Bayesian methods for characterizing unknown parameters of material models

DOE PAGES

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

Bayesian methods for characterizing unknown parameters of material models

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

Emery, J. M.; Grigoriu, M. D.; Field Jr., R. V.

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

Towards physics of neural processes and behavior.

PubMed

Latash, Mark L

2016-10-01

Behavior of biological systems is based on basic physical laws, common across inanimate and living systems, and currently unknown physical laws that are specific for living systems. Living systems are able to unite basic laws of physics into chains and clusters leading to new stable and pervasive relations among variables (new physical laws) involving new parameters and to modify these parameters in a purposeful way. Examples of such laws are presented starting from the tonic stretch reflex. Further, the idea of control with referent coordinates is formulated and merged with the idea of hierarchical control and the principle of abundance. The notion of controlled stability of behaviors is linked to the idea of structured variability, which is a common feature across living systems and actions. The explanatory and predictive power of this approach is illustrated with respect to the control of both intentional and unintentional movements, the phenomena of equifinality and its violations, preparation to quick actions, development of motor skills, changes with aging and neurological disorders, and perception. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Routine Fitting of Kinetic Data to Models

PubMed Central

Berman, Mones; Shahn, Ezra; Weiss, Marjory F.

1962-01-01

A mathematical formalism is presented for use with digital computers to permit the routine fitting of data to physical and mathematical models. Given a set of data, the mathematical equations describing a model, initial conditions for an experiment, and initial estimates for the values of model parameters, the computer program automatically proceeds to obtain a least squares fit of the data by an iterative adjustment of the values of the parameters. When the experimental measures are linear combinations of functions, the linear coefficients for a least squares fit may also be calculated. The values of both the parameters of the model and the coefficients for the sum of functions may be unknown independent variables, unknown dependent variables, or known constants. In the case of dependence, only linear dependencies are provided for in routine use. The computer program includes a number of subroutines, each one of which performs a special task. This permits flexibility in choosing various types of solutions and procedures. One subroutine, for example, handles linear differential equations, another, special non-linear functions, etc. The use of analytic or numerical solutions of equations is possible. PMID:13867975

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

DOE PAGES

Higdon, Dave; McDonnell, Jordan D.; Schunck, Nicolas; ...

2015-02-05

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based modelmore » $$\\eta (\\theta )$$, where θ denotes the uncertain, best input setting. Hence the statistical model is of the form $$y=\\eta (\\theta )+\\epsilon ,$$ where $$\\epsilon $$ accounts for measurement, and possibly other, error sources. When nonlinearity is present in $$\\eta (\\cdot )$$, the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model $$\\eta (\\cdot )$$. This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. Lastly, we also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory.« less

Bayesian nonlinear structural FE model and seismic input identification for damage assessment of civil structures

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Li, Yong; Conte, Joel P.

2017-09-01

A methodology is proposed to update mechanics-based nonlinear finite element (FE) models of civil structures subjected to unknown input excitation. The approach allows to jointly estimate unknown time-invariant model parameters of a nonlinear FE model of the structure and the unknown time histories of input excitations using spatially-sparse output response measurements recorded during an earthquake event. The unscented Kalman filter, which circumvents the computation of FE response sensitivities with respect to the unknown model parameters and unknown input excitations by using a deterministic sampling approach, is employed as the estimation tool. The use of measurement data obtained from arrays of heterogeneous sensors, including accelerometers, displacement sensors, and strain gauges is investigated. Based on the estimated FE model parameters and input excitations, the updated nonlinear FE model can be interrogated to detect, localize, classify, and assess damage in the structure. Numerically simulated response data of a three-dimensional 4-story 2-by-1 bay steel frame structure with six unknown model parameters subjected to unknown bi-directional horizontal seismic excitation, and a three-dimensional 5-story 2-by-1 bay reinforced concrete frame structure with nine unknown model parameters subjected to unknown bi-directional horizontal seismic excitation are used to illustrate and validate the proposed methodology. The results of the validation studies show the excellent performance and robustness of the proposed algorithm to jointly estimate unknown FE model parameters and unknown input excitations.

Integrated direct/indirect adaptive robust motion trajectory tracking control of pneumatic cylinders

NASA Astrophysics Data System (ADS)

Meng, Deyuan; Tao, Guoliang; Zhu, Xiaocong

2013-09-01

This paper studies the precision motion trajectory tracking control of a pneumatic cylinder driven by a proportional-directional control valve. An integrated direct/indirect adaptive robust controller is proposed. The controller employs a physical model based indirect-type parameter estimation to obtain reliable estimates of unknown model parameters, and utilises a robust control method with dynamic compensation type fast adaptation to attenuate the effects of parameter estimation errors, unmodelled dynamics and disturbances. Due to the use of projection mapping, the robust control law and the parameter adaption algorithm can be designed separately. Since the system model uncertainties are unmatched, the recursive backstepping technology is adopted to design the robust control law. Extensive comparative experimental results are presented to illustrate the effectiveness of the proposed controller and its performance robustness to parameter variations and sudden disturbances.

Two similar cases of elderly women with moderate abdominal pain and pneumoperitoneum of unknown origin: a surgeon's successful conservative management.

PubMed

Vinzens, Fabrizio; Zumstein, Valentin; Bieg, Christian; Ackermann, Christoph

2016-05-26

Patients presenting with abdominal pain and pneumoperitoneum in radiological examination usually require emergency explorative laparoscopy or laparotomy. Pneumoperitoneum mostly associates with gastrointestinal perforation. There are very few cases where surgery can be avoided. We present 2 cases of pneumoperitoneum with unknown origin and successful conservative treatment. Both patients were elderly women presenting to our emergency unit, with moderate abdominal pain. There was neither medical intervention nor trauma in their medical history. Physical examination revealed mild abdominal tenderness, but no clinical sign of peritonitis. Cardiopulmonary examination remained unremarkable. Blood studies showed only slight abnormalities, in particular, inflammation parameters were not significantly increased. Finally, obtained CTs showed free abdominal gas of unknown origin in both cases. We performed conservative management with nil per os, nasogastric tube, total parenteral nutrition and prophylactic antibiotics. After 2 weeks, both were discharged home. 2016 BMJ Publishing Group Ltd.

Probabilistic and deterministic aspects of linear estimation in geodesy. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Dermanis, A.

1976-01-01

Recent advances in observational techniques related to geodetic work (VLBI, laser ranging) make it imperative that more consideration should be given to modeling problems. Uncertainties in the effect of atmospheric refraction, polar motion and precession-nutation parameters, cannot be dispensed with in the context of centimeter level geodesy. Even physical processes that have generally been previously altogether neglected (station motions) must now be taken into consideration. The problem of modeling functions of time or space, or at least their values at observation points (epochs) is explored. When the nature of the function to be modeled is unknown. The need to include a limited number of terms and to a priori decide upon a specific form may result in a representation which fails to sufficiently approximate the unknown function. An alternative approach of increasing application is the modeling of unknown functions as stochastic processes.

System and method for motor parameter estimation

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

Luhrs, Bin; Yan, Ting

2014-03-18

A system and method for determining unknown values of certain motor parameters includes a motor input device connectable to an electric motor having associated therewith values for known motor parameters and an unknown value of at least one motor parameter. The motor input device includes a processing unit that receives a first input from the electric motor comprising values for the known motor parameters for the electric motor and receive a second input comprising motor data on a plurality of reference motors, including values for motor parameters corresponding to the known motor parameters of the electric motor and values formore » motor parameters corresponding to the at least one unknown motor parameter value of the electric motor. The processor determines the unknown value of the at least one motor parameter from the first input and the second input and determines a motor management strategy for the electric motor based thereon.« less

Smart watch-based coaching with tiotropium and olodaterol ameliorates physical activity in patients with chronic obstructive pulmonary disease

PubMed Central

Hataji, Osamu; Nishii, Yoichi; Ito, Kentaro; Sakaguchi, Tadashi; Saiki, Haruko; Suzuki, Yuta; D'Alessandro-Gabazza, Corina; Fujimoto, Hajime; Kobayashi, Tetsu; Gabazza, Esteban C.; Taguchi, Osamu

2017-01-01

Combined therapy with tiotropium and olodaterol notably improves parameters of lung function and quality of life in patients with chronic obstructive pulmonary disease (COPD) compared to mono-components; however, its effect on physical activity is unknown. The present study evaluated whether combination therapy affects daily physical performance in patients with COPD under a smart watch-based encouragement program. This was a non-blinded clinical trial with no randomization or placebo control. A total of 20 patients with COPD were enrolled in the present study. The patients carried an accelerometer for 4 weeks; they received no therapy during the first 2 weeks but they were treated with combined tiotropium and olodaterol under a smart watch-based encouragement program for the last 2 weeks. The pulmonary function test, COPD assessment test, 6-min walk distance and parameters of physical activity were significantly improved (P<0.05) by combination therapy under smart watch-based coaching compared with values prior to treatment. To the best of our knowledge, the present study for the first time provides evidence that smart watch-based coaching in combination with tiotropium and olodaterol may improve daily physical activity in chronic obstructive pulmonary disease. PMID:29104624

Smart watch-based coaching with tiotropium and olodaterol ameliorates physical activity in patients with chronic obstructive pulmonary disease.

PubMed

Hataji, Osamu; Nishii, Yoichi; Ito, Kentaro; Sakaguchi, Tadashi; Saiki, Haruko; Suzuki, Yuta; D'Alessandro-Gabazza, Corina; Fujimoto, Hajime; Kobayashi, Tetsu; Gabazza, Esteban C; Taguchi, Osamu

2017-11-01

Combined therapy with tiotropium and olodaterol notably improves parameters of lung function and quality of life in patients with chronic obstructive pulmonary disease (COPD) compared to mono-components; however, its effect on physical activity is unknown. The present study evaluated whether combination therapy affects daily physical performance in patients with COPD under a smart watch-based encouragement program. This was a non-blinded clinical trial with no randomization or placebo control. A total of 20 patients with COPD were enrolled in the present study. The patients carried an accelerometer for 4 weeks; they received no therapy during the first 2 weeks but they were treated with combined tiotropium and olodaterol under a smart watch-based encouragement program for the last 2 weeks. The pulmonary function test, COPD assessment test, 6-min walk distance and parameters of physical activity were significantly improved (P<0.05) by combination therapy under smart watch-based coaching compared with values prior to treatment. To the best of our knowledge, the present study for the first time provides evidence that smart watch-based coaching in combination with tiotropium and olodaterol may improve daily physical activity in chronic obstructive pulmonary disease.

Adaptive method for electron bunch profile prediction

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

Scheinker, Alexander; Gessner, Spencer

2015-10-01

We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. The simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrial controlmore » system. The main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET. © 2015 authors. Published by the American Physical Society.« less

Multiple concurrent recursive least squares identification with application to on-line spacecraft mass-property identification

NASA Technical Reports Server (NTRS)

Wilson, Edward (Inventor)

2006-01-01

The present invention is a method for identifying unknown parameters in a system having a set of governing equations describing its behavior that cannot be put into regression form with the unknown parameters linearly represented. In this method, the vector of unknown parameters is segmented into a plurality of groups where each individual group of unknown parameters may be isolated linearly by manipulation of said equations. Multiple concurrent and independent recursive least squares identification of each said group run, treating other unknown parameters appearing in their regression equation as if they were known perfectly, with said values provided by recursive least squares estimation from the other groups, thereby enabling the use of fast, compact, efficient linear algorithms to solve problems that would otherwise require nonlinear solution approaches. This invention is presented with application to identification of mass and thruster properties for a thruster-controlled spacecraft.

Phases and interfaces from real space atomically resolved data: Physics-based deep data image analysis

DOE PAGES

Vasudevan, Rama K.; Ziatdinov, Maxim; Jesse, Stephen; ...

2016-08-12

Advances in electron and scanning probe microscopies have led to a wealth of atomically resolved structural and electronic data, often with ~1–10 pm precision. However, knowledge generation from such data requires the development of a physics-based robust framework to link the observed structures to macroscopic chemical and physical descriptors, including single phase regions, order parameter fields, interfaces, and structural and topological defects. Here, we develop an approach based on a synergy of sliding window Fourier transform to capture the local analog of traditional structure factors combined with blind linear unmixing of the resultant 4D data set. This deep data analysismore » is ideally matched to the underlying physics of the problem and allows reconstruction of the a priori unknown structure factors of individual components and their spatial localization. We demonstrate the principles of this approach using a synthetic data set and further apply it for extracting chemical and physically relevant information from electron and scanning tunneling microscopy data. Furthermore, this method promises to dramatically speed up crystallographic analysis in atomically resolved data, paving the road toward automatic local structure–property determinations in crystalline and quasi-ordered systems, as well as systems with competing structural and electronic order parameters.« less

Investigation on Insar Time Series Deformation Model Considering Rheological Parameters for Soft Clay Subgrade Monitoring

NASA Astrophysics Data System (ADS)

Xing, X.; Yuan, Z.; Chen, L. F.; Yu, X. Y.; Xiao, L.

2018-04-01

The stability control is one of the major technical difficulties in the field of highway subgrade construction engineering. Building deformation model is a crucial step for InSAR time series deformation monitoring. Most of the InSAR deformation models for deformation monitoring are pure empirical mathematical models, without considering the physical mechanism of the monitored object. In this study, we take rheology into consideration, inducing rheological parameters into traditional InSAR deformation models. To assess the feasibility and accuracy for our new model, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. In order to solve the unknows of the non-linear rheological model, three algorithms: Gauss-Newton (GN), Levenberg-Marquarat (LM), and Genetic Algorithm (GA), are utilized and compared to estimate the unknown parameters. Considering both the calculation efficiency and accuracy, GA is chosen as the final choice for the new model in our case study. Preliminary real data experiment is conducted with use of 17 TerraSAR-X Stripmap images (with a 3-m resolution). With the new deformation model and GA aforementioned, the unknown rheological parameters over all the high coherence points are obtained and the LOS deformation (the low-pass component) sequences are generated.

cosmoabc: Likelihood-free inference for cosmology

NASA Astrophysics Data System (ADS)

Ishida, Emille E. O.; Vitenti, Sandro D. P.; Penna-Lima, Mariana; Trindade, Arlindo M.; Cisewski, Jessi; M.; de Souza, Rafael; Cameron, Ewan; Busti, Vinicius C.

2015-05-01

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogs. cosmoabc is a Python Approximate Bayesian Computation (ABC) sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code can be coupled to an external simulator to allow incorporation of arbitrary distance and prior functions. When coupled with the numcosmo library, it has been used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function.

Synchronization between uncertain nonidentical networks with quantum chaotic behavior

NASA Astrophysics Data System (ADS)

Li, Wenlin; Li, Chong; Song, Heshan

2016-11-01

Synchronization between uncertain nonidentical networks with quantum chaotic behavior is researched. The identification laws of unknown parameters in state equations of network nodes, the adaptive laws of configuration matrix elements and outer coupling strengths are determined based on Lyapunov theorem. The conditions of realizing synchronization between uncertain nonidentical networks are discussed and obtained. Further, Jaynes-Cummings model in physics are taken as the nodes of two networks and simulation results show that the synchronization performance between networks is very stable.

Simple robust control laws for robot manipulators. Part 2: Adaptive case

NASA Technical Reports Server (NTRS)

Bayard, D. S.; Wen, J. T.

1987-01-01

A new class of asymptotically stable adaptive control laws is introduced for application to the robotic manipulator. Unlike most applications of adaptive control theory to robotic manipulators, this analysis addresses the nonlinear dynamics directly without approximation, linearization, or ad hoc assumptions, and utilizes a parameterization based on physical (time-invariant) quantities. This approach is made possible by using energy-like Lyapunov functions which retain the nonlinear character and structure of the dynamics, rather than simple quadratic forms which are ubiquitous to the adaptive control literature, and which have bound the theory tightly to linear systems with unknown parameters. It is a unique feature of these results that the adaptive forms arise by straightforward certainty equivalence adaptation of their nonadaptive counterparts found in the companion to this paper (i.e., by replacing unknown quantities by their estimates) and that this simple approach leads to asymptotically stable closed-loop adaptive systems. Furthermore, it is emphasized that this approach does not require convergence of the parameter estimates (i.e., via persistent excitation), invertibility of the mass matrix estimate, or measurement of the joint accelerations.

Neutrino Physics in the NOvA Experiment

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

Sanchez, Mayly

2016-09-19

The objective of the experimental neutrino physics program at ISU is to contribute to the NOvA experiment in order to enable the measurement of the unknown neutrino parameters: the CP violation phase and the mass hierarchy. In the Summer of 2015, the NOvA Collaboration released results from the first year of data collected by the experiment. The ISU group played an important role in various aspects of these results including authoring one of the two resulting publications. During this project period and with the support of this grant the PI and her group made leading contributions both in data analysismore » and operations to the NOvA experiment.« less

Identifying quantum phase transitions with adversarial neural networks

NASA Astrophysics Data System (ADS)

Huembeli, Patrick; Dauphin, Alexandre; Wittek, Peter

2018-04-01

The identification of phases of matter is a challenging task, especially in quantum mechanics, where the complexity of the ground state appears to grow exponentially with the size of the system. Traditionally, physicists have to identify the relevant order parameters for the classification of the different phases. We here follow a radically different approach: we address this problem with a state-of-the-art deep learning technique, adversarial domain adaptation. We derive the phase diagram of the whole parameter space starting from a fixed and known subspace using unsupervised learning. This method has the advantage that the input of the algorithm can be directly the ground state without any ad hoc feature engineering. Furthermore, the dimension of the parameter space is unrestricted. More specifically, the input data set contains both labeled and unlabeled data instances. The first kind is a system that admits an accurate analytical or numerical solution, and one can recover its phase diagram. The second type is the physical system with an unknown phase diagram. Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture. Once these layers are trained, we can attach an unsupervised learner to the network to find phase transitions. We show the success of this technique by applying it on several paradigmatic models: the Ising model with different temperatures, the Bose-Hubbard model, and the Su-Schrieffer-Heeger model with disorder. The method finds unknown transitions successfully and predicts transition points in close agreement with standard methods. This study opens the door to the classification of physical systems where the phase boundaries are complex such as the many-body localization problem or the Bose glass phase.

Temporal Changes in Technical and Physical Performances During a Small-Sided Game in Elite Youth Soccer Players

PubMed Central

Moreira, Alexandre; Saldanha Aoki, Marcelo; Carling, Chris; Alan Rodrigues Lopes, Rafael; Felipe Schultz de Arruda, Ademir; Lima, Marcelo; Cesar Correa, Umberto; Bradley, Paul S

2016-01-01

Background There have been claims that small-sided games (SSG) may generate an appropriate environment to develop youth players’ technical performance associated to game-related problem solving. However, the temporal change in technical performance parameters of youth players during SSG is still unknown. Objectives The aim of this study was to examine temporal changes in technical and physical performances during a small-sided game (SSG) in elite soccer players. Methods Sixty elite youth players (age 14.8 ± 0.2 yr; stature 177 ± 5 cm; body mass 66.2 ± 4.7 kg) completed a 5 v 5 SSG using two repetitions of 8 minutes interspersed by 3 minutes of passive recovery. To evaluate temporal changes in performance, the data were analysed across 4 minutes quarters. Physical performance parameters included the total distance covered (TDC), the frequency of sprints (>18 km•h-1), accelerations and decelerations (> 2.0 m•s-2 and - 2.0 m•s-2), metabolic power (W•kg-1), training impulse (TRIMP), TDC: TRIMP, number of impacts, and body load. Technical performance parameters included goal attempts, total number of tackles, tackles and interceptions, total number of passes, and passes effectiveness. Results All physical performance parameters decreased from the first to the last quarter with notable declines in TDC, metabolic power and the frequency of sprints, accelerations and decelerations (P < 0.05; moderate to very large ES: 1.08 - 3.30). However, technical performance parameters did not vary across quarters (P > 0.05; trivial ES for 1st v 4th quarters: 0.15 - 0.33). Conclusions The data demonstrate that technical performance is maintained despite substantial declines in physical performance during a SSG in elite youth players. This finding may have implications for designing SSG’s for elite youth players to ensure physical, technical and tactical capabilities are optimized. Modifications in player number, pitch dimensions, rules, coach encouragement, for instance, should be included taking into account the main aim of a given session and then focused on overloading physical or technical elements. PMID:28144411

Temporal Changes in Technical and Physical Performances During a Small-Sided Game in Elite Youth Soccer Players.

PubMed

Moreira, Alexandre; Saldanha Aoki, Marcelo; Carling, Chris; Alan Rodrigues Lopes, Rafael; Felipe Schultz de Arruda, Ademir; Lima, Marcelo; Cesar Correa, Umberto; Bradley, Paul S

2016-12-01

There have been claims that small-sided games (SSG) may generate an appropriate environment to develop youth players' technical performance associated to game-related problem solving. However, the temporal change in technical performance parameters of youth players during SSG is still unknown. The aim of this study was to examine temporal changes in technical and physical performances during a small-sided game (SSG) in elite soccer players. Sixty elite youth players (age 14.8 ± 0.2 yr; stature 177 ± 5 cm; body mass 66.2 ± 4.7 kg) completed a 5 v 5 SSG using two repetitions of 8 minutes interspersed by 3 minutes of passive recovery. To evaluate temporal changes in performance, the data were analysed across 4 minutes quarters. Physical performance parameters included the total distance covered (TDC), the frequency of sprints (>18 km•h -1 ), accelerations and decelerations (> 2.0 m•s -2 and - 2.0 m•s -2 ), metabolic power (W•kg -1 ), training impulse (TRIMP), TDC: TRIMP, number of impacts, and body load. Technical performance parameters included goal attempts, total number of tackles, tackles and interceptions, total number of passes, and passes effectiveness. All physical performance parameters decreased from the first to the last quarter with notable declines in TDC, metabolic power and the frequency of sprints, accelerations and decelerations (P < 0.05; moderate to very large ES: 1.08 - 3.30). However, technical performance parameters did not vary across quarters (P > 0.05; trivial ES for 1st v 4th quarters: 0.15 - 0.33). The data demonstrate that technical performance is maintained despite substantial declines in physical performance during a SSG in elite youth players. This finding may have implications for designing SSG's for elite youth players to ensure physical, technical and tactical capabilities are optimized. Modifications in player number, pitch dimensions, rules, coach encouragement, for instance, should be included taking into account the main aim of a given session and then focused on overloading physical or technical elements.

Mean-square state and parameter estimation for stochastic linear systems with Gaussian and Poisson noises

NASA Astrophysics Data System (ADS)

Basin, M.; Maldonado, J. J.; Zendejo, O.

2016-07-01

This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.

Numerical solution of system of boundary value problems using B-spline with free parameter

NASA Astrophysics Data System (ADS)

Gupta, Yogesh

2017-01-01

This paper deals with method of B-spline solution for a system of boundary value problems. The differential equations are useful in various fields of science and engineering. Some interesting real life problems involve more than one unknown function. These result in system of simultaneous differential equations. Such systems have been applied to many problems in mathematics, physics, engineering etc. In present paper, B-spline and B-spline with free parameter methods for the solution of a linear system of second-order boundary value problems are presented. The methods utilize the values of cubic B-spline and its derivatives at nodal points together with the equations of the given system and boundary conditions, ensuing into the linear matrix equation.

Mechanical Properties of Transcription

NASA Astrophysics Data System (ADS)

Sevier, Stuart A.; Levine, Herbert

2017-06-01

The mechanical properties of transcription have recently been shown to play a central role in gene expression. However, a full physical characterization of this central biological process is lacking. In this Letter, we introduce a simple description of the basic physical elements of transcription where RNA elongation, RNA polymerase rotation, and DNA supercoiling are coupled. The resulting framework describes the relative amount of RNA polymerase rotation and DNA supercoiling that occurs during RNA elongation. Asymptotic behavior is derived and can be used to experimentally extract unknown mechanical parameters of transcription. Mechanical limits to transcription are incorporated through the addition of a DNA supercoiling-dependent RNA polymerase velocity. This addition can lead to transcriptional stalling and resulting implications for gene expression, chromatin structure and genome organization are discussed.

Modeling of feed-forward control using the partial least squares regression method in the tablet compression process.

PubMed

Hattori, Yusuke; Otsuka, Makoto

2017-05-30

In the pharmaceutical industry, the implementation of continuous manufacturing has been widely promoted in lieu of the traditional batch manufacturing approach. More specially, in recent years, the innovative concept of feed-forward control has been introduced in relation to process analytical technology. In the present study, we successfully developed a feed-forward control model for the tablet compression process by integrating data obtained from near-infrared (NIR) spectra and the physical properties of granules. In the pharmaceutical industry, batch manufacturing routinely allows for the preparation of granules with the desired properties through the manual control of process parameters. On the other hand, continuous manufacturing demands the automatic determination of these process parameters. Here, we proposed the development of a control model using the partial least squares regression (PLSR) method. The most significant feature of this method is the use of dataset integrating both the NIR spectra and the physical properties of the granules. Using our model, we determined that the properties of products, such as tablet weight and thickness, need to be included as independent variables in the PLSR analysis in order to predict unknown process parameters. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of an analytical solution for the Budyko watershed parameter in terms of catchment physical features

NASA Astrophysics Data System (ADS)

Reaver, N.; Kaplan, D. A.; Jawitz, J. W.

2017-12-01

The Budyko hypothesis states that a catchment's long-term water and energy balances are dependent on two relatively easy to measure quantities: rainfall depth and potential evaporation. This hypothesis is expressed as a simple function, the Budyko equation, which allows for the prediction of a catchment's actual evapotranspiration and discharge from measured rainfall depth and potential evaporation, data which are widely available. However, the two main analytically derived forms of the Budyko equation contain a single unknown watershed parameter, whose value varies across catchments; variation in this parameter has been used to explain the hydrological behavior of different catchments. The watershed parameter is generally thought of as a lumped quantity that represents the influence of all catchment biophysical features (e.g. soil type and depth, vegetation type, timing of rainfall, etc). Previous work has shown that the parameter is statistically correlated with catchment properties, but an explicit expression has been elusive. While the watershed parameter can be determined empirically by fitting the Budyko equation to measured data in gauged catchments where actual evapotranspiration can be estimated, this limits the utility of the framework for predicting impacts to catchment hydrology due to changing climate and land use. In this study, we developed an analytical solution for the lumped catchment parameter for both forms of the Budyko equation. We combined these solutions with a statistical soil moisture model to obtain analytical solutions for the Budyko equation parameter as a function of measurable catchment physical features, including rooting depth, soil porosity, and soil wilting point. We tested the predictive power of these solutions using the U.S. catchments in the MOPEX database. We also compared the Budyko equation parameter estimates generated from our analytical solutions (i.e. predicted parameters) with those obtained through the calibration of the Budyko equation to discharge data (i.e. empirical parameters), and found good agreement. These results suggest that it is possible to predict the Budyko equation watershed parameter directly from physical features, even for ungauged catchments.

Minimal Unified Resolution to R_{K^{(*)}} and R(D^{(*)}) Anomalies with Lepton Mixing.

PubMed

Choudhury, Debajyoti; Kundu, Anirban; Mandal, Rusa; Sinha, Rahul

2017-10-13

It is a challenging task to explain, in terms of a simple and compelling new physics scenario, the intriguing discrepancies between the standard model expectations and the data for the neutral-current observables R_{K} and R_{K^{*}}, as well as the charged-current observables R(D) and R(D^{*}). We show that this can be achieved in an effective theory with only two unknown parameters. In addition, this class of models predicts some interesting signatures in the context of both B decays as well as high-energy collisions.

Adaptive control of Parkinson's state based on a nonlinear computational model with unknown parameters.

PubMed

Su, Fei; Wang, Jiang; Deng, Bin; Wei, Xi-Le; Chen, Ying-Yuan; Liu, Chen; Li, Hui-Yan

2015-02-01

The objective here is to explore the use of adaptive input-output feedback linearization method to achieve an improved deep brain stimulation (DBS) algorithm for closed-loop control of Parkinson's state. The control law is based on a highly nonlinear computational model of Parkinson's disease (PD) with unknown parameters. The restoration of thalamic relay reliability is formulated as the desired outcome of the adaptive control methodology, and the DBS waveform is the control input. The control input is adjusted in real time according to estimates of unknown parameters as well as the feedback signal. Simulation results show that the proposed adaptive control algorithm succeeds in restoring the relay reliability of the thalamus, and at the same time achieves accurate estimation of unknown parameters. Our findings point to the potential value of adaptive control approach that could be used to regulate DBS waveform in more effective treatment of PD.

Dynamic Modeling from Flight Data with Unknown Time Skews

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.

2016-01-01

A method for estimating dynamic model parameters from flight data with unknown time skews is described and demonstrated. The method combines data reconstruction, nonlinear optimization, and equation-error parameter estimation in the frequency domain to accurately estimate both dynamic model parameters and the relative time skews in the data. Data from a nonlinear F-16 aircraft simulation with realistic noise, instrumentation errors, and arbitrary time skews were used to demonstrate the approach. The approach was further evaluated using flight data from a subscale jet transport aircraft, where the measured data were known to have relative time skews. Comparison of modeling results obtained from time-skewed and time-synchronized data showed that the method accurately estimates both dynamic model parameters and relative time skew parameters from flight data with unknown time skews.

Parameter identifiability of linear dynamical systems

NASA Technical Reports Server (NTRS)

Glover, K.; Willems, J. C.

1974-01-01

It is assumed that the system matrices of a stationary linear dynamical system were parametrized by a set of unknown parameters. The question considered here is, when can such a set of unknown parameters be identified from the observed data? Conditions for the local identifiability of a parametrization are derived in three situations: (1) when input/output observations are made, (2) when there exists an unknown feedback matrix in the system and (3) when the system is assumed to be driven by white noise and only output observations are made. Also a sufficient condition for global identifiability is derived.

Parameter identification of thermophilic anaerobic degradation of valerate.

PubMed

Flotats, Xavier; Ahring, Birgitte K; Angelidaki, Irini

2003-01-01

The considered mathematical model of the decomposition of valerate presents three unknown kinetic parameters, two unknown stoichiometric coefficients, and three unknown initial concentrations for biomass. Applying a structural identifiability study, we concluded that it is necessary to perform simultaneous batch experiments with different initial conditions for estimating these parameters. Four simultaneous batch experiments were conducted at 55 degrees C, characterized by four different initial acetate concentrations. Product inhibition of valerate degradation by acetate was considered. Practical identification was done optimizing the sum of the multiple determination coefficients for all measured state variables and for all experiments simultaneously. The estimated values of kinetic parameters and stoichiometric coefficients were characterized by the parameter correlation matrix, the confidence interval, and the student's t-test at 5% significance level with positive results except for the saturation constant, for which more experiments for improving its identifiability should be conducted. In this article, we discuss kinetic parameter estimation methods.

Fatigue and physical fitness of mildly disabled persons with multiple sclerosis: a cross-sectional study.

PubMed

Valet, Maxime; Lejeune, Thierry; Glibert, Yumiko; Hakizimana, Jean C; Van Pesch, Vincent; El Sankari, Souraya; Detrembleur, Christine; Stoquart, Gaëtan

2017-09-01

Fatigue is frequent and disabling in persons with multiple sclerosis (pwMS) with mild neurological disability. These patients also have impaired physical fitness. Whether mildly disabled pwMS are deconditioned, and this deconditioning is linked to fatigue, remains unknown. Our aim is to determine the physical fitness of mildly disabled patients with multiple sclerosis and study its relationship with perceived fatigue and to link perceived fatigue with other parameters. Twenty patients (14 women; mean age: 45.5 years) with mild disability (Expanded Disability Status Scale 0-4) underwent a 2-min walking test, Timed Up-and-Go test, aerobic capacity testing, and isometric knee extension testing to assess strength and neuromuscular fatigability. They completed questionnaires assessing perceived fatigue, psychological status, and physical activity. Correlation coefficients and multivariate regression were used to analyze the relationships among variables. Seventeen (85%) patients reported a high level of fatigue. Thirteen (65%) patients had subnormal aerobic capacity. Fatigue was weakly to moderately associated with aerobic capacity, mobility, walking capacity, depression, and neuromuscular fatigability. An association of disease duration, aerobic capacity, and the neuromuscular fatigability index explained 65.1% of fatigue. A high proportion of pwMS with mild neurological disability are fatigued and deconditioned. Perceived fatigue is linked to aerobic capacity, neuromuscular fatigability, depression, mobility, and walking capacity. Focusing on these parameters could help in the management of fatigue.

Discrete-time switching periodic adaptive control for time-varying parameters with unknown periodicity

NASA Astrophysics Data System (ADS)

Yu, Miao; Huang, Deqing; Yang, Wanqiu

2018-06-01

In this paper, we address the problem of unknown periodicity for a class of discrete-time nonlinear parametric systems without assuming any growth conditions on the nonlinearities. The unknown periodicity hides in the parametric uncertainties, which is difficult to estimate with existing techniques. By incorporating a logic-based switching mechanism, we identify the period and bound of unknown parameter simultaneously. Lyapunov-based analysis is given to demonstrate that a finite number of switchings can guarantee the asymptotic tracking for the nonlinear parametric systems. The simulation result also shows the efficacy of the proposed switching periodic adaptive control approach.

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

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

Marzouk, Youssef

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesianmore » inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.« less

GLRT-based array receivers for the detection of a known signal with unknown parameters corrupted by noncircular interferences

NASA Astrophysics Data System (ADS)

Chevalier, Pascal; Oukaci, Abdelkader; Delmas, Jean-Pierre

2011-12-01

The detection of a known signal with unknown parameters in the presence of noise plus interferences (called total noise) whose covariance matrix is unknown is an important problem which has received much attention these last decades for applications such as radar, satellite localization or time acquisition in radio communications. However, most of the available receivers assume a second order (SO) circular (or proper) total noise and become suboptimal in the presence of SO noncircular (or improper) interferences, potentially present in the previous applications. The scarce available receivers which take the potential SO noncircularity of the total noise into account have been developed under the restrictive condition of a known signal with known parameters or under the assumption of a random signal. For this reason, following a generalized likelihood ratio test (GLRT) approach, the purpose of this paper is to introduce and to analyze the performance of different array receivers for the detection of a known signal, with different sets of unknown parameters, corrupted by an unknown noncircular total noise. To simplify the study, we limit the analysis to rectilinear known useful signals for which the baseband signal is real, which concerns many applications.

Lightning Charge Retrievals: Dimensional Reduction, LDAR Constraints, and a First Comparison with LIS Satellite Data

NASA Technical Reports Server (NTRS)

Koshak, W. J.; Krider, E. P.; Murray, N.; Boccippio, D. J.

2007-01-01

A "dimensional reduction" (DR) method is introduced for analyzing lightning field changes (DELTAEs) whereby the number of unknowns in a discrete two-charge model is reduced from the standard eight (x, y, z, Q, x', y', z', Q') to just four (x, y, z, Q). The four unknowns (x, y, z, Q) are found by performing a numerical minimization of a chi-square function. At each step of the minimization, an Overdetermined Fixed Matrix (OFM) method is used to immediately retrieve the best "residual source" (x', y', z', Q'), given the values of (x, y, z, Q). In this way, all 8 parameters (x, y, z, Q, x', y', z', Q') are found, yet a numerical search of only 4 parameters (x, y, z, Q) is required. The DR method has been used to analyze lightning-caused DeltaEs derived from multiple ground-based electric field measurements at the NASA Kennedy Space Center (KSC) and USAF Eastern Range (ER). The accuracy of the DR method has been assessed by comparing retrievals with data provided by the Lightning Detection And Ranging (LDAR) system at the KSC-ER, and from least squares error estimation theory, and the method is shown to be a useful "stand-alone" charge retrieval tool. Since more than one charge distribution describes a finite set of DELTAEs (i.e., solutions are non-unique), and since there can exist appreciable differences in the physical characteristics of these solutions, not all DR solutions are physically acceptable. Hence, an alternative and more accurate method of analysis is introduced that uses LDAR data to constrain the geometry of the charge solutions, thereby removing physically unacceptable retrievals. The charge solutions derived from this method are shown to compare well with independent satellite- and ground-based observations of lightning in several Florida storms.

Separation of Trend and Chaotic Components of Time Series and Estimation of Their Characteristics by Linear Splines

NASA Astrophysics Data System (ADS)

Kryanev, A. V.; Ivanov, V. V.; Romanova, A. O.; Sevastyanov, L. A.; Udumyan, D. K.

2018-03-01

This paper considers the problem of separating the trend and the chaotic component of chaotic time series in the absence of information on the characteristics of the chaotic component. Such a problem arises in nuclear physics, biomedicine, and many other applied fields. The scheme has two stages. At the first stage, smoothing linear splines with different values of smoothing parameter are used to separate the "trend component." At the second stage, the method of least squares is used to find the unknown variance σ2 of the noise component.

Complete synchronization of uncertain chaotic systems via a single proportional adaptive controller: A comparative study

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

Ahmad, Israr, E-mail: iak-2000plus@yahoo.com; Saaban, Azizan Bin, E-mail: azizan.s@uum.edu.my; Ibrahim, Adyda Binti, E-mail: adyda@uum.edu.my

This paper addresses a comparative computational study on the synchronization quality, cost and converging speed for two pairs of identical chaotic and hyperchaotic systems with unknown time-varying parameters. It is assumed that the unknown time-varying parameters are bounded. Based on the Lyapunov stability theory and using the adaptive control method, a single proportional controller is proposed to achieve the goal of complete synchronizations. Accordingly, appropriate adaptive laws are designed to identify the unknown time-varying parameters. The designed control strategy is easy to implement in practice. Numerical simulations results are provided to verify the effectiveness of the proposed synchronization scheme.

Prediction of scaling physics laws for proton acceleration with extended parameter space of the NIF ARC

NASA Astrophysics Data System (ADS)

Bhutwala, Krish; Beg, Farhat; Mariscal, Derek; Wilks, Scott; Ma, Tammy

2017-10-01

The Advanced Radiographic Capability (ARC) laser at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory is the world's most energetic short-pulse laser. It comprises four beamlets, each of substantial energy ( 1.5 kJ), extended short-pulse duration (10-30 ps), and large focal spot (>=50% of energy in 150 µm spot). This allows ARC to achieve proton and light ion acceleration via the Target Normal Sheath Acceleration (TNSA) mechanism, but it is yet unknown how proton beam characteristics scale with ARC-regime laser parameters. As theory has also not yet been validated for laser-generated protons at ARC-regime laser parameters, we attempt to formulate the scaling physics of proton beam characteristics as a function of laser energy, intensity, focal spot size, pulse length, target geometry, etc. through a review of relevant proton acceleration experiments from laser facilities across the world. These predicted scaling laws should then guide target design and future diagnostics for desired proton beam experiments on the NIF ARC. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and funded by the LLNL LDRD program under tracking code 17-ERD-039.

On synchronisation of a class of complex chaotic systems with complex unknown parameters via integral sliding mode control

NASA Astrophysics Data System (ADS)

Tirandaz, Hamed; Karami-Mollaee, Ali

2018-06-01

Chaotic systems demonstrate complex behaviour in their state variables and their parameters, which generate some challenges and consequences. This paper presents a new synchronisation scheme based on integral sliding mode control (ISMC) method on a class of complex chaotic systems with complex unknown parameters. Synchronisation between corresponding states of a class of complex chaotic systems and also convergence of the errors of the system parameters to zero point are studied. The designed feedback control vector and complex unknown parameter vector are analytically achieved based on the Lyapunov stability theory. Moreover, the effectiveness of the proposed methodology is verified by synchronisation of the Chen complex system and the Lorenz complex systems as the leader and the follower chaotic systems, respectively. In conclusion, some numerical simulations related to the synchronisation methodology is given to illustrate the effectiveness of the theoretical discussions.

How to Frame the Un-Known? The Odd Alliance of Design and "Fundamental Physics" in a Design School

ERIC Educational Resources Information Center

Gentes, Annie; Renon, Anne-Lyse; Bobroff, Julien

2017-01-01

This paper analyzes the introduction of fundamental physics in design education as a pedagogical method that trains designers to create with the un-known. It studies how three workshops offered design students to work on: superconductivity in 2011, quantum physics in 2013 and light and optics in 2014. The authors observe that introducing physics…

Comparison of methods for the detection of gravitational waves from unknown neutron stars

NASA Astrophysics Data System (ADS)

Walsh, S.; Pitkin, M.; Oliver, M.; D'Antonio, S.; Dergachev, V.; Królak, A.; Astone, P.; Bejger, M.; Di Giovanni, M.; Dorosh, O.; Frasca, S.; Leaci, P.; Mastrogiovanni, S.; Miller, A.; Palomba, C.; Papa, M. A.; Piccinni, O. J.; Riles, K.; Sauter, O.; Sintes, A. M.

2016-12-01

Rapidly rotating neutron stars are promising sources of continuous gravitational wave radiation for the LIGO and Virgo interferometers. The majority of neutron stars in our galaxy have not been identified with electromagnetic observations. All-sky searches for isolated neutron stars offer the potential to detect gravitational waves from these unidentified sources. The parameter space of these blind all-sky searches, which also cover a large range of frequencies and frequency derivatives, presents a significant computational challenge. Different methods have been designed to perform these searches within acceptable computational limits. Here we describe the first benchmark in a project to compare the search methods currently available for the detection of unknown isolated neutron stars. The five methods compared here are individually referred to as the PowerFlux, sky Hough, frequency Hough, Einstein@Home, and time domain F -statistic methods. We employ a mock data challenge to compare the ability of each search method to recover signals simulated assuming a standard signal model. We find similar performance among the four quick-look search methods, while the more computationally intensive search method, Einstein@Home, achieves up to a factor of two higher sensitivity. We find that the absence of a second derivative frequency in the search parameter space does not degrade search sensitivity for signals with physically plausible second derivative frequencies. We also report on the parameter estimation accuracy of each search method, and the stability of the sensitivity in frequency and frequency derivative and in the presence of detector noise.

