washoff parameters commonly used in these models have been presented previously. In this paper, parameter identification techniques for estimating the accumulation parameters from measured runoff quality data are presented along with a sensitivity analysis of the parameters. Results from application of the techniques and the sensitivity analysis suggest a need for data quantifying the magnitude and identifying the shape of constituent accumulation curves. An exponential accumulation curve is shown to be more general than the linear accumulation curves used in most urban runoff quality models. When determining accumulation rates, attention needs to be given to the effects of residual amounts of constituents remaining after the previous period of storm runoff or street sweeping.
Modenese, Luca; Ceseracciu, Elena; Reggiani, Monica; Lloyd, David G
2016-01-25
A challenging aspect of subject specific musculoskeletal modeling is the estimation of muscle parameters, especially optimal fiber length and tendon slack length. In this study, the method for scaling musculotendon parameters published by Winby et al. (2008), J. Biomech. 41, 1682-1688, has been reformulated, generalized and applied to two cases of practical interest: 1) the adjustment of muscle parameters in the entire lower limb following linear scaling of a generic model and 2) their estimation "from scratch" in a subject specific model of the hip joint created from medical images. In the first case, the procedure maintained the muscles׳ operating range between models with mean errors below 2.3% of the reference model normalized fiber length value. In the second case, a subject specific model of the hip joint was created using segmented bone geometries and muscle volumes publicly available for a cadaveric specimen from the Living Human Digital Library (LHDL). Estimated optimal fiber lengths were found to be consistent with those of a previously published dataset for all 27 considered muscle bundles except gracilis. However, computed tendon slack lengths differed from tendon lengths measured in the LHDL cadaver, suggesting that tendon slack length should be determined via optimization in subject-specific applications. Overall, the presented methodology could adjust the parameters of a scaled model and enabled the estimation of muscle parameters in newly created subject specific models. All data used in the analyses are of public domain and a tool implementing the algorithm is available at https://simtk.org/home/opt_muscle_par. PMID:26776930
An automatic and effective parameter optimization method for model tuning
NASA Astrophysics Data System (ADS)
Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.
2015-11-01
Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.
Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment.
Qiao, Hong; Xi, Xuanyang; Li, Yinlin; Wu, Wei; Li, Fengfu
2015-11-01
Recently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position- and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational
Baker, Syed Murtuza; Poskar, C Hart; Junker, Björn H
2011-01-01
In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison. PMID:21989173
NASA Astrophysics Data System (ADS)
Weigand, M.; Kemna, A.
2016-06-01
Spectral induced polarization (SIP) data are commonly analysed using phenomenological models. Among these models the Cole-Cole (CC) model is the most popular choice to describe the strength and frequency dependence of distinct polarization peaks in the data. More flexibility regarding the shape of the spectrum is provided by decomposition schemes. Here the spectral response is decomposed into individual responses of a chosen elementary relaxation model, mathematically acting as kernel in the involved integral, based on a broad range of relaxation times. A frequently used kernel function is the Debye model, but also the CC model with some other a priorly specified frequency dispersion (e.g. Warburg model) has been proposed as kernel in the decomposition. The different decomposition approaches in use, also including conductivity and resistivity formulations, pose the question to which degree the integral spectral parameters typically derived from the obtained relaxation time distribution are biased by the approach itself. Based on synthetic SIP data sampled from an ideal CC response, we here investigate how the two most important integral output parameters deviate from the corresponding CC input parameters. We find that the total chargeability may be underestimated by up to 80 per cent and the mean relaxation time may be off by up to three orders of magnitude relative to the original values, depending on the frequency dispersion of the analysed spectrum and the proximity of its peak to the frequency range limits considered in the decomposition. We conclude that a quantitative comparison of SIP parameters across different studies, or the adoption of parameter relationships from other studies, for example when transferring laboratory results to the field, is only possible on the basis of a consistent spectral analysis procedure. This is particularly important when comparing effective CC parameters with spectral parameters derived from decomposition results.
Tube-Load Model Parameter Estimation for Monitoring Arterial Hemodynamics
Zhang, Guanqun; Hahn, Jin-Oh; Mukkamala, Ramakrishna
2011-01-01
A useful model of the arterial system is the uniform, lossless tube with parametric load. This tube-load model is able to account for wave propagation and reflection (unlike lumped-parameter models such as the Windkessel) while being defined by only a few parameters (unlike comprehensive distributed-parameter models). As a result, the parameters may be readily estimated by accurate fitting of the model to available arterial pressure and flow waveforms so as to permit improved monitoring of arterial hemodynamics. In this paper, we review tube-load model parameter estimation techniques that have appeared in the literature for monitoring wave reflection, large artery compliance, pulse transit time, and central aortic pressure. We begin by motivating the use of the tube-load model for parameter estimation. We then describe the tube-load model, its assumptions and validity, and approaches for estimating its parameters. We next summarize the various techniques and their experimental results while highlighting their advantages over conventional techniques. We conclude the review by suggesting future research directions and describing potential applications. PMID:22053157
Extraction of exposure modeling parameters of thick resist
NASA Astrophysics Data System (ADS)
Liu, Chi; Du, Jinglei; Liu, Shijie; Duan, Xi; Luo, Boliang; Zhu, Jianhua; Guo, Yongkang; Du, Chunlei
2004-12-01
Experimental and theoretical analysis indicates that many nonlinear factors existing in the exposure process of thick resist can remarkably affect the PAC concentration distribution in the resist. So the effects should be fully considered in the exposure model of thick resist, and exposure parameters should not be treated as constants because there exists certain relationship between the parameters and resist thickness. In this paper, an enhanced Dill model for the exposure process of thick resist is presented, and the experimental setup for measuring exposure parameters of thick resist is developed. We measure the intensity transmittance curve of thick resist AZ4562 under different processing conditions, and extract the corresponding exposure parameters based on the experiment results and the calculations from the beam propagation matrix of the resist films. With these modified modeling parameters and enhanced Dill model, simulation of thick-resist exposure process can be effectively developed in the future.
Interfacial free energy adjustable phase field crystal model for homogeneous nucleation.
Guo, Can; Wang, Jincheng; Wang, Zhijun; Li, Junjie; Guo, Yaolin; Huang, Yunhao
2016-05-18
To describe the homogeneous nucleation process, an interfacial free energy adjustable phase-field crystal model (IPFC) was proposed by reconstructing the energy functional of the original phase field crystal (PFC) methodology. Compared with the original PFC model, the additional interface term in the IPFC model effectively can adjust the magnitude of the interfacial free energy, but does not affect the equilibrium phase diagram and the interfacial energy anisotropy. The IPFC model overcame the limitation that the interfacial free energy of the original PFC model is much less than the theoretical results. Using the IPFC model, we investigated some basic issues in homogeneous nucleation. From the viewpoint of simulation, we proceeded with an in situ observation of the process of cluster fluctuation and obtained quite similar snapshots to colloidal crystallization experiments. We also counted the size distribution of crystal-like clusters and the nucleation rate. Our simulations show that the size distribution is independent of the evolution time, and the nucleation rate remains constant after a period of relaxation, which are consistent with experimental observations. The linear relation between logarithmic nucleation rate and reciprocal driving force also conforms to the steady state nucleation theory. PMID:27117814
Identification of parameters of discrete-continuous models
Cekus, Dawid Warys, Pawel
2015-03-10
In the paper, the parameters of a discrete-continuous model have been identified on the basis of experimental investigations and formulation of optimization problem. The discrete-continuous model represents a cantilever stepped Timoshenko beam. The mathematical model has been formulated and solved according to the Lagrange multiplier formalism. Optimization has been based on the genetic algorithm. The presented proceeding’s stages make the identification of any parameters of discrete-continuous systems possible.
Inverse estimation of parameters for an estuarine eutrophication model
Shen, J.; Kuo, A.Y.
1996-11-01
An inverse model of an estuarine eutrophication model with eight state variables is developed. It provides a framework to estimate parameter values of the eutrophication model by assimilation of concentration data of these state variables. The inverse model using the variational technique in conjunction with a vertical two-dimensional eutrophication model is general enough to be applicable to aid model calibration. The formulation is illustrated by conducting a series of numerical experiments for the tidal Rappahannock River, a western shore tributary of the Chesapeake Bay. The numerical experiments of short-period model simulations with different hypothetical data sets and long-period model simulations with limited hypothetical data sets demonstrated that the inverse model can be satisfactorily used to estimate parameter values of the eutrophication model. The experiments also showed that the inverse model is useful to address some important questions, such as uniqueness of the parameter estimation and data requirements for model calibration. Because of the complexity of the eutrophication system, degrading of speed of convergence may occur. Two major factors which cause degradation of speed of convergence are cross effects among parameters and the multiple scales involved in the parameter system.
Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models.
Krivitsky, Pavel N; Handcock, Mark S; Morris, Martina
2011-07-01
Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS). PMID:21691424
Remote Sensing-based Methodologies for Snow Model Adjustments in Operational Streamflow Prediction
NASA Astrophysics Data System (ADS)
Bender, S.; Miller, W. P.; Bernard, B.; Stokes, M.; Oaida, C. M.; Painter, T. H.
2015-12-01
Water management agencies rely on hydrologic forecasts issued by operational agencies such as NOAA's Colorado Basin River Forecast Center (CBRFC). The CBRFC has partnered with the Jet Propulsion Laboratory (JPL) under funding from NASA to incorporate research-oriented, remotely-sensed snow data into CBRFC operations and to improve the accuracy of CBRFC forecasts. The partnership has yielded valuable analysis of snow surface albedo as represented in JPL's MODIS Dust Radiative Forcing in Snow (MODDRFS) data, across the CBRFC's area of responsibility. When dust layers within a snowpack emerge, reducing the snow surface albedo, the snowmelt rate may accelerate. The CBRFC operational snow model (SNOW17) is a temperature-index model that lacks explicit representation of snowpack surface albedo. CBRFC forecasters monitor MODDRFS data for emerging dust layers and may manually adjust SNOW17 melt rates. A technique was needed for efficient and objective incorporation of the MODDRFS data into SNOW17. Initial development focused in Colorado, where dust-on-snow events frequently occur. CBRFC forecasters used retrospective JPL-CBRFC analysis and developed a quantitative relationship between MODDRFS data and mean areal temperature (MAT) data. The relationship was used to generate adjusted, MODDRFS-informed input for SNOW17. Impacts of the MODDRFS-SNOW17 MAT adjustment method on snowmelt-driven streamflow prediction varied spatially and with characteristics of the dust deposition events. The largest improvements occurred in southwestern Colorado, in years with intense dust deposition events. Application of the method in other regions of Colorado and in "low dust" years resulted in minimal impact. The MODDRFS-SNOW17 MAT technique will be implemented in CBRFC operations in late 2015, prior to spring 2016 runoff. Collaborative investigation of remote sensing-based adjustment methods for the CBRFC operational hydrologic forecasting environment will continue over the next several years.
Analysis of Hydrogeologic Conceptual Model and Parameter Uncertainty
Meyer, Philip D.; Nicholson, Thomas J.; Mishra, Srikanta
2003-06-24
A systematic methodology for assessing hydrogeologic conceptual model, parameter, and scenario uncertainties is being developed to support technical reviews of environmental assessments related to decommissioning of nuclear facilities. The first major task being undertaken is to produce a coupled parameter and conceptual model uncertainty assessment methodology. This task is based on previous studies that have primarily dealt individually with these two types of uncertainties. Conceptual model uncertainty analysis is based on the existence of alternative conceptual models that are generated using a set of clearly stated guidelines targeted at the needs of NRC staff. Parameter uncertainty analysis makes use of generic site characterization data as well as site-specific characterization and monitoring data to evaluate parameter uncertainty in each of the alternative conceptual models. Propagation of parameter uncertainty will be carried out through implementation of a general stochastic model of groundwater flow and transport in the saturated and unsaturated zones. Evaluation of prediction uncertainty will make use of Bayesian model averaging and visualization of model results. The goal of this study is to develop a practical tool to quantify uncertainties in the conceptual model and parameters identified in performance assessments.
“A model of mother-child Adjustment in Arab Muslim Immigrants to the US”
Hough, Edythe s; Templin, Thomas N; Kulwicki, Anahid; Ramaswamy, Vidya; Katz, Anne
2009-01-01
We examined the mother-child adjustment and child behavior problems in Arab Muslim immigrant families residing in the U.S.A. The sample of 635 mother-child dyads was comprised of mothers who emigrated from 1989 or later and had at least one early adolescent child between the ages of 11 to 15 years old who was also willing to participate. Arabic speaking research assistants collected the data from the mothers and children using established measures of maternal and child stressors, coping, and social support; maternal distress; parent-child relationship; and child behavior problems. A structural equation model (SEM) was specified a priori with 17 predicted pathways. With a few exceptions, the final SEM model was highly consistent with the proposed model and had a good fit to the data. The model accounted for 67% of the variance in child behavior problems. Child stressors, mother-child relationship, and maternal stressors were the causal variables that contributed the most to child behavior problems. The model also accounted for 27% of the variance in mother-child relationship. Child active coping, child gender, mother’s education, and maternal distress were all predictive of the mother-child relationship. Mother-child relationship also mediated the effects of maternal distress and child active coping on child behavior problems. These findings indicate that immigrant mothers contribute greatly to adolescent adjustment, both as a source of risk and protection. These findings also suggest that intervening with immigrant mothers to reduce their stress and strengthening the parent-child relationship are two important areas for promoting adolescent adjustment. PMID:19758737
A spatial model of bird abundance as adjusted for detection probability
Gorresen, P.M.; Mcmillan, G.P.; Camp, R.J.; Pratt, T.K.
2009-01-01
Modeling the spatial distribution of animals can be complicated by spatial and temporal effects (i.e. spatial autocorrelation and trends in abundance over time) and other factors such as imperfect detection probabilities and observation-related nuisance variables. Recent advances in modeling have demonstrated various approaches that handle most of these factors but which require a degree of sampling effort (e.g. replication) not available to many field studies. We present a two-step approach that addresses these challenges to spatially model species abundance. Habitat, spatial and temporal variables were handled with a Bayesian approach which facilitated modeling hierarchically structured data. Predicted abundance was subsequently adjusted to account for imperfect detection and the area effectively sampled for each species. We provide examples of our modeling approach for two endemic Hawaiian nectarivorous honeycreepers: 'i'iwi Vestiaria coccinea and 'apapane Himatione sanguinea. ?? 2009 Ecography.
NASA Astrophysics Data System (ADS)
Wu, Bo; Hu, Han; Guo, Jian
2014-04-01
Lunar topographic information is essential for lunar scientific investigations and exploration missions. Lunar orbiter imagery and laser altimeter data are two major data sources for lunar topographic modeling. Most previous studies have processed the imagery and laser altimeter data separately for lunar topographic modeling, and there are usually inconsistencies between the derived lunar topographic models. This paper presents a novel combined block adjustment approach to integrate multiple strips of the Chinese Chang'E-2 imagery and NASA's Lunar Reconnaissance Orbiter (LRO) Laser Altimeter (LOLA) data for precision lunar topographic modeling. The participants of the combined block adjustment include the orientation parameters of the Chang'E-2 images, the intra-strip tie points derived from the Chang'E-2 stereo images of the same orbit, the inter-strip tie points derived from the overlapping area of two neighbor Chang'E-2 image strips, and the LOLA points. Two constraints are incorporated into the combined block adjustment including a local surface constraint and an orbit height constraint, which are specifically designed to remedy the large inconsistencies between the Chang'E-2 and LOLA data sets. The output of the combined block adjustment is the improved orientation parameters of the Chang'E-2 images and ground coordinates of the LOLA points, from which precision lunar topographic models can be generated. The performance of the developed approach was evaluated using the Chang'E-2 imagery and LOLA data in the Sinus Iridum area and the Apollo 15 landing area. The experimental results revealed that the mean absolute image residuals between the Chang'E-2 image strips were drastically reduced from tens of pixels before the adjustment to sub-pixel level after adjustment. Digital elevation models (DEMs) with 20 m resolution were generated using the Chang'E-2 imagery after the combined block adjustment. Comparison of the Chang'E-2 DEM with the LOLA DEM showed a good
Computationally Inexpensive Identification of Non-Informative Model Parameters
NASA Astrophysics Data System (ADS)
Mai, J.; Cuntz, M.; Kumar, R.; Zink, M.; Samaniego, L. E.; Schaefer, D.; Thober, S.; Rakovec, O.; Musuuza, J. L.; Craven, J. R.; Spieler, D.; Schrön, M.; Prykhodko, V.; Dalmasso, G.; Langenberg, B.; Attinger, S.
2014-12-01
Sensitivity analysis is used, for example, to identify parameters which induce the largest variability in model output and are thus informative during calibration. Variance-based techniques are employed for this purpose, which unfortunately require a large number of model evaluations and are thus ineligible for complex environmental models. We developed, therefore, a computational inexpensive screening method, which is based on Elementary Effects, that automatically separates informative and non-informative model parameters. The method was tested using the mesoscale hydrologic model (mHM) with 52 parameters. The model was applied in three European catchments with different hydrological characteristics, i.e. Neckar (Germany), Sava (Slovenia), and Guadalquivir (Spain). The method identified the same informative parameters as the standard Sobol method but with less than 1% of model runs. In Germany and Slovenia, 22 of 52 parameters were informative mostly in the formulations of evapotranspiration, interflow and percolation. In Spain 19 of 52 parameters were informative with an increased importance of soil parameters. We showed further that Sobol' indexes calculated for the subset of informative parameters are practically the same as Sobol' indexes before the screening but the number of model runs was reduced by more than 50%. The model mHM was then calibrated twice in the three test catchments. First all 52 parameters were taken into account and then only the informative parameters were calibrated while all others are kept fixed. The Nash-Sutcliffe efficiencies were 0.87 and 0.83 in Germany, 0.89 and 0.88 in Slovenia, and 0.86 and 0.85 in Spain, respectively. This minor loss of at most 4% in model performance comes along with a substantial decrease of at least 65% in model evaluations. In summary, we propose an efficient screening method to identify non-informative model parameters that can be discarded during further applications. We have shown that sensitivity
An automatic and effective parameter optimization method for model tuning
NASA Astrophysics Data System (ADS)
Zhang, T.; Li, L.; Lin, Y.; Xue, W.; Xie, F.; Xu, H.; Huang, X.
2015-05-01
Physical parameterizations in General Circulation Models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
The determination of oxide surface charging parameters for a predictive metal adsorption model.
Schreier, Marc; Feltes, Theresa E; Schaal, Melanie T; Regalbuto, John R
2010-08-15
The procurement of oxide surface charging parameters has been a widely researched topic in recent years [1-30]. In this study, a one-site, two-pK surface charging mechanism is used in combination with a diffuse double-layer description of the electric double-layer to fit pH shift data over silica and alumina. From these fits of pH data, with no further adjustment of parameters, metal adsorption can be predicted over both supports to a reasonable degree of accuracy. A multi-dimensional optimization procedure employing a Nelder-Mead simplex algorithm is used to optimize the DeltapK (pK(2)-pK(1)) parameter to obtain a best fit of the pH shift data with fixed PZC and hydroxyl density (N(s)). The resulting set of parameters is then used with no adjustment in a purely electrostatic adsorption model (the Revised Physical Adsorption or RPA model) in order to predict anionic chloroplatinic acid (CPA, [PtCl(6)](-2)) adsorption on alumina and cationic platinum tetraammine (PTA, [Pt(NH(3))(4)](+2)) adsorption on alumina and silica. The optimization procedure developed in this study gives reasonable values of the DeltapK compared to other values reported in the literature, with fits to the pH shift data at various oxide loadings with relative errors below 2.8%. PMID:20478569
NASA Astrophysics Data System (ADS)
Chen, Y.; Li, J.; Xu, H.
2015-10-01
Physically based distributed hydrological models discrete the terrain of the whole catchment into a number of grid cells at fine resolution, and assimilate different terrain data and precipitation to different cells, and are regarded to have the potential to improve the catchment hydrological processes simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters, but unfortunately, the uncertanties associated with this model parameter deriving is very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study, the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using PSO algorithm and to test its competence and to improve its performances, the second is to explore the possibility of improving physically based distributed hydrological models capability in cathcment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improverd Particle Swarm Optimization (PSO) algorithm is developed for the parameter optimization of Liuxihe model in catchment flood forecasting, the improvements include to adopt the linear decreasing inertia weight strategy to change the inertia weight, and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show that the improved PSO algorithm could be
Observational Constraint of Aerosol Effects on the CMIP5 Inter-model Spread of Adjusted Forcings
NASA Astrophysics Data System (ADS)
Chen, J.; Wennberg, P. O.; Jiang, J. H.; Su, H.; Bordoni, S.
2013-12-01
The simulated global-mean temperature (GMT) change over the past 150 years is quite consistent across CMIP5 climate models and also consistent with the observations. However, the predicted future GMT under the identical CO2 forcing is divergent. This paradox is partly due to the errors in the predicted GMT produced by historical greenhouse gas (GHG) forcing being compensated by the parameterization of aerosol cloud radiative forcing. Historical increases in anthropogenic aerosols exert an overall (but highly uncertain) cooling effect in the climate system, which partially offsets the warming due to well mixed greenhouse gases (WMGHGs). Because aerosol concentrations are predicted to eventually decrease in future scenarios, climate change becomes dominated by warming due to the WMGHG. This change in the relative importance of forcing by aerosol versus WMGHG makes apparent the substantial differences in prediction of climate by WMGHG forcing. Here we investigate the role of aerosols in the context of adjusted forcing changes in the historical runs and the effect of aerosols on the cloud feedback. Our preliminary results suggest that models which are more sensitive to the increase in concentration of CO2 have a larger aerosol radiative cooling effect. By comparing the historicalMisc runs and historicalGHG runs, we find that aerosols exert a potential impact on the cloud adjusted forcings, especially shortwave cloud adjusted forcings. We use the CLIPSO, MISR and CERES data as the benchmark to evaluate the present aerosol simulations. Using satellite observations to assess the relative reliability of the different model responses and to constrain the simulated aerosol radiative forcing will contribute significantly to reducing the across model spread in future climate simulations and identifying some missing physical processes.
NASA Astrophysics Data System (ADS)
Yang, Ge; Barry, John. F.; Shuman, Edward; Demille, David
2010-03-01
We have constructed a field programmable gate array (FPGA) based lock-in amplifier/PID servo controller for use in laser frequency locking and other applications. Our system is constructed from a commercial FPGA evaluation board with total cost less than 400 and no additional electronic component is required. FPGA technology allows us to implement parallel real-time signal processing with great flexibility. Internal parameters such as the modulation frequency, phase delay, gains and filter time constants, etc. can be changed on the fly within a very wide dynamic range through an iPod-like interface. This system was used to lock a tunable diode laser to an external Fabry Perot cavity with piezo and current feedback. A loop bandwidth of 200 kHz was achieved, limited only by the slow ADCs available on the FPGA board. Further improvements in both hardware and software seem possible, and will be discussed.
Estimation of Graded Response Model Parameters Using MULTILOG.
ERIC Educational Resources Information Center
Baker, Frank B.
1997-01-01
Describes an idiosyncracy of the MULTILOG (D. Thissen, 1991) parameter estimation process discovered during a simulation study involving the graded response model. A misordering reflected in boundary function location parameter estimates resulted in a large negative contribution to the true score followed by a large positive contribution. These…
Parameter variability estimation using stochastic response surface model updating
NASA Astrophysics Data System (ADS)
Fang, Sheng-En; Zhang, Qiu-Hu; Ren, Wei-Xin
2014-12-01
From a practical point of view, uncertainties existing in structural parameters and measurements must be handled in order to provide reliable structural condition evaluations. At this moment, deterministic model updating loses its practicability and a stochastic updating procedure should be employed seeking for statistical properties of parameters and responses. Presently this topic has not been well investigated on account of its greater complexity in theoretical configuration and difficulty in inverse problem solutions after involving uncertainty analyses. Due to it, this paper attempts to develop a stochastic model updating method for parameter variability estimation. Uncertain parameters and responses are correlated through stochastic response surface models, which are actually explicit polynomial chaos expansions based on Hermite polynomials. Then by establishing a stochastic inverse problem, parameter means and standard deviations are updated in a separate and successive way. For the purposes of problem simplification and optimization efficiency, in each updating iteration stochastic response surface models are reconstructed to avoid the construction and analysis of sensitivity matrices. Meanwhile, in the interest of investigating the effects of parameter variability on responses, a parameter sensitivity analysis method has been developed based on the derivation of polynomial chaos expansions. Lastly the feasibility and reliability of the proposed methods have been validated using a numerical beam and then a set of nominally identical metal plates. After comparing with a perturbation method, it is found that the proposed method can estimate parameter variability with satisfactory accuracy and the complexity of the inverse problem can be highly reduced resulting in cost-efficient optimization.
NASA Astrophysics Data System (ADS)
Bates, P. D.; Neal, J. C.; Fewtrell, T. J.
2012-12-01
In this we paper we consider two related questions. First, we address the issue of how much physical complexity is necessary in a model in order to simulate floodplain inundation to within validation data error. This is achieved through development of a single code/multiple physics hydraulic model (LISFLOOD-FP) where different degrees of complexity can be switched on or off. Different configurations of this code are applied to four benchmark test cases, and compared to the results of a number of industry standard models. Second we address the issue of how parameter sensitivity and transferability change with increasing complexity using numerical experiments with models of different physical and geometric intricacy. Hydraulic models are a good example system with which to address such generic modelling questions as: (1) they have a strong physical basis; (2) there is only one set of equations to solve; (3) they require only topography and boundary conditions as input data; and (4) they typically require only a single free parameter, namely boundary friction. In terms of complexity required we show that for the problem of sub-critical floodplain inundation a number of codes of different dimensionality and resolution can be found to fit uncertain model validation data equally well, and that in this situation Occam's razor emerges as a useful logic to guide model selection. We find also find that model skill usually improves more rapidly with increases in model spatial resolution than increases in physical complexity, and that standard approaches to testing hydraulic models against laboratory data or analytical solutions may fail to identify this important fact. Lastly, we find that in benchmark testing studies significant differences can exist between codes with identical numerical solution techniques as a result of auxiliary choices regarding the specifics of model implementation that are frequently unreported by code developers. As a consequence, making sound
Elizur, Y; Ziv, M
2001-01-01
While heterosexist family undermining has been demonstrated to be a developmental risk factor in the life of persons with same-gender orientation, the issue of protective family factors is both controversial and relatively neglected. In this study of Israeli gay males (N = 114), we focused on the interrelations of family support, family acceptance and family knowledge of gay orientation, and gay male identity formation, and their effects on mental health and self-esteem. A path model was proposed based on the hypotheses that family support, family acceptance, family knowledge, and gay identity formation have an impact on psychological adjustment, and that family support has an effect on gay identity formation that is mediated by family acceptance. The assessment of gay identity formation was based on an established stage model that was streamlined for cross-cultural practice by defining three basic processes of same-gender identity formation: self-definition, self-acceptance, and disclosure (Elizur & Mintzer, 2001). The testing of our conceptual path model demonstrated an excellent fit with the data. An alternative model that hypothesized effects of gay male identity on family acceptance and family knowledge did not fit the data. Interpreting these results, we propose that the main effect of family support/acceptance on gay identity is related to the process of disclosure, and that both general family support and family acceptance of same-gender orientation play a significant role in the psychological adjustment of gay men. PMID:11444052
Retrospective forecast of ETAS model with daily parameters estimate
NASA Astrophysics Data System (ADS)
Falcone, Giuseppe; Murru, Maura; Console, Rodolfo; Marzocchi, Warner; Zhuang, Jiancang
2016-04-01
We present a retrospective ETAS (Epidemic Type of Aftershock Sequence) model based on the daily updating of free parameters during the background, the learning and the test phase of a seismic sequence. The idea was born after the 2011 Tohoku-Oki earthquake. The CSEP (Collaboratory for the Study of Earthquake Predictability) Center in Japan provided an appropriate testing benchmark for the five 1-day submitted models. Of all the models, only one was able to successfully predict the number of events that really happened. This result was verified using both the real time and the revised catalogs. The main cause of the failure was in the underestimation of the forecasted events, due to model parameters maintained fixed during the test. Moreover, the absence in the learning catalog of an event similar to the magnitude of the mainshock (M9.0), which drastically changed the seismicity in the area, made the learning parameters not suitable to describe the real seismicity. As an example of this methodological development we show the evolution of the model parameters during the last two strong seismic sequences in Italy: the 2009 L'Aquila and the 2012 Reggio Emilia episodes. The achievement of the model with daily updated parameters is compared with that of same model where the parameters remain fixed during the test time.
Parameter Estimates in Differential Equation Models for Population Growth
ERIC Educational Resources Information Center
Winkel, Brian J.
2011-01-01
We estimate the parameters present in several differential equation models of population growth, specifically logistic growth models and two-species competition models. We discuss student-evolved strategies and offer "Mathematica" code for a gradient search approach. We use historical (1930s) data from microbial studies of the Russian biologist,…
Variational estimation of process parameters in a simplified atmospheric general circulation model
NASA Astrophysics Data System (ADS)
Lv, Guokun; Koehl, Armin; Stammer, Detlef
2016-04-01
Parameterizations are used to simulate effects of unresolved sub-grid-scale processes in current state-of-the-art climate model. The values of the process parameters, which determine the model's climatology, are usually manually adjusted to reduce the difference of model mean state to the observed climatology. This process requires detailed knowledge of the model and its parameterizations. In this work, a variational method was used to estimate process parameters in the Planet Simulator (PlaSim). The adjoint code was generated using automatic differentiation of the source code. Some hydrological processes were switched off to remove the influence of zero-order discontinuities. In addition, the nonlinearity of the model limits the feasible assimilation window to about 1day, which is too short to tune the model's climatology. To extend the feasible assimilation window, nudging terms for all state variables were added to the model's equations, which essentially suppress all unstable directions. In identical twin experiments, we found that the feasible assimilation window could be extended to over 1-year and accurate parameters could be retrieved. Although the nudging terms transform to a damping of the adjoint variables and therefore tend to erases the information of the data over time, assimilating climatological information is shown to provide sufficient information on the parameters. Moreover, the mechanism of this regularization is discussed.
Uncertainty in dual permeability model parameters for structured soils
Arora, B.; Mohanty, B. P.; McGuire, J. T.
2013-01-01
Successful application of dual permeability models (DPM) to predict contaminant transport is contingent upon measured or inversely estimated soil hydraulic and solute transport parameters. The difficulty in unique identification of parameters for the additional macropore- and matrix-macropore interface regions, and knowledge about requisite experimental data for DPM has not been resolved to date. Therefore, this study quantifies uncertainty in dual permeability model parameters of experimental soil columns with different macropore distributions (single macropore, and low- and high-density multiple macropores). Uncertainty evaluation is conducted using adaptive Markov chain Monte Carlo (AMCMC) and conventional Metropolis-Hastings (MH) algorithms while assuming 10 out of 17 parameters to be uncertain or random. Results indicate that AMCMC resolves parameter correlations and exhibits fast convergence for all DPM parameters while MH displays large posterior correlations for various parameters. This study demonstrates that the choice of parameter sampling algorithms is paramount in obtaining unique DPM parameters when information on covariance structure is lacking, or else additional information on parameter correlations must be supplied to resolve the problem of equifinality of DPM parameters. This study also highlights the placement and significance of matrix-macropore interface in flow experiments of soil columns with different macropore densities. Histograms for certain soil hydraulic parameters display tri-modal characteristics implying that macropores are drained first followed by the interface region and then by pores of the matrix domain in drainage experiments. Results indicate that hydraulic properties and behavior of the matrix-macropore interface is not only a function of saturated hydraulic conductivity of the macroporematrix interface (Ksa) and macropore tortuosity (lf) but also of other parameters of the matrix and macropore domains. PMID:24478531
NASA Astrophysics Data System (ADS)
Xia, Youlong; Yang, Zong-Liang; Stoffa, Paul L.; Sen, Mrinal K.
2005-01-01
Most previous land-surface model calibration studies have defined global ranges for their parameters to search for optimal parameter sets. Little work has been conducted to study the impacts of realistic versus global ranges as well as model complexities on the calibration and uncertainty estimates. The primary purpose of this paper is to investigate these impacts by employing Bayesian Stochastic Inversion (BSI) to the Chameleon Surface Model (CHASM). The CHASM was designed to explore the general aspects of land-surface energy balance representation within a common modeling framework that can be run from a simple energy balance formulation to a complex mosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem, importance sampling, and very fast simulated annealing. The model forcing data and surface flux data were collected at seven sites representing a wide range of climate and vegetation conditions. For each site, four experiments were performed with simple and complex CHASM formulations as well as realistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parameter sets were used for each run. The results show that the use of global and realistic ranges gives similar simulations for both modes for most sites, but the global ranges tend to produce some unreasonable optimal parameter values. Comparison of simple and complex modes shows that the simple mode has more parameters with unreasonable optimal values. Use of parameter ranges and model complexities have significant impacts on frequency distribution of parameters, marginal posterior probability density functions, and estimates of uncertainty of simulated sensible and latent heat fluxes. Comparison between model complexity and parameter ranges shows that the former has more significant impacts on parameter and uncertainty estimations.
Determination of stellar parameters using binary system models
NASA Astrophysics Data System (ADS)
Blay, Georgina; Lovekin, Catherine
2015-12-01
Stellar parameters can be constrained more tightly with binary systems than can typically be done with single stars. We used a freely available binary fitting code to determine the best fitting parameters of a collection of potential eclipsing binary systems observed with the Kepler satellite. These model fits constrain the mass ratio, radii ratio, surface brightness ratio, and the orbital inclination of both stars in the binary system. The frequencies of these pulsations can then be determined and used to constrain asteroseismic models.
Agricultural and Environmental Input Parameters for the Biosphere Model
K. Rasmuson; K. Rautenstrauch
2004-09-14
This analysis is one of 10 technical reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model). It documents development of agricultural and environmental input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for the repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships between the major activities and their products (the analysis and model reports) that were planned in ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters.
Combined Estimation of Hydrogeologic Conceptual Model and Parameter Uncertainty
Meyer, Philip D.; Ye, Ming; Neuman, Shlomo P.; Cantrell, Kirk J.
2004-03-01
The objective of the research described in this report is the development and application of a methodology for comprehensively assessing the hydrogeologic uncertainties involved in dose assessment, including uncertainties associated with conceptual models, parameters, and scenarios. This report describes and applies a statistical method to quantitatively estimate the combined uncertainty in model predictions arising from conceptual model and parameter uncertainties. The method relies on model averaging to combine the predictions of a set of alternative models. Implementation is driven by the available data. When there is minimal site-specific data the method can be carried out with prior parameter estimates based on generic data and subjective prior model probabilities. For sites with observations of system behavior (and optionally data characterizing model parameters), the method uses model calibration to update the prior parameter estimates and model probabilities based on the correspondence between model predictions and site observations. The set of model alternatives can contain both simplified and complex models, with the requirement that all models be based on the same set of data. The method was applied to the geostatistical modeling of air permeability at a fractured rock site. Seven alternative variogram models of log air permeability were considered to represent data from single-hole pneumatic injection tests in six boreholes at the site. Unbiased maximum likelihood estimates of variogram and drift parameters were obtained for each model. Standard information criteria provided an ambiguous ranking of the models, which would not justify selecting one of them and discarding all others as is commonly done in practice. Instead, some of the models were eliminated based on their negligibly small updated probabilities and the rest were used to project the measured log permeabilities by kriging onto a rock volume containing the six boreholes. These four
NASA Astrophysics Data System (ADS)
Chen, Y.; Li, J.; Xu, H.
2016-01-01
Physically based distributed hydrological models (hereafter referred to as PBDHMs) divide the terrain of the whole catchment into a number of grid cells at fine resolution and assimilate different terrain data and precipitation to different cells. They are regarded to have the potential to improve the catchment hydrological process simulation and prediction capability. In the early stage, physically based distributed hydrological models are assumed to derive model parameters from the terrain properties directly, so there is no need to calibrate model parameters. However, unfortunately the uncertainties associated with this model derivation are very high, which impacted their application in flood forecasting, so parameter optimization may also be necessary. There are two main purposes for this study: the first is to propose a parameter optimization method for physically based distributed hydrological models in catchment flood forecasting by using particle swarm optimization (PSO) algorithm and to test its competence and to improve its performances; the second is to explore the possibility of improving physically based distributed hydrological model capability in catchment flood forecasting by parameter optimization. In this paper, based on the scalar concept, a general framework for parameter optimization of the PBDHMs for catchment flood forecasting is first proposed that could be used for all PBDHMs. Then, with the Liuxihe model as the study model, which is a physically based distributed hydrological model proposed for catchment flood forecasting, the improved PSO algorithm is developed for the parameter optimization of the Liuxihe model in catchment flood forecasting. The improvements include adoption of the linearly decreasing inertia weight strategy to change the inertia weight and the arccosine function strategy to adjust the acceleration coefficients. This method has been tested in two catchments in southern China with different sizes, and the results show
Accuracy of Aerodynamic Model Parameters Estimated from Flight Test Data
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; Klein, Vladislav
1997-01-01
An important put of building mathematical models based on measured date is calculating the accuracy associated with statistical estimates of the model parameters. Indeed, without some idea of this accuracy, the parameter estimates themselves have limited value. An expression is developed for computing quantitatively correct parameter accuracy measures for maximum likelihood parameter estimates when the output residuals are colored. This result is important because experience in analyzing flight test data reveals that the output residuals from maximum likelihood estimation are almost always colored. The calculations involved can be appended to conventional maximum likelihood estimation algorithms. Monte Carlo simulation runs were used to show that parameter accuracy measures from the new technique accurately reflect the quality of the parameter estimates from maximum likelihood estimation without the need for correction factors or frequency domain analysis of the output residuals. The technique was applied to flight test data from repeated maneuvers flown on the F-18 High Alpha Research Vehicle. As in the simulated cases, parameter accuracy measures from the new technique were in agreement with the scatter in the parameter estimates from repeated maneuvers, whereas conventional parameter accuracy measures were optimistic.