M-MRAC Backstepping for Systems with Unknown Virtual Control Coefficients

NASA Technical Reports Server (NTRS)

Stepanyan, Vahram; Krishnakumar, Kalmanje

2015-01-01

The paper presents an over-parametrization free certainty equivalence state feedback backstepping adaptive control design method for systems of any relative degree with unmatched uncertainties and unknown virtual control coefficients. It uses a fast prediction model to estimate the unknown parameters, which is independent of the control design. It is shown that the system's input and output tracking errors can be systematically decreased by the proper choice of the design parameters. The benefits of the approach are demonstrated in numerical simulations.

Accelerator and reactor complementarity in coherent neutrino-nucleus scattering

NASA Astrophysics Data System (ADS)

Dent, James B.; Dutta, Bhaskar; Liao, Shu; Newstead, Jayden L.; Strigari, Louis E.; Walker, Joel W.

2018-02-01

We study the complementarity between accelerator and reactor coherent elastic neutrino-nucleus elastic scattering (CE ν NS ) experiments for constraining new physics in the form of nonstandard neutrino interactions (NSI). First, considering just data from the recent observation by the Coherent experiment, we explore interpretive degeneracies that emerge when activating either two or four unknown NSI parameters. Next, we demonstrate that simultaneous treatment of reactor and accelerator experiments, each employing at least two distinct target materials, can break a degeneracy between up and down flavor-diagonal NSI terms that survives analysis of neutrino oscillation experiments. Considering four flavor-diagonal (e e /μ μ ) up- and down-type NSI parameters, we find that all terms can be measured with high local precision (to a width as small as ˜5 % in Fermi units) by next-generation experiments, although discrete reflection ambiguities persist.

Stochastic approximation methods-Powerful tools for simulation and optimization: A survey of some recent work on multi-agent systems and cyber-physical systems

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

Yin, George; Wang, Le Yi; Zhang, Hongwei

2014-12-10

Stochastic approximation methods have found extensive and diversified applications. Recent emergence of networked systems and cyber-physical systems has generated renewed interest in advancing stochastic approximation into a general framework to support algorithm development for information processing and decisions in such systems. This paper presents a survey on some recent developments in stochastic approximation methods and their applications. Using connected vehicles in platoon formation and coordination as a platform, we highlight some traditional and new methodologies of stochastic approximation algorithms and explain how they can be used to capture essential features in networked systems. Distinct features of networked systems with randomlymore » switching topologies, dynamically evolving parameters, and unknown delays are presented, and control strategies are provided.« less

Parameter estimation of qubit states with unknown phase parameter

NASA Astrophysics Data System (ADS)

Suzuki, Jun

2015-02-01

We discuss a problem of parameter estimation for quantum two-level system, qubit system, in presence of unknown phase parameter. We analyze trade-off relations for mean square errors (MSEs) when estimating relevant parameters with separable measurements based on known precision bounds; the symmetric logarithmic derivative (SLD) Cramér-Rao (CR) bound and Hayashi-Gill-Massar (HGM) bound. We investigate the optimal measurement which attains the HGM bound and discuss its properties. We show that the HGM bound for relevant parameters can be attained asymptotically by using some fraction of given n quantum states to estimate the phase parameter. We also discuss the Holevo bound which can be attained asymptotically by a collective measurement.

Poroelastic Modeling as a Proof of Concept for Modular Representation of Coupled Geophysical Processes

NASA Astrophysics Data System (ADS)

Walker, R. L., II; Knepley, M.; Aminzadeh, F.

2017-12-01

We seek to use the tools provided by the Portable, Extensible Toolkit for Scientific Computation (PETSc) to represent a multiphysics problem in a form that decouples the element definition from the fully coupled equation through the use of pointwise functions that imitate the strong form of the governing equation. This allows allows individual physical processes to be expressed as independent kernels that may be then coupled with the existing finite element framework, PyLith, and capitalizes upon the flexibility offered by the solver, data management, and time stepping algorithms offered by PETSc. To demonstrate a characteristic example of coupled geophysical simulation devised in this manner, we present a model of a synthetic poroelastic environment, with and without the consideration of inertial effects, with fluid initially represented as a single phase. Matrix displacement and fluid pressure serve as the desired unknowns, with the option for various model parameters represented as dependent variables of the central unknowns. While independent of PyLith, this model also serves to showcase the adaptability of physics kernels for synthetic forward modeling. In addition, we seek to expand the base case to demonstrate the impact of modeling fluid as single phase compressible versus a single incompressible phase. As a goal, we also seek to include multiphase fluid modeling, as well as capillary effects.

Fear and C-reactive protein cosynergize annual pulse increases in healthy adults

PubMed Central

Shenhar-Tsarfaty, Shani; Yayon, Nadav; Waiskopf, Nir; Shapira, Itzhak; Toker, Sharon; Zaltser, David; Berliner, Shlomo; Ritov, Ya'acov; Soreq, Hermona

2015-01-01

Recent international terror outbreaks notably involve long-term mental health risks to the exposed population, but whether physical health risks are also anticipated has remained unknown. Here, we report fear of terror-induced annual increases in resting heart rate (pulse), a notable risk factor of all-cause mortality. Partial least squares analysis based on 325 measured parameters successfully predicted annual pulse increases, inverse to the expected age-related pulse decline, in approximately 4.1% of a cohort of 17,380 apparently healthy active Israeli adults. Nonbiased hierarchical regression analysis among 27 of those parameters identified pertinent fear of terror combined with the inflammatory biomarker C-reactive protein as prominent coregulators of the observed annual pulse increases. In comparison, basal pulse primarily depended on general physiological parameters and reduced cholinergic control over anxiety and inflammation, together indicating that consistent exposure to terror threats ignites fear-induced exacerbation of preexisting neuro-immune risks of all-cause mortality. PMID:25535364

Optimization of a Thermodynamic Model Using a Dakota Toolbox Interface

NASA Astrophysics Data System (ADS)

Cyrus, J.; Jafarov, E. E.; Schaefer, K. M.; Wang, K.; Clow, G. D.; Piper, M.; Overeem, I.

2016-12-01

Scientific modeling of the Earth physical processes is an important driver of modern science. The behavior of these scientific models is governed by a set of input parameters. It is crucial to choose accurate input parameters that will also preserve the corresponding physics being simulated in the model. In order to effectively simulate real world processes the models output data must be close to the observed measurements. To achieve this optimal simulation, input parameters are tuned until we have minimized the objective function, which is the error between the simulation model outputs and the observed measurements. We developed an auxiliary package, which serves as a python interface between the user and DAKOTA. The package makes it easy for the user to conduct parameter space explorations, parameter optimizations, as well as sensitivity analysis while tracking and storing results in a database. The ability to perform these analyses via a Python library also allows the users to combine analysis techniques, for example finding an approximate equilibrium with optimization then immediately explore the space around it. We used the interface to calibrate input parameters for the heat flow model, which is commonly used in permafrost science. We performed optimization on the first three layers of the permafrost model, each with two thermal conductivity coefficients input parameters. Results of parameter space explorations indicate that the objective function not always has a unique minimal value. We found that gradient-based optimization works the best for the objective functions with one minimum. Otherwise, we employ more advanced Dakota methods such as genetic optimization and mesh based convergence in order to find the optimal input parameters. We were able to recover 6 initially unknown thermal conductivity parameters within 2% accuracy of their known values. Our initial tests indicate that the developed interface for the Dakota toolbox could be used to perform analysis and optimization on a `black box' scientific model more efficiently than using just Dakota.

An eigenvalue approach for the automatic scaling of unknowns in model-based reconstructions: Application to real-time phase-contrast flow MRI.

PubMed

Tan, Zhengguo; Hohage, Thorsten; Kalentev, Oleksandr; Joseph, Arun A; Wang, Xiaoqing; Voit, Dirk; Merboldt, K Dietmar; Frahm, Jens

2017-12-01

The purpose of this work is to develop an automatic method for the scaling of unknowns in model-based nonlinear inverse reconstructions and to evaluate its application to real-time phase-contrast (RT-PC) flow magnetic resonance imaging (MRI). Model-based MRI reconstructions of parametric maps which describe a physical or physiological function require the solution of a nonlinear inverse problem, because the list of unknowns in the extended MRI signal equation comprises multiple functional parameters and all coil sensitivity profiles. Iterative solutions therefore rely on an appropriate scaling of unknowns to numerically balance partial derivatives and regularization terms. The scaling of unknowns emerges as a self-adjoint and positive-definite matrix which is expressible by its maximal eigenvalue and solved by power iterations. The proposed method is applied to RT-PC flow MRI based on highly undersampled acquisitions. Experimental validations include numerical phantoms providing ground truth and a wide range of human studies in the ascending aorta, carotid arteries, deep veins during muscular exercise and cerebrospinal fluid during deep respiration. For RT-PC flow MRI, model-based reconstructions with automatic scaling not only offer velocity maps with high spatiotemporal acuity and much reduced phase noise, but also ensure fast convergence as well as accurate and precise velocities for all conditions tested, i.e. for different velocity ranges, vessel sizes and the simultaneous presence of signals with velocity aliasing. In summary, the proposed automatic scaling of unknowns in model-based MRI reconstructions yields quantitatively reliable velocities for RT-PC flow MRI in various experimental scenarios. Copyright © 2017 John Wiley & Sons, Ltd.

Benefits from bremsstrahlung distribution evaluation to get unknown information from specimen in SEM and TEM

NASA Astrophysics Data System (ADS)

Eggert, F.; Camus, P. P.; Schleifer, M.; Reinauer, F.

2018-01-01

The energy-dispersive X-ray spectrometer (EDS or EDX) is a commonly used device to characterise the composition of investigated material in scanning and transmission electron microscopes (SEM and TEM). One major benefit compared to wavelength-dispersive X-ray spectrometers (WDS) is that EDS systems collect the entire spectrum simultaneously. Therefore, not only are all emitted characteristic X-ray lines in the spectrum, but also the complete bremsstrahlung distribution is included. It is possible to get information about the specimen even from this radiation, which is usually perceived more as a disturbing background. This is possible by using theoretical model knowledge about bremsstrahlung excitation and absorption in the specimen in comparison to the actual measured spectrum. The core aim of this investigation is to present a method for better bremsstrahlung fitting in unknown geometry cases by variation of the geometry parameters and to utilise this knowledge also for characteristic radiation evaluation. A method is described, which allows the parameterisation of the true X-ray absorption conditions during spectrum acquisition. An ‘effective tilt’ angle parameter is determined by evaluation of the bremsstrahlung shape of the measured SEM spectra. It is useful for bremsstrahlung background approximation, with exact calculations of the absorption edges below the characteristic peaks, required for P/B-ZAF model based quantification methods. It can even be used for ZAF based quantification models as a variable input parameter. The analytical results are then much more reliable for the different absorption effects from irregular specimen surfaces because the unknown absorption dependency is considered. Finally, the method is also applied for evaluation of TEM spectra. In this case, the real physical parameter optimisation is with sample thickness (mass thickness), which is influencing the emitted and measured spectrum due to different absorption with TEM measurements. The effects are in the very low energy part of the spectrum, and are much more visible with most recent windowless TEM detectors. The thickness of the sample can be determined in this way from the measured bremsstrahlung spectrum shape.

Physical limits of flow sensing in the left-right organizer

PubMed Central

Ferreira, Rita R; Vilfan, Andrej; Jülicher, Frank; Supatto, Willy; Vermot, Julien

2017-01-01

Fluid flows generated by motile cilia are guiding the establishment of the left-right asymmetry of the body in the vertebrate left-right organizer. Competing hypotheses have been proposed: the direction of flow is sensed either through mechanosensation, or via the detection of chemical signals transported in the flow. We investigated the physical limits of flow detection to clarify which mechanisms could be reliably used for symmetry breaking. We integrated parameters describing cilia distribution and orientation obtained in vivo in zebrafish into a multiscale physical study of flow generation and detection. Our results show that the number of immotile cilia is too small to ensure robust left and right determination by mechanosensing, given the large spatial variability of the flow. However, motile cilia could sense their own motion by a yet unknown mechanism. Finally, transport of chemical signals by the flow can provide a simple and reliable mechanism of asymmetry establishment. DOI: http://dx.doi.org/10.7554/eLife.25078.001 PMID:28613157

Bayesian statistics and Monte Carlo methods

NASA Astrophysics Data System (ADS)

Koch, K. R.

2018-03-01

The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability. If the statements refer to the numerical values of variables, the so-called random variables, univariate and multivariate distributions follow. They lead to the point estimation by which unknown quantities, i.e. unknown parameters, are computed from measurements. The unknown parameters are random variables, they are fixed quantities in traditional statistics which is not founded on Bayes' theorem. Bayesian statistics therefore recommends itself for Monte Carlo methods, which generate random variates from given distributions. Monte Carlo methods, of course, can also be applied in traditional statistics. The unknown parameters, are introduced as functions of the measurements, and the Monte Carlo methods give the covariance matrix and the expectation of these functions. A confidence region is derived where the unknown parameters are situated with a given probability. Following a method of traditional statistics, hypotheses are tested by determining whether a value for an unknown parameter lies inside or outside the confidence region. The error propagation of a random vector by the Monte Carlo methods is presented as an application. If the random vector results from a nonlinearly transformed vector, its covariance matrix and its expectation follow from the Monte Carlo estimate. This saves a considerable amount of derivatives to be computed, and errors of the linearization are avoided. The Monte Carlo method is therefore efficient. If the functions of the measurements are given by a sum of two or more random vectors with different multivariate distributions, the resulting distribution is generally not known. TheMonte Carlo methods are then needed to obtain the covariance matrix and the expectation of the sum.

Physical modeling in geomorphology: are boundary conditions necessary?

NASA Astrophysics Data System (ADS)

Cantelli, A.

2012-12-01

Referring to the physical experimental design in geomorphology, boundary conditions are key elements that determine the quality of the results and therefore the study development. For years engineers have modeled structures, such as dams and bridges, with high precision and excellent results. Until the last decade, a great part of the physical experimental work in geomorphology has been developed with an engineer-like approach, requiring an accurate scaling analysis to determine inflow parameters and initial geometrical conditions. However, during the last decade, the way we have been approaching physical experiments has significantly changed. In particular, boundary conditions and initial conditions are considered unknown factors that need to be discovered during the experiment. This new philosophy leads to a more demanding data acquisition process but relaxes the obligation to a priori know the appropriate input and initial conditions and provides the flexibility to discover those data. Here I am going to present some practical examples of this experimental approach in deepwater geomorphology; some questions about scaling of turbidity currents and a new large experimental facility built at the Universidade Federal do Rio Grande do Sul, Brasil.

Regularized Semiparametric Estimation for Ordinary Differential Equations

PubMed Central

Li, Yun; Zhu, Ji; Wang, Naisyin

2015-01-01

Ordinary differential equations (ODEs) are widely used in modeling dynamic systems and have ample applications in the fields of physics, engineering, economics and biological sciences. The ODE parameters often possess physiological meanings and can help scientists gain better understanding of the system. One key interest is thus to well estimate these parameters. Ideally, constant parameters are preferred due to their easy interpretation. In reality, however, constant parameters can be too restrictive such that even after incorporating error terms, there could still be unknown sources of disturbance that lead to poor agreement between observed data and the estimated ODE system. In this paper, we address this issue and accommodate short-term interferences by allowing parameters to vary with time. We propose a new regularized estimation procedure on the time-varying parameters of an ODE system so that these parameters could change with time during transitions but remain constants within stable stages. We found, through simulation studies, that the proposed method performs well and tends to have less variation in comparison to the non-regularized approach. On the theoretical front, we derive finite-sample estimation error bounds for the proposed method. Applications of the proposed method to modeling the hare-lynx relationship and the measles incidence dynamic in Ontario, Canada lead to satisfactory and meaningful results. PMID:26392639

Global 3ν oscillation analysis: Status of unknown parameters and future systematic challenges for ORCA and PINGU

NASA Astrophysics Data System (ADS)

Capozzi, Francesco; Lisi, Eligio; Marrone, Antonio

2016-04-01

Within the standard 3ν oscillation framework, we illustrate the status of currently unknown oscillation parameters: the θ23 octant, the mass hierarchy (normal or inverted), and the possible CP-violating phase δ, as derived by a (preliminary) global analysis of oscillation data available in 2015. We then discuss some challenges that will be faced by future, high-statistics analyses of spectral data, starting with one-dimensional energy spectra in reactor experiments, and concluding with two-dimensional energy-angle spectra in large-volume atmospheric experiments. It is shown that systematic uncertainties in the spectral shapes can noticeably affect the prospective sensitivities to unknown oscillation parameters, in particular to the mass hierarchy.

Market-Based Coordination of Thermostatically Controlled Loads—Part II: Unknown Parameters and Case Studies

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

Li, Sen; Zhang, Wei; Lian, Jianming

This two-part paper considers the coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. The companion paper (Part I) formulates the problem and proposes a load coordination framework using the mechanism design approach. To address the unknown parameters, Part II of this paper presents a joint state and parameter estimation framework based on the expectation maximization algorithm. The overall framework is then validated using real-world weather data andmore » price data, and is compared with other approaches in terms of aggregated power response. Simulation results indicate that our coordination framework can effectively improve the efficiency of the power grid operations and reduce power congestion at key times.« less

Estimating unknown input parameters when implementing the NGA ground-motion prediction equations in engineering practice

USGS Publications Warehouse

Kaklamanos, James; Baise, Laurie G.; Boore, David M.

2011-01-01

The ground-motion prediction equations (GMPEs) developed as part of the Next Generation Attenuation of Ground Motions (NGA-West) project in 2008 are becoming widely used in seismic hazard analyses. However, these new models are considerably more complicated than previous GMPEs, and they require several more input parameters. When employing the NGA models, users routinely face situations in which some of the required input parameters are unknown. In this paper, we present a framework for estimating the unknown source, path, and site parameters when implementing the NGA models in engineering practice, and we derive geometrically-based equations relating the three distance measures found in the NGA models. Our intent is for the content of this paper not only to make the NGA models more accessible, but also to help with the implementation of other present or future GMPEs.

Parameter estimation in a structural acoustic system with fully nonlinear coupling conditions

NASA Technical Reports Server (NTRS)

Banks, H. T.; Smith, Ralph C.

1994-01-01

A methodology for estimating physical parameters in a class of structural acoustic systems is presented. The general model under consideration consists of an interior cavity which is separated from an exterior noise source by an enclosing elastic structure. Piezoceramic patches are bonded to or embedded in the structure; these can be used both as actuators and sensors in applications ranging from the control of interior noise levels to the determination of structural flaws through nondestructive evaluation techniques. The presence and excitation of patches, however, changes the geometry and material properties of the structure as well as involves unknown patch parameters, thus necessitating the development of parameter estimation techniques which are applicable in this coupled setting. In developing a framework for approximation, parameter estimation and implementation, strong consideration is given to the fact that the input operator is unbonded due to the discrete nature of the patches. Moreover, the model is weakly nonlinear. As a result of the coupling mechanism between the structural vibrations and the interior acoustic dynamics. Within this context, an illustrating model is given, well-posedness and approximations results are discussed and an applicable parameter estimation methodology is presented. The scheme is then illustrated through several numerical examples with simulations modeling a variety of commonly used structural acoustic techniques for systems excitations and data collection.

Adaptive control based on an on-line parameter estimation of an upper limb exoskeleton.

PubMed

Riani, Akram; Madani, Tarek; Hadri, Abdelhafid El; Benallegue, Abdelaziz

2017-07-01

This paper presents an adaptive control strategy for an upper-limb exoskeleton based on an on-line dynamic parameter estimator. The objective is to improve the control performance of this system that plays a critical role in assisting patients for shoulder, elbow and wrist joint movements. In general, the dynamic parameters of the human limb are unknown and differ from a person to another, which degrade the performances of the exoskeleton-human control system. For this reason, the proposed control scheme contains a supplementary loop based on a new efficient on-line estimator of the dynamic parameters. Indeed, the latter is acting upon the parameter adaptation of the controller to ensure the performances of the system in the presence of parameter uncertainties and perturbations. The exoskeleton used in this work is presented and a physical model of the exoskeleton interacting with a 7 Degree of Freedom (DoF) upper limb model is generated using the SimMechanics library of MatLab/Simulink. To illustrate the effectiveness of the proposed approach, an example of passive rehabilitation movements is performed using multi-body dynamic simulation. The aims is to maneuver the exoskeleton that drive the upper limb to track desired trajectories in the case of the passive arm movements.

Adaptive method for electron bunch profile prediction

DOE PAGES

Scheinker, Alexander; Gessner, Spencer

2015-10-15

We report on an experiment performed at the Facility for Advanced Accelerator Experimental Tests (FACET) at SLAC National Accelerator Laboratory, in which a new adaptive control algorithm, one with known, bounded update rates, despite operating on analytically unknown cost functions, was utilized in order to provide quasi-real-time bunch property estimates of the electron beam. Multiple parameters, such as arbitrary rf phase settings and other time-varying accelerator properties, were simultaneously tuned in order to match a simulated bunch energy spectrum with a measured energy spectrum. Thus, the simple adaptive scheme was digitally implemented using matlab and the experimental physics and industrialmore » control system. Finally, the main result is a nonintrusive, nondestructive, real-time diagnostic scheme for prediction of bunch profiles, as well as other beam parameters, the precise control of which are important for the plasma wakefield acceleration experiments being explored at FACET.« less

A sensitive search for unknown spectral emission lines in the diffuse X-ray background with XMM-Newton

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

Gewering-Peine, A.; Horns, D.; Schmitt, J.H.M.M., E-mail: alexander.gewering-peine@desy.de, E-mail: dieter.horns@desy.de, E-mail: jschmitt@hs.uni-hamburg.de

The Standard Model of particle physics can be extended to include sterile (right-handed) neutrinos or axions to solve the dark matter problem. Depending upon the mixing angle between active and sterile neutrinos, the latter have the possibility to decay into monoenergetic active neutrinos and photons in the keV-range while axions can couple to two photons. We have used data taken with the X-ray telescope XMM-Newton for the search of line emissions. We used pointings with high exposures and expected dark matter column densities with respect to the dark matter halo of the Milky Way. The posterior predictive p-value analysis hasmore » been applied to locate parameter space regions which favour additional emission lines. In addition, upper limits of the parameter space of the models have been generated such that the preexisting limits have been significantly improved.« less

Kron-Branin modelling of ultra-short pulsed signal microelectrode

NASA Astrophysics Data System (ADS)

Xu, Zhifei; Ravelo, Blaise; Liu, Yang; Zhao, Lu; Delaroche, Fabien; Vurpillot, Francois

2018-06-01

An uncommon circuit modelling of microelectrode for ultra-short signal propagation is developed. The proposed model is based on the Tensorial Analysis of Network (TAN) using the Kron-Branin (KB) formalism. The systemic graph topology equivalent to the considered structure problem is established by assuming as unknown variables the branch currents. The TAN mathematical solution is determined after the KB characteristic matrix identification. The TAN can integrate various structure physical parameters. As proof of concept, via hole ended microelectrodes implemented on Kapton substrate were designed, fabricated and tested. The 0.1-MHz-to-6-GHz S-parameter KB model, simulation and measurement are in good agreement. In addition, time-domain analyses with nanosecond duration pulse signals were carried out to predict the microelectrode signal integrity. The modelled microstrip electrode is usually integrated in the atom probe tomography. The proposed unfamiliar KB method is particularly beneficial with respect to the computation speed and adaptability to various structures.

Half-blind remote sensing image restoration with partly unknown degradation

NASA Astrophysics Data System (ADS)

Xie, Meihua; Yan, Fengxia

2017-01-01

The problem of image restoration has been extensively studied for its practical importance and theoretical interest. This paper mainly discusses the problem of image restoration with partly unknown kernel. In this model, the degraded kernel function is known but its parameters are unknown. With this model, we should estimate the parameters in Gaussian kernel and the real image simultaneity. For this new problem, a total variation restoration model is put out and an intersect direction iteration algorithm is designed. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) are used to measure the performance of the method. Numerical results show that we can estimate the parameters in kernel accurately, and the new method has both much higher PSNR and much higher SSIM than the expectation maximization (EM) method in many cases. In addition, the accuracy of estimation is not sensitive to noise. Furthermore, even though the support of the kernel is unknown, we can also use this method to get accurate estimation.

Heat generation/absorption and nonlinear radiation effects on stagnation point flow of nanofluid along a moving surface

NASA Astrophysics Data System (ADS)

Soomro, Feroz Ahmed; Haq, Rizwan Ul; Al-Mdallal, Qasem M.; Zhang, Qiang

2018-03-01

In this study, heat generation/absorption effects are studied in the presence of nonlinear thermal radiation along a moving slip surface. Uniform magnetic field and convective condition along the stretching surface are adjusted to deal the slip mechanisms in term of Brownian motion and thermophoresis for nanofluid. The mathematical model is constructed in the form of coupled partial differential equations. By introducing the suitable similarity transformation, system of coupled nonlinear ordinary differential equations are obtained. Finite difference approach is implemented to obtain the unknown functions of velocity, temperature, nanoparticle concentration. To deduct the effects at the surface, physical quantities of interest are computed under the effects of controlled physical parameters. Present numerical solutions are validated via numerical comparison with existing published work for limiting cases. Present study indicates that due to increase in both Brownian motion and thermophoresis, the Nusselt number decreases while Sherwood number shows the gradual increase.

The mean time-limited crash rate of stock price

NASA Astrophysics Data System (ADS)

Li, Yun-Xian; Li, Jiang-Cheng; Yang, Ai-Jun; Tang, Nian-Sheng

2017-05-01

In this article we investigate the occurrence of stock market crash in an economy cycle. Bayesian approach, Heston model and statistical-physical method are considered. Specifically, Heston model and an effective potential are employed to address the dynamic changes of stock price. Bayesian approach has been utilized to estimate the Heston model's unknown parameters. Statistical physical method is used to investigate the occurrence of stock market crash by calculating the mean time-limited crash rate. The real financial data from the Shanghai Composite Index is analyzed with the proposed methods. The mean time-limited crash rate of stock price is used to describe the occurrence of stock market crash in an economy cycle. The monotonous and nonmonotonous behaviors are observed in the behavior of the mean time-limited crash rate versus volatility of stock for various cross correlation coefficient between volatility and price. Also a minimum occurrence of stock market crash matching an optimal volatility is discovered.

Ceres: predictions for near-surface water ice stability and implications for plume generating processes

USGS Publications Warehouse

Titus, Timothy N.

2015-01-01

This paper will constrain the possible sources and processes for the formation of recently observed H2O vapor plumes above the surface of the dwarf planet Ceres. Two hypotheses have been proposed: (1) cryovolcanism where the water source is the mantle and the heating source is still unknown or (2) comet-like sublimation where near-surface water ice is vaporized by seasonally increasing solar insolation. We test hypothesis #2, comet-like near-surface sublimation, by using a thermal model to examine the stability of water-ice in the near surface. For a reasonable range of physical parameters (thermal inertia, surface roughness, slopes), we find that water ice is only stable at latitudes higher than ~40-60 degrees. These results indicate that either (a) the physical properties of Ceres are unlike our expectations or (b) an alternative to comet-like sublimation, such as the cryovolcanism hypothesis, must be invoked.

Identification of linear system models and state estimators for controls

NASA Technical Reports Server (NTRS)

Chen, Chung-Wen

1992-01-01

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

Resistin, interleukin-6, tumor necrosis factor-alpha, and human semen parameters in the presence of leukocytospermia, smoking habit, and varicocele.

PubMed

Moretti, Elena; Collodel, Giulia; Mazzi, Lucia; Campagna, MariaStella; Iacoponi, Francesca; Figura, Natale

2014-08-01

To explore the relationships between resistin, interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) and semen parameters, sperm apoptosis, and necrosis in infertile patients and in control subjects with unknown reproductive potential with/without smoking habits, leukocytospermia, and varicocele. Prospective study. Sperm laboratory. A total of 110 selected men. Family history, clinical/physical examination, ELISA determination (resistin, IL-6, TNF-α), semen analysis, annexin V/propidium iodide assay. Relationships among resistin, IL-6, and TNF-α and semen parameters in the presence of smoking habits, varicocele, leukocytospermia, and in infertile subjects. Resistin level was higher in semen than in serum. Resistin semen levels showed negative correlations with sperm motility and positive correlations with apoptotic, necrotic sperm and TNF-α and IL-6 levels. Resistin, TNF-α, and IL-6 levels were higher in smokers compared with nonsmokers and in cases with leukocytospermia, in which an increase in necrotic sperm and a decrease in the number of sperm with normal morphology and motility were observed. Cytokine levels were significantly higher in infertile patients compared with control subjects with unknown reproductive potential. A total of 74.5% of infertile patients showed leukocytospermia. Semen resistin correlated with IL-6, TNF-α, and sperm quality; in cases of leukocytospermia and smoking habits, resistin concentrations were increased, suggesting that resistin may play a regulatory role in inflammation of the male reproductive system. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Tune-stabilized, non-scaling, fixed-field, alternating gradient accelerator

DOEpatents

Johnstone, Carol J [Warrenville, IL

2011-02-01

A FFAG is a particle accelerator having turning magnets with a linear field gradient for confinement and a large edge angle to compensate for acceleration. FODO cells contain focus magnets and defocus magnets that are specified by a number of parameters. A set of seven equations, called the FFAG equations relate the parameters to one another. A set of constraints, call the FFAG constraints, constrain the FFAG equations. Selecting a few parameters, such as injection momentum, extraction momentum, and drift distance reduces the number of unknown parameters to seven. Seven equations with seven unknowns can be solved to yield the values for all the parameters and to thereby fully specify a FFAG.

An Enhanced Box-Wing Solar Radiation pressure model for BDS and initial results

NASA Astrophysics Data System (ADS)

Zhao, Qunhe; Wang, Xiaoya; Hu, Xiaogong; Guo, Rui; Shang, Lin; Tang, Chengpan; Shao, Fan

2016-04-01

Solar radiation pressure forces are the largest non-gravitational perturbations acting on GNSS satellites, which is difficult to be accurately modeled due to the complicated and changing satellite attitude and unknown surface material characteristics. By the end of 2015, there are more than 50 stations of the Multi-GNSS Experiment(MGEX) set-up by the IGS. The simple box-plate model relies on coarse assumptions about the dimensions and optical properties of the satellite due to lack of more detailed information. So, a physical model based on BOX-WING model is developed, which is more sophisticated and more detailed physical structure has been taken into account, then calculating pressure forces according to the geometric relations between light rays and surfaces. All the MGEX stations and IGS core stations had been processed for precise orbit determination tests with GPS and BDS observations. Calculation range covers all the two kinds of Eclipsing and non-eclipsing periods in 2015, and we adopted the un-differential observation mode and more accurate values of satellite phase centers. At first, we tried nine parameters model, and then eliminated the parameters with strong correlation between them, came into being five parameters of the model. Five parameters were estimated, such as solar scale, y-bias, three material coefficients of solar panel, x-axis and z-axis panels. Initial results showed that, in the period of yaw-steering mode, use of Enhanced ADBOXW model results in small improvement for IGSO and MEO satellites, and the Root-Mean-Square(RMS) error value of one-day arc orbit decreased by about 10%~30% except for C08 and C14. The new model mainly improved the along track acceleration, up to 30% while in the radial track was not obvious. The Satellite Laser Ranging(SLR) validation showed, however, that this model had higher prediction accuracy in the period of orbit-normal mode, compared to GFZ multi-GNSS orbit products, as well with relative post-processing results. Because of the system bias and unknown reasons, GEO satellites had bad results, when after adding some Chinese regional stations, there had an obviously improvement of the orbit precision. This model can be used as a priori model to help build experience models for the later works.

Variations on Bayesian Prediction and Inference

DTIC Science & Technology

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Immunometabolic parameters in overweight dogs during weight loss with or without an exercise program.

PubMed

Vitger, A D; Stallknecht, B M; Miles, J E; Hansen, S L; Vegge, A; Bjørnvad, C R

2017-04-01

The influence of physical activity on metabolic health in overweight dogs is unknown. This study was conducted to evaluate biomarkers of immunometabolic health in relation to changes in physical activity and adiposity. Client-owned overweight dogs participated in a 12-wk intervention based on caloric restriction combined with a training program (fitness and diet [FD] group, n = 8), or caloric restriction alone (diet-only [DO] group, n = 8). Physical activity was monitored by accelerometry. All dogs were fed the same diet and achieved similar weight loss. Fasting blood samples were collected before and after 6- and 12-wk intervention. Insulin resistance was evaluated from plasma insulin and C-peptide as well as homeostasis model assessment. Inflammation and dyslipidemia were evaluated from circulating leptin, adiponectin, C-reactive protein (CRP), monocyte chemoattractant factor-1 (MCP-1), interleukin-8 (IL-8), and cholesterol. Accelerometer counts in both groups were high compared with previous reports of physical activity in overweight dogs. No difference in blood parameters was evident between groups, evaluated by linear mixed-effects model (P > 0.05). Within the groups, the following changes were significant by t-test (P < 0.05): leptin decreased in both groups. Within the FD group, IL-8, MCP-1, and CRP decreased at 6 wk and IL-8 and cholesterol at 12 wk. Within the DO group, C-peptide and HOMA decreased at 6 wk and C-peptide at 12 wk. We conclude that, for both groups, weight loss resulted in minor indications of improved immunometabolic health, whereas this level of physical activity did not add further benefits. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

A continuous optimization approach for inferring parameters in mathematical models of regulatory networks.

PubMed

Deng, Zhimin; Tian, Tianhai

2014-07-29

The advances of systems biology have raised a large number of sophisticated mathematical models for describing the dynamic property of complex biological systems. One of the major steps in developing mathematical models is to estimate unknown parameters of the model based on experimentally measured quantities. However, experimental conditions limit the amount of data that is available for mathematical modelling. The number of unknown parameters in mathematical models may be larger than the number of observation data. The imbalance between the number of experimental data and number of unknown parameters makes reverse-engineering problems particularly challenging. To address the issue of inadequate experimental data, we propose a continuous optimization approach for making reliable inference of model parameters. This approach first uses a spline interpolation to generate continuous functions of system dynamics as well as the first and second order derivatives of continuous functions. The expanded dataset is the basis to infer unknown model parameters using various continuous optimization criteria, including the error of simulation only, error of both simulation and the first derivative, or error of simulation as well as the first and second derivatives. We use three case studies to demonstrate the accuracy and reliability of the proposed new approach. Compared with the corresponding discrete criteria using experimental data at the measurement time points only, numerical results of the ERK kinase activation module show that the continuous absolute-error criteria using both function and high order derivatives generate estimates with better accuracy. This result is also supported by the second and third case studies for the G1/S transition network and the MAP kinase pathway, respectively. This suggests that the continuous absolute-error criteria lead to more accurate estimates than the corresponding discrete criteria. We also study the robustness property of these three models to examine the reliability of estimates. Simulation results show that the models with estimated parameters using continuous fitness functions have better robustness properties than those using the corresponding discrete fitness functions. The inference studies and robustness analysis suggest that the proposed continuous optimization criteria are effective and robust for estimating unknown parameters in mathematical models.

Synchronization of coupled different chaotic FitzHugh-Nagumo neurons with unknown parameters under communication-direction-dependent coupling.

PubMed

Iqbal, Muhammad; Rehan, Muhammad; Khaliq, Abdul; Saeed-ur-Rehman; Hong, Keum-Shik

2014-01-01

This paper investigates the chaotic behavior and synchronization of two different coupled chaotic FitzHugh-Nagumo (FHN) neurons with unknown parameters under external electrical stimulation (EES). The coupled FHN neurons of different parameters admit unidirectional and bidirectional gap junctions in the medium between them. Dynamical properties, such as the increase in synchronization error as a consequence of the deviation of neuronal parameters for unlike neurons, the effect of difference in coupling strengths caused by the unidirectional gap junctions, and the impact of large time-delay due to separation of neurons, are studied in exploring the behavior of the coupled system. A novel integral-based nonlinear adaptive control scheme, to cope with the infeasibility of the recovery variable, for synchronization of two coupled delayed chaotic FHN neurons of different and unknown parameters under uncertain EES is derived. Further, to guarantee robust synchronization of different neurons against disturbances, the proposed control methodology is modified to achieve the uniformly ultimately bounded synchronization. The parametric estimation errors can be reduced by selecting suitable control parameters. The effectiveness of the proposed control scheme is illustrated via numerical simulations.

The Approximate Bayesian Computation methods in the localization of the atmospheric contamination source

NASA Astrophysics Data System (ADS)

Kopka, P.; Wawrzynczak, A.; Borysiewicz, M.

2015-09-01

In many areas of application, a central problem is a solution to the inverse problem, especially estimation of the unknown model parameters to model the underlying dynamics of a physical system precisely. In this situation, the Bayesian inference is a powerful tool to combine observed data with prior knowledge to gain the probability distribution of searched parameters. We have applied the modern methodology named Sequential Approximate Bayesian Computation (S-ABC) to the problem of tracing the atmospheric contaminant source. The ABC is technique commonly used in the Bayesian analysis of complex models and dynamic system. Sequential methods can significantly increase the efficiency of the ABC. In the presented algorithm, the input data are the on-line arriving concentrations of released substance registered by distributed sensor network from OVER-LAND ATMOSPHERIC DISPERSION (OLAD) experiment. The algorithm output are the probability distributions of a contamination source parameters i.e. its particular location, release rate, speed and direction of the movement, start time and duration. The stochastic approach presented in this paper is completely general and can be used in other fields where the parameters of the model bet fitted to the observable data should be found.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, J.; Zeng, L.

2013-12-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameter identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from indirect concentration measurements in identifying unknown source parameters such as the release time, strength and location. In this approach, the sampling location that gives the maximum relative entropy is selected as the optimal one. Once the sampling location is determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown source parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. Compared with the traditional optimal design, which is based on the Gaussian linear assumption, the method developed in this study can cope with arbitrary nonlinearity. It can be used to assist in groundwater monitor network design and identification of unknown contaminant sources. Contours of the expected information gain. The optimal observing location corresponds to the maximum value. Posterior marginal probability densities of unknown parameters, the thick solid black lines are for the designed location. For comparison, other 7 lines are for randomly chosen locations. The true values are denoted by vertical lines. It is obvious that the unknown parameters are estimated better with the desinged location.

An Integrated Approach to Parameter Learning in Infinite-Dimensional Space

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

Boyd, Zachary M.; Wendelberger, Joanne Roth

The availability of sophisticated modern physics codes has greatly extended the ability of domain scientists to understand the processes underlying their observations of complicated processes, but it has also introduced the curse of dimensionality via the many user-set parameters available to tune. Many of these parameters are naturally expressed as functional data, such as initial temperature distributions, equations of state, and controls. Thus, when attempting to find parameters that match observed data, being able to navigate parameter-space becomes highly non-trivial, especially considering that accurate simulations can be expensive both in terms of time and money. Existing solutions include batch-parallel simulations,more » high-dimensional, derivative-free optimization, and expert guessing, all of which make some contribution to solving the problem but do not completely resolve the issue. In this work, we explore the possibility of coupling together all three of the techniques just described by designing user-guided, batch-parallel optimization schemes. Our motivating example is a neutron diffusion partial differential equation where the time-varying multiplication factor serves as the unknown control parameter to be learned. We find that a simple, batch-parallelizable, random-walk scheme is able to make some progress on the problem but does not by itself produce satisfactory results. After reducing the dimensionality of the problem using functional principal component analysis (fPCA), we are able to track the progress of the solver in a visually simple way as well as viewing the associated principle components. This allows a human to make reasonable guesses about which points in the state space the random walker should try next. Thus, by combining the random walker's ability to find descent directions with the human's understanding of the underlying physics, it is possible to use expensive simulations more efficiently and more quickly arrive at the desired parameter set.« less

Constraining the Magmatic System at Mount St. Helens (2004-2008) Using Bayesian Inversion With Physics-Based Models Including Gas Escape and Crystallization

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

Wong, Ying -Qi; Segall, Paul; Bradley, Andrew

Physics-based models of volcanic eruptions track conduit processes as functions of depth and time. When used in inversions, these models permit integration of diverse geological and geophysical data sets to constrain important parameters of magmatic systems. We develop a 1-D steady state conduit model for effusive eruptions including equilibrium crystallization and gas transport through the conduit and compare with the quasi-steady dome growth phase of Mount St. Helens in 2005. Viscosity increase resulting from pressure-dependent crystallization leads to a natural transition from viscous flow to frictional sliding on the conduit margin. Erupted mass flux depends strongly on wall rock andmore » magma permeabilities due to their impact on magma density. Including both lateral and vertical gas transport reveals competing effects that produce nonmonotonic behavior in the mass flux when increasing magma permeability. Using this physics-based model in a Bayesian inversion, we link data sets from Mount St. Helens such as extrusion flux and earthquake depths with petrological data to estimate unknown model parameters, including magma chamber pressure and water content, magma permeability constants, conduit radius, and friction along the conduit walls. Even with this relatively simple model and limited data, we obtain improved constraints on important model parameters. We find that the magma chamber had low (<5 wt %) total volatiles and that the magma permeability scale is well constrained at ~10 –11.4m 2 to reproduce observed dome rock porosities. Here, compared with previous results, higher magma overpressure and lower wall friction are required to compensate for increased viscous resistance while keeping extrusion rate at the observed value.« less

Constraining the Magmatic System at Mount St. Helens (2004-2008) Using Bayesian Inversion With Physics-Based Models Including Gas Escape and Crystallization

NASA Astrophysics Data System (ADS)

Wong, Ying-Qi; Segall, Paul; Bradley, Andrew; Anderson, Kyle

2017-10-01

Physics-based models of volcanic eruptions track conduit processes as functions of depth and time. When used in inversions, these models permit integration of diverse geological and geophysical data sets to constrain important parameters of magmatic systems. We develop a 1-D steady state conduit model for effusive eruptions including equilibrium crystallization and gas transport through the conduit and compare with the quasi-steady dome growth phase of Mount St. Helens in 2005. Viscosity increase resulting from pressure-dependent crystallization leads to a natural transition from viscous flow to frictional sliding on the conduit margin. Erupted mass flux depends strongly on wall rock and magma permeabilities due to their impact on magma density. Including both lateral and vertical gas transport reveals competing effects that produce nonmonotonic behavior in the mass flux when increasing magma permeability. Using this physics-based model in a Bayesian inversion, we link data sets from Mount St. Helens such as extrusion flux and earthquake depths with petrological data to estimate unknown model parameters, including magma chamber pressure and water content, magma permeability constants, conduit radius, and friction along the conduit walls. Even with this relatively simple model and limited data, we obtain improved constraints on important model parameters. We find that the magma chamber had low (<5 wt %) total volatiles and that the magma permeability scale is well constrained at ˜10-11.4m2 to reproduce observed dome rock porosities. Compared with previous results, higher magma overpressure and lower wall friction are required to compensate for increased viscous resistance while keeping extrusion rate at the observed value.