Hirozawa, Anne M; Montez-Rath, Maria E; Johnson, Elizabeth C; Solnit, Stephen A; Drennan, Michael J; Katz, Mitchell H; Marx, Rani
2016-01-01
We compared prospective risk adjustment models for adjusting patient panels at the San Francisco Department of Public Health. We used 4 statistical models (linear regression, two-part model, zero-inflated Poisson, and zero-inflated negative binomial) and 4 subsets of predictor variables (age/gender categories, chronic diagnoses, homelessness, and a loss to follow-up indicator) to predict primary care visit frequency. Predicted visit frequency was then used to calculate patient weights and adjusted panel sizes. The two-part model using all predictor variables performed best (R = 0.20). This model, designed specifically for safety net patients, may prove useful for panel adjustment in other public health settings. PMID:27576054
Parameter Estimation and Model Selection in Computational Biology
Lillacci, Gabriele; Khammash, Mustafa
2010-01-01
A central challenge in computational modeling of biological systems is the determination of the model parameters. Typically, only a fraction of the parameters (such as kinetic rate constants) are experimentally measured, while the rest are often fitted. The fitting process is usually based on experimental time course measurements of observables, which are used to assign parameter values that minimize some measure of the error between these measurements and the corresponding model prediction. The measurements, which can come from immunoblotting assays, fluorescent markers, etc., tend to be very noisy and taken at a limited number of time points. In this work we present a new approach to the problem of parameter selection of biological models. We show how one can use a dynamic recursive estimator, known as extended Kalman filter, to arrive at estimates of the model parameters. The proposed method follows. First, we use a variation of the Kalman filter that is particularly well suited to biological applications to obtain a first guess for the unknown parameters. Secondly, we employ an a posteriori identifiability test to check the reliability of the estimates. Finally, we solve an optimization problem to refine the first guess in case it should not be accurate enough. The final estimates are guaranteed to be statistically consistent with the measurements. Furthermore, we show how the same tools can be used to discriminate among alternate models of the same biological process. We demonstrate these ideas by applying our methods to two examples, namely a model of the heat shock response in E. coli, and a model of a synthetic gene regulation system. The methods presented are quite general and may be applied to a wide class of biological systems where noisy measurements are used for parameter estimation or model selection. PMID:20221262
SPOTting Model Parameters Using a Ready-Made Python Package
Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz
2015-01-01
The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function. PMID:26680783
SPOTting Model Parameters Using a Ready-Made Python Package.
Houska, Tobias; Kraft, Philipp; Chamorro-Chavez, Alejandro; Breuer, Lutz
2015-01-01
The choice for specific parameter estimation methods is often more dependent on its availability than its performance. We developed SPOTPY (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of methods typically used to calibrate, analyze and optimize parameters for a wide range of ecological models. SPOTPY currently contains eight widely used algorithms, 11 objective functions, and can sample from eight parameter distributions. SPOTPY has a model-independent structure and can be run in parallel from the workstation to large computation clusters using the Message Passing Interface (MPI). We tested SPOTPY in five different case studies to parameterize the Rosenbrock, Griewank and Ackley functions, a one-dimensional physically based soil moisture routine, where we searched for parameters of the van Genuchten-Mualem function and a calibration of a biogeochemistry model with different objective functions. The case studies reveal that the implemented SPOTPY methods can be used for any model with just a minimal amount of code for maximal power of parameter optimization. They further show the benefit of having one package at hand that includes number of well performing parameter search methods, since not every case study can be solved sufficiently with every algorithm or every objective function. PMID:26680783
An Effective Parameter Screening Strategy for High Dimensional Watershed Models
NASA Astrophysics Data System (ADS)
Khare, Y. P.; Martinez, C. J.; Munoz-Carpena, R.
2014-12-01
Watershed simulation models can assess the impacts of natural and anthropogenic disturbances on natural systems. These models have become important tools for tackling a range of water resources problems through their implementation in the formulation and evaluation of Best Management Practices, Total Maximum Daily Loads, and Basin Management Action Plans. For accurate applications of watershed models they need to be thoroughly evaluated through global uncertainty and sensitivity analyses (UA/SA). However, due to the high dimensionality of these models such evaluation becomes extremely time- and resource-consuming. Parameter screening, the qualitative separation of important parameters, has been suggested as an essential step before applying rigorous evaluation techniques such as the Sobol' and Fourier Amplitude Sensitivity Test (FAST) methods in the UA/SA framework. The method of elementary effects (EE) (Morris, 1991) is one of the most widely used screening methodologies. Some of the common parameter sampling strategies for EE, e.g. Optimized Trajectories [OT] (Campolongo et al., 2007) and Modified Optimized Trajectories [MOT] (Ruano et al., 2012), suffer from inconsistencies in the generated parameter distributions, infeasible sample generation time, etc. In this work, we have formulated a new parameter sampling strategy - Sampling for Uniformity (SU) - for parameter screening which is based on the principles of the uniformity of the generated parameter distributions and the spread of the parameter sample. A rigorous multi-criteria evaluation (time, distribution, spread and screening efficiency) of OT, MOT, and SU indicated that SU is superior to other sampling strategies. Comparison of the EE-based parameter importance rankings with those of Sobol' helped to quantify the qualitativeness of the EE parameter screening approach, reinforcing the fact that one should use EE only to reduce the resource burden required by FAST/Sobol' analyses but not to replace it.
Identification of patient specific parameters for a minimal cardiac model.
Hann, C E; Chase, J G; Shaw, G M; Smith, B W
2004-01-01
A minimal cardiac model has been developed which accurately captures the essential dynamics of the cardiovascular system (CVS). This paper develops an integral based parameter identification method for fast and accurate identification of patient specific parameters for this minimal model. The integral method is implemented using a single chamber model to prove the concept, and turns a previously nonlinear and nonconvex optimization problem into a linear and convex problem. The method can be readily extended to the full minimal cardiac model and enables rapid identification of model parameters to match a particular patient condition in clinical real time (3-5 minutes). This information can then be used to assist medical staff in understanding, diagnosis and treatment selection. PMID:17271801
Parameter Identification in a Tuberculosis Model for Cameroon
Moualeu-Ngangue, Dany Pascal; Röblitz, Susanna; Ehrig, Rainald; Deuflhard, Peter
2015-01-01
A deterministic model of tuberculosis in Cameroon is designed and analyzed with respect to its transmission dynamics. The model includes lack of access to treatment and weak diagnosis capacity as well as both frequency- and density-dependent transmissions. It is shown that the model is mathematically well-posed and epidemiologically reasonable. Solutions are non-negative and bounded whenever the initial values are non-negative. A sensitivity analysis of model parameters is performed and the most sensitive ones are identified by means of a state-of-the-art Gauss-Newton method. In particular, parameters representing the proportion of individuals having access to medical facilities are seen to have a large impact on the dynamics of the disease. The model predicts that a gradual increase of these parameters could significantly reduce the disease burden on the population within the next 15 years. PMID:25874885
Application of physical parameter identification to finite element models
NASA Technical Reports Server (NTRS)
Bronowicki, Allen J.; Lukich, Michael S.; Kuritz, Steven P.
1986-01-01
A time domain technique for matching response predictions of a structural dynamic model to test measurements is developed. Significance is attached to prior estimates of physical model parameters and to experimental data. The Bayesian estimation procedure allows confidence levels in predicted physical and modal parameters to be obtained. Structural optimization procedures are employed to minimize an error functional with physical model parameters describing the finite element model as design variables. The number of complete FEM analyses are reduced using approximation concepts, including the recently developed convoluted Taylor series approach. The error function is represented in closed form by converting free decay test data to a time series model using Prony' method. The technique is demonstrated on simulated response of a simple truss structure.
Parameter identification in a tuberculosis model for Cameroon.
Moualeu-Ngangue, Dany Pascal; Röblitz, Susanna; Ehrig, Rainald; Deuflhard, Peter
2015-01-01
A deterministic model of tuberculosis in Cameroon is designed and analyzed with respect to its transmission dynamics. The model includes lack of access to treatment and weak diagnosis capacity as well as both frequency- and density-dependent transmissions. It is shown that the model is mathematically well-posed and epidemiologically reasonable. Solutions are non-negative and bounded whenever the initial values are non-negative. A sensitivity analysis of model parameters is performed and the most sensitive ones are identified by means of a state-of-the-art Gauss-Newton method. In particular, parameters representing the proportion of individuals having access to medical facilities are seen to have a large impact on the dynamics of the disease. The model predicts that a gradual increase of these parameters could significantly reduce the disease burden on the population within the next 15 years. PMID:25874885
NASA Astrophysics Data System (ADS)
Hamimid, M.; Mimoune, S. M.; Feliachi, M.
2012-07-01
In this present work, the minor hysteresis loops model based on parameters scaling of the modified Jiles-Atherton model is evaluated by using judicious expressions. These expressions give the minor hysteresis loops parameters as a function of the major hysteresis loop ones. They have exponential form and are obtained by parameters identification using the stochastic optimization method “simulated annealing”. The main parameters influencing the data fitting are three parameters, the pinning parameter k, the mean filed parameter α and the parameter which characterizes the shape of anhysteretic magnetization curve a. To validate this model, calculated minor hysteresis loops are compared with measured ones and good agreements are obtained.
Parameter Sensitivity Evaluation of the CLM-Crop model
NASA Astrophysics Data System (ADS)
Drewniak, B. A.; Zeng, X.; Mametjanov, A.; Anitescu, M.; Norris, B.; Kotamarthi, V. R.
2011-12-01
In order to improve carbon cycling within Earth System Models, crop representation for corn, spring wheat, and soybean species has been incorporated into the latest version of the Community Land Model (CLM), the land surface model in the Community Earth System Model. As a means to evaluate and improve the CLM-Crop model, we will determine the sensitivity of various crop parameters on carbon fluxes (such as GPP and NEE), yields, and soil organic matter. The sensitivity analysis will perform small perturbations over a range of values for each parameter on individual grid sites, for comparison with AmeriFlux data, as well as globally so crop model parameters can be improved. Over 20 parameters have been identified for evaluation in this study including carbon-nitrogen ratios for leaves, stems, roots, and organs; fertilizer applications; growing degree days for each growth stage; and more. Results from this study will be presented to give a better understanding of the sensitivity of the various parameters used to represent crops, which will help improve the overall model performance and aid with determining future influences climate change will have on cropland ecosystems.
NASA Technical Reports Server (NTRS)
Sovers, O. J.; Jacobs, C. S.
1994-01-01
This report is a revision of the document Observation Model and Parameter Partials for the JPL VLBI Parameter Estimation Software 'MODEST'---1991, dated August 1, 1991. It supersedes that document and its four previous versions (1983, 1985, 1986, and 1987). A number of aspects of the very long baseline interferometry (VLBI) model were improved from 1991 to 1994. Treatment of tidal effects is extended to model the effects of ocean tides on universal time and polar motion (UTPM), including a default model for nearly diurnal and semidiurnal ocean tidal UTPM variations, and partial derivatives for all (solid and ocean) tidal UTPM amplitudes. The time-honored 'K(sub 1) correction' for solid earth tides has been extended to include analogous frequency-dependent response of five tidal components. Partials of ocean loading amplitudes are now supplied. The Zhu-Mathews-Oceans-Anisotropy (ZMOA) 1990-2 and Kinoshita-Souchay models of nutation are now two of the modeling choices to replace the increasingly inadequate 1980 International Astronomical Union (IAU) nutation series. A rudimentary model of antenna thermal expansion is provided. Two more troposphere mapping functions have been added to the repertoire. Finally, corrections among VLBI observations via the model of Treuhaft and lanyi improve modeling of the dynamic troposphere. A number of minor misprints in Rev. 4 have been corrected.
Liu, S.; Anderson, P.; Zhou, G.; Kauffman, B.; Hughes, F.; Schimel, D.; Watson, Vicente; Tosi, Joseph
2008-01-01
Objectively assessing the performance of a model and deriving model parameter values from observations are critical and challenging in landscape to regional modeling. In this paper, we applied a nonlinear inversion technique to calibrate the ecosystem model CENTURY against carbon (C) and nitrogen (N) stock measurements collected from 39 mature tropical forest sites in seven life zones in Costa Rica. Net primary productivity from the Moderate-Resolution Imaging Spectroradiometer (MODIS), C and N stocks in aboveground live biomass, litter, coarse woody debris (CWD), and in soils were used to calibrate the model. To investigate the resolution of available observations on the number of adjustable parameters, inversion was performed using nine setups of adjustable parameters. Statistics including observation sensitivity, parameter correlation coefficient, parameter sensitivity, and parameter confidence limits were used to evaluate the information content of observations, resolution of model parameters, and overall model performance. Results indicated that soil organic carbon content, soil nitrogen content, and total aboveground biomass carbon had the highest information contents, while measurements of carbon in litter and nitrogen in CWD contributed little to the parameter estimation processes. The available information could resolve the values of 2-4 parameters. Adjusting just one parameter resulted in under-fitting and unacceptable model performance, while adjusting five parameters simultaneously led to over-fitting. Results further indicated that the MODIS NPP values were compressed as compared with the spatial variability of net primary production (NPP) values inferred from inverse modeling. Using inverse modeling to infer NPP and other sensitive model parameters from C and N stock observations provides an opportunity to utilize data collected by national to regional forest inventory systems to reduce the uncertainties in the carbon cycle and generate valuable
A distributed parameter wire model for transient electrical discharges
NASA Astrophysics Data System (ADS)
Maier, William B., II; Kadish, A.; Sutherland, C. D.; Robiscoe, R. T.
1990-06-01
This paper presents a three-dimensional model developed for freely propagating electrical discharges, such as lightning and punch-through arcs. In this model, charge transport is described by a nonlinear differential equation containing two phenomenological parameters characteristic of the medium and the arc; the electromagnetic field is described by Maxwell's equations. Using this model, a cylindrically symmetric small-diameter discharge is analyzed. It is shown that the model predicts discharge properties consistent with experimentally known phenomena.
Material parameter computation for multi-layered vocal fold models
Schmidt, Bastian; Stingl, Michael; Leugering, Günter; Berry, David A.; Döllinger, Michael
2011-01-01
Today, the prevention and treatment of voice disorders is an ever-increasing health concern. Since many occupations rely on verbal communication, vocal health is necessary just to maintain one’s livelihood. Commonly applied models to study vocal fold vibrations and air flow distributions are self sustained physical models of the larynx composed of artificial silicone vocal folds. Choosing appropriate mechanical parameters for these vocal fold models while considering simplifications due to manufacturing restrictions is difficult but crucial for achieving realistic behavior. In the present work, a combination of experimental and numerical approaches to compute material parameters for synthetic vocal fold models is presented. The material parameters are derived from deformation behaviors of excised human larynges. The resulting deformations are used as reference displacements for a tracking functional to be optimized. Material optimization was applied to three-dimensional vocal fold models based on isotropic and transverse-isotropic material laws, considering both a layered model with homogeneous material properties on each layer and an inhomogeneous model. The best results exhibited a transversal-isotropic inhomogeneous (i.e., not producible) model. For the homogeneous model (three layers), the transversal-isotropic material parameters were also computed for each layer yielding deformations similar to the measured human vocal fold deformations. PMID:21476672
Parameters of cosmological models and recent astronomical observations
Sharov, G.S.; Vorontsova, E.G. E-mail: elenavor@inbox.ru
2014-10-01
For different gravitational models we consider limitations on their parameters coming from recent observational data for type Ia supernovae, baryon acoustic oscillations, and from 34 data points for the Hubble parameter H(z) depending on redshift. We calculate parameters of 3 models describing accelerated expansion of the universe: the ΛCDM model, the model with generalized Chaplygin gas (GCG) and the multidimensional model of I. Pahwa, D. Choudhury and T.R. Seshadri. In particular, for the ΛCDM model 1σ estimates of parameters are: H{sub 0}=70.262±0.319 km {sup -1}Mp {sup -1}, Ω{sub m}=0.276{sub -0.008}{sup +0.009}, Ω{sub Λ}=0.769±0.029, Ω{sub k}=-0.045±0.032. The GCG model under restriction 0α≥ is reduced to the ΛCDM model. Predictions of the multidimensional model essentially depend on 3 data points for H(z) with z≥2.3.
Environmental Transport Input Parameters for the Biosphere Model
M. Wasiolek
2004-09-10
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]).
Inhalation Exposure Input Parameters for the Biosphere Model
K. Rautenstrauch
2004-09-10
This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.
Parameter uncertainty analysis of a biokinetic model of caesium.
Li, W B; Klein, W; Blanchardon, E; Puncher, M; Leggett, R W; Oeh, U; Breustedt, B; Noßke, D; Lopez, M A
2015-01-01
Parameter uncertainties for the biokinetic model of caesium (Cs) developed by Leggett et al. were inventoried and evaluated. The methods of parameter uncertainty analysis were used to assess the uncertainties of model predictions with the assumptions of model parameter uncertainties and distributions. Furthermore, the importance of individual model parameters was assessed by means of sensitivity analysis. The calculated uncertainties of model predictions were compared with human data of Cs measured in blood and in the whole body. It was found that propagating the derived uncertainties in model parameter values reproduced the range of bioassay data observed in human subjects at different times after intake. The maximum ranges, expressed as uncertainty factors (UFs) (defined as a square root of ratio between 97.5th and 2.5th percentiles) of blood clearance, whole-body retention and urinary excretion of Cs predicted at earlier time after intake were, respectively: 1.5, 1.0 and 2.5 at the first day; 1.8, 1.1 and 2.4 at Day 10 and 1.8, 2.0 and 1.8 at Day 100; for the late times (1000 d) after intake, the UFs were increased to 43, 24 and 31, respectively. The model parameters of transfer rates between kidneys and blood, muscle and blood and the rate of transfer from kidneys to urinary bladder content are most influential to the blood clearance and to the whole-body retention of Cs. For the urinary excretion, the parameters of transfer rates from urinary bladder content to urine and from kidneys to urinary bladder content impact mostly. The implication and effect on the estimated equivalent and effective doses of the larger uncertainty of 43 in whole-body retention in the later time, say, after Day 500 will be explored in a successive work in the framework of EURADOS. PMID:24743755
NASA Technical Reports Server (NTRS)
Sovers, O. J.; Fanselow, J. L.
1987-01-01
This report is a revision of the document of the same title (1986), dated August 1, which it supersedes. Model changes during 1986 and 1987 included corrections for antenna feed rotation, refraction in modelling antenna axis offsets, and an option to employ improved values of the semiannual and annual nutation amplitudes. Partial derivatives of the observables with respect to an additional parameter (surface temperature) are now available. New versions of two figures representing the geometric delay are incorporated. The expressions for the partial derivatives with respect to the nutation parameters have been corrected to include contributions from the dependence of UTI on nutation. The authors hope to publish revisions of this document in the future, as modeling improvements warrant.
NASA Astrophysics Data System (ADS)
Sovers, O. J.; Fanselow, J. L.
1987-12-01
This report is a revision of the document of the same title (1986), dated August 1, which it supersedes. Model changes during 1986 and 1987 included corrections for antenna feed rotation, refraction in modelling antenna axis offsets, and an option to employ improved values of the semiannual and annual nutation amplitudes. Partial derivatives of the observables with respect to an additional parameter (surface temperature) are now available. New versions of two figures representing the geometric delay are incorporated. The expressions for the partial derivatives with respect to the nutation parameters have been corrected to include contributions from the dependence of UTI on nutation. The authors hope to publish revisions of this document in the future, as modeling improvements warrant.
Environmental Transport Input Parameters for the Biosphere Model
M. A. Wasiolek
2003-06-27
This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699], Section 6.2). Parameter values
Structural and parameter uncertainty in Bayesian cost-effectiveness models
Jackson, Christopher H; Sharples, Linda D; Thompson, Simon G
2010-01-01
Health economic decision models are subject to various forms of uncertainty, including uncertainty about the parameters of the model and about the model structure. These uncertainties can be handled within a Bayesian framework, which also allows evidence from previous studies to be combined with the data. As an example, we consider a Markov model for assessing the cost-effectiveness of implantable cardioverter defibrillators. Using Markov chain Monte Carlo posterior simulation, uncertainty about the parameters of the model is formally incorporated in the estimates of expected cost and effectiveness. We extend these methods to include uncertainty about the choice between plausible model structures. This is accounted for by averaging the posterior distributions from the competing models using weights that are derived from the pseudo-marginal-likelihood and the deviance information criterion, which are measures of expected predictive utility. We also show how these cost-effectiveness calculations can be performed efficiently in the widely used software WinBUGS. PMID:20383261
Global-scale regionalization of hydrologic model parameters
NASA Astrophysics Data System (ADS)
Beck, Hylke; van Dijk, Albert; de Roo, Ad; Miralles, Diego; Schellekens, Jaap; McVicar, Tim; Bruijnzeel, Sampurno
2016-04-01
Current state-of-the-art models typically applied at continental to global scales (hereafter called macro-scale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10--10 000~km^2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the ten most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially-uniform (i.e., averaged calibrated) parameters for 79~% of the evaluation catchments. Substantial improvements were evident for all major Köppen-Geiger climate types and even for evaluation catchments >5000~km distance from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV using regionalized parameters outperformed nine state-of-the-art macro-scale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via http://water.jrc.ec.europa.eu/HBV/.
Global-scale regionalization of hydrologic model parameters
NASA Astrophysics Data System (ADS)
Beck, Hylke E.; van Dijk, Albert I. J. M.; de Roo, Ad; Miralles, Diego G.; McVicar, Tim R.; Schellekens, Jaap; Bruijnzeel, L. Adrian
2016-05-01
Current state-of-the-art models typically applied at continental to global scales (hereafter called macroscale) tend to use a priori parameters, resulting in suboptimal streamflow (Q) simulation. For the first time, a scheme for regionalization of model parameters at the global scale was developed. We used data from a diverse set of 1787 small-to-medium sized catchments (10-10,000 km2) and the simple conceptual HBV model to set up and test the scheme. Each catchment was calibrated against observed daily Q, after which 674 catchments with high calibration and validation scores, and thus presumably good-quality observed Q and forcing data, were selected to serve as donor catchments. The calibrated parameter sets for the donors were subsequently transferred to 0.5° grid cells with similar climatic and physiographic characteristics, resulting in parameter maps for HBV with global coverage. For each grid cell, we used the 10 most similar donor catchments, rather than the single most similar donor, and averaged the resulting simulated Q, which enhanced model performance. The 1113 catchments not used as donors were used to independently evaluate the scheme. The regionalized parameters outperformed spatially uniform (i.e., averaged calibrated) parameters for 79% of the evaluation catchments. Substantial improvements were evident for all major Köppen-Geiger climate types and even for evaluation catchments > 5000 km distant from the donors. The median improvement was about half of the performance increase achieved through calibration. HBV with regionalized parameters outperformed nine state-of-the-art macroscale models, suggesting these might also benefit from the new regionalization scheme. The produced HBV parameter maps including ancillary data are available via www.gloh2o.org.
Climate Model Sensitivity to Moist Convection Parameter Perturbations
NASA Astrophysics Data System (ADS)
Bernstein, D. N.; Neelin, J. D.
2014-12-01
The perturbed physics ensemble in this study examines the impact of poorly constrained parameters in representations of subgrid-scale processes affecting moist convection using the National Center for Atmospheric Research fully coupled ocean-atmosphere Community Earth System Model. An ensemble of historical period simulations and an ensemble of end-of-the-century simulations under the Representative Concentration Pathway 8.5 scenario for global warming quantify some of the implications of parameter uncertainty for simulation of precipitation processes in current climate and in projections of climate change. Regional precipitation patterns prove highly sensitive to the parameter perturbations, especially in the tropics. In the historical period, nonlinear parameter response with local changes exceeding 3 mm/day is noted. Over the full range of parameters, the error with respect to observations is within the range typical of different climate models used in the Coupled Model Intercomparison Project phase 5 (CMIP5). For parameter perturbations within this range, differences in the end of century projections for global warming precipitation change also regionally exceed 3 mm/day, also comparable to differences in global warming predictions among the CMIP5 models. These results suggest that improving constraints within moist convective parameterized processes based on better assessment against observations in the historical period will be required to reduce the range of uncertainty in regional projections of precipitation change.
Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies
ERIC Educational Resources Information Center
Smith, Carrie E.; Cribbie, Robert A.
2013-01-01
When structural equation modeling (SEM) analyses are conducted, significance tests for all important model relationships (parameters including factor loadings, covariances, etc.) are typically conducted at a specified nominal Type I error rate ([alpha]). Despite the fact that many significance tests are often conducted in SEM, rarely is…
Estimability of Parameters in the Generalized Graded Unfolding Model.
ERIC Educational Resources Information Center
Roberts, James S.; Donoghue, John R.; Laughlin, James E.
The generalized graded unfolding model (GGUM) (J. Roberts, J. Donoghue, and J. Laughlin, 1998) is an item response theory model designed to analyze binary or graded responses that are based on a proximity relation. The purpose of this study was to assess conditions under which item parameter estimation accuracy increases or decreases, with special…
Separability of Item and Person Parameters in Response Time Models.
ERIC Educational Resources Information Center
Van Breukelen, Gerard J. P.
1997-01-01
Discusses two forms of separability of item and person parameters in the context of response time models. The first is "separate sufficiency," and the second is "ranking independence." For each form a theorem stating sufficient conditions is proved. The two forms are shown to include several cases of models from psychometric and biometric…
Global Characterization of Model Parameter Space Using Information Topology
NASA Astrophysics Data System (ADS)
Transtrum, Mark
A generic parameterized model is a mapping between parameters and data and is naturally interpreted as a prediction manifold embedded in data space. In this interpretation, known as Information Geometry, the Fisher Information Matrix (FIM) is a Riemannian metric that measures the identifiability of the model parameters. Varying the experimental conditions (e.g., times at which measurements are made) alters both the FIM and the geometric properties of the model. However, several global features of the model manifold (e.g., edges and corners) are invariant to changes in experimental conditions as long as the FIM is not singular. Invariance of these features to changing experimental conditions generates an ''Information Topology'' that globally characterizes a model's parameter space and reflects the underlying physical principles from which the model was derived. Understanding a model's information topology can give insights into the emergent physics that controls a system's collective behavior, identify reduced models and describe the relationship among them, and determine which parameter combinations will be difficult to identify for various experimental conditions.
Atmospheric turbulence parameters for modeling wind turbine dynamics
NASA Technical Reports Server (NTRS)
Holley, W. E.; Thresher, R. W.
1982-01-01
A model which can be used to predict the response of wind turbines to atmospheric turbulence is given. The model was developed using linearized aerodynamics for a three-bladed rotor and accounts for three turbulent velocity components as well as velocity gradients across the rotor disk. Typical response power spectral densities are shown. The system response depends critically on three wind and turbulence parameters, and models are presented to predict desired response statistics. An equation error method, which can be used to estimate the required parameters from field data, is also presented.
Distributed activation energy model parameters of some Turkish coals
Gunes, M.; Gunes, S.K.
2008-07-01
A multi-reaction model based on distributed activation energy has been applied to some Turkish coals. The kinetic parameters of distributed activation energy model were calculated via computer program developed for this purpose. It was observed that the values of mean of activation energy distribution vary between 218 and 248 kJ/mol, and the values of standard deviation of activation energy distribution vary between 32 and 70 kJ/mol. The correlations between kinetic parameters of the distributed activation energy model and certain properties of coal have been investigated.
Maximum likelihood estimation for distributed parameter models of flexible spacecraft
NASA Technical Reports Server (NTRS)
Taylor, L. W., Jr.; Williams, J. L.
1989-01-01
A distributed-parameter model of the NASA Solar Array Flight Experiment spacecraft structure is constructed on the basis of measurement data and analyzed to generate a priori estimates of modal frequencies and mode shapes. A Newton-Raphson maximum-likelihood algorithm is applied to determine the unknown parameters, using a truncated model for the estimation and the full model for the computation of the higher modes. Numerical results are presented in a series of graphs and briefly discussed, and the significant improvement in computation speed obtained by parallel implementation of the method on a supercomputer is noted.
Uncertainty Analysis and Parameter Estimation For Nearshore Hydrodynamic Models
NASA Astrophysics Data System (ADS)
Ardani, S.; Kaihatu, J. M.
2012-12-01
Numerical models represent deterministic approaches used for the relevant physical processes in the nearshore. Complexity of the physics of the model and uncertainty involved in the model inputs compel us to apply a stochastic approach to analyze the robustness of the model. The Bayesian inverse problem is one powerful way to estimate the important input model parameters (determined by apriori sensitivity analysis) and can be used for uncertainty analysis of the outputs. Bayesian techniques can be used to find the range of most probable parameters based on the probability of the observed data and the residual errors. In this study, the effect of input data involving lateral (Neumann) boundary conditions, bathymetry and off-shore wave conditions on nearshore numerical models are considered. Monte Carlo simulation is applied to a deterministic numerical model (the Delft3D modeling suite for coupled waves and flow) for the resulting uncertainty analysis of the outputs (wave height, flow velocity, mean sea level and etc.). Uncertainty analysis of outputs is performed by random sampling from the input probability distribution functions and running the model as required until convergence to the consistent results is achieved. The case study used in this analysis is the Duck94 experiment, which was conducted at the U.S. Army Field Research Facility at Duck, North Carolina, USA in the fall of 1994. The joint probability of model parameters relevant for the Duck94 experiments will be found using the Bayesian approach. We will further show that, by using Bayesian techniques to estimate the optimized model parameters as inputs and applying them for uncertainty analysis, we can obtain more consistent results than using the prior information for input data which means that the variation of the uncertain parameter will be decreased and the probability of the observed data will improve as well. Keywords: Monte Carlo Simulation, Delft3D, uncertainty analysis, Bayesian techniques
Improvement of Continuous Hydrologic Models and HMS SMA Parameters Reduction
NASA Astrophysics Data System (ADS)
Rezaeian Zadeh, Mehdi; Zia Hosseinipour, E.; Abghari, Hirad; Nikian, Ashkan; Shaeri Karimi, Sara; Moradzadeh Azar, Foad
2010-05-01
Hydrological models can help us to predict stream flows and associated runoff volumes of rainfall events within a watershed. There are many different reasons why we need to model the rainfall-runoff processes of for a watershed. However, the main reason is the limitation of hydrological measurement techniques and the costs of data collection at a fine scale. Generally, we are not able to measure all that we would like to know about a given hydrological systems. This is very particularly the case for ungauged catchments. Since the ultimate aim of prediction using models is to improve decision-making about a hydrological problem, therefore, having a robust and efficient modeling tool becomes an important factor. Among several hydrologic modeling approaches, continuous simulation has the best predictions because it can model dry and wet conditions during a long-term period. Continuous hydrologic models, unlike event based models, account for a watershed's soil moisture balance over a long-term period and are suitable for simulating daily, monthly, and seasonal streamflows. In this paper, we describe a soil moisture accounting (SMA) algorithm added to the hydrologic modeling system (HEC-HMS) computer program. As is well known in the hydrologic modeling community one of the ways for improving a model utility is the reduction of input parameters. The enhanced model developed in this study is applied to Khosrow Shirin Watershed, located in the north-west part of Fars Province in Iran, a data limited watershed. The HMS SMA algorithm divides the potential path of rainfall onto a watershed into five zones. The results showed that the output of HMS SMA is insensitive with the variation of many parameters such as soil storage and soil percolation rate. The study's objective is to remove insensitive parameters from the model input using Multi-objective sensitivity analysis. Keywords: Continuous Hydrologic Modeling, HMS SMA, Multi-objective sensitivity analysis, SMA Parameters
NASA Astrophysics Data System (ADS)
Ebato, Yuki; Miyata, Tatsuhiko
2016-05-01
Ornstein-Zernike (OZ) integral equation theory is known to overestimate the excess internal energy, Uex, pressure through the virial route, Pv, and excess chemical potential, μex, for one-component Lennard-Jones (LJ) fluids under hypernetted chain (HNC) and Kovalenko-Hirata (KH) approximatons. As one of the bridge correction methods to improve the precision of these thermodynamic quantities, it was shown in our previous paper that the method to apparently adjust σ parameter in the LJ potential is effective [T. Miyata and Y. Ebato, J. Molec. Liquids. 217, 75 (2016)]. In our previous paper, we evaluated the actual variation in the σ parameter by using a fitting procedure to molecular dynamics (MD) results. In this article, we propose an alternative method to determine the actual variation in the σ parameter. The proposed method utilizes a condition that the virial and compressibility pressures coincide with each other. This method can correct OZ theory without a fitting procedure to MD results, and possesses characteristics of keeping a form of HNC and/or KH closure. We calculate the radial distribution function, pressure, excess internal energy, and excess chemical potential for one-component LJ fluids to check the performance of our proposed bridge function. We discuss the precision of these thermodynamic quantities by comparing with MD results. In addition, we also calculate a corrected gas-liquid coexistence curve based on a corrected KH-type closure and compare it with MD results.
Microscopic calculation of interacting boson model parameters by potential-energy surface mapping
Bentley, I.; Frauendorf, S.
2011-06-15
A coherent state technique is used to generate an interacting boson model (IBM) Hamiltonian energy surface which is adjusted to match a mean-field energy surface. This technique allows the calculation of IBM Hamiltonian parameters, prediction of properties of low-lying collective states, as well as the generation of probability distributions of various shapes in the ground state of transitional nuclei, the last two of which are of astrophysical interest. The results for krypton, molybdenum, palladium, cadmium, gadolinium, dysprosium, and erbium nuclei are compared with experiment.
Soil-related Input Parameters for the Biosphere Model
A. J. Smith
2003-07-02
This analysis is one of the technical reports containing documentation of the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the Total System Performance Assessment (TSPA) for the geologic repository at Yucca Mountain. The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A graphical representation of the documentation hierarchy for the ERMYN biosphere model is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003 [163602]). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. ''The Biosphere Model Report'' (BSC 2003 [160699]) describes in detail the conceptual model as well as the mathematical model and its input parameters. The purpose of this analysis was to develop the biosphere model parameters needed to evaluate doses from pathways associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation and ash
Bayesian methods for characterizing unknown parameters of material models
Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.
2016-02-04
A Bayesian framework is developed for characterizing the unknown parameters of probabilistic models for material properties. In this framework, the unknown parameters are viewed as random and described by their posterior distributions obtained from prior information and measurements of quantities of interest that are observable and depend on the unknown parameters. The proposed Bayesian method is applied to characterize an unknown spatial correlation of the conductivity field in the definition of a stochastic transport equation and to solve this equation by Monte Carlo simulation and stochastic reduced order models (SROMs). As a result, the Bayesian method is also employed tomore » characterize unknown parameters of material properties for laser welds from measurements of peak forces sustained by these welds.« less
Automatic Determination of the Conic Coronal Mass Ejection Model Parameters
NASA Technical Reports Server (NTRS)
Pulkkinen, A.; Oates, T.; Taktakishvili, A.
2009-01-01
Characterization of the three-dimensional structure of solar transients using incomplete plane of sky data is a difficult problem whose solutions have potential for societal benefit in terms of space weather applications. In this paper transients are characterized in three dimensions by means of conic coronal mass ejection (CME) approximation. A novel method for the automatic determination of cone model parameters from observed halo CMEs is introduced. The method uses both standard image processing techniques to extract the CME mass from white-light coronagraph images and a novel inversion routine providing the final cone parameters. A bootstrap technique is used to provide model parameter distributions. When combined with heliospheric modeling, the cone model parameter distributions will provide direct means for ensemble predictions of transient propagation in the heliosphere. An initial validation of the automatic method is carried by comparison to manually determined cone model parameters. It is shown using 14 halo CME events that there is reasonable agreement, especially between the heliocentric locations of the cones derived with the two methods. It is argued that both the heliocentric locations and the opening half-angles of the automatically determined cones may be more realistic than those obtained from the manual analysis
Parameter Estimation for Single Diode Models of Photovoltaic Modules
Hansen, Clifford
2015-03-01
Many popular models for photovoltaic system performance employ a single diode model to compute the I - V curve for a module or string of modules at given irradiance and temperature conditions. A single diode model requires a number of parameters to be estimated from measured I - V curves. Many available parameter estimation methods use only short circuit, o pen circuit and maximum power points for a single I - V curve at standard test conditions together with temperature coefficients determined separately for individual cells. In contrast, module testing frequently records I - V curves over a wide range of irradi ance and temperature conditions which, when available , should also be used to parameterize the performance model. We present a parameter estimation method that makes use of a fu ll range of available I - V curves. We verify the accuracy of the method by recov ering known parameter values from simulated I - V curves . We validate the method by estimating model parameters for a module using outdoor test data and predicting the outdoor performance of the module.
Modeling Chinese ionospheric layer parameters based on EOF analysis
NASA Astrophysics Data System (ADS)
Yu, You; Wan, Weixing
2016-04-01
Using 24-ionosonde observations in and around China during the 20th solar cycle, an assimilative model is constructed to map the ionospheric layer parameters (foF2, hmF2, M(3000)F2, and foE) over China based on empirical orthogonal function (EOF) analysis. First, we decompose the background maps from the International Reference Ionosphere model 2007 (IRI-07) into different EOF modes. The obtained EOF modes consist of two factors: the EOF patterns and the corresponding EOF amplitudes. These two factors individually reflect the spatial distributions (e.g., the latitudinal dependence such as the equatorial ionization anomaly structure and the longitude structure with east-west difference) and temporal variations on different time scales (e.g., solar cycle, annual, semiannual, and diurnal variations) of the layer parameters. Then, the EOF patterns and long-term observations of ionosondes are assimilated to get the observed EOF amplitudes, which are further used to construct the Chinese Ionospheric Maps (CIMs) of the layer parameters. In contrast with the IRI-07 model, the mapped CIMs successfully capture the inherent temporal and spatial variations of the ionospheric layer parameters. Finally, comparison of the modeled (EOF and IRI-07 model) and observed values reveals that the EOF model reproduces the observation with smaller root-mean-square errors and higher linear correlation co- efficients. In addition, IRI discrepancy at the low latitude especially for foF2 is effectively removed by EOF model.