Constraining the Magmatic System at Mount St. Helens (2004-2008) Using Bayesian Inversion With Physics-Based Models Including Gas Escape and Crystallization

DOE PAGES

Wong, Ying -Qi; Segall, Paul; Bradley, Andrew; ...

2017-10-04

Physics-based models of volcanic eruptions track conduit processes as functions of depth and time. When used in inversions, these models permit integration of diverse geological and geophysical data sets to constrain important parameters of magmatic systems. We develop a 1-D steady state conduit model for effusive eruptions including equilibrium crystallization and gas transport through the conduit and compare with the quasi-steady dome growth phase of Mount St. Helens in 2005. Viscosity increase resulting from pressure-dependent crystallization leads to a natural transition from viscous flow to frictional sliding on the conduit margin. Erupted mass flux depends strongly on wall rock andmore » magma permeabilities due to their impact on magma density. Including both lateral and vertical gas transport reveals competing effects that produce nonmonotonic behavior in the mass flux when increasing magma permeability. Using this physics-based model in a Bayesian inversion, we link data sets from Mount St. Helens such as extrusion flux and earthquake depths with petrological data to estimate unknown model parameters, including magma chamber pressure and water content, magma permeability constants, conduit radius, and friction along the conduit walls. Even with this relatively simple model and limited data, we obtain improved constraints on important model parameters. We find that the magma chamber had low (<5 wt %) total volatiles and that the magma permeability scale is well constrained at ~10 –11.4m 2 to reproduce observed dome rock porosities. Here, compared with previous results, higher magma overpressure and lower wall friction are required to compensate for increased viscous resistance while keeping extrusion rate at the observed value.« less

Constraining the magmatic system at Mount St. Helens (2004–2008) using Bayesian inversion with physics-based models including gas escape and crystallization

USGS Publications Warehouse

Wong, Ying-Qi; Segall, Paul; Bradley, Andrew; Anderson, Kyle R.

2017-01-01

Physics-based models of volcanic eruptions track conduit processes as functions of depth and time. When used in inversions, these models permit integration of diverse geological and geophysical data sets to constrain important parameters of magmatic systems. We develop a 1-D steady state conduit model for effusive eruptions including equilibrium crystallization and gas transport through the conduit and compare with the quasi-steady dome growth phase of Mount St. Helens in 2005. Viscosity increase resulting from pressure-dependent crystallization leads to a natural transition from viscous flow to frictional sliding on the conduit margin. Erupted mass flux depends strongly on wall rock and magma permeabilities due to their impact on magma density. Including both lateral and vertical gas transport reveals competing effects that produce nonmonotonic behavior in the mass flux when increasing magma permeability. Using this physics-based model in a Bayesian inversion, we link data sets from Mount St. Helens such as extrusion flux and earthquake depths with petrological data to estimate unknown model parameters, including magma chamber pressure and water content, magma permeability constants, conduit radius, and friction along the conduit walls. Even with this relatively simple model and limited data, we obtain improved constraints on important model parameters. We find that the magma chamber had low (<5wt%) total volatiles and that the magma permeability scale is well constrained at ~10-11.4 m2 to reproduce observed dome rock porosities. Compared with previous results, higher magma overpressure and lower wall friction are required to compensate for increased viscous resistance while keeping extrusion rate at the observed value.

Inferring the parameters of a Markov process from snapshots of the steady state

NASA Astrophysics Data System (ADS)

Dettmer, Simon L.; Berg, Johannes

2018-02-01

We seek to infer the parameters of an ergodic Markov process from samples taken independently from the steady state. Our focus is on non-equilibrium processes, where the steady state is not described by the Boltzmann measure, but is generally unknown and hard to compute, which prevents the application of established equilibrium inference methods. We propose a quantity we call propagator likelihood, which takes on the role of the likelihood in equilibrium processes. This propagator likelihood is based on fictitious transitions between those configurations of the system which occur in the samples. The propagator likelihood can be derived by minimising the relative entropy between the empirical distribution and a distribution generated by propagating the empirical distribution forward in time. Maximising the propagator likelihood leads to an efficient reconstruction of the parameters of the underlying model in different systems, both with discrete configurations and with continuous configurations. We apply the method to non-equilibrium models from statistical physics and theoretical biology, including the asymmetric simple exclusion process (ASEP), the kinetic Ising model, and replicator dynamics.

Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

PubMed

Zhu, Changxing; Zhao, Hongbo; Zhao, Ming

2014-01-01

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

Acoustically Driven Fluid and Particle Motion in Confined and Leaky Systems

NASA Astrophysics Data System (ADS)

Barnkob, Rune; Nama, Nitesh; Ren, Liqiang; Huang, Tony Jun; Costanzo, Francesco; Kähler, Christian J.

2018-01-01

The acoustic motion of fluids and particles in confined and acoustically leaky systems is receiving increasing attention for its use in medicine and biotechnology. A number of contradicting physical and numerical models currently exist, but their validity is uncertain due to the unavailability of hard-to-access experimental data for validation. We provide experimental benchmarking data by measuring 3D particle trajectories and demonstrate that the particle trajectories can be described numerically without any fitting parameter by a reduced-fluid model with leaky impedance-wall conditions. The results reveal the hitherto unknown existence of a pseudo-standing wave that drives the acoustic streaming as well as the acoustic radiation force on suspended particles.

Experimental modelling of fragmentation applied to volcanic explosions

NASA Astrophysics Data System (ADS)

Haug, Øystein Thordén; Galland, Olivier; Gisler, Galen R.

2013-12-01

Explosions during volcanic eruptions cause fragmentation of magma and host rock, resulting in fragments with sizes ranging from boulders to fine ash. The products can be described by fragment size distributions (FSD), which commonly follow power laws with exponent D. The processes that lead to power-law distributions and the physical parameters that control D remain unknown. We developed a quantitative experimental procedure to study the physics of the fragmentation process through time. The apparatus consists of a Hele-Shaw cell containing a layer of cohesive silica flour that is fragmented by a rapid injection of pressurized air. The evolving fragmentation of the flour is monitored with a high-speed camera, and the images are analysed to obtain the evolution of the number of fragments (N), their average size (A), and the FSD. Using the results from our image-analysis procedure, we find transient empirical laws for N, A and the exponent D of the power-law FSD as functions of the initial air pressure. We show that our experimental procedure is a promising tool for unravelling the complex physics of fragmentation during phreatomagmatic and phreatic eruptions.

SU-E-J-150: Four-Dimensional Cone-Beam CT Algorithm by Extraction of Physical and Motion Parameter of Mobile Targets Retrospective to Image Reconstruction with Motion Modeling

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

Ali, I; Ahmad, S; Alsbou, N

Purpose: To develop 4D-cone-beam CT (CBCT) algorithm by motion modeling that extracts actual length, CT numbers level and motion amplitude of a mobile target retrospective to image reconstruction by motion modeling. Methods: The algorithm used three measurable parameters: apparent length and blurred CT number distribution of a mobile target obtained from CBCT images to determine actual length, CT-number value of the stationary target, and motion amplitude. The predictions of this algorithm were tested with mobile targets that with different well-known sizes made from tissue-equivalent gel which was inserted into a thorax phantom. The phantom moved sinusoidally in one-direction to simulatemore » respiratory motion using eight amplitudes ranging 0–20mm. Results: Using this 4D-CBCT algorithm, three unknown parameters were extracted that include: length of the target, CT number level, speed or motion amplitude for the mobile targets retrospective to image reconstruction. The motion algorithms solved for the three unknown parameters using measurable apparent length, CT number level and gradient for a well-defined mobile target obtained from CBCT images. The motion model agreed with measured apparent lengths which were dependent on the actual target length and motion amplitude. The gradient of the CT number distribution of the mobile target is dependent on the stationary CT number level, actual target length and motion amplitude. Motion frequency and phase did not affect the elongation and CT number distribution of the mobile target and could not be determined. Conclusion: A 4D-CBCT motion algorithm was developed to extract three parameters that include actual length, CT number level and motion amplitude or speed of mobile targets directly from reconstructed CBCT images without prior knowledge of the stationary target parameters. This algorithm provides alternative to 4D-CBCT without requirement to motion tracking and sorting of the images into different breathing phases which has potential applications in diagnostic CT imaging and radiotherapy.« less

A motion algorithm to extract physical and motion parameters of mobile targets from cone-beam computed tomographic images.

PubMed

Alsbou, Nesreen; Ahmad, Salahuddin; Ali, Imad

2016-05-17

A motion algorithm has been developed to extract length, CT number level and motion amplitude of a mobile target from cone-beam CT (CBCT) images. The algorithm uses three measurable parameters: Apparent length and blurred CT number distribution of a mobile target obtained from CBCT images to determine length, CT-number value of the stationary target, and motion amplitude. The predictions of this algorithm are tested with mobile targets having different well-known sizes that are made from tissue-equivalent gel which is inserted into a thorax phantom. The phantom moves sinusoidally in one-direction to simulate respiratory motion using eight amplitudes ranging 0-20 mm. Using this motion algorithm, three unknown parameters are extracted that include: Length of the target, CT number level, speed or motion amplitude for the mobile targets from CBCT images. The motion algorithm solves for the three unknown parameters using measured length, CT number level and gradient for a well-defined mobile target obtained from CBCT images. The motion model agrees with the measured lengths which are dependent on the target length and motion amplitude. The gradient of the CT number distribution of the mobile target is dependent on the stationary CT number level, the target length and motion amplitude. Motion frequency and phase do not affect the elongation and CT number distribution of the mobile target and could not be determined. A motion algorithm has been developed to extract three parameters that include length, CT number level and motion amplitude or speed of mobile targets directly from reconstructed CBCT images without prior knowledge of the stationary target parameters. This algorithm provides alternative to 4D-CBCT without requirement of motion tracking and sorting of the images into different breathing phases. The motion model developed here works well for tumors that have simple shapes, high contrast relative to surrounding tissues and move nearly in regular motion pattern that can be approximated with a simple sinusoidal function. This algorithm has potential applications in diagnostic CT imaging and radiotherapy in terms of motion management.

Nonlinear adaptive control system design with asymptotically stable parameter estimation error

NASA Astrophysics Data System (ADS)

Mishkov, Rumen; Darmonski, Stanislav

2018-01-01

The paper presents a new general method for nonlinear adaptive system design with asymptotic stability of the parameter estimation error. The advantages of the approach include asymptotic unknown parameter estimation without persistent excitation and capability to directly control the estimates transient response time. The method proposed modifies the basic parameter estimation dynamics designed via a known nonlinear adaptive control approach. The modification is based on the generalised prediction error, a priori constraints with a hierarchical parameter projection algorithm, and the stable data accumulation concepts. The data accumulation principle is the main tool for achieving asymptotic unknown parameter estimation. It relies on the parametric identifiability system property introduced. Necessary and sufficient conditions for exponential stability of the data accumulation dynamics are derived. The approach is applied in a nonlinear adaptive speed tracking vector control of a three-phase induction motor.

Assessment of gait parameters and fatigue in MS patients during inpatient rehabilitation: a pilot trial.

PubMed

Sacco, Rosaria; Bussman, Rita; Oesch, Peter; Kesselring, Jürg; Beer, Serafin

2011-05-01

Gait impairment and fatigue are common and disabling problems in multiple sclerosis (MS). Characterisation of abnormal gait in MS patients has been done mainly using observational studies and simple walking tests providing only limited quantitative and no qualitative data, or using intricate and time-consuming assessment procedures. In addition, the correlation of gait impairments with fatigue is largely unknown. The aim of this study was to characterise spatio-temporal gait parameters by a simple and easy-to-use gait analysis system (GAITRite®) in MS patients compared with healthy controls, and to analyse changes and correlation with fatigue during inpatient rehabilitation. Twenty-four MS patients (EDSS <6.5) admitted for inpatient rehabilitation and 19 healthy subjects were evaluated using the GAITRite® Functional Ambulation System. Between-group differences and changes of gait parameters during inpatient rehabilitation were analysed, and correlation with fatigue, using the Wurzburg Fatigue Inventory for Multiple Sclerosis (WEIMuS), was determined. Compared to healthy controls MS patients showed significant impairments in different spatio-temporal gait parameters, which showed a significant improvement during inpatient rehabilitation. Different gait parameters were correlated with fatigue physical score, and change of gait parameters was correlated with improvement of fatigue. Spatio-temporal gait analysis is helpful to assess specific walking impairments in MS patients and subtle changes during rehabilitation. Correlation with fatigue may indicate a possible negative impact of fatigue on rehabilitation outcome.

Market-Based Coordination of Thermostatically Controlled Loads—Part I: A Mechanism Design Formulation

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

Li, Sen; Zhang, Wei; Lian, Jianming

This paper focuses on the coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives. The problem involves designing the bidding and market clearing strategy to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. Using the mechanism design approach, we propose a market-based coordination framework, which can effectively incorporate heterogeneous load dynamics, systematically deal with user preferences, account for the unknown load model parameters, and enable the real-world implementation with limited communication resources. This paper is divided into two parts. Part I presents a mathematical formulation of themore » problem and develops a coordination framework using the mechanism design approach. Part II presents a learning scheme to account for the unknown load model parameters, and evaluates the proposed framework through realistic simulations.« less

A Sparse Bayesian Learning Algorithm for White Matter Parameter Estimation from Compressed Multi-shell Diffusion MRI.

PubMed

Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Sapiro, Guillermo; Lenglet, Christophe

2017-09-01

We propose a sparse Bayesian learning algorithm for improved estimation of white matter fiber parameters from compressed (under-sampled q-space) multi-shell diffusion MRI data. The multi-shell data is represented in a dictionary form using a non-monoexponential decay model of diffusion, based on continuous gamma distribution of diffusivities. The fiber volume fractions with predefined orientations, which are the unknown parameters, form the dictionary weights. These unknown parameters are estimated with a linear un-mixing framework, using a sparse Bayesian learning algorithm. A localized learning of hyperparameters at each voxel and for each possible fiber orientations improves the parameter estimation. Our experiments using synthetic data from the ISBI 2012 HARDI reconstruction challenge and in-vivo data from the Human Connectome Project demonstrate the improvements.

The first dozen years of the history of ITEP Theoretical Physics Laboratory

NASA Astrophysics Data System (ADS)

Ioffe, B. L.

2013-01-01

The theoretical investigations at ITEP in the years 1945 - 1958 are reviewed. There are exposed the most important theoretical results, obtained in the following branches of physics: (1) the theory of nuclear reactors on thermal neutrons; (2) the hydrogen bomb project ("Tube" in USSR and "Classical Super" in USA); (3) radiation theory; (4) low temperature physics; (5) quantum electrodynamics and quantum field theories; (6) parity violation in weak interactions, the theory of β-decay and other weak processes; (7) strong interaction and nuclear physics. To the review are added the English translations of a few papers, originally published in Russian, but unknown (or almost unknown) to Western readers.

A Guided-Inquiry Approach to the Sodium Borohydride Reduction and Grignard Reaction of Carbonyl Compounds

ERIC Educational Resources Information Center

Rosenberg, Robert E.

2007-01-01

The guided-inquiry approach is applied to the reactions of sodium borohydride and phenyl magnesium bromide with benzaldehyde, benzophenone, benzoic anhydride, and ethyl benzoate. Each team of four students receives four unknowns. Students identify the unknowns and their reaction products by using the physical state of the unknown, an…

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras.

PubMed

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-08-30

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme.

Target Capturing Control for Space Robots with Unknown Mass Properties: A Self-Tuning Method Based on Gyros and Cameras

PubMed Central

Li, Zhenyu; Wang, Bin; Liu, Hong

2016-01-01

Satellite capturing with free-floating space robots is still a challenging task due to the non-fixed base and unknown mass property issues. In this paper gyro and eye-in-hand camera data are adopted as an alternative choice for solving this problem. For this improved system, a new modeling approach that reduces the complexity of system control and identification is proposed. With the newly developed model, the space robot is equivalent to a ground-fixed manipulator system. Accordingly, a self-tuning control scheme is applied to handle such a control problem including unknown parameters. To determine the controller parameters, an estimator is designed based on the least-squares technique for identifying the unknown mass properties in real time. The proposed method is tested with a credible 3-dimensional ground verification experimental system, and the experimental results confirm the effectiveness of the proposed control scheme. PMID:27589748

Teaching-learning-based Optimization Algorithm for Parameter Identification in the Design of IIR Filters

NASA Astrophysics Data System (ADS)

Singh, R.; Verma, H. K.

2013-12-01

This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.

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

Selle, J.E.

A modification was made to the Kaufman method of calculating binary phase diagrams to permit calculation of intra-rare earth diagrams. Atomic volumes for all phases, real or hypothetical, are necessary to determine interaction parameters for calculation of complete diagrams. The procedures used to determine unknown atomic volumes are describes. Also, procedures are described for determining lattice stability parameters for unknown transformations. Results are presented on the calculation of intra-rare earth diagrams between both trivalent and divalent rare earths. 13 refs., 36 figs., 11 tabs.

Assessment of the accuracy of plasma shape reconstruction by the Cauchy condition surface method in JT-60SA

NASA Astrophysics Data System (ADS)

Miyata, Y.; Suzuki, T.; Takechi, M.; Urano, H.; Ide, S.

2015-07-01

For the purpose of stable plasma equilibrium control and detailed analysis, it is essential to reconstruct an accurate plasma boundary on the poloidal cross section in tokamak devices. The Cauchy condition surface (CCS) method is a numerical approach for calculating the spatial distribution of the magnetic flux outside a hypothetical surface and reconstructing the plasma boundary from the magnetic measurements located outside the plasma. The accuracy of the plasma shape reconstruction has been assessed by comparing the CCS method and an equilibrium calculation in JT-60SA with a high elongation and triangularity of plasma shape. The CCS, on which both Dirichlet and Neumann conditions are unknown, is defined as a hypothetical surface located inside the real plasma region. The accuracy of the plasma shape reconstruction is sensitive to the CCS free parameters such as the number of unknown parameters and the shape in JT-60SA. It is found that the optimum number of unknown parameters and the size of the CCS that minimizes errors in the reconstructed plasma shape are in proportion to the plasma size. Furthermore, it is shown that the accuracy of the plasma shape reconstruction is greatly improved using the optimum number of unknown parameters and shape of the CCS, and the reachable reconstruction errors in plasma shape and locations of strike points are within the target ranges in JT-60SA.

Concurrent hyperthermia estimation schemes based on extended Kalman filtering and reduced-order modelling.

PubMed

Potocki, J K; Tharp, H S

1993-01-01

The success of treating cancerous tissue with heat depends on the temperature elevation, the amount of tissue elevated to that temperature, and the length of time that the tissue temperature is elevated. In clinical situations the temperature of most of the treated tissue volume is unknown, because only a small number of temperature sensors can be inserted into the tissue. A state space model based on a finite difference approximation of the bioheat transfer equation (BHTE) is developed for identification purposes. A full-order extended Kalman filter (EKF) is designed to estimate both the unknown blood perfusion parameters and the temperature at unmeasured locations. Two reduced-order estimators are designed as computationally less intensive alternatives to the full-order EKF. Simulation results show that the success of the estimation scheme depends strongly on the number and location of the temperature sensors. Superior results occur when a temperature sensor exists in each unknown blood perfusion zone, and the number of sensors is at least as large as the number of unknown perfusion zones. Unacceptable results occur when there are more unknown perfusion parameters than temperature sensors, or when the sensors are placed in locations that do not sample the unknown perfusion information.

An Adaptive Moving Target Imaging Method for Bistatic Forward-Looking SAR Using Keystone Transform and Optimization NLCS.

PubMed

Li, Zhongyu; Wu, Junjie; Huang, Yulin; Yang, Haiguang; Yang, Jianyu

2017-01-23

Bistatic forward-looking SAR (BFSAR) is a kind of bistatic synthetic aperture radar (SAR) system that can image forward-looking terrain in the flight direction of an aircraft. Until now, BFSAR imaging theories and methods for a stationary scene have been researched thoroughly. However, for moving-target imaging with BFSAR, the non-cooperative movement of the moving target induces some new issues: (I) large and unknown range cell migration (RCM) (including range walk and high-order RCM); (II) the spatial-variances of the Doppler parameters (including the Doppler centroid and high-order Doppler) are not only unknown, but also nonlinear for different point-scatterers. In this paper, we put forward an adaptive moving-target imaging method for BFSAR. First, the large and unknown range walk is corrected by applying keystone transform over the whole received echo, and then, the relationships among the unknown high-order RCM, the nonlinear spatial-variances of the Doppler parameters, and the speed of the mover, are established. After that, using an optimization nonlinear chirp scaling (NLCS) technique, not only can the unknown high-order RCM be accurately corrected, but also the nonlinear spatial-variances of the Doppler parameters can be balanced. At last, a high-order polynomial filter is applied to compress the whole azimuth data of the moving target. Numerical simulations verify the effectiveness of the proposed method.

NWP model forecast skill optimization via closure parameter variations

NASA Astrophysics Data System (ADS)

Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

2012-04-01

We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Uncertain viscoelastic models with fractional order: A new spectral tau method to study the numerical simulations of the solution

NASA Astrophysics Data System (ADS)

Ahmadian, A.; Ismail, F.; Salahshour, S.; Baleanu, D.; Ghaemi, F.

2017-12-01

The analysis of the behaviors of physical phenomena is important to discover significant features of the character and the structure of mathematical models. Frequently the unknown parameters involve in the models are assumed to be unvarying over time. In reality, some of them are uncertain and implicitly depend on several factors. In this study, to consider such uncertainty in variables of the models, they are characterized based on the fuzzy notion. We propose here a new model based on fractional calculus to deal with the Kelvin-Voigt (KV) equation and non-Newtonian fluid behavior model with fuzzy parameters. A new and accurate numerical algorithm using a spectral tau technique based on the generalized fractional Legendre polynomials (GFLPs) is developed to solve those problems under uncertainty. Numerical simulations are carried out and the analysis of the results highlights the significant features of the new technique in comparison with the previous findings. A detailed error analysis is also carried out and discussed.

Cosmic-ray antiprotons, positrons, and gamma rays from halo dark matter annihilation

NASA Technical Reports Server (NTRS)

Rudaz, S.; Stecker, F. W.

1988-01-01

The subject of cosmic ray antiproton production is reexamined by considering other choices for the nature of the Majorana fermion chi other than the photino considered in a previous article. The calculations are extended to include cosmic-ray positrons and cosmic gamma rays as annihilation products. Taking chi to be a generic higgsino or simply a heavy Majorana neutrino with standard couplings to the Z-zero boson allows the previous interpretation of the cosmic antiproton data to be maintained. In this case also, the annihilation cross section can be calculated independently of unknown particle physics parameters. Whereas the relic density of photinos with the choice of parameters in the previous paper turned out to be only a few percent of the closure density, the corresponding value for Omega in the generic higgsino or Majorana case is about 0.2, in excellent agreement with the value associated with galaxies and one which is sufficient to give the halo mass.

COSMOABC: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Ishida, E. E. O.; Vitenti, S. D. P.; Penna-Lima, M.; Cisewski, J.; de Souza, R. S.; Trindade, A. M. M.; Cameron, E.; Busti, V. C.; COIN Collaboration

2015-11-01

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present COSMOABC, a Python ABC sampler featuring a Population Monte Carlo variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled COSMOABC with the NUMCOSMO library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. COSMOABC is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX.

Dendrobium officinale Orchid Extract Prevents Ovariectomy-Induced Osteoporosis in Vivo and Inhibits RANKL-Induced Osteoclast Differentiation in Vitro

PubMed Central

Wang, Qi; Zi, Cheng-Ting; Wang, Jing; Wang, Yu-Na; Huang, Ye-Wei; Fu, Xue-Qi; Wang, Xuan-Jun; Sheng, Jun

2018-01-01

Background: Dendrobium officinale, a traditional Chinese medical herb with high value that is widely used in Asia, possesses many positive effects on human health, including anti-chronic inflammation, anti-obesity, and immune modulation properties; however, whether D. officinale has inhibitory effects on postmenopausal osteoporosis remains unknown. Objective: We investigated the effects of D. officinale extract (DOE) on ovariectomy-induced bone loss in vivo and on osteoclastogenesis in vitro. Methods: In vivo, female rats were divided into a sham-operated (sham) group and five ovariectomized (OVX) subgroups: OVX with vehicle (OVX), OVX with Xian-Ling-Gu-Bao capsule (240 mg/kg body weight/day), and OVX with low-, medium-, and high-dose DOE (150, 300, and 600 mg/kg body weight/day, respectively). Animals in each group were administered their corresponding treatments for 13 weeks. Body weight, serum biochemical parameters, uterine and femoral physical parameters, bone mineral density (BMD), bone biomechanical properties, and bone microarchitecture were obtained. In vitro, the effects of DOE on osteoclastogenesis were examined using RAW264.7 cells. The effects of DOE on osteoclastogenesis and the expression of osteoclast-specific marker genes and proteins were determined. Results: DOE effectively ameliorated serum biochemical parameters, especially alleviated estradiol (E2) deficiency and maintained calcium and phosphorus homeostasis. DOE improved uterine and femoral physical parameters. In addition, DOE improved femoral BMD and biomechanical properties. DOE significantly ameliorated bone microarchitecture. Moreover, DOE inhibited osteoclastogenesis independent of its cytoxicity and suppressed the expression of osteoclast-specific marker genes and proteins. Conclusion: DOE can effectively prevent ovariectomy-induced bone loss in vivo and inhibit osteoclastogenesis in vitro. PMID:29379436

Dendrobium officinale Orchid Extract Prevents Ovariectomy-Induced Osteoporosis in Vivo and Inhibits RANKL-Induced Osteoclast Differentiation in Vitro.

PubMed

Wang, Qi; Zi, Cheng-Ting; Wang, Jing; Wang, Yu-Na; Huang, Ye-Wei; Fu, Xue-Qi; Wang, Xuan-Jun; Sheng, Jun

2017-01-01

Background: Dendrobium officinale , a traditional Chinese medical herb with high value that is widely used in Asia, possesses many positive effects on human health, including anti-chronic inflammation, anti-obesity, and immune modulation properties; however, whether D. officinale has inhibitory effects on postmenopausal osteoporosis remains unknown. Objective: We investigated the effects of D. officinale extract (DOE) on ovariectomy-induced bone loss in vivo and on osteoclastogenesis in vitro . Methods: In vivo , female rats were divided into a sham-operated (sham) group and five ovariectomized (OVX) subgroups: OVX with vehicle (OVX), OVX with Xian-Ling-Gu-Bao capsule (240 mg/kg body weight/day), and OVX with low-, medium-, and high-dose DOE (150, 300, and 600 mg/kg body weight/day, respectively). Animals in each group were administered their corresponding treatments for 13 weeks. Body weight, serum biochemical parameters, uterine and femoral physical parameters, bone mineral density (BMD), bone biomechanical properties, and bone microarchitecture were obtained. In vitro , the effects of DOE on osteoclastogenesis were examined using RAW264.7 cells. The effects of DOE on osteoclastogenesis and the expression of osteoclast-specific marker genes and proteins were determined. Results: DOE effectively ameliorated serum biochemical parameters, especially alleviated estradiol (E2) deficiency and maintained calcium and phosphorus homeostasis. DOE improved uterine and femoral physical parameters. In addition, DOE improved femoral BMD and biomechanical properties. DOE significantly ameliorated bone microarchitecture. Moreover, DOE inhibited osteoclastogenesis independent of its cytoxicity and suppressed the expression of osteoclast-specific marker genes and proteins. Conclusion: DOE can effectively prevent ovariectomy-induced bone loss in vivo and inhibit osteoclastogenesis in vitro .

Simplifying the complexity of a coupled carbon turnover and pesticide degradation model

NASA Astrophysics Data System (ADS)

Marschmann, Gianna; Erhardt, André H.; Pagel, Holger; Kügler, Philipp; Streck, Thilo

2016-04-01

The mechanistic one-dimensional model PECCAD (PEsticide degradation Coupled to CArbon turnover in the Detritusphere; Pagel et al. 2014, Biogeochemistry 117, 185-204) has been developed as a tool to elucidate regulation mechanisms of pesticide degradation in soil. A feature of this model is that it integrates functional traits of microorganisms, identifiable by molecular tools, and physicochemical processes such as transport and sorption that control substrate availability. Predicting the behavior of microbially active interfaces demands a fundamental understanding of factors controlling their dynamics. Concepts from dynamical systems theory allow us to study general properties of the model such as its qualitative behavior, intrinsic timescales and dynamic stability: Using a Latin hypercube method we sampled the parameter space for physically realistic steady states of the PECCAD ODE system and set up a numerical continuation and bifurcation problem with the open-source toolbox MatCont in order to obtain a complete classification of the dynamical system's behaviour. Bifurcation analysis reveals an equilibrium state of the system entirely controlled by fungal kinetic parameters. The equilibrium is generally unstable in response to small perturbations except for a small band in parameter space where the pesticide pool is stable. Time scale separation is a phenomenon that occurs in almost every complex open physical system. Motivated by the notion of "initial-stage" and "late-stage" decomposers and the concept of r-, K- or L-selected microbial life strategies, we test the applicability of geometric singular perturbation theory to identify fast and slow time scales of PECCAD. Revealing a generic fast-slow structure would greatly simplify the analysis of complex models of organic matter turnover by reducing the number of unknowns and parameters and providing a systematic mathematical framework for studying their properties.

Optimizing the choice of spin-squeezed states for detecting and characterizing quantum processes

DOE PAGES

Rozema, Lee A.; Mahler, Dylan H.; Blume-Kohout, Robin; ...

2014-11-07

Quantum metrology uses quantum states with no classical counterpart to measure a physical quantity with extraordinary sensitivity or precision. Most such schemes characterize a dynamical process by probing it with a specially designed quantum state. The success of such a scheme usually relies on the process belonging to a particular one-parameter family. If this assumption is violated, or if the goal is to measure more than one parameter, a different quantum state may perform better. In the most extreme case, we know nothing about the process and wish to learn everything. This requires quantum process tomography, which demands an informationallymore » complete set of probe states. It is very convenient if this set is group covariant—i.e., each element is generated by applying an element of the quantum system’s natural symmetry group to a single fixed fiducial state. In this paper, we consider metrology with 2-photon (“biphoton”) states and report experimental studies of different states’ sensitivity to small, unknown collective SU( 2) rotations [“ SU( 2) jitter”]. Maximally entangled N00 N states are the most sensitive detectors of such a rotation, yet they are also among the worst at fully characterizing an a priori unknown process. We identify (and confirm experimentally) the best SU( 2)-covariant set for process tomography; these states are all less entangled than the N00 N state, and are characterized by the fact that they form a 2-design.« less

Calibration strategies for a groundwater model in a highly dynamic alpine floodplain

USGS Publications Warehouse

Foglia, L.; Burlando, P.; Hill, Mary C.; Mehl, S.

2004-01-01

Most surface flows to the 20-km-long Maggia Valley in Southern Switzerland are impounded and the valley is being investigated to determine environmental flow requirements. The aim of the investigation is the devel-opment of a modelling framework that simulates the dynamics of the ground-water, hydrologic, and ecologic systems. Because of the multi-scale nature of the modelling framework, large-scale models are first developed to provide the boundary conditions for more detailed models of reaches that are of eco-logical importance. We describe here the initial (large-scale) groundwa-ter/surface water model and its calibration in relation to initial and boundary conditions. A MODFLOW-2000 model was constructed to simulate the inter-action of groundwater and surface water and was developed parsimoniously to avoid modelling artefacts and parameter inconsistencies. Model calibration includes two steady-state conditions, with and without recharge to the aquifer from the adjoining hillslopes. Parameters are defined to represent areal re-charge, hydraulic conductivity of the aquifer (up to 5 classes), and streambed hydraulic conductivity. Model performance was investigated following two system representation. The first representation assumed unknown flow input at the northern end of the groundwater domain and unknown lateral inflow. The second representation used simulations of the lateral flow obtained by means of a raster-based, physically oriented and continuous in time rainfall-runoff (R-R) model. Results based on these two representations are compared and discussed.

Dynamic parameter identification of robot arms with servo-controlled electrical motors

NASA Astrophysics Data System (ADS)

Jiang, Zhao-Hui; Senda, Hiroshi

2005-12-01

This paper addresses the issue of dynamic parameter identification of the robot manipulator with servo-controlled electrical motors. An assumption is made that all kinematical parameters, such as link lengths, are known, and only dynamic parameters containing mass, moment of inertia, and their functions need to be identified. First, we derive dynamics of the robot arm with a linear form of the unknown dynamic parameters by taking dynamic characteristics of the motor and servo unit into consideration. Then, we implement the parameter identification approach to identify the unknown parameters with respect to individual link separately. A pseudo-inverse matrix is used for formulation of the parameter identification. The optimal solution is guaranteed in a sense of least-squares of the mean errors. A Direct Drive (DD) SCARA type industrial robot arm AdeptOne is used as an application example of the parameter identification. Simulations and experiments for both open loop and close loop controls are carried out. Comparison of the results confirms the correctness and usefulness of the parameter identification and the derived dynamic model.

An almost-parameter-free harmony search algorithm for groundwater pollution source identification.

PubMed

Jiang, Simin; Zhang, Yali; Wang, Pei; Zheng, Maohui

2013-01-01

The spatiotemporal characterization of unknown sources of groundwater pollution is frequently encountered in environmental problems. This study adopts a simulation-optimization approach that combines a contaminant transport simulation model with a heuristic harmony search algorithm to identify unknown pollution sources. In the proposed methodology, an almost-parameter-free harmony search algorithm is developed. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem, and the identified results indicate that the proposed almost-parameter-free harmony search algorithm-based optimization model can give satisfactory estimations, even when the irregular geometry, erroneous monitoring data, and prior information shortage of potential locations are considered.

Supernovas y Cosmología

NASA Astrophysics Data System (ADS)

Folatelli, G.

Supernovae are very relevant astrophysical objects because they indicate the violent end of certain stars and because they alter the interstellar medium. But most importantly, they have become an extremely useful tool for measuring cosmological distances. Based on highly precise distances to type Ia supernovae it was possible to find out that the expansion of the universe is currently accelerated. This led to introducing the concept of ``dark energy'' as a dominant and yet unknown component of the cosmos. In this article we will describe the method of distance measurements that leads to the determination of cosmological parameters. We will briefly review the current status of the field with emphasis on the importance of improving our knowledge about the physical nature of supernovae. FULL TEXT IN SPANISH

Disturbance Accommodating Adaptive Control with Application to Wind Turbines

NASA Technical Reports Server (NTRS)

Frost, Susan

2012-01-01

Adaptive control techniques are well suited to applications that have unknown modeling parameters and poorly known operating conditions. Many physical systems experience external disturbances that are persistent or continually recurring. Flexible structures and systems with compliance between components often form a class of systems that fail to meet standard requirements for adaptive control. For these classes of systems, a residual mode filter can restore the ability of the adaptive controller to perform in a stable manner. New theory will be presented that enables adaptive control with accommodation of persistent disturbances using residual mode filters. After a short introduction to some of the control challenges of large utility-scale wind turbines, this theory will be applied to a high-fidelity simulation of a wind turbine.

Procedures and results related to the direct determination of gravity anomalies from satellite and terrestrial gravity data

NASA Technical Reports Server (NTRS)

Rapp, R. H.

1974-01-01

The equations needed for the incorporation of gravity anomalies as unknown parameters in an orbit determination program are described. These equations were implemented in the Geodyn computer program which was used to process optical satellite observations. The arc dependent parameter unknowns, 184 unknown 15 deg and coordinates of 7 tracking stations were considered. Up to 39 arcs (5 to 7 days) involving 10 different satellites, were processed. An anomaly solution from the satellite data and a combination solution with 15 deg terrestrial anomalies were made. The limited data samples indicate that the method works. The 15 deg anomalies from various solutions and the potential coefficients implied by the different solutions are reported.

SU-E-J-252: A Motion Algorithm to Extract Physical and Motion Parameters of a Mobile Target in Cone-Beam Computed Tomographic Imaging Retrospective to Image Reconstruction

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

Ali, I; Ahmad, S; Alsbou, N

Purpose: A motion algorithm was developed to extract actual length, CT-numbers and motion amplitude of a mobile target imaged with cone-beam-CT (CBCT) retrospective to image-reconstruction. Methods: The motion model considered a mobile target moving with a sinusoidal motion and employed three measurable parameters: apparent length, CT number level and gradient of a mobile target obtained from CBCT images to extract information about the actual length and CT number value of the stationary target and motion amplitude. The algorithm was verified experimentally with a mobile phantom setup that has three targets with different sizes manufactured from homogenous tissue-equivalent gel material embeddedmore » into a thorax phantom. The phantom moved sinusoidal in one-direction using eight amplitudes (0–20mm) and a frequency of 15-cycles-per-minute. The model required imaging parameters such as slice thickness, imaging time. Results: This motion algorithm extracted three unknown parameters: length of the target, CT-number-level, motion amplitude for a mobile target retrospective to CBCT image reconstruction. The algorithm relates three unknown parameters to measurable apparent length, CT-number-level and gradient for well-defined mobile targets obtained from CBCT images. The motion model agreed with measured apparent lengths which were dependent on actual length of the target and motion amplitude. The cumulative CT-number for a mobile target was dependent on CT-number-level of the stationary target and motion amplitude. The gradient of the CT-distribution of mobile target is dependent on the stationary CT-number-level, actual target length along the direction of motion, and motion amplitude. Motion frequency and phase did not affect the elongation and CT-number distributions of mobile targets when imaging time included several motion cycles. Conclusion: The motion algorithm developed in this study has potential applications in diagnostic CT imaging and radiotherapy to extract actual length, size and CT-numbers distorted by motion in CBCT imaging. The model provides further information about motion of the target.« less

Monte Carlo sensitivity analysis of unknown parameters in hazardous materials transportation risk assessment.

PubMed

Pet-Armacost, J J; Sepulveda, J; Sakude, M

1999-12-01

The US Department of Transportation was interested in the risks associated with transporting Hydrazine in tanks with and without relief devices. Hydrazine is both highly toxic and flammable, as well as corrosive. Consequently, there was a conflict as to whether a relief device should be used or not. Data were not available on the impact of relief devices on release probabilities or the impact of Hydrazine on the likelihood of fires and explosions. In this paper, a Monte Carlo sensitivity analysis of the unknown parameters was used to assess the risks associated with highway transport of Hydrazine. To help determine whether or not relief devices should be used, fault trees and event trees were used to model the sequences of events that could lead to adverse consequences during transport of Hydrazine. The event probabilities in the event trees were derived as functions of the parameters whose effects were not known. The impacts of these parameters on the risk of toxic exposures, fires, and explosions were analyzed through a Monte Carlo sensitivity analysis and analyzed statistically through an analysis of variance. The analysis allowed the determination of which of the unknown parameters had a significant impact on the risks. It also provided the necessary support to a critical transportation decision even though the values of several key parameters were not known.

Bayesian estimation of a source term of radiation release with approximately known nuclide ratios

NASA Astrophysics Data System (ADS)

Tichý, Ondřej; Šmídl, Václav; Hofman, Radek

2016-04-01

We are concerned with estimation of a source term in case of an accidental release from a known location, e.g. a power plant. Usually, the source term of an accidental release of radiation comprises of a mixture of nuclide. The gamma dose rate measurements do not provide a direct information on the source term composition. However, physical properties of respective nuclide (deposition properties, decay half-life) can be used when uncertain information on nuclide ratios is available, e.g. from known reactor inventory. The proposed method is based on linear inverse model where the observation vector y arise as a linear combination y = Mx of a source-receptor-sensitivity (SRS) matrix M and the source term x. The task is to estimate the unknown source term x. The problem is ill-conditioned and further regularization is needed to obtain a reasonable solution. In this contribution, we assume that nuclide ratios of the release is known with some degree of uncertainty. This knowledge is used to form the prior covariance matrix of the source term x. Due to uncertainty in the ratios the diagonal elements of the covariance matrix are considered to be unknown. Positivity of the source term estimate is guaranteed by using multivariate truncated Gaussian distribution. Following Bayesian approach, we estimate all parameters of the model from the data so that y, M, and known ratios are the only inputs of the method. Since the inference of the model is intractable, we follow the Variational Bayes method yielding an iterative algorithm for estimation of all model parameters. Performance of the method is studied on simulated 6 hour power plant release where 3 nuclide are released and 2 nuclide ratios are approximately known. The comparison with method with unknown nuclide ratios will be given to prove the usefulness of the proposed approach. This research is supported by EEA/Norwegian Financial Mechanism under project MSMT-28477/2014 Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI).

Bayesian Markov Chain Monte Carlo inversion for weak anisotropy parameters and fracture weaknesses using azimuthal elastic impedance

NASA Astrophysics Data System (ADS)

Chen, Huaizhen; Pan, Xinpeng; Ji, Yuxin; Zhang, Guangzhi

2017-08-01

A system of aligned vertical fractures and fine horizontal shale layers combine to form equivalent orthorhombic media. Weak anisotropy parameters and fracture weaknesses play an important role in the description of orthorhombic anisotropy (OA). We propose a novel approach of utilizing seismic reflection amplitudes to estimate weak anisotropy parameters and fracture weaknesses from observed seismic data, based on azimuthal elastic impedance (EI). We first propose perturbation in stiffness matrix in terms of weak anisotropy parameters and fracture weaknesses, and using the perturbation and scattering function, we derive PP-wave reflection coefficient and azimuthal EI for the case of an interface separating two OA media. Then we demonstrate an approach to first use a model constrained damped least-squares algorithm to estimate azimuthal EI from partially incidence-phase-angle-stack seismic reflection data at different azimuths, and then extract weak anisotropy parameters and fracture weaknesses from the estimated azimuthal EI using a Bayesian Markov Chain Monte Carlo inversion method. In addition, a new procedure to construct rock physics effective model is presented to estimate weak anisotropy parameters and fracture weaknesses from well log interpretation results (minerals and their volumes, porosity, saturation, fracture density, etc.). Tests on synthetic and real data indicate that unknown parameters including elastic properties (P- and S-wave impedances and density), weak anisotropy parameters and fracture weaknesses can be estimated stably in the case of seismic data containing a moderate noise, and our approach can make a reasonable estimation of anisotropy in a fractured shale reservoir.

Theoretical relationship between elastic wave velocity and electrical resistivity

NASA Astrophysics Data System (ADS)

Lee, Jong-Sub; Yoon, Hyung-Koo

2015-05-01

Elastic wave velocity and electrical resistivity have been commonly applied to estimate stratum structures and obtain subsurface soil design parameters. Both elastic wave velocity and electrical resistivity are related to the void ratio; the objective of this study is therefore to suggest a theoretical relationship between the two physical parameters. Gassmann theory and Archie's equation are applied to propose a new theoretical equation, which relates the compressional wave velocity to shear wave velocity and electrical resistivity. The piezo disk element (PDE) and bender element (BE) are used to measure the compressional and shear wave velocities, respectively. In addition, the electrical resistivity is obtained by using the electrical resistivity probe (ERP). The elastic wave velocity and electrical resistivity are recorded in several types of soils including sand, silty sand, silty clay, silt, and clay-sand mixture. The appropriate input parameters are determined based on the error norm in order to increase the reliability of the proposed relationship. The predicted compressional wave velocities from the shear wave velocity and electrical resistivity are similar to the measured compressional velocities. This study demonstrates that the new theoretical relationship may be effectively used to predict the unknown geophysical property from the measured values.

Numerical solution to generalized Burgers'-Fisher equation using Exp-function method hybridized with heuristic computation.

PubMed

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems.

Numerical Solution to Generalized Burgers'-Fisher Equation Using Exp-Function Method Hybridized with Heuristic Computation

PubMed Central

Malik, Suheel Abdullah; Qureshi, Ijaz Mansoor; Amir, Muhammad; Malik, Aqdas Naveed; Haq, Ihsanul

2015-01-01

In this paper, a new heuristic scheme for the approximate solution of the generalized Burgers'-Fisher equation is proposed. The scheme is based on the hybridization of Exp-function method with nature inspired algorithm. The given nonlinear partial differential equation (NPDE) through substitution is converted into a nonlinear ordinary differential equation (NODE). The travelling wave solution is approximated by the Exp-function method with unknown parameters. The unknown parameters are estimated by transforming the NODE into an equivalent global error minimization problem by using a fitness function. The popular genetic algorithm (GA) is used to solve the minimization problem, and to achieve the unknown parameters. The proposed scheme is successfully implemented to solve the generalized Burgers'-Fisher equation. The comparison of numerical results with the exact solutions, and the solutions obtained using some traditional methods, including adomian decomposition method (ADM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), show that the suggested scheme is fairly accurate and viable for solving such problems. PMID:25811858

Reconstructing high-dimensional two-photon entangled states via compressive sensing

PubMed Central

Tonolini, Francesco; Chan, Susan; Agnew, Megan; Lindsay, Alan; Leach, Jonathan

2014-01-01

Accurately establishing the state of large-scale quantum systems is an important tool in quantum information science; however, the large number of unknown parameters hinders the rapid characterisation of such states, and reconstruction procedures can become prohibitively time-consuming. Compressive sensing, a procedure for solving inverse problems by incorporating prior knowledge about the form of the solution, provides an attractive alternative to the problem of high-dimensional quantum state characterisation. Using a modified version of compressive sensing that incorporates the principles of singular value thresholding, we reconstruct the density matrix of a high-dimensional two-photon entangled system. The dimension of each photon is equal to d = 17, corresponding to a system of 83521 unknown real parameters. Accurate reconstruction is achieved with approximately 2500 measurements, only 3% of the total number of unknown parameters in the state. The algorithm we develop is fast, computationally inexpensive, and applicable to a wide range of quantum states, thus demonstrating compressive sensing as an effective technique for measuring the state of large-scale quantum systems. PMID:25306850

Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.

PubMed

Ullah, Azmat; Malik, Suheel Abdullah; Alimgeer, Khurram Saleem

2018-01-01

In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.

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

NASA Astrophysics Data System (ADS)

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

2006-12-01

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

The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems.

PubMed

White, Andrew; Tolman, Malachi; Thames, Howard D; Withers, Hubert Rodney; Mason, Kathy A; Transtrum, Mark K

2016-12-01

We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model's discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system-a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model.

EFFECTS OF AROMATHERAPY MASSAGE ON THE SLEEP QUALITY AND PHYSIOLOGICAL PARAMETERS OF PATIENTS IN A SURGICAL INTENSIVE CARE UNIT.

PubMed

Özlü, Zeynep Karaman; Bilican, Pınar

2017-01-01

Surgical pain is experienced by inpatients with clinical, disease-related concerns, unknown encounters after surgery, quality of sleep, restrictions in position after surgery is known to be serious. The study was conducted to determine the effect of aromatherapy massage on quality of sleep and physiological parameters in surgical intensive care patients. This is an experimental study. The sample of this study consisted of 60 patients who were divided into two groups as experimental group and control group including 30 patients in each one. The participants were postoperative patients, absent complications, who were unconscious and extubated. A data collection form on personal characteristics of the patients, a registration form on their physical parameters and the Richards-Campbell Sleep Scale (RCSQ) were used to collect the data of the study. The Richards-Campbell Sleep Scale indicated that while the experimental group had a mean score of 53.80 ± 13.20, the control group had a mean score of 29.08 ± 9.71 and there was a statistically significant difference between mean scores of the groups. In a comparison of physiologic parameters, only diastolic blood pressure measuring between parameters in favor of an assembly as a statistically significant difference was detected. Results of the study showed that aromatherapy massage enhanced the sleep quality of patients in a surgical intensive care unit and resulted in some positive changes in their physiological parameters.

EFFECTS OF AROMATHERAPY MASSAGE ON THE SLEEP QUALITY AND PHYSIOLOGICAL PARAMETERS OF PATIENTS IN A SURGICAL INTENSIVE CARE UNIT

PubMed Central

Özlü, Zeynep Karaman; Bilican, Pınar

2017-01-01

Background: Surgical pain is experienced by inpatients with clinical, disease-related concerns, unknown encounters after surgery, quality of sleep, restrictions in position after surgery is known to be serious. The study was conducted to determine the effect of aromatherapy massage on quality of sleep and physiological parameters in surgical intensive care patients. Materials and Methods: This is an experimental study. The sample of this study consisted of 60 patients who were divided into two groups as experimental group and control group including 30 patients in each one. The participants were postoperative patients, absent complications, who were unconscious and extubated. A data collection form on personal characteristics of the patients, a registration form on their physical parameters and the Richards-Campbell Sleep Scale (RCSQ) were used to collect the data of the study. Results: The Richards-Campbell Sleep Scale indicated that while the experimental group had a mean score of 53.80 ± 13.20, the control group had a mean score of 29.08 ± 9.71 and there was a statistically significant difference between mean scores of the groups. In a comparison of physiologic parameters, only diastolic blood pressure measuring between parameters in favor of an assembly as a statistically significant difference was detected. Conclusions: Results of the study showed that aromatherapy massage enhanced the sleep quality of patients in a surgical intensive care unit and resulted in some positive changes in their physiological parameters. PMID:28480419

The Limitations of Model-Based Experimental Design and Parameter Estimation in Sloppy Systems

PubMed Central

Tolman, Malachi; Thames, Howard D.; Mason, Kathy A.

2016-01-01

We explore the relationship among experimental design, parameter estimation, and systematic error in sloppy models. We show that the approximate nature of mathematical models poses challenges for experimental design in sloppy models. In many models of complex biological processes it is unknown what are the relevant physical mechanisms that must be included to explain system behaviors. As a consequence, models are often overly complex, with many practically unidentifiable parameters. Furthermore, which mechanisms are relevant/irrelevant vary among experiments. By selecting complementary experiments, experimental design may inadvertently make details that were ommitted from the model become relevant. When this occurs, the model will have a large systematic error and fail to give a good fit to the data. We use a simple hyper-model of model error to quantify a model’s discrepancy and apply it to two models of complex biological processes (EGFR signaling and DNA repair) with optimally selected experiments. We find that although parameters may be accurately estimated, the discrepancy in the model renders it less predictive than it was in the sloppy regime where systematic error is small. We introduce the concept of a sloppy system–a sequence of models of increasing complexity that become sloppy in the limit of microscopic accuracy. We explore the limits of accurate parameter estimation in sloppy systems and argue that identifying underlying mechanisms controlling system behavior is better approached by considering a hierarchy of models of varying detail rather than focusing on parameter estimation in a single model. PMID:27923060

Inverse modeling with RZWQM2 to predict water quality

USDA-ARS?s Scientific Manuscript database

Agricultural systems models such as RZWQM2 are complex and have numerous parameters that are unknown and difficult to estimate. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals...

Perm-Fit: a new program to estimate permeability at high P-T conditions

NASA Astrophysics Data System (ADS)

Moulas, Evangelos; Madonna, Claudio

2016-04-01

Several geological processes are controlled by porous fluid flow. The circulation of porous fluids influences many physical phenomena and in turn it depends on the rock permeability. The permeability of rocks is a physical property that needs to be measured since it depends on many factors such as secondary porosity (fractures etc). We present a numerical approach to estimate permeability using the transient step method (Brace et al., 1968). When a non-reacting, compressible fluid is considered in a relative incompressible solid matrix, the only unknown parameter in the equations of porous flow is permeability. Porosity is assumed to be known and the physical properties of the fluid (compressibility, density, viscosity) are taken from the NIST database. Forward numerical calculations for different values of permeability are used and the results are compared to experimental measurements. The extracted permeability value is the one that minimizes the misfit between experimental and numerical results. The uncertainty on the value of permeability is estimated using a Monte Carlo method. REFERENCES Brace, W.F., Walsh J.B., & Frangos, W.T. 1968: Permeability of Granite under High Pressure, Journal of Geophysical Research, 73, 6, 2225-2236

NeuroPhysics: Studying how neurons create the perception of space-time using Physics' tools and techniques

NASA Astrophysics Data System (ADS)

Dhingra, Shonali; Sandler, Roman; Rios, Rodrigo; Vuong, Cliff; Mehta, Mayank

All animals naturally perceive the abstract concept of space-time. A brain region called the Hippocampus is known to be important in creating these perceptions, but the underlying mechanisms are unknown. In our lab we employ several experimental and computational techniques from Physics to tackle this fundamental puzzle. Experimentally, we use ideas from Nanoscience and Materials Science to develop techniques to measure the activity of hippocampal neurons, in freely-behaving animals. Computationally, we develop models to study neuronal activity patterns, which are point processes that are highly stochastic and multidimensional. We then apply these techniques to collect and analyze neuronal signals from rodents while they're exploring space in Real World or Virtual Reality with various stimuli. Our findings show that under these conditions neuronal activity depends on various parameters, such as sensory cues including visual and auditory, and behavioral cues including, linear and angular, position and velocity. Further, neuronal networks create internally-generated rhythms, which influence perception of space and time. In totality, these results further our understanding of how the brain develops a cognitive map of our surrounding space, and keep track of time.

Assessment of the accuracy of plasma shape reconstruction by the Cauchy condition surface method in JT-60SA

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

Miyata, Y.; Suzuki, T.; Takechi, M.

2015-07-15

For the purpose of stable plasma equilibrium control and detailed analysis, it is essential to reconstruct an accurate plasma boundary on the poloidal cross section in tokamak devices. The Cauchy condition surface (CCS) method is a numerical approach for calculating the spatial distribution of the magnetic flux outside a hypothetical surface and reconstructing the plasma boundary from the magnetic measurements located outside the plasma. The accuracy of the plasma shape reconstruction has been assessed by comparing the CCS method and an equilibrium calculation in JT-60SA with a high elongation and triangularity of plasma shape. The CCS, on which both Dirichletmore » and Neumann conditions are unknown, is defined as a hypothetical surface located inside the real plasma region. The accuracy of the plasma shape reconstruction is sensitive to the CCS free parameters such as the number of unknown parameters and the shape in JT-60SA. It is found that the optimum number of unknown parameters and the size of the CCS that minimizes errors in the reconstructed plasma shape are in proportion to the plasma size. Furthermore, it is shown that the accuracy of the plasma shape reconstruction is greatly improved using the optimum number of unknown parameters and shape of the CCS, and the reachable reconstruction errors in plasma shape and locations of strike points are within the target ranges in JT-60SA.« less

Dynamical Behavior of Meteor in AN Atmosphere: Theory vs Observations

NASA Astrophysics Data System (ADS)

Gritsevich, Maria

Up to now the only quantities which directly follow from the available meteor observations are its brightness, the height above sea level, the length along the trajectory, and as a consequence its velocity as a function of time. Other important parameters like meteoroid's mass, its shape, bulk and grain density, temperature remain unknown and should be found based on physical theories and special experiments. In this study I will consider modern methods for evaluating meteoroid parameters from observational data, and some of their applications. The study in particular takes an approach in modelling the meteoroids' mass and other properties from the aerodynamical point of view, e.g. from the rate of body deceleration in the atmosphere as opposed to conventionally used luminosity [1]. An analytical model of the atmospheric entry is calculated for registered meteors using published observational data and evaluating parameters describing drag, ablation and rotation rate of meteoroid along the luminous segment of the trajectory. One of the special features of this approach is the possibility of considering a change in body shape during its motion in the atmosphere. The correct mathematical modelling of meteor events is necessary for further studies of consequences for collisions of cosmic bodies with the Earth [2]. It also helps us to estimate the key parameters of the meteoroids, including deceleration, pre-entry mass, terminal mass, ablation coefficient, effective destruction enthalpy, and heat-transfer coefficient. With this information, one can use models for the dust influx onto Earth to estimate the number of meteors detected by a camera of a given sensitivity. References 1. Gritsevich M. I. Determination of Parameters of Meteor Bodies based on Flight Obser-vational Data // Advances in Space Research, 44, p. 323-334, 2009. 2. Gritsevich M. I., Stulov V. P. and Turchak L. I. Classification of Consequences for Col-lisions of Natural Cosmic Bodies with the Earth // Doklady Physics, 54, p. 499-503, 2009.

Distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms.

PubMed

Wang, Huiwei; Liao, Xiaofeng; Wang, Zidong; Huang, Tingwen; Chen, Guo

2016-01-01

In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm. Copyright © 2015 Elsevier Ltd. All rights reserved.

Adaptive exponential synchronization of complex-valued Cohen-Grossberg neural networks with known and unknown parameters.

PubMed

Hu, Jin; Zeng, Chunna

2017-02-01

The complex-valued Cohen-Grossberg neural network is a special kind of complex-valued neural network. In this paper, the synchronization problem of a class of complex-valued Cohen-Grossberg neural networks with known and unknown parameters is investigated. By using Lyapunov functionals and the adaptive control method based on parameter identification, some adaptive feedback schemes are proposed to achieve synchronization exponentially between the drive and response systems. The results obtained in this paper have extended and improved some previous works on adaptive synchronization of Cohen-Grossberg neural networks. Finally, two numerical examples are given to demonstrate the effectiveness of the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

Analysis of multinomial models with unknown index using data augmentation

USGS Publications Warehouse

Royle, J. Andrew; Dorazio, R.M.; Link, W.A.

2007-01-01

Multinomial models with unknown index ('sample size') arise in many practical settings. In practice, Bayesian analysis of such models has proved difficult because the dimension of the parameter space is not fixed, being in some cases a function of the unknown index. We describe a data augmentation approach to the analysis of this class of models that provides for a generic and efficient Bayesian implementation. Under this approach, the data are augmented with all-zero detection histories. The resulting augmented dataset is modeled as a zero-inflated version of the complete-data model where an estimable zero-inflation parameter takes the place of the unknown multinomial index. Interestingly, data augmentation can be justified as being equivalent to imposing a discrete uniform prior on the multinomial index. We provide three examples involving estimating the size of an animal population, estimating the number of diabetes cases in a population using the Rasch model, and the motivating example of estimating the number of species in an animal community with latent probabilities of species occurrence and detection.

A new zonation algorithm with parameter estimation using hydraulic head and subsidence observations.

PubMed

Zhang, Meijing; Burbey, Thomas J; Nunes, Vitor Dos Santos; Borggaard, Jeff

2014-01-01

Parameter estimation codes such as UCODE_2005 are becoming well-known tools in groundwater modeling investigations. These programs estimate important parameter values such as transmissivity (T) and aquifer storage values (Sa ) from known observations of hydraulic head, flow, or other physical quantities. One drawback inherent in these codes is that the parameter zones must be specified by the user. However, such knowledge is often unknown even if a detailed hydrogeological description is available. To overcome this deficiency, we present a discrete adjoint algorithm for identifying suitable zonations from hydraulic head and subsidence measurements, which are highly sensitive to both elastic (Sske) and inelastic (Sskv) skeletal specific storage coefficients. With the advent of interferometric synthetic aperture radar (InSAR), distributed spatial and temporal subsidence measurements can be obtained. A synthetic conceptual model containing seven transmissivity zones, one aquifer storage zone and three interbed zones for elastic and inelastic storage coefficients were developed to simulate drawdown and subsidence in an aquifer interbedded with clay that exhibits delayed drainage. Simulated delayed land subsidence and groundwater head data are assumed to be the observed measurements, to which the discrete adjoint algorithm is called to create approximate spatial zonations of T, Sske , and Sskv . UCODE-2005 is then used to obtain the final optimal parameter values. Calibration results indicate that the estimated zonations calculated from the discrete adjoint algorithm closely approximate the true parameter zonations. This automation algorithm reduces the bias established by the initial distribution of zones and provides a robust parameter zonation distribution. © 2013, National Ground Water Association.

Improved central confidence intervals for the ratio of Poisson means

NASA Astrophysics Data System (ADS)

Cousins, R. D.

The problem of confidence intervals for the ratio of two unknown Poisson means was "solved" decades ago, but a closer examination reveals that the standard solution is far from optimal from the frequentist point of view. We construct a more powerful set of central confidence intervals, each of which is a (typically proper) subinterval of the corresponding standard interval. They also provide upper and lower confidence limits which are more restrictive than the standard limits. The construction follows Neyman's original prescription, though discreteness of the Poisson distribution and the presence of a nuisance parameter (one of the unknown means) lead to slightly conservative intervals. Philosophically, the issue of the appropriateness of the construction method is similar to the issue of conditioning on the margins in 2×2 contingency tables. From a frequentist point of view, the new set maintains (over) coverage of the unknown true value of the ratio of means at each stated confidence level, even though the new intervals are shorter than the old intervals by any measure (except for two cases where they are identical). As an example, when the number 2 is drawn from each Poisson population, the 90% CL central confidence interval on the ratio of means is (0.169, 5.196), rather than (0.108, 9.245). In the cited literature, such confidence intervals have applications in numerous branches of pure and applied science, including agriculture, wildlife studies, manufacturing, medicine, reliability theory, and elementary particle physics.

Studies into the nature of cosmic acceleration: Dark energy or a modification to gravity on cosmological scales

NASA Astrophysics Data System (ADS)

Dossett, Jason Nicholas

Since its discovery more than a decade ago, the problem of cosmic acceleration has become one of the largest in cosmology and physics as a whole. An unknown dark energy component of the universe is often invoked to explain this observation. Mathematically, this works because inserting a cosmic fluid with a negative equation of state into Einstein's equations provides an accelerated expansion. There are, however, alternative explanations for the observed cosmic acceleration. Perhaps the most promising of the alternatives is that, on the very largest cosmological scales, general relativity needs to be extended or a new, modified gravity theory must be used. Indeed, many modified gravity models are not only able to replicate the observed accelerated expansion without dark energy, but are also more compatible with a unified theory of physics. Thus it is the goal of this dissertation to develop and study robust tests that will be able to distinguish between these alternative theories of gravity and the need for a dark energy component of the universe. We will study multiple approaches using the growth history of large-scale structure in the universe as a way to accomplish this task. These approaches include studying what is known as the growth index parameter, a parameter that describes the logarithmic growth rate of structure in the universe, which describes the rate of formation of clusters and superclusters of galaxies over the entire age of the universe. We will explore the effectiveness of this parameter to distinguish between general relativity and modifications to gravity physics given realistic expectations of results from future experiments. Next, we will explore the modified growth formalism wherein deviations from the growth expected in general relativity are parameterized via changes to the growth equations, i.e. the perturbed Einstein's equations. We will also explore the impact of spatial curvature on these tests. Finally, we will study how dark energy with some unusual properties will affect the conclusiveness of these tests.

Capillary-Physics Mechanism of Elastic-Wave Mobilization of Residual Oil

NASA Astrophysics Data System (ADS)

Beresnev, I. A.; Pennington, W. D.; Turpening, R. M.

2003-12-01

Much attention has been given to the possibility of vibratory mobilization of residual oil as a method of enhanced recovery. The common features of the relevant applications have nonetheless been inconsistency in the results of field tests and the lack of understanding of a physical mechanism that would explain variable experiences. Such a mechanism can be found in the physics of capillary trapping of oil ganglia, driven through the pore channels by an external pressure gradient. Entrapping of ganglia occurs due to the capillary pressure building on the downstream meniscus entering a narrow pore throat. The resulting internal-pressure imbalance acts against the external gradient, which needs to exceed a certain threshold to carry the ganglion through. The ganglion flow thus exhibits the properties of the Bingham (yield-stress) flow, not the Darcy flow. The application of vibrations is equivalent to the addition of an oscillatory forcing to the constant gradient. When this extra forcing acts along the gradient, an instant "unplugging" occurs, while, when the vibration reverses direction, the flow is plugged. This asymmetry results in an average non-zero flow over one period of vibration, which explains the mobilization effect. The minimum-amplitude and maximum-frequency thresholds apply for the mobilization to occur. When the vibration amplitude exceeds a certain "saturation" level, the flow returns to the Darcy regime. The criterion of the mobilization of a particular ganglion involves the parameters of both the medium (pore geometry, interfacial and wetting properties, fluid viscosity) and the oscillatory field (amplitude and frequency). The medium parameters vary widely under natural conditions. It follows that an elastic wave with a given amplitude and frequency will always produce a certain mobilization effect, mobilizing some ganglia and leaving others intact. The exact macroscopic effect is hard to predict, as it will represent a response of the populations of ganglia with unknown parameter distributions. The variability of responses to vibratory stimulation should thus be expected.

Patterns of Physical Activity Outside of School Time among Japanese Junior High School Students

ERIC Educational Resources Information Center

He, Li; Ishii, Kaori; Shibata, Ai; Adachi, Minoru; Nonoue, Keiko; Oka, Koichiro

2013-01-01

Background: Physical activity is beneficial for adolescent health. The physical activity patterns of Japanese adolescents are relatively unknown. Therefore, this study aimed to describe the current patterns of physical activity and to identify sex and grade differences among them. Methods: The participants comprised 714 Japanese adolescents aged…

Inverse and forward modeling under uncertainty using MRE-based Bayesian approach

NASA Astrophysics Data System (ADS)

Hou, Z.; Rubin, Y.

2004-12-01

A stochastic inverse approach for subsurface characterization is proposed and applied to shallow vadose zone at a winery field site in north California and to a gas reservoir at the Ormen Lange field site in the North Sea. The approach is formulated in a Bayesian-stochastic framework, whereby the unknown parameters are identified in terms of their statistical moments or their probabilities. Instead of the traditional single-valued estimation /prediction provided by deterministic methods, the approach gives a probability distribution for an unknown parameter. This allows calculating the mean, the mode, and the confidence interval, which is useful for a rational treatment of uncertainty and its consequences. The approach also allows incorporating data of various types and different error levels, including measurements of state variables as well as information such as bounds on or statistical moments of the unknown parameters, which may represent prior information. To obtain minimally subjective prior probabilities required for the Bayesian approach, the principle of Minimum Relative Entropy (MRE) is employed. The approach is tested in field sites for flow parameters identification and soil moisture estimation in the vadose zone and for gas saturation estimation at great depth below the ocean floor. Results indicate the potential of coupling various types of field data within a MRE-based Bayesian formalism for improving the estimation of the parameters of interest.

Mixed linear-non-linear inversion of crustal deformation data: Bayesian inference of model, weighting and regularization parameters

NASA Astrophysics Data System (ADS)

Fukuda, Jun'ichi; Johnson, Kaj M.

2010-06-01

We present a unified theoretical framework and solution method for probabilistic, Bayesian inversions of crustal deformation data. The inversions involve multiple data sets with unknown relative weights, model parameters that are related linearly or non-linearly through theoretic models to observations, prior information on model parameters and regularization priors to stabilize underdetermined problems. To efficiently handle non-linear inversions in which some of the model parameters are linearly related to the observations, this method combines both analytical least-squares solutions and a Monte Carlo sampling technique. In this method, model parameters that are linearly and non-linearly related to observations, relative weights of multiple data sets and relative weights of prior information and regularization priors are determined in a unified Bayesian framework. In this paper, we define the mixed linear-non-linear inverse problem, outline the theoretical basis for the method, provide a step-by-step algorithm for the inversion, validate the inversion method using synthetic data and apply the method to two real data sets. We apply the method to inversions of multiple geodetic data sets with unknown relative data weights for interseismic fault slip and locking depth. We also apply the method to the problem of estimating the spatial distribution of coseismic slip on faults with unknown fault geometry, relative data weights and smoothing regularization weight.

Radio Detection of Cosmic Rays-Achievements and Future Potential

NASA Astrophysics Data System (ADS)

Huege, Tim

When modern efforts for radio detection of cosmic rays started about a decade ago, hopes were high but the true potential was unknown. Since then, we have achieved a detailed understanding of the radio emission physics and have consequently succeeded in developing sophisticated detection schemes and analysis approaches. In particular, we have demonstrated that the important air-shower parameters arrival direction, particle energy and depth of shower maximum can be reconstructed reliably from radio measurements, with a precision that is comparable with that of other detection techniques. At the same time, limitations inherent to the radio-emission mechanisms have become apparent. In this article, I shortly review the capabilities of radio detection in the very high-frequency band, and discuss the potential for future application in existing and new experiments for cosmic-ray detection.

Model Calibration with Censored Data

DOE PAGES

Cao, Fang; Ba, Shan; Brenneman, William A.; ...

2017-06-28

Here, the purpose of model calibration is to make the model predictions closer to reality. The classical Kennedy-O'Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the Kennedy-O'Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied tomore » study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large.« less

Handling the unknown soil hydraulic parameters in data assimilation for unsaturated flow problems

NASA Astrophysics Data System (ADS)

Lange, Natascha; Erdal, Daniel; Neuweiler, Insa

2017-04-01

Model predictions of flow in the unsaturated zone require the soil hydraulic parameters. However, these parameters cannot be determined easily in applications, in particular if observations are indirect and cover only a small range of possible states. Correlation of parameters or their correlation in the range of states that are observed is a problem, as different parameter combinations may reproduce approximately the same measured water content. In field campaigns this problem can be helped by adding more measurement devices. Often, observation networks are designed to feed models for long term prediction purposes (i.e. for weather forecasting). A popular way of making predictions with such kind of observations are data assimilation methods, like the ensemble Kalman filter (Evensen, 1994). These methods can be used for parameter estimation if the unknown parameters are included in the state vector and updated along with the model states. Given the difficulties related to estimation of the soil hydraulic parameters in general, it is questionable, though, whether these methods can really be used for parameter estimation under natural conditions. Therefore, we investigate the ability of the ensemble Kalman filter to estimate the soil hydraulic parameters. We use synthetic identical twin-experiments to guarantee full knowledge of the model and the true parameters. We use the van Genuchten model to describe the soil water retention and relative permeability functions. This model is unfortunately prone to the above mentioned pseudo-correlations of parameters. Therefore, we also test the simpler Russo Gardner model, which is less affected by that problem, in our experiments. The total number of unknown parameters is varied by considering different layers of soil. Besides, we study the influence of the parameter updates on the water content predictions. We test different iterative filter approaches and compare different observation strategies for parameter identification. Considering heterogeneous soils, we discuss the representativeness of different observation types to be used for the assimilation. G. Evensen. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans, 99(C5):10143-10162, 1994

Mass properties measurement system dynamics

NASA Technical Reports Server (NTRS)

Doty, Keith L.

1993-01-01

The MPMS mechanism possess two revolute degrees-of-freedom and allows the user to measure the mass, center of gravity, and the inertia tensor of an unknown mass. The dynamics of the Mass Properties Measurement System (MPMS) from the Lagrangian approach to illustrate the dependency of the motion on the unknown parameters.

Adaptive control of stochastic linear systems with unknown parameters. M.S. Thesis

NASA Technical Reports Server (NTRS)

Ku, R. T.

1972-01-01

The problem of optimal control of linear discrete-time stochastic dynamical system with unknown and, possibly, stochastically varying parameters is considered on the basis of noisy measurements. It is desired to minimize the expected value of a quadratic cost functional. Since the simultaneous estimation of the state and plant parameters is a nonlinear filtering problem, the extended Kalman filter algorithm is used. Several qualitative and asymptotic properties of the open loop feedback optimal control and the enforced separation scheme are discussed. Simulation results via Monte Carlo method show that, in terms of the performance measure, for stable systems the open loop feedback optimal control system is slightly better than the enforced separation scheme, while for unstable systems the latter scheme is far better.

Distributed Optimization Design of Continuous-Time Multiagent Systems With Unknown-Frequency Disturbances.

PubMed

Wang, Xinghu; Hong, Yiguang; Yi, Peng; Ji, Haibo; Kang, Yu

2017-05-24

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

Stochastic Inversion of 2D Magnetotelluric Data

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

Chen, Jinsong

2010-07-01

The algorithm is developed to invert 2D magnetotelluric (MT) data based on sharp boundary parametrization using a Bayesian framework. Within the algorithm, we consider the locations and the resistivity of regions formed by the interfaces are as unknowns. We use a parallel, adaptive finite-element algorithm to forward simulate frequency-domain MT responses of 2D conductivity structure. Those unknown parameters are spatially correlated and are described by a geostatistical model. The joint posterior probability distribution function is explored by Markov Chain Monte Carlo (MCMC) sampling methods. The developed stochastic model is effective for estimating the interface locations and resistivity. Most importantly, itmore » provides details uncertainty information on each unknown parameter. Hardware requirements: PC, Supercomputer, Multi-platform, Workstation; Software requirements C and Fortan; Operation Systems/version is Linux/Unix or Windows« less

Three-dimensional cinematography with control object of unknown shape.

PubMed

Dapena, J; Harman, E A; Miller, J A

1982-01-01

A technique for reconstruction of three-dimensional (3D) motion which involves a simple filming procedure but allows the deduction of coordinates in large object volumes was developed. Internal camera parameters are calculated from measurements of the film images of two calibrated crosses while external camera parameters are calculated from the film images of points in a control object of unknown shape but at least one known length. The control object, which includes the volume in which the activity is to take place, is formed by a series of poles placed at unknown locations, each carrying two targets. From the internal and external camera parameters, and from locations of the images of point in the films of the two cameras, 3D coordinates of the point can be calculated. Root mean square errors of the three coordinates of points in a large object volume (5m x 5m x 1.5m) were 15 mm, 13 mm, 13 mm and 6 mm, and relative errors in lengths averaged 0.5%, 0.7% and 0.5%, respectively.

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study.

PubMed

Ogihara, Takeshi; Mita, Tomoya; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal-metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. UMIN000010932.

Estimating the Maximum Magnitude of Induced Earthquakes With Dynamic Rupture Simulations

NASA Astrophysics Data System (ADS)

Gilmour, E.; Daub, E. G.

2017-12-01

Seismicity in Oklahoma has been sharply increasing as the result of wastewater injection. The earthquakes, thought to be induced from changes in pore pressure due to fluid injection, nucleate along existing faults. Induced earthquakes currently dominate central and eastern United States seismicity (Keranen et al. 2016). Induced earthquakes have only been occurring in the central US for a short time; therefore, too few induced earthquakes have been observed in this region to know their maximum magnitude. The lack of knowledge regarding the maximum magnitude of induced earthquakes means that large uncertainties exist in the seismic hazard for the central United States. While induced earthquakes follow the Gutenberg-Richter relation (van der Elst et al. 2016), it is unclear if there are limits to their magnitudes. An estimate of the maximum magnitude of the induced earthquakes is crucial for understanding their impact on seismic hazard. While other estimates of the maximum magnitude exist, those estimates are observational or statistical, and cannot take into account the possibility of larger events that have not yet been observed. Here, we take a physical approach to studying the maximum magnitude based on dynamic ruptures simulations. We run a suite of two-dimensional ruptures simulations to physically determine how ruptures propagate. The simulations use the known parameters of principle stress orientation and rupture locations. We vary the other unknown parameters of the ruptures simulations to obtain a large number of rupture simulation results reflecting different possible sets of parameters, and use these results to train a neural network to complete the ruptures simulations. Then using a Markov Chain Monte Carlo method to check different combinations of parameters, the trained neural network is used to create synthetic magnitude-frequency distributions to compare to the real earthquake catalog. This method allows us to find sets of parameters that are consistent with earthquakes observed in Oklahoma and find which parameters effect the rupture propagation. Our results show that the stress orientation and magnitude, pore pressure, and friction properties combine to determine the final magnitude of the simulated event.

Stochastic filtering for damage identification through nonlinear structural finite element model updating

NASA Astrophysics Data System (ADS)

Astroza, Rodrigo; Ebrahimian, Hamed; Conte, Joel P.

2015-03-01

This paper describes a novel framework that combines advanced mechanics-based nonlinear (hysteretic) finite element (FE) models and stochastic filtering techniques to estimate unknown time-invariant parameters of nonlinear inelastic material models used in the FE model. Using input-output data recorded during earthquake events, the proposed framework updates the nonlinear FE model of the structure. The updated FE model can be directly used for damage identification and further used for damage prognosis. To update the unknown time-invariant parameters of the FE model, two alternative stochastic filtering methods are used: the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). A three-dimensional, 5-story, 2-by-1 bay reinforced concrete (RC) frame is used to verify the proposed framework. The RC frame is modeled using fiber-section displacement-based beam-column elements with distributed plasticity and is subjected to the ground motion recorded at the Sylmar station during the 1994 Northridge earthquake. The results indicate that the proposed framework accurately estimate the unknown material parameters of the nonlinear FE model. The UKF outperforms the EKF when the relative root-mean-square error of the recorded responses are compared. In addition, the results suggest that the convergence of the estimate of modeling parameters is smoother and faster when the UKF is utilized.

A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks

PubMed Central

Zaikin, Alexey; Míguez, Joaquín

2017-01-01

We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introducing dynamical noise in the model and assuming that the partial observations of the system are contaminated by additive noise, we enable a principled mechanism to represent experimental uncertainties in the synthesis of the multicellular system and pave the way for the design of probabilistic methods for the estimation of any unknowns in the model. Within this setup, we tackle the Bayesian estimation of a subset of the model parameters. Specifically, we compare three Monte Carlo based numerical methods for the approximation of the posterior probability density function of the unknown parameters given a set of partial and noisy observations of the system. The schemes we assess are the particle Metropolis-Hastings (PMH) algorithm, the nonlinear population Monte Carlo (NPMC) method and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) scheme. We present an extensive numerical simulation study, which shows that while the three techniques can effectively solve the problem there are significant differences both in estimation accuracy and computational efficiency. PMID:28797087

Underwater passive acoustic localization of Pacific walruses in the northeastern Chukchi Sea.

PubMed

Rideout, Brendan P; Dosso, Stan E; Hannay, David E

2013-09-01

This paper develops and applies a linearized Bayesian localization algorithm based on acoustic arrival times of marine mammal vocalizations at spatially-separated receivers which provides three-dimensional (3D) location estimates with rigorous uncertainty analysis. To properly account for uncertainty in receiver parameters (3D hydrophone locations and synchronization times) and environmental parameters (water depth and sound-speed correction), these quantities are treated as unknowns constrained by prior estimates and prior uncertainties. Unknown scaling factors on both the prior and arrival-time uncertainties are estimated by minimizing Akaike's Bayesian information criterion (a maximum entropy condition). Maximum a posteriori estimates for sound source locations and times, receiver parameters, and environmental parameters are calculated simultaneously using measurements of arrival times for direct and interface-reflected acoustic paths. Posterior uncertainties for all unknowns incorporate both arrival time and prior uncertainties. Monte Carlo simulation results demonstrate that, for the cases considered here, linearization errors are small and the lack of an accurate sound-speed profile does not cause significant biases in the estimated locations. A sequence of Pacific walrus vocalizations, recorded in the Chukchi Sea northwest of Alaska, is localized using this technique, yielding a track estimate and uncertainties with an estimated speed comparable to normal walrus swim speeds.

Modern control concepts in hydrology. [parameter identification in adaptive stochastic control approach

NASA Technical Reports Server (NTRS)

Duong, N.; Winn, C. B.; Johnson, G. R.

1975-01-01

Two approaches to an identification problem in hydrology are presented, based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time-invariant or time-dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and confirm the results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

A new chaotic communication scheme based on adaptive synchronization.

PubMed

Xiang-Jun, Wu

2006-12-01

A new chaotic communication scheme using adaptive synchronization technique of two unified chaotic systems is proposed. Different from the existing secure communication methods, the transmitted signal is modulated into the parameter of chaotic systems. The adaptive synchronization technique is used to synchronize two identical chaotic systems embedded in the transmitter and the receiver. It is assumed that the parameter of the receiver system is unknown. Based on the Lyapunov stability theory, an adaptive control law is derived to make the states of two identical unified chaotic systems with unknown system parameters asymptotically synchronized; thus the parameter of the receiver system is identified. Then the recovery of the original information signal in the receiver is successfully achieved on the basis of the estimated parameter. It is noticed that the time required for recovering the information signal and the accuracy of the recovered signal very sensitively depends on the frequency of the information signal. Numerical results have verified the effectiveness of the proposed scheme.

Estimation of time- and state-dependent delays and other parameters in functional differential equations

NASA Technical Reports Server (NTRS)

Murphy, K. A.

1988-01-01

A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

Estimation of time- and state-dependent delays and other parameters in functional differential equations

NASA Technical Reports Server (NTRS)

Murphy, K. A.

1990-01-01

A parameter estimation algorithm is developed which can be used to estimate unknown time- or state-dependent delays and other parameters (e.g., initial condition) appearing within a nonlinear nonautonomous functional differential equation. The original infinite dimensional differential equation is approximated using linear splines, which are allowed to move with the variable delay. The variable delays are approximated using linear splines as well. The approximation scheme produces a system of ordinary differential equations with nice computational properties. The unknown parameters are estimated within the approximating systems by minimizing a least-squares fit-to-data criterion. Convergence theorems are proved for time-dependent delays and state-dependent delays within two classes, which say essentially that fitting the data by using approximations will, in the limit, provide a fit to the data using the original system. Numerical test examples are presented which illustrate the method for all types of delay.

Novel adaptive neural control design for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

NASA Astrophysics Data System (ADS)

Bu, Xiangwei; Wu, Xiaoyan; He, Guangjun; Huang, Jiaqi

2016-03-01

This paper investigates the design of a novel adaptive neural controller for the longitudinal dynamics of a flexible air-breathing hypersonic vehicle with control input constraints. To reduce the complexity of controller design, the vehicle dynamics is decomposed into the velocity subsystem and the altitude subsystem, respectively. For each subsystem, only one neural network is utilized to approach the lumped unknown function. By employing a minimal-learning parameter method to estimate the norm of ideal weight vectors rather than their elements, there are only two adaptive parameters required for neural approximation. Thus, the computational burden is lower than the ones derived from neural back-stepping schemes. Specially, to deal with the control input constraints, additional systems are exploited to compensate the actuators. Lyapunov synthesis proves that all the closed-loop signals involved are uniformly ultimately bounded. Finally, simulation results show that the adopted compensation scheme can tackle actuator constraint effectively and moreover velocity and altitude can stably track their reference trajectories even when the physical limitations on control inputs are in effect.