Simultaneous estimation of parameters in the bivariate Emax model.
Magnusdottir, Bergrun T; Nyquist, Hans
2015-12-10
In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation. PMID:26190048
Parameter selection and testing the soil water model SOIL
NASA Astrophysics Data System (ADS)
McGechan, M. B.; Graham, R.; Vinten, A. J. A.; Douglas, J. T.; Hooda, P. S.
1997-08-01
The soil water and heat simulation model SOIL was tested for its suitability to study the processes of transport of water in soil. Required parameters, particularly soil hydraulic parameters, were determined by field and laboratory tests for some common soil types and for soils subjected to contrasting treatments of long-term grassland and tilled land under cereal crops. Outputs from simulations were shown to be in reasonable agreement with independently measured field drain outflows and soil water content histories.
Application of physical parameter identification to finite-element models
NASA Technical Reports Server (NTRS)
Bronowicki, Allen J.; Lukich, Michael S.; Kuritz, Steven P.
1987-01-01
The time domain parameter identification method described previously is applied to TRW's Large Space Structure Truss Experiment. Only control sensors and actuators are employed in the test procedure. The fit of the linear structural model to the test data is improved by more than an order of magnitude using a physically reasonable parameter set. The electro-magnetic control actuators are found to contribute significant damping due to a combination of eddy current and back electro-motive force (EMF) effects. Uncertainties in both estimated physical parameters and modal behavior variables are given.
Estimation of nonlinear pilot model parameters including time delay.
NASA Technical Reports Server (NTRS)
Schiess, J. R.; Roland, V. R.; Wells, W. R.
1972-01-01
Investigation of the feasibility of using a Kalman filter estimator for the identification of unknown parameters in nonlinear dynamic systems with a time delay. The problem considered is the application of estimation theory to determine the parameters of a family of pilot models containing delayed states. In particular, the pilot-plant dynamics are described by differential-difference equations of the retarded type. The pilot delay, included as one of the unknown parameters to be determined, is kept in pure form as opposed to the Pade approximations generally used for these systems. Problem areas associated with processing real pilot response data are included in the discussion.
SU-E-T-247: Multi-Leaf Collimator Model Adjustments Improve Small Field Dosimetry in VMAT Plans
Young, L; Yang, F
2014-06-01
Purpose: The Elekta beam modulator linac employs a 4-mm micro multileaf collimator (MLC) backed by a fixed jaw. Out-of-field dose discrepancies between treatment planning system (TPS) calculations and output water phantom measurements are caused by the 1-mm leaf gap required for all moving MLCs in a VMAT arc. In this study, MLC parameters are optimized to improve TPS out-of-field dose approximations. Methods: Static 2.4 cm square fields were created with a 1-mm leaf gap for MLCs that would normally park behind the jaw. Doses in the open field and leaf gap were measured with an A16 micro ion chamber and EDR2 film for comparison with corresponding point doses in the Pinnacle TPS. The MLC offset table and tip radius were adjusted until TPS point doses agreed with photon measurements. Improvements to the beam models were tested using static arcs consisting of square fields ranging from 1.6 to 14.0 cm, with 45° collimator rotation, and 1-mm leaf gap to replicate VMAT conditions. Gamma values for the 3-mm distance, 3% dose difference criteria were evaluated using standard QA procedures with a cylindrical detector array. Results: The best agreement in point doses within the leaf gap and open field was achieved by offsetting the default rounded leaf end table by 0.1 cm and adjusting the leaf tip radius to 13 cm. Improvements in TPS models for 6 and 10 MV photon beams were more significant for smaller field sizes 3.6 cm or less where the initial gamma factors progressively increased as field size decreased, i.e. for a 1.6cm field size, the Gamma increased from 56.1% to 98.8%. Conclusion: The MLC optimization techniques developed will achieve greater dosimetric accuracy in small field VMAT treatment plans for fixed jaw linear accelerators. Accurate predictions of dose to organs at risk may reduce adverse effects of radiotherapy.
A model of the western Laurentide Ice Sheet, using observations of glacial isostatic adjustment
NASA Astrophysics Data System (ADS)
Gowan, Evan J.; Tregoning, Paul; Purcell, Anthony; Montillet, Jean-Philippe; McClusky, Simon
2016-05-01
We present the results of a new numerical model of the late glacial western Laurentide Ice Sheet, constrained by observations of glacial isostatic adjustment (GIA), including relative sea level indicators, uplift rates from permanent GPS stations, contemporary differential lake level change, and postglacial tilt of glacial lake level indicators. The later two datasets have been underutilized in previous GIA based ice sheet reconstructions. The ice sheet model, called NAICE, is constructed using simple ice physics on the basis of changing margin location and basal shear stress conditions in order to produce ice volumes required to match GIA. The model matches the majority of the observations, while maintaining a relatively realistic ice sheet geometry. Our model has a peak volume at 18,000 yr BP, with a dome located just east of Great Slave Lake with peak thickness of 4000 m, and surface elevation of 3500 m. The modelled ice volume loss between 16,000 and 14,000 yr BP amounts to about 7.5 m of sea level equivalent, which is consistent with the hypothesis that a large portion of Meltwater Pulse 1A was sourced from this part of the ice sheet. The southern part of the ice sheet was thin and had a low elevation profile. This model provides an accurate representation of ice thickness and paleo-topography, and can be used to assess present day uplift and infer past climate.
Procedures for adjusting regional regression models of urban-runoff quality using local data
Hoos, A.B.; Sisolak, J.K.
1993-01-01
Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for
Parameter Estimation for a crop model: separate and joint calibration of soil and plant parameters
NASA Astrophysics Data System (ADS)
Hildebrandt, A.; Jackisch, C.; Luis, S.
2008-12-01
Vegetation plays a major role both in the atmospheric and terrestrial water cycle. A great deal of vegetation cover in the developed world consists of agricultural used land (i.e. 44 % of the territory of the EU). Therefore, crop models have become increasingly prominent for studying the impact of Global Change both on economic welfare as well as on influence of vegetation on climate, and feedbacks with hydrological processes. By doing so, it is implied that crop models properly reflect the soil water balance and vertical exchange with the atmosphere. Although crop models can be incorporated in Surface Vegetation Atmosphere Transfer Schemes for that purpose, their main focus has traditionally not been on predicting water and energy fluxes, but yield. In this research we use data from two lysimeters in Brandis (Saxony, Germany), which have been planted with the crops of the surrounding farm, to test the capability of the crop model in SWAP. The lysimeters contain different natural soil cores, leading to substantially different yield. This experiment gives the opportunity to test, if the crop model is portable - that is if a calibrated crop can be moved between different locations. When using the default parameters for the respective environment, the model does neither quantitatively nor qualitatively reproduce the difference in yield and LAI for the different lysimeters. The separate calibration of soil and plant parameter was poor compared to the joint calibration of plant and soil parameters. This suggests that the model is not portable, but needs to be calibrated for individual locations, based on measurements or expert knowledge.
Control of the SCOLE configuration using distributed parameter models
NASA Technical Reports Server (NTRS)
Hsiao, Min-Hung; Huang, Jen-Kuang
1994-01-01
A continuum model for the SCOLE configuration has been derived using transfer matrices. Controller designs for distributed parameter systems have been analyzed. Pole-assignment controller design is considered easy to implement but stability is not guaranteed. An explicit transfer function of dynamic controllers has been obtained and no model reduction is required before the controller is realized. One specific LQG controller for continuum models had been derived, but other optimal controllers for more general performances need to be studied.
Parameter fitting for piano sound synthesis by physical modeling
NASA Astrophysics Data System (ADS)
Bensa, Julien; Gipouloux, Olivier; Kronland-Martinet, Richard
2005-07-01
A difficult issue in the synthesis of piano tones by physical models is to choose the values of the parameters governing the hammer-string model. In fact, these parameters are hard to estimate from static measurements, causing the synthesis sounds to be unrealistic. An original approach that estimates the parameters of a piano model, from the measurement of the string vibration, by minimizing a perceptual criterion is proposed. The minimization process that was used is a combination of a gradient method and a simulated annealing algorithm, in order to avoid convergence problems in case of multiple local minima. The criterion, based on the tristimulus concept, takes into account the spectral energy density in three bands, each allowing particular parameters to be estimated. The optimization process has been run on signals measured on an experimental setup. The parameters thus estimated provided a better sound quality than the one obtained using a global energetic criterion. Both the sound's attack and its brightness were better preserved. This quality gain was obtained for parameter values very close to the initial ones, showing that only slight deviations are necessary to make synthetic sounds closer to the real ones.
Quantifying the parameters of Prusiner's heterodimer model for prion replication
NASA Astrophysics Data System (ADS)
Li, Z. R.; Liu, G. R.; Mi, D.
2005-02-01
A novel approach for the determination of parameters in prion replication kinetics is developed based on Prusiner's heterodimer model. It is proposed to employ a simple 2D HP lattice model and a two-state transition theory to determine kinetic parameters that play the key role in the prion replication process. The simulation results reveal the most important facts observed in the prion diseases, including the long incubation time, rapid death following symptom manifestation, the effect of inoculation size, different mechanisms of the familial and infectious prion diseases, etc. Extensive simulation with various thermodynamic parameters shows that the Prusiner's heterodimer model is applicable, and the putative protein X plays a critical role in replication of the prion disease.
Radar altimeter waveform modeled parameter recovery. [SEASAT-1 data
NASA Technical Reports Server (NTRS)
1981-01-01
Satellite-borne radar altimeters include waveform sampling gates providing point samples of the transmitted radar pulse after its scattering from the ocean's surface. Averages of the waveform sampler data can be fitted by varying parameters in a model mean return waveform. The theoretical waveform model used is described as well as a general iterative nonlinear least squares procedures used to obtain estimates of parameters characterizing the modeled waveform for SEASAT-1 data. The six waveform parameters recovered by the fitting procedure are: (1) amplitude; (2) time origin, or track point; (3) ocean surface rms roughness; (4) noise baseline; (5) ocean surface skewness; and (6) altitude or off-nadir angle. Additional practical processing considerations are addressed and FORTRAN source listing for subroutines used in the waveform fitting are included. While the description is for the Seasat-1 altimeter waveform data analysis, the work can easily be generalized and extended to other radar altimeter systems.
Synchronous Generator Model Parameter Estimation Based on Noisy Dynamic Waveforms
NASA Astrophysics Data System (ADS)
Berhausen, Sebastian; Paszek, Stefan
2016-01-01
In recent years, there have occurred system failures in many power systems all over the world. They have resulted in a lack of power supply to a large number of recipients. To minimize the risk of occurrence of power failures, it is necessary to perform multivariate investigations, including simulations, of power system operating conditions. To conduct reliable simulations, the current base of parameters of the models of generating units, containing the models of synchronous generators, is necessary. In the paper, there is presented a method for parameter estimation of a synchronous generator nonlinear model based on the analysis of selected transient waveforms caused by introducing a disturbance (in the form of a pseudorandom signal) in the generator voltage regulation channel. The parameter estimation was performed by minimizing the objective function defined as a mean square error for deviations between the measurement waveforms and the waveforms calculated based on the generator mathematical model. A hybrid algorithm was used for the minimization of the objective function. In the paper, there is described a filter system used for filtering the noisy measurement waveforms. The calculation results of the model of a 44 kW synchronous generator installed on a laboratory stand of the Institute of Electrical Engineering and Computer Science of the Silesian University of Technology are also given. The presented estimation method can be successfully applied to parameter estimation of different models of high-power synchronous generators operating in a power system.
SPOTting model parameters using a ready-made Python package
NASA Astrophysics Data System (ADS)
Houska, Tobias; Kraft, Philipp; Breuer, Lutz
2015-04-01
The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for
Parameter uncertainty and interaction in complex environmental models
NASA Astrophysics Data System (ADS)
Spear, Robert C.; Grieb, Thomas M.; Shang, Nong
1994-11-01
Recently developed models for the estimation of risks arising from the release of toxic chemicals from hazardous waste sites are inherently complex both structurally and parametrically. To better understand the impact of uncertainty and interaction in the high-dimensional parameter spaces of these models, the set of procedures termed regional sensitivity analysis has been extended and applied to the groundwater pathway of the MMSOILS model. The extension consists of a tree-structured density estimation technique which allows the characterization of complex interaction in that portion of the parameter space which gives rise to successful simulation. Results show that the parameter space can be partitioned into small, densely populated regions and relatively large, sparsely populated regions. From the high-density regions one can identify the important or controlling parameters as well as the interaction between parameters in different local areas of the space. This new tool can provide guidance in the analysis and interpretation of site-specific application of these complex models.
Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations
NASA Technical Reports Server (NTRS)
Hanson, Andrea; Reed, Erik; Cavanagh, Peter
2011-01-01
Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.
[Parameter uncertainty analysis for urban rainfall runoff modelling].
Huang, Jin-Liang; Lin, Jie; Du, Peng-Fei
2012-07-01
An urban watershed in Xiamen was selected to perform the parameter uncertainty analysis for urban stormwater runoff modeling in terms of identification and sensitivity analysis based on storm water management model (SWMM) using Monte-Carlo sampling and regionalized sensitivity analysis (RSA) algorithm. Results show that Dstore-Imperv, Dstore-Perv and Curve Number (CN) are the identifiable parameters with larger K-S values in hydrological and hydraulic module, and the rank of K-S values in hydrological and hydraulic module is Dstore-Imperv > CN > Dstore-Perv > N-Perv > conductivity > Con-Mann > N-Imperv. With regards to water quality module, the parameters in exponent washoff model including Coefficient and Exponent and the Max. Buildup parameter of saturation buildup model in three land cover types are the identifiable parameters with the larger K-S values. In comparison, the K-S value of rate constant in three landuse/cover types is smaller than that of Max. Buildup, Coefficient and Exponent. PMID:23002595
Adjusting Satellite Rainfall Error in Mountainous Areas for Flood Modeling Applications
NASA Astrophysics Data System (ADS)
Zhang, X.; Anagnostou, E. N.; Astitha, M.; Vergara, H. J.; Gourley, J. J.; Hong, Y.
2014-12-01
This study aims to investigate the use of high-resolution Numerical Weather Prediction (NWP) for evaluating biases of satellite rainfall estimates of flood-inducing storms in mountainous areas and associated improvements in flood modeling. Satellite-retrieved precipitation has been considered as a feasible data source for global-scale flood modeling, given that satellite has the spatial coverage advantage over in situ (rain gauges and radar) observations particularly over mountainous areas. However, orographically induced heavy precipitation events tend to be underestimated and spatially smoothed by satellite products, which error propagates non-linearly in flood simulations.We apply a recently developed retrieval error and resolution effect correction method (Zhang et al. 2013*) on the NOAA Climate Prediction Center morphing technique (CMORPH) product based on NWP analysis (or forecasting in the case of real-time satellite products). The NWP rainfall is derived from the Weather Research and Forecasting Model (WRF) set up with high spatial resolution (1-2 km) and explicit treatment of precipitation microphysics.In this study we will show results on NWP-adjusted CMORPH rain rates based on tropical cyclones and a convective precipitation event measured during NASA's IPHEX experiment in the South Appalachian region. We will use hydrologic simulations over different basins in the region to evaluate propagation of bias correction in flood simulations. We show that the adjustment reduced the underestimation of high rain rates thus moderating the strong rainfall magnitude dependence of CMORPH rainfall bias, which results in significant improvement in flood peak simulations. Further study over Blue Nile Basin (western Ethiopia) will be investigated and included in the presentation. *Zhang, X. et al. 2013: Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas. J. Hydrometeor, 14, 1844-1858.
A Bayesian approach to parameter estimation in HIV dynamical models.
Putter, H; Heisterkamp, S H; Lange, J M A; de Wolf, F
2002-08-15
In the context of a mathematical model describing HIV infection, we discuss a Bayesian modelling approach to a non-linear random effects estimation problem. The model and the data exhibit a number of features that make the use of an ordinary non-linear mixed effects model intractable: (i) the data are from two compartments fitted simultaneously against the implicit numerical solution of a system of ordinary differential equations; (ii) data from one compartment are subject to censoring; (iii) random effects for one variable are assumed to be from a beta distribution. We show how the Bayesian framework can be exploited by incorporating prior knowledge on some of the parameters, and by combining the posterior distributions of the parameters to obtain estimates of quantities of interest that follow from the postulated model. PMID:12210633
Electron-N/sub 2/ scattering calculations with a parameter-free model polarization potential
Morrison, M.A.; Saha, B.C.; Gibson, T.L.
1987-10-15
We have extended our variationally determined nonadiabatic polarization potential (Gibson and Morrison, Phys. Rev. A 29, 2497 (1984)) to the e-N/sub 2/ system and calculated elastic, total momentum transfer, and rotational excitation cross sections. This model potential, which requires no scaling and contains no adjustable parameters, is presented in tabular and analytic (fitted) form for possible use in future studies. We evaluated the static potential at the near-Hartree-Fock level of accuracy and included exchange effects exactly via the linear algebraic method of Collins and Schneider (Phys. Rev. A 24, 2387 (1981)). Diverse cross sections based on this model are in excellent agreement with existing experiment. We also compare various scattering quantities calculated with our model to prior theoretical results and to newly determined numbers using two other model potentials: a cutoff phenomenological form and the correlation-polarization potential of O'Connell and Lane (Phys. Rev. A 27, 1893 (1983)).
Estimation of the parameters of ETAS models by Simulated Annealing
Lombardi, Anna Maria
2015-01-01
This paper proposes a new algorithm to estimate the maximum likelihood parameters of an Epidemic Type Aftershock Sequences (ETAS) model. It is based on Simulated Annealing, a versatile method that solves problems of global optimization and ensures convergence to a global optimum. The procedure is tested on both simulated and real catalogs. The main conclusion is that the method performs poorly as the size of the catalog decreases because the effect of the correlation of the ETAS parameters is more significant. These results give new insights into the ETAS model and the efficiency of the maximum-likelihood method within this context. PMID:25673036
Climate change decision-making: Model & parameter uncertainties explored
Dowlatabadi, H.; Kandlikar, M.; Linville, C.
1995-12-31
A critical aspect of climate change decision-making is uncertainties in current understanding of the socioeconomic, climatic and biogeochemical processes involved. Decision-making processes are much better informed if these uncertainties are characterized and their implications understood. Quantitative analysis of these uncertainties serve to inform decision makers about the likely outcome of policy initiatives, and help set priorities for research so that outcome ambiguities faced by the decision-makers are reduced. A family of integrated assessment models of climate change have been developed at Carnegie Mellon. These models are distinguished from other integrated assessment efforts in that they were designed from the outset to characterize and propagate parameter, model, value, and decision-rule uncertainties. The most recent of these models is ICAM 2.1. This model includes representation of the processes of demographics, economic activity, emissions, atmospheric chemistry, climate and sea level change and impacts from these changes and policies for emissions mitigation, and adaptation to change. The model has over 800 objects of which about one half are used to represent uncertainty. In this paper we show, that when considering parameter uncertainties, the relative contribution of climatic uncertainties are most important, followed by uncertainties in damage calculations, economic uncertainties and direct aerosol forcing uncertainties. When considering model structure uncertainties we find that the choice of policy is often dominated by model structure choice, rather than parameter uncertainties.
Inhalation Exposure Input Parameters for the Biosphere Model
M. Wasiolek
2006-06-05
This analysis is one of the technical reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), referred to in this report as the biosphere model. ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. ''Inhalation Exposure Input Parameters for the Biosphere Model'' is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the biosphere model is presented in Figure 1-1 (based on BSC 2006 [DIRS 176938]). This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and how this analysis report contributes to biosphere modeling. This analysis report defines and justifies values of atmospheric mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of the biosphere model to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception. This report is concerned primarily with the
Force Field Independent Metal Parameters Using a Nonbonded Dummy Model
2014-01-01
The cationic dummy atom approach provides a powerful nonbonded description for a range of alkaline-earth and transition-metal centers, capturing both structural and electrostatic effects. In this work we refine existing literature parameters for octahedrally coordinated Mn2+, Zn2+, Mg2+, and Ca2+, as well as providing new parameters for Ni2+, Co2+, and Fe2+. In all the cases, we are able to reproduce both M2+–O distances and experimental solvation free energies, which has not been achieved to date for transition metals using any other model. The parameters have also been tested using two different water models and show consistent performance. Therefore, our parameters are easily transferable to any force field that describes nonbonded interactions using Coulomb and Lennard-Jones potentials. Finally, we demonstrate the stability of our parameters in both the human and Escherichia coli variants of the enzyme glyoxalase I as showcase systems, as both enzymes are active with a range of transition metals. The parameters presented in this work provide a valuable resource for the molecular simulation community, as they extend the range of metal ions that can be studied using classical approaches, while also providing a starting point for subsequent parametrization of new metal centers. PMID:24670003
Force field independent metal parameters using a nonbonded dummy model.
Duarte, Fernanda; Bauer, Paul; Barrozo, Alexandre; Amrein, Beat Anton; Purg, Miha; Aqvist, Johan; Kamerlin, Shina Caroline Lynn
2014-04-24
The cationic dummy atom approach provides a powerful nonbonded description for a range of alkaline-earth and transition-metal centers, capturing both structural and electrostatic effects. In this work we refine existing literature parameters for octahedrally coordinated Mn(2+), Zn(2+), Mg(2+), and Ca(2+), as well as providing new parameters for Ni(2+), Co(2+), and Fe(2+). In all the cases, we are able to reproduce both M(2+)-O distances and experimental solvation free energies, which has not been achieved to date for transition metals using any other model. The parameters have also been tested using two different water models and show consistent performance. Therefore, our parameters are easily transferable to any force field that describes nonbonded interactions using Coulomb and Lennard-Jones potentials. Finally, we demonstrate the stability of our parameters in both the human and Escherichia coli variants of the enzyme glyoxalase I as showcase systems, as both enzymes are active with a range of transition metals. The parameters presented in this work provide a valuable resource for the molecular simulation community, as they extend the range of metal ions that can be studied using classical approaches, while also providing a starting point for subsequent parametrization of new metal centers. PMID:24670003
Facial motion parameter estimation and error criteria in model-based image coding
NASA Astrophysics Data System (ADS)
Liu, Yunhai; Yu, Lu; Yao, Qingdong
2000-04-01
Model-based image coding has been given extensive attention due to its high subject image quality and low bit-rates. But the estimation of object motion parameter is still a difficult problem, and there is not a proper error criteria for the quality assessment that are consistent with visual properties. This paper presents an algorithm of the facial motion parameter estimation based on feature point correspondence and gives the motion parameter error criteria. The facial motion model comprises of three parts. The first part is the global 3-D rigid motion of the head, the second part is non-rigid translation motion in jaw area, and the third part consists of local non-rigid expression motion in eyes and mouth areas. The feature points are automatically selected by a function of edges, brightness and end-node outside the blocks of eyes and mouth. The numbers of feature point are adjusted adaptively. The jaw translation motion is tracked by the changes of the feature point position of jaw. The areas of non-rigid expression motion can be rebuilt by using block-pasting method. The estimation approach of motion parameter error based on the quality of reconstructed image is suggested, and area error function and the error function of contour transition-turn rate are used to be quality criteria. The criteria reflect the image geometric distortion caused by the error of estimated motion parameters properly.
Macroscopic singlet oxygen model incorporating photobleaching as an input parameter
NASA Astrophysics Data System (ADS)
Kim, Michele M.; Finlay, Jarod C.; Zhu, Timothy C.
2015-03-01
A macroscopic singlet oxygen model for photodynamic therapy (PDT) has been used extensively to calculate the reacted singlet oxygen concentration for various photosensitizers. The four photophysical parameters (ξ, σ, β, δ) and threshold singlet oxygen dose ([1O2]r,sh) can be found for various drugs and drug-light intervals using a fitting algorithm. The input parameters for this model include the fluence, photosensitizer concentration, optical properties, and necrosis radius. An additional input variable of photobleaching was implemented in this study to optimize the results. Photobleaching was measured by using the pre-PDT and post-PDT sensitizer concentrations. Using the RIF model of murine fibrosarcoma, mice were treated with a linear source with fluence rates from 12 - 150 mW/cm and total fluences from 24 - 135 J/cm. The two main drugs investigated were benzoporphyrin derivative monoacid ring A (BPD) and 2-[1-hexyloxyethyl]-2-devinyl pyropheophorbide-a (HPPH). Previously published photophysical parameters were fine-tuned and verified using photobleaching as the additional fitting parameter. Furthermore, photobleaching can be used as an indicator of the robustness of the model for the particular mouse experiment by comparing the experimental and model-calculated photobleaching ratio.
Global parameter estimation for thermodynamic models of transcriptional regulation.
Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N
2013-07-15
Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. PMID:23726942
Generating Effective Models and Parameters for RNA Genetic Circuits.
Hu, Chelsea Y; Varner, Jeffrey D; Lucks, Julius B
2015-08-21
RNA genetic circuitry is emerging as a powerful tool to control gene expression. However, little work has been done to create a theoretical foundation for RNA circuit design. A prerequisite to this is a quantitative modeling framework that accurately describes the dynamics of RNA circuits. In this work, we develop an ordinary differential equation model of transcriptional RNA genetic circuitry, using an RNA cascade as a test case. We show that parameter sensitivity analysis can be used to design a set of four simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions to determine the 13 parameters of the model. The resulting model accurately recapitulates the dynamic behavior of the cascade, and can be easily extended to predict the function of new cascade variants that utilize new elements with limited additional characterization experiments. Interestingly, we show that inconsistencies between model predictions and experiments led to the model-guided discovery of a previously unknown maturation step required for RNA regulator function. We also determine circuit parameters in two different batches of TX-TL, and show that batch-to-batch variation can be attributed to differences in parameters that are directly related to the concentrations of core gene expression machinery. We anticipate the RNA circuit models developed here will inform the creation of computer aided genetic circuit design tools that can incorporate the growing number of RNA regulators, and that the parametrization method will find use in determining functional parameters of a broad array of natural and synthetic regulatory systems. PMID:26046393
Considerations for parameter optimization and sensitivity in climate models.
Neelin, J David; Bracco, Annalisa; Luo, Hao; McWilliams, James C; Meyerson, Joyce E
2010-12-14
Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention--here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models. PMID:21115841
Lehmann, E. D.; Deutsch, T.
1992-01-01
Joe Daniels is a 41 year old, 76kg male, insulin-treated diabetic patient who was diagnosed as being diabetic in 1972, at the age of 22. Joe recently found that he was having hypoglycaemic symptoms. Using self-monitoring blood glucose equipment glycaemic levels below 3.0 mmol/l were recorded at least once a week while hyperglycaemic readings (> 16 mmol/l) were observed 2-3 times per week. Joe came into hospital to have his glycaemic control improved as doctors were concerned about the risks of him suffering a serious hypoglycaemic attack. Using some of the data collected by Joe while in hospital we will demonstrate how a computer model of glucose-insulin interaction in type I diabetes can be used interactively to teach diabetic patients about their diabetes and educate them to adjust their own insulin injections and diet. PMID:1482868
Parameter space of the Rulkov chaotic neuron model
NASA Astrophysics Data System (ADS)
Wang, Caixia; Cao, Hongjun
2014-06-01
The parameter space of the two dimensional Rulkov chaotic neuron model is taken into account by using the qualitative analysis, the co-dimension 2 bifurcation, the center manifold theorem, and the normal form. The goal is intended to clarify analytically different dynamics and firing regimes of a single neuron in a two dimensional parameter space. Our research demonstrates the origin that there exist very rich nonlinear dynamics and complex biological firing regimes lies in different domains and their boundary curves in the two dimensional parameter plane. We present the parameter domains of fixed points, the saddle-node bifurcation, the supercritical/subcritical Neimark-Sacker bifurcation, stability conditions of non hyperbolic fixed points and quasiperiodic solutions. Based on these parameter domains, it is easy to know that the Rulkov chaotic neuron model can produce what kinds of firing regimes as well as their transition mechanisms. These results are very useful for building-up a large-scale neuron network with different biological functional roles and cognitive activities, especially in establishing some specific neuron network models of neurological diseases.
UPDATING THE FREIGHT TRUCK STOCK ADJUSTMENT MODEL: 1997 VEHICLE INVENTORY AND USE SURVEY DATA
Davis, S.C.
2000-11-16
The Energy Information Administration's (EIA's) National Energy Modeling System (NEMS) Freight Truck Stock Adjustment Model (FTSAM) was created in 1995 relying heavily on input data from the 1992 Economic Census, Truck Inventory and Use Survey (TIUS). The FTSAM is part of the NEMS Transportation Sector Model, which provides baseline energy projections and analyzes the impacts of various technology scenarios on consumption, efficiency, and carbon emissions. The base data for the FTSAM can be updated every five years as new Economic Census information is released. Because of expertise in using the TIUS database, Oak Ridge National Laboratory (ORNL) was asked to assist the EIA when the new Economic Census data were available. ORNL provided the necessary base data from the 1997 Vehicle Inventory and Use Survey (VIUS) and other sources to update the FTSAM. The next Economic Census will be in the year 2002. When those data become available, the EIA will again want to update the FTSAM using the VIUS. This report, which details the methodology of estimating and extracting data from the 1997 VIUS Microdata File, should be used as a guide for generating the data from the next VIUS so that the new data will be as compatible as possible with the data in the model.
Modelling Biophysical Parameters of Maize Using Landsat 8 Time Series
NASA Astrophysics Data System (ADS)
Dahms, Thorsten; Seissiger, Sylvia; Conrad, Christopher; Borg, Erik
2016-06-01
Open and free access to multi-frequent high-resolution data (e.g. Sentinel - 2) will fortify agricultural applications based on satellite data. The temporal and spatial resolution of these remote sensing datasets directly affects the applicability of remote sensing methods, for instance a robust retrieving of biophysical parameters over the entire growing season with very high geometric resolution. In this study we use machine learning methods to predict biophysical parameters, namely the fraction of absorbed photosynthetic radiation (FPAR), the leaf area index (LAI) and the chlorophyll content, from high resolution remote sensing. 30 Landsat 8 OLI scenes were available in our study region in Mecklenburg-Western Pomerania, Germany. In-situ data were weekly to bi-weekly collected on 18 maize plots throughout the summer season 2015. The study aims at an optimized prediction of biophysical parameters and the identification of the best explaining spectral bands and vegetation indices. For this purpose, we used the entire in-situ dataset from 24.03.2015 to 15.10.2015. Random forest and conditional inference forests were used because of their explicit strong exploratory and predictive character. Variable importance measures allowed for analysing the relation between the biophysical parameters with respect to the spectral response, and the performance of the two approaches over the plant stock evolvement. Classical random forest regression outreached the performance of conditional inference forests, in particular when modelling the biophysical parameters over the entire growing period. For example, modelling biophysical parameters of maize for the entire vegetation period using random forests yielded: FPAR: R² = 0.85; RMSE = 0.11; LAI: R² = 0.64; RMSE = 0.9 and chlorophyll content (SPAD): R² = 0.80; RMSE=4.9. Our results demonstrate the great potential in using machine-learning methods for the interpretation of long-term multi-frequent remote sensing datasets to model
A Hamiltonian Model of Generator With AVR and PSS Parameters*
NASA Astrophysics Data System (ADS)
Qian, Jing.; Zeng, Yun.; Zhang, Lixiang.; Xu, Tianmao.
Take the typical thyristor excitation system including the automatic voltage regulator (AVR) and the power system stabilizer (PSS) as an example, the supply rate of AVR and PSS branch are selected as the energy function of controller, and that is added to the Hamiltonian function of the generator to compose the total energy function. By proper transformation, the standard form of the Hamiltonian model of the generator including AVR and PSS is derived. The structure matrix and damping matrix of the model include feature parameters of AVR and PSS, which gives a foundation to study the interaction mechanism of parameters between AVR, PSS and the generator. Finally, the structural relationships and interactions of the system model are studied, the results show that the relationship of structure and damping characteristic reflected by model consistent with practical system.
Prediction of interest rate using CKLS model with stochastic parameters
Ying, Khor Chia; Hin, Pooi Ah
2014-06-19
The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ{sup (j)} of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j′-th time point where j≤j′≤j+n. To model the variation of φ{sup (j)}, we assume that φ{sup (j)} depends on φ{sup (j−m)}, φ{sup (j−m+1)},…, φ{sup (j−1)} and the interest rate r{sub j+n} at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value r{sub j+n+1} of the interest rate at the next time point when the value r{sub j+n} of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate r{sub j+n+d} at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.
Prediction of interest rate using CKLS model with stochastic parameters
NASA Astrophysics Data System (ADS)
Ying, Khor Chia; Hin, Pooi Ah
2014-06-01
The Chan, Karolyi, Longstaff and Sanders (CKLS) model is a popular one-factor model for describing the spot interest rates. In this paper, the four parameters in the CKLS model are regarded as stochastic. The parameter vector φ(j) of four parameters at the (J+n)-th time point is estimated by the j-th window which is defined as the set consisting of the observed interest rates at the j'-th time point where j≤j'≤j+n. To model the variation of φ(j), we assume that φ(j) depends on φ(j-m), φ(j-m+1),…, φ(j-1) and the interest rate rj+n at the (j+n)-th time point via a four-dimensional conditional distribution which is derived from a [4(m+1)+1]-dimensional power-normal distribution. Treating the (j+n)-th time point as the present time point, we find a prediction interval for the future value rj+n+1 of the interest rate at the next time point when the value rj+n of the interest rate is given. From the above four-dimensional conditional distribution, we also find a prediction interval for the future interest rate rj+n+d at the next d-th (d≥2) time point. The prediction intervals based on the CKLS model with stochastic parameters are found to have better ability of covering the observed future interest rates when compared with those based on the model with fixed parameters.
Atmosphere models and the determination of stellar parameters
NASA Astrophysics Data System (ADS)
Martins, F.
2014-11-01
We present the basic concepts necessary to build atmosphere models for any type of star. We then illustrate how atmosphere models can be used to determine stellar parameters. We focus on the effects of line-blanketing for hot stars, and on non-LTE and three dimensional effects for cool stars. We illustrate the impact of these effects on the determination of the ages of stars from the HR diagram.
Parabolic problems with parameters arising in evolution model for phytromediation
NASA Astrophysics Data System (ADS)
Sahmurova, Aida; Shakhmurov, Veli
2012-12-01
The past few decades, efforts have been made to clean sites polluted by heavy metals as chromium. One of the new innovative methods of eradicating metals from soil is phytoremediation. This uses plants to pull metals from the soil through the roots. This work develops a system of differential equations with parameters to model the plant metal interaction of phytoremediation (see [1]).
Long wave infrared polarimetric model: theory, measurements and parameters
NASA Astrophysics Data System (ADS)
Wellems, David; Ortega, Steve; Bowers, David; Boger, Jim; Fetrow, Matthew
2006-10-01
Material parameters, which include the complex index of refraction, (n,k), and surface roughness, are needed to determine passive long wave infrared (LWIR) polarimetric radiance. A single scatter microfacet bi-direction reflectance distribution function (BRDF) is central to the energy conserving (EC) model which determines emitted and reflected polarized surface radiance. Model predictions are compared to LWIR polarimetric data. An ellipsometry approach is described for finding an effective complex index of refraction or (n,k) averaged over the 8.5-9.5 µm wavelength range. The reflected S3/S2 ratios, where S2 and S3 are components of the Stokes (Born and Wolf 1975 Principles of Optics (London: Pergamon) p 30) vector, are used to determine (n,k). An imaging polarimeter with a rotating retarder is utilized to measure the Stokes vector. Effective (n,k) and two EC optical roughness parameters are presented for roughened glass and several unprepared, typical outdoor materials including metals and paints. A two parameter slope distribution function is introduced which is more flexible in modelling the source reflected intensity profiles or BRDF data than one parameter Cauchy or Gaussian distributions (Jordan et al 1996 Appl. Opt. 35 3585-90 Priest and Meier 2002 Opt. Eng. 41 992). The glass results show that the (n,k) needed to model polarimetric emission and scatter differ from that for a smooth surface and that surface roughness reduces the degree of linear polarization.
A Fully Conditional Estimation Procedure for Rasch Model Parameters.