Hydraulics of epiphreatic flow of a karst aquifer

NASA Astrophysics Data System (ADS)

Gabrovšek, Franci; Peric, Borut; Kaufmann, Georg

2018-05-01

The nature of epiphreatic flow remains an important research challenge in karst hydrology. This study focuses on the flood propagation along the epiphreatic system of Reka-Timavo system (Kras/Carso Plateau, Slovenia/Italy). It is based on long-term monitoring of basic physical parameters (pressure/level, temperature, specific electric conductivity) of ground water in six active caves belonging to the flow system. The system vigorously responds to flood events, with stage rising >100 m in some of the caves. Besides presenting the response of the system to flood events of different scales, the work focuses on the interpretation of recorded hydrographs in view of the known distribution and size of conduits and basic hydraulic relations. Furthermore, the hydrographs were used to infer the unknown geometry between the observation points. This way, the main flow restrictors, overflow passages and large epiphreatic storages were identified. The assumptions were tested with a hydraulic model, where the inversion procedure was used for an additional parameter optimisation. Time series of temperature and specific electric conductivity were used to assess the apparent velocities of flow between consecutive points.

Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling

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

Stevens, Garrison N.; Atamturktur, Sez; Brown, D. Andrew

Rapid advancements in parallel computing over the last two decades have enabled simulations of complex, coupled systems through partitioning. In partitioned analysis, independently developed constituent models communicate, representing dependencies between multiple physical phenomena that occur in the full system. Figure 1 schematically demonstrates a coupled system with two constituent models, each resolving different physical behavior. In this figure, the constituent model, denoted as the “consumer,” relies upon some input parameter that is being provided by the constituent model acting as a “feeder”. The role of the feeder model is to map operating conditions (i.e. those that are stimulating the process)more » to consumer inputs, thus providing functional inputs to the consumer model*. Problems arise if the feeder model cannot be built–a challenge that is prevalent for highly complex systems in extreme operational conditions that push the limits of our understanding of underlying physical behavior. Often, these are also the situations where separate-effect experiments isolating the physical phenomena are not available; meaning that experimentally determining the unknown constituent behavior is not possible (Bauer and Holland, 1995; Unal et al., 2013), and that integral-effect experiments that reflect the behavior of the complete system tend to be the only available observations. In this paper, the authors advocate for the usefulness of integral-effect experiments in furthering a model developer’s knowledge of the physics principles governing the system behavior of interest.« less

Statistical inference of empirical constituents in partitioned analysis from integral-effect experiments: An application in thermo-mechanical coupling

DOE PAGES

Stevens, Garrison N.; Atamturktur, Sez; Brown, D. Andrew; ...

2018-04-16

Rapid advancements in parallel computing over the last two decades have enabled simulations of complex, coupled systems through partitioning. In partitioned analysis, independently developed constituent models communicate, representing dependencies between multiple physical phenomena that occur in the full system. Figure 1 schematically demonstrates a coupled system with two constituent models, each resolving different physical behavior. In this figure, the constituent model, denoted as the “consumer,” relies upon some input parameter that is being provided by the constituent model acting as a “feeder”. The role of the feeder model is to map operating conditions (i.e. those that are stimulating the process)more » to consumer inputs, thus providing functional inputs to the consumer model*. Problems arise if the feeder model cannot be built–a challenge that is prevalent for highly complex systems in extreme operational conditions that push the limits of our understanding of underlying physical behavior. Often, these are also the situations where separate-effect experiments isolating the physical phenomena are not available; meaning that experimentally determining the unknown constituent behavior is not possible (Bauer and Holland, 1995; Unal et al., 2013), and that integral-effect experiments that reflect the behavior of the complete system tend to be the only available observations. In this paper, the authors advocate for the usefulness of integral-effect experiments in furthering a model developer’s knowledge of the physics principles governing the system behavior of interest.« less

Nonlinear Kalman filters for calibration in radio interferometry

NASA Astrophysics Data System (ADS)

Tasse, C.

2014-06-01

The data produced by the new generation of interferometers are affected by a wide variety of partially unknown complex effects such as pointing errors, phased array beams, ionosphere, troposphere, Faraday rotation, or clock drifts. Most algorithms addressing direction-dependent calibration solve for the effective Jones matrices, and cannot constrain the underlying physical quantities of the radio interferometry measurement equation (RIME). A related difficulty is that they lack robustness in the presence of low signal-to-noise ratios, and when solving for moderate to large numbers of parameters they can be subject to ill-conditioning. These effects can have dramatic consequences in the image plane such as source or even thermal noise suppression. The advantage of solvers directly estimating the physical terms appearing in the RIME is that they can potentially reduce the number of free parameters by orders of magnitudes while dramatically increasing the size of usable data, thereby improving conditioning. We present here a new calibration scheme based on a nonlinear version of the Kalman filter that aims at estimating the physical terms appearing in the RIME. We enrich the filter's structure with a tunable data representation model, together with an augmented measurement model for regularization. Using simulations we show that it can properly estimate the physical effects appearing in the RIME. We found that this approach is particularly useful in the most extreme cases such as when ionospheric and clock effects are simultaneously present. Combined with the ability to provide prior knowledge on the expected structure of the physical instrumental effects (expected physical state and dynamics), we obtain a fairly computationally cheap algorithm that we believe to be robust, especially in low signal-to-noise regimes. Potentially, the use of filters and other similar methods can represent an improvement for calibration in radio interferometry, under the condition that the effects corrupting visibilities are understood and analytically stable. Recursive algorithms are particularly well adapted for pre-calibration and sky model estimate in a streaming way. This may be useful for the SKA-type instruments that produce huge amounts of data that have to be calibrated before being averaged.

Bayesian approach to the analysis of neutron Brillouin scattering data on liquid metals

NASA Astrophysics Data System (ADS)

De Francesco, A.; Guarini, E.; Bafile, U.; Formisano, F.; Scaccia, L.

2016-08-01

When the dynamics of liquids and disordered systems at mesoscopic level is investigated by means of inelastic scattering (e.g., neutron or x ray), spectra are often characterized by a poor definition of the excitation lines and spectroscopic features in general and one important issue is to establish how many of these lines need to be included in the modeling function and to estimate their parameters. Furthermore, when strongly damped excitations are present, commonly used and widespread fitting algorithms are particularly affected by the choice of initial values of the parameters. An inadequate choice may lead to an inefficient exploration of the parameter space, resulting in the algorithm getting stuck in a local minimum. In this paper, we present a Bayesian approach to the analysis of neutron Brillouin scattering data in which the number of excitation lines is treated as unknown and estimated along with the other model parameters. We propose a joint estimation procedure based on a reversible-jump Markov chain Monte Carlo algorithm, which efficiently explores the parameter space, producing a probabilistic measure to quantify the uncertainty on the number of excitation lines as well as reliable parameter estimates. The method proposed could turn out of great importance in extracting physical information from experimental data, especially when the detection of spectral features is complicated not only because of the properties of the sample, but also because of the limited instrumental resolution and count statistics. The approach is tested on generated data set and then applied to real experimental spectra of neutron Brillouin scattering from a liquid metal, previously analyzed in a more traditional way.

Physical condition for the slowing down of cosmic acceleration

NASA Astrophysics Data System (ADS)

Zhang, Ming-Jian; Xia, Jun-Qing

2018-04-01

The possible slowing down of cosmic acceleration was widely studied. However, judgment on this effect in different dark energy parameterizations was very ambiguous. Moreover, the reason of generating these uncertainties was still unknown. In the present paper, we analyze the derivative of deceleration parameter q‧ (z) using the Gaussian processes. This model-independent reconstruction suggests that no slowing down of acceleration is presented within 95% C.L. from the Union2.1 and JLA supernova data. However, q‧ (z) from the observational H (z) data is a little smaller than zero at 95% C.L., which indicates that future H (z) data may have a potential to test this effect. From the evolution of q‧ (z), we present an interesting constraint on the dark energy and observational data. The physical constraint clearly solves the problem of why some dark energy models cannot produce this effect in previous work. Comparison between the constraint and observational data also shows that most of current data are not in the allowed regions. This implies a reason of why current data cannot convincingly measure this effect.

A Brief History of Physics in China,

DTIC Science & Technology

1982-08-31

birth and death unknown) of the Yuan dynasty stated that lotus seeds were initially submerged in four different salt solutions whose concentration was...34 documented the construction metho. of the south-pointing cart by Yian Su (960-1040) in 1027 and Wu De-Ren (dates of birth and death unknown) in 1107. The...book also documented how Lu Dao-Long (dates of birth and death unknown) constructed the distance-registering cart in 1027. According to the record

The retrieval of a buried cylindrical obstacle by a constrained modified gradient method in the H-polarization case and for Maxwellian materials

NASA Astrophysics Data System (ADS)

Lambert, M.; Lesselier, D.; Kooij, B. J.

1998-10-01

The retrieval of an unknown, possibly inhomogeneous, penetrable cylindrical obstacle buried entirely in a known homogeneous half-space - the constitutive material parameters of the obstacle and of its embedding obey a Maxwell model - is considered from single- or multiple-frequency aspect-limited data collected by ideal sensors located in air above the embedding half-space, when a small number of time-harmonic transverse electric (TE)-polarized line sources - the magnetic field H is directed along the axis of the cylinder - is also placed in air. The wavefield is modelled from a rigorous H-field domain integral-differential formulation which involves the dot product of the gradients of the single component of H and of the Green function of the stratified environment times a scalar-valued contrast function which contains the obstacle parameters (the frequency-independent, position-dependent relative permittivity and conductivity). A modified gradient method is developed in order to reconstruct the maps of such parameters within a prescribed search domain from the iterative minimization of a cost functional which incorporates both the error in reproducing the data and the error on the field built inside this domain. Non-physical values are excluded and convergence reached by incorporating in the solution algorithm, from a proper choice of unknowns, the condition that the relative permittivity be larger than or equal to 1, and the conductivity be non-negative. The efficiency of the constrained method is illustrated from noiseless and noisy synthetic data acquired independently. The importance of the choice of the initial values of the sought quantities, the need for a periodic refreshment of the constitutive parameters to avoid the algorithm providing inconsistent results, and the interest of a frequency-hopping strategy to obtain finer and finer features of the obstacle when the frequency is raised, are underlined. It is also shown that though either the permittivity map or the conductivity map can be obtained for a fair variety of cases, retrieving both of them may be difficult unless further information is made available.

Parameter Optimization for Feature and Hit Generation in a General Unknown Screening Method-Proof of Concept Study Using a Design of Experiment Approach for a High Resolution Mass Spectrometry Procedure after Data Independent Acquisition.

PubMed

Elmiger, Marco P; Poetzsch, Michael; Steuer, Andrea E; Kraemer, Thomas

2018-03-06

High resolution mass spectrometry and modern data independent acquisition (DIA) methods enable the creation of general unknown screening (GUS) procedures. However, even when DIA is used, its potential is far from being exploited, because often, the untargeted acquisition is followed by a targeted search. Applying an actual GUS (including untargeted screening) produces an immense amount of data that must be dealt with. An optimization of the parameters regulating the feature detection and hit generation algorithms of the data processing software could significantly reduce the amount of unnecessary data and thereby the workload. Design of experiment (DoE) approaches allow a simultaneous optimization of multiple parameters. In a first step, parameters are evaluated (crucial or noncrucial). Second, crucial parameters are optimized. The aim in this study was to reduce the number of hits, without missing analytes. The obtained parameter settings from the optimization were compared to the standard settings by analyzing a test set of blood samples spiked with 22 relevant analytes as well as 62 authentic forensic cases. The optimization lead to a marked reduction of workload (12.3 to 1.1% and 3.8 to 1.1% hits for the test set and the authentic cases, respectively) while simultaneously increasing the identification rate (68.2 to 86.4% and 68.8 to 88.1%, respectively). This proof of concept study emphasizes the great potential of DoE approaches to master the data overload resulting from modern data independent acquisition methods used for general unknown screening procedures by optimizing software parameters.

Computational methods for estimation of parameters in hyperbolic systems

NASA Technical Reports Server (NTRS)

Banks, H. T.; Ito, K.; Murphy, K. A.

1983-01-01

Approximation techniques for estimating spatially varying coefficients and unknown boundary parameters in second order hyperbolic systems are discussed. Methods for state approximation (cubic splines, tau-Legendre) and approximation of function space parameters (interpolatory splines) are outlined and numerical findings for use of the resulting schemes in model "one dimensional seismic inversion' problems are summarized.

Adaptive estimation of nonlinear parameters of a nonholonomic spherical robot using a modified fuzzy-based speed gradient algorithm

NASA Astrophysics Data System (ADS)

Roozegar, Mehdi; Mahjoob, Mohammad J.; Ayati, Moosa

2017-05-01

This paper deals with adaptive estimation of the unknown parameters and states of a pendulum-driven spherical robot (PDSR), which is a nonlinear in parameters (NLP) chaotic system with parametric uncertainties. Firstly, the mathematical model of the robot is deduced by applying the Newton-Euler methodology for a system of rigid bodies. Then, based on the speed gradient (SG) algorithm, the states and unknown parameters of the robot are estimated online for different step length gains and initial conditions. The estimated parameters are updated adaptively according to the error between estimated and true state values. Since the errors of the estimated states and parameters as well as the convergence rates depend significantly on the value of step length gain, this gain should be chosen optimally. Hence, a heuristic fuzzy logic controller is employed to adjust the gain adaptively. Simulation results indicate that the proposed approach is highly encouraging for identification of this NLP chaotic system even if the initial conditions change and the uncertainties increase; therefore, it is reliable to be implemented on a real robot.

Characterizing unknown systematics in large scale structure surveys

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

Agarwal, Nishant; Ho, Shirley; Myers, Adam D.

Photometric large scale structure (LSS) surveys probe the largest volumes in the Universe, but are inevitably limited by systematic uncertainties. Imperfect photometric calibration leads to biases in our measurements of the density fields of LSS tracers such as galaxies and quasars, and as a result in cosmological parameter estimation. Earlier studies have proposed using cross-correlations between different redshift slices or cross-correlations between different surveys to reduce the effects of such systematics. In this paper we develop a method to characterize unknown systematics. We demonstrate that while we do not have sufficient information to correct for unknown systematics in the data,more » we can obtain an estimate of their magnitude. We define a parameter to estimate contamination from unknown systematics using cross-correlations between different redshift slices and propose discarding bins in the angular power spectrum that lie outside a certain contamination tolerance level. We show that this method improves estimates of the bias using simulated data and further apply it to photometric luminous red galaxies in the Sloan Digital Sky Survey as a case study.« less

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

Kato, Go

We consider the situation where s replicas of a qubit with an unknown state and its orthogonal k replicas are given as an input, and we try to make c clones of the qubit with the unknown state. As a function of s, k, and c, we obtain the optimal fidelity between the qubit with an unknown state and the clone by explicitly giving a completely positive trace-preserving (CPTP) map that represents a cloning machine. We discuss dependency of the fidelity on the values of the parameters s, k, and c.

Simulation of spatial and temporal properties of aftershocks by means of the fiber bundle model

NASA Astrophysics Data System (ADS)

Monterrubio-Velasco, Marisol; Zúñiga, F. R.; Márquez-Ramírez, Victor Hugo; Figueroa-Soto, Angel

2017-11-01

The rupture processes of any heterogeneous material constitute a complex physical problem. Earthquake aftershocks show temporal and spatial behaviors which are consequence of the heterogeneous stress distribution and multiple rupturing following the main shock. This process is difficult to model deterministically due to the number of parameters and physical conditions, which are largely unknown. In order to shed light on the minimum requirements for the generation of aftershock clusters, in this study, we perform a simulation of the main features of such a complex process by means of a fiber bundle (FB) type model. The FB model has been widely used to analyze the fracture process in heterogeneous materials. It is a simple but powerful tool that allows modeling the main characteristics of a medium such as the brittle shallow crust of the earth. In this work, we incorporate spatial properties, such as the Coulomb stress change pattern, which help simulate observed characteristics of aftershock sequences. In particular, we introduce a parameter ( P) that controls the probability of spatial distribution of initial loads. Also, we use a "conservation" parameter ( π), which accounts for the load dissipation of the system, and demonstrate its influence on the simulated spatio-temporal patterns. Based on numerical results, we find that P has to be in the range 0.06 < P < 0.30, whilst π needs to be limited by a very narrow range ( 0.60 < π < 0.66) in order to reproduce aftershocks pattern characteristics which resemble those of observed sequences. This means that the system requires a small difference in the spatial distribution of initial stress, and a very particular fraction of load transfer in order to generate realistic aftershocks.

A dynamical approach in exploring the unknown mass in the Solar system using pulsar timing arrays

NASA Astrophysics Data System (ADS)

Guo, Y. J.; Lee, K. J.; Caballero, R. N.

2018-04-01

The error in the Solar system ephemeris will lead to dipolar correlations in the residuals of pulsar timing array for widely separated pulsars. In this paper, we utilize such correlated signals, and construct a Bayesian data-analysis framework to detect the unknown mass in the Solar system and to measure the orbital parameters. The algorithm is designed to calculate the waveform of the induced pulsar-timing residuals due to the unmodelled objects following the Keplerian orbits in the Solar system. The algorithm incorporates a Bayesian-analysis suit used to simultaneously analyse the pulsar-timing data of multiple pulsars to search for coherent waveforms, evaluate the detection significance of unknown objects, and to measure their parameters. When the object is not detectable, our algorithm can be used to place upper limits on the mass. The algorithm is verified using simulated data sets, and cross-checked with analytical calculations. We also investigate the capability of future pulsar-timing-array experiments in detecting the unknown objects. We expect that the future pulsar-timing data can limit the unknown massive objects in the Solar system to be lighter than 10-11-10-12 M⊙, or measure the mass of Jovian system to a fractional precision of 10-8-10-9.

Unknown loads affect force production capacity in early phases of bench press throws.

PubMed

Hernández Davó, J L; Sabido Solana, R; Sarabia Marínm, J M; Sánchez Martos, Á; Moya Ramón, M

2015-10-01

Explosive strength training aims to improve force generation in early phases of movement due to its importance in sport performance. The present study examined the influence of lack of knowledge about the load lifted in explosive parameters during bench press throws. Thirteen healthy young men (22.8±2.0 years) participated in the study. Participants performed bench press throws with three different loads (30, 50 and 70% of 1 repetition maximum) in two different conditions (known and unknown loads). In unknown condition, loads were changed within sets in each repetition and participants did not know the load, whereas in known condition the load did not change within sets and participants had knowledge about the load lifted. Results of repeated-measures ANOVA revealed that unknown conditions involves higher power in the first 30, 50, 100 and 150 ms with the three loads, higher values of ratio of force development in those first instants, and differences in time to reach maximal rate of force development with 50 and 70% of 1 repetition maximum. This study showed that unknown conditions elicit higher values of explosive parameters in early phases of bench press throws, thereby this kind of methodology could be considered in explosive strength training.

Risk Assessment of Bone Fracture During Space Exploration Missions to the Moon and Mars

NASA Technical Reports Server (NTRS)

Lewandowski, Beth E.; Myers, Jerry G.; Nelson, Emily S.; Licatta, Angelo; Griffin, Devon

2007-01-01

The possibility of a traumatic bone fracture in space is a concern due to the observed decrease in astronaut bone mineral density (BMD) during spaceflight and because of the physical demands of the mission. The Bone Fracture Risk Module (BFxRM) was developed to quantify the probability of fracture at the femoral neck and lumbar spine during space exploration missions. The BFxRM is scenario-based, providing predictions for specific activities or events during a particular space mission. The key elements of the BFxRM are the mission parameters, the biomechanical loading models, the bone loss and fracture models and the incidence rate of the activity or event. Uncertainties in the model parameters arise due to variations within the population and unknowns associated with the effects of the space environment. Consequently, parameter distributions were used in Monte Carlo simulations to obtain an estimate of fracture probability under real mission scenarios. The model predicts an increase in the probability of fracture as the mission length increases and fracture is more likely in the higher gravitational field of Mars than on the moon. The resulting probability predictions and sensitivity analyses of the BFxRM can be used as an engineering tool for mission operation and resource planning in order to mitigate the risk of bone fracture in space.

Risk Assessment of Bone Fracture During Space Exploration Missions to the Moon and Mars

NASA Technical Reports Server (NTRS)

Lewandowski, Beth E.; Myers, Jerry G.; Nelson, Emily S.; Griffin, Devon

2008-01-01

The possibility of a traumatic bone fracture in space is a concern due to the observed decrease in astronaut bone mineral density (BMD) during spaceflight and because of the physical demands of the mission. The Bone Fracture Risk Module (BFxRM) was developed to quantify the probability of fracture at the femoral neck and lumbar spine during space exploration missions. The BFxRM is scenario-based, providing predictions for specific activities or events during a particular space mission. The key elements of the BFxRM are the mission parameters, the biomechanical loading models, the bone loss and fracture models and the incidence rate of the activity or event. Uncertainties in the model parameters arise due to variations within the population and unknowns associated with the effects of the space environment. Consequently, parameter distributions were used in Monte Carlo simulations to obtain an estimate of fracture probability under real mission scenarios. The model predicts an increase in the probability of fracture as the mission length increases and fracture is more likely in the higher gravitational field of Mars than on the moon. The resulting probability predictions and sensitivity analyses of the BFxRM can be used as an engineering tool for mission operation and resource planning in order to mitigate the risk of bone fracture in space.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

PubMed

Ding, A Adam; Wu, Hulin

2014-10-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression

PubMed Central

Ding, A. Adam; Wu, Hulin

2015-01-01

We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method. PMID:26401093

Groebner Basis Solutions to Satellite Trajectory Control by Pole Placement

NASA Astrophysics Data System (ADS)

Kukelova, Z.; Krsek, P.; Smutny, V.; Pajdla, T.

2013-09-01

Satellites play an important role, e.g., in telecommunication, navigation and weather monitoring. Controlling their trajectories is an important problem. In [1], an approach to the pole placement for the synthesis of a linear controller has been presented. It leads to solving five polynomial equations in nine unknown elements of the state space matrices of a compensator. This is an underconstrained system and therefore four of the unknown elements need to be considered as free parameters and set to some prior values to obtain a system of five equations in five unknowns. In [1], this system was solved for one chosen set of free parameters with the help of Dixon resultants. In this work, we study and present Groebner basis solutions to this problem of computation of a dynamic compensator for the satellite for different combinations of input free parameters. We show that the Groebner basis method for solving systems of polynomial equations leads to very simple solutions for all combinations of free parameters. These solutions require to perform only the Gauss-Jordan elimination of a small matrix and computation of roots of a single variable polynomial. The maximum degree of this polynomial is not greater than six in general but for most combinations of the input free parameters its degree is even lower. [1] B. Palancz. Application of Dixon resultant to satellite trajectory control by pole placement. Journal of Symbolic Computation, Volume 50, March 2013, Pages 79-99, Elsevier.

A Laboratory Exercise in Physics: Determining Single Capacitances and Series and Parallel Combinations of Capacitance.

ERIC Educational Resources Information Center

Schlenker, Richard M.

This document presents a series of physics experiments which allow students to determine the value of unknown electrical capacitors. The exercises include both parallel and series connected capacitors. (SL)

Method and apparatus for sensor fusion

NASA Technical Reports Server (NTRS)

Krishen, Kumar (Inventor); Shaw, Scott (Inventor); Defigueiredo, Rui J. P. (Inventor)

1991-01-01

Method and apparatus for fusion of data from optical and radar sensors by error minimization procedure is presented. The method was applied to the problem of shape reconstruction of an unknown surface at a distance. The method involves deriving an incomplete surface model from an optical sensor. The unknown characteristics of the surface are represented by some parameter. The correct value of the parameter is computed by iteratively generating theoretical predictions of the radar cross sections (RCS) of the surface, comparing the predicted and the observed values for the RCS, and improving the surface model from results of the comparison. Theoretical RCS may be computed from the surface model in several ways. One RCS prediction technique is the method of moments. The method of moments can be applied to an unknown surface only if some shape information is available from an independent source. The optical image provides the independent information.

Estimating the frequency interval of a regularly spaced multicomponent harmonic line signal in colored noise

NASA Astrophysics Data System (ADS)

Frazer, Gordon J.; Anderson, Stuart J.

1997-10-01

The radar returns from some classes of time-varying point targets can be represented by the discrete-time signal plus noise model: xt equals st plus [vt plus (eta) t] equals (summation)i equals o P minus 1 Aiej2(pi f(i)/f(s)t) plus vt plus (eta) t, t (epsilon) 0, . . ., N minus 1, fi equals kfI plus fo where the received signal xt corresponds to the radar return from the target of interest from one azimuth-range cell. The signal has an unknown number of components, P, unknown complex amplitudes Ai and frequencies fi. The frequency parameters fo and fI are unknown, although constrained such that fo less than fI/2 and parameter k (epsilon) {minus u, . . ., minus 2, minus 1, 0, 1, 2, . . ., v} is constrained such that the component frequencies fi are bound by (minus fs/2, fs/2). The noise term vt, is typically colored, and represents clutter, interference and various noise sources. It is unknown, except that (summation)tvt2 less than infinity; in general, vt is not well modelled as an auto-regressive process of known order. The additional noise term (eta) t represents time-invariant point targets in the same azimuth-range cell. An important characteristic of the target is the unknown parameter, fI, representing the frequency interval between harmonic lines. It is desired to determine an estimate of fI from N samples of xt. We propose an algorithm to estimate fI based on Thomson's harmonic line F-Test, which is part of the multi-window spectrum estimation method and demonstrate the proposed estimator applied to target echo time series collected using an experimental HF skywave radar.

Reconstruction of signals with unknown spectra in information field theory with parameter uncertainty

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

Ensslin, Torsten A.; Frommert, Mona

2011-05-15

The optimal reconstruction of cosmic metric perturbations and other signals requires knowledge of their power spectra and other parameters. If these are not known a priori, they have to be measured simultaneously from the same data used for the signal reconstruction. We formulate the general problem of signal inference in the presence of unknown parameters within the framework of information field theory. To solve this, we develop a generic parameter-uncertainty renormalized estimation (PURE) technique. As a concrete application, we address the problem of reconstructing Gaussian signals with unknown power-spectrum with five different approaches: (i) separate maximum-a-posteriori power-spectrum measurement and subsequentmore » reconstruction, (ii) maximum-a-posteriori reconstruction with marginalized power-spectrum, (iii) maximizing the joint posterior of signal and spectrum, (iv) guessing the spectrum from the variance in the Wiener-filter map, and (v) renormalization flow analysis of the field-theoretical problem providing the PURE filter. In all cases, the reconstruction can be described or approximated as Wiener-filter operations with assumed signal spectra derived from the data according to the same recipe, but with differing coefficients. All of these filters, except the renormalized one, exhibit a perception threshold in case of a Jeffreys prior for the unknown spectrum. Data modes with variance below this threshold do not affect the signal reconstruction at all. Filter (iv) seems to be similar to the so-called Karhune-Loeve and Feldman-Kaiser-Peacock estimators for galaxy power spectra used in cosmology, which therefore should also exhibit a marginal perception threshold if correctly implemented. We present statistical performance tests and show that the PURE filter is superior to the others, especially if the post-Wiener-filter corrections are included or in case an additional scale-independent spectral smoothness prior can be adopted.« less

Physics of Canopy Boundary Layer Resistance for Better Quantification of Sensitivity of Deforestation Scenarios

NASA Astrophysics Data System (ADS)

Ragi, K. B.; Patel, R.

2015-12-01

A great deal of studies focused on deforestation scenarios in the tropical rainforests. Though all these efforts are useful in the understanding of its response to climate, the systematic understanding of uncertainties in representation of physical processes related to vegetation through sensitivity studies is imperative antecedently to understand the real role of vegetation in changing the climate. It is understood that the dense vegetation fluxes energy and moisture to the atmosphere. But, how much a specific process/a group of processes in the surface conditions of a specific area helps flux energy, moisture and tracers is unknown due to lack of process sensitivity studies and uncertain due to malfunctioning of processes. In this presentation, we have found a faulty parameterization, through process sensitivity studies, that would abet in energy and moisture fluxes to the atmosphere. The model we have employed is the Common Land Model2014. The area we have chosen is the Congolese rainforest. We have discovered the flaw in the leaf boundary layer resistance (LBLR), through sensitivity studies in the LSMs, especially in the dense forest regions. This LBLR is over-parameterized with constant heat transfer coefficient and characteristic dimension of leaves; and friction velocity. However, it is too scant because of overlooking of significant complex physics of turbulence and canopy roughness boundary layer to function it realistically. Our sensitivity results show the deficiency of this process and we have formulated canopy boundary layer resistance, instead of LBLR, with depending variables such as LAI, roughness length, vegetation temperature using appropriate thermo-fluid dynamical principles. We are running the sensitivity experiments with new formulations for setting the parameter values for the data not available so far. This effort would lead to better physics for the land-use change studies and demand for the retrieval of new parameters from satellite/field experiments such as leaf mass per area and specific heat capacity of vegetation.

Effectiveness of a 6-month home-based training program in Prader-Willi patients.

PubMed

Vismara, Luca; Cimolin, Veronica; Grugni, Graziano; Galli, Manuela; Parisio, Cinzia; Sibilia, Olivia; Capodaglio, Paolo

2010-01-01

In addition to hypotonia and relative sarcopenia, patients with Prader-Willi syndrome (PWS) show reduced spontaneous physical activity and gait disorders. Scant evidence exists that daily muscle training increases their lean mass and physical activity levels. Whether adequate long-term physical training is feasible and effective in improving muscle function and gait in PWS is still unknown. Eleven adult PWS patients (mean age: 33.8±4.3 years; mean BMI: 43.3±5.9 kg/m(2)) admitted to our hospital were enrolled in this study. During their hospital stay they attended a 2-week rehabilitation program which included supervised exercise sessions. At discharge, Group 1 (6 patients) continued the same exercises at home for 6 months, while Group 2 (5 patients) did not continue home-based training. They were assessed at admission (PRE), at 2 weeks (POST1) and at 6 months (POST2). The assessment consisted of a clinical examination, 3D gait analysis and muscle strength measurement with an isokinetic dynamometer. After 2 weeks of supervised training (POST1), no significant changes in spatial-temporal gait parameters were observed, although significant improvements in ankle dorsal flexion during stance and swing and knee flexor strength were evidenced by 3D gait analysis and dynamometry in all patients. Following 6 months of home training (POST2), Group 1 had showed significant improvements in cadence and reduced knee hyperextension in mid-stance. Ankle plantar and dorsal flexors isokinetic strength had improved significantly at 120° s(-1), whereas Group 2 showed no changes in their spatial-temporal and kinematic parameters. The present study reinforces the idea that even in participants with PWS who present with a distinctive psychological profile, long-term group interventions are feasible and effective in improving their overall physical functioning. Providing an effective and simple home-based training program represents a continuum of the rehabilitation process outside the hospital, which is a crucial issue in chronic conditions. Copyright © 2010 Elsevier Ltd. All rights reserved.

Alaskan Native Games--A Cross-Cultural Addition to the Physical Education Curriculum.

ERIC Educational Resources Information Center

Frey, Richard D.; Allen, Mike

1989-01-01

Importing traditional, yet unknown, physical activities from different cultures is an exciting way to add creativity and imagination to the physical education curriculum. Explanations, accompanied by teaching hints, are given of several traditional Alaskan native games which have been successfully used with K-Six students in the Anchorage School…

Cost-Effectiveness of Ready for Recess to Promote Physical Activity in Children

ERIC Educational Resources Information Center

Wang, Hongmei; Li, Tao; Siahpush, Mohammad; Chen, Li-Wu; Huberty, Jennifer

2017-01-01

Background: Many school-based recess interventions have been shown to be effective in increasing physical activity but their relative efficiency compared to other school-based programs are unknown. This study examined the cost-effectiveness of Ready for Recess, a program designed to increase students' physical activity in 2 elementary schools.…

Are American Children and Youth Fit?: It's Time We Learned

ERIC Educational Resources Information Center

Morrow, James R., Jr.

2005-01-01

The current state of physical fitness in American youth is unknown. While evidence exists that obesity levels are increasing in children and youth, data on declines in physical fitness measures (i.e., cardiorespiratory and musculoskeletal fitness) are lacking. Tracking of physical fitness components has been poorly done. Surveillance of behaviors…

A parameter optimization tool for evaluating the physical consistency of the plot-scale water budget of the integrated eco-hydrological model GEOtop in complex terrain

NASA Astrophysics Data System (ADS)

Bertoldi, Giacomo; Cordano, Emanuele; Brenner, Johannes; Senoner, Samuel; Della Chiesa, Stefano; Niedrist, Georg

2017-04-01

In mountain regions, the plot- and catchment-scale water and energy budgets are controlled by a complex interplay of different abiotic (i.e. topography, geology, climate) and biotic (i.e. vegetation, land management) controlling factors. When integrated, physically-based eco-hydrological models are used in mountain areas, there are a large number of parameters, topographic and boundary conditions that need to be chosen. However, data on soil and land-cover properties are relatively scarce and do not reflect the strong variability at the local scale. For this reason, tools for uncertainty quantification and optimal parameters identification are essential not only to improve model performances, but also to identify most relevant parameters to be measured in the field and to evaluate the impact of different assumptions for topographic and boundary conditions (surface, lateral and subsurface water and energy fluxes), which are usually unknown. In this contribution, we present the results of a sensitivity analysis exercise for a set of 20 experimental stations located in the Italian Alps, representative of different conditions in terms of topography (elevation, slope, aspect), land use (pastures, meadows, and apple orchards), soil type and groundwater influence. Besides micrometeorological parameters, each station provides soil water content at different depths, and in three stations (one for each land cover) eddy covariance fluxes. The aims of this work are: (I) To present an approach for improving calibration of plot-scale soil moisture and evapotranspiration (ET). (II) To identify the most sensitive parameters and relevant factors controlling temporal and spatial differences among sites. (III) Identify possible model structural deficiencies or uncertainties in boundary conditions. Simulations have been performed with the GEOtop 2.0 model, which is a physically-based, fully distributed integrated eco-hydrological model that has been specifically designed for mountain regions, since it considers the effect of topography on radiation and water fluxes and integrates a snow module. A new automatic sensitivity and optimization tool based on the Particle Swarm Optimization theory has been developed, available as R package on https://github.com/EURAC-Ecohydro/geotopOptim2. The model, once calibrated for soil and vegetation parameters, predicts the plot-scale temporal SMC dynamics of SMC and ET with a RMSE of about 0.05 m3/m3 and 40 W/m2, respectively. However, the model tends to underestimate ET during summer months over apple orchards. Results show how most sensitive parameters are both soil and canopy structural properties. However, ranking is affected by the choice of the target function and local topographic conditions. In particular, local slope/aspect influences results in stations located over hillslopes, but with marked seasonal differences. Results for locations in the valley floor are strongly controlled by the choice of the bottom water flux boundary condition. The poorer model performances in simulating ET over apple orchards could be explained by a model structural deficiency in representing the stomatal control on vapor pressure deficit for this particular type of vegetation. The results of this sensitivity could be extended to other physically distributed models, and also provide valuable insights for optimizing new experimental designs.

Cosmology with interaction in the dark sector

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

Costa, F. E. M.; Barboza, E. M. Jr.; Alcaniz, J. S.

2009-06-15

Unless some unknown symmetry in nature prevents or suppresses a nonminimal coupling in the dark sector, the dark energy field may interact with the pressureless component of dark matter. In this paper, we investigate some cosmological consequences of a general model of interacting dark matter-dark energy characterized by a dimensionless parameter {epsilon}. We derive a coupled scalar field version for this general class of scenarios and carry out a joint statistical analysis involving type Ia supernovae data (Legacy and Constitution sets), measurements of baryon acoustic oscillation peaks at z=0.20 (2dFGRS) and z=0.35 (SDSS), and measurements of the Hubble evolution H(z).more » For the specific case of vacuum decay (w=-1), we find that, although physically forbidden, a transfer of energy from dark matter to dark energy is favored by the data.« less

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh–Nagumo Neurons under Direction-Dependent Coupling

PubMed Central

Iqbal, Muhammad; Rehan, Muhammad; Hong, Keum-Shik

2018-01-01

This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh–Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the synchronization of uncertain and perturbed neurons, communicating in a medium of bidirectional coupling. The neurons are assumed to be different and interconnected in a ring structure. The strength of the gap junctions is taken to be different for each link in the network, owing to the inter-neuronal coupling medium properties. Robust adaptive control mechanism based on Lyapunov stability analysis is employed and theoretical criteria are derived to realize the synchronization of the network of four FHN neurons in a ring form with unknown parameters under direction-dependent coupling and disturbances. The proposed scheme for synchronization of dissimilar neurons, under external electrical stimuli, coupled in a ring communication topology, having all parameters unknown, and subject to directional coupling medium and perturbations, is addressed for the first time as per our knowledge. To demonstrate the efficacy of the proposed strategy, simulation results are provided. PMID:29535622

A highly precise frequency-based method for estimating the tension of an inclined cable with unknown boundary conditions

NASA Astrophysics Data System (ADS)

Ma, Lin

2017-11-01

This paper develops a method for precisely determining the tension of an inclined cable with unknown boundary conditions. First, the nonlinear motion equation of an inclined cable is derived, and a numerical model of the motion of the cable is proposed using the finite difference method. The proposed numerical model includes the sag-extensibility, flexural stiffness, inclination angle and rotational stiffness at two ends of the cable. Second, the influence of the dynamic parameters of the cable on its frequencies is discussed in detail, and a method for precisely determining the tension of an inclined cable is proposed based on the derivatives of the eigenvalues of the matrices. Finally, a multiparameter identification method is developed that can simultaneously identify multiple parameters, including the rotational stiffness at two ends. This scheme is applicable to inclined cables with varying sag, varying flexural stiffness and unknown boundary conditions. Numerical examples indicate that the method provides good precision. Because the parameters of cables other than tension (e.g., the flexural stiffness and rotational stiffness at the ends) are not accurately known in practical engineering, the multiparameter identification method could further improve the accuracy of cable tension measurements.

Reciprocating and Screw Compressor semi-empirical models for establishing minimum energy performance standards

NASA Astrophysics Data System (ADS)

Javed, Hassan; Armstrong, Peter

2015-08-01

The efficiency bar for a Minimum Equipment Performance Standard (MEPS) generally aims to minimize energy consumption and life cycle cost of a given chiller type and size category serving a typical load profile. Compressor type has a significant chiller performance impact. Performance of screw and reciprocating compressors is expressed in terms of pressure ratio and speed for a given refrigerant and suction density. Isentropic efficiency for a screw compressor is strongly affected by under- and over-compression (UOC) processes. The theoretical simple physical UOC model involves a compressor-specific (but sometimes unknown) volume index parameter and the real gas properties of the refrigerant used. Isentropic efficiency is estimated by the UOC model and a bi-cubic, used to account for flow, friction and electrical losses. The unknown volume index, a smoothing parameter (to flatten the UOC model peak) and bi-cubic coefficients are identified by curve fitting to minimize an appropriate residual norm. Chiller performance maps are produced for each compressor type by selecting optimized sub-cooling and condenser fan speed options in a generic component-based chiller model. SEER is the sum of hourly load (from a typical building in the climate of interest) and specific power for the same hourly conditions. An empirical UAE cooling load model, scalable to any equipment capacity, is used to establish proposed UAE MEPS. Annual electricity use and cost, determined from SEER and annual cooling load, and chiller component cost data are used to find optimal chiller designs and perform life-cycle cost comparison between screw and reciprocating compressor-based chillers. This process may be applied to any climate/load model in order to establish optimized MEPS for any country and/or region.

K→(ππ)(I=2) decay amplitude from lattice QCD.

PubMed

Blum, T; Boyle, P A; Christ, N H; Garron, N; Goode, E; Izubuchi, T; Jung, C; Kelly, C; Lehner, C; Lightman, M; Liu, Q; Lytle, A T; Mawhinney, R D; Sachrajda, C T; Soni, A; Sturm, C

2012-04-06

We report on the first realistic ab initio calculation of a hadronic weak decay, that of the amplitude A(2) for a kaon to decay into two π mesons with isospin 2. We find ReA(2)=(1.436±0.063(stat)±0.258(syst))10(-8) GeV in good agreement with the experimental result and for the hitherto unknown imaginary part we find ImA(2)=-(6.83±0.51(stat)±1.30(syst))10(-13) GeV. Moreover combining our result for ImA(2) with experimental values of ReA(2), ReA(0), and ε'/ε, we obtain the following value for the unknown ratio ImA(0)/ReA(0) within the standard model: ImA(0)/ReA(0)=-1.63(19)(stat)(20(syst)×10(-4). One consequence of these results is that the contribution from ImA(2) to the direct CP violation parameter ε' (the so-called Electroweak Penguin contribution) is Re(ε'/ε)(EWP)=-(6.52±0.49(stat)±1.24(syst))×10(-4). We explain why this calculation of A(2) represents a major milestone for lattice QCD and discuss the exciting prospects for a full quantitative understanding of CP violation in kaon decays. © 2012 American Physical Society

Robust Control Design for Uncertain Nonlinear Dynamic Systems

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Andrews, Lindsey; Giesy, Daniel P.

2012-01-01

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Parameter and state estimation in a Neisseria meningitidis model: A study case of Niger

NASA Astrophysics Data System (ADS)

Bowong, S.; Mountaga, L.; Bah, A.; Tewa, J. J.; Kurths, J.