ERIC Educational Resources Information Center
Choppin, Bruce
A strategy for overcoming problems with the Rasch model's inability to handle missing data involves a pairwise algorithm which manipulates the data matrix to separate out the information needed for the estimation of item difficulty parameters in a test. The method of estimation compares two or three items at a time, separating out the ability…
Investigation of land use effects on Nash model parameters
NASA Astrophysics Data System (ADS)
Niazi, Faegheh; Fakheri Fard, Ahmad; Nourani, Vahid; Goodrich, David; Gupta, Hoshin
2015-04-01
Flood forecasting is of great importance in hydrologic planning, hydraulic structure design, water resources management and sustainable designs like flood control and management. Nash's instantaneous unit hydrograph is frequently used for simulating hydrological response in natural watersheds. Urban hydrology is gaining more attention due to population increases and associated construction escalation. Rapid development of urban areas affects the hydrologic processes of watersheds by decreasing soil permeability, flood base flow, lag time and increase in flood volume, peak runoff rates and flood frequency. In this study the influence of urbanization on the significant parameters of the Nash model have been investigated. These parameters were calculated using three popular methods (i.e. moment, root mean square error and random sampling data generation), in a small watershed consisting of one natural sub-watershed which drains into a residentially developed sub-watershed in the city of Sierra Vista, Arizona. The results indicated that for all three methods, the lag time, which is product of Nash parameters "K" and "n", in the natural sub-watershed is greater than the developed one. This logically implies more storage and/or attenuation in the natural sub-watershed. The median K and n parameters derived from the three methods using calibration events were tested via a set of verification events. The results indicated that all the three method have acceptable accuracy in hydrograph simulation. The CDF curves and histograms of the parameters clearly show the difference of the Nash parameter values between the natural and developed sub-watersheds. Some specific upper and lower percentile values of the median of the generated parameters (i.e. 10, 20 and 30 %) were analyzed to future investigates the derived parameters. The model was sensitive to variations in the value of the uncertain K and n parameter. Changes in n are smaller than K in both sub-watersheds indicating
Punamäki, R L; Qouta, S; el Sarraj, E
1997-08-01
The relations between traumatic events, perceived parenting styles, children's resources, political activity, and psychological adjustment were examined among 108 Palestinian boys and girls of 11-12 years of age. The results showed that exposure to traumatic events increased psychological adjustment problems directly and via 2 mediating paths. First, the more traumatic events children had experienced, the more negative parenting they experienced. And, the poorer they perceived parenting, the more they suffered from high neuroticism and low self-esteem. Second, the more traumatic events children had experienced, the more political activity they showed, and the more active they were, the more they suffered from psychological adjustment problems. Good perceived parenting protected children's psychological adjustment by making them less vulnerable in two ways. First, traumatic events decreased their intellectual, creative, and cognitive resources, and a lack of resources predicted many psychological adjustment problems in a model excluding perceived parenting. Second, political activity increased psychological adjustment problems in the same model, but not in the model including good parenting. PMID:9306648
Integrating microbial diversity in soil carbon dynamic models parameters
NASA Astrophysics Data System (ADS)
Louis, Benjamin; Menasseri-Aubry, Safya; Leterme, Philippe; Maron, Pierre-Alain; Viaud, Valérie
2015-04-01
Faced with the numerous concerns about soil carbon dynamic, a large quantity of carbon dynamic models has been developed during the last century. These models are mainly in the form of deterministic compartment models with carbon fluxes between compartments represented by ordinary differential equations. Nowadays, lots of them consider the microbial biomass as a compartment of the soil organic matter (carbon quantity). But the amount of microbial carbon is rarely used in the differential equations of the models as a limiting factor. Additionally, microbial diversity and community composition are mostly missing, although last advances in soil microbial analytical methods during the two past decades have shown that these characteristics play also a significant role in soil carbon dynamic. As soil microorganisms are essential drivers of soil carbon dynamic, the question about explicitly integrating their role have become a key issue in soil carbon dynamic models development. Some interesting attempts can be found and are dominated by the incorporation of several compartments of different groups of microbial biomass in terms of functional traits and/or biogeochemical compositions to integrate microbial diversity. However, these models are basically heuristic models in the sense that they are used to test hypotheses through simulations. They have rarely been confronted to real data and thus cannot be used to predict realistic situations. The objective of this work was to empirically integrate microbial diversity in a simple model of carbon dynamic through statistical modelling of the model parameters. This work is based on available experimental results coming from a French National Research Agency program called DIMIMOS. Briefly, 13C-labelled wheat residue has been incorporated into soils with different pedological characteristics and land use history. Then, the soils have been incubated during 104 days and labelled and non-labelled CO2 fluxes have been measured at ten
Inhalation Exposure Input Parameters for the Biosphere Model
M. A. Wasiolek
2003-09-24
This analysis is one of the nine reports that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2003a) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents a set of input parameters for the biosphere model, and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the Total System Performance Assessment (TSPA) for a Yucca Mountain repository. This report, ''Inhalation Exposure Input Parameters for the Biosphere Model'', is one of the five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (BSC 2003b). It should be noted that some documents identified in Figure 1-1 may be under development at the time this report is issued and therefore not available at that time. This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this analysis report. This analysis report defines and justifies values of mass loading, which is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Measurements of mass loading are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air surrounding crops and concentrations in air inhaled by a receptor. Concentrations in air to which the
High speed parameter estimation for a homogenized energy model
NASA Astrophysics Data System (ADS)
Ernstberger, Jon M.
Industrial, commercial, military, biomedical, and civilian uses of smart materials are increasingly investigated for high performance applications. These compounds couple applied field or thermal energy to mechanical forces that are generated within the material. The devices utilizing these compounds are often much smaller than their traditional counterparts and provide greater design capabilities and energy efficiency. The relations that couple field and mechanical energies are often hysteretic and nonlinear. To accurately control devices employing these compounds, models must quantify these effects. Further, since these compounds exhibit environment-dependent behavior, the models must be robust for accurate actuator quantification. In this dissertation, we investigate the construction of models that characterize these internal mechanisms and that manifest themselves in material deformation in a hysteretic fashion. Results of previously-presented model formulations are given. New techniques for generating model components are presented which reduce the computational load for parameter estimations. The use of various deterministic and stochastic search algorithms for parameter estimation are discussed with strengths and weaknesses of each examined. New end-user graphical tools for properly initiating the parameter estimation are also presented. Finally, results from model fits to data from ferroelectric---e.g., Lead Zirconate Titanate (PZT)---and ferromagnetic---e.g., Terfenol-D---materials are presented.
The Trauma Outcome Process Assessment Model: A Structural Equation Model Examination of Adjustment
ERIC Educational Resources Information Center
Borja, Susan E.; Callahan, Jennifer L.
2009-01-01
This investigation sought to operationalize a comprehensive theoretical model, the Trauma Outcome Process Assessment, and test it empirically with structural equation modeling. The Trauma Outcome Process Assessment reflects a robust body of research and incorporates known ecological factors (e.g., family dynamics, social support) to explain…
Improving a regional model using reduced complexity and parameter estimation
Kelson, Victor A.; Hunt, Randall J.; Haitjema, Henk M.
2002-01-01
The availability of powerful desktop computers and graphical user interfaces for ground water flow models makes possible the construction of ever more complex models. A proposed copper-zinc sulfide mine in northern Wisconsin offers a unique case in which the same hydrologic system has been modeled using a variety of techniques covering a wide range of sophistication and complexity. Early in the permitting process, simple numerical models were used to evaluate the necessary amount of water to be pumped from the mine, reductions in streamflow, and the drawdowns in the regional aquifer. More complex models have subsequently been used in an attempt to refine the predictions. Even after so much modeling effort, questions regarding the accuracy and reliability of the predictions remain. We have performed a new analysis of the proposed mine using the two-dimensional analytic element code GFLOW coupled with the nonlinear parameter estimation code UCODE. The new model is parsimonious, containing fewer than 10 parameters, and covers a region several times larger in areal extent than any of the previous models. The model demonstrates the suitability of analytic element codes for use with parameter estimation codes. The simplified model results are similar to the more complex models; predicted mine inflows and UCODE-derived 95% confidence intervals are consistent with the previous predictions. More important, the large areal extent of the model allowed us to examine hydrological features not included in the previous models, resulting in new insights about the effects that far-field boundary conditions can have on near-field model calibration and parameterization. In this case, the addition of surface water runoff into a lake in the headwaters of a stream while holding recharge constant moved a regional ground watershed divide and resulted in some of the added water being captured by the adjoining basin. Finally, a simple analytical solution was used to clarify the GFLOW model
Assessment of Model Parameters Interdependency of a Conceptual Rainfall-Runoff Model
NASA Astrophysics Data System (ADS)
Das, T.; Bárdossy, A.; Zehe, E.
2006-12-01
Conceptual rainfall-runoff models are widely used tools in hydrology. Contrary to more complex physically- based distributed models, the required input data are readily available for most applications in the world. In addition to their modest data requirement, conceptual models are usually simple and relatively easy to apply. However, for partly or fully conceptual models, some parameters cannot be considered as physically measured quantities and thus have to be estimated on the basis of the available data and information. However, in the range of input data, it is often not possible to find one unique parameter set, i.e. a number of parameter sets can lead to similar model results (known as equifinality). Nevertheless, the model parameter sets which lead to equally good model performance may have interesting internal structures. The issue of equifinality following the internal model structures was investigated in this paper using two examples. The first example is one which uses a simple two parameter sediment transport model in a river. A large number of parameter pairs was generated randomly. The results indicated that they both can be taken from a wide interval of possible values which might lead to satisfactory model performance. However, a well structured set is obtained if one investigates the set of parameters as pairs. The second example was given by using model parameters of the modified HBV conceptual rainfall-runoff model. One hundred independent calibration runs for the HBV model were carried out. These runs were done using the automatic calibration procedure based on the simulated optimization algorithm; each run using a different, randomly selected initial seed value required for the calibration algorithm. No explicit dependence between the parameters was considered. The results demonstrated that parameters of rainfall-runoff models often can not be identified as individual values. A large set of possible parameters can lead to a similar model
Parameter Calibration of Mini-LEO Hill Slope Model
NASA Astrophysics Data System (ADS)
Siegel, H.
2015-12-01
The mini-LEO hill slope, located at Biosphere 2, is a small-scale catchment model that is used to study the ways landscapes change in response to biological, chemical, and hydrological processes. Previous experiments have shown that soil heterogeneity can develop as a result of groundwater flow; changing the characteristics of the landscape. To determine whether or not flow has caused heterogeneity within the mini-LEO hill slope, numerical models were used to simulate the observed seepage flow, water table height, and storativity. To begin a numerical model of the hill slope was created using CATchment Hydrology (CATHY). The model was then brought to an initial steady state by applying a rainfall event of 5mm/day for 180 days. Then a specific rainfall experiment of alternating intensities was applied to the model. Next, a parameter calibration was conducted, to fit the model to the observed data, by changing soil parameters individually. The parameters of the best fitting calibration were taken to be the most representative of those present within the mini-LEO hill slope. Our model concluded that heterogeneities had indeed arisen as a result of the rainfall event, resulting in a lower hydraulic conductivity downslope. The lower hydraulic conductivity downslope in turn caused in an increased storage of water and a decrease in seepage flow compared to homogeneous models. This shows that the hydraulic processes acting within a landscape can change the very characteristics of the landscape itself, namely the permeability and conductivity of the soil. In the future results from the excavation of soil in mini-LEO can be compared to the models results to improve the model and validate its findings.
A new glacial isostatic adjustment model of the Innuitian Ice Sheet, Arctic Canada
NASA Astrophysics Data System (ADS)
Simon, K. M.; James, T. S.; Dyke, A. S.
2015-07-01
A reconstruction of the Innuitian Ice Sheet (IIS) is developed that incorporates first-order constraints on its spatial extent and history as suggested by regional glacial geology studies. Glacial isostatic adjustment modelling of this ice sheet provides relative sea-level predictions that are in good agreement with measurements of post-glacial sea-level change at 18 locations. The results indicate peak thicknesses of the Innuitian Ice Sheet of approximately 1600 m, up to 400 m thicker than the minimum peak thicknesses estimated from glacial geology studies, but between approximately 1000 to 1500 m thinner than the peak thicknesses present in previous GIA models. The thickness history of the best-fit Innuitian Ice Sheet model developed here, termed SJD15, differs from the ICE-5G reconstruction and provides an improved fit to sea-level measurements from the lowland sector of the ice sheet. Both models provide a similar fit to relative sea-level measurements from the alpine sector. The vertical crustal motion predictions of the best-fit IIS model are in general agreement with limited GPS observations, after correction for a significant elastic crustal response to present-day ice mass change. The new model provides approximately 2.7 m equivalent contribution to global sea-level rise, an increase of +0.6 m compared to the Innuitian portion of ICE-5G. SJD15 is qualitatively more similar to the recent ICE-6G ice sheet reconstruction, which appears to also include more spatially extensive ice cover in the Innuitian region than ICE-5G.
Soil-Related Input Parameters for the Biosphere Model
A. J. Smith
2004-09-09
This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure was defined as AP-SIII.9Q, ''Scientific Analyses''. This
Space geodetic techniques for global modeling of ionospheric peak parameters
NASA Astrophysics Data System (ADS)
Alizadeh, M. Mahdi; Schuh, Harald; Schmidt, Michael
The rapid development of new technological systems for navigation, telecommunication, and space missions which transmit signals through the Earth’s upper atmosphere - the ionosphere - makes the necessity of precise, reliable and near real-time models of the ionospheric parameters more crucial. In the last decades space geodetic techniques have turned into a capable tool for measuring ionospheric parameters in terms of Total Electron Content (TEC) or the electron density. Among these systems, the current space geodetic techniques, such as Global Navigation Satellite Systems (GNSS), Low Earth Orbiting (LEO) satellites, satellite altimetry missions, and others have found several applications in a broad range of commercial and scientific fields. This paper aims at the development of a three-dimensional integrated model of the ionosphere, by using various space geodetic techniques and applying a combination procedure for computation of the global model of electron density. In order to model ionosphere in 3D, electron density is represented as a function of maximum electron density (NmF2), and its corresponding height (hmF2). NmF2 and hmF2 are then modeled in longitude, latitude, and height using two sets of spherical harmonic expansions with degree and order 15. To perform the estimation, GNSS input data are simulated in such a way that the true position of the satellites are detected and used, but the STEC values are obtained through a simulation procedure, using the IGS VTEC maps. After simulating the input data, the a priori values required for the estimation procedure are calculated using the IRI-2012 model and also by applying the ray-tracing technique. The estimated results are compared with F2-peak parameters derived from the IRI model to assess the least-square estimation procedure and moreover, to validate the developed maps, the results are compared with the raw F2-peak parameters derived from the Formosat-3/Cosmic data.
Realistic uncertainties on Hapke model parameters from photometric measurement
NASA Astrophysics Data System (ADS)
Schmidt, Frédéric; Fernando, Jennifer
2015-11-01
The single particle phase function describes the manner in which an average element of a granular material diffuses the light in the angular space usually with two parameters: the asymmetry parameter b describing the width of the scattering lobe and the backscattering fraction c describing the main direction of the scattering lobe. Hapke proposed a convenient and widely used analytical model to describe the spectro-photometry of granular materials. Using a compilation of the published data, Hapke (Hapke, B. [2012]. Icarus 221, 1079-1083) recently studied the relationship of b and c for natural examples and proposed the hockey stick relation (excluding b > 0.5 and c > 0.5). For the moment, there is no theoretical explanation for this relationship. One goal of this article is to study a possible bias due to the retrieval method. We expand here an innovative Bayesian inversion method in order to study into detail the uncertainties of retrieved parameters. On Emission Phase Function (EPF) data, we demonstrate that the uncertainties of the retrieved parameters follow the same hockey stick relation, suggesting that this relation is due to the fact that b and c are coupled parameters in the Hapke model instead of a natural phenomena. Nevertheless, the data used in the Hapke (Hapke, B. [2012]. Icarus 221, 1079-1083) compilation generally are full Bidirectional Reflectance Diffusion Function (BRDF) that are shown not to be subject to this artifact. Moreover, the Bayesian method is a good tool to test if the sampling geometry is sufficient to constrain the parameters (single scattering albedo, surface roughness, b, c , opposition effect). We performed sensitivity tests by mimicking various surface scattering properties and various single image-like/disk resolved image, EPF-like and BRDF-like geometric sampling conditions. The second goal of this article is to estimate the favorable geometric conditions for an accurate estimation of photometric parameters in order to provide
Model and parameter uncertainty in IDF relationships under climate change
NASA Astrophysics Data System (ADS)
Chandra, Rupa; Saha, Ujjwal; Mujumdar, P. P.
2015-05-01
Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty.
NASA Astrophysics Data System (ADS)
Yang, M. F.
In this research we present a stylized model to find the optimal strategy for integrated vendor-buyer inventory model with fuzzy annual demand and fuzzy adjustable production rate. This model with such consideration is based on the total cost optimization under a common stock strategy. However, the supposition of known annual demand and adjustable production rate in most related publications may not be realistic. This paper proposes the triangular fuzzy number of annual demand and adjustable production rate and then employs the signed distance, to find the estimation of the common total cost in the fuzzy sense and derives the corresponding optimal buyer`s quantity consequently and the integer number of lots in which the items are delivered from the vendor to the purchaser. A numerical example is provided and the results of fuzzy and crisp models are compared.
NASA Astrophysics Data System (ADS)
Żyliński, Marek; Niewiadomski, Wiktor; Strasz, Anna; GÄ siorowska, Anna; Berka, Martin; Młyńczak, Marcel; Cybulski, Gerard
2015-09-01
The haemodynamics of the arterial system can be described by the three-elements Windkessel model. As it is a lumped model, it does not account for pulse wave propagation phenomena: pulse wave velocity, reflection, and pulse pressure profile changes during propagation. The Modelflowmethod uses this model to calculate stroke volume and total peripheral resistance (TPR) from pulse pressure obtained from finger; the reliability of this method is questioned. The model parameters are: aortic input impedance (Zo), TPR, and arterial compliance (Cw). They were obtained from studies of human aorta preparation. Individual adjustment is performed based on the subject's age and gender. As Cw is also affected by diseases, this may lead to inaccuracies. Moreover, the Modelflowmethod transforms the pulse pressure recording from the finger (Finapres©) into a remarkably different pulse pressure in the aorta using a predetermined transfer function — another source of error. In the present study, we indicate a way to include in the Windkessel model information obtained by adding carotid pulse recording to the finger pressure measurement. This information allows individualization of the values of Cw and Zo. It also seems reasonable to utilize carotid pulse, which better reflects aortic pressure, to individualize the transfer function. Despite its simplicity, the Windkessel model describes essential phenomena in the arterial system remarkably well; therefore, it seems worthwhile to check whether individualization of its parameters would increase the reliability of results obtained with this model.
Mass balance model parameter transferability on a tropical glacier
NASA Astrophysics Data System (ADS)
Gurgiser, Wolfgang; Mölg, Thomas; Nicholson, Lindsey; Kaser, Georg
2013-04-01
The mass balance and melt water production of glaciers is of particular interest in the Peruvian Andes where glacier melt water has markedly increased water supply during the pronounced dry seasons in recent decades. However, the melt water contribution from glaciers is projected to decrease with appreciable negative impacts on the local society within the coming decades. Understanding mass balance processes on tropical glaciers is a prerequisite for modeling present and future glacier runoff. As a first step towards this aim we applied a process-based surface mass balance model in order to calculate observed ablation at two stakes in the ablation zone of Shallap Glacier (4800 m a.s.l., 9°S) in the Cordillera Blanca, Peru. Under the tropical climate, the snow line migrates very frequently across most of the ablation zone all year round causing large temporal and spatial variations of glacier surface conditions and related ablation. Consequently, pronounced differences between the two chosen stakes and the two years were observed. Hourly records of temperature, humidity, wind speed, short wave incoming radiation, and precipitation are available from an automatic weather station (AWS) on the moraine near the glacier for the hydrological years 2006/07 and 2007/08 while stake readings are available at intervals of between 14 to 64 days. To optimize model parameters, we used 1000 model simulations in which the most sensitive model parameters were varied randomly within their physically meaningful ranges. The modeled surface height change was evaluated against the two stake locations in the lower ablation zone (SH11, 4760m) and in the upper ablation zone (SH22, 4816m), respectively. The optimal parameter set for each point achieved good model skill but if we transfer the best parameter combination from one stake site to the other stake site model errors increases significantly. The same happens if we optimize the model parameters for each year individually and transfer
Estimating demographic parameters using hidden process dynamic models.
Gimenez, Olivier; Lebreton, Jean-Dominique; Gaillard, Jean-Michel; Choquet, Rémi; Pradel, Roger
2012-12-01
Structured population models are widely used in plant and animal demographic studies to assess population dynamics. In matrix population models, populations are described with discrete classes of individuals (age, life history stage or size). To calibrate these models, longitudinal data are collected at the individual level to estimate demographic parameters. However, several sources of uncertainty can complicate parameter estimation, such as imperfect detection of individuals inherent to monitoring in the wild and uncertainty in assigning a state to an individual. Here, we show how recent statistical models can help overcome these issues. We focus on hidden process models that run two time series in parallel, one capturing the dynamics of the true states and the other consisting of observations arising from these underlying possibly unknown states. In a first case study, we illustrate hidden Markov models with an example of how to accommodate state uncertainty using Frequentist theory and maximum likelihood estimation. In a second case study, we illustrate state-space models with an example of how to estimate lifetime reproductive success despite imperfect detection, using a Bayesian framework and Markov Chain Monte Carlo simulation. Hidden process models are a promising tool as they allow population biologists to cope with process variation while simultaneously accounting for observation error. PMID:22373775
Comparison of Cone Model Parameters for Halo Coronal Mass Ejections
NASA Astrophysics Data System (ADS)
Na, Hyeonock; Moon, Y.-J.; Jang, Soojeong; Lee, Kyoung-Sun; Kim, Hae-Yeon
2013-11-01
Halo coronal mass ejections (HCMEs) are a major cause of geomagnetic storms, hence their three-dimensional structures are important for space weather. We compare three cone models: an elliptical-cone model, an ice-cream-cone model, and an asymmetric-cone model. These models allow us to determine three-dimensional parameters of HCMEs such as radial speed, angular width, and the angle [ γ] between sky plane and cone axis. We compare these parameters obtained from three models using 62 HCMEs observed by SOHO/LASCO from 2001 to 2002. Then we obtain the root-mean-square (RMS) error between the highest measured projection speeds and their calculated projection speeds from the cone models. As a result, we find that the radial speeds obtained from the models are well correlated with one another ( R > 0.8). The correlation coefficients between angular widths range from 0.1 to 0.48 and those between γ-values range from -0.08 to 0.47, which is much smaller than expected. The reason may be the different assumptions and methods. The RMS errors between the highest measured projection speeds and the highest estimated projection speeds of the elliptical-cone model, the ice-cream-cone model, and the asymmetric-cone model are 376 km s-1, 169 km s-1, and 152 km s-1. We obtain the correlation coefficients between the location from the models and the flare location ( R > 0.45). Finally, we discuss strengths and weaknesses of these models in terms of space-weather application.
Parameter identification in dynamical models of anaerobic waste water treatment.
Müller, T G; Noykova, N; Gyllenberg, M; Timmer, J
2002-01-01
Biochemical reactions can often be formulated mathematically as ordinary differential equations. In the process of modeling, the main questions that arise are concerned with structural identifiability, parameter estimation and practical identifiability. To clarify these questions and the methods how to solve them, we analyze two different second order models for anaerobic waste water treatment processes using two data sets obtained from different experimental setups. In both experiments only biogas production rate was measured which complicates the analysis considerably. We show that proving structural identifiability of the mathematical models with currently used methods fails. Therefore, we introduce a new, general method based on the asymptotic behavior of the maximum likelihood estimator to show local structural identifiability. For parameter estimation we use the multiple shooting approach which is described. Additionally we show that the Hessian matrix approach to compute confidence intervals fails in our examples while a method based on Monte Carlo Simulation works well. PMID:11965253
Multiple beam interference model for measuring parameters of a capillary.
Xu, Qiwei; Tian, Wenjing; You, Zhihong; Xiao, Jinghua
2015-08-01
A multiple beam interference model based on the ray tracing method and interference theory is built to analyze the interference patterns of a capillary tube filled with a liquid. The relations between the angular widths of the interference fringes and the parameters of both the capillary and liquid are derived. Based on these relations, an approach is proposed to simultaneously determine four parameters of the capillary, i.e., the inner and outer radii of the capillary, the refractive indices of the liquid, and the wall material. PMID:26368114
Inversion of canopy reflectance models for estimation of vegetation parameters
NASA Technical Reports Server (NTRS)
Goel, Narendra S.
1987-01-01
One of the keys to successful remote sensing of vegetation is to be able to estimate important agronomic parameters like leaf area index (LAI) and biomass (BM) from the bidirectional canopy reflectance (CR) data obtained by a space-shuttle or satellite borne sensor. One approach for such an estimation is through inversion of CR models which relate these parameters to CR. The feasibility of this approach was shown. The overall objective of the research carried out was to address heretofore uninvestigated but important fundamental issues, develop the inversion technique further, and delineate its strengths and limitations.
Order parameter in complex dipolar structures: Microscopic modeling
NASA Astrophysics Data System (ADS)
Prosandeev, S.; Bellaiche, L.
2008-02-01
Microscopic models have been used to reveal the existence of an order parameter that is associated with many complex dipolar structures in magnets and ferroelectrics. This order parameter involves a double cross product of the local dipoles with their positions. It provides a measure of subtle microscopic features, such as the helicity of the two domains inherent to onion states, curvature of the dipolar pattern in flower states, or characteristics of sets of vortices with opposite chirality (e.g., distance between the vortex centers and/or the magnitude of their local dipoles).
Considering Measurement Model Parameter Errors in Static and Dynamic Systems
NASA Astrophysics Data System (ADS)
Woodbury, Drew P.; Majji, Manoranjan; Junkins, John L.
2011-07-01
In static systems, state values are estimated using traditional least squares techniques based on a redundant set of measurements. Inaccuracies in measurement model parameter estimates can lead to significant errors in the state estimates. This paper describes a technique that considers these parameters in a modified least squares framework. It is also shown that this framework leads to the minimum variance solution. Both batch and sequential (recursive) least squares methods are described. One static system and one dynamic system are used as examples to show the benefits of the consider least squares methodology.
Ziadinov, I.; Mathis, A.; Trachsel, D.; Rysmukhambetova, A.; Abdyjaparov, T. A.; Kuttubaev, O. T.; Deplazes, P.; Torgerson, P. R.
2008-01-01
Echinococcosis is a major emerging zoonosis in central Asia. A cross-sectional study of dogs in four villages in rural Kyrgyzstan was undertaken to investigate the epidemiology and transmission of Echinococcus spp. A total of 466 dogs were examined by arecoline purgation for the presence of Echinococcus granulosus and Echinococcus multilocularis. In addition, a faecal sample from each dog was examined for taeniid eggs. Any taeniid eggs found were investigated using PCR techniques (multiplex and single target PCR) to improve the diagnostic sensitivity by confirming the presence of Echinococcus spp. and to identify E. granulosus strains. A total of 83 (18%) dogs had either E. granulosus adults in purge material and/or E. granulosus eggs in their faeces as confirmed by PCR. Three genotypes of E. granulosus: G1, G4 and the G6/7 complex were shown to be present in these dogs through subsequent sequence analysis. Purge analysis combined with PCR identified 50 dogs that were infected with adult E. multilocularis and/or had E. multilocularis eggs in their faeces (11%). Bayesian techniques were employed to estimate the true prevalence, the diagnostic sensitivity and specificity of the procedures used and the transmission parameters. The sensitivity of arecoline purgation for the detection of echinococcosis in dogs was rather low, with a value of 38% (Credible intervals (CIs) 27–50%) for E. granulosus and 21% (CIs 11–34%) for E. multilocularis. The specificity of arecoline purgation was assumed to be 100%. The sensitivity of coproscopy followed by PCR of the isolated eggs was calculated as 78% (CIs 57–87%) for E. granulosus and 50% (CIs 29–72%) for E. multilocularis with specificity of 93% (CIs 88–96%) and 100% (CIs 97–100), respectively. The 93% specificity of the coprological-PCR for E. granulosus could suggest coprophagia rather than true infections. After adjusting for the sensitivity of the diagnostic procedures, the estimated true prevalence of infection of
Modelling the cut-off resolution parameter in the PANS method for turbulence simulation
NASA Astrophysics Data System (ADS)
Basara, Branislav; Hanjalic, Kemal
2014-11-01
The Partially-Averaged Navier-Stokes (PANS) approach, designed to resolve a part of the turbulence spectrum, adjusts seamlessly from the Reynolds-Averaged Navier-Stokes (RANS) to the Direct Numerical Solution (DNS) of the Navier-Stokes equations. This turbulence closure, derived from a RANS model, supports any filter width or scale resolution. We choose the PANS model as the basis for the present analysis of options for the model resolution parameter, but the conclusions derived are applicable to other partially resolved closure methods. Namely, in the conventional well-established PANS, the resolution parameter is obtained from the grid spacing and the integral turbulence length scale. The latter is obtained usually by summing up the resolved turbulence, while the unresolved motion is computed from the modelled equation. Several formulations have been shown to provide reliable and accurate results for many test flows. However, serious impediments have been noted in some applications such as moving domains and transient boundaries because too long calculations of the average velocity make this approach impractical. We analysed some recent alternative approaches which use the turbulent-to-mean-strain-rate time scale aimed at avoiding the on-line calculations of the resolved kinetic energy required for calculations of the input resolution parameter. Comparisons of several approaches will be shown in detail and conclusions drawn on the merits of each method.
NASA Technical Reports Server (NTRS)
Luthcke, S. B.; Marshall, J. A.
1992-01-01
The TOPEX/Poseidon spacecraft was launched on August 10, 1992 to study the Earth's oceans. To achieve maximum benefit from the altimetric data it is to collect, mission requirements dictate that TOPEX/Poseidon's orbit must be computed at an unprecedented level of accuracy. To reach our pre-launch radial orbit accuracy goals, the mismodeling of the radiative nonconservative forces of solar radiation, Earth albedo an infrared re-radiation, and spacecraft thermal imbalances cannot produce in combination more than a 6 cm rms error over a 10 day period. Similarly, the 10-day drag modeling error cannot exceed 3 cm rms. In order to satisfy these requirements, a 'box-wing' representation of the satellite has been developed in which, the satellite is modelled as the combination of flat plates arranged in the shape of a box and a connected solar array. The radiative/thermal nonconservative forces acting on each of the eight surfaces are computed independently, yielding vector accelerations which are summed to compute the total aggregate effect on the satellite center-of-mass. Select parameters associated with the flat plates are adjusted to obtain a better representation of the satellite acceleration history. This study analyzes the estimation of these parameters from simulated TOPEX/Poseidon laser data in the presence of both nonconservative and gravity model errors. A 'best choice' of estimated parameters is derived and the ability to meet mission requirements with the 'box-wing' model evaluated.
NASA Astrophysics Data System (ADS)
Root, Bart; Tarasov, Lev; van der Wal, Wouter
2014-05-01
The global ice budget is still under discussion because the observed 120-130 m eustatic sea level equivalent since the Last Glacial Maximum (LGM) can not be explained by the current knowledge of land-ice melt after the LGM. One possible location for the missing ice is the Barents Sea Region, which was completely covered with ice during the LGM. This is deduced from relative sea level observations on Svalbard, Novaya Zemlya and the North coast of Scandinavia. However, there are no observations in the middle of the Barents Sea that capture the post-glacial uplift. With increased precision and longer time series of monthly gravity observations of the GRACE satellite mission it is possible to constrain Glacial Isostatic Adjustment in the center of the Barents Sea. This study investigates the extra constraint provided by GRACE data for modeling the past ice geometry in the Barents Sea. We use CSR release 5 data from February 2003 to July 2013. The GRACE data is corrected for the past 10 years of secular decline of glacier ice on Svalbard, Novaya Zemlya and Frans Joseph Land. With numerical GIA models for a radially symmetric Earth, we model the expected gravity changes and compare these with the GRACE observations after smoothing with a 250 km Gaussian filter. The comparisons show that for the viscosity profile VM5a, ICE-5G has too strong a gravity signal compared to GRACE. The regional calibrated ice sheet model (GLAC) of Tarasov appears to fit the amplitude of the GRACE signal. However, the GRACE data are very sensitive to the ice-melt correction, especially for Novaya Zemlya. Furthermore, the ice mass should be more concentrated to the middle of the Barents Sea. Alternative viscosity models confirm these conclusions.
Pursuing parameters for critical-density dark matter models
NASA Astrophysics Data System (ADS)
Liddle, Andrew R.; Lyth, David H.; Schaefer, R. K.; Shafi, Q.; Viana, Pedro T. P.
1996-07-01
We present an extensive comparison of models of structure formation with observations, based on linear and quasi-linear theory. We assume a critical matter density, and study both cold dark matter models and cold plus hot dark matter models. We explore a wide range of parameters, by varying the fraction of hot dark matter , the Hubble parameter h and the spectral index of density perturbations n, and allowing for the possibility of gravitational waves from inflation influencing large-angle microwave background anisotropies. New calculations are made of the transfer functions describing the linear power spectrum, with special emphasis on improving the accuracy on short scales where there are strong constraints. For assessing early object formation, the transfer functions are explicitly evaluated at the appropriate redshift. The observations considered are the four-year COBE observations of microwave background anisotropies, peculiar velocity flows, the galaxy correlation function, and the abundances of galaxy clusters, quasars and damped Lyman alpha systems. Each observation is interpreted in terms of the power spectrum filtered by a top-hat window function. We find that there remains a viable region of parameter space for critical-density models when all the dark matter is cold, though h must be less than 0.5 before any fit is found and n significantly below unity is preferred. Once a hot dark matter component is invoked, a wide parameter space is acceptable, including n 1. The allowed region is characterized by 0.35 and 0.60 n 1.25, at 95 per cent confidence on at least one piece of data. There is no useful lower bound on h, and for curious combinations of the other parameters it is possible to fit the data with h as high as 0.65.
Yan, Ying; Yi, Grace Y
2016-07-01
Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods. PMID:26328545
Test models for improving filtering with model errors through stochastic parameter estimation
Gershgorin, B.; Harlim, J. Majda, A.J.
2010-01-01
The filtering skill for turbulent signals from nature is often limited by model errors created by utilizing an imperfect model for filtering. Updating the parameters in the imperfect model through stochastic parameter estimation is one way to increase filtering skill and model performance. Here a suite of stringent test models for filtering with stochastic parameter estimation is developed based on the Stochastic Parameterization Extended Kalman Filter (SPEKF). These new SPEKF-algorithms systematically correct both multiplicative and additive biases and involve exact formulas for propagating the mean and covariance including the parameters in the test model. A comprehensive study is presented of robust parameter regimes for increasing filtering skill through stochastic parameter estimation for turbulent signals as the observation time and observation noise are varied and even when the forcing is incorrectly specified. The results here provide useful guidelines for filtering turbulent signals in more complex systems with significant model errors.
Four-dimensional data assimilation applied to photochemical air quality modeling is used to suggest adjustments to the emissions inventory of the Atlanta, Georgia metropolitan area. In this approach, a three-dimensional air quality model, coupled with direct sensitivity analys...
Linear parameter varying battery model identification using subspace methods
NASA Astrophysics Data System (ADS)
Hu, Y.; Yurkovich, S.
2011-03-01
The advent of hybrid and plug-in hybrid electric vehicles has created a demand for more precise battery pack management systems (BMS). Among methods used to design various components of a BMS, such as state-of-charge (SoC) estimators, model based approaches offer a good balance between accuracy, calibration effort and implementability. Because models used for these approaches are typically low in order and complexity, the traditional approach is to identify linear (or slightly nonlinear) models that are scheduled based on operating conditions. These models, formally known as linear parameter varying (LPV) models, tend to be difficult to identify because they contain a large amount of coefficients that require calibration. Consequently, the model identification process can be very laborious and time-intensive. This paper describes a comprehensive identification algorithm that uses linear-algebra-based subspace methods to identify a parameter varying state variable model that can describe the input-to-output dynamics of a battery under various operating conditions. Compared with previous methods, this approach is much faster and provides the user with information on the order of the system without placing an a priori structure on the system matrices. The entire process and various nuances are demonstrated using data collected from a lithium ion battery, and the focus is on applications for energy storage in automotive applications.
Unrealistic parameter estimates in inverse modelling: A problem or a benefit for model calibration?
Poeter, E.P.; Hill, M.C.
1996-01-01
Estimation of unrealistic parameter values by inverse modelling is useful for constructed model discrimination. This utility is demonstrated using the three-dimensional, groundwater flow inverse model MODFLOWP to estimate parameters in a simple synthetic model where the true conditions and character of the errors are completely known. When a poorly constructed model is used, unreasonable parameter values are obtained even when using error free observations and true initial parameter values. This apparent problem is actually a benefit because it differentiates accurately and inaccurately constructed models. The problems seem obvious for a synthetic problem in which the truth is known, but are obscure when working with field data. Situations in which unrealistic parameter estimates indicate constructed model problems are illustrated in applications of inverse modelling to three field sites and to complex synthetic test cases in which it is shown that prediction accuracy also suffers when constructed models are inaccurate.
Soares, Ana Paula; Guisande, M Adelina; Diniz, António M; Almeida, Leandro S
2006-05-01
This article presents a model of interaction of personal and contextual variables in the prediction of academic performance and psychosocial development of Portuguese college students. The sample consists of 560 first-year college students of the University of Minho. The path analysis results suggest that initial expectations of the students' involvement in academic life constituted an effective predictor of their involvement during their first year; as well as the social climate of the classroom influenced their involvement, well-being and levels of satisfaction obtained. However, these relationships were not strong enough to influence the criterion variables integrated in the model (academic performance and psychosocial development). Academic performance was predicted by the high school grades and college entrance examination scores, and the level of psychosocial development was determined by the level of development showed at the time they entered college. Though more research is needed, these results point to the importance of students' pre-college characteristics when we are considering the quality of their college adjustment process. PMID:17296040
Dynamic imaging model and parameter optimization for a star tracker.
Yan, Jinyun; Jiang, Jie; Zhang, Guangjun
2016-03-21
Under dynamic conditions, star spots move across the image plane of a star tracker and form a smeared star image. This smearing effect increases errors in star position estimation and degrades attitude accuracy. First, an analytical energy distribution model of a smeared star spot is established based on a line segment spread function because the dynamic imaging process of a star tracker is equivalent to the static imaging process of linear light sources. The proposed model, which has a clear physical meaning, explicitly reflects the key parameters of the imaging process, including incident flux, exposure time, velocity of a star spot in an image plane, and Gaussian radius. Furthermore, an analytical expression of the centroiding error of the smeared star spot is derived using the proposed model. An accurate and comprehensive evaluation of centroiding accuracy is obtained based on the expression. Moreover, analytical solutions of the optimal parameters are derived to achieve the best performance in centroid estimation. Finally, we perform numerical simulations and a night sky experiment to validate the correctness of the dynamic imaging model, the centroiding error expression, and the optimal parameters. PMID:27136791
Modeling crash spatial heterogeneity: random parameter versus geographically weighting.
Xu, Pengpeng; Huang, Helai
2015-02-01
The widely adopted techniques for regional crash modeling include the negative binomial model (NB) and Bayesian negative binomial model with conditional autoregressive prior (CAR). The outputs from both models consist of a set of fixed global parameter estimates. However, the impacts of predicting variables on crash counts might not be stationary over space. This study intended to quantitatively investigate this spatial heterogeneity in regional safety modeling using two advanced approaches, i.e., random parameter negative binomial model (RPNB) and semi-parametric geographically weighted Poisson regression model (S-GWPR). Based on a 3-year data set from the county of Hillsborough, Florida, results revealed that (1) both RPNB and S-GWPR successfully capture the spatially varying relationship, but the two methods yield notably different sets of results; (2) the S-GWPR performs best with the highest value of Rd(2) as well as the lowest mean absolute deviance and Akaike information criterion measures. Whereas the RPNB is comparable to the CAR, in some cases, it provides less accurate predictions; (3) a moderately significant spatial correlation is found in the residuals of RPNB and NB, implying the inadequacy in accounting for the spatial correlation existed across adjacent zones. As crash data are typically collected with reference to location dimension, it is desirable to firstly make use of the geographical component to explore explicitly spatial aspects of the crash data (i.e., the spatial heterogeneity, or the spatially structured varying relationships), then is the unobserved heterogeneity by non-spatial or fuzzy techniques. The S-GWPR is proven to be more appropriate for regional crash modeling as the method outperforms the global models in capturing the spatial heterogeneity occurring in the relationship that is model, and compared with the non-spatial model, it is capable of accounting for the spatial correlation in crash data. PMID:25460087
Enhancing multiple-point geostatistical modeling: 1. Graph theory and pattern adjustment
NASA Astrophysics Data System (ADS)
Tahmasebi, Pejman; Sahimi, Muhammad
2016-03-01
In recent years, higher-order geostatistical methods have been used for modeling of a wide variety of large-scale porous media, such as groundwater aquifers and oil reservoirs. Their popularity stems from their ability to account for qualitative data and the great flexibility that they offer for conditioning the models to hard (quantitative) data, which endow them with the capability for generating realistic realizations of porous formations with very complex channels, as well as features that are mainly a barrier to fluid flow. One group of such models consists of pattern-based methods that use a set of data points for generating stochastic realizations by which the large-scale structure and highly-connected features are reproduced accurately. The cross correlation-based simulation (CCSIM) algorithm, proposed previously by the authors, is a member of this group that has been shown to be capable of simulating multimillion cell models in a matter of a few CPU seconds. The method is, however, sensitive to pattern's specifications, such as boundaries and the number of replicates. In this paper the original CCSIM algorithm is reconsidered and two significant improvements are proposed for accurately reproducing large-scale patterns of heterogeneities in porous media. First, an effective boundary-correction method based on the graph theory is presented by which one identifies the optimal cutting path/surface for removing the patchiness and discontinuities in the realization of a porous medium. Next, a new pattern adjustment method is proposed that automatically transfers the features in a pattern to one that seamlessly matches the surrounding patterns. The original CCSIM algorithm is then combined with the two methods and is tested using various complex two- and three-dimensional examples. It should, however, be emphasized that the methods that we propose in this paper are applicable to other pattern-based geostatistical simulation methods.
Optimizing Parameters of Process-Based Terrestrial Ecosystem Model with Particle Filter
NASA Astrophysics Data System (ADS)
Ito, A.
2014-12-01
Present terrestrial ecosystem models still contain substantial uncertainties, as model intercomparison studies have shown, because of poor model constraint by observational data. So, development of advanced methodology of data-model fusion, or data-assimilation, is an important task to reduce the uncertainties and improve model predictability. In this study, I apply the Particle filter (or Sequential Monte Carlo filer) to optimize parameters of a process-based terrestrial ecosystem model (VISIT). The Particle filter is one of the data-assimilation methods, in which probability distribution of model state is approximated by many samples of parameter set (i.e., particle). This is a computationally intensive method and applicable to nonlinear systems; this is an advantage of the method in comparison with other techniques like Ensemble Kalman filter and variational method. At several sites, I used flux measurement data of atmosphere-ecosystem CO2 exchange in sequential and non-sequential manners. In the sequential data assimilation, a time-series data at 30-min or daily steps were used to optimize gas-exchange-related parameters; this method would be also effective to assimilate satellite observational data. On the other hand, in the non-sequential case, annual or long-term mean budget was adjusted to observations; this method would be also effective to assimilate carbon stock data. Although there remain technical issues (e.g., appropriate number of particles and likelihood function), I demonstrate that the Partile filter is an effective method of data-assimilation for process-based models, enhancing collaboration between field and model researchers.
Estimation of Time-Varying Pilot Model Parameters
NASA Technical Reports Server (NTRS)
Zaal, Peter M. T.; Sweet, Barbara T.
2011-01-01
Human control behavior is rarely completely stationary over time due to fatigue or loss of attention. In addition, there are many control tasks for which human operators need to adapt their control strategy to vehicle dynamics that vary in time. In previous studies on the identification of time-varying pilot control behavior wavelets were used to estimate the time-varying frequency response functions. However, the estimation of time-varying pilot model parameters was not considered. Estimating these parameters can be a valuable tool for the quantification of different aspects of human time-varying manual control. This paper presents two methods for the estimation of time-varying pilot model parameters, a two-step method using wavelets and a windowed maximum likelihood estimation method. The methods are evaluated using simulations of a closed-loop control task with time-varying pilot equalization and vehicle dynamics. Simulations are performed with and without remnant. Both methods give accurate results when no pilot remnant is present. The wavelet transform is very sensitive to measurement noise, resulting in inaccurate parameter estimates when considerable pilot remnant is present. Maximum likelihood estimation is less sensitive to pilot remnant, but cannot detect fast changes in pilot control behavior.
HOM study and parameter calculation of the TESLA cavity model
NASA Astrophysics Data System (ADS)
Zeng, Ri-Hua; Schuh, Marcel; Gerigk, Frank; Wegner, Rolf; Pan, Wei-Min; Wang, Guang-Wei; Liu, Rong
2010-01-01
The Superconducting Proton Linac (SPL) is the project for a superconducting, high current H-accelerator at CERN. To find dangerous higher order modes (HOMs) in the SPL superconducting cavities, simulation and analysis for the cavity model using simulation tools are necessary. The existing TESLA 9-cell cavity geometry data have been used for the initial construction of the models in HFSS. Monopole, dipole and quadrupole modes have been obtained by applying different symmetry boundaries on various cavity models. In calculation, scripting language in HFSS was used to create scripts to automatically calculate the parameters of modes in these cavity models (these scripts are also available in other cavities with different cell numbers and geometric structures). The results calculated automatically are then compared with the values given in the TESLA paper. The optimized cavity model with the minimum error will be taken as the base for further simulation of the SPL cavities.
Neural mass model parameter identification for MEG/EEG
NASA Astrophysics Data System (ADS)
Kybic, Jan; Faugeras, Olivier; Clerc, Maureen; Papadopoulo, Théo
2007-03-01
Electroencephalography (EEG) and magnetoencephalography (MEG) have excellent time resolution. However, the poor spatial resolution and small number of sensors do not permit to reconstruct a general spatial activation pattern. Moreover, the low signal to noise ratio (SNR) makes accurate reconstruction of a time course also challenging. We therefore propose to use constrained reconstruction, modeling the relevant part of the brain using a neural mass model: There is a small number of zones that are considered as entities, neurons within a zone are assumed to be activated simultaneously. The location and spatial extend of the zones as well as the interzonal connection pattern can be determined from functional MRI (fMRI), diffusion tensor MRI (DTMRI), and other anatomical and brain mapping observation techniques. The observation model is linear, its deterministic part is known from EEG/MEG forward modeling, the statistics of the stochastic part can be estimated. The dynamics of the neural model is described by a moderate number of parameters that can be estimated from the recorded EEG/MEG data. We explicitly model the long-distance communication delays. Our parameters have physiological meaning and their plausible range is known. Since the problem is highly nonlinear, a quasi-Newton optimization method with random sampling and automatic success evaluation is used. The actual connection topology can be identified from several possibilities. The method was tested on synthetic data as well as on true MEG somatosensory-evoked field (SEF) data.
Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J
2014-01-01
Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model. PMID:24989866
Empirical flow parameters : a tool for hydraulic model validity
Asquith, William H.; Burley, Thomas E.; Cleveland, Theodore G.
2013-01-01
The objectives of this project were (1) To determine and present from existing data in Texas, relations between observed stream flow, topographic slope, mean section velocity, and other hydraulic factors, to produce charts such as Figure 1 and to produce empirical distributions of the various flow parameters to provide a methodology to "check if model results are way off!"; (2) To produce a statistical regional tool to estimate mean velocity or other selected parameters for storm flows or other conditional discharges at ungauged locations (most bridge crossings) in Texas to provide a secondary way to compare such values to a conventional hydraulic modeling approach. (3.) To present ancillary values such as Froude number, stream power, Rosgen channel classification, sinuosity, and other selected characteristics (readily determinable from existing data) to provide additional information to engineers concerned with the hydraulic-soil-foundation component of transportation infrastructure.
Glacial isostatic adjustment in Fennoscandia from GRACE data and comparison with geodynamical models
NASA Astrophysics Data System (ADS)
Steffen, Holger; Denker, Heiner; Müller, Jürgen
2008-10-01
The Earth's gravity field observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission shows variations due to the integral effect of mass variations in the atmosphere, hydrosphere and geosphere. Several institutions, such as the GeoForschungsZentrum (GFZ) Potsdam, the University of Texas at Austin, Center for Space Research (CSR) and the Jet Propulsion Laboratory (JPL), Pasadena, provide GRACE monthly solutions, which differ slightly due to the application of different reduction models and centre-specific processing schemes. The GRACE data are used to investigate the mass variations in Fennoscandia, an area which is strongly influenced by glacial isostatic adjustment (GIA). Hence the focus is set on the computation of secular trends. Different filters (e.g. isotropic and non-isotropic filters) are discussed for the removal of high frequency noise to permit the extraction of the GIA signal. The resulting GRACE based mass variations are compared to global hydrology models (WGHM, LaDWorld) in order to (a) separate possible hydrological signals and (b) validate the hydrology models with regard to long period and secular components. In addition, a pattern matching algorithm is applied to localise the uplift centre, and finally the GRACE signal is compared with the results from a geodynamical modelling. The GRACE data clearly show temporal gravity variations in Fennoscandia. The secular variations are in good agreement with former studies and other independent data. The uplift centre is located over the Bothnian Bay, and the whole uplift area comprises the Scandinavian Peninsula and Finland. The secular variations derived from the GFZ, CSR and JPL monthly solutions differ up to 20%, which is not statistically significant, and the largest signal of about 1.2 μGal/year is obtained from the GFZ solution. Besides the GIA signal, two peaks with positive trend values of about 0.8 μGal/year exist in central eastern Europe, which are not GIA-induced, and
Automated parameter estimation for biological models using Bayesian statistical model checking
2015-01-01
Background Probabilistic models have gained widespread acceptance in the systems biology community as a useful way to represent complex biological systems. Such models are developed using existing knowledge of the structure and dynamics of the system, experimental observations, and inferences drawn from statistical analysis of empirical data. A key bottleneck in building such models is that some system variables cannot be measured experimentally. These variables are incorporated into the model as numerical parameters. Determining values of these parameters that justify existing experiments and provide reliable predictions when model simulations are performed is a key research problem. Domain experts usually estimate the values of these parameters by fitting the model to experimental data. Model fitting is usually expressed as an optimization problem that requires minimizing a cost-function which measures some notion of distance between the model and the data. This optimization problem is often solved by combining local and global search methods that tend to perform well for the specific application domain. When some prior information about parameters is available, methods such as Bayesian inference are commonly used for parameter learning. Choosing the appropriate parameter search technique requires detailed domain knowledge and insight into the underlying system. Results Using an agent-based model of the dynamics of acute inflammation, we demonstrate a novel parameter estimation algorithm by discovering the amount and schedule of doses of bacterial lipopolysaccharide that guarantee a set of observed clinical outcomes with high probability. We synthesized values of twenty-eight unknown parameters such that the parameterized model instantiated with these parameter values satisfies four specifications describing the dynamic behavior of the model. Conclusions We have developed a new algorithmic technique for discovering parameters in complex stochastic models of
NASA Astrophysics Data System (ADS)
Menna, F.; Nocerino, E.; Troisi, S.; Remondino, F.
2015-04-01
The surveying and 3D modelling of objects that extend both below and above the water level, such as ships, harbour structures, offshore platforms, are still an open issue. Commonly, a combined and simultaneous survey is the adopted solution, with acoustic/optical sensors respectively in underwater and in air (most common) or optical/optical sensors both below and above the water level. In both cases, the system must be calibrated and a ship is to be used and properly equipped with also a navigation system for the alignment of sequential 3D point clouds. Such a system is usually highly expensive and has been proved to work with still structures. On the other hand for free floating objects it does not provide a very practical solution. In this contribution, a flexible, low-cost alternative for surveying floating objects is presented. The method is essentially based on photogrammetry, employed for surveying and modelling both the emerged and submerged parts of the object. Special targets, named Orientation Devices, are specifically designed and adopted for the successive alignment of the two photogrammetric models (underwater and in air). A typical scenario where the proposed procedure can be particularly suitable and effective is the case of a ship after an accident whose damaged part is underwater and necessitate to be measured (Figure 1). The details of the mathematical procedure are provided in the paper, together with a critical explanation of the results obtained from the adoption of the method for the survey of a small pleasure boat in floating condition.
An assessment of the ICE6G_C(VM5a) glacial isostatic adjustment model
NASA Astrophysics Data System (ADS)
Purcell, A.; Tregoning, P.; Dehecq, A.
2016-05-01
The recent release of the next-generation global ice history model, ICE6G_C(VM5a), is likely to be of interest to a wide range of disciplines including oceanography (sea level studies), space gravity (mass balance studies), glaciology, and, of course, geodynamics (Earth rheology studies). In this paper we make an assessment of some aspects of the ICE6G_C(VM5a) model and show that the published present-day radial uplift rates are too high along the eastern side of the Antarctic Peninsula (by ˜8.6 mm/yr) and beneath the Ross Ice Shelf (by ˜5 mm/yr). Furthermore, the published spherical harmonic coefficients—which are meant to represent the dimensionless present-day changes due to glacial isostatic adjustment (GIA)—contain excessive power for degree ≥90, do not agree with physical expectations and do not represent accurately the ICE6G_C(VM5a) model. We show that the excessive power in the high-degree terms produces erroneous uplift rates when the empirical relationship of Purcell et al. (2011) is applied, but when correct Stokes coefficients are used, the empirical relationship produces excellent agreement with the fully rigorous computation of the radial velocity field, subject to the caveats first noted by Purcell et al. (2011). Using the Australian National University (ANU) groups CALSEA software package, we recompute the present-day GIA signal for the ice thickness history and Earth rheology used by Peltier et al. (2015) and provide dimensionless Stokes coefficients that can be used to correct satellite altimetry observations for GIA over oceans and by the space gravity community to separate GIA and present-day mass balance change signals. We denote the new data sets as ICE6G_ANU.
NASA Astrophysics Data System (ADS)
Sim, Minseob; Park, Hyunbin; Kim, Shiho
2015-11-01
We have presented both modeling and a method for extracting parasitic thermal conductance as well as intrinsic device parameters of a thermoelectric module based on information readily available in vendor datasheets. An equivalent circuit model that is compatible with circuit simulators is derived, followed by a methodology for extracting both intrinsic and parasitic model parameters. For the first time, the effective thermal resistance of the ceramic and copper interconnect layers of the thermoelectric module is extracted using only parameters listed in vendor datasheets. In the experimental condition, including under condition of varying electric current, the parameters extracted from the model accurately reproduce the performance of commercial thermoelectric modules.
Assessment of an adjustment factor to model radar range dependent error
NASA Astrophysics Data System (ADS)
Sebastianelli, S.; Russo, F.; Napolitano, F.; Baldini, L.
2012-09-01
Quantitative radar precipitation estimates are affected by errors determined by many causes such as radar miscalibration, range degradation, attenuation, ground clutter, variability of Z-R relation, variability of drop size distribution, vertical air motion, anomalous propagation and beam-blocking. Range degradation (including beam broadening and sampling of precipitation at an increasing altitude)and signal attenuation, determine a range dependent behavior of error. The aim of this work is to model the range-dependent error through an adjustment factor derived from the G/R ratio trend against the range, where G and R are the corresponding rain gauge and radar rainfall amounts computed at each rain gauge location. Since range degradation and signal attenuation effects are negligible close to the radar, resultsshowthatwithin 40 km from radar the overall range error is independent of the distance from Polar 55C and no range-correction is needed. Nevertheless, up to this distance, the G/R ratiocan showa concave trend with the range, which is due to the melting layer interception by the radar beam during stratiform events.
Comparison of Two Foreign Body Retrieval Devices with Adjustable Loops in a Swine Model
Konya, Andras
2006-12-15
The purpose of the study was to compare two similar foreign body retrieval devices, the Texan{sup TM} (TX) and the Texan LONGhorn{sup TM} (TX-LG), in a swine model. Both devices feature a {<=}30-mm adjustable loop. Capture times and total procedure times for retrieving foreign bodies from the infrarenal aorta, inferior vena cava, and stomach were compared. All attempts with both devices (TX, n = 15; TX-LG, n = 14) were successful. Foreign bodies in the vasculature were captured quickly using both devices (mean {+-} SD, 88 {+-} 106 sec for TX vs 67 {+-} 42 sec for TX-LG) with no significant difference between them. The TX-LG, however, allowed significantly better capture times than the TX in the stomach (p = 0.022), Overall, capture times for the TX-LG were significantly better than for the TX (p = 0.029). There was no significant difference between the total procedure times in any anatomic region. TX-LG performed significantly better than the TX in the stomach and therefore overall. The better torque control and maneuverability of TX-LG resulted in better performance in large anatomic spaces.
Determination of structure parameters in molecular tunnelling ionisation model
NASA Astrophysics Data System (ADS)
Wang, Jun-Ping; Zhao, Song-Feng; Zhang, Cai-Rong; Li, Wei; Zhou, Xiao-Xin
2014-04-01
We extracted the accurate structure parameters in a molecular tunnelling ionisation model (the so-called MO-ADK model) for 23 selected linear molecules including some inner orbitals. The molecular wave functions with the correct asymptotic behaviour are obtained by solving the time-independent Schrödinger equation with B-spline functions and molecular potentials numerically constructed using the modified Leeuwen-Baerends (LBα) model. We show that the orientation-dependent ionisation rate reflects the shape of the ionising orbitals in general. The influences of the Stark shifts of the energy levels on the orientation-dependent ionisation rates of the polar molecules are studied. We also examine the angle-dependent ionisation rates (or probabilities) based on the MO-ADK model by comparing with the molecular strong-field approximation calculations and with recent experimental measurements.
Parameter Estimation in a Delay Differential Model of ENSO
NASA Astrophysics Data System (ADS)
Roux, J.; Gerchinovitz, S.; Ghil, M.
2009-04-01
In this talk, we present very generic statistical methods to perform parameter estimation in a Delay Differential Equation. Our reference DDE is the toy model of El Nino/Southern Oscillation introduced by Ghil, Zaliapin and Thompson (2008). We first recall some properties of this model in comparison with other models, together with basic results in Functional Differential Equation theory. We then briefly describe two statistical estimation procedures (the very classic Ordinary Least Squares estimator computed via simulated annealing, and a new two stage method based on nonparametric regression using the Nadaraya-Watson kernel). We finally comment on the numerical tests we performed on simulated noised data. These results encourage further application of this kind of methods to more complex (and more realistic) models of ENSO, to other problems in the Geosciences or to other fields.
NASA Astrophysics Data System (ADS)
Pankoke, S.; Buck, B.; Woelfel, H. P.
1998-08-01
Long-term whole-body vibrations can cause degeneration of the lumbar spine. Therefore existing degeneration has to be assessed as well as industrial working places to prevent further damage. Hence, the mechanical stress in the lumbar spine—especially in the three lower vertebrae—has to be known. This stress can be expressed as internal forces. These internal forces cannot be evaluated experimentally, because force transducers cannot be implementated in the force lines because of ethical reasons. Thus it is necessary to calculate the internal forces with a dynamic mathematical model of sitting man.A two dimensional dynamic Finite Element model of sitting man is presented which allows calculation of these unknown internal forces. The model is based on an anatomic representation of the lower lumbar spine (L3-L5). This lumber spine model is incorporated into a dynamic model of the upper torso with neck, head and arms as well as a model of the body caudal to the lumbar spine with pelvis and legs. Additionally a simple dynamic representation of the viscera is used. All these parts are modelled as rigid bodies connected by linear stiffnesses. Energy dissipation is modelled by assigning modal damping ratio to the calculated undamped eigenvalues. Geometry and inertial properties of the model are determined according to human anatomy. Stiffnesses of the spine model are derived from static in-vitro experiments in references [1] and [2]. Remaining stiffness parameters and parameters for energy dissipation are determined by using parameter identification to fit measurements in reference [3]. The model, which is available in 3 different postures, allows one to adjust its parameters for body height and body mass to the values of the person for which internal forces have to be calculated.
NASA Astrophysics Data System (ADS)
Norton, P. A., II
2015-12-01
The U. S. Geological Survey is developing a National Hydrologic Model (NHM) to support consistent hydrologic modeling across the conterminous United States (CONUS). The Precipitation-Runoff Modeling System (PRMS) simulates daily hydrologic and energy processes in watersheds, and is used for the NHM application. For PRMS each watershed is divided into hydrologic response units (HRUs); by default each HRU is assumed to have a uniform hydrologic response. The Geospatial Fabric (GF) is a database containing initial parameter values for input to PRMS and was created for the NHM. The parameter values in the GF were derived from datasets that characterize the physical features of the entire CONUS. The NHM application is composed of more than 100,000 HRUs from the GF. Selected parameter values commonly are adjusted by basin in PRMS using an automated calibration process based on calibration targets, such as streamflow. Providing each HRU with distinct values that captures variability within the CONUS may improve simulation performance of the NHM. During calibration of the NHM by HRU, selected parameter values are adjusted for PRMS based on calibration targets, such as streamflow, snow water equivalent (SWE) and actual evapotranspiration (AET). Simulated SWE, AET, and runoff were compared to value ranges derived from multiple sources (e.g. the Snow Data Assimilation System, the Moderate Resolution Imaging Spectroradiometer (i.e. MODIS) Global Evapotranspiration Project, the Simplified Surface Energy Balance model, and the Monthly Water Balance Model). This provides each HRU with a distinct set of parameter values that captures the variability within the CONUS, leading to improved model performance. We present simulation results from the NHM after preliminary calibration, including the results of basin-level calibration for the NHM using: 1) default initial GF parameter values, and 2) parameter values calibrated by HRU.
Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models
NASA Astrophysics Data System (ADS)
Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea
2014-05-01
Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.
NASA Astrophysics Data System (ADS)
Addor, Nans; Rohrer, Marco; Furrer, Reinhard; Seibert, Jan
2016-03-01
Bias adjustment methods usually do not account for the origins of biases in climate models and instead perform empirical adjustments. Biases in the synoptic circulation are for instance often overlooked when postprocessing regional climate model (RCM) simulations driven by general circulation models (GCMs). Yet considering atmospheric circulation helps to establish links between the synoptic and the regional scale, and thereby provides insights into the physical processes leading to RCM biases. Here we investigate how synoptic circulation biases impact regional climate simulations and influence our ability to mitigate biases in precipitation and temperature using quantile mapping. We considered 20 GCM-RCM combinations from the ENSEMBLES project and characterized the dominant atmospheric flow over the Alpine domain using circulation types. We report in particular a systematic overestimation of the frequency of westerly flow in winter. We show that it contributes to the generalized overestimation of winter precipitation over Switzerland, and this wet regional bias can be reduced by improving the simulation of synoptic circulation. We also demonstrate that statistical bias adjustment relying on quantile mapping is sensitive to circulation biases, which leads to residual errors in the postprocessed time series. Overall, decomposing GCM-RCM time series using circulation types reveals connections missed by analyses relying on monthly or seasonal values. Our results underscore the necessity to better diagnose process misrepresentation in climate models to progress with bias adjustment and impact modeling.
Accelerated gravitational wave parameter estimation with reduced order modeling.
Canizares, Priscilla; Field, Scott E; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2015-02-20
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ∼30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ∼70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable. PMID:25763948
Order-parameter model for unstable multilane traffic flow
NASA Astrophysics Data System (ADS)
Lubashevsky, Ihor A.; Mahnke, Reinhard
2000-11-01
We discuss a phenomenological approach to the description of unstable vehicle motion on multilane highways that explains in a simple way the observed sequence of the ``free flow <--> synchronized mode <--> jam'' phase transitions as well as the hysteresis in these transitions. We introduce a variable called an order parameter that accounts for possible correlations in the vehicle motion at different lanes. So, it is principally due to the ``many-body'' effects in the car interaction in contrast to such variables as the mean car density and velocity being actually the zeroth and first moments of the ``one-particle'' distribution function. Therefore, we regard the order parameter as an additional independent state variable of traffic flow. We assume that these correlations are due to a small group of ``fast'' drivers and by taking into account the general properties of the driver behavior we formulate a governing equation for the order parameter. In this context we analyze the instability of homogeneous traffic flow that manifested itself in the above-mentioned phase transitions and gave rise to the hysteresis in both of them. Besides, the jam is characterized by the vehicle flows at different lanes which are independent of one another. We specify a certain simplified model in order to study the general features of the car cluster self-formation under the ``free flow <--> synchronized motion'' phase transition. In particular, we show that the main local parameters of the developed cluster are determined by the state characteristics of vehicle motion only.
Accelerated Gravitational Wave Parameter Estimation with Reduced Order Modeling
NASA Astrophysics Data System (ADS)
Canizares, Priscilla; Field, Scott E.; Gair, Jonathan; Raymond, Vivien; Smith, Rory; Tiglio, Manuel
2015-02-01
Inferring the astrophysical parameters of coalescing compact binaries is a key science goal of the upcoming advanced LIGO-Virgo gravitational-wave detector network and, more generally, gravitational-wave astronomy. However, current approaches to parameter estimation for these detectors require computationally expensive algorithms. Therefore, there is a pressing need for new, fast, and accurate Bayesian inference techniques. In this Letter, we demonstrate that a reduced order modeling approach enables rapid parameter estimation to be performed. By implementing a reduced order quadrature scheme within the LIGO Algorithm Library, we show that Bayesian inference on the 9-dimensional parameter space of nonspinning binary neutron star inspirals can be sped up by a factor of ˜30 for the early advanced detectors' configurations (with sensitivities down to around 40 Hz) and ˜70 for sensitivities down to around 20 Hz. This speedup will increase to about 150 as the detectors improve their low-frequency limit to 10 Hz, reducing to hours analyses which could otherwise take months to complete. Although these results focus on interferometric gravitational wave detectors, the techniques are broadly applicable to any experiment where fast Bayesian analysis is desirable.
An assessment of the ICE6G_C (VM5A) glacial isostatic adjustment model
NASA Astrophysics Data System (ADS)
Purcell, Anthony; Tregoning, Paul; Dehecq, Amaury
2016-04-01
The recent release of the next-generation global ice history model, ICE6G_C(VM5a) [Peltier et al., 2015, Argus et al. 2014] is likely to be of interest to a wide range of disciplines including oceanography (sea level studies), space gravity (mass balance studies), glaciology and, of course, geodynamics (Earth rheology studies). In this presentation I will assess some aspects of the ICE6G_C(VM5a) model and the accompanying published data sets. I will demonstrate that the published present-day radial uplift rates are too high along the eastern side of the Antarctic Peninsula (by ˜8.6 mm/yr) and beneath the Ross Ice Shelf (by ˜5 mm/yr). Further, the published spherical harmonic coefficients - which are meant to represent the dimensionless present-day changes due to glacial isostatic adjustment (GIA) - will be shown to contain excessive power for degree ≥ 90, to be physically implausible and to not represent accurately the ICE6G_C(VM5a) model. The excessive power in the high degree terms produces erroneous uplift rates when the empirical relationship of Purcell et al. [2011] is applied but, when correct Stokes' coefficients are used, the empirical relationship will be shown to produce excellent agreement with the fully rigorous computation of the radial velocity field, subject to the caveats first noted by Purcell et al. [2011]. Finally, a global radial velocity field for the present-day GIA signal, and corresponding Stoke's coefficients will be presented for the ICE6GC ice model history using the VM5a rheology model. These results have been obtained using the ANU group's CALSEA software package and can be used to correct satellite altimetry observations for GIA over oceans and by the space gravity community to separate GIA and present-day mass balance change signals without any of the shortcomings of the previously published data-sets. We denote the new data sets ICE6G_ANU.
Computational approaches to parameter estimation and model selection in immunology
NASA Astrophysics Data System (ADS)
Baker, C. T. H.; Bocharov, G. A.; Ford, J. M.; Lumb, P. M.; Norton, S. J.; Paul, C. A. H.; Junt, T.; Krebs, P.; Ludewig, B.
2005-12-01
One of the significant challenges in biomathematics (and other areas of science) is to formulate meaningful mathematical models. Our problem is to decide on a parametrized model which is, in some sense, most likely to represent the information in a set of observed data. In this paper, we illustrate the computational implementation of an information-theoretic approach (associated with a maximum likelihood treatment) to modelling in immunology.The approach is illustrated by modelling LCMV infection using a family of models based on systems of ordinary differential and delay differential equations. The models (which use parameters that have a scientific interpretation) are chosen to fit data arising from experimental studies of virus-cytotoxic T lymphocyte kinetics; the parametrized models that result are arranged in a hierarchy by the computation of Akaike indices. The practical illustration is used to convey more general insight. Because the mathematical equations that comprise the models are solved numerically, the accuracy in the computation has a bearing on the outcome, and we address this and other practical details in our discussion.
Bazzazian, S; Besharat, M A
2012-01-01
The aim of this study was to develop and test a model of adjustment to type I diabetes. Three hundred young adults (172 females and 128 males) with type I diabetes were asked to complete the Adult Attachment Inventory (AAI), the Brief Illness Perception Questionnaire (Brief IPQ), Task-oriented subscale of the Coping Inventory for Stressful Situations (CISS), D-39, and well-being subscale of the Mental Health Inventory (MHI). HbA1c was obtained from laboratory examination. Results from structural equation analysis partly supported the hypothesized model. Secure and avoidant attachment styles were found to have effects on illness perception, ambivalent attachment style did not have significant effect on illness perception. Three attachment styles had significant effect on task-oriented coping strategy. Avoidant attachment had negative direct effect on adjustment too. Regression effects of illness perception and task-oriented coping strategy on adjustment were positive. Therefore, positive illness perception and more usage of task-oriented coping strategy predict better adjustment to diabetes. So, the results confirmed the theoretical bases and empirical evidence of effectiveness of attachment styles in adjustment to chronic disease and can be helpful in devising preventive policies, determining high-risk maladjusted patients, and planning special psychological treatment. PMID:21678193
Ratios as a size adjustment in morphometrics.
Albrecht, G H; Gelvin, B R; Hartman, S E
1993-08-01
Simple ratios in which a measurement variable is divided by a size variable are commonly used but known to be inadequate for eliminating size correlations from morphometric data. Deficiencies in the simple ratio can be alleviated by incorporating regression coefficients describing the bivariate relationship between the measurement and size variables. Recommendations have included: 1) subtracting the regression intercept to force the bivariate relationship through the origin (intercept-adjusted ratios); 2) exponentiating either the measurement or the size variable using an allometry coefficient to achieve linearity (allometrically adjusted ratios); or 3) both subtracting the intercept and exponentiating (fully adjusted ratios). These three strategies for deriving size-adjusted ratios imply different data models for describing the bivariate relationship between the measurement and size variables (i.e., the linear, simple allometric, and full allometric models, respectively). Algebraic rearrangement of the equation associated with each data model leads to a correctly formulated adjusted ratio whose expected value is constant (i.e., size correlation is eliminated). Alternatively, simple algebra can be used to derive an expected value function for assessing whether any proposed ratio formula is effective in eliminating size correlations. Some published ratio adjustments were incorrectly formulated as indicated by expected values that remain a function of size after ratio transformation. Regression coefficients incorporated into adjusted ratios must be estimated using least-squares regression of the measurement variable on the size variable. Use of parameters estimated by any other regression technique (e.g., major axis or reduced major axis) results in residual correlations between size and the adjusted measurement variable. Correctly formulated adjusted ratios, whose parameters are estimated by least-squares methods, do control for size correlations. The size-adjusted
Modelling rock-avalanche induced impact waves: Sensitivity of the model chains to model parameters
NASA Astrophysics Data System (ADS)
Schaub, Yvonne; Huggel, Christian
2014-05-01
New lakes are forming in high-mountain areas all over the world due to glacier recession. Often they will be located below steep, destabilized flanks and are therefore exposed to impacts from rock-/ice-avalanches. Several events worldwide are known, where an outburst flood has been triggered by such an impact. In regions such as in the European Alps or in the Cordillera Blanca in Peru, where valley bottoms are densely populated, these far-travelling, high-magnitude events can result in major disasters. Usually natural hazards are assessed as single hazardous processes, for the above mentioned reasons, however, development of assessment and reproduction methods of the hazardous process chain for the purpose of hazard map generation have to be brought forward. A combination of physical process models have already been suggested and illustrated by means of lake outburst in the Cordillera Blanca, Peru, where on April 11th 2010 an ice-avalanche of approx. 300'000m3 triggered an impact wave, which overtopped the 22m freeboard of the rock-dam for 5 meters and caused and outburst flood which travelled 23 km to the city of Carhuaz. We here present a study, where we assessed the sensitivity of the model chain from ice-avalanche and impact wave to single parameters considering rock-/ice-avalanche modeling by RAMMS and impact wave modeling by IBER. Assumptions on the initial rock-/ice-avalanche volume, calibration of the friction parameters in RAMMS and assumptions on erosion considered in RAMMS were parameters tested regarding their influence on overtopping parameters that are crucial for outburst flood modeling. Further the transformation of the RAMMS-output (flow height and flow velocities on the shoreline of the lake) into an inflow-hydrograph for IBER was also considered a possible source of uncertainties. Overtopping time, volume, and wave height as much as mean and maximum discharge were considered decisive parameters for the outburst flood modeling and were therewith
NASA Astrophysics Data System (ADS)
Luo, Yue; Ye, Shujun; Wu, Jichun; Wang, Hanmei; Jiao, Xun
2016-05-01
Land-subsidence prediction depends on an appropriate subsidence model and the calibration of its parameter values. A modified inverse procedure is developed and applied to calibrate five parameters in a compacting confined aquifer system using records of field data from vertical extensometers and corresponding hydrographs. The inverse procedure of COMPAC (InvCOMPAC) has been used in the past for calibrating vertical hydraulic conductivity of the aquitards, nonrecoverable and recoverable skeletal specific storages of the aquitards, skeletal specific storage of the aquifers, and initial preconsolidation stress within the aquitards. InvCOMPAC is modified to increase robustness in this study. There are two main differences in the modified InvCOMPAC model (MInvCOMPAC). One is that field data are smoothed before diagram analysis to reduce local oscillation of data and remove abnormal data points. A robust locally weighted regression method is applied to smooth the field data. The other difference is that the Newton-Raphson method, with a variable scale factor, is used to conduct the computer-based inverse adjustment procedure. MInvCOMPAC is then applied to calibrate parameters in a land subsidence model of Shanghai, China. Five parameters of aquifers and aquitards at 15 multiple-extensometer sites are calibrated. Vertical deformation of sedimentary layers can be predicted by the one-dimensional COMPAC model with these calibrated parameters at extensometer sites. These calibrated parameters could also serve as good initial values for parameters of three-dimensional regional land subsidence models of Shanghai.
Anisotropic effects on constitutive model parameters of aluminum alloys
NASA Astrophysics Data System (ADS)
Brar, Nachhatter S.; Joshi, Vasant S.
2012-03-01
Simulation of low velocity impact on structures or high velocity penetration in armor materials heavily rely on constitutive material models. Model constants are determined from tension, compression or torsion stress-strain at low and high strain rates at different temperatures. These model constants are required input to computer codes (LS-DYNA, DYNA3D or SPH) to accurately simulate fragment impact on structural components made of high strength 7075-T651 aluminum alloy. Johnson- Cook model constants determined for Al7075-T651 alloy bar material failed to simulate correctly the penetration into 1' thick Al-7075-T651plates. When simulation go well beyond minor parameter tweaking and experimental results show drastically different behavior it becomes important to determine constitutive parameters from the actual material used in impact/penetration experiments. To investigate anisotropic effects on the yield/flow stress of this alloy quasi-static and high strain rate tensile tests were performed on specimens fabricated in the longitudinal "L", transverse "T", and thickness "TH" directions of 1' thick Al7075 Plate. While flow stress at a strain rate of ~1/s as well as ~1100/s in the thickness and transverse directions are lower than the longitudinal direction. The flow stress in the bar was comparable to flow stress in the longitudinal direction of the plate. Fracture strain data from notched tensile specimens fabricated in the L, T, and Thickness directions of 1' thick plate are used to derive fracture constants.