2016-12-01

Neisseria meningitidis (Nm) is a major cause of bacterial meningitidis outbreaks in Africa and the Middle East. The availability of yearly reported meningitis cases in the African meningitis belt offers the opportunity to analyze the transmission dynamics and the impact of control strategies. In this paper, we propose a method for the estimation of state variables that are not accessible to measurements and an unknown parameter in a Nm model. We suppose that the yearly number of Nm induced mortality and the total population are known inputs, which can be obtained from data, and the yearly number of new Nm cases is the model output. We also suppose that the Nm transmission rate is an unknown parameter. We first show how the recruitment rate into the population can be estimated using real data of the total population and Nm induced mortality. Then, we use an auxiliary system called observer whose solutions converge exponentially to those of the original model. This observer does not use the unknown infection transmission rate but only uses the known inputs and the model output. This allows us to estimate unmeasured state variables such as the number of carriers that play an important role in the transmission of the infection and the total number of infected individuals within a human community. Finally, we also provide a simple method to estimate the unknown Nm transmission rate. In order to validate the estimation results, numerical simulations are conducted using real data of Niger.

Guaranteeing robustness of structural condition monitoring to environmental variability

NASA Astrophysics Data System (ADS)

Van Buren, Kendra; Reilly, Jack; Neal, Kyle; Edwards, Harry; Hemez, François

2017-01-01

Advances in sensor deployment and computational modeling have allowed significant strides to be recently made in the field of Structural Health Monitoring (SHM). One widely used SHM strategy is to perform a vibration analysis where a model of the structure's pristine (undamaged) condition is compared with vibration response data collected from the physical structure. Discrepancies between model predictions and monitoring data can be interpreted as structural damage. Unfortunately, multiple sources of uncertainty must also be considered in the analysis, including environmental variability, unknown model functional forms, and unknown values of model parameters. Not accounting for these sources of uncertainty can lead to false-positives or false-negatives in the structural condition assessment. To manage the uncertainty, we propose a robust SHM methodology that combines three technologies. A time series algorithm is trained using "baseline" data to predict the vibration response, compare predictions to actual measurements collected on a potentially damaged structure, and calculate a user-defined damage indicator. The second technology handles the uncertainty present in the problem. An analysis of robustness is performed to propagate this uncertainty through the time series algorithm and obtain the corresponding bounds of variation of the damage indicator. The uncertainty description and robustness analysis are both inspired by the theory of info-gap decision-making. Lastly, an appropriate "size" of the uncertainty space is determined through physical experiments performed in laboratory conditions. Our hypothesis is that examining how the uncertainty space changes throughout time might lead to superior diagnostics of structural damage as compared to only monitoring the damage indicator. This methodology is applied to a portal frame structure to assess if the strategy holds promise for robust SHM. (Publication approved for unlimited, public release on October-28-2015, LA-UR-15-28442, unclassified.)

Parameter Estimation for a Turbulent Buoyant Jet Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Christopher, Jason D.; Wimer, Nicholas T.; Hayden, Torrey R. S.; Lapointe, Caelan; Grooms, Ian; Rieker, Gregory B.; Hamlington, Peter E.

2016-11-01

Approximate Bayesian Computation (ABC) is a powerful tool that allows sparse experimental or other "truth" data to be used for the prediction of unknown model parameters in numerical simulations of real-world engineering systems. In this presentation, we introduce the ABC approach and then use ABC to predict unknown inflow conditions in simulations of a two-dimensional (2D) turbulent, high-temperature buoyant jet. For this test case, truth data are obtained from a simulation with known boundary conditions and problem parameters. Using spatially-sparse temperature statistics from the 2D buoyant jet truth simulation, we show that the ABC method provides accurate predictions of the true jet inflow temperature. The success of the ABC approach in the present test suggests that ABC is a useful and versatile tool for engineering fluid dynamics research.

"Physics is supposed to be spoken of with a bit of irony": unknown speech by L D Landau, 8 April 1960

NASA Astrophysics Data System (ADS)

Druzhinin, P. A.

2018-01-01

A previously unknown speech by Lev Landau dated 8 April 1960 has been published, as transcribed from a unique tape recording obtained from the Russian State Phonogram Archive (Moscow). This is Landau's only true public speech known to have been recorded.

Rhelogical constraints on ridge formation on Icy Satellites

NASA Astrophysics Data System (ADS)

Rudolph, M. L.; Manga, M.

2010-12-01

The processes responsible for forming ridges on Europa remain poorly understood. We use a continuum damage mechanics approach to model ridge formation. The main objectives of this contribution are to constrain (1) choice of rheological parameters and (2) maximum ridge size and rate of formation. The key rheological parameters to constrain appear in the evolution equation for a damage variable (D): ˙ {D} = B <<σ >>r}(1-D){-k-α D (p)/(μ ) and in the equation relating damage accumulation to volumetric changes, Jρ 0 = δ (1-D). Similar damage evolution laws have been applied to terrestrial glaciers and to the analysis of rock mechanics experiments. However, it is reasonable to expect that, like viscosity, the rheological constants B, α , and δ depend strongly on temperature, composition, and ice grain size. In order to determine whether the damage model is appropriate for Europa’s ridges, we must find values of the unknown damage parameters that reproduce ridge topography. We perform a suite of numerical experiments to identify the region of parameter space conducive to ridge production and show the sensitivity to changes in each unknown parameter.

Systematic Review of Service-Learning in Youth Physical Activity Settings

ERIC Educational Resources Information Center

Carson, Russell L.; Raguse, Allison L.

2014-01-01

The extent to which service-learning exists in the field of kinesiology broadly, and more specifically related to the physical activity of youth, remains largely unknown. The purpose of this study was to conduct a systematic review of the service-learning literature in kinesiology, with a specific focus on youth physical activity settings.…

The Impact of Participation in Extra-Curricular Physical Activity on Males from Disadvantaged Schools

ERIC Educational Resources Information Center

Belton, Sarahjane; Prior, Paul; Wickel, Eric; Woods, Catherine

2017-01-01

Extra-curricular physical activity (ECPA) may have an important role to play in the health and well-being of adolescents, but the actual benefits are relatively unknown. This study examined ECPA participation amongst adolescent males (age 12-15 years) from disadvantaged backgrounds, and evaluated its impact on overall physical activity (PA)…

Relationships between lifestyle patterns and cardio-renal-metabolic parameters in patients with type 2 diabetes mellitus: A cross-sectional study

PubMed Central

Ogihara, Takeshi; Osonoi, Yusuke; Osonoi, Takeshi; Saito, Miyoko; Tamasawa, Atsuko; Nakayama, Shiho; Someya, Yuki; Ishida, Hidenori; Gosho, Masahiko; Kanazawa, Akio; Watada, Hirotaka

2017-01-01

Introduction While individuals tend to show accumulation of certain lifestyle patterns, the effect of such patterns in real daily life on cardio-renal—metabolic parameters remains largely unknown. This study aimed to assess clustering of lifestyle patterns and investigate the relationships between such patterns and cardio-renal-metabolic parameters. Participants and methods The study participants were 726 Japanese type 2 diabetes mellitus (T2DM) outpatients free of history of cardiovascular diseases. The relationship between lifestyle patterns and cardio-renal-metabolic parameters was investigated by linear and logistic regression analyses. Results Factor analysis identified three lifestyle patterns. Subjects characterized by evening type, poor sleep quality and depressive status (type 1 pattern) had high levels of HbA1c, alanine aminotransferase and albuminuria. Subjects characterized by high consumption of food, alcohol and cigarettes (type 2 pattern) had high levels of γ-glutamyl transpeptidase, triglycerides, HDL-cholesterol, blood pressure, and brachial-ankle pulse wave velocity. Subjects characterized by high physical activity (type 3 pattern) had low uric acid and mild elevation of alanine aminotransferase and aspartate aminotransferase. In multivariate regression analysis adjusted by age, gender and BMI, type 1 pattern was associated with higher HbA1c levels, systolic BP and brachial-ankle pulse wave velocity. Type 2 pattern was associated with higher HDL-cholesterol levels, triglycerides, aspartate aminotransferase, ɤ- glutamyl transpeptidase levels, and diastolic BP. Conclusions The study identified three lifestyle patterns that were associated with distinct cardio-metabolic-renal parameters in T2DM patients. Trial registration UMIN000010932 PMID:28273173

Parameter Estimation for a Pulsating Turbulent Buoyant Jet Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Christopher, Jason; Wimer, Nicholas; Lapointe, Caelan; Hayden, Torrey; Grooms, Ian; Rieker, Greg; Hamlington, Peter

2017-11-01

Approximate Bayesian Computation (ABC) is a powerful tool that allows sparse experimental or other ``truth'' data to be used for the prediction of unknown parameters, such as flow properties and boundary conditions, in numerical simulations of real-world engineering systems. Here we introduce the ABC approach and then use ABC to predict unknown inflow conditions in simulations of a two-dimensional (2D) turbulent, high-temperature buoyant jet. For this test case, truth data are obtained from a direct numerical simulation (DNS) with known boundary conditions and problem parameters, while the ABC procedure utilizes lower fidelity large eddy simulations. Using spatially-sparse statistics from the 2D buoyant jet DNS, we show that the ABC method provides accurate predictions of true jet inflow parameters. The success of the ABC approach in the present test suggests that ABC is a useful and versatile tool for predicting flow information, such as boundary conditions, that can be difficult to determine experimentally.

Distributed weighted least-squares estimation with fast convergence for large-scale systems.

PubMed

Marelli, Damián Edgardo; Fu, Minyue

2015-01-01

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods.

Distributed weighted least-squares estimation with fast convergence for large-scale systems☆

PubMed Central

Marelli, Damián Edgardo; Fu, Minyue

2015-01-01

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods. PMID:25641976

Modern control concepts in hydrology

NASA Technical Reports Server (NTRS)

Duong, N.; Johnson, G. R.; Winn, C. B.

1974-01-01

Two approaches to an identification problem in hydrology are presented based upon concepts from modern control and estimation theory. The first approach treats the identification of unknown parameters in a hydrologic system subject to noisy inputs as an adaptive linear stochastic control problem; the second approach alters the model equation to account for the random part in the inputs, and then uses a nonlinear estimation scheme to estimate the unknown parameters. Both approaches use state-space concepts. The identification schemes are sequential and adaptive and can handle either time invariant or time dependent parameters. They are used to identify parameters in the Prasad model of rainfall-runoff. The results obtained are encouraging and conform with results from two previous studies; the first using numerical integration of the model equation along with a trial-and-error procedure, and the second, by using a quasi-linearization technique. The proposed approaches offer a systematic way of analyzing the rainfall-runoff process when the input data are imbedded in noise.

Bootstrap Standard Errors for Maximum Likelihood Ability Estimates When Item Parameters Are Unknown

ERIC Educational Resources Information Center

Patton, Jeffrey M.; Cheng, Ying; Yuan, Ke-Hai; Diao, Qi

2014-01-01

When item parameter estimates are used to estimate the ability parameter in item response models, the standard error (SE) of the ability estimate must be corrected to reflect the error carried over from item calibration. For maximum likelihood (ML) ability estimates, a corrected asymptotic SE is available, but it requires a long test and the…

The relationship between physical activity, physical fitness and overweight in adolescents: a systematic review of studies published in or after 2000

PubMed Central

2013-01-01

Background Not only in adults but also in children and adolescents, obesity increases the risk for several health disorders. In turn, many factors including genetic variations and environmental influences (e.g. physical activity) increase the risk of obesity. For instance, 25 to 40 percent of people inherit a predisposition for a high body mass index (BMI). The purpose of this systematic review was to summarize current cross-sectional and longitudinal studies on physical activity, fitness and overweight in adolescents and to identify mediator and moderator effects by evaluating the interaction between these three parameters. Methods The electronic academic databases PubMed, SportDiscus, WEB OF KNOWLEDGE and Ovid were searched for studies on physical activity, fitness and overweight in adolescents aged 11 to 19 years (cross-sectional studies) and in adolescents up to 23 years old (longitudinal studies) published in English in or after 2000. Results Twelve cross-sectional and two longitudinal studies were included. Only four studies analyzed the interaction among physical activity, fitness and overweight in adolescents and reported inconsistent results. All other studies analyzed the relationship between either physical activity and overweight, or between fitness and overweight. Overweight—here including obesity—was inversely related to physical activity. Similarly, all studies reported inverse relations between physical fitness and overweight. Mediator and moderator effects were detected in the interrelationship of BMI, fitness and physical activity. Overall, a distinction of excessive body weight as cause or effect of low levels of physical activity and fitness is lacking. Conclusions The small number of studies on the interrelationship of BMI, fitness and physical activity emphasizes the need for longitudinal studies that would reveal 1) the causality between physical activity and overweight / fitness and overweight and 2) the causal interrelationships among overweight, physical activity and fitness. These results must be carefully interpreted given the lack of distinction between self-reported and objective physical activity and that studies analyzing the metabolic syndrome or cardiovascular disease were not considered. The importance of physical activity or fitness in predicting overweight remains unknown. PMID:23375072

1r2dinv: A finite-difference model for inverse analysis of two dimensional linear or radial groundwater flow

USGS Publications Warehouse

Bohling, Geoffrey C.; Butler, J.J.

2001-01-01

We have developed a program for inverse analysis of two-dimensional linear or radial groundwater flow problems. The program, 1r2dinv, uses standard finite difference techniques to solve the groundwater flow equation for a horizontal or vertical plane with heterogeneous properties. In radial mode, the program simulates flow to a well in a vertical plane, transforming the radial flow equation into an equivalent problem in Cartesian coordinates. The physical parameters in the model are horizontal or x-direction hydraulic conductivity, anisotropy ratio (vertical to horizontal conductivity in a vertical model, y-direction to x-direction in a horizontal model), and specific storage. The program allows the user to specify arbitrary and independent zonations of these three parameters and also to specify which zonal parameter values are known and which are unknown. The Levenberg-Marquardt algorithm is used to estimate parameters from observed head values. Particularly powerful features of the program are the ability to perform simultaneous analysis of heads from different tests and the inclusion of the wellbore in the radial mode. These capabilities allow the program to be used for analysis of suites of well tests, such as multilevel slug tests or pumping tests in a tomographic format. The combination of information from tests stressing different vertical levels in an aquifer provides the means for accurately estimating vertical variations in conductivity, a factor profoundly influencing contaminant transport in the subsurface. ?? 2001 Elsevier Science Ltd. All rights reserved.

The contribution of NOAA/CMDL ground-based measurements to understanding long-term stratospheric changes

NASA Astrophysics Data System (ADS)

Montzka, S. A.; Butler, J. H.; Dutton, G.; Thompson, T. M.; Hall, B.; Mondeel, D. J.; Elkins, J. W.

2005-05-01

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

State, Parameter, and Unknown Input Estimation Problems in Active Automotive Safety Applications

NASA Astrophysics Data System (ADS)

Phanomchoeng, Gridsada

A variety of driver assistance systems such as traction control, electronic stability control (ESC), rollover prevention and lane departure avoidance systems are being developed by automotive manufacturers to reduce driver burden, partially automate normal driving operations, and reduce accidents. The effectiveness of these driver assistance systems can be significant enhanced if the real-time values of several vehicle parameters and state variables, namely tire-road friction coefficient, slip angle, roll angle, and rollover index, can be known. Since there are no inexpensive sensors available to measure these variables, it is necessary to estimate them. However, due to the significant nonlinear dynamics in a vehicle, due to unknown and changing plant parameters, and due to the presence of unknown input disturbances, the design of estimation algorithms for this application is challenging. This dissertation develops a new approach to observer design for nonlinear systems in which the nonlinearity has a globally (or locally) bounded Jacobian. The developed approach utilizes a modified version of the mean value theorem to express the nonlinearity in the estimation error dynamics as a convex combination of known matrices with time varying coefficients. The observer gains are then obtained by solving linear matrix inequalities (LMIs). A number of illustrative examples are presented to show that the developed approach is less conservative and more useful than the standard Lipschitz assumption based nonlinear observer. The developed nonlinear observer is utilized for estimation of slip angle, longitudinal vehicle velocity, and vehicle roll angle. In order to predict and prevent vehicle rollovers in tripped situations, it is necessary to estimate the vertical tire forces in the presence of unknown road disturbance inputs. An approach to estimate unknown disturbance inputs in nonlinear systems using dynamic model inversion and a modified version of the mean value theorem is presented. The developed theory is used to estimate vertical tire forces and predict tripped rollovers in situations involving road bumps, potholes, and lateral unknown force inputs. To estimate the tire-road friction coefficients at each individual tire of the vehicle, algorithms to estimate longitudinal forces and slip ratios at each tire are proposed. Subsequently, tire-road friction coefficients are obtained using recursive least squares parameter estimators that exploit the relationship between longitudinal force and slip ratio at each tire. The developed approaches are evaluated through simulations with industry standard software, CARSIM, with experimental tests on a Volvo XC90 sport utility vehicle and with experimental tests on a 1/8th scaled vehicle. The simulation and experimental results show that the developed approaches can reliably estimate the vehicle parameters and state variables needed for effective ESC and rollover prevention applications.

PHYSICS OF OUR DAYS: Dark energy and universal antigravitation

NASA Astrophysics Data System (ADS)

Chernin, A. D.

2008-03-01

Universal antigravitation, a new physical phenomenon discovered astronomically at distances of 5 to 8 billion light years, manifests itself as cosmic repulsion that acts between distant galaxies and overcomes their gravitational attraction, resulting in the accelerating expansion of the Universe. The source of the antigravitation is not galaxies or any other bodies of nature but a previously unknown form of mass/energy that has been termed dark energy. Dark energy accounts for 70 to 80% of the total mass and energy of the Universe and, in macroscopic terms, is a kind of continuous medium that fills the entire space of the Universe and is characterized by positive density and negative pressure. With its physical nature and microscopic structure unknown, dark energy is among the most critical challenges fundamental science faces in the twenty-first century.

Application of identified sensitive physical parameters in reducing the uncertainty of numerical simulation

NASA Astrophysics Data System (ADS)

Sun, Guodong; Mu, Mu

2016-04-01

An important source of uncertainty, which then causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. There are many physical parameters in numerical models in the atmospheric and oceanic sciences, and it would cost a great deal to reduce uncertainties in all physical parameters. Therefore, finding a subset of these parameters, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund-Potsdam-Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach. The results imply that nonlinear interactions among parameters play a key role in the uncertainty of numerical simulations in arid and semi-arid regions of China compared to those in northern, northeastern and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply "target observations" to reduce the uncertainties in model parameters.

A multiwave range test for obstacle reconstructions with unknown physical properties

NASA Astrophysics Data System (ADS)

Potthast, Roland; Schulz, Jochen

2007-08-01

We develop a new multiwave version of the range test for shape reconstruction in inverse scattering theory. The range test [R. Potthast, et al., A `range test' for determining scatterers with unknown physical properties, Inverse Problems 19(3) (2003) 533-547] has originally been proposed to obtain knowledge about an unknown scatterer when the far field pattern for only one plane wave is given. Here, we extend the method to the case of multiple waves and show that the full shape of the unknown scatterer can be reconstructed. We further will clarify the relation between the range test methods, the potential method [A. Kirsch, R. Kress, On an integral equation of the first kind in inverse acoustic scattering, in: Inverse Problems (Oberwolfach, 1986), Internationale Schriftenreihe zur Numerischen Mathematik, vol. 77, Birkhauser, Basel, 1986, pp. 93-102] and the singular sources method [R. Potthast, Point sources and multipoles in inverse scattering theory, Habilitation Thesis, Gottingen, 1999]. In particular, we propose a new version of the Kirsch-Kress method using the range test and a new approach to the singular sources method based on the range test and potential method. Numerical examples of reconstructions for all four methods are provided.

Scenario-Based Case Study Analysis of Asteroid Mitigation in the Short Response Time Regime

NASA Astrophysics Data System (ADS)

Seery, B.; Greenaugh, K. C.

2017-12-01

Asteroid impact on Earth is a rare but inevitable occurrence, with potentially cataclysmic consequences. If a pending impact is discovered, mitigation options include civil-defense preparations as well as missions to deflect the asteroid and/or robustly disrupt and disperse it to an extent that only a negligible fraction remains on a threatening path (National Research Council's "Defending the Planet," 2010). If discovered with sufficient warning time, a kinetic impactor can deflect smaller objects, but response delays can rule out the option. If a body is too large to deflect by kinetic impactor, or the time for response is insufficient, deflection or disruption can be achieved with a nuclear device. The use of nuclear ablation is considered within the context of current capabilities, requiring no need for nuclear testing. Existing, well-understood devices are sufficient for the largest known Potentially Hazardous Objects (PHOs). The National Aeronautics and Space Administration/Goddard Space Flight Center and the Department of Energy/National Nuclear Security Administration are collaborating to determine the critical characterization issues that define the boundaries for the asteroid-deflection options. Drawing from such work, we examine the timeline for a deflection mission, and how to provide the best opportunity for an impactor to suffice by minimizing the response time. This integrated problem considers the physical process of the deflection method (impact or ablation), along with the spacecraft, launch capability, risk analysis, and the available intercept flight trajectories. Our joint DOE/NASA team has conducted case study analysis of three distinctly different PHOs, on a hypothetical earth impacting trajectory. The size of the design reference bodies ranges from 100 - 500 meters in diameter, with varying physical parameters such as composition, spin state, and metallicity, to name a few. We assemble the design reference of the small body in question using known values for key parameters and expert elicitation to make educated guesses on the unknown parameters, including an estimate of the overall uncertainties in those values. Our scenario-based systems approach includes 2-D and 3-D physics-based modeling and simulations.

Low Physical Fitness Levels in Older Adults with ID: Results of the HA-ID Study

ERIC Educational Resources Information Center

Hilgenkamp, Thessa I. M.; van Wijck, Ruud; Evenhuis, Heleen M.

2012-01-01

Physical fitness is as important to aging adults with ID as in the general population, but to date, the physical fitness levels of this group are unknown. Comfortable walking speed, muscle strength (grip strength), muscle endurance (30 s Chair stand) and cardiorespiratory endurance (10 m incremental shuttle walking test) were tested in a sample of…

A study of parameter identification

NASA Technical Reports Server (NTRS)

Herget, C. J.; Patterson, R. E., III

1978-01-01

A set of definitions for deterministic parameter identification ability were proposed. Deterministic parameter identificability properties are presented based on four system characteristics: direct parameter recoverability, properties of the system transfer function, properties of output distinguishability, and uniqueness properties of a quadratic cost functional. Stochastic parameter identifiability was defined in terms of the existence of an estimation sequence for the unknown parameters which is consistent in probability. Stochastic parameter identifiability properties are presented based on the following characteristics: convergence properties of the maximum likelihood estimate, properties of the joint probability density functions of the observations, and properties of the information matrix.

Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space

PubMed Central

Chen, Min; Hashimoto, Koichi

2017-01-01

Motivated by biological interests in analyzing navigation behaviors of flying animals, we attempt to build a system measuring their motion states. To do this, in this paper, we build a vision system to detect unknown fast moving objects within a given space, calculating their motion parameters represented by positions and poses. We proposed a novel method to detect reliable interest points from images of moving objects, which can be hardly detected by general purpose interest point detectors. 3D points reconstructed using these interest points are then grouped and maintained for detected objects, according to a careful schedule, considering appearance and perspective changes. In the estimation step, a method is introduced to adapt the robust estimation procedure used for dense point set to the case for sparse set, reducing the potential risk of greatly biased estimation. Experiments are conducted against real scenes, showing the capability of the system of detecting multiple unknown moving objects and estimating their positions and poses. PMID:29206189

Design and analysis of adaptive Super-Twisting sliding mode control for a microgyroscope.

PubMed

Feng, Zhilin; Fei, Juntao

2018-01-01

This paper proposes a novel adaptive Super-Twisting sliding mode control for a microgyroscope under unknown model uncertainties and external disturbances. In order to improve the convergence rate of reaching the sliding surface and the accuracy of regulating and trajectory tracking, a high order Super-Twisting sliding mode control strategy is employed, which not only can combine the advantages of the traditional sliding mode control with the Super-Twisting sliding mode control, but also guarantee that the designed control system can reach the sliding surface and equilibrium point in a shorter finite time from any initial state and avoid chattering problems. In consideration of unknown parameters of micro gyroscope system, an adaptive algorithm based on Lyapunov stability theory is designed to estimate the unknown parameters and angular velocity of microgyroscope. Finally, the effectiveness of the proposed scheme is demonstrated by simulation results. The comparative study between adaptive Super-Twisting sliding mode control and conventional sliding mode control demonstrate the superiority of the proposed method.

Comparing Different Approaches of Bias Correction for Ability Estimation in IRT Models. Research Report. ETS RR-08-13

ERIC Educational Resources Information Center

Lee, Yi-Hsuan; Zhang, Jinming

2008-01-01

The method of maximum-likelihood is typically applied to item response theory (IRT) models when the ability parameter is estimated while conditioning on the true item parameters. In practice, the item parameters are unknown and need to be estimated first from a calibration sample. Lewis (1985) and Zhang and Lu (2007) proposed the expected response…

Extensions of Rasch's Multiplicative Poisson Model.

ERIC Educational Resources Information Center

Jansen, Margo G. H.; van Duijn, Marijtje A. J.

1992-01-01

A model developed by G. Rasch that assumes scores on some attainment tests can be realizations of a Poisson process is explained and expanded by assuming a prior distribution, with fixed but unknown parameters, for the subject parameters. How additional between-subject and within-subject factors can be incorporated is discussed. (SLD)

Cookbook asymptotics for spiral and scroll waves in excitable media.

PubMed

Margerit, Daniel; Barkley, Dwight

2002-09-01

Algebraic formulas predicting the frequencies and shapes of waves in a reaction-diffusion model of excitable media are presented in the form of four recipes. The formulas themselves are based on a detailed asymptotic analysis (published elsewhere) of the model equations at leading order and first order in the asymptotic parameter. The importance of the first order contribution is stressed throughout, beginning with a discussion of the Fife limit, Fife scaling, and Fife regime. Recipes are given for spiral waves and detailed comparisons are presented between the asymptotic predictions and the solutions of the full reaction-diffusion equations. Recipes for twisted scroll waves with straight filaments are given and again comparisons are shown. The connection between the asymptotic results and filament dynamics is discussed, and one of the previously unknown coefficients in the theory of filament dynamics is evaluated in terms of its asymptotic expansion. (c) 2002 American Institute of Physics.

Analysis of structural response data using discrete modal filters. M.S. Thesis

NASA Technical Reports Server (NTRS)

Freudinger, Lawrence C.

1991-01-01

The application of reciprocal modal vectors to the analysis of structural response data is described. Reciprocal modal vectors are constructed using an existing experimental modal model and an existing frequency response matrix of a structure, and can be assembled into a matrix that effectively transforms the data from the physical space to a modal space within a particular frequency range. In other words, the weighting matrix necessary for modal vector orthogonality (typically the mass matrix) is contained within the reciprocal model matrix. The underlying goal of this work is mostly directed toward observing the modal state responses in the presence of unknown, possibly closed loop forcing functions, thus having an impact on both operating data analysis techniques and independent modal space control techniques. This study investigates the behavior of reciprocol modal vectors as modal filters with respect to certain calculation parameters and their performance with perturbed system frequency response data.

MECHANISMS IN ENDOCRINOLOGY: Mechanisms and evaluation of bone fragility in type 1 diabetes mellitus.

PubMed

Hough, F S; Pierroz, D D; Cooper, C; Ferrari, S L

2016-04-01

Subjects with type 1 diabetes mellitus (T1DM) have decreased bone mineral density and an up to sixfold increase in fracture risk. Yet bone fragility is not commonly regarded as another unique complication of diabetes. Both animals with experimentally induced insulin deficiency syndromes and patients with T1DM have impaired osteoblastic bone formation, with or without increased bone resorption. Insulin/IGF1 deficiency appears to be a major pathogenetic mechanism involved, along with glucose toxicity, marrow adiposity, inflammation, adipokine and other metabolic alterations that may all play a role on altering bone turnover. In turn, increasing physical activity in children with diabetes as well as good glycaemic control appears to provide some improvement of bone parameters, although robust clinical studies are still lacking. In this context, the role of osteoporosis drugs remains unknown. © 2016 European Society of Endocrinology.

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

Fang, Ke; Linden, Tim, E-mail: kefang@umd.edu, E-mail: linden.70@osu.edu

Radio observations at multiple frequencies have detected a significant isotropic emission component between 22 MHz and 10 GHz, commonly termed the ARCADE-2 Excess. The origin of this radio emission is unknown, as the intensity, spectrum and isotropy of the signal are difficult to model with either traditional astrophysical mechanisms or novel physics such as dark matter annihilation. We posit a new model capable of explaining the key components of the excess radio emission. Specifically, we show that the re-acceleration of non-thermal electrons via turbulence in merging galaxy clusters are capable of explaining the intensity, spectrum, and isotropy of the ARCADE-2more » data. We examine the parameter spaces of cluster re-acceleration, magnetic field, and merger rate, finding that the radio excess can be reproduced assuming reasonable assumptions for each. Finally, we point out that future observations will definitively confirm or rule-out the contribution of cluster mergers to the isotropic radio background.« less

Two-band induced superconductivity in single-layer graphene and topological insulator bismuth selenide

NASA Astrophysics Data System (ADS)

Talantsev, E. F.; Crump, W. P.; Tallon, J. L.

2018-01-01

Proximity-induced superconductivity in single-layer graphene (SLG) and in topological insulators represent almost ideal examples of superconductivity in two dimensions. Fundamental mechanisms governing superconductivity in the 2D limit are of central interest for modern condensed-matter physics. To deduce fundamental parameters of superconductor/graphene/superconductor and superconductor/bismuth selenide/superconductor junctions we investigate the self-field critical currents in these devices using the formalism of the Ambegaokar-Baratoff model. Our central finding is that the induced superconducting state in SLG and bismuth selenide each exhibits gapping on two superconducting bands. Based on recent results obtained on ultra-thin films of natural superconductors, including single-atomic layer of iron selenide, double and triple atomic layers of gallium, and several atomic layer tantalum disulphide, we conclude that a two-band induced superconducting state in SLG and bismuth selenide is part of a wider, more general multiple-band phenomenology of currently unknown origin.

Benchmarks for single-phase flow in fractured porous media

NASA Astrophysics Data System (ADS)

Flemisch, Bernd; Berre, Inga; Boon, Wietse; Fumagalli, Alessio; Schwenck, Nicolas; Scotti, Anna; Stefansson, Ivar; Tatomir, Alexandru

2018-01-01

This paper presents several test cases intended to be benchmarks for numerical schemes for single-phase fluid flow in fractured porous media. A number of solution strategies are compared, including a vertex and two cell-centred finite volume methods, a non-conforming embedded discrete fracture model, a primal and a dual extended finite element formulation, and a mortar discrete fracture model. The proposed benchmarks test the schemes by increasing the difficulties in terms of network geometry, e.g. intersecting fractures, and physical parameters, e.g. low and high fracture-matrix permeability ratio as well as heterogeneous fracture permeabilities. For each problem, the results presented are the number of unknowns, the approximation errors in the porous matrix and in the fractures with respect to a reference solution, and the sparsity and condition number of the discretized linear system. All data and meshes used in this study are publicly available for further comparisons.

Accelerator-based Neutrino Physics at Fermilab

NASA Astrophysics Data System (ADS)

Dukes, Edmond

2008-10-01

The discovery of neutrino mass has excited great interest in elucidating the properties of neutrinos and their role in nature. Experiments around the world take advantage of solar, atmospheric, reactor, and accelerator sources of neutrinos. Accelerator-based sources are particularly convenient since their parameters can be tuned to optimize the measurement in question. At Fermilab an extensive neutrino program includes the MiniBooNE, SciBooNE, and MINOS experiments. Two major new experiments, MINERvA and NOvA, are being constructed, plans for a high-intensity neutrino source to DUSEL are underway, and an R&D effort towards a large liquid argon detector is being pursued. The NOvA experiment intends to search for electron neutrino appearance using a massive surface detector 811 km from Fermilab. In addition to measuring the last unknown mixing angle, theta(13), NOvA has the possibility of seeing matter-antimatter asymmetries in neutrinos and resolving the ordering of the neutrino mass states.

Large-Scale Experimental Planetary Science Meets Planetary Defense: Deorbiting an Asteroidal Satellite

NASA Technical Reports Server (NTRS)

Cintala, M. J.; Durda, D. D.; Housen, K. R.

2005-01-01

Other than remote-sensing and spacecraft-derived data, the only information that exists regarding the physical and chemical properties of asteroids is that inferred through calculations, numerical simulations, extrapolation of experiments, and meteorite studies. Our understanding of the dynamics of accretion of planetesimals, collisional disruption of asteroids, and the macroscopic, shock-induced modification of the surfaces of such small objects is also, for the most part, founded on similar inferences. While considerable strides have been made in improving the state of asteroid science, too many unknowns remain to assert that we understand the parameters necessary for the more practical problem of deflecting an asteroid or asteroid pair on an Earth-intersecting trajectory. Many of these deficiencies could be reduced or eliminated by intentionally deorbiting an asteroidal satellite and monitoring the resulting collision between it and the primary asteroid, a capability that is well within the limitations of current technology.

Phenomenological model for transient deformation based on state variables

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

Jackson, M S; Cho, C W; Alexopoulos, P

The state variable theory of Hart, while providing a unified description of plasticity-dominated deformation, exhibits deficiencies when it is applied to transient deformation phenomena at stresses below yield. It appears that the description of stored anelastic strain is oversimplified. Consideration of a simple physical picture based on continuum dislocation pileups suggests that the neglect of weak barriers to dislocation motion is the source of these inadequacies. An appropriately modified description incorporating such barriers then allows the construction of a macroscopic model including transient effects. Although the flow relations for the microplastic element required in the new theory are not known,more » tentative assignments may be made for such functions. The model then exhibits qualitatively correct behavior when tensile, loading-unloading, reverse loading, and load relaxation tests are simulated. Experimental procedures are described for determining the unknown parameters and functions in the new model.« less

Atmospheric radiance interpolation for the modeling of hyperspectral data

NASA Astrophysics Data System (ADS)

Fuehrer, Perry; Healey, Glenn; Rauch, Brian; Slater, David; Ratkowski, Anthony

2008-04-01

The calibration of data from hyperspectral sensors to spectral radiance enables the use of physical models to predict measured spectra. Since environmental conditions are often unknown, material detection algorithms have emerged that utilize predicted spectra over ranges of environmental conditions. The predicted spectra are typically generated by a radiative transfer (RT) code such as MODTRAN TM. Such techniques require the specification of a set of environmental conditions. This is particularly challenging in the LWIR for which temperature and atmospheric constituent profiles are required as inputs for the RT codes. We have developed an automated method for generating environmental conditions to obtain a desired sampling of spectra in the sensor radiance domain. Our method provides a way of eliminating the usual problems encountered, because sensor radiance spectra depend nonlinearly on the environmental parameters, when model conditions are specified by a uniform sampling of environmental parameters. It uses an initial set of radiance vectors concatenated over a set of conditions to define the mapping from environmental conditions to sensor spectral radiance. This approach enables a given number of model conditions to span the space of desired radiance spectra and improves both the accuracy and efficiency of detection algorithms that rely upon use of predicted spectra.

Synchronization in complex oscillator networks and smart grids.

PubMed

Dörfler, Florian; Chertkov, Michael; Bullo, Francesco

2013-02-05

The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A widely adopted model of a coupled oscillator network is characterized by a population of heterogeneous phase oscillators, a graph describing the interaction among them, and diffusive and sinusoidal coupling. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here, we present a unique, concise, and closed-form condition for synchronization of the fully nonlinear, nonequilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters; they are statistically correct for almost all networks; and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks, such as electrical power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex network scenarios and in smart grid applications.

Low energy secondary cosmic ray flux (gamma rays) monitoring and its constrains

NASA Astrophysics Data System (ADS)

Raghav, Anil; Bhaskar, Ankush; Yadav, Virendra; Bijewar, Nitinkumar

2015-02-01

Temporal variation of secondary cosmic rays (SCR) flux was measured during the full and new moon and days close to them at Department of Physics, University of Mumbai, Mumbai (Geomagnetic latitude: 10.6 °N), India. The measurements were done by using NaI (Tl) scintillation detector with energy threshold of 200 keV. The SCR flux showed sudden enhancement for approximately about 2 hour during few days out of all observations. The maximum enhancement in SCR flux is about 200 % as compared to the diurnal trend of SCR temporal variations. Weather parameters (temperature and relative humidity) were continuously monitored during all observations. The influences of geomagnetic field, interplanetary parameters and tidal effect on SCR flux have been considered. Summed spectra corresponding to enhancement duration indicates appearance of atmospheric radioactivity which shows single gamma ray line. Detail investigation revealed the presence of radioactive Ar41. Present study indicates origin of Ar41 could be due to anthropogenic source or due to gravitational tidal forces. This measurements point out limitations on low energy SCR flux monitoring. This study will help many researchers in measurements of SCR flux during eclipses and to find unknown mechanism behind decrease/increase in SCR flux during solar/lunar eclipse.

Heat exchange between a bouncing drop and a superhydrophobic substrate

PubMed Central

Shiri, Samira; Bird, James C.

2017-01-01

The ability to enhance or limit heat transfer between a surface and impacting drops is important in applications ranging from industrial spray cooling to the thermal regulation of animals in cold rain. When these surfaces are micro/nanotextured and hydrophobic, or superhydrophobic, an impacting drop can spread and recoil over trapped air pockets so quickly that it can completely bounce off the surface. It is expected that this short contact time limits heat transfer; however, the amount of heat exchanged and precise role of various parameters, such as the drop size, are unknown. Here, we demonstrate that the amount of heat exchanged between a millimeter-sized water drop and a superhydrophobic surface will be orders of magnitude less when the drop bounces than when it sticks. Through a combination of experiments and theory, we show that the heat transfer process on superhydrophobic surfaces is independent of the trapped gas. Instead, we find that, for a given spreading factor, the small fraction of heat transferred is controlled by two dimensionless groupings of physical parameters: one that relates the thermal properties of the drop and bulk substrate and the other that characterizes the relative thermal, inertial, and capillary dynamics of the drop. PMID:28630306

A Coarse Alignment Method Based on Digital Filters and Reconstructed Observation Vectors

PubMed Central

Xu, Xiang; Xu, Xiaosu; Zhang, Tao; Li, Yao; Wang, Zhicheng

2017-01-01

In this paper, a coarse alignment method based on apparent gravitational motion is proposed. Due to the interference of the complex situations, the true observation vectors, which are calculated by the apparent gravity, are contaminated. The sources of the interference are analyzed in detail, and then a low-pass digital filter is designed in this paper for eliminating the high-frequency noise of the measurement observation vectors. To extract the effective observation vectors from the inertial sensors’ outputs, a parameter recognition and vector reconstruction method are designed, where an adaptive Kalman filter is employed to estimate the unknown parameters. Furthermore, a robust filter, which is based on Huber’s M-estimation theory, is developed for addressing the outliers of the measurement observation vectors due to the maneuver of the vehicle. A comprehensive experiment, which contains a simulation test and physical test, is designed to verify the performance of the proposed method, and the results show that the proposed method is equivalent to the popular apparent velocity method in swaying mode, but it is superior to the current methods while in moving mode when the strapdown inertial navigation system (SINS) is under entirely self-contained conditions. PMID:28353682

Fractal morphometry of cell complexity.

PubMed

Losa, Gabriele A

2002-01-01

Irregularity and self-similarity under scale changes are the main attributes of the morphological complexity of both normal and abnormal cells and tissues. In other words, the shape of a self-similar object does not change when the scale of measurement changes, because each part of it looks similar to the original object. However, the size and geometrical parameters of an irregular object do differ when it is examined at increasing resolution, which reveals more details. Significant progress has been made over the past three decades in understanding how irregular shapes and structures in the physical and biological sciences can be analysed. Dominant influences have been the discovery of a new practical geometry of Nature, now known as fractal geometry, and the continuous improvements in computation capabilities. Unlike conventional Euclidean geometry, which was developed to describe regular and ideal geometrical shapes which are practically unknown in nature, fractal geometry can be used to measure the fractal dimension, contour length, surface area and other dimension parameters of almost all irregular and complex biological tissues. We have used selected examples to illustrate the application of the fractal principle to measuring irregular and complex membrane ultrastructures of cells at specific functional and pathological stage.

Optimal hemodynamic response model for functional near-infrared spectroscopy

PubMed Central

Kamran, Muhammad A.; Jeong, Myung Yung; Mannan, Malik M. N.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650–950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > tcritical and p-value < 0.05). PMID:26136668

Optimal hemodynamic response model for functional near-infrared spectroscopy.

PubMed

Kamran, Muhammad A; Jeong, Myung Yung; Mannan, Malik M N

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an emerging non-invasive brain imaging technique and measures brain activities by means of near-infrared light of 650-950 nm wavelengths. The cortical hemodynamic response (HR) differs in attributes at different brain regions and on repetition of trials, even if the experimental paradigm is kept exactly the same. Therefore, an HR model that can estimate such variations in the response is the objective of this research. The canonical hemodynamic response function (cHRF) is modeled by two Gamma functions with six unknown parameters (four of them to model the shape and other two to scale and baseline respectively). The HRF model is supposed to be a linear combination of HRF, baseline, and physiological noises (amplitudes and frequencies of physiological noises are supposed to be unknown). An objective function is developed as a square of the residuals with constraints on 12 free parameters. The formulated problem is solved by using an iterative optimization algorithm to estimate the unknown parameters in the model. Inter-subject variations in HRF and physiological noises have been estimated for better cortical functional maps. The accuracy of the algorithm has been verified using 10 real and 15 simulated data sets. Ten healthy subjects participated in the experiment and their HRF for finger-tapping tasks have been estimated and analyzed. The statistical significance of the estimated activity strength parameters has been verified by employing statistical analysis (i.e., t-value > t critical and p-value < 0.05).