Hydrological Modelling and Parameter Identification for Green Roof
NASA Astrophysics Data System (ADS)
Lo, W.; Tung, C.
2012-12-01
Green roofs, a multilayered system covered by plants, can be used to replace traditional concrete roofs as one of various measures to mitigate the increasing stormwater runoff in the urban environment. Moreover, facing the high uncertainty of the climate change, the present engineering method as adaptation may be regarded as improper measurements; reversely, green roofs are unregretful and flexible, and thus are rather important and suitable. The related technology has been developed for several years and the researches evaluating the stormwater reduction performance of green roofs are ongoing prosperously. Many European counties, cities in the U.S., and other local governments incorporate green roof into the stormwater control policy. Therefore, in terms of stormwater management, it is necessary to develop a robust hydrologic model to quantify the efficacy of green roofs over different types of designs and environmental conditions. In this research, a physical based hydrologic model is proposed to simulate water flowing process in the green roof system. In particular, the model adopts the concept of water balance, bringing a relatively simple and intuitive idea. Also, the research compares the two methods in the surface water balance calculation. One is based on Green-Ampt equation, and the other is under the SCS curve number calculation. A green roof experiment is designed to collect weather data and water discharge. Then, the proposed model is verified with these observed data; furthermore, the parameters using in the model are calibrated to find appropriate values in the green roof hydrologic simulation. This research proposes a simple physical based hydrologic model and the measures to determine parameters for the model.
Parameter estimation for models of ligninolytic and cellulolytic enzyme kinetics
Wang, Gangsheng; Post, Wilfred M; Mayes, Melanie; Frerichs, Joshua T; Jagadamma, Sindhu
2012-01-01
While soil enzymes have been explicitly included in the soil organic carbon (SOC) decomposition models, there is a serious lack of suitable data for model parameterization. This study provides well-documented enzymatic parameters for application in enzyme-driven SOC decomposition models from a compilation and analysis of published measurements. In particular, we developed appropriate kinetic parameters for five typical ligninolytic and cellulolytic enzymes ( -glucosidase, cellobiohydrolase, endo-glucanase, peroxidase, and phenol oxidase). The kinetic parameters included the maximum specific enzyme activity (Vmax) and half-saturation constant (Km) in the Michaelis-Menten equation. The activation energy (Ea) and the pH optimum and sensitivity (pHopt and pHsen) were also analyzed. pHsen was estimated by fitting an exponential-quadratic function. The Vmax values, often presented in different units under various measurement conditions, were converted into the same units at a reference temperature (20 C) and pHopt. Major conclusions are: (i) Both Vmax and Km were log-normal distributed, with no significant difference in Vmax exhibited between enzymes originating from bacteria or fungi. (ii) No significant difference in Vmax was found between cellulases and ligninases; however, there was significant difference in Km between them. (iii) Ligninases had higher Ea values and lower pHopt than cellulases; average ratio of pHsen to pHopt ranged 0.3 0.4 for the five enzymes, which means that an increase or decrease of 1.1 1.7 pH units from pHopt would reduce Vmax by 50%. (iv) Our analysis indicated that the Vmax values from lab measurements with purified enzymes were 1 2 orders of magnitude higher than those for use in SOC decomposition models under field conditions.
Sensitivity Analysis of Parameters in Linear-Quadratic Radiobiologic Modeling
Fowler, Jack F.
2009-04-01
Purpose: Radiobiologic modeling is increasingly used to estimate the effects of altered treatment plans, especially for dose escalation. The present article shows how much the linear-quadratic (LQ) (calculated biologically equivalent dose [BED] varies when individual parameters of the LQ formula are varied by {+-}20% and by 1%. Methods: Equivalent total doses (EQD2 = normalized total doses (NTD) in 2-Gy fractions for tumor control, acute mucosal reactions, and late complications were calculated using the linear- quadratic formula with overall time: BED = nd (1 + d/ [{alpha}/{beta}]) - log{sub e}2 (T - Tk) / {alpha}Tp, where BED is BED = total dose x relative effectiveness (RE = nd (1 + d/ [{alpha}/{beta}]). Each of the five biologic parameters in turn was altered by {+-}10%, and the altered EQD2s tabulated; the difference was finally divided by 20. EQD2 or NTD is obtained by dividing BED by the RE for 2-Gy fractions, using the appropriate {alpha}/{beta} ratio. Results: Variations in tumor and acute mucosal EQD ranged from 0.1% to 0.45% per 1% change in each parameter for conventional schedules, the largest variation being caused by overall time. Variations in 'late' EQD were 0.4% to 0.6% per 1% change in the only biologic parameter, the {alpha}/{beta} ratio. For stereotactic body radiotherapy schedules, variations were larger, up to 0.6 to 0.9 for tumor and 1.6% to 1.9% for late, per 1% change in parameter. Conclusions: Robustness occurs similar to that of equivalent uniform dose (EUD), for the same reasons. Total dose, dose per fraction, and dose-rate cause their major effects, as well known.
ERIC Educational Resources Information Center
Rulison, Kelly L.; Gest, Scott D.; Loken, Eric; Welsh, Janet A.
2010-01-01
The association between affiliating with aggressive peers and behavioral, social and psychological adjustment was examined. Students initially in 3rd, 4th, and 5th grade (N = 427) were followed biannually through 7th grade. Students' peer-nominated groups were identified. Multilevel modeling was used to examine the independent contributions of…
The Effectiveness of the Strength-Centered Career Adjustment Model for Dual-Career Women in Taiwan
ERIC Educational Resources Information Center
Wang, Yu-Chen; Tien, Hsiu-Lan Shelley
2011-01-01
The authors investigated the effectiveness of a Strength-Centered Career Adjustment Model for dual-career women (N = 28). Fourteen women in the experimental group received strength-centered career counseling for 6 to 8 sessions; the 14 women in the control group received test services in 1 to 2 sessions. All participants completed the Personal…
ERIC Educational Resources Information Center
Hawkins, Amy L.; Haskett, Mary E.
2014-01-01
Background: Abused children's internal working models (IWM) of relationships are known to relate to their socioemotional adjustment, but mechanisms through which negative representations increase vulnerability to maladjustment have not been explored. We sought to expand the understanding of individual differences in IWM of abused children and…
Parameter Estimation for a Model of Space-Time Rainfall
NASA Astrophysics Data System (ADS)
Smith, James A.; Karr, Alan F.
1985-08-01
In this paper, parameter estimation procedures, based on data from a network of rainfall gages, are developed for a class of space-time rainfall models. The models, which are designed to represent the spatial distribution of daily rainfall, have three components, one that governs the temporal occurrence of storms, a second that distributes rain cells spatially for a given storm, and a third that determines the rainfall pattern within a rain cell. Maximum likelihood and method of moments procedures are developed. We illustrate that limitations on model structure are imposed by restricting data sources to rain gage networks. The estimation procedures are applied to a 240-mi2 (621 km2) catchment in the Potomac River basin.
Modelling of some parameters from thermoelectric power plants
NASA Astrophysics Data System (ADS)
Popa, G. N.; Diniş, C. M.; Deaconu, S. I.; Maksay, Şt; Popa, I.
2016-02-01
Paper proposing new mathematical models for the main electrical parameters (active power P, reactive power Q of power supplies) and technological (mass flow rate of steam M from boiler and dust emission E from the output of precipitator) from a thermoelectric power plants using industrial plate-type electrostatic precipitators with three sections used in electrical power plants. The mathematical models were used experimental results taken from industrial facility, from boiler and plate-type electrostatic precipitators with three sections, and has used the least squares method for their determination. The modelling has been used equations of degree 1, 2 and 3. The equations were determined between dust emission depending on active power of power supplies and mass flow rate of steam from boiler, and, also, depending on reactive power of power supplies and mass flow rate of steam from boiler. These equations can be used to control the process from electrostatic precipitators.
Hoos, Anne B.; Patel, Anant R.
1996-01-01
Model-adjustment procedures were applied to the combined data bases of storm-runoff quality for Chattanooga, Knoxville, and Nashville, Tennessee, to improve predictive accuracy for storm-runoff quality for urban watersheds in these three cities and throughout Middle and East Tennessee. Data for 45 storms at 15 different sites (five sites in each city) constitute the data base. Comparison of observed values of storm-runoff load and event-mean concentration to the predicted values from the regional regression models for 10 constituents shows prediction errors, as large as 806,000 percent. Model-adjustment procedures, which combine the regional model predictions with local data, are applied to improve predictive accuracy. Standard error of estimate after model adjustment ranges from 67 to 322 percent. Calibration results may be biased due to sampling error in the Tennessee data base. The relatively large values of standard error of estimate for some of the constituent models, although representing significant reduction (at least 50 percent) in prediction error compared to estimation with unadjusted regional models, may be unacceptable for some applications. The user may wish to collect additional local data for these constituents and repeat the analysis, or calibrate an independent local regression model.
Lumped-parameter tissue temperature-blood perfusion model of a cold-stressed fingertip.
Shitzer, A; Stroschein, L A; Gonzalez, R R; Pandolf, K B
1996-05-01
A lumped-parameter model of a fingertip is presented. The semispherical model includes the effects of heat storage, heat exchange with the environment, and heat transport by blood perfusion. The thermal insulation on the surface of the fingertip is represented by the overall heat transfer coefficient that is calculated by common engineering formulas. The model is solved analytically for the simple case of constant blood perfusion rate. The general case of variable blood perfusion rates is solved by an Euler finite difference technique. At this stage, the model does not include active control mechanisms of blood perfusion. Thus the effects of cold-induced vasodilatation have to be superimposed and are modeled by symmetrical triangular waveforms because these were found to best depict the behavior of fingers exposed to cold environments. Results of this model were compared with experimental data obtained in two separate studies. One included 60-min infrared thermograms of the dorsal surface of bare hands of sedentary subjects horizontally suspended on a fish net in a 0 degree C environment. Another study, on gloved finger temperatures, involved 0 and -6.7 degrees C environments. Fingertip (nail bed) temperatures of both these studies were compared with model predictions. Blood perfusion rates were assumed and adjusted within physiologically reasonable limits. Comparison of measured and computed temperature records showed very good conformity in both cases studied. PMID:8727573
Anisotropic Effects on Constitutive Model Parameters of Aluminum Alloys
NASA Astrophysics Data System (ADS)
Brar, Nachhatter; Joshi, Vasant
2011-06-01
Simulation of low velocity impact on structures or high velocity penetration in armor materials heavily rely on constitutive material models. The model constants are required input to computer codes (LS-DYNA, DYNA3D or SPH) to accurately simulate fragment impact on structural components made of high strength 7075-T651 aluminum alloys. Johnson-Cook model constants determined for Al7075-T651 alloy bar material failed to simulate correctly the penetration into 1' thick Al-7075-T651plates. When simulations go well beyond minor parameter tweaking and experimental results are drastically different it is important to determine constitutive parameters from the actual material used in impact/penetration experiments. To investigate anisotropic effects on the yield/flow stress of this alloy we performed quasi-static and high strain rate tensile tests on specimens fabricated in the longitudinal, transverse, and thickness directions of 1' thick Al7075-T651 plate. Flow stresses at a strain rate of ~1100/s in the longitudinal and transverse direction are similar around 670MPa and decreases to 620 MPa in the thickness direction. These data are lower than the flow stress of 760 MPa measured in Al7075-T651 bar stock.
Bayesian parameter estimation for stochastic models of biological cell migration
NASA Astrophysics Data System (ADS)
Dieterich, Peter; Preuss, Roland
2013-08-01
Cell migration plays an essential role under many physiological and patho-physiological conditions. It is of major importance during embryonic development and wound healing. In contrast, it also generates negative effects during inflammation processes, the transmigration of tumors or the formation of metastases. Thus, a reliable quantification and characterization of cell paths could give insight into the dynamics of these processes. Typically stochastic models are applied where parameters are extracted by fitting models to the so-called mean square displacement of the observed cell group. We show that this approach has several disadvantages and problems. Therefore, we propose a simple procedure directly relying on the positions of the cell's trajectory and the covariance matrix of the positions. It is shown that the covariance is identical with the spatial aging correlation function for the supposed linear Gaussian models of Brownian motion with drift and fractional Brownian motion. The technique is applied and illustrated with simulated data showing a reliable parameter estimation from single cell paths.
NASA Astrophysics Data System (ADS)
Chakraborty, Shuvendu; Debnath, Ujjal; Jamil, Mubasher; Myrzakulov, Ratbay
2012-07-01
In this work, we have calculated the deceleration parameter, statefinder parameters and EoS parameters for different dark energy models with variable G correction in homogeneous, isotropic and non-flat universe for Kaluza-Klein Cosmology. The statefinder parameters have been obtained in terms of some observable parameters like dimensionless density parameter, EoS parameter and Hubble parameter for holographic dark energy, new agegraphic dark energy and generalized Chaplygin gas models.
Microbial Communities Model Parameter Calculation for TSPA/SR
D. Jolley
2001-07-16
This calculation has several purposes. First the calculation reduces the information contained in ''Committed Materials in Repository Drifts'' (BSC 2001a) to useable parameters required as input to MING V1.O (CRWMS M&O 1998, CSCI 30018 V1.O) for calculation of the effects of potential in-drift microbial communities as part of the microbial communities model. The calculation is intended to replace the parameters found in Attachment II of the current In-Drift Microbial Communities Model revision (CRWMS M&O 2000c) with the exception of Section 11-5.3. Second, this calculation provides the information necessary to supercede the following DTN: M09909SPAMING1.003 and replace it with a new qualified dataset (see Table 6.2-1). The purpose of this calculation is to create the revised qualified parameter input for MING that will allow {Delta}G (Gibbs Free Energy) to be corrected for long-term changes to the temperature of the near-field environment. Calculated herein are the quadratic or second order regression relationships that are used in the energy limiting calculations to potential growth of microbial communities in the in-drift geochemical environment. Third, the calculation performs an impact review of a new DTN: M00012MAJIONIS.000 that is intended to replace the currently cited DTN: GS9809083 12322.008 for water chemistry data used in the current ''In-Drift Microbial Communities Model'' revision (CRWMS M&O 2000c). Finally, the calculation updates the material lifetimes reported on Table 32 in section 6.5.2.3 of the ''In-Drift Microbial Communities'' AMR (CRWMS M&O 2000c) based on the inputs reported in BSC (2001a). Changes include adding new specified materials and updating old materials information that has changed.
Parameter optimization in differential geometry based solvation models.
Wang, Bao; Wei, G W
2015-10-01
Differential geometry (DG) based solvation models are a new class of variational implicit solvent approaches that are able to avoid unphysical solvent-solute boundary definitions and associated geometric singularities, and dynamically couple polar and non-polar interactions in a self-consistent framework. Our earlier study indicates that DG based non-polar solvation model outperforms other methods in non-polar solvation energy predictions. However, the DG based full solvation model has not shown its superiority in solvation analysis, due to its difficulty in parametrization, which must ensure the stability of the solution of strongly coupled nonlinear Laplace-Beltrami and Poisson-Boltzmann equations. In this work, we introduce new parameter learning algorithms based on perturbation and convex optimization theories to stabilize the numerical solution and thus achieve an optimal parametrization of the DG based solvation models. An interesting feature of the present DG based solvation model is that it provides accurate solvation free energy predictions for both polar and non-polar molecules in a unified formulation. Extensive numerical experiment demonstrates that the present DG based solvation model delivers some of the most accurate predictions of the solvation free energies for a large number of molecules. PMID:26450304
Important Scaling Parameters for Testing Model-Scale Helicopter Rotors
NASA Technical Reports Server (NTRS)
Singleton, Jeffrey D.; Yeager, William T., Jr.
1998-01-01
An investigation into the effects of aerodynamic and aeroelastic scaling parameters on model scale helicopter rotors has been conducted in the NASA Langley Transonic Dynamics Tunnel. The effect of varying Reynolds number, blade Lock number, and structural elasticity on rotor performance has been studied and the performance results are discussed herein for two different rotor blade sets at two rotor advance ratios. One set of rotor blades were rigid and the other set of blades were dynamically scaled to be representative of a main rotor design for a utility class helicopter. The investigation was con-densities permits the acquisition of data for several Reynolds and Lock number combinations.
2014-01-01
Background Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms. Results The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input–output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard
NASA Technical Reports Server (NTRS)
1979-01-01
The computer model for erythropoietic control was adapted to the mouse system by altering system parameters originally given for the human to those which more realistically represent the mouse. Parameter values were obtained from a variety of literature sources. Using the mouse model, the mouse was studied as a potential experimental model for spaceflight. Simulation studies of dehydration and hypoxia were performed. A comparison of system parameters for the mouse and human models is presented. Aside from the obvious differences expected in fluid volumes, blood flows and metabolic rates, larger differences were observed in the following: erythrocyte life span, erythropoietin half-life, and normal arterial pO2.
A Lumped Parameter Model for Feedback Studies in Tokamaks
NASA Astrophysics Data System (ADS)
Chance, M. S.; Chu, M. S.; Okabayashi, M.; Glasser, A. H.
2004-11-01
A lumped circuit model of the feedback stabilization studies in tokamaks is calculated. This work parallels the formulation by Boozer^a, is analogous to the studies done on axisymmetric modes^b, and generalizes the cylindrical model^c. The lumped circuit parameters are derived from the DCON derived eigenfunctions of the plasma, the resistive shell and the feedback coils. The inductances are calculated using the VACUUM code which is designed to calculate the responses between the various elements in the feedback system. The results are compared with the normal mode^d and the system identification^e approaches. ^aA.H. Boozer, Phys. Plasmas 5, 3350 (1998). ^b E.A. Lazarus et al., Nucl. Fusion 30, 111 (1990). ^c M. Okabayashi et al., Nucl. Fusion 38, 1607 (1998). ^dM.S. Chu et al., Nucl. Fusion 43, 441 (2003). ^eY.Q. Liu et al., Phys. Plasmas 7, 3681 (2000).
Breakdown parameter for kinetic modeling of multiscale gas flows.
Meng, Jianping; Dongari, Nishanth; Reese, Jason M; Zhang, Yonghao
2014-06-01
Multiscale methods built purely on the kinetic theory of gases provide information about the molecular velocity distribution function. It is therefore both important and feasible to establish new breakdown parameters for assessing the appropriateness of a fluid description at the continuum level by utilizing kinetic information rather than macroscopic flow quantities alone. We propose a new kinetic criterion to indirectly assess the errors introduced by a continuum-level description of the gas flow. The analysis, which includes numerical demonstrations, focuses on the validity of the Navier-Stokes-Fourier equations and corresponding kinetic models and reveals that the new criterion can consistently indicate the validity of continuum-level modeling in both low-speed and high-speed flows at different Knudsen numbers. PMID:25019910
Data Assimilation and Adjusted Spherical Harmonic Model of VTEC Map over Thailand
NASA Astrophysics Data System (ADS)
Klinngam, Somjai; Maruyama, Takashi; Tsugawa, Takuya; Ishii, Mamoru; Supnithi, Pornchai; Chiablaem, Athiwat
2016-07-01
The global navigation satellite system (GNSS) and high frequency (HF) communication are vulnerable to the ionospheric irregularities, especially when the signal travels through the low-latitude region and around the magnetic equator known as equatorial ionization anomaly (EIA) region. In order to study the ionospheric effects to the communications performance in this region, the regional map of the observed total electron content (TEC) can show the characteristic and irregularities of the ionosphere. In this work, we develop the two-dimensional (2D) map of vertical TEC (VTEC) over Thailand using the adjusted spherical harmonic model (ASHM) and the data assimilation technique. We calculate the VTEC from the receiver independent exchange (RINEX) files recorded by the dual-frequency global positioning system (GPS) receivers on July 8th, 2012 (quiet day) at 12 stations around Thailand: 0° to 25°E and 95°N to 110°N. These stations are managed by Department of Public Works and Town & Country Planning (DPT), Thailand, and the South East Asia Low-latitude ionospheric Network (SEALION) project operated by National Institute of Information and Communications Technology (NICT), Japan, and King Mongkut's Institute of Technology Ladkrabang (KMITL). We compute the median observed VTEC (OBS-VTEC) in the grids with the spatial resolution of 2.5°x5° in latitude and longitude and time resolution of 2 hours. We assimilate the OBS-VTEC with the estimated VTEC from the International Reference Ionosphere model (IRI-VTEC) as well as the ionosphere map exchange (IONEX) files provided by the International GNSS Service (IGS-VTEC). The results show that the estimation of the 15-degree ASHM can be improved when both of IRI-VTEC and IGS-VTEC are weighted by the latitude-dependent factors before assimilating with the OBS-VTEC. However, the IRI-VTEC assimilation can improve the ASHM estimation more than the IGS-VTEC assimilation. Acknowledgment: This work is partially funded by the
A novel criterion for determination of material model parameters
NASA Astrophysics Data System (ADS)
Andrade-Campos, A.; de-Carvalho, R.; Valente, R. A. F.
2011-05-01
Parameter identification problems have emerged due to the increasing demanding of precision in the numerical results obtained by Finite Element Method (FEM) software. High result precision can only be obtained with confident input data and robust numerical techniques. The determination of parameters should always be performed confronting numerical and experimental results leading to the minimum difference between them. However, the success of this task is dependent of the specification of the cost/objective function, defined as the difference between the experimental and the numerical results. Recently, various objective functions have been formulated to assess the errors between the experimental and computed data (Lin et al., 2002; Cao and Lin, 2008; among others). The objective functions should be able to efficiently lead the optimisation process. An ideal objective function should have the following properties: (i) all the experimental data points on the curve and all experimental curves should have equal opportunity to be optimised; and (ii) different units and/or the number of curves in each sub-objective should not affect the overall performance of the fitting. These two criteria should be achieved without manually choosing the weighting factors. However, for some non-analytical specific problems, this is very difficult in practice. Null values of experimental or numerical values also turns the task difficult. In this work, a novel objective function for constitutive model parameter identification is presented. It is a generalization of the work of Cao and Lin and it is suitable for all kinds of constitutive models and mechanical tests, including cyclic tests and Baushinger tests with null values.
Standard model parameters and the search for new physics
Marciano, W.J.
1988-04-01
In these lectures, my aim is to present an up-to-date status report on the standard model and some key tests of electroweak unification. Within that context, I also discuss how and where hints of new physics may emerge. To accomplish those goals, I have organized my presentation as follows: I discuss the standard model parameters with particular emphasis on the gauge coupling constants and vector boson masses. Examples of new physics appendages are also briefly commented on. In addition, because these lectures are intended for students and thus somewhat pedagogical, I have included an appendix on dimensional regularization and a simple computational example that employs that technique. Next, I focus on weak charged current phenomenology. Precision tests of the standard model are described and up-to-date values for the Cabibbo-Kobayashi-Maskawa (CKM) mixing matrix parameters are presented. Constraints implied by those tests for a 4th generation, supersymmetry, extra Z/prime/ bosons, and compositeness are also discussed. I discuss weak neutral current phenomenology and the extraction of sin/sup 2/ /theta//sub W/ from experiment. The results presented there are based on a recently completed global analysis of all existing data. I have chosen to concentrate that discussion on radiative corrections, the effect of a heavy top quark mass, and implications for grand unified theories (GUTS). The potential for further experimental progress is also commented on. I depart from the narrowest version of the standard model and discuss effects of neutrino masses and mixings. I have chosen to concentrate on oscillations, the Mikheyev-Smirnov- Wolfenstein (MSW) effect, and electromagnetic properties of neutrinos. On the latter topic, I will describe some recent work on resonant spin-flavor precession. Finally, I conclude with a prospectus on hopes for the future. 76 refs.
Model structure and parameter identification in soil carbon models using incubation data
NASA Astrophysics Data System (ADS)
Sierra, Carlos
2015-04-01
Models of soil organic matter dynamics play an important role in integrating different sources of information and help to predict future behavior of carbon stocks and fluxes in soils. In particular, compartment-based models have proved successful at integrating data from laboratory and field experiments to estimate the range of cycling rates of organic matter found in different soils. Complex models with particular mechanisms explaining processes related to the stabilization and destabilization of organic matter usually include a large number of parameters than simpler models that omit detailed mechanisms. This poses a challenge to parameterize complex models. Depending on the type of data available, the estimation of parameters in complex models may lead to identifiability problems, i.e. obtaining different combinations of parameters that give equally good predictions in relation to the observed data. In this contribution, I explore the problem of identifiability in soil organic matter models, pointing out combinations of empirical data and model structure that can minimize identifiability issues. In particular, I will show how common datasets from incubation experiments can only help to uniquely identify small number of parameters for simple models. Isotopic data and soil fractionations can help to reduce identifiability issues, but only to a limited extend. In medium-complexity models including stabilization and destabilization mechanisms, only up to 4 to 5 parameters may be uniquely identified when a full set of respiration fluxes, stocks, fractions and isotopic data are integrated to inform parameter estimation.
Variational methods to estimate terrestrial ecosystem model parameters
NASA Astrophysics Data System (ADS)
Delahaies, Sylvain; Roulstone, Ian
2016-04-01
Carbon is at the basis of the chemistry of life. Its ubiquity in the Earth system is the result of complex recycling processes. Present in the atmosphere in the form of carbon dioxide it is adsorbed by marine and terrestrial ecosystems and stored within living biomass and decaying organic matter. Then soil chemistry and a non negligible amount of time transform the dead matter into fossil fuels. Throughout this cycle, carbon dioxide is released in the atmosphere through respiration and combustion of fossils fuels. Model-data fusion techniques allow us to combine our understanding of these complex processes with an ever-growing amount of observational data to help improving models and predictions. The data assimilation linked ecosystem carbon (DALEC) model is a simple box model simulating the carbon budget allocation for terrestrial ecosystems. Over the last decade several studies have demonstrated the relative merit of various inverse modelling strategies (MCMC, ENKF, 4DVAR) to estimate model parameters and initial carbon stocks for DALEC and to quantify the uncertainty in the predictions. Despite its simplicity, DALEC represents the basic processes at the heart of more sophisticated models of the carbon cycle. Using adjoint based methods we study inverse problems for DALEC with various data streams (8 days MODIS LAI, monthly MODIS LAI, NEE). The framework of constraint optimization allows us to incorporate ecological common sense into the variational framework. We use resolution matrices to study the nature of the inverse problems and to obtain data importance and information content for the different type of data. We study how varying the time step affect the solutions, and we show how "spin up" naturally improves the conditioning of the inverse problems.
Conger, R D; Patterson, G R; Ge, X
1995-02-01
In this study of parental stress and adolescent adjustment, experiences of negative life events during the recent past were used to generate a measure of acute stress. In addition, multiple indicators based on reports from various informants were used to estimate latent constructs for parental depression, discipline practices, and adolescent adjustment. Employing 2 independent samples of families from 2 different regions of the country (rural Iowa and a medium-sized city in Oregon), structural equation models were used to test the hypothesis that in intact families acute stress experienced by parents is linked to boys' adjustment (average age equaled 11.8 years in the Oregon sample, 12.7 years in the Iowa sample) through 2 different causal mechanisms. The findings showed that parental stress was related to adjustment through stress-related parental depression that is, in turn, correlated with disrupted discipline practices. Poor discipline appears to provide the direct link with developmental outcomes. The structural equation model (SEM) used to test the proposed mediational process was consistent with the data for mothers and boys from both the Oregon and the Iowa samples. The similarity in results was less clear for fathers and boys. Implications of these results for future replication studies are discussed. PMID:7497831
Telzer, Eva H; Yuen, Cynthia; Gonzales, Nancy; Fuligni, Andrew J
2016-07-01
The acculturation gap-distress model purports that immigrant children acculturate faster than do their parents, resulting in an acculturation gap that leads to family and youth maladjustment. However, empirical support for the acculturation gap-distress model has been inconclusive. In the current study, 428 Mexican-American adolescents (50.2 % female) and their primary caregivers independently completed questionnaires assessing their levels of American and Mexican cultural orientation, family functioning, and youth adjustment. Contrary to the acculturation gap-distress model, acculturation gaps were not associated with poorer family or youth functioning. Rather, adolescents with higher levels of Mexican cultural orientations showed positive outcomes, regardless of their parents' orientations to either American or Mexican cultures. Findings suggest that youths' heritage cultural maintenance may be most important for their adjustment. PMID:26759225
Analysing DNA structural parameters using a mesoscopic model
NASA Astrophysics Data System (ADS)
Amarante, Tauanne D.; Weber, Gerald
2014-03-01
The Peyrard-Bishop model is a mesoscopic approximation to model DNA and RNA molecules. Several variants of this model exists, from 3D Hamiltonians, including torsional angles, to simpler 2D versions. Currently, we are able to parametrize the 2D variants of the model which allows us to extract important information about the molecule. For example, with this technique we were able recently to obtain the hydrogen bonds of RNA from melting temperatures, which previously were obtainable only from NMR measurements. Here, we take the 3D torsional Hamiltonian and set the angles to zero. Curiously, in doing this we do not recover the traditional 2D Hamiltonians. Instead, we obtain a different 2D Hamiltonian which now includes a base pair step distance, commonly known as rise. A detailed knowledge of the rise distance is important as it determines the overall length of the DNA molecule. This 2D Hamiltonian provides us with the exciting prospect of obtaining DNA structural parameters from melting temperatures. Our results of the rise distance at low salt concentration are in good qualitative agreement with those from several published x-ray measurements. We also found an important dependence of the rise distance with salt concentration. In contrast to our previous calculations, the elastic constants now show little dependence with salt concentrations which appears to be closer to what is seen experimentally in DNA flexibility experiments.
The S-parameter in holographic technicolor models
NASA Astrophysics Data System (ADS)
Agashe, Kaustubh; Csáki, Csaba; Reece, Matthew; Grojean, Christophe
2007-12-01
We study the S parameter, considering especially its sign, in models of electroweak symmetry breaking (EWSB) in extra dimensions, with fermions localized near the UV brane. Such models are conjectured to be dual to 4D strong dynamics triggering EWSB. The motivation for such a study is that a negative value of S can significantly ameliorate the constraints from electroweak precision data on these models, allowing lower mass scales (TeV or below) for the new particles and leading to easier discovery at the LHC. We first extend an earlier proof of S>0 for EWSB by boundary conditions in arbitrary metric to the case of general kinetic functions for the gauge fields or arbitrary kinetic mixing. We then consider EWSB in the bulk by a Higgs VEV showing that S is positive for arbitrary metric and Higgs profile, assuming that the effects from higher-dimensional operators in the 5D theory are sub-leading and can therefore be neglected. For the specific case of AdS5 with a power law Higgs profile, we also show that S ~ +O(1), including effects of possible kinetic mixing from higher-dimensional operator (of NDA size) in the 5D theory. Therefore, our work strongly suggests that S is positive in calculable models in extra dimensions.
Hierarchical parameter identification in models of respiratory mechanics.
Schranz, C; Knöbel, C; Kretschmer, J; Zhao, Z; Möller, K
2011-11-01
Potential harmful effects of ventilation therapy could be reduced by model-based predictions of the effects of ventilator settings to the patient. To obtain optimal predictions, the model has to be individualized based on patients' data. Given a nonlinear model, the result of parameter identification using iterative numerical methods depends on initial estimates. In this work, a feasible hierarchical identification process is proposed and compared to the commonly implemented direct approach with randomized initial values. The hierarchical approach is exemplarily illustrated by identifying the viscoelastic model (VEM) of respiratory mechanics, whose a priori identifiability was proven. To demonstrate its advantages over the direct approach, two different data sources were employed. First, correctness of the approach was shown with simulation data providing controllable conditions. Second, the clinical potential was evaluated under realistic conditions using clinical data from 13 acute respiratory distress syndrome (ARDS) patients. Simulation data revealed that the success rate of the direct approach exponentially decreases with increasing deviation of the initial estimates while the hierarchical approach always obtained the correct solution. The average computing time using clinical data for the direct approach equals 4.77 s (SD = 1.32) and 2.41 s (SD = 0.01) for the hierarchical approach. These investigations demonstrate that a hierarchical approach may be beneficial with respect to robustness and efficiency using simulated and clinical data. PMID:21880567
NASA Astrophysics Data System (ADS)
Sharifi, A.; Kalin, L.; Hantush, M. M.
2013-12-01
The Generalized Likelihood Uncertainty Estimation (GLUE) method is a practical tool for evaluating parameter uncertainty and distinguishing behavioral parameter sets, which are deemed acceptable in reproducing observed behavior of a system, from non-behavioral sets. When a conventional GLUE methodology is applied to a complex geochemical model, depending on the type of observed constituent used for model verification, parameters effecting more than one process might end up having different behavioral distributions. To overcome this problem, we propose a Stepwise GLUE procedure (StepGLUE), which can better identify the behavioral distributions of the model parameters regardless of the data being used for model verification. StepGLUE method uses a three step approach for identifying parameter behavioral domains that produce optimal results for all model constituents. In step 0, model parameters are divided into two groups: group A consisting of parameters exclusive to a single constituent (e.g. denitrification rate, which only effects nitrate pool) and group B consisting of parameters affecting more than constituent (e.g. nitrification rate which engages both ammonia and nitrate related processes). In step 1, for each constituent (like nitrate), we identify the most sensitive parameters through Kolmogorov-Smirnov (KS) test, using a single-constituent likelihood measure. If any of the parameters listed in group A end up being sensitive, new parameter values are generated for them according to their behavioral distributions. This process is necessary to avoid carrying over parameter uncertainty of one constituent to other constituents in the model. At the end of step one, new series of Monte Carlo simulations are performed with modified parameters. In step 2 (parameter oriented phase), we re-evaluate constituent sensitivity to all model parameters using KS test, however, this time the focus is on parameters in group B. For each of the group B parameters that show up in
Langlois, C; Simon, L; Lécuyer, Ch
2003-12-01
A time-dependent box model is developed to calculate oxygen isotope compositions of bone phosphate as a function of environmental and physiological parameters. Input and output oxygen fluxes related to body water and bone reservoirs are scaled to the body mass. The oxygen fluxes are evaluated by stoichiometric scaling to the calcium accretion and resorption rates, assuming a pure hydroxylapatite composition for the bone and tooth mineral. The model shows how the diet composition, body mass, ambient relative humidity and temperature may control the oxygen isotope composition of bone phosphate. The model also computes how bones and teeth record short-term variations in relative humidity, air temperature and delta18O of drinking water, depending on body mass. The documented diversity of oxygen isotope fractionation equations for vertebrates is accounted for by our model when for each specimen the physiological and diet parameters are adjusted in the living range of environmental conditions. PMID:14711171
A Key Challenge in Global HRM: Adding New Insights to Existing Expatriate Spouse Adjustment Models
ERIC Educational Resources Information Center
Gupta, Ritu; Banerjee, Pratyush; Gaur, Jighyasu
2012-01-01
This study is an attempt to strengthen the existing knowledge about factors affecting the adjustment process of the trailing expatriate spouse and the subsequent impact of any maladjustment or expatriate failure. We conducted a qualitative enquiry using grounded theory methodology with 26 Indian spouses who had to deal with their partner's…
ERIC Educational Resources Information Center
Donaldson, Tarryn; Earl, Joanne K.; Muratore, Alexa M.
2010-01-01
Extending earlier research, this study explores individual (e.g. demographic and health characteristics), psychosocial (e.g. mastery and planning) and organizational factors (e.g. conditions of workforce exit) influencing retirement adjustment. Survey data were collected from 570 semi-retired and retired men and women aged 45 years and older.…
ERIC Educational Resources Information Center
Wong, Jessica Y.; Earl, Joanne K.
2009-01-01
This cross-sectional study examines three predictors of retirement adjustment: individual (demographic and health), psychosocial (work centrality), and organizational (conditions of workforce exit). It also examines the effect of work centrality on post-retirement activity levels. Survey data was collected from 394 retirees (aged 45-93 years).…
ERIC Educational Resources Information Center
Ray, Corey E.; Elliott, Stephen N.
2006-01-01
This study examined the hypothesized relationship between social adjustment, as measured by perceived social support, self-concept, and social skills, and performance on academic achievement tests. Participants included 27 teachers and 77 fourth- and eighth-grade students with diverse academic and behavior competencies. Teachers were asked to…
Divorce Stress and Adjustment Model: Locus of Control and Demographic Predictors.
ERIC Educational Resources Information Center
Barnet, Helen Smith
This study depicts the divorce process over three time periods: predivorce decision phase, divorce proper, and postdivorce. Research has suggested that persons with a more internal locus of control experience less intense and shorter intervals of stress during the divorce proper and better postdivorce adjustment than do persons with a more…
ERIC Educational Resources Information Center
Sonderegger, Robi; Barrett, Paula M.; Creed, Peter A.
2004-01-01
Building on previous cultural adjustment profile work by Sonderegger and Barrett (2004), the aim of this study was to propose an organised structure for a number of single risk factors that have been linked to acculturative-stress in young migrants. In recognising that divergent situational characteristics (e.g., school level, gender, residential…
ERIC Educational Resources Information Center
Asberg, Kia K.; Bowers, Clint; Renk, Kimberly; McKinney, Cliff
2008-01-01
Today's society puts constant demands on the time and resources of all individuals, with the resulting stress promoting a decline in psychological adjustment. Emerging adults are not exempt from this experience, with an alarming number reporting excessive levels of stress and stress-related problems. As a result, the present study addresses the…
Barks, C.S.