Monolithic multigrid method for the coupled Stokes flow and deformable porous medium system

NASA Astrophysics Data System (ADS)

Luo, P.; Rodrigo, C.; Gaspar, F. J.; Oosterlee, C. W.

2018-01-01

The interaction between fluid flow and a deformable porous medium is a complicated multi-physics problem, which can be described by a coupled model based on the Stokes and poroelastic equations. A monolithic multigrid method together with either a coupled Vanka smoother or a decoupled Uzawa smoother is employed as an efficient numerical technique for the linear discrete system obtained by finite volumes on staggered grids. A specialty in our modeling approach is that at the interface of the fluid and poroelastic medium, two unknowns from the different subsystems are defined at the same grid point. We propose a special discretization at and near the points on the interface, which combines the approximation of the governing equations and the considered interface conditions. In the decoupled Uzawa smoother, Local Fourier Analysis (LFA) helps us to select optimal values of the relaxation parameter appearing. To implement the monolithic multigrid method, grid partitioning is used to deal with the interface updates when communication is required between two subdomains. Numerical experiments show that the proposed numerical method has an excellent convergence rate. The efficiency and robustness of the method are confirmed in numerical experiments with typically small realistic values of the physical coefficients.

The Outer Limits: English.

ERIC Educational Resources Information Center

Tyler, Barbara R.; Biesekerski, Joan

The Quinmester course "The Outer Limits" involves an exploration of unknown worlds, mental and physical, through fiction and nonfiction. Its purpose is to focus attention on the ongoing conquest of the frontiers of the mind, the physical world, and outer space. The subject matter includes identification and investigation of unknown…

Determination of power system component parameters using nonlinear dead beat estimation method

NASA Astrophysics Data System (ADS)

Kolluru, Lakshmi

Power systems are considered the most complex man-made wonders in existence today. In order to effectively supply the ever increasing demands of the consumers, power systems are required to remain stable at all times. Stability and monitoring of these complex systems are achieved by strategically placed computerized control centers. State and parameter estimation is an integral part of these facilities, as they deal with identifying the unknown states and/or parameters of the systems. Advancements in measurement technologies and the introduction of phasor measurement units (PMU) provide detailed and dynamic information of all measurements. Accurate availability of dynamic measurements provides engineers the opportunity to expand and explore various possibilities in power system dynamic analysis/control. This thesis discusses the development of a parameter determination algorithm for nonlinear power systems, using dynamic data obtained from local measurements. The proposed algorithm was developed by observing the dead beat estimator used in state space estimation of linear systems. The dead beat estimator is considered to be very effective as it is capable of obtaining the required results in a fixed number of steps. The number of steps required is related to the order of the system and the number of parameters to be estimated. The proposed algorithm uses the idea of dead beat estimator and nonlinear finite difference methods to create an algorithm which is user friendly and can determine the parameters fairly accurately and effectively. The proposed algorithm is based on a deterministic approach, which uses dynamic data and mathematical models of power system components to determine the unknown parameters. The effectiveness of the algorithm is tested by implementing it to identify the unknown parameters of a synchronous machine. MATLAB environment is used to create three test cases for dynamic analysis of the system with assumed known parameters. Faults are introduced in the virtual test systems and the dynamic data obtained in each case is analyzed and recorded. Ideally, actual measurements are to be provided to the algorithm. As the measurements are not readily available the data obtained from simulations is fed into the determination algorithm as inputs. The obtained results are then compared to the original (or assumed) values of the parameters. The results obtained suggest that the algorithm is able to determine the parameters of a synchronous machine when crisp data is available.

A unifying view of synchronization for data assimilation in complex nonlinear networks

NASA Astrophysics Data System (ADS)

Abarbanel, Henry D. I.; Shirman, Sasha; Breen, Daniel; Kadakia, Nirag; Rey, Daniel; Armstrong, Eve; Margoliash, Daniel

2017-12-01

Networks of nonlinear systems contain unknown parameters and dynamical degrees of freedom that may not be observable with existing instruments. From observable state variables, we want to estimate the connectivity of a model of such a network and determine the full state of the model at the termination of a temporal observation window during which measurements transfer information to a model of the network. The model state at the termination of a measurement window acts as an initial condition for predicting the future behavior of the network. This allows the validation (or invalidation) of the model as a representation of the dynamical processes producing the observations. Once the model has been tested against new data, it may be utilized as a predictor of responses to innovative stimuli or forcing. We describe a general framework for the tasks involved in the "inverse" problem of determining properties of a model built to represent measured output from physical, biological, or other processes when the measurements are noisy, the model has errors, and the state of the model is unknown when measurements begin. This framework is called statistical data assimilation and is the best one can do in estimating model properties through the use of the conditional probability distributions of the model state variables, conditioned on observations. There is a very broad arena of applications of the methods described. These include numerical weather prediction, properties of nonlinear electrical circuitry, and determining the biophysical properties of functional networks of neurons. Illustrative examples will be given of (1) estimating the connectivity among neurons with known dynamics in a network of unknown connectivity, and (2) estimating the biophysical properties of individual neurons in vitro taken from a functional network underlying vocalization in songbirds.

Rendezvous with connectivity preservation for multi-robot systems with an unknown leader

NASA Astrophysics Data System (ADS)

Dong, Yi

2018-02-01

This paper studies the leader-following rendezvous problem with connectivity preservation for multi-agent systems composed of uncertain multi-robot systems subject to external disturbances and an unknown leader, both of which are generated by a so-called exosystem with parametric uncertainty. By combining internal model design, potential function technique and adaptive control, two distributed control strategies are proposed to maintain the connectivity of the communication network, to achieve the asymptotic tracking of all the followers to the output of the unknown leader system, as well as to reject unknown external disturbances. It is also worth to mention that the uncertain parameters in the multi-robot systems and exosystem are further allowed to belong to unknown and unbounded sets when applying the second fully distributed control law containing a dynamic gain inspired by high-gain adaptive control or self-tuning regulator.

Design of a DNA chip for detection of unknown genetically modified organisms (GMOs).

PubMed

Nesvold, Håvard; Kristoffersen, Anja Bråthen; Holst-Jensen, Arne; Berdal, Knut G

2005-05-01

Unknown genetically modified organisms (GMOs) have not undergone a risk evaluation, and hence might pose a danger to health and environment. There are, today, no methods for detecting unknown GMOs. In this paper we propose a novel method intended as a first step in an approach for detecting unknown genetically modified (GM) material in a single plant. A model is designed where biological and combinatorial reduction rules are applied to a set of DNA chip probes containing all possible sequences of uniform length n, creating probes capable of detecting unknown GMOs. The model is theoretically tested for Arabidopsis thaliana Columbia, and the probabilities for detecting inserts and receiving false positives are assessed for various parameters for this organism. From a theoretical standpoint, the model looks very promising but should be tested further in the laboratory. The model and algorithms will be available upon request to the corresponding author.

Adaptive fuzzy prescribed performance control for MIMO nonlinear systems with unknown control direction and unknown dead-zone inputs.

PubMed

Shi, Wuxi; Luo, Rui; Li, Baoquan

2017-01-01

In this study, an adaptive fuzzy prescribed performance control approach is developed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with unknown control direction and unknown dead-zone inputs. The properties of symmetric matrix are exploited to design adaptive fuzzy prescribed performance controller, and a Nussbaum-type function is incorporated in the controller to estimate the unknown control direction. This method has two prominent advantages: it does not require the priori knowledge of control direction and only three parameters need to be updated on-line for this MIMO systems. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Globally Convergent Numerical Methods for Coefficient Inverse Problems

DTIC Science & Technology

2008-09-23

backgrounds. Probing radiations are usually thought as electric and acoustic waves for the first two applications and light originated by lasers in...fundamental laws of physics. Electric , acoustic or light scattering properties of both unknown targets and the backgrounds are described by coefficients of...with the back-reflected data here, Army applications are quite feasible. The 2-D inverse problem of the determination of the unknown electric

Methodological issues regarding power of classical test theory (CTT) and item response theory (IRT)-based approaches for the comparison of patient-reported outcomes in two groups of patients - a simulation study

PubMed Central

2010-01-01

Background Patients-Reported Outcomes (PRO) are increasingly used in clinical and epidemiological research. Two main types of analytical strategies can be found for these data: classical test theory (CTT) based on the observed scores and models coming from Item Response Theory (IRT). However, whether IRT or CTT would be the most appropriate method to analyse PRO data remains unknown. The statistical properties of CTT and IRT, regarding power and corresponding effect sizes, were compared. Methods Two-group cross-sectional studies were simulated for the comparison of PRO data using IRT or CTT-based analysis. For IRT, different scenarios were investigated according to whether items or person parameters were assumed to be known, to a certain extent for item parameters, from good to poor precision, or unknown and therefore had to be estimated. The powers obtained with IRT or CTT were compared and parameters having the strongest impact on them were identified. Results When person parameters were assumed to be unknown and items parameters to be either known or not, the power achieved using IRT or CTT were similar and always lower than the expected power using the well-known sample size formula for normally distributed endpoints. The number of items had a substantial impact on power for both methods. Conclusion Without any missing data, IRT and CTT seem to provide comparable power. The classical sample size formula for CTT seems to be adequate under some conditions but is not appropriate for IRT. In IRT, it seems important to take account of the number of items to obtain an accurate formula. PMID:20338031

Characterizing heterogeneous properties of cerebral aneurysms with unknown stress-free geometry: a precursor to in vivo identification.

PubMed

Zhao, Xuefeng; Raghavan, Madhavan L; Lu, Jia

2011-05-01

Knowledge of elastic properties of cerebral aneurysms is crucial for understanding the biomechanical behavior of the lesion. However, characterizing tissue properties using in vivo motion data presents a tremendous challenge. Aside from the limitation of data accuracy, a pressing issue is that the in vivo motion does not expose the stress-free geometry. This is compounded by the nonlinearity, anisotropy, and heterogeneity of the tissue behavior. This article introduces a method for identifying the heterogeneous properties of aneurysm wall tissue under unknown stress-free configuration. In the proposed approach, an accessible configuration is taken as the reference; the unknown stress-free configuration is represented locally by a metric tensor describing the prestrain from the stress-free configuration to the reference configuration. Material parameters are identified together with the metric tensor pointwisely. The paradigm is tested numerically using a forward-inverse analysis loop. An image-derived sac is considered. The aneurysm tissue is modeled as an eightply laminate whose constitutive behavior is described by an anisotropic hyperelastic strain-energy function containing four material parameters. The parameters are assumed to vary continuously in two assigned patterns to represent two types of material heterogeneity. Nine configurations between the diastolic and systolic pressures are generated by forward quasi-static finite element analyses. These configurations are fed to the inverse analysis to delineate the material parameters and the metric tensor. The recovered and the assigned distributions are in good agreement. A forward verification is conducted by comparing the displacement solutions obtained from the recovered and the assigned material parameters at a different pressure. The nodal displacements are found in excellent agreement.

A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund-Potsdam-Jena (LPJ) model as an example

NASA Astrophysics Data System (ADS)

Sun, Guodong; Mu, Mu

2017-05-01

An important source of uncertainty, which causes further uncertainty in numerical simulations, is that residing in the parameters describing physical processes in numerical models. Therefore, finding a subset among numerous physical parameters in numerical models in the atmospheric and oceanic sciences, which are relatively more sensitive and important parameters, and reducing the errors in the physical parameters in this subset would be a far more efficient way to reduce the uncertainties involved in simulations. In this context, we present a new approach based on the conditional nonlinear optimal perturbation related to parameter (CNOP-P) method. The approach provides a framework to ascertain the subset of those relatively more sensitive and important parameters among the physical parameters. The Lund-Potsdam-Jena (LPJ) dynamical global vegetation model was utilized to test the validity of the new approach in China. The results imply that nonlinear interactions among parameters play a key role in the identification of sensitive parameters in arid and semi-arid regions of China compared to those in northern, northeastern, and southern China. The uncertainties in the numerical simulations were reduced considerably by reducing the errors of the subset of relatively more sensitive and important parameters. The results demonstrate that our approach not only offers a new route to identify relatively more sensitive and important physical parameters but also that it is viable to then apply "target observations" to reduce the uncertainties in model parameters.

Application of a net-based baseline correction scheme to strong-motion records of the 2011 Mw 9.0 Tohoku earthquake

NASA Astrophysics Data System (ADS)

Tu, Rui; Wang, Rongjiang; Zhang, Yong; Walter, Thomas R.

2014-06-01

The description of static displacements associated with earthquakes is traditionally achieved using GPS, EDM or InSAR data. In addition, displacement histories can be derived from strong-motion records, allowing an improvement of geodetic networks at a high sampling rate and a better physical understanding of earthquake processes. Strong-motion records require a correction procedure appropriate for baseline shifts that may be caused by rotational motion, tilting and other instrumental effects. Common methods use an empirical bilinear correction on the velocity seismograms integrated from the strong-motion records. In this study, we overcome the weaknesses of an empirically based bilinear baseline correction scheme by using a net-based criterion to select the timing parameters. This idea is based on the physical principle that low-frequency seismic waveforms at neighbouring stations are coherent if the interstation distance is much smaller than the distance to the seismic source. For a dense strong-motion network, it is plausible to select the timing parameters so that the correlation coefficient between the velocity seismograms of two neighbouring stations is maximized after the baseline correction. We applied this new concept to the KiK-Net and K-Net strong-motion data available for the 2011 Mw 9.0 Tohoku earthquake. We compared the derived coseismic static displacement with high-quality GPS data, and with the results obtained using empirical methods. The results show that the proposed net-based approach is feasible and more robust than the individual empirical approaches. The outliers caused by unknown problems in the measurement system can be easily detected and quantified.

THE EFFECT OF WARM DARK MATTER ON GALAXY PROPERTIES: CONSTRAINTS FROM THE STELLAR MASS FUNCTION AND THE TULLY-FISHER RELATION

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

Kang, Xi; Maccio, Andrea V.; Dutton, Aaron A.

2013-04-10

In this paper, we combine high-resolution N-body simulations with a semi-analytical model of galaxy formation to study the effects of a possible warm dark matter (WDM) component on the observable properties of galaxies. We compare three WDM models with a dark matter (DM) mass of 0.5, 0.75, and 2.0 keV with the standard cold dark matter case. For a fixed set of parameters describing the baryonic physics, the WDM models predict fewer galaxies at low (stellar) masses, as expected due to the suppression of power on small scales, while no substantial difference is found at the high-mass end. However, thesemore » differences in the stellar mass function vanish when a different set of parameters is used to describe the (largely unknown) galaxy formation processes. We show that it is possible to break this degeneracy between DM properties and the parameterization of baryonic physics by combining observations on the stellar mass function with the Tully-Fisher relation (the relation between stellar mass and the rotation velocity at large galactic radii as probed by resolved H I rotation curves). WDM models with a too warm candidate (m{sub {nu}} < 0.75 keV) cannot simultaneously reproduce the stellar mass function and the Tully-Fisher relation. We conclude that accurate measurements of the galaxy stellar mass function and the link between galaxies and DM halos down to the very low mass end can give very tight constraints on the nature of DM candidates.« less

The Evolution and Physical Parameters of WN3/O3s: A New Type of Wolf–Rayet Star

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

Neugent, Kathryn F.; Massey, Philip; Hillier, D. John

As part of a search for Wolf–Rayet (WR) stars in the Magellanic Clouds, we have discovered a new type of WR star in the Large Magellanic Cloud (LMC). These stars have both strong emission lines, as well as He ii and Balmer absorption lines and spectroscopically resemble a WN3 and O3V binary pair. However, they are visually too faint to be WN3+O3V binary systems. We have found nine of these WN3/O3s, making up ∼6% of the population of LMC WRs. Using cmfgen, we have successfully modeled their spectra as single stars and have compared the physical parameters with those ofmore » more typical LMC WNs. Their temperatures are around 100,000 K, a bit hotter than the majority of WN stars (by around 10,000 K), though a few hotter WNs are known. The abundances are what you would expect for CNO equilibrium. However, most anomalous are their mass-loss rates, which are more like that of an O-type star than a WN star. While their evolutionary status is uncertain, their low mass-loss rates and wind velocities suggest that they are not products of homogeneous evolution. It is possible instead that these stars represent an intermediate stage between O stars and WNs. Since WN3/O3 stars are unknown in the Milky Way, we suspect that their formation depends upon metallicity, and we are investigating this further by a deep survey in M33, which possesses a metallicity gradient.« less

The Pillars of Creation revisited with MUSE: gas kinematics and high-mass stellar feedback traced by optical spectroscopy

NASA Astrophysics Data System (ADS)

McLeod, A. F.; Dale, J. E.; Ginsburg, A.; Ercolano, B.; Gritschneder, M.; Ramsay, S.; Testi, L.

2015-06-01

Integral field unit (IFU) data of the iconic Pillars of Creation in M16 are presented. The ionization structure of the pillars was studied in great detail over almost the entire visible wavelength range, and maps of the relevant physical parameters, e.g. extinction, electron density, electron temperature, line-of-sight velocity of the ionized and neutral gas are shown. In agreement with previous authors, we find that the pillar tips are being ionized and photoevaporated by the massive members of the nearby cluster NGC 6611. They display a stratified ionization structure where the emission lines peak in a descending order according to their ionization energies. The IFU data allowed us to analyse the kinematics of the photoevaporative flow in terms of the stratified ionization structure, and we find that, in agreement with simulations, the photoevaporative flow is traced by a blueshift in the position-velocity profile. The gas kinematics and ionization structure have allowed us to produce a sketch of the 3D geometry of the Pillars, positioning the pillars with respect to the ionizing cluster stars. We use a novel method to detect a previously unknown bipolar outflow at the tip of the middle pillar and suggest that it has an embedded protostar as its driving source. Furthermore we identify a candidate outflow in the leftmost pillar. With the derived physical parameters and ionic abundances, we estimate a mass-loss rate due to the photoevaporative flow of 70 M⊙ Myr-1 which yields an expected lifetime of approximately 3 Myr.

The Use of One-Sample Prediction Intervals for Estimating CO2 Scrubber Canister Durations

DTIC Science & Technology

2012-10-01

Grade and 812 D-Grade Sofnolime.3 Definitions According to Devore,4 A CI (confidence interval) refers to a parameter, or population ... characteristic , whose value is fixed but unknown to us. In contrast, a future value of Y is not a parameter but instead a random variable; for this

Changes of Pain Perception, Autonomic Function, and Endocrine Parameters during Treatment of Anorectic Adolescents

ERIC Educational Resources Information Center

Bar, Karl-Jurgen; Boettger, Silke; Wagner, Gerd; Wilsdorf, Christine; Gerhard, Uwe Jens; Boettger, Michael K.; Blanz, Bernhard; Sauer, Heinrich

2006-01-01

Objectives: The underlying mechanisms of reduced pain perception in anorexia nervosa (AN) are unknown. To gain more insight into the pathology, the authors investigated pain perception, autonomic function, and endocrine parameters before and during successful treatment of adolescent AN patients. Method: Heat pain perception was assessed in 15…

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative–quantitative modeling

PubMed Central

Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

2012-01-01

Summary: Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if–then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLabTM-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. Availability: ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/ Contact: stefan.streif@ovgu.de PMID:22451270

ADMIT: a toolbox for guaranteed model invalidation, estimation and qualitative-quantitative modeling.

PubMed

Streif, Stefan; Savchenko, Anton; Rumschinski, Philipp; Borchers, Steffen; Findeisen, Rolf

2012-05-01

Often competing hypotheses for biochemical networks exist in the form of different mathematical models with unknown parameters. Considering available experimental data, it is then desired to reject model hypotheses that are inconsistent with the data, or to estimate the unknown parameters. However, these tasks are complicated because experimental data are typically sparse, uncertain, and are frequently only available in form of qualitative if-then observations. ADMIT (Analysis, Design and Model Invalidation Toolbox) is a MatLab(TM)-based tool for guaranteed model invalidation, state and parameter estimation. The toolbox allows the integration of quantitative measurement data, a priori knowledge of parameters and states, and qualitative information on the dynamic or steady-state behavior. A constraint satisfaction problem is automatically generated and algorithms are implemented for solving the desired estimation, invalidation or analysis tasks. The implemented methods built on convex relaxation and optimization and therefore provide guaranteed estimation results and certificates for invalidity. ADMIT, tutorials and illustrative examples are available free of charge for non-commercial use at http://ifatwww.et.uni-magdeburg.de/syst/ADMIT/

Distributed Time-Varying Formation Robust Tracking for General Linear Multiagent Systems With Parameter Uncertainties and External Disturbances.

PubMed

Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang

2017-05-18

This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.

Multiparameter Estimation in Networked Quantum Sensors

NASA Astrophysics Data System (ADS)

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-01

We introduce a general model for a network of quantum sensors, and we use this model to consider the following question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or nonlinear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.

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

NASA Astrophysics Data System (ADS)

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

2016-02-01

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

Multisite EPR oximetry from multiple quadrature harmonics.

PubMed

Ahmad, R; Som, S; Johnson, D H; Zweier, J L; Kuppusamy, P; Potter, L C

2012-01-01

Multisite continuous wave (CW) electron paramagnetic resonance (EPR) oximetry using multiple quadrature field modulation harmonics is presented. First, a recently developed digital receiver is used to extract multiple harmonics of field modulated projection data. Second, a forward model is presented that relates the projection data to unknown parameters, including linewidth at each site. Third, a maximum likelihood estimator of unknown parameters is reported using an iterative algorithm capable of jointly processing multiple quadrature harmonics. The data modeling and processing are applicable for parametric lineshapes under nonsaturating conditions. Joint processing of multiple harmonics leads to 2-3-fold acceleration of EPR data acquisition. For demonstration in two spatial dimensions, both simulations and phantom studies on an L-band system are reported. Copyright © 2011 Elsevier Inc. All rights reserved.

Parametric system identification of catamaran for improving controller design

NASA Astrophysics Data System (ADS)

Timpitak, Surasak; Prempraneerach, Pradya; Pengwang, Eakkachai

2018-01-01

This paper presents an estimation of simplified dynamic model for only surge- and yaw- motions of catamaran by using system identification (SI) techniques to determine associated unknown parameters. These methods will enhance the performance of designing processes for the motion control system of Unmanned Surface Vehicle (USV). The simulation results demonstrate an effective way to solve for damping forces and to determine added masses by applying least-square and AutoRegressive Exogenous (ARX) methods. Both methods are then evaluated according to estimated parametric errors from the vehicle’s dynamic model. The ARX method, which yields better estimated accuracy, can then be applied to identify unknown parameters as well as to help improving a controller design of a real unmanned catamaran.

Quantum pattern recognition with multi-neuron interactions

NASA Astrophysics Data System (ADS)

Fard, E. Rezaei; Aghayar, K.; Amniat-Talab, M.

2018-03-01

We present a quantum neural network with multi-neuron interactions for pattern recognition tasks by a combination of extended classic Hopfield network and adiabatic quantum computation. This scheme can be used as an associative memory to retrieve partial patterns with any number of unknown bits. Also, we propose a preprocessing approach to classifying the pattern space S to suppress spurious patterns. The results of pattern clustering show that for pattern association, the number of weights (η ) should equal the numbers of unknown bits in the input pattern ( d). It is also remarkable that associative memory function depends on the location of unknown bits apart from the d and load parameter α.

Elder physical abuse.

PubMed

Young, Lisa M

2014-11-01

Physical abuse of the elderly is a significant public health concern. The true prevalence of all types is unknown, and under-reporting is known to be significant. The geriatric population is projected to increase dramatically over the next 10 years, and the number of abused individuals is projected to increase also. It is critical that health care providers feel competent in addressing physical elder abuse. This article presents cases illustrating the variety of presenting symptoms that may be attributed to physical elder abuse. Copyright © 2014 Elsevier Inc. All rights reserved.

Bayesian Modeling of Exposure and Airflow Using Two-Zone Models

PubMed Central

Zhang, Yufen; Banerjee, Sudipto; Yang, Rui; Lungu, Claudiu; Ramachandran, Gurumurthy

2009-01-01

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters. PMID:19403840

Application of empirical and dynamical closure methods to simple climate models

NASA Astrophysics Data System (ADS)

Padilla, Lauren Elizabeth

This dissertation applies empirically- and physically-based methods for closure of uncertain parameters and processes to three model systems that lie on the simple end of climate model complexity. Each model isolates one of three sources of closure uncertainty: uncertain observational data, large dimension, and wide ranging length scales. They serve as efficient test systems toward extension of the methods to more realistic climate models. The empirical approach uses the Unscented Kalman Filter (UKF) to estimate the transient climate sensitivity (TCS) parameter in a globally-averaged energy balance model. Uncertainty in climate forcing and historical temperature make TCS difficult to determine. A range of probabilistic estimates of TCS computed for various assumptions about past forcing and natural variability corroborate ranges reported in the IPCC AR4 found by different means. Also computed are estimates of how quickly uncertainty in TCS may be expected to diminish in the future as additional observations become available. For higher system dimensions the UKF approach may become prohibitively expensive. A modified UKF algorithm is developed in which the error covariance is represented by a reduced-rank approximation, substantially reducing the number of model evaluations required to provide probability densities for unknown parameters. The method estimates the state and parameters of an abstract atmospheric model, known as Lorenz 96, with accuracy close to that of a full-order UKF for 30-60% rank reduction. The physical approach to closure uses the Multiscale Modeling Framework (MMF) to demonstrate closure of small-scale, nonlinear processes that would not be resolved directly in climate models. A one-dimensional, abstract test model with a broad spatial spectrum is developed. The test model couples the Kuramoto-Sivashinsky equation to a transport equation that includes cloud formation and precipitation-like processes. In the test model, three main sources of MMF error are evaluated independently. Loss of nonlinear multi-scale interactions and periodic boundary conditions in closure models were dominant sources of error. Using a reduced order modeling approach to maximize energy content allowed reduction of the closure model dimension up to 75% without loss in accuracy. MMF and a comparable alternative model peformed equally well compared to direct numerical simulation.

Estimating age from recapture data: integrating incremental growth measures with ancillary data to infer age-at-length

USGS Publications Warehouse

Eaton, Mitchell J.; Link, William A.

2011-01-01

Estimating the age of individuals in wild populations can be of fundamental importance for answering ecological questions, modeling population demographics, and managing exploited or threatened species. Significant effort has been devoted to determining age through the use of growth annuli, secondary physical characteristics related to age, and growth models. Many species, however, either do not exhibit physical characteristics useful for independent age validation or are too rare to justify sacrificing a large number of individuals to establish the relationship between size and age. Length-at-age models are well represented in the fisheries and other wildlife management literature. Many of these models overlook variation in growth rates of individuals and consider growth parameters as population parameters. More recent models have taken advantage of hierarchical structuring of parameters and Bayesian inference methods to allow for variation among individuals as functions of environmental covariates or individual-specific random effects. Here, we describe hierarchical models in which growth curves vary as individual-specific stochastic processes, and we show how these models can be fit using capture–recapture data for animals of unknown age along with data for animals of known age. We combine these independent data sources in a Bayesian analysis, distinguishing natural variation (among and within individuals) from measurement error. We illustrate using data for African dwarf crocodiles, comparing von Bertalanffy and logistic growth models. The analysis provides the means of predicting crocodile age, given a single measurement of head length. The von Bertalanffy was much better supported than the logistic growth model and predicted that dwarf crocodiles grow from 19.4 cm total length at birth to 32.9 cm in the first year and 45.3 cm by the end of their second year. Based on the minimum size of females observed with hatchlings, reproductive maturity was estimated to be at nine years. These size benchmarks are believed to represent thresholds for important demographic parameters; improved estimates of age, therefore, will increase the precision of population projection models. The modeling approach that we present can be applied to other species and offers significant advantages when multiple sources of data are available and traditional aging techniques are not practical.

Soil biota and agriculture production in conventional and organic farming

NASA Astrophysics Data System (ADS)

Schrama, Maarten; de Haan, Joj; Carvalho, Sabrina; Kroonen, Mark; Verstegen, Harry; Van der Putten, Wim

2015-04-01

Sustainable food production for a growing world population requires a healthy soil that can buffer environmental extremes and minimize its losses. There are currently two views on how to achieve this: by intensifying conventional agriculture or by developing organically based agriculture. It has been established that yields of conventional agriculture can be 20% higher than of organic agriculture. However, high yields of intensified conventional agriculture trade off with loss of soil biodiversity, leaching of nutrients, and other unwanted ecosystem dis-services. One of the key explanations for the loss of nutrients and GHG from intensive agriculture is that it results in high dynamics of nutrient losses, and policy has aimed at reducing temporal variation. However, little is known about how different agricultural practices affect spatial variation, and it is unknown how soil fauna acts this. In this study we compare the spatial and temporal variation of physical, chemical and biological parameters in a long term (13-year) field experiment with two conventional farming systems (low and medium organic matter input) and one organic farming system (high organic matter input) and we evaluate the impact on ecosystem services that these farming systems provide. Soil chemical (N availability, N mineralization, pH) and soil biological parameters (nematode abundance, bacterial and fungal biomass) show considerably higher spatial variation under conventional farming than under organic farming. Higher variation in soil chemical and biological parameters coincides with the presence of 'leaky' spots (high nitrate leaching) in conventional farming systems, which shift unpredictably over the course of one season. Although variation in soil physical factors (soil organic matter, soil aggregation, soil moisture) was similar between treatments, but averages were higher under organic farming, indicating more buffered conditions for nutrient cycling. All these changes coincide with pronounced shifts in soil fauna composition (nematodes, earthworms) and an increase in earthworm activity. Hence, more buffered conditions and shifts in soil fauna composition under organic farming may underlie the observed reduction in spatial variation of soil chemical and biological parameters, which in turn correlates positively with a long-term increase in yield. Our study highlights the need for both policymakers and farmers alike to support spatial stability-increasing farming.

Dark matter, baryogenesis and neutrino oscillations from right-handed neutrinos

NASA Astrophysics Data System (ADS)

Canetti, Laurent; Drewes, Marco; Frossard, Tibor; Shaposhnikov, Mikhail

2013-05-01

We show that, leaving aside accelerated cosmic expansion, all experimental data in high energy physics that are commonly agreed to require physics beyond the Standard Model can be explained when completing the model by three right-handed neutrinos that can be searched for using present-day experimental techniques. The model that realizes this scenario is known as the Neutrino Minimal Standard Model (νMSM). In this article we give a comprehensive summary of all known constraints in the νMSM, along with a pedagogical introduction to the model. We present the first complete quantitative study of the parameter space of the model where no physics beyond the νMSM is needed to simultaneously explain neutrino oscillations, dark matter, and the baryon asymmetry of the Universe. The key new point of our analysis is leptogenesis after sphaleron freeze-out, which leads to resonant dark matter production, thus evading the constraints on sterile neutrino dark matter from structure formation and x-ray searches. This requires one to track the time evolution of left- and right-handed neutrino abundances from hot big bang initial conditions down to temperatures below the QCD scale. We find that the interplay of resonant amplifications, CP-violating flavor oscillations, scatterings, and decays leads to a number of previously unknown constraints on the sterile neutrino properties. We furthermore reanalyze bounds from past collider experiments and big bang nucleosynthesis in the face of recent evidence for a nonzero neutrino mixing angle θ13. We combine all our results with existing constraints on dark matter properties from astrophysics and cosmology. Our results provide a guideline for future experimental searches for sterile neutrinos. A summary of the constraints on sterile neutrino masses and mixings has appeared in Canetti et al. [Phys. Rev. Lett. 110, 061801 (2013)PRLTAO0031-9007]. In this article we provide all details of our calculations and give constraints on other model parameters.

Stromgren photometry of A-stars - A test of physical parameter determination

NASA Astrophysics Data System (ADS)

Torra, J.; Figueras, F.; Jordi, C.; Rossello, G.

1990-08-01

By use of known published values for Teff, log g, and Mv, a check on a procedure (Figueras et al, 1990) for determining the physical parameters of A v-type stars from Stromgren photometry has been performed. External errors for the calculated physical parameters have been obtained.

Activity Specificity, Physical and Psychosocial Dimensions.

ERIC Educational Resources Information Center

Hatfield, Frederick C.

The position is taken that the physical parameters of one's involvement in activity learning depend in large measure upon the objectives of the participant. General comments regarding the physical parameters of most activity classes are made. Underlying commonalities existing among these parameters are identified as: (1) freedom from disease; (2)…

Stochastic analysis of experimentally determined physical parameters of HPMC:NiCl{sub 2} polymer composites

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

Thejas, Urs G.; Somashekar, R., E-mail: rs@physics.uni-mysore.ac.in; Sangappa, Y.

A stochastic approach to explain the variation of physical parameters in polymer composites is discussed in this study. We have given a statistical model to derive the characteristic variation of physical parameters as a function of dopant concentration. Results of X-ray diffraction study and conductivity have been taken to validate this function, which can be extended to any of the physical parameters and polymer composites. For this study we have considered a polymer composites of HPMC doped with various concentrations of Nickel Chloride.

Trace-transform invariants of tracks of high-velocity jets from the surface of tungsten droplets in the plasma flow

NASA Astrophysics Data System (ADS)

Gulyaev, P.; Jordan, V.; Gulyaev, I.; Dolmatov, A.

2017-05-01

The paper presents the analysis of the recorded tracks of high-velocity emission in the air-argon plasma flow during breaking up of tungsten microdroplets. This new physical effect of optical emission involves two stages. The first one includes thermionic emission of electrons from the surface of the melted tungsten droplet of 100-200 μm size and formation of the charged sphere of 3-5 mm diameter. After it reaches the breakdown electric potential, it collapses and produces a spherical shock wave and luminous radiation. The second stage includes previously unknown physical phenomenon of narrowly directed energy jet with velocity exceeding 4000 m/s from the surface of the tungsten droplet. The luminous spherical collapse and high-velocity jets were recorded using CMOS photo-array operating in a global shutter charge storage mode. Special features of the CMOS array scanning algorithm affect formation of distinctive signs of the recorded tracks, which stay invariant to trace transform (TT) with specific functional. The series of concentric circles were adopted as primitive object models (patterns) used in TT at the spherical collapse stage and linear segment of fixed thickness - at the high-velocity emission stage. The two invariants of the physical object, motion velocity and optical brightness distribution in the motion front, were adopted as desired identification features of tracks. The analytical expressions of the relation of 2D TT parameters and physical object motion invariants were obtained. The equations for spherical collapse stage correspond to Radon-Nikodym transform.

Hybrid modeling of nitrate fate in large catchments using fuzzy-rules

NASA Astrophysics Data System (ADS)

van der Heijden, Sven; Haberlandt, Uwe

2010-05-01

Especially for nutrient balance simulations, physically based ecohydrological modeling needs an abundance of measured data and model parameters, which for large catchments all too often are not available in sufficient spatial or temporal resolution or are simply unknown. For efficient large-scale studies it is thus beneficial to have methods at one's disposal which are parsimonious concerning the number of model parameters and the necessary input data. One such method is fuzzy-rule based modeling, which compared to other machine-learning techniques has the advantages to produce models (the fuzzy-rules) which are physically interpretable to a certain extent, and to allow the explicit introduction of expert knowledge through pre-defined rules. The study focuses on the application of fuzzy-rule based modeling for nitrate simulation in large catchments, in particular concerning decision support. Fuzzy-rule based modeling enables the generation of simple, efficient, easily understandable models with nevertheless satisfactory accuracy for problems of decision support. The chosen approach encompasses a hybrid metamodeling, which includes the generation of fuzzy-rules with data originating from physically based models as well as a coupling with a physically based water balance model. For the generation of the needed training data and also as coupled water balance model the ecohydrological model SWAT is employed. The conceptual model divides the nitrate pathway into three parts. The first fuzzy-module calculates nitrate leaching with the percolating water from soil surface to groundwater, the second module simulates groundwater passage, and the final module replaces the in-stream processes. The aim of this modularization is to create flexibility for using each of the modules on its own, for changing or completely replacing it. For fuzzy-rule based modeling this can explicitly mean that the re-training of one of the modules with newly available data will be possible without problem, while the module assembly does not have to be modified. Apart from the concept of hybrid metamodeling first results are presented for the fuzzy-module for nitrate passage through the unsaturated zone.

Probabilistic short-term forecasting of eruption rate at Kīlauea Volcano using a physics-based model

NASA Astrophysics Data System (ADS)

Anderson, K. R.

2016-12-01

Deterministic models of volcanic eruptions yield predictions of future activity conditioned on uncertainty in the current state of the system. Physics-based eruption models are well-suited for deterministic forecasting as they can relate magma physics with a wide range of observations. Yet, physics-based eruption forecasting is strongly limited by an inadequate understanding of volcanic systems, and the need for eruption models to be computationally tractable. At Kīlauea Volcano, Hawaii, episodic depressurization-pressurization cycles of the magma system generate correlated, quasi-exponential variations in ground deformation and surface height of the active summit lava lake. Deflations are associated with reductions in eruption rate, or even brief eruptive pauses, and thus partly control lava flow advance rates and associated hazard. Because of the relatively well-understood nature of Kīlauea's shallow magma plumbing system, and because more than 600 of these events have been recorded to date, they offer a unique opportunity to refine a physics-based effusive eruption forecasting approach and apply it to lava eruption rates over short (hours to days) time periods. A simple physical model of the volcano ascribes observed data to temporary reductions in magma supply to an elastic reservoir filled with compressible magma. This model can be used to predict the evolution of an ongoing event, but because the mechanism that triggers events is unknown, event durations are modeled stochastically from previous observations. A Bayesian approach incorporates diverse data sets and prior information to simultaneously estimate uncertain model parameters and future states of the system. Forecasts take the form of probability distributions for eruption rate or cumulative erupted volume at some future time. Results demonstrate the significant uncertainties that still remain even for short-term eruption forecasting at a well-monitored volcano - but also the value of a physics-based, mixed deterministic-probabilistic eruption forecasting approach in reducing and quantifying these uncertainties.

Multi-objective optimization in quantum parameter estimation

NASA Astrophysics Data System (ADS)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Via generalized function projective synchronization in nonlinear Schrödinger equation for secure communication

NASA Astrophysics Data System (ADS)

Zhao, L. W.; Du, J. G.; Yin, J. L.

2018-05-01

This paper proposes a novel secured communication scheme in a chaotic system by applying generalized function projective synchronization of the nonlinear Schrödinger equation. This phenomenal approach guarantees a secured and convenient communication. Our study applied the Melnikov theorem with an active control strategy to suppress chaos in the system. The transmitted information signal is modulated into the parameter of the nonlinear Schrödinger equation in the transmitter and it is assumed that the parameter of the receiver system is unknown. Based on the Lyapunov stability theory and the adaptive control technique, the controllers are designed to make two identical nonlinear Schrödinger equation with the unknown parameter asymptotically synchronized. The numerical simulation results of our study confirmed the validity, effectiveness and the feasibility of the proposed novel synchronization method and error estimate for a secure communication. The Chaos masking signals of the information communication scheme, further guaranteed a safer and secured information communicated via this approach.

A review of the meteorological parameters which affect aerial application

NASA Technical Reports Server (NTRS)

Christensen, L. S.; Frost, W.

1979-01-01

The ambient wind field and temperature gradient were found to be the most important parameters. Investigation results indicated that the majority of meteorological parameters affecting dispersion were interdependent and the exact mechanism by which these factors influence the particle dispersion was largely unknown. The types and approximately ranges of instrumented capabilities for a systematic study of the significant meteorological parameters influencing aerial applications were defined. Current mathematical dispersion models were also briefly reviewed. Unfortunately, a rigorous dispersion model which could be applied to aerial application was not available.

Highly adaptive tests for group differences in brain functional connectivity.

PubMed

Kim, Junghi; Pan, Wei

2015-01-01

Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.

Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions.

PubMed

Yu, Zhaoxu; Li, Shugang; Yu, Zhaosheng; Li, Fangfei

2018-04-01

This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.

77 FR 50465 - Certain Small Diameter Carbon and Alloy Seamless Standard, Line and Pressure Pipe From Romania...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-21

...- 795, and the American Petroleum Institute (API) 5L specifications and meeting the physical parameters... standard, line, or pressure pipe applications and meeting the physical parameters described below.... The scope of this review includes all seamless pipe meeting the physical parameters described above...

Pentaerythritol Tetranitrate (PETN) Surveillance by HPLC-MS: Instrumental Parameters Development

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

Harvey, C A; Meissner, R

Surveillance of PETN Homologs in the stockpile here at LLNL is currently carried out by high performance liquid chromatography (HPLC) with ultra violet (UV) detection. Identification of unknown chromatographic peaks with this detection scheme is severely limited. The design agency is aware of the limitations of this methodology and ordered this study to develop instrumental parameters for the use of a currently owned mass spectrometer (MS) as the detection system. The resulting procedure would be a ''drop-in'' replacement for the current surveillance method (ERD04-524). The addition of quadrupole mass spectrometry provides qualitative identification of PETN and its homologs (Petrin, DiPEHN,more » TriPEON, and TetraPEDN) using a LLNL generated database, while providing mass clues to the identity of unknown chromatographic peaks.« less

An efficient and flexible Abel-inversion method for noisy data

NASA Astrophysics Data System (ADS)

Antokhin, Igor I.

2016-12-01

We propose an efficient and flexible method for solving the Abel integral equation of the first kind, frequently appearing in many fields of astrophysics, physics, chemistry, and applied sciences. This equation represents an ill-posed problem, thus solving it requires some kind of regularization. Our method is based on solving the equation on a so-called compact set of functions and/or using Tikhonov's regularization. A priori constraints on the unknown function, defining a compact set, are very loose and can be set using simple physical considerations. Tikhonov's regularization in itself does not require any explicit a priori constraints on the unknown function and can be used independently of such constraints or in combination with them. Various target degrees of smoothness of the unknown function may be set, as required by the problem at hand. The advantage of the method, apart from its flexibility, is that it gives uniform convergence of the approximate solution to the exact solution, as the errors of input data tend to zero. The method is illustrated on several simulated models with known solutions. An example of astrophysical application of the method is also given.