1995-01-01
Storm-runoff water-quality data were used to verify and, when appropriate, adjust regional regression models previously developed to estimate urban storm- runoff loads and mean concentrations in Little Rock, Arkansas. Data collected at 5 representative sites during 22 storms from June 1992 through January 1994 compose the Little Rock data base. Comparison of observed values (0) of storm-runoff loads and mean concentrations to the predicted values (Pu) from the regional regression models for nine constituents (chemical oxygen demand, suspended solids, total nitrogen, total ammonia plus organic nitrogen as nitrogen, total phosphorus, dissolved phosphorus, total recoverable copper, total recoverable lead, and total recoverable zinc) shows large prediction errors ranging from 63 to several thousand percent. Prediction errors for six of the regional regression models are less than 100 percent, and can be considered reasonable for water-quality models. Differences between 0 and Pu are due to variability in the Little Rock data base and error in the regional models. Where applicable, a model adjustment procedure (termed MAP-R-P) based upon regression with 0 against Pu was applied to improve predictive accuracy. For 11 of the 18 regional water-quality models, 0 and Pu are significantly correlated, that is much of the variation in 0 is explained by the regional models. Five of these 11 regional models consistently overestimate O; therefore, MAP-R-P can be used to provide a better estimate. For the remaining seven regional models, 0 and Pu are not significanfly correlated, thus neither the unadjusted regional models nor the MAP-R-P is appropriate. A simple estimator, such as the mean of the observed values may be used if the regression models are not appropriate. Standard error of estimate of the adjusted models ranges from 48 to 130 percent. Calibration results may be biased due to the limited data set sizes in the Little Rock data base. The relatively large values of
Impact of parameter uncertainty on carbon sequestration modeling
NASA Astrophysics Data System (ADS)
Bandilla, K.; Celia, M. A.
2013-12-01
Geologic carbon sequestration through injection of supercritical carbon dioxide (CO2) into the subsurface is one option to reduce anthropogenic CO¬2 emissions. Widespread industrial-scale deployment, on the order of giga-tonnes of CO2 injected per year, will be necessary for carbon sequestration to make a significant contribution to solving the CO2 problem. Deep saline formations are suitable targets for CO2 sequestration due to their large storage capacity, high injectivity, and favorable pressure and temperature regimes. Due to the large areal extent of saline formations, and the need to inject very large amounts of CO2, multiple sequestration operations are likely to be developed in the same formation. The injection-induced migration of both CO2 and resident formation fluids (brine) needs to be predicted to determine the feasibility of industrial-scale deployment of carbon sequestration. Due to the larger spatial scale of the domain, many of the modeling parameters (e.g., permeability) will be highly uncertain. In this presentation we discuss a sensitivity analysis of both pressure response and CO2 plume migration to variations of model parameters such as permeability, compressibility and temperature. The impact of uncertainty in the stratigraphic succession is also explored. The sensitivity analysis is conducted using a numerical vertically-integrated modeling approach. The Illinois Basin, USA is selected as the test site for this study, due to its large storage capacity and large number of stationary CO2 sources. As there is currently only one active CO2 injection operation in the Illinois Basin, a hypothetical injection scenario is used, where CO2 is injected at the locations of large CO2 emitters related to electricity generation, ethanol production and hydrocarbon refinement. The Area of Review (AoR) is chosen as the comparison metric, as it includes both the CO2 plume size and pressure response.
Incorporation of shuttle CCT parameters in computer simulation models
NASA Technical Reports Server (NTRS)
Huntsberger, Terry
1990-01-01
Computer simulations of shuttle missions have become increasingly important during recent years. The complexity of mission planning for satellite launch and repair operations which usually involve EVA has led to the need for accurate visibility and access studies. The PLAID modeling package used in the Man-Systems Division at Johnson currently has the necessary capabilities for such studies. In addition, the modeling package is used for spatial location and orientation of shuttle components for film overlay studies such as the current investigation of the hydrogen leaks found in the shuttle flight. However, there are a number of differences between the simulation studies and actual mission viewing. These include image blur caused by the finite resolution of the CCT monitors in the shuttle and signal noise from the video tubes of the cameras. During the course of this investigation the shuttle CCT camera and monitor parameters are incorporated into the existing PLAID framework. These parameters are specific for certain camera/lens combinations and the SNR characteristics of these combinations are included in the noise models. The monitor resolution is incorporated using a Gaussian spread function such as that found in the screen phosphors in the shuttle monitors. Another difference between the traditional PLAID generated images and actual mission viewing lies in the lack of shadows and reflections of light from surfaces. Ray tracing of the scene explicitly includes the lighting and material characteristics of surfaces. The results of some preliminary studies using ray tracing techniques for the image generation process combined with the camera and monitor effects are also reported.
NASA Astrophysics Data System (ADS)
Lindholm, Brian E.; West, Robert L.
1994-09-01
A design parameter based update methodology for updating finite models based on the results of experimental dynamics tests is presented. In the proposed method, analyst-selected design parameters are updated with the objective of making realistic changes to a finite element model that will enable the model to more accurately predict the behavior of the structure. This process of 'reconciling' the finite element model with experimental data seeks to bring uncertainty in design parameters into the formulation for realistic updates of the model parameters. The reconciliation process becomes a problem of system identification. Since the finite element model is a spatial model, the high spatial density measurement of the structure's operating shape by the scanning laser-Doppler vibrometer is highly desirable. The reconciliation process updates the selected design parameters by solving a non-linear least-squares problem in which the differences between laser-based velocity measurements and analytically derived structural velocity fields are minimized over the entire structure. In the formulation, design or model parameters with greatest uncertainty are identified first, retaining statistical qualification on the estimates. This method lends itself to cross-validation of the model over the entire structure as well as at several frequencies of interest or over a frequency range. Model order analysis can also be performed within the process to ensure that the correct model is identified. The experimental velocity field is obtained by sinusoidally exciting the test structure at a given frequency and acquiring steady-state velocity data with a scanning laser-Doppler vibrometer. Conceptually, the laser-based measurements are samples of the structure's velocity field of operating shape. The finite element formulation used to generate the analytical steady-state velocity field is derived using either a dynamic stiffness finite element formulation or a static stiffness
Modeling parameter extraction for DNQ-novolak thick film resists
NASA Astrophysics Data System (ADS)
Henderson, Clifford L.; Scheer, Steven A.; Tsiartas, Pavlos C.; Rathsack, Benjamen M.; Sagan, John P.; Dammel, Ralph R.; Erdmann, Andreas; Willson, C. Grant
1998-06-01
Optical lithography with special thick film DNQ-novolac photoresists have been practiced for many years to fabricate microstructures that require feature heights ranging from several to hundreds of microns such as thin film magnetic heads. It is common in these thick film photoresist systems to observe interesting non-uniform profiles with narrow regions near the top surface of the film that transition into broader and more concave shapes near the bottom of the resist profile. A number of explanations have been proposed for these various observations including the formation of `dry skins' at the resist surface and the presence of solvent gradients in the film which serve to modify the local development rate of the photoresist. There have been few detailed experimental studies of the development behavior of thick films resists. This has been due to part to the difficulty in studying these films with conventional dissolution rate monitors (DRMs). In general, this lack of experimental data along with other factors has made simulation and modeling of thick film resist performance difficult. As applications such as thin film head manufacturing drive to smaller features with higher aspect ratios, the need for accurate thick film simulation capability continues to grow. A new multi-wavelength DRM tool has been constructed and used in conjunction with a resist bleaching tool and rigorous parameter extraction techniques to establish exposure and development parameters for two thick film resists, AZTM 4330-RS and AZTM 9200. Simulations based on these parameters show good agreement to resist profiles for these two resists.
NASA Astrophysics Data System (ADS)
Sun, Y.; Hou, Z.; Huang, M.; Tian, F.; Leung, L. R.
2013-04-01
This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC) Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent - as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.
Sun, Yu; Hou, Zhangshuan; Huang, Maoyi; Tian, Fuqiang; Leung, Lai-Yung R.
2013-12-10
This study demonstrates the possibility of inverting hydrologic parameters using surface flux and runoff observations in version 4 of the Community Land Model (CLM4). Previous studies showed that surface flux and runoff calculations are sensitive to major hydrologic parameters in CLM4 over different watersheds, and illustrated the necessity and possibility of parameter calibration. Two inversion strategies, the deterministic least-square fitting and stochastic Markov-Chain Monte-Carlo (MCMC) - Bayesian inversion approaches, are evaluated by applying them to CLM4 at selected sites. The unknowns to be estimated include surface and subsurface runoff generation parameters and vadose zone soil water parameters. We find that using model parameters calibrated by the least-square fitting provides little improvements in the model simulations but the sampling-based stochastic inversion approaches are consistent - as more information comes in, the predictive intervals of the calibrated parameters become narrower and the misfits between the calculated and observed responses decrease. In general, parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or runoff observations. Temporal resolution of observations has larger impacts on the results of inverse modeling using heat flux data than runoff data. Soil and vegetation cover have important impacts on parameter sensitivities, leading to the different patterns of posterior distributions of parameters at different sites. Overall, the MCMC-Bayesian inversion approach effectively and reliably improves the simulation of CLM under different climates and environmental conditions. Bayesian model averaging of the posterior estimates with different reference acceptance probabilities can smooth the posterior distribution and provide more reliable parameter estimates, but at the expense of wider uncertainty bounds.
Modeling soil detachment capacity by rill flow using hydraulic parameters
NASA Astrophysics Data System (ADS)
Wang, Dongdong; Wang, Zhanli; Shen, Nan; Chen, Hao
2016-04-01
The relationship between soil detachment capacity (Dc) by rill flow and hydraulic parameters (e.g., flow velocity, shear stress, unit stream power, stream power, and unit energy) at low flow rates is investigated to establish an accurate experimental model. Experiments are conducted using a 4 × 0.1 m rill hydraulic flume with a constant artificial roughness on the flume bed. The flow rates range from 0.22 × 10-3 m2 s-1 to 0.67 × 10-3 m2 s-1, and the slope gradients vary from 15.8% to 38.4%. Regression analysis indicates that the Dc by rill flow can be predicted using the linear equations of flow velocity, stream power, unit stream power, and unit energy. Dc by rill flow that is fitted to shear stress can be predicted with a power function equation. Predictions based on flow velocity, unit energy, and stream power are powerful, but those based on shear stress, especially on unit stream power, are relatively poor. The prediction based on flow velocity provides the best estimates of Dc by rill flow because of the simplicity and availability of its measurements. Owing to error in measuring flow velocity at low flow rates, the predictive abilities of Dc by rill flow using all hydraulic parameters are relatively lower in this study compared with the results of previous research. The measuring accuracy of experiments for flow velocity should be improved in future research.
Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model
NASA Astrophysics Data System (ADS)
Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.
2012-12-01
Agro-Land Surface Models (agro-LSM) have been developed from the coupling of specific crop models and large-scale generic vegetation models. They aim at accounting for the spatial distribution and variability of energy, water and carbon fluxes within soil-vegetation-atmosphere continuum with a particular emphasis on how crop phenology and agricultural management practice influence the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty in these models is related to the many parameters included in the models' equations. In this study, we quantify the parameter-based uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS on a multi-regional approach with data from sites in Australia, La Reunion and Brazil. First, the main source of uncertainty for the output variables NPP, GPP, and sensible heat flux (SH) is determined through a screening of the main parameters of the model on a multi-site basis leading to the selection of a subset of most sensitive parameters causing most of the uncertainty. In a second step, a sensitivity analysis is carried out on the parameters selected from the screening analysis at a regional scale. For this, a Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used. First, we quantify the sensitivity of the output variables to individual input parameters on a regional scale for two regions of intensive sugar cane cultivation in Australia and Brazil. Then, we quantify the overall uncertainty in the simulation's outputs propagated from the uncertainty in the input parameters. Seven parameters are identified by the screening procedure as driving most of the uncertainty in the agro-LSM ORCHIDEE-STICS model output at all sites. These parameters control photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), root
Extracting Structure Parameters of Dimers for Molecular Tunneling Ionization Model
NASA Astrophysics Data System (ADS)
Song-Feng, Zhao; Fang, Huang; Guo-Li, Wang; Xiao-Xin, Zhou
2016-03-01
We determine structure parameters of the highest occupied molecular orbital (HOMO) of 27 dimers for the molecular tunneling ionization (so called MO-ADK) model of Tong et al. [Phys. Rev. A 66 (2002) 033402]. The molecular wave functions with correct asymptotic behavior are obtained by solving the time-independent Schrödinger equation with B-spline functions and molecular potentials which are numerically created using the density functional theory. We examine the alignment-dependent tunneling ionization probabilities from MO-ADK model for several molecules by comparing with the molecular strong-field approximation (MO-SFA) calculations. We show the molecular Perelomov–Popov–Terent'ev (MO-PPT) can successfully give the laser wavelength dependence of ionization rates (or probabilities). Based on the MO-PPT model, two diatomic molecules having valence orbital with antibonding systems (i.e., Cl2, Ne2) show strong ionization suppression when compared with their corresponding closest companion atoms. Supported by National Natural Science Foundation of China under Grant Nos. 11164025, 11264036, 11465016, 11364038, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20116203120001, and the Basic Scientific Research Foundation for Institution of Higher Learning of Gansu Province
Extracting Structure Parameters of Dimers for Molecular Tunneling Ionization Model
NASA Astrophysics Data System (ADS)
Zhao, Song-Feng; Huang, Fang; Wang, Guo-Li; Zhou, Xiao-Xin
2016-03-01
We determine structure parameters of the highest occupied molecular orbital (HOMO) of 27 dimers for the molecular tunneling ionization (so called MO-ADK) model of Tong et al. [Phys. Rev. A 66 (2002) 033402]. The molecular wave functions with correct asymptotic behavior are obtained by solving the time-independent Schrödinger equation with B-spline functions and molecular potentials which are numerically created using the density functional theory. We examine the alignment-dependent tunneling ionization probabilities from MO-ADK model for several molecules by comparing with the molecular strong-field approximation (MO-SFA) calculations. We show the molecular Perelomov-Popov-Terent'ev (MO-PPT) can successfully give the laser wavelength dependence of ionization rates (or probabilities). Based on the MO-PPT model, two diatomic molecules having valence orbital with antibonding systems (i.e., Cl2, Ne2) show strong ionization suppression when compared with their corresponding closest companion atoms. Supported by National Natural Science Foundation of China under Grant Nos. 11164025, 11264036, 11465016, 11364038, the Specialized Research Fund for the Doctoral Program of Higher Education of China under Grant No. 20116203120001, and the Basic Scientific Research Foundation for Institution of Higher Learning of Gansu Province
Sound propagation and absorption in foam - A distributed parameter model.
NASA Technical Reports Server (NTRS)
Manson, L.; Lieberman, S.
1971-01-01
Liquid-base foams are highly effective sound absorbers. A better understanding of the mechanisms of sound absorption in foams was sought by exploration of a mathematical model of bubble pulsation and coupling and the development of a distributed-parameter mechanical analog. A solution by electric-circuit analogy was thus obtained and transmission-line theory was used to relate the physical properties of the foams to the characteristic impedance and propagation constants of the analog transmission line. Comparison of measured physical properties of the foam with values obtained from measured acoustic impedance and propagation constants and the transmission-line theory showed good agreement. We may therefore conclude that the sound propagation and absorption mechanisms in foam are accurately described by the resonant response of individual bubbles coupled to neighboring bubbles.
NASA Astrophysics Data System (ADS)
Wentworth, Mami Tonoe
Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification
NASA Astrophysics Data System (ADS)
Cotroneo, Vincenzo; Davis, William N.; Reid, Paul B.; Schwartz, Daniel A.; Trolier-McKinstry, Susan; Wilke, Rudeger H. T.
2011-09-01
The present generation of X-ray telescopes emphasizes either high image quality (e.g. Chandra with sub-arc second resolution) or large effective area (e.g. XMM-Newton), while future observatories under consideration (e.g. Athena, AXSIO) aim to greatly enhance the effective area, while maintaining moderate (~10 arc-seconds) image quality. To go beyond the limits of present and planned missions, the use of thin adjustable optics for the control of low-order figure error is needed to obtain the high image quality of precisely figured mirrors along with the large effective area of thin mirrors. The adjustable mirror prototypes under study at Smithsonian Astrophysical Observatory are based on two different principles and designs: 1) thin film lead-zirconate-titanate (PZT) piezoelectric actuators directly deposited on the mirror back surface, with the strain direction parallel to the glass surface (for sub-arc-second angular resolution and large effective area), and 2) conventional leadmagnesium- niobate (PMN) electrostrictive actuators with their strain direction perpendicular to the mirror surface (for 3-5 arc second resolution and moderate effective area). We have built and operated flat test mirrors of these adjustable optics. We present the comparison between theoretical influence functions as obtained by finite element analysis and the measured influence functions obtained from the two test configurations.
Bertipaglia, T S; Carreño, L O D; Aspilcueta-Borquis, R R; Boligon, A A; Farah, M M; Gomes, F J; Machado, C H C; Rey, F S B; da Fonseca, R
2015-08-01
Random regression models (RRM) and multitrait models (MTM) were used to estimate genetic parameters for growth traits in Brazilian Brahman cattle and to compare the estimated breeding values obtained by these 2 methodologies. For RRM, 78,641 weight records taken between 60 and 550 d of age from 16,204 cattle were analyzed, and for MTM, the analysis consisted of 17,385 weight records taken at the same ages from 12,925 cattle. All models included the fixed effects of contemporary group and the additive genetic, maternal genetic, and animal permanent environmental effects and the quadratic effect of age at calving (AAC) as covariate. For RRM, the AAC was nested in the animal's age class. The best RRM considered cubic polynomials and the residual variance heterogeneity (5 levels). For MTM, the weights were adjusted for standard ages. For RRM, additive heritability estimates ranged from 0.42 to 0.75, and for MTM, the estimates ranged from 0.44 to 0.72 for both models at 60, 120, 205, 365, and 550 d of age. The maximum maternal heritability estimate (0.08) was at 140 d for RRM, but for MTM, it was highest at weaning (0.09). The magnitude of the genetic correlations was generally from moderate to high. The RRM adequately modeled changes in variance or covariance with age, and provided there was sufficient number of samples, increased accuracy in the estimation of the genetic parameters can be expected. Correlation of bull classifications were different in both methods and at all the ages evaluated, especially at high selection intensities, which could affect the response to selection. PMID:26440161
Achleitner, S; Rinderer, M; Kirnbauer, R
2009-01-01
For the Tyrolean part of the river Inn, a hybrid model for flood forecast has been set up and is currently in its test phase. The system is a hybrid system which comprises of a hydraulic 1D model for the river Inn, and the hydrological models HQsim (Rainfall-runoff-discharge model) and the snow and ice melt model SES for modeling the rainfall runoff form non-glaciated and glaciated tributary catchment respectively. Within this paper the focus is put on the hydrological modeling of the totally 49 connected non-glaciated catchments realized with the software HQsim. In the course of model calibration, the identification of the most sensitive parameters is important aiming at an efficient calibration procedure. The indicators used for explaining the parameter sensitivities were chosen specifically for the purpose of flood forecasting. Finally five model parameters could be identified as being sensitive for model calibration when aiming for a well calibrated model for flood conditions. In addition two parameters were identified which are sensitive in situations where the snow line plays an important role. PMID:19759453
Shapiro, Y; Moran, D; Epstein, Y; Stroschein, L; Pandolf, K B
1995-05-01
Under outdoor conditions this model was over estimating sweat loss response in shaded (low solar radiation) environments, and underestimating the response when solar radiation was high (open field areas). The present study was conducted in order to adjust the model to be applicable under outdoor environmental conditions. Four groups of fit acclimated subjects participated in the study. They were exposed to three climatic conditions (30 degrees, 65% rh; 31 degrees C, 40% rh; and 40 degrees C, 20% rh) and three levels of metabolic rate (100, 300 and 450 W) in shaded and sunny areas while wearing shorts, cotton fatigues (BDUs) or protective garments. The original predictive equation for sweat loss was adjusted for the outdoor conditions by evaluating separately the radiative heat exchange, short-wave absorption in the body and long-wave emission from the body to the atmosphere and integrating them in the required evaporation component (Ereq) of the model, as follows: Hr = 1.5SL0.6/I(T) (watt) H1 = 0.047Me.th/I(T) (watt), where SL is solar radiation (W.m-2), Me.th is the Stephan Boltzman constant, and I(T) is the effective clothing insulation coefficient. This adjustment revealed a high correlation between the measured and expected values of sweat loss (r = 0.99, p < 0.0001). PMID:7737107
NASA Astrophysics Data System (ADS)
Sun, Y.; Hou, Z.; Huang, M.; Tian, F.; Leung, L.
2012-12-01
This study aims at demonstrating the possibility of calibrating hydrologic parameters using surface flux and streamflow observations in version 4 of the Community Land Model (CLM4). Previously we showed that surface flux and streamflow calculations are sensitive to several key hydrologic parameters in CLM4, and discussed the necessity and possibility of parameter calibration. In this study, we evaluate performances of several different inversion strategies, including least-square fitting, quasi Monte-Carlo (QMC) sampling based Bayesian updating, and a Markov-Chain Monte-Carlo (MCMC) Bayesian inversion approach. The parameters to be calibrated include the surface and subsurface runoff generation parameters and vadose zone soil water parameters. We discuss the effects of surface flux and streamflow observations on the inversion results and compare their consistency and reliability using both monthly and daily observations at various flux tower and MOPEX sites. We find that the sampling-based stochastic inversion approaches behaved consistently - as more information comes in, the predictive intervals of the calibrated parameters as well as the misfits between the calculated and observed observations decrease. In general, the parameters that are identified to be significant through sensitivity analyses and statistical tests are better calibrated than those with weak or nonlinear impacts on flux or streamflow observations. We also evaluated the possibility of probabilistic model averaging for more consistent parameter estimation.
Parameter Estimation and Parameterization Uncertainty Using Bayesian Model Averaging
NASA Astrophysics Data System (ADS)
Tsai, F. T.; Li, X.
2007-12-01
This study proposes Bayesian model averaging (BMA) to address parameter estimation uncertainty arisen from non-uniqueness in parameterization methods. BMA provides a means of incorporating multiple parameterization methods for prediction through the law of total probability, with which an ensemble average of hydraulic conductivity distribution is obtained. Estimation uncertainty is described by the BMA variances, which contain variances within and between parameterization methods. BMA shows the facts that considering more parameterization methods tends to increase estimation uncertainty and estimation uncertainty is always underestimated using a single parameterization method. Two major problems in applying BMA to hydraulic conductivity estimation using a groundwater inverse method will be discussed in the study. The first problem is the use of posterior probabilities in BMA, which tends to single out one best method and discard other good methods. This problem arises from Occam's window that only accepts models in a very narrow range. We propose a variance window to replace Occam's window to cope with this problem. The second problem is the use of Kashyap information criterion (KIC), which makes BMA tend to prefer high uncertain parameterization methods due to considering the Fisher information matrix. We found that Bayesian information criterion (BIC) is a good approximation to KIC and is able to avoid controversial results. We applied BMA to hydraulic conductivity estimation in the 1,500-foot sand aquifer in East Baton Rouge Parish, Louisiana.
Dynamic parameters in models of atmospheric vortex structures
NASA Astrophysics Data System (ADS)
Dobryshman, E. M.; Kochina, V. G.; Letunova, T. A.
2013-09-01
Vortex simulation and the computation of fields of dynamic parameters of vortex structures (velocity, rotor velocity, and helicity) are carried out with the use of exact hydrodynamic equations in a cylindrical coordinate system. Components of centripetal and Coriolis accelerations are taken into account in the initial equations. Internal and external solutions are defined. Internal solutions ignore the disturbances of the pressure field, but they are considered in external solutions. The simulation is carried out so that the effect of accounting for spatial coordinates on the structure of the above fields is pronounced. It is shown that the initial kinetic energy of rotating motion transforms into the kinetic energy of radial and vertical velocity components in models with centripetal acceleration. In models with Coriolis acceleration, the Rossby effect is clearly pronounced. The method of an "inverse problem" is used for finding external solutions, i.e., reconstruction of the pressure field at specified velocity components. Computations have shown that tangential components mainly contribute to the velocity and helicity vortex moduli at the initial stage.
FEM numerical model study of electrosurgical dispersive electrode design parameters.
Pearce, John A
2015-08-01
Electrosurgical dispersive electrodes must safely carry the surgical current in monopolar procedures, such as those used in cutting, coagulation and radio frequency ablation (RFA). Of these, RFA represents the most stringent design constraint since ablation currents are often more than 1 to 2 Arms (continuous) for several minutes depending on the size of the lesion desired and local heat transfer conditions at the applicator electrode. This stands in contrast to standard surgical activations, which are intermittent, and usually less than 1 Arms, but for several seconds at a time. Dispersive electrode temperature rise is also critically determined by the sub-surface skin anatomy, thicknesses of the subcutaneous and supra-muscular fat, etc. Currently, we lack fundamental engineering design criteria that provide an estimating framework for preliminary designs of these electrodes. The lack of a fundamental design framework means that a large number of experiments must be conducted in order to establish a reasonable design. Previously, an attempt to correlate maximum temperatures in experimental work with the average current density-time product failed to yield a good match. This paper develops and applies a new measure of an electrode stress parameter that correlates well with both the previous experimental data and with numerical models of other electrode shapes. The finite element method (FEM) model work was calibrated against experimental RF lesions in porcine skin to establish the fundamental principle underlying dispersive electrode performance. The results can be used in preliminary electrode design calculations, experiment series design and performance evaluation. PMID:26736814
Mechanical models for insect locomotion: stability and parameter studies
NASA Astrophysics Data System (ADS)
Schmitt, John; Holmes, Philip
2001-08-01
We extend the analysis of simple models for the dynamics of insect locomotion in the horizontal plane, developed in [Biol. Cybern. 83 (6) (2000) 501] and applied to cockroach running in [Biol. Cybern. 83 (6) (2000) 517]. The models consist of a rigid body with a pair of effective legs (each representing the insect’s support tripod) placed intermittently in ground contact. The forces generated may be prescribed as functions of time, or developed by compression of a passive leg spring. We find periodic gaits in both cases, and show that prescribed (sinusoidal) forces always produce unstable gaits, unless they are allowed to rotate with the body during stride, in which case a (small) range of physically unrealistic stable gaits does exist. Stability is much more robust in the passive spring case, in which angular momentum transfer at touchdown/liftoff can result in convergence to asymptotically straight motions with bounded yaw, fore-aft and lateral velocity oscillations. Using a non-dimensional formulation of the equations of motion, we also develop exact and approximate scaling relations that permit derivation of gait characteristics for a range of leg stiffnesses, lengths, touchdown angles, body masses and inertias, from a single gait family computed at ‘standard’ parameter values.
Sensitivity of numerical dispersion modeling to explosive source parameters
Baskett, R.L. ); Cederwall, R.T. )
1991-02-13
The calculation of downwind concentrations from non-traditional sources, such as explosions, provides unique challenges to dispersion models. The US Department of Energy has assigned the Atmospheric Release Advisory Capability (ARAC) at the Lawrence Livermore National Laboratory (LLNL) the task of estimating the impact of accidental radiological releases to the atmosphere anywhere in the world. Our experience includes responses to over 25 incidents in the past 16 years, and about 150 exercises a year. Examples of responses to explosive accidents include the 1980 Titan 2 missile fuel explosion near Damascus, Arkansas and the hydrogen gas explosion in the 1986 Chernobyl nuclear power plant accident. Based on judgment and experience, we frequently estimate the source geometry and the amount of toxic material aerosolized as well as its particle size distribution. To expedite our real-time response, we developed some automated algorithms and default assumptions about several potential sources. It is useful to know how well these algorithms perform against real-world measurements and how sensitive our dispersion model is to the potential range of input values. In this paper we present the algorithms we use to simulate explosive events, compare these methods with limited field data measurements, and analyze their sensitivity to input parameters. 14 refs., 7 figs., 2 tabs.
Constitutive modeling of ascending thoracic aortic aneurysms using microstructural parameters.
Pasta, Salvatore; Phillippi, Julie A; Tsamis, Alkiviadis; D'Amore, Antonio; Raffa, Giuseppe M; Pilato, Michele; Scardulla, Cesare; Watkins, Simon C; Wagner, William R; Gleason, Thomas G; Vorp, David A
2016-02-01
Ascending thoracic aortic aneurysm (ATAA) has been associated with diminished biomechanical strength and disruption in the collagen fiber microarchitecture. Additionally, the congenital bicuspid aortic valve (BAV) leads to a distinct extracellular matrix structure that may be related to ATAA development at an earlier age than degenerative aneurysms arising in patients with the morphological normal tricuspid aortic valve (TAV). The purpose of this study was to model the fiber-reinforced mechanical response of ATAA specimens from patients with either BAV or TAV. This was achieved by combining image-analysis derived parameters of collagen fiber dispersion and alignment with tensile testing data. Then, numerical simulations were performed to assess the role of anisotropic constitutive formulation on the wall stress distribution of aneurysmal aorta. Results indicate that both BAV ATAA and TAV ATAA have altered collagen fiber architecture in the medial plane of experimentally-dissected aortic tissues when compared to normal ascending aortic specimens. The study findings highlight that differences in the collagen fiber distribution mostly influences the resulting wall stress distribution rather than the peak stress. We conclude that fiber-reinforced constitutive modeling that takes into account the collagen fiber defect inherent to the aneurysmal ascending aorta is paramount for accurate finite element predictions and ultimately for biomechanical-based indicators to reliably distinguish the more from the less 'malignant' ATAAs. PMID:26669606
NASA Astrophysics Data System (ADS)
Kim, Tae-Jeong; Kim, Ki-Young; Shin, Dong-Hoon; Kwon, Hyun-Han
2015-04-01
It has been widely acknowledged that the appropriate simulation of natural streamflow at ungauged sites is one of the fundamental challenges to hydrology community. In particular, the key to reliable runoff simulation in ungauged basins is a reliable rainfall-runoff model and a parameter estimation. In general, parameter estimation in rainfall-runoff models is a complex issue due to an insufficient hydrologic data. This study aims to regionalize the parameters of the continuous rainfall-runoff model in conjunction with Bayesian statistical techniques to facilitate uncertainty analysis. First, this study uses the Bayesian Markov Chain Monte Carlo scheme for the Sacramento rainfall-runoff model that has been widely used around the world. The Sacramento model is calibrated against daily runoff observation, and thirteen parameters of the model are optimized as well as posterior distributor distributions for each parameter are derived. Second, we applied Bayesian generalized linear regression model to set of the parameters with basin characteristics (e.g. area and slope), to obtain a functional relationship between pairs of variables. The proposed model was validated in two gauged watersheds in accordance with the efficiency criteria such as the Nash-Sutcliffe efficiency, coefficient of efficiency, index of agreement and coefficient of correlation. The future study will be further focused on uncertainty analysis to fully incorporate propagation of the uncertainty into the regionalization framework. KEYWORDS: Ungauge, Parameter, Sacramento, Generalized linear model, Regionalization Acknowledgement This research was supported by a Grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by the Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and the Korea Agency for Infrastructure Technology Advancement (KAIA).
NKG201xGIA - first results for a new model of glacial isostatic adjustment in Fennoscandia
NASA Astrophysics Data System (ADS)
Steffen, Holger; Barletta, Valentina; Kollo, Karin; Milne, Glenn A.; Nordman, Maaria; Olsson, Per-Anders; Simpson, Matthew J. R.; Tarasov, Lev; Ågren, Jonas
2016-04-01
Glacial isostatic adjustment (GIA) is a dominant process in northern Europe, which is observed with several geodetic and geophysical methods. The observed land uplift due to this process amounts to about 1 cm/year in the northern Gulf of Bothnia. GIA affects the establishment and maintenance of reliable geodetic and gravimetric reference networks in the Nordic countries. To support a high level of accuracy in the determination of position, adequate corrections have to be applied with dedicated models. Currently, there are efforts within a Nordic Geodetic Commission (NKG) activity towards a model of glacial isostatic adjustment for Fennoscandia. The new model, NKG201xGIA, to be developed in the near future will complement the forthcoming empirical NKG land uplift model, which will substitute the currently used empirical land uplift model NKG2005LU (Ågren & Svensson, 2007). Together, the models will be a reference for vertical and horizontal motion, gravity and geoid change and more. NKG201xGIA will also provide uncertainty estimates for each field. Following former investigations, the GIA model is based on a combination of an ice and an earth model. The selected reference ice model, GLAC, for Fennoscandia, the Barents/Kara seas and the British Isles is provided by Lev Tarasov and co-workers. Tests of different ice and earth models will be performed based on the expertise of each involved modeler. This includes studies on high resolution ice sheets, different rheologies, lateral variations in lithosphere and mantle viscosity and more. This will also be done in co-operation with scientists outside NKG who help in the development and testing of the model. References Ågren, J., Svensson, R. (2007): Postglacial Land Uplift Model and System Definition for the New Swedish Height System RH 2000. Reports in Geodesy and Geographical Information Systems Rapportserie, LMV-Rapport 4, Lantmäteriet, Gävle.
Hard-coded parameters have the largest impact on fluxes of the land surface model Noah-MP
NASA Astrophysics Data System (ADS)
Cuntz, M.; Mai, J.; Samaniego, L. E.; Clark, M. P.; Wulfmeyer, V.; Attinger, S.; Thober, S.
2015-12-01
Land surface models incorporate a large number of processes, described by physical and empirical equations. The agilityof the models to react to different meteorological conditions is artificially constrained by having hard-codedparameters in their equations. The land surface model Noah with multiple process options (Noah-MP) is one of the standard land surface schemes in WRFand gives the flexibility to experiment with several model parameterizations of biophysical and hydrologicalprocesses. The model has around 80 parameters per plant functional type or soil class, which are given in tabulatedform and which can be adjusted. Here we looked into the model code in considerable detail and found another 140hard-coded values in all parameterizations, called hidden parameters here, of which around 50-60 are active inspecific combinations of the process options. We quantify global parametric sensitivities (SI) for the traditional and the hidden parameters for five model outputsin 12 MOPEX catchments of very different local hydro-meteorologies. Outputs are photosynthesis, transpiration,latent heat, surface and underground runoff. Photosynthesis is mostly sensitive to parameters describing plant physiology. Its second largest SI is for a hiddenparameter that partitions incoming into direct and diffuse radiation. Transpiration shows very similar SI asphotosynthesis. The SI of latent heat are, however, very different to transpiration. Its largest SI is observed for ahidden parameter in the formulation of soil surface resistance, due to low transpiration in Noah-MP. Surface runoff ismostly sensitive to soil and infiltration parameters. But it is also sensitive to almost all hidden snow parameters,which are about 40% of all hidden parameters. The largest SI of surface runoff is to the albedo of fresh snow and thesecond largest to the thermal conductivity of snow. Sensitive parameters for underground runoff, finally, are a mixtureof those of latent heat and surface runoff. In
Simultaneous model discrimination and parameter estimation in dynamic models of cellular systems
2013-01-01
Background Model development is a key task in systems biology, which typically starts from an initial model candidate and, involving an iterative cycle of hypotheses-driven model modifications, leads to new experimentation and subsequent model identification steps. The final product of this cycle is a satisfactory refined model of the biological phenomena under study. During such iterative model development, researchers frequently propose a set of model candidates from which the best alternative must be selected. Here we consider this problem of model selection and formulate it as a simultaneous model selection and parameter identification problem. More precisely, we consider a general mixed-integer nonlinear programming (MINLP) formulation for model selection and identification, with emphasis on dynamic models consisting of sets of either ODEs (ordinary differential equations) or DAEs (differential algebraic equations). Results We solved the MINLP formulation for model selection and identification using an algorithm based on Scatter Search (SS). We illustrate the capabilities and efficiency of the proposed strategy with a case study considering the KdpD/KdpE system regulating potassium homeostasis in Escherichia coli. The proposed approach resulted in a final model that presents a better fit to the in silico generated experimental data. Conclusions The presented MINLP-based optimization approach for nested-model selection and identification is a powerful methodology for model development in systems biology. This strategy can be used to perform model selection and parameter estimation in one single step, thus greatly reducing the number of experiments and computations of traditional modeling approaches. PMID:23938131
Tommasi, C.; May, C.
2010-09-30
The DKL-optimality criterion has been recently proposed for the dual problem of model discrimination and parameter estimation, for the case of two rival models. A sequential version of the DKL-optimality criterion is herein proposed in order to discriminate and efficiently estimate more than two nested non-linear models. Our sequential method is inspired by the procedure of Biswas and Chaudhuri (2002), which is however useful only in the set up of nested linear models.
Kleinman, Lawrence C; Norton, Edward C
2009-01-01
Objective To develop and validate a general method (called regression risk analysis) to estimate adjusted risk measures from logistic and other nonlinear multiple regression models. We show how to estimate standard errors for these estimates. These measures could supplant various approximations (e.g., adjusted odds ratio [AOR]) that may diverge, especially when outcomes are common. Study Design Regression risk analysis estimates were compared with internal standards as well as with Mantel–Haenszel estimates, Poisson and log-binomial regressions, and a widely used (but flawed) equation to calculate adjusted risk ratios (ARR) from AOR. Data Collection Data sets produced using Monte Carlo simulations. Principal Findings Regression risk analysis accurately estimates ARR and differences directly from multiple regression models, even when confounders are continuous, distributions are skewed, outcomes are common, and effect size is large. It is statistically sound and intuitive, and has properties favoring it over other methods in many cases. Conclusions Regression risk analysis should be the new standard for presenting findings from multiple regression analysis of dichotomous outcomes for cross-sectional, cohort, and population-based case–control studies, particularly when outcomes are common or effect size is large. PMID:18793213
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, Va: American Psychiatric Publishing. 2013. Powell AD. Grief, bereavement, and adjustment disorders. In: Stern TA, Rosenbaum ...
NASA Astrophysics Data System (ADS)
Swaminathan-Gopalan, Krishnan; Stephani, Kelly A.
2016-02-01
A systematic approach for calibrating the direct simulation Monte Carlo (DSMC) collision model parameters to achieve consistency in the transport processes is presented. The DSMC collision cross section model parameters are calibrated for high temperature atmospheric conditions by matching the collision integrals from DSMC against ab initio based collision integrals that are currently employed in the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) and Data Parallel Line Relaxation (DPLR) high temperature computational fluid dynamics solvers. The DSMC parameter values are computed for the widely used Variable Hard Sphere (VHS) and the Variable Soft Sphere (VSS) models using the collision-specific pairing approach. The recommended best-fit VHS/VSS parameter values are provided over a temperature range of 1000-20 000 K for a thirteen-species ionized air mixture. Use of the VSS model is necessary to achieve consistency in transport processes of ionized gases. The agreement of the VSS model transport properties with the transport properties as determined by the ab initio collision integral fits was found to be within 6% in the entire temperature range, regardless of the composition of the mixture. The recommended model parameter values can be readily applied to any gas mixture involving binary collisional interactions between the chemical species presented for the specified temperature range.
Accurate analytical method for the extraction of solar cell model parameters
NASA Astrophysics Data System (ADS)
Phang, J. C. H.; Chan, D. S. H.; Phillips, J. R.
1984-05-01
Single diode solar cell model parameters are rapidly extracted from experimental data by means of the presently derived analytical expressions. The parameter values obtained have a less than 5 percent error for most solar cells, in light of the extraction of model parameters for two cells of differing quality which were compared with parameters extracted by means of the iterative method.
Observation model and parameter partials for the JPL geodetic GPS modeling software GPSOMC
NASA Technical Reports Server (NTRS)
Sovers, O. J.; Border, J. S.
1988-01-01
The physical models employed in GPSOMC and the modeling module of the GIPSY software system developed at JPL for analysis of geodetic Global Positioning Satellite (GPS) measurements are described. Details of the various contributions to range and phase observables are given, as well as the partial derivatives of the observed quantities with respect to model parameters. A glossary of parameters is provided to enable persons doing data analysis to identify quantities in the current report with their counterparts in the computer programs. There are no basic model revisions, with the exceptions of an improved ocean loading model and some new options for handling clock parametrization. Such misprints as were discovered were corrected. Further revisions include modeling improvements and assurances that the model description is in accord with the current software.
ERIC Educational Resources Information Center
DeAyala, R. J.; Koch, William R.
A nominal response model-based computerized adaptive testing procedure (nominal CAT) was implemented using simulated data. Ability estimates from the nominal CAT were compared to those from a CAT based upon the three-parameter logistic model (3PL CAT). Furthermore, estimates from both CAT procedures were compared with the known true abilities used…
Rock thermal conductivity as key parameter for geothermal numerical models
NASA Astrophysics Data System (ADS)
Di Sipio, Eloisa; Chiesa, Sergio; Destro, Elisa; Galgaro, Antonio; Giaretta, Aurelio; Gola, Gianluca; Manzella, Adele
2013-04-01
The geothermal energy applications are undergoing a rapid development. However, there are still several challenges in the successful exploitation of geothermal energy resources. In particular, a special effort is required to characterize the thermal properties of the ground along with the implementation of efficient thermal energy transfer technologies. This paper focuses on understanding the quantitative contribution that geosciences can receive from the characterization of rock thermal conductivity. The thermal conductivity of materials is one of the main input parameters in geothermal modeling since it directly controls the steady state temperature field. An evaluation of this thermal property is required in several fields, such as Thermo-Hydro-Mechanical multiphysics analysis of frozen soils, designing ground source heat pumps plant, modeling the deep geothermal reservoirs structure, assessing the geothermal potential of subsoil. Aim of this study is to provide original rock thermal conductivity values useful for the evaluation of both low and high enthalpy resources at regional or local scale. To overcome the existing lack of thermal conductivity data of sedimentary, igneous and metamorphic rocks, a series of laboratory measurements has been performed on several samples, collected in outcrop, representative of the main lithologies of the regions included in the VIGOR Project (southern Italy). Thermal properties tests were carried out both in dry and wet conditions, using a C-Therm TCi device, operating following the Modified Transient Plane Source method.Measurements were made at standard laboratory conditions on samples both water saturated and dehydrated with a fan-forced drying oven at 70 ° C for 24 hr, for preserving the mineral assemblage and preventing the change of effective porosity. Subsequently, the samples have been stored in an air-conditioned room while bulk density, solid volume and porosity were detected. The measured thermal conductivity
Model-Based Material Parameter Estimation for Terahertz Reflection Spectroscopy
NASA Astrophysics Data System (ADS)
Kniffin, Gabriel Paul
Many materials such as drugs and explosives have characteristic spectral signatures in the terahertz (THz) band. These unique signatures imply great promise for spectral detection and classification using THz radiation. While such spectral features are most easily observed in transmission, real-life imaging systems will need to identify materials of interest from reflection measurements, often in non-ideal geometries. One important, yet commonly overlooked source of signal corruption is the etalon effect -- interference phenomena caused by multiple reflections from dielectric layers of packaging and clothing likely to be concealing materials of interest in real-life scenarios. This thesis focuses on the development and implementation of a model-based material parameter estimation technique, primarily for use in reflection spectroscopy, that takes the influence of the etalon effect into account. The technique is adapted from techniques developed for transmission spectroscopy of thin samples and is demonstrated using measured data taken at the Northwest Electromagnetic Research Laboratory (NEAR-Lab) at Portland State University. Further tests are conducted, demonstrating the technique's robustness against measurement noise and common sources of error.
Observation model and parameter partials for the JPL geodetic (GPS) modeling software 'GPSOMC'
NASA Technical Reports Server (NTRS)
Sovers, O. J.
1990-01-01
The physical models employed in GPSOMC, the modeling module of the GIPSY software system developed at JPL for analysis of geodetic Global Positioning Satellite (GPS) measurements are described. Details of the various contributions to range and phase observables are given, as well as the partial derivatives of the observed quantities with respect to model parameters. A glossary of parameters is provided to enable persons doing data analysis to identify quantities with their counterparts in the computer programs. The present version is the second revision of the original document which it supersedes. The modeling is expanded to provide the option of using Cartesian station coordinates; parameters for the time rates of change of universal time and polar motion are also introduced.
NASA Astrophysics Data System (ADS)
Srivastava, Prashant K.; O'Neill, Peggy; Han, Dawei; Rico-Ramirez, Miguel A.; Petropoulos, George P.; Islam, Tanvir; Gupta, Manika
2015-04-01
Roughness parameterization is necessary for nearly all soil moisture retrieval algorithms such as single or dual channel algorithms, L-band Microwave Emission of Biosphere (LMEB), Land Parameter Retrieval Model (LPRM), etc. At present, roughness parameters can be obtained either by field experiments, although obtaining field measurements all over the globe is nearly impossible, or by using a land cover-based look up table, which is not always accurate everywhere for individual fields. From a catalogue of models available in the technical literature domain, the LPRM model was used here because of its robust nature and applicability to a wide range of frequencies. LPRM needs several parameters for soil moisture retrieval -- in particular, roughness parameters (h and Q) are important for calculating reflectivity. In this study, the h and Q parameters are optimized using the soil moisture deficit (SMD) estimated from the probability distributed model (PDM) and Soil Moisture and Ocean Salinity (SMOS) brightness temperatures following the Levenberg-Marquardt (LM) algorithm over the Brue catchment, Southwest of England, U.K.. The catchment is predominantly a pasture land with moderate topography. The PDM-based SMD is used as it is calibrated and validated using locally available ground-based information, suitable for large scale areas such as catchments. The optimal h and Q parameters are determined by maximizing the correlation between SMD and LPRM retrieved soil moisture. After optimization the values of h and Q have been found to be 0.32 and 0.15, respectively. For testing the usefulness of the estimated roughness parameters, a separate set of SMOS datasets are taken into account for soil moisture retrieval using the LPRM model and optimized roughness parameters. The overall analysis indicates a satisfactory result when compared against the SMD information. This work provides quantitative values of roughness parameters suitable for large scale applications. The
Observation model and parameter partials for the JPL VLBI parameter estimation software MODEST/1991
NASA Technical Reports Server (NTRS)
Sovers, O. J.
1991-01-01
A revision is presented of MASTERFIT-1987, which it supersedes. Changes during 1988 to 1991 included introduction of the octupole component of solid Earth tides, the NUVEL tectonic motion model, partial derivatives for the precession constant and source position rates, the option to correct for source structure, a refined model for antenna offsets, modeling the unique antenna at Richmond, FL, improved nutation series due to Zhu, Groten, and Reigber, and reintroduction of the old (Woolard) nutation series for simulation purposes. Text describing the relativistic transformations and gravitational contributions to the delay model was also revised in order to reflect the computer code more faithfully.
Multi-Variable Model-Based Parameter Estimation Model for Antenna Radiation Pattern Prediction
NASA Technical Reports Server (NTRS)
Deshpande, Manohar D.; Cravey, Robin L.
2002-01-01
A new procedure is presented to develop multi-variable model-based parameter estimation (MBPE) model to predict far field intensity of antenna. By performing MBPE model development procedure on a single variable at a time, the present method requires solution of smaller size matrices. The utility of the present method is demonstrated by determining far field intensity due to a dipole antenna over a frequency range of 100-1000 MHz and elevation angle range of 0-90 degrees.
A Note on the Item Information Function of the Four-Parameter Logistic Model
ERIC Educational Resources Information Center
Magis, David
2013-01-01
This article focuses on four-parameter logistic (4PL) model as an extension of the usual three-parameter logistic (3PL) model with an upper asymptote possibly different from 1. For a given item with fixed item parameters, Lord derived the value of the latent ability level that maximizes the item information function under the 3PL model. The…
McKinney, Cliff; Renk, Kimberly
2008-06-01
Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample for this study consisted of 151 male and 324 female late adolescents, who reported on their mothers' and fathers' parenting style, their family environment, their mothers' and fathers' expectations for them, the conflict that they experience with their mothers and fathers, and their own adjustment. Overall, the variables had significant relationships with one another. Further, the male-father, male-mother, and female-father structural equation models that were examined suggested that parenting style has an indirect relationship with late adolescents' adjustment through characteristics of the family environment and the conflict that is experienced in families; such findings were not evident for the female-mother model. Thus, the examination of parent-late adolescent interactions should occur in the context of the gender of parents and their late adolescents. PMID:17710537
NASA Astrophysics Data System (ADS)
Passegger, V. M.; Wende-von Berg, S.; Reiners, A.
2016-03-01
M-dwarf stars are the most numerous stars in the Universe; they span a wide range in mass and are in the focus of ongoing and planned exoplanet surveys. To investigate and understand their physical nature, detailed spectral information and accurate stellar models are needed. We use a new synthetic atmosphere model generation and compare model spectra to observations. To test the model accuracy, we compared the models to four benchmark stars with atmospheric parameters for which independent information from interferometric radius measurements is available. We used χ2-based methods to determine parameters from high-resolution spectroscopic observations. Our synthetic spectra are based on the new PHOENIX grid that uses the ACES description for the equation of state. This is a model generation expected to be especially suitable for the low-temperature atmospheres. We identified suitable spectral tracers of atmospheric parameters and determined the uncertainties in Teff, log g, and [Fe/H] resulting from degeneracies between parameters and from shortcomings of the model atmospheres. The inherent uncertainties we find are σTeff = 35 K, σlog g = 0.14, and σ[Fe/H] = 0.11. The new model spectra achieve a reliable match to our observed data; our results for Teff and log g are consistent with literature values to within 1σ. However, metallicities reported from earlier photometric and spectroscopic calibrations in some cases disagree with our results by more than 3σ. A possible explanation are systematic errors in earlier metallicity determinations that were based on insufficient descriptions of the cool atmospheres. At this point, however, we cannot definitely identify the reason for this discrepancy, but our analysis indicates that there is a large uncertainty in the accuracy of M-dwarf parameter estimates. Based on observations carried out with UVES at ESO VLT.
Garabedian, Stephen P.
1986-01-01
A nonlinear, least-squares regression technique for the estimation of ground-water flow model parameters was applied to the regional aquifer underlying the eastern Snake River Plain, Idaho. The technique uses a computer program to simulate two-dimensional, steady-state ground-water flow. Hydrologic data for the 1980 water year were used to calculate recharge rates, boundary fluxes, and spring discharges. Ground-water use was estimated from irrigated land maps and crop consumptive-use figures. These estimates of ground-water withdrawal, recharge rates, and boundary flux, along with leakance, were used as known values in the model calibration of transmissivity. Leakance values were adjusted between regression solutions by comparing model-calculated to measured spring discharges. In other simulations, recharge and leakance also were calibrated as prior-information regression parameters, which limits the variation of these parameters using a normalized standard error of estimate. Results from a best-fit model indicate a wide areal range in transmissivity from about 0.05 to 44 feet squared per second and in leakance from about 2.2x10 -9 to 6.0 x 10 -8 feet per second per foot. Along with parameter values, model statistics also were calculated, including the coefficient of correlation between calculated and observed head (0.996), the standard error of the estimates for head (40 feet), and the parameter coefficients of variation (about 10-40 percent). Additional boundary flux was added in some areas during calibration to achieve proper fit to ground-water flow directions. Model fit improved significantly when areas that violated model assumptions were removed. It also improved slightly when y-direction (northwest-southeast) transmissivity values were larger than x-direction (northeast-southwest) transmissivity values. The model was most sensitive to changes in recharge, and in some areas, to changes in transmissivity, particularly near the spring discharge area from
Frey Law, Laura A; Shields, Richard K
2005-01-01
Background Mathematical muscle models may be useful for the determination of appropriate musculoskeletal stresses that will safely maintain the integrity of muscle and bone following spinal cord injury. Several models have been proposed to represent paralyzed muscle, but there have not been any systematic comparisons of modelling approaches to better understand the relationships between model parameters and muscle contractile properties. This sensitivity analysis of simulated muscle forces using three currently available mathematical models provides insight into the differences in modelling strategies as well as any direct parameter associations with simulated muscle force properties. Methods Three mathematical muscle models were compared: a traditional linear model with 3 parameters and two contemporary nonlinear models each with 6 parameters. Simulated muscle forces were calculated for two stimulation patterns (constant frequency and initial doublet trains) at three frequencies (5, 10, and 20 Hz). A sensitivity analysis of each model was performed by altering a single parameter through a range of 8 values, while the remaining parameters were kept at baseline values. Specific simulated force characteristics were determined for each stimulation pattern and each parameter increment. Significant parameter influences for each simulated force property were determined using ANOVA and Tukey's follow-up tests (α ≤ 0.05), and compared to previously reported parameter definitions. Results Each of the 3 linear model's parameters most clearly influence either simulated force magnitude or speed properties, consistent with previous parameter definitions. The nonlinear models' parameters displayed greater redundancy between force magnitude and speed properties. Further, previous parameter definitions for one of the nonlinear models were consistently supported, while the other was only partially supported by this analysis. Conclusion These three mathematical models use
Neural Models: An Option to Estimate Seismic Parameters of Accelerograms
NASA Astrophysics Data System (ADS)
Alcántara, L.; García, S.; Ovando-Shelley, E.; Macías, M. A.
2014-12-01
Seismic instrumentation for recording strong earthquakes, in Mexico, goes back to the 60´s due the activities carried out by the Institute of Engineering at Universidad Nacional Autónoma de México. However, it was after the big earthquake of September 19, 1985 (M=8.1) when the project of seismic instrumentation assumes a great importance. Currently, strong ground motion networks have been installed for monitoring seismic activity mainly along the Mexican subduction zone and in Mexico City. Nevertheless, there are other major regions and cities that can be affected by strong earthquakes and have not yet begun their seismic instrumentation program or this is still in development.Because of described situation some relevant earthquakes (e.g. Huajuapan de León Oct 24, 1980 M=7.1, Tehuacán Jun 15, 1999 M=7 and Puerto Escondido Sep 30, 1999 M= 7.5) have not been registered properly in some cities, like Puebla and Oaxaca, and that were damaged during those earthquakes. Fortunately, the good maintenance work carried out in the seismic network has permitted the recording of an important number of small events in those cities. So in this research we present a methodology based on the use of neural networks to estimate significant duration and in some cases the response spectra for those seismic events. The neural model developed predicts significant duration in terms of magnitude, epicenter distance, focal depth and soil characterization. Additionally, for response spectra we used a vector of spectral accelerations. For training the model we selected a set of accelerogram records obtained from the small events recorded in the strong motion instruments installed in the cities of Puebla and Oaxaca. The final results show that neural networks as a soft computing tool that use a multi-layer feed-forward architecture provide good estimations of the target parameters and they also have a good predictive capacity to estimate strong ground motion duration and response spectra.
Stochastic modelling of daily rainfall in Nigeria: intra-annual variation of model parameters
NASA Astrophysics Data System (ADS)
Jimoh, O. D.; Webster, P.
1999-09-01
A Markov model of order 1 may be used to describe the occurrence of wet and dry days in Nigeria. Such models feature two parameter sets; P01 to characterise the probability of a wet day following a dry day and P11 to characterise the probability of a wet day following a wet day. The model parameter sets, when estimated from historical records, are characterised by a distinctive seasonal behaviour. However, the comparison of this seasonal behaviour between rainfall stations is hampered by the noise reflecting the high variability of parameters on successive days. The first part of this article is concerned with methods for smoothing these inherently noisy parameter sets. Smoothing has been approached using Fourier series, averaging techniques, or a combination thereof. It has been found that different methods generally perform well with respect to estimation of the average number of wet events and the frequency duration curves of wet and dry events. Parameterisation of the P01 parameter set is more successful than the P11 in view of the relatively small number of wet events lasting two or more days. The second part of the article is concerned with describing the regional variation in smoothed parameter sets. There is a systematic variation in the P01 parameter set as one moves northwards. In contrast, there is limited regional variation in the P11 set. Although this regional variation in P01 appears to be related to the gradual movement of the Inter Tropical Convergence Zone, the contrasting behaviour of the two parameter sets is difficult to explain on physical grounds.
An investigation of the material and model parameters for a constitutive model for MSMAs
NASA Astrophysics Data System (ADS)
Dikes, Jason; Feigenbaum, Heidi; Ciocanel, Constantin
2015-04-01
A two dimensional constitutive model capable of predicting the magneto-mechanical response of a magnetic shape memory alloy (MSMA) has been developed and calibrated using a zero field-variable stress test1. This calibration approach is easy to perform and facilitates a faster evaluation of the three calibration constants required by the model (vs. five calibration constants required by previous models2,3). The calibration constants generated with this approach facilitate good model predictions of constant field-variable stress tests, for a wide range of loading conditions1. However, the same calibration constants yield less accurate model predictions for constant stress-variable field tests. Deployment of a separate calibration method for this type of loading, using a varying field-zero stress calibration test, also didn't lead to improved model predictions of this loading case. As a result, a sensitivity analysis was performed on most model and material parameters to identify which of them may influence model predictions the most, in both types of loading conditions. The sensitivity analysis revealed that changing most of these parameters did not improve model predictions for all loading types. Only the anisotropy coefficient was found to improve significantly field controlled model predictions and slightly worsen model predictions for stress controlled cases. This suggests that either the value of the anisotropy coefficient (which is provided by the manufacturer) is not accurate, or that the model is missing features associated with the magnetic energy of the material.
NASA Astrophysics Data System (ADS)
Reusser, D. E.; Zehe, E.
2011-07-01
In this paper we investigate the use of hydrological models as learning tools to help improve our understanding of the hydrological functioning of a catchment. With the model as a hypothetical conceptualization of how dominant hydrological processes contribute to catchment-scale response, we investigate three questions: (1) During which periods does the model (not) reproduce observed quantities and dynamics? (2) What is the nature of the error during times of bad model performance? (3) Which model components are responsible for this error? To investigate these questions, we combine a method for detecting repeating patterns of typical differences between model and observations (time series of grouped errors, TIGER) with a method for identifying the active model components during each simulation time step based on parameter sensitivity (temporal dynamics of parameter sensitivities, TEDPAS). The approach generates a time series of occurrence of dominant error types and time series of parameter sensitivities. A synoptic discussion of these time series highlights deficiencies in the assumptions about the functioning of the catchment. The approach is demonstrated for the Weisseritz headwater catchment in the eastern Ore Mountains. Our results indicate that the WaSiM-ETH complex grid-based model is not a sufficient working hypothesis for the functioning of the Weisseritz catchment and point toward future steps that can help improve our understanding of the catchment.
NASA Astrophysics Data System (ADS)
Zhang, Juying; Hum Na, Yong; Caracappa, Peter F.; Xu, X. George
2009-10-01
This paper describes the development of a pair of adult male and adult female computational phantoms that are compatible with anatomical parameters for the 50th percentile population as specified by the International Commission on Radiological Protection (ICRP). The phantoms were designed entirely using polygonal mesh surfaces—a Boundary REPresentation (BREP) geometry that affords the ability to efficiently deform the shape and size of individual organs, as well as the body posture. A set of surface mesh models, from Anatomium™ 3D P1 V2.0, including 140 organs (out of 500 available) was adopted to supply the basic anatomical representation at the organ level. The organ masses were carefully adjusted to agree within 0.5% relative error with the reference values provided in the ICRP Publication 89. The finalized phantoms have been designated the RPI adult male (RPI-AM) and adult female (RPI-AF) phantoms. For the purposes of organ dose calculations using the MCNPX Monte Carlo code, these phantoms were subsequently converted to voxel formats. Monoenergetic photons between 10 keV and 10 MeV in six standard external photon source geometries were considered in this study: four parallel beams (anterior-posterior, posterior-anterior, left lateral and right lateral), one rotational and one isotropic. The results are tabulated as fluence-to-organ-absorbed-dose conversion coefficients and fluence-to-effective-dose conversion coefficients and compared against those derived from the ICRP computational phantoms, REX and REGINA. A general agreement was found for the effective dose from these two sets of phantoms for photon energies greater than about 300 keV. However, for low-energy photons and certain individual organs, the absorbed doses exhibit profound differences due to specific anatomical features. For example, the position of the arms affects the dose to the lung by more than 20% below 300 keV in the lateral source directions, and the vertical position of the testes
Zhang, Juying; Na, Yong Hum; Caracappa, Peter F; Xu, X George
2010-01-01
This paper describes the development of a pair of adult male and adult female computational phantoms that are compatible with anatomical parameters for the 50th percentile population as specified by the International Commission on Radiological Protection (ICRP). The phantoms were designed entirely using polygonal mesh surfaces—a Boundary REPresentation (BREP) geometry that affords the ability to efficiently deform the shape and size of individual organs, as well as the body posture. A set of surface mesh models, from Anatomium™ 3D P1 V2.0, including 140 organs (out of 500 available) was adopted to supply the basic anatomical representation at the organ level. The organ masses were carefully adjusted to agree within 0.5% relative error with the reference values provided in the ICRP Publication 89. The finalized phantoms have been designated the RPI adult male (RPI-AM) and adult female (RPI-AF) phantoms. For the purposes of organ dose calculations using the MCNPX Monte Carlo code, these phantoms were subsequently converted to voxel formats. Monoenergetic photons between 10 keV and 10 MeV in six standard external photon source geometries were considered in this study: four parallel beams (anterior–posterior, posterior–anterior, left lateral and right lateral), one rotational and one isotropic. The results are tabulated as fluence-to-organ-absorbed-dose conversion coefficients and fluence-to-effective-dose conversion coefficients and compared against those derived from the ICRP computational phantoms, REX and REGINA. A general agreement was found for the effective dose from these two sets of phantoms for photon energies greater than about 300 keV. However, for low-energy photons and certain individual organs, the absorbed doses exhibit profound differences due to specific anatomical features. For example, the position of the arms affects the dose to the lung by more than 20% below 300 keV in the lateral source directions, and the vertical position of the testes
Dependence of red edge on eddy viscosity model parameters
NASA Technical Reports Server (NTRS)
Deupree, R. G.; Cole, P. W.
1980-01-01
The dependence of the red edge location on the two fundamental free parameters in the eddy viscosity treatment was extensively studied. It is found that the convective flux is rather insensitive to any reasonable or allowed value of the two free parameters of the treatment. This must be due in part to the fact that the convective flux is determined more by the properties of the hydrogen ionization region than by differences in convective structure. The changes in the effective temperature of the red edge of the RR Lyrae gap resulting from these parameter variations is quite small (approximately 150 K). This is true both because the parameter variation causes only small changes and because large changes in the convective flux are required to produce any significant change in red edge location. The possible changes found are substantially less than the approximately 600 K required to change the predicted helium abundance mass fraction from 0.3 to 0.2.
Nuclear magnetic resonance parameters of atomic xenon dissolved in Gay-Berne model liquid crystal.
Lintuvuori, Juho; Straka, Michal; Vaara, Juha
2007-03-01
We present constant-pressure Monte Carlo simulations of nuclear magnetic resonance (NMR) spectral parameters, nuclear magnetic shielding relative to the free atom as well as nuclear quadrupole coupling, for atomic xenon dissolved in a model thermotropic liquid crystal. The solvent is described by Gay-Berne (GB) molecules with parametrization kappa=4.4, kappa{'}=20.0 , and mu=nu=1 . The reduced pressure of P{*}=2.0 is used. Previous simulations of a pure GB system with this parametrization have shown that upon lowering the temperature, the model exhibits isotropic, nematic, smectic- A , and smectic- B /molecular crystal phases. We introduce spherical xenon solutes and adjust the energy and length scales of the GB-Xe interaction to those of the GB-GB interaction. This is done through first principles quantum chemical calculations carried out for a dimer of model mesogens as well as the mesogen-xenon complex. We preparametrize quantum chemically the Xe nuclear shielding and quadrupole coupling tensors when interacting with the model mesogen, and use the parametrization in a pairwise additive fashion in the analysis of the simulation. We present the temperature evolution of {129/131}Xe shielding and 131Xe quadrupole coupling in the different phases of the GB model. From the simulations, separate isotropic and anisotropic contributions to the experimentally available total shielding can be obtained. At the experimentally relevant concentration, the presence of the xenon atoms does not significantly affect the phase behavior as compared to the pure GB model. The simulations reproduce many of the characteristic experimental features of Xe NMR in real thermotropic LCs: Discontinuity in the value or trends of the shielding and quadrupole coupling at the nematic-isotropic and smectic-A-nematic phase transitions, nonlinear shift evolution in the nematic phase reflecting the behavior of the orientational order parameter, and decreasing shift in the smectic-A phase. The last
Nuclear magnetic resonance parameters of atomic xenon dissolved in Gay-Berne model liquid crystal
NASA Astrophysics Data System (ADS)
Lintuvuori, Juho; Straka, Michal; Vaara, Juha
2007-03-01
We present constant-pressure Monte Carlo simulations of nuclear magnetic resonance (NMR) spectral parameters, nuclear magnetic shielding relative to the free atom as well as nuclear quadrupole coupling, for atomic xenon dissolved in a model thermotropic liquid crystal. The solvent is described by Gay-Berne (GB) molecules with parametrization κ=4.4 , κ'=20.0 , and μ=ν=1 . The reduced pressure of P⋆=2.0 is used. Previous simulations of a pure GB system with this parametrization have shown that upon lowering the temperature, the model exhibits isotropic, nematic, smectic- A , and smectic- B /molecular crystal phases. We introduce spherical xenon solutes and adjust the energy and length scales of the GB-Xe interaction to those of the GB-GB interaction. This is done through first principles quantum chemical calculations carried out for a dimer of model mesogens as well as the mesogen-xenon complex. We preparametrize quantum chemically the Xe nuclear shielding and quadrupole coupling tensors when interacting with the model mesogen, and use the parametrization in a pairwise additive fashion in the analysis of the simulation. We present the temperature evolution of Xe129/131 shielding and Xe131 quadrupole coupling in the different phases of the GB model. From the simulations, separate isotropic and anisotropic contributions to the experimentally available total shielding can be obtained. At the experimentally relevant concentration, the presence of the xenon atoms does not significantly affect the phase behavior as compared to the pure GB model. The simulations reproduce many of the characteristic experimental features of Xe NMR in real thermotropic LCs: Discontinuity in the value or trends of the shielding and quadrupole coupling at the nematic-isotropic and smectic- A -nematic phase transitions, nonlinear shift evolution in the nematic phase reflecting the behavior of the orientational order parameter, and decreasing shift in the smectic- A phase. The last
Ecosystem Modeling of College Drinking: Parameter Estimation and Comparing Models to Data*
Ackleh, Azmy S.; Fitzpatrick, Ben G.; Scribner, Richard; Simonsen, Neal; Thibodeaux, Jeremy J.
2009-01-01
Recently we developed a model composed of five impulsive differential equations that describes the changes in drinking patterns (that persist at epidemic level) amongst college students. Many of the model parameters cannot be measured directly from data; thus, an inverse problem approach, which chooses the set of parameters that results in the “best” model to data fit, is crucial for using this model as a predictive tool. The purpose of this paper is to present the procedure and results of an unconventional approach to parameter estimation that we developed after more common approaches were unsuccessful for our specific problem. The results show that our model provides a good fit to survey data for 32 campuses. Using these parameter estimates, we examined the effect of two hypothetical intervention policies: 1) reducing environmental wetness, and 2) penalizing students who are caught drinking. The results suggest that reducing campus wetness may be a very effective way of reducing heavy episodic (binge) drinking on a college campus, while a policy that penalizes students who drink is not nearly as effective. PMID:20161275
NASA Technical Reports Server (NTRS)
Hyland, D. C.
1983-01-01
A stochastic structural control model is described. In contrast to the customary deterministic model, the stochastic minimum data/maximum entropy model directly incorporates the least possible a priori parameter information. The approach is to adopt this model as the basic design model, thus incorporating the effects of parameter uncertainty at a fundamental level, and design mean-square optimal controls (that is, choose the control law to minimize the average of a quadratic performance index over the parameter ensemble).
NASA Astrophysics Data System (ADS)
Lewis, L. H.; Gao, J.; Jiles, D. C.; Welch, D. O.
1996-04-01
The Jiles-Atherton theory is based on considerations of the dependence of energy dissipation within a magnetic material resulting from changes in its magnetization. The algorithm based on the theory yields five computed model parameters, MS, a, α, k, and c, which represent the saturation magnetization, the effective domain density, the mean exchange coupling between the effective domains, the flexibility of domain walls and energy-dissipative features in the microstructure, respectively. Model parameters were calculated from the algorithm and linked with the physical attributes of a set of three related melt-quenched permanent magnets based on the Nd2Fe14B composition. Measured magnetic parameters were used as inputs into the model to reproduce the experimental hysteresis curves. The results show that two of the calculated parameters, the saturation magnetization MS and the effective coercivity k, agree well with their directly determined analogs. The calculated a and α parameters provide support for the concept of increased intergranular exchange coupling upon die upsetting, and decreased intergranular exchange coupling with the addition of gallium.
Parameter sensitivity and uncertainty analysis for a storm surge and wave model
NASA Astrophysics Data System (ADS)
Bastidas, L. A.; Knighton, J.; Kline, S. W.
2015-10-01
Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991) utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland). The sensitive model parameters (of eleven total considered) include wind drag, the depth-induced breaking γB, and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters and depth-induced breaking αB) and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large amount of interactions between parameters and a non-linear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.
[Study on the automatic parameters identification of water pipe network model].
Jia, Hai-Feng; Zhao, Qi-Feng
2010-01-01
Based on the problems analysis on development and application of water pipe network model, the model parameters automatic identification is regarded as a kernel bottleneck of model's application in water supply enterprise. The methodology of water pipe network model parameters automatic identification based on GIS and SCADA database is proposed. Then the kernel algorithm of model parameters automatic identification is studied, RSA (Regionalized Sensitivity Analysis) is used for automatic recognition of sensitive parameters, and MCS (Monte-Carlo Sampling) is used for automatic identification of parameters, the detail technical route based on RSA and MCS is presented. The module of water pipe network model parameters automatic identification is developed. At last, selected a typical water pipe network as a case, the case study on water pipe network model parameters automatic identification is conducted and the satisfied results are achieved. PMID:20329520
NASA Astrophysics Data System (ADS)
Chen, P.; Tung, C.
2012-12-01
Green roof has the advantage to lower building temperature; therefore it has been applied a lot nowadays to indoor temperature adjustment. This study builds a coupled heat and mass transfer model, in which the water vapor in the substrate is taken into consideration, based on the concept of energy balance. With the parameters optimized by Tabu search algorithm, data from the experiment is used to validate the model. In the study, both the model and the experimental green roof of this study consist of four layers: canopy, substrate, drainage and concrete rooftop. Heat flux of each layer is calculated in the model, using energy balance equations as well as some numerical methods to simulate water-related thermal effect in soil, to see the heat transfer process. The experiment site locates on the rooftop of Hydrotech Research Institute, National Taiwan University, Taiwan. Since the material of the substrate layer has high porosity, the results show a contradiction of energy conservation when neglecting the influence of water. It is found that the parameters identified by Tabu search seem reasonable for the experiment. The main contribution of the study is to construct a thermal model for green roof with parameter optimization procedure, which can be used as an effective assessment method to quantify the heat-reduced performance of green roof on the underlying building.
Bao, J Y
1991-04-01
The commonly used microforceps have a much greater opening distance and spring resistance than needed. A piece of plastic ring or rubber band can be used to adjust the opening distance and reduce most of the spring resistance, making the user feel more comfortable and less fatigued. PMID:2051437
ERIC Educational Resources Information Center
Custer, Michael; Sharairi, Sid; Yamazaki, Kenji; Signatur, Diane; Swift, David; Frey, Sharon
2008-01-01
The present study compared item and ability invariance as well as model-data fit between the one-parameter (1PL) and three-parameter (3PL) Item Response Theory (IRT) models utilizing real data across five grades; second through sixth as well as simulated data at second, fourth and sixth grade. At each grade, the 1PL and 3PL IRT models were run…
NASA Astrophysics Data System (ADS)
Williamson, Daniel; Goldstein, Michael; Allison, Lesley; Blaker, Adam; Challenor, Peter; Jackson, Laura; Yamazaki, Kuniko
2013-10-01
We apply an established statistical methodology called history matching to constrain the parameter space of a coupled non-flux-adjusted climate model (the third Hadley Centre Climate Model; HadCM3) by using a 10,000-member perturbed physics ensemble and observational metrics. History matching uses emulators (fast statistical representations of climate models that include a measure of uncertainty in the prediction of climate model output) to rule out regions of the parameter space of the climate model that are inconsistent with physical observations given the relevant uncertainties. Our methods rule out about half of the parameter space of the climate model even though we only use a small number of historical observations. We explore 2 dimensional projections of the remaining space and observe a region whose shape mainly depends on parameters controlling cloud processes and one ocean mixing parameter. We find that global mean surface air temperature (SAT) is the dominant constraint of those used, and that the others provide little further constraint after matching to SAT. The Atlantic meridional overturning circulation (AMOC) has a non linear relationship with SAT and is not a good proxy for the meridional heat transport in the unconstrained parameter space, but these relationships are linear in our reduced space. We find that the transient response of the AMOC to idealised CO2 forcing at 1 and 2 % per year shows a greater average reduction in strength in the constrained parameter space than in the unconstrained space. We test extended ranges of a number of parameters of HadCM3 and discover that no part of the extended ranges can by ruled out using any of our constraints. Constraining parameter space using easy to emulate observational metrics prior to analysis of more complex processes is an important and powerful tool. It can remove complex and irrelevant behaviour in unrealistic parts of parameter space, allowing the processes in question to be more easily
Dynamic Modeling of Adjustable-Speed Pumped Storage Hydropower Plant: Preprint
Muljadi, E.; Singh, M.; Gevorgian, V.; Mohanpurkar, M.; Havsapian, R.; Koritarov, V.
2015-04-06
Hydropower is the largest producer of renewable energy in the U.S. More than 60% of the total renewable generation comes from hydropower. There is also approximately 22 GW of pumped storage hydropower (PSH). Conventional PSH uses a synchronous generator, and thus the rotational speed is constant at synchronous speed. This work details a hydrodynamic model and generator/power converter dynamic model. The optimization of the hydrodynamic model is executed by the hydro-turbine controller, and the electrical output real/reactive power is controlled by the power converter. All essential controllers to perform grid-interface functions and provide ancillary services are included in the model.
Ruivo, M. C.; Costa, P.; Sousa, C. A. de; Hansen, H.
2010-08-05
The equation of state and the critical behavior around the critical end point are studied in the framework of the Polyakov-Nambu-Jona-Lasinio model. We prove that a convenient choice of the model parameters is crucial to get the correct description of isentropic trajectories. The physical relevance of the effects of the regularization procedure is insured by the agreement with general thermodynamic requirements. The results are compared with simple thermodynamic expectations and lattice data.
NASA Astrophysics Data System (ADS)
Morin, José A.; Ibarra, Borja; Cao, Francisco J.
2016-05-01
Single-molecule manipulation experiments of molecular motors provide essential information about the rate and conformational changes of the steps of the reaction located along the manipulation coordinate. This information is not always sufficient to define a particular kinetic cycle. Recent single-molecule experiments with optical tweezers showed that the DNA unwinding activity of a Phi29 DNA polymerase mutant presents a complex pause behavior, which includes short and long pauses. Here we show that different kinetic models, considering different connections between the active and the pause states, can explain the experimental pause behavior. Both the two independent pause model and the two connected pause model are able to describe the pause behavior of a mutated Phi29 DNA polymerase observed in an optical tweezers single-molecule experiment. For the two independent pause model all parameters are fixed by the observed data, while for the more general two connected pause model there is a range of values of the parameters compatible with the observed data (which can be expressed in terms of two of the rates and their force dependencies). This general model includes models with indirect entry and exit to the long-pause state, and also models with cycling in both directions. Additionally, assuming that detailed balance is verified, which forbids cycling, this reduces the ranges of the values of the parameters (which can then be expressed in terms of one rate and its force dependency). The resulting model interpolates between the independent pause model and the indirect entry and exit to the long-pause state model