A spline-based parameter estimation technique for static models of elastic structures

NASA Technical Reports Server (NTRS)

Dutt, P.; Taasan, S.

1986-01-01

The problem of identifying the spatially varying coefficient of elasticity using an observed solution to the forward problem is considered. Under appropriate conditions this problem can be treated as a first order hyperbolic equation in the unknown coefficient. Some continuous dependence results are developed for this problem and a spline-based technique is proposed for approximating the unknown coefficient, based on these results. The convergence of the numerical scheme is established and error estimates obtained.

A summary report on the search for current technologies and developers to develop depth profiling/physical parameter end effectors

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

Nguyen, Q.H.

1994-09-12

This report documents the search strategies and results for available technologies and developers to develop tank waste depth profiling/physical parameter sensors. Sources searched include worldwide research reports, technical papers, journals, private industries, and work at Westinghouse Hanford Company (WHC) at Richland site. Tank waste physical parameters of interest are: abrasiveness, compressive strength, corrosiveness, density, pH, particle size/shape, porosity, radiation, settling velocity, shear strength, shear wave velocity, tensile strength, temperature, viscosity, and viscoelasticity. A list of related articles or sources for each physical parameters is provided.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Component spectra extraction from terahertz measurements of unknown mixtures.

PubMed

Li, Xian; Hou, D B; Huang, P J; Cai, J H; Zhang, G X

2015-10-20

The aim of this work is to extract component spectra from unknown mixtures in the terahertz region. To that end, a method, hard modeling factor analysis (HMFA), was applied to resolve terahertz spectral matrices collected from the unknown mixtures. This method does not require any expertise of the user and allows the consideration of nonlinear effects such as peak variations or peak shifts. It describes the spectra using a peak-based nonlinear mathematic model and builds the component spectra automatically by recombination of the resolved peaks through correlation analysis. Meanwhile, modifications on the method were made to take the features of terahertz spectra into account and to deal with the artificial baseline problem that troubles the extraction process of some terahertz spectra. In order to validate the proposed method, simulated wideband terahertz spectra of binary and ternary systems and experimental terahertz absorption spectra of amino acids mixtures were tested. In each test, not only the number of pure components could be correctly predicted but also the identified pure spectra had a good similarity with the true spectra. Moreover, the proposed method associated the molecular motions with the component extraction, making the identification process more physically meaningful and interpretable compared to other methods. The results indicate that the HMFA method with the modifications can be a practical tool for identifying component terahertz spectra in completely unknown mixtures. This work reports the solution to this kind of problem in the terahertz region for the first time, to the best of the authors' knowledge, and represents a significant advance toward exploring physical or chemical mechanisms of unknown complex systems by terahertz spectroscopy.

Gender Gaps and Gendered Action in a First-Year Physics Laboratory

ERIC Educational Resources Information Center

Day, James; Stang, Jared B.; Holmes, N. G.; Kumar, Dhaneesh; Bonn, D. A.

2016-01-01

It is established that male students outperform female students on almost all commonly used physics concept inventories. However, there is significant variation in the factors that contribute to the gap, as well as the direction in which they influence it. It is presently unknown if such a gender gap exists on the relatively new Concise Data…

What Do We Know about "How" to Promote Physical Activity to Adolescents? A Mapping Review

ERIC Educational Resources Information Center

Bush, Paula Louise; García Bengoechea, Enrique

2015-01-01

To date, adolescent physical activity (PA) intervention research has focused on the school setting and suggests a need to extend interventions beyond this setting to influence teenagers' overall level of PA. But, the relative effectiveness of PA promotion strategies that can be part of such multi-setting interventions remains unknown. We completed…

The Influence of Epoch Length on Physical Activity Patterns Varies by Child's Activity Level

ERIC Educational Resources Information Center

Nettlefold, Lindsay; Naylor, P. J.; Warburton, Darren E. R.; Bredin, Shannon S. D.; Race, Douglas; McKay, Heather A.

2016-01-01

Purpose: Patterns of physical activity (PA) and sedentary time, including volume of bouted activity, are important health indicators. However, the effect of accelerometer epoch length on measurement of these patterns and associations with health outcomes in children remain unknown. Method: We measured activity patterns in 308 children (52% girls,…

One-Dimensional, Two-Phase Flow Modeling Toward Interpreting Motor Slag Expulsion Phenomena

NASA Technical Reports Server (NTRS)

Kibbey, Timothy P.

2012-01-01

Aluminum oxide slag accumulation and expulsion was previously shown to be a player in various solid rocket motor phenomena, including the Space Shuttle's Reusable Solid Rocket Motor (RSRM) pressure perturbation, or "blip," and phantom moment. In the latter case, such un ]commanded side accelerations near the end of burn have also been identified in several other motor systems. However, efforts to estimate the mass expelled during a given event have come up short. Either bulk calculations are performed without enough physics present, or multiphase, multidimensional Computational Fluid Dynamic analyses are performed that give a snapshot in time and space but do not always aid in grasping the general principle. One ]dimensional, two ]phase compressible flow calculations yield an analytical result for nozzle flow under certain assumptions. This can be carried further to relate the bulk motor parameters of pressure, thrust, and mass flow rate under the different exhaust conditions driven by the addition of condensed phase mass flow. An unknown parameter is correlated to airflow testing with water injection where mass flow rates and pressure are known. Comparison is also made to full ]scale static test motor data where thrust and pressure changes are known and similar behavior is shown. The end goal is to be able to include the accumulation and flow of slag in internal ballistics predictions. This will allow better prediction of the tailoff when much slag is ejected and of mass retained versus time, believed to be a contributor to the widely-observed "flight knockdown" parameter.

Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

NASA Astrophysics Data System (ADS)

Lipponen, Antti; Mielonen, Tero; Pitkänen, Mikko R. A.; Levy, Robert C.; Sawyer, Virginia R.; Romakkaniemi, Sami; Kolehmainen, Ville; Arola, Antti

2018-03-01

We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15 %) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

Multiparameter Estimation in Networked Quantum Sensors

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

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

Multiparameter Estimation in Networked Quantum Sensors

DOE PAGES

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-21

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less

Using Lunar Module Shadows To Scale the Effects of Rocket Exhaust Plumes

NASA Technical Reports Server (NTRS)

2008-01-01

Excavating granular materials beneath a vertical jet of gas involves several physical mechanisms. These occur, for example, beneath the exhaust plume of a rocket landing on the soil of the Moon or Mars. We performed a series of experiments and simulations (Figure 1) to provide a detailed view of the complex gas-soil interactions. Measurements taken from the Apollo lunar landing videos (Figure 2) and from photographs of the resulting terrain helped demonstrate how the interactions extrapolate into the lunar environment. It is important to understand these processes at a fundamental level to support the ongoing design of higher fidelity numerical simulations and larger-scale experiments. These are needed to enable future lunar exploration wherein multiple hardware assets will be placed on the Moon within short distances of one another. The high-velocity spray of soil from the landing spacecraft must be accurately predicted and controlled or it could erode the surfaces of nearby hardware. This analysis indicated that the lunar dust is ejected at an angle of less than 3 degrees above the surface, the results of which can be mitigated by a modest berm of lunar soil. These results assume that future lunar landers will use a single engine. The analysis would need to be adjusted for a multiengine lander. Figure 3 is a detailed schematic of the Lunar Module camera calibration math model. In this chart, formulas relating the known quantities, such as sun angle and Lunar Module dimensions, to the unknown quantities are depicted. The camera angle PSI is determined by measurement of the imaged aspect ratio of a crater, where the crater is assumed to be circular. The final solution is the determination of the camera calibration factor, alpha. Figure 4 is a detailed schematic of the dust angle math model, which again relates known to unknown parameters. The known parameters now include the camera calibration factor and Lunar Module dimensions. The final computation is the ejected dust angle, as a function of Lunar Module altitude.

Nacre tablet thickness records formation temperature in modern and fossil shells

DOE PAGES

Gilbert, Pupa U. P. A.; Bergmann, Kristin D.; Myers, Corinne E.; ...

2016-12-15

Nacre, the iridescent outer lining of pearls and inner lining of many mollusk shells, is made of periodic, parallel, organic sheets alternating with aragonite (CaCO 3) tablet layers. Nacre tablet thickness (TT) generates both nacre's iridescence and its remarkable resistance to fracture. Despite extensive studies on how nacre forms, the mechanisms controlling TT remain unknown, even though they determine the most conspicuous of nacre's characteristics, visible even to the naked eye.Thermodynamics predicts that temperature (T) will affect both physical and chemical components of biomineralized skeletons. The chemical composition of biominerals is well-established to record environmental parameters, and has therefore beenmore » extensively used in paleoclimate studies. The physical structure, however, has been hypothesized but never directly demonstrated to depend on the environment. Here we observe that the physical TT in nacre from modern and fossil shallow-water shells of the bivalves Pinna and Atrina correlates with T as measured by the carbonate clumped isotope thermometer. Based on the observed TT vs. T correlation, we anticipate that TT will be used as a paleothermometer, useful to estimate paleotemperature in shallow-water paleoenvironments. Here we successfully test the proposed new nacre TT thermometer on two Jurassic Pinna shells. The increase of TT with T is consistent with greater aragonite growth rate at higher T, and with greater metabolic rate at higher T. Thus, it reveals a complex, T-dependent biophysical mechanism for nacre formation.« less

Nacre tablet thickness records formation temperature in modern and fossil shells

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

Gilbert, Pupa U. P. A.; Bergmann, Kristin D.; Myers, Corinne E.

Nacre, the iridescent outer lining of pearls and inner lining of many mollusk shells, is made of periodic, parallel, organic sheets alternating with aragonite (CaCO 3) tablet layers. Nacre tablet thickness (TT) generates both nacre's iridescence and its remarkable resistance to fracture. Despite extensive studies on how nacre forms, the mechanisms controlling TT remain unknown, even though they determine the most conspicuous of nacre's characteristics, visible even to the naked eye.Thermodynamics predicts that temperature (T) will affect both physical and chemical components of biomineralized skeletons. The chemical composition of biominerals is well-established to record environmental parameters, and has therefore beenmore » extensively used in paleoclimate studies. The physical structure, however, has been hypothesized but never directly demonstrated to depend on the environment. Here we observe that the physical TT in nacre from modern and fossil shallow-water shells of the bivalves Pinna and Atrina correlates with T as measured by the carbonate clumped isotope thermometer. Based on the observed TT vs. T correlation, we anticipate that TT will be used as a paleothermometer, useful to estimate paleotemperature in shallow-water paleoenvironments. Here we successfully test the proposed new nacre TT thermometer on two Jurassic Pinna shells. The increase of TT with T is consistent with greater aragonite growth rate at higher T, and with greater metabolic rate at higher T. Thus, it reveals a complex, T-dependent biophysical mechanism for nacre formation.« less

How Far Is Quasar UV/Optical Variability from a Damped Random Walk at Low Frequency?

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

Guo Hengxiao; Wang Junxian; Cai Zhenyi

Studies have shown that UV/optical light curves of quasars can be described using the prevalent damped random walk (DRW) model, also known as the Ornstein–Uhlenbeck process. A white noise power spectral density (PSD) is expected at low frequency in this model; however, a direct observational constraint to the low-frequency PSD slope is difficult due to the limited lengths of the light curves available. Meanwhile, quasars show scatter in their DRW parameters that is too large to be attributed to uncertainties in the measurements and dependence on the variation of known physical factors. In this work we present simulations showing that,more » if the low-frequency PSD deviates from the DRW, the red noise leakage can naturally produce large scatter in the variation parameters measured from simulated light curves. The steeper the low-frequency PSD slope, the larger scatter we expect. Based on observations of SDSS Stripe 82 quasars, we find that the low-frequency PSD slope should be no steeper than −1.3. The actual slope could be flatter, which consequently requires that the quasar variabilities should be influenced by other unknown factors. We speculate that the magnetic field and/or metallicity could be such additional factors.« less

Waves propagating over a two-layer porous barrier on a seabed

NASA Astrophysics Data System (ADS)

Lin, Qiang; Meng, Qing-rui; Lu, Dong-qiang

2018-05-01

A research of wave propagation over a two-layer porous barrier, each layer of which is with different values of porosity and friction, is conducted with a theoretical model in the frame of linear potential flow theory. The model is more appropriate when the seabed consists of two different properties, such as rocks and breakwaters. It is assumed that the fluid is inviscid and incompressible and the motion is irrotational. The wave numbers in the porous region are complex ones, which are related to the decaying and propagating behaviors of wave modes. With the aid of the eigenfunction expansions, a new inner product of the eigenfunctions in the two-layer porous region is proposed to simplify the calculation. The eigenfunctions, under this new definition, possess the orthogonality from which the expansion coefficients can be easily deduced. Selecting the optimum truncation of the series, we derive a closed system of simultaneous linear equations for the same number of the unknown reflection and transmission coefficients. The effects of several physical parameters, including the porosity, friction, width, and depth of the porous barrier, on the dispersion relation, reflection and transmission coefficients are discussed in detail through the graphical representations of the solutions. It is concluded that these parameters have certain impacts on the reflection and transmission energy.

Some Physical and Computational Issues in Land Surface Data Assimilation of Satellite Skin Temperatures

NASA Astrophysics Data System (ADS)

Mackaro, Scott M.; McNider, Richard T.; Biazar, Arastoo Pour

2012-03-01

Skin temperatures that reflect the radiating temperature of a surface observed by infrared radiometers are one of the most widely available products from polar orbiting and geostationary satellites and the most commonly used satellite data in land surface assimilation. Past work has indicated that a simple land surface scheme with a few key parameters constrained by observations such as skin temperatures may be preferable to complex land use schemes with many unknown parameters. However, a true radiating skin temperature is sometimes not a prognostic variable in weather forecast models. Additionally, recent research has shown that skin temperatures cannot be directly used in surface similarity forms for inferring fluxes. This paper examines issues encountered in using satellite derived skin temperatures to improve surface flux specifications in weather forecast and air quality models. Attention is given to iterations necessary when attempting to nudge the surface energy budget equation to a desired state. Finally, the issue of mathematical operator splitting is examined in which the surface energy budget calculations are split with the atmospheric vertical diffusion calculations. However, the high level of connectivity between the surface and first atmospheric level means that the operator splitting leads to high frequency oscillations. These oscillations may hinder the assimilation of skin temperature derived moisture fluxes.

Bayesian prediction of future ice sheet volume using local approximation Markov chain Monte Carlo methods

NASA Astrophysics Data System (ADS)

Davis, A. D.; Heimbach, P.; Marzouk, Y.

2017-12-01

We develop a Bayesian inverse modeling framework for predicting future ice sheet volume with associated formal uncertainty estimates. Marine ice sheets are drained by fast-flowing ice streams, which we simulate using a flowline model. Flowline models depend on geometric parameters (e.g., basal topography), parameterized physical processes (e.g., calving laws and basal sliding), and climate parameters (e.g., surface mass balance), most of which are unknown or uncertain. Given observations of ice surface velocity and thickness, we define a Bayesian posterior distribution over static parameters, such as basal topography. We also define a parameterized distribution over variable parameters, such as future surface mass balance, which we assume are not informed by the data. Hyperparameters are used to represent climate change scenarios, and sampling their distributions mimics internal variation. For example, a warming climate corresponds to increasing mean surface mass balance but an individual sample may have periods of increasing or decreasing surface mass balance. We characterize the predictive distribution of ice volume by evaluating the flowline model given samples from the posterior distribution and the distribution over variable parameters. Finally, we determine the effect of climate change on future ice sheet volume by investigating how changing the hyperparameters affects the predictive distribution. We use state-of-the-art Bayesian computation to address computational feasibility. Characterizing the posterior distribution (using Markov chain Monte Carlo), sampling the full range of variable parameters and evaluating the predictive model is prohibitively expensive. Furthermore, the required resolution of the inferred basal topography may be very high, which is often challenging for sampling methods. Instead, we leverage regularity in the predictive distribution to build a computationally cheaper surrogate over the low dimensional quantity of interest (future ice sheet volume). Continual surrogate refinement guarantees asymptotic sampling from the predictive distribution. Directly characterizing the predictive distribution in this way allows us to assess the ice sheet's sensitivity to climate variability and change.

Model Reduction via Principe Component Analysis and Markov Chain Monte Carlo (MCMC) Methods

NASA Astrophysics Data System (ADS)

Gong, R.; Chen, J.; Hoversten, M. G.; Luo, J.

2011-12-01

Geophysical and hydrogeological inverse problems often include a large number of unknown parameters, ranging from hundreds to millions, depending on parameterization and problems undertaking. This makes inverse estimation and uncertainty quantification very challenging, especially for those problems in two- or three-dimensional spatial domains. Model reduction technique has the potential of mitigating the curse of dimensionality by reducing total numbers of unknowns while describing the complex subsurface systems adequately. In this study, we explore the use of principal component analysis (PCA) and Markov chain Monte Carlo (MCMC) sampling methods for model reduction through the use of synthetic datasets. We compare the performances of three different but closely related model reduction approaches: (1) PCA methods with geometric sampling (referred to as 'Method 1'), (2) PCA methods with MCMC sampling (referred to as 'Method 2'), and (3) PCA methods with MCMC sampling and inclusion of random effects (referred to as 'Method 3'). We consider a simple convolution model with five unknown parameters as our goal is to understand and visualize the advantages and disadvantages of each method by comparing their inversion results with the corresponding analytical solutions. We generated synthetic data with noise added and invert them under two different situations: (1) the noised data and the covariance matrix for PCA analysis are consistent (referred to as the unbiased case), and (2) the noise data and the covariance matrix are inconsistent (referred to as biased case). In the unbiased case, comparison between the analytical solutions and the inversion results show that all three methods provide good estimates of the true values and Method 1 is computationally more efficient. In terms of uncertainty quantification, Method 1 performs poorly because of relatively small number of samples obtained, Method 2 performs best, and Method 3 overestimates uncertainty due to inclusion of random effects. However, in the biased case, only Method 3 correctly estimates all the unknown parameters, and both Methods 1 and 2 provide wrong values for the biased parameters. The synthetic case study demonstrates that if the covariance matrix for PCA analysis is inconsistent with true models, the PCA methods with geometric or MCMC sampling will provide incorrect estimates.

Integrating Advanced Physical Training Programs into the Marine Corps

DTIC Science & Technology

2009-02-20

all of which are available to the public for use. However, the most popular training program amongst Marines is CrossFit6. While CrossFit is a...the CrossFit program and consequently a fee is required to participate in the CrossFit 3 P90X, Extreme Body Workout, (unknown... CrossFit : Forging Elite Fitness, (unknown, CrossFit : Forging Elite Fitness n.d.), CrossFit , as advertised on its website, is a principal strength and

Fresnel's original interpretation of complex numbers in 19th century optics

NASA Astrophysics Data System (ADS)

Karam, Ricardo

2018-04-01

In 1823, Fresnel published an original (physical) interpretation of complex numbers in his investigations of refraction and reflection of polarized light. This is arguably the first time that complex numbers were given a physical interpretation, which led to a better understanding of elliptical and circular polarizations. This rather unknown episode of the history of physics is described in this work, and some of the pedagogical lessons that can be extracted from it are discussed.

Mass and p-factor of the Type II Cepheid OGLE-LMC-T2CEP-098 in a Binary System

NASA Astrophysics Data System (ADS)

Pilecki, Bogumił; Gieren, Wolfgang; Smolec, Radosław; Pietrzyński, Grzegorz; Thompson, Ian B.; Anderson, Richard I.; Bono, Giuseppe; Soszyński, Igor; Kervella, Pierre; Nardetto, Nicolas; Taormina, Mónica; Stȩpień, Kazimierz; Wielgórski, Piotr

2017-06-01

We present the results of a study of the type II Cepheid (P puls = 4.974 days) in the eclipsing binary system OGLE-LMC-T2CEP-098 (P orb = 397.2 days). The Cepheid belongs to the peculiar W Vir group, for which the evolutionary status is virtually unknown. It is the first single-lined system with a pulsating component analyzed using the method developed by Pilecki et al. We show that the presence of a pulsator makes it possible to derive accurate physical parameters of the stars even if radial velocities can be measured for only one of the components. We have used four different methods to limit and estimate the physical parameters, eventually obtaining precise results by combining pulsation theory with the spectroscopic and photometric solutions. The Cepheid radius, mass, and temperature are 25.3+/- 0.2 {R}⊙ , 1.51+/- 0.09 {M}⊙ , and 5300+/- 100 {{K}}, respectively, while its companion has a similar size (26.3 {R}⊙ ), but is more massive (6.8 {M}⊙ ) and hotter (9500 K). Our best estimate for the p-factor of the Cepheid is 1.30+/- 0.03. The mass, position on the period-luminosity diagram, and pulsation amplitude indicate that the pulsating component is very similar to the Anomalous Cepheids, although it has a much longer period and is redder in color. The very unusual combination of the components suggest that the system has passed through a mass-transfer phase in its evolution. More complicated internal structure would then explain its peculiarity. This paper includes data gathered with the 6.5 m Magellan Clay Telescope at Las Campanas Observatory, Chile.

Dose-Response Evaluation of Braslet-M Occlusion Cuffs

NASA Technical Reports Server (NTRS)

Ebert, Douglas; Garcia, Kathleen; Sargsyan, Ashot E.; Ham, David; Hamilton, Douglas; Dulchavsky, Scott A.

2010-01-01

Introduction: Braslet-M is a set of special elasticized thigh cuffs used by the Russian space agency to reduce the effects of the head-ward fluid shift during early adaptation to microgravity by sequestering fluid in the lower extremities. Currently, no imaging modalities are used in the calibration of the device, and the pressure required to produce a predictable physiological response is unknown. This investigation intends to relate the pressure exerted by the cuffs to the extent of fluid redistribution and commensurate physiological effects. Materials and Methods: Ten healthy subjects with standardized fluid intake participated in the study. Data collection included femoral and internal jugular vein imaging in two orthogonal planes, pulsed Doppler of cervical and femoral vessels and middle cerebral artery, optic nerve imaging, and echocardiography. Braslet-M cuff pressure was monitored at the skin interface using pre-calibrated pressure sensors. Using 6 and 30 head-down tilt in two separate sessions, the effect of Braslet-M was assessed while incrementally tightening the cuffs. Cuffs were then simultaneously released to document the resulting hemodynamic change. Results: Preliminary analysis shows correlation between physical pressure exerted by the Braslet-M device and several parameters such as jugular and femoral vein cross-sections, resistivity of the lower extremity vascular bed, and others. A number of parameters reflect blood redistribution and will be used to determine the therapeutic range of the device and to prevent unsafe application. Conclusion: Braslet-M exerts a physical effect that can be measured and correlated with many changes in central and peripheral hemodynamics. Analysis of the full data set will be required to make definitive recommendations regarding the range of safe therapeutic application. Objective data and subjective responses suggest that a safer and equally effective use of Braslet can be achieved when compared with the current non-imaging calibration techniques.

Noise parameter estimation for poisson corrupted images using variance stabilization transforms.

PubMed

Jin, Xiaodan; Xu, Zhenyu; Hirakawa, Keigo

2014-03-01

Noise is present in all images captured by real-world image sensors. Poisson distribution is said to model the stochastic nature of the photon arrival process and agrees with the distribution of measured pixel values. We propose a method for estimating unknown noise parameters from Poisson corrupted images using properties of variance stabilization. With a significantly lower computational complexity and improved stability, the proposed estimation technique yields noise parameters that are comparable in accuracy to the state-of-art methods.

An easy-to-use tool for the evaluation of leachate production at landfill sites.

PubMed

Grugnaletti, Matteo; Pantini, Sara; Verginelli, Iason; Lombardi, Francesco

2016-09-01

A simulation program for the evaluation of leachate generation at landfill sites is herein presented. The developed tool is based on a water balance model that accounts for all the key processes influencing leachate generation through analytical and empirical equations. After a short description of the tool, different simulations on four Italian landfill sites are shown. The obtained results revealed that when literature values were assumed for the unknown input parameters, the model provided a rough estimation of the leachate production measured in the field. In this case, indeed, the deviations between observed and predicted data appeared, in some cases, significant. Conversely, by performing a preliminary calibration for some of the unknown input parameters (e.g. initial moisture content of wastes, compression index), in nearly all cases the model performances significantly improved. These results although showed the potential capability of a water balance model to estimate the leachate production at landfill sites also highlighted the intrinsic limitation of a deterministic approach to accurately forecast the leachate production over time. Indeed, parameters such as the initial water content of incoming waste and the compression index, that have a great influence on the leachate production, may exhibit temporal variation due to seasonal changing of weather conditions (e.g. rainfall, air humidity) as well as to seasonal variability in the amount and type of specific waste fractions produced (e.g. yard waste, food, plastics) that make their prediction quite complicated. In this sense, we believe that a tool such as the one proposed in this work that requires a limited number of unknown parameters, can be easier handled to quantify the uncertainties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamic identification of axial force and boundary restraints in tie rods and cables with uncertainty quantification using Set Inversion Via Interval Analysis

NASA Astrophysics Data System (ADS)

Kernicky, Timothy; Whelan, Matthew; Al-Shaer, Ehab

2018-06-01

A methodology is developed for the estimation of internal axial force and boundary restraints within in-service, prismatic axial force members of structural systems using interval arithmetic and contractor programming. The determination of the internal axial force and end restraints in tie rods and cables using vibration-based methods has been a long standing problem in the area of structural health monitoring and performance assessment. However, for structural members with low slenderness where the dynamics are significantly affected by the boundary conditions, few existing approaches allow for simultaneous identification of internal axial force and end restraints and none permit for quantifying the uncertainties in the parameter estimates due to measurement uncertainties. This paper proposes a new technique for approaching this challenging inverse problem that leverages the Set Inversion Via Interval Analysis algorithm to solve for the unknown axial forces and end restraints using natural frequency measurements. The framework developed offers the ability to completely enclose the feasible solutions to the parameter identification problem, given specified measurement uncertainties for the natural frequencies. This ability to propagate measurement uncertainty into the parameter space is critical towards quantifying the confidence in the individual parameter estimates to inform decision-making within structural health diagnosis and prognostication applications. The methodology is first verified with simulated data for a case with unknown rotational end restraints and then extended to a case with unknown translational and rotational end restraints. A laboratory experiment is then presented to demonstrate the application of the methodology to an axially loaded rod with progressively increased end restraint at one end.

Insight into nuclear body formation of phytochromes through stochastic modelling and experiment.

PubMed

Grima, Ramon; Sonntag, Sebastian; Venezia, Filippo; Kircher, Stefan; Smith, Robert W; Fleck, Christian

2018-05-01

Spatial relocalization of proteins is crucial for the correct functioning of living cells. An interesting example of spatial ordering is the light-induced clustering of plant photoreceptor proteins. Upon irradiation by white or red light, the red light-active phytochrome, phytochrome B, enters the nucleus and accumulates in large nuclear bodies. The underlying physical process of nuclear body formation remains unclear, but phytochrome B is thought to coagulate via a simple protein-protein binding process. We measure, for the first time, the distribution of the number of phytochrome B-containing nuclear bodies as well as their volume distribution. We show that the experimental data cannot be explained by a stochastic model of nuclear body formation via simple protein-protein binding processes using physically meaningful parameter values. Rather modelling suggests that the data is consistent with a two step process: a fast nucleation step leading to macroparticles followed by a subsequent slow step in which the macroparticles bind to form the nuclear body. An alternative explanation for the observed nuclear body distribution is that the phytochromes bind to a so far unknown molecular structure. We believe it is likely this result holds more generally for other nuclear body-forming plant photoreceptors and proteins. Creative Commons Attribution license.

Generation of Coherent Structures After Cosmic Inflation

NASA Astrophysics Data System (ADS)

Gleiser, Marcelo

2013-04-01

The transition from inflation to power-law expansion is a rich nonlinear nonequilibrium physical process. For this reason, much is still unknown about this epoch in early universe physics, which has been dubbed the ``new big bang" by many colleagues. Here I describe results from the past few years of research, some of which in collaboration with Noah Graham and Nik Stamatopoulos, where we explored the generation on extended structures at the end of inflation known as oscillons. In particular, in hybrid inflation models we solve the coupled Einstein-Klein-Gordon equations to find that as the field responsible for inflating the universe rolls down to oscillate about its minimum, it triggers the formation of long-lived two-field oscillons, which can contribute up to 20% of the total energy density of the universe. We show that these oscillons emerge for a wide range of parameters consistent with WMAP 7-year data. These objects contain total energy of about 25x10^20 GeV, localized in a region of approximate radius 6x10-26 cm. We argue that these structures could have played a key role during the reheating of the universe, influencing the reheating temperature. We also explore the notion that these objects will appear in most symmetry-breaking phase transitions.

Role of pressure anisotropy on relativistic compact stars

NASA Astrophysics Data System (ADS)

Maurya, S. K.; Banerjee, Ayan; Hansraj, Sudan

2018-02-01

We investigate a compact spherically symmetric relativistic body with anisotropic particle pressure profiles. The distribution possesses characteristics relevant to modeling compact stars within the framework of general relativity. For this purpose, we consider a spatial metric potential of Korkina and Orlyanskii [Ukr. Phys. J. 36, 885 (1991)] type in order to solve the Einstein field equations. An additional prescription we make is that the pressure anisotropy parameter takes the functional form proposed by Lake [Phys. Rev. D 67, 104015 (2003), 10.1103/PhysRevD.67.104015]. Specifying these two geometric quantities allows for further analysis to be carried out in determining unknown constants and obtaining a limit of the mass-radius diagram, which adequately describes compact strange star candidates like Her X-1 and SMC X-1. Using the anisotropic Tolman-Oppenheimer-Volkoff equations, we explore the hydrostatic equilibrium and the stability of such compact objects. Then, we investigate other physical features of this model, such as the energy conditions, speeds of sound, and compactness of the star, in detail and show that our results satisfy all the required elementary conditions for a physically acceptable stellar model. The results obtained are useful in analyzing the stability of other anisotropic compact objects like white dwarfs, neutron stars, and gravastars.

Using synthetic biology to make cells tomorrow's test tubes.

PubMed

Garcia, Hernan G; Brewster, Robert C; Phillips, Rob

2016-04-18

The main tenet of physical biology is that biological phenomena can be subject to the same quantitative and predictive understanding that physics has afforded in the context of inanimate matter. However, the inherent complexity of many of these biological processes often leads to the derivation of complex theoretical descriptions containing a plethora of unknown parameters. Such complex descriptions pose a conceptual challenge to the establishment of a solid basis for predictive biology. In this article, we present various exciting examples of how synthetic biology can be used to simplify biological systems and distill these phenomena down to their essential features as a means to enable their theoretical description. Here, synthetic biology goes beyond previous efforts to engineer nature and becomes a tool to bend nature to understand it. We discuss various recent and classic experiments featuring applications of this synthetic approach to the elucidation of problems ranging from bacteriophage infection, to transcriptional regulation in bacteria and in developing embryos, to evolution. In all of these examples, synthetic biology provides the opportunity to turn cells into the equivalent of a test tube, where biological phenomena can be reconstituted and our theoretical understanding put to test with the same ease that these same phenomena can be studied in the in vitro setting.

[Correlation between physical characteristics of sticks and quality of traditional Chinese medicine pills prepared by plastic molded method].

PubMed

Wang, Ling; Xian, Jiechen; Hong, Yanlong; Lin, Xiao; Feng, Yi

2012-05-01

To quantify the physical characteristics of sticks of traditional Chinese medicine (TCM) honeyed pills prepared by the plastic molded method and the correlation of adhesiveness and plasticity-related parameters of sticks and quality of pills, in order to find major parameters and the appropriate range impacting pill quality. Sticks were detected by texture analyzer for their physical characteristic parameters such as hardness and compression action, and pills were observed by visual evaluation for their quality. The correlation of both data was determined by the stepwise discriminant analysis. Stick physical characteristic parameter l(CD) can exactly depict the adhesiveness, with the discriminant equation of Y0 - Y1 = 6.415 - 41.594l(CD). When Y0 < Y1, pills were scattered well; when Y0 > Y1, pills were adhesive with each other. Pills' physical characteristic parameters l(CD) and l(AC), Ar, Tr can exactly depict smoothness of pills, with the discriminant equation of Z0 - Z1 = -195.318 + 78.79l(AC) - 3 258. 982Ar + 3437.935Tr. When Z0 < Z1, pills were smooth on surface. When Z0 > Z1, pills were rough on surface. The stepwise discriminant analysis is made to show the obvious correlation between key physical characteristic parameters l(CD) and l(AC), Ar, Tr of sticks and appearance quality of pills, defining the molding process for preparing pills by the plastic molded and qualifying ranges of key physical characteristic parameters characterizing intermediate sticks, in order to provide theoretical basis for prescription screening and technical parameter adjustment for pills.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

DOE PAGES

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.; ...

2018-01-17

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

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

Boiteau, Rene M.; Hoyt, David W.; Nicora, Carrie D.

Here, we introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS 2), and NMR in a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS 2 approach is well suited for discovery ofmore » new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.« less

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

PubMed Central

Hoyt, David W.; Nicora, Carrie D.; Kinmonth-Schultz, Hannah A.; Ward, Joy K.

2018-01-01

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases. PMID:29342073

Identifying mechanical property parameters of planetary soil using in-situ data obtained from exploration rovers

NASA Astrophysics Data System (ADS)

Ding, Liang; Gao, Haibo; Liu, Zhen; Deng, Zongquan; Liu, Guangjun

2015-12-01

Identifying the mechanical property parameters of planetary soil based on terramechanics models using in-situ data obtained from autonomous planetary exploration rovers is both an important scientific goal and essential for control strategy optimization and high-fidelity simulations of rovers. However, identifying all the terrain parameters is a challenging task because of the nonlinear and coupling nature of the involved functions. Three parameter identification methods are presented in this paper to serve different purposes based on an improved terramechanics model that takes into account the effects of slip, wheel lugs, etc. Parameter sensitivity and coupling of the equations are analyzed, and the parameters are grouped according to their sensitivity to the normal force, resistance moment and drawbar pull. An iterative identification method using the original integral model is developed first. In order to realize real-time identification, the model is then simplified by linearizing the normal and shearing stresses to derive decoupled closed-form analytical equations. Each equation contains one or two groups of soil parameters, making step-by-step identification of all the unknowns feasible. Experiments were performed using six different types of single-wheels as well as a four-wheeled rover moving on planetary soil simulant. All the unknown model parameters were identified using the measured data and compared with the values obtained by conventional experiments. It is verified that the proposed iterative identification method provides improved accuracy, making it suitable for scientific studies of soil properties, whereas the step-by-step identification methods based on simplified models require less calculation time, making them more suitable for real-time applications. The models have less than 10% margin of error comparing with the measured results when predicting the interaction forces and moments using the corresponding identified parameters.

Autopilot for frequency-modulation atomic force microscopy.

PubMed

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

NASA Astrophysics Data System (ADS)

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri

2015-10-01

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loops require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.

Autopilot for frequency-modulation atomic force microscopy

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

Kuchuk, Kfir; Schlesinger, Itai; Sivan, Uri, E-mail: phsivan@tx.technion.ac.il

2015-10-15

One of the most challenging aspects of operating an atomic force microscope (AFM) is finding optimal feedback parameters. This statement applies particularly to frequency-modulation AFM (FM-AFM), which utilizes three feedback loops to control the cantilever excitation amplitude, cantilever excitation frequency, and z-piezo extension. These loops are regulated by a set of feedback parameters, tuned by the user to optimize stability, sensitivity, and noise in the imaging process. Optimization of these parameters is difficult due to the coupling between the frequency and z-piezo feedback loops by the non-linear tip-sample interaction. Four proportional-integral (PI) parameters and two lock-in parameters regulating these loopsmore » require simultaneous optimization in the presence of a varying unknown tip-sample coupling. Presently, this optimization is done manually in a tedious process of trial and error. Here, we report on the development and implementation of an algorithm that computes the control parameters automatically. The algorithm reads the unperturbed cantilever resonance frequency, its quality factor, and the z-piezo driving signal power spectral density. It analyzes the poles and zeros of the total closed loop transfer function, extracts the unknown tip-sample transfer function, and finds four PI parameters and two lock-in parameters for the frequency and z-piezo control loops that optimize the bandwidth and step response of the total system. Implementation of the algorithm in a home-built AFM shows that the calculated parameters are consistently excellent and rarely require further tweaking by the user. The new algorithm saves the precious time of experienced users, facilitates utilization of FM-AFM by casual users, and removes the main hurdle on the way to fully automated FM-AFM.« less

Bayesian source term determination with unknown covariance of measurements

NASA Astrophysics Data System (ADS)

Belal, Alkomiet; Tichý, Ondřej; Šmídl, Václav

2017-04-01

Determination of a source term of release of a hazardous material into the atmosphere is a very important task for emergency response. We are concerned with the problem of estimation of the source term in the conventional linear inverse problem, y = Mx, where the relationship between the vector of observations y is described using the source-receptor-sensitivity (SRS) matrix M and the unknown source term x. Since the system is typically ill-conditioned, the problem is recast as an optimization problem minR,B(y - Mx)TR-1(y - Mx) + xTB-1x. The first term minimizes the error of the measurements with covariance matrix R, and the second term is a regularization of the source term. There are different types of regularization arising for different choices of matrices R and B, for example, Tikhonov regularization assumes covariance matrix B as the identity matrix multiplied by scalar parameter. In this contribution, we adopt a Bayesian approach to make inference on the unknown source term x as well as unknown R and B. We assume prior on x to be a Gaussian with zero mean and unknown diagonal covariance matrix B. The covariance matrix of the likelihood R is also unknown. We consider two potential choices of the structure of the matrix R. First is the diagonal matrix and the second is a locally correlated structure using information on topology of the measuring network. Since the inference of the model is intractable, iterative variational Bayes algorithm is used for simultaneous estimation of all model parameters. The practical usefulness of our contribution is demonstrated on an application of the resulting algorithm to real data from the European Tracer Experiment (ETEX). This research is supported by EEA/Norwegian Financial Mechanism under project MSMT-28477/2014 Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI).

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities.

PubMed

Zouari, Farouk; Ibeas, Asier; Boulkroune, Abdesselem; Cao, Jinde; Mehdi Arefi, Mohammad

2018-06-01

This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control directions, unknown actuator nonlinearities and output constraints. Firstly, the mean value theorem and the Gaussian error function are introduced to eliminate the difficulties that arise from the nonaffine structures and the unknown actuator nonlinearities, respectively. Secondly, the immeasurable tracking error variables are suitably estimated by constructing a fractional-order linear observer. Thirdly, the neural network, the Razumikhin Lemma, the variable separation approach, and the smooth Nussbaum-type function are used to deal with the uncertain nonlinear dynamics, the unknown time-varying delays, the nonstrict feedback and the unknown control directions, respectively. Fourthly, asymmetric barrier Lyapunov functions are employed to overcome the violation of the output constraints and to tune online the parameters of the adaptive neural controller. Through rigorous analysis, it is proved that the boundedness of all variables in the closed-loop system and the semi global asymptotic tracking are ensured without transgression of the constraints. The principal contributions of this study can be summarized as follows: (1) based on Caputo's definitions and new lemmas, methods concerning the controllability, observability and stability analysis of integer-order systems are extended to fractional-order ones, (2) the output tracking objective for a relatively large class of uncertain systems is achieved with a simple controller and less tuning parameters. Finally, computer-simulation studies from the robotic field are given to demonstrate the effectiveness of the proposed controller. Copyright © 2018 Elsevier Ltd. All rights reserved.

MXLKID: a maximum likelihood parameter identifier. [In LRLTRAN for CDC 7600

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

Gavel, D.T.

MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. A detailed mathematical description of the algorithm is given in the appendices. 24 figures, 6 tables.

Physics-Based Methods of Failure Analysis and Diagnostics in Human Space Flight

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry Georgievich; Hafiychuk, Vasyl Nmn; Osipov, Viatcheslav V.; Patterson-Hine, F. Ann

2010-01-01

The Integrated Health Management (IHM) for the future aerospace systems requires to interface models of multiple subsystems in an efficient and accurate information environment at the earlier stages of system design. The complexity of modern aeronautic and aircraft systems (including e.g. the power distribution, flight control, solid and liquid motors) dictates employment of hybrid models and high-level reasoners for analysing mixed continuous and discrete information flow involving multiple modes of operation in uncertain environments, unknown state variables, heterogeneous software and hardware components. To provide the information link between key design/performance parameters and high-level reasoners we rely on development of multi-physics performance models, distributed sensors networks, and fault diagnostic and prognostic (FD&P) technologies in close collaboration with system designers. The main challenges of our research are related to the in-flight assessment of the structural stability, engine performance, and trajectory control. The main goal is to develop an intelligent IHM that not only enhances components and system reliability, but also provides a post-flight feedback helping to optimize design of the next generation of aerospace systems. Our efforts are concentrated on several directions of the research. One of the key components of our strategy is an innovative approach to the diagnostics/prognostics based on the real time dynamical inference (DI) technologies extended to encompass hybrid systems with hidden state trajectories. The major investments are into the multiphysics performance modelling that provides an access of the FD&P technologies to the main performance parameters of e.g. solid and liquid rocket motors and composite materials of the nozzle and case. Some of the recent results of our research are discussed in this chapter. We begin by introducing the problem of dynamical inference of stochastic nonlinear models and reviewing earlier results. Next, we present our analytical approach to the solution of this problem based on the path integral formulation. The resulting algorithm does not require an extensive global search for the model parameters, provides optimal compensation for the effects of dynamical noise, and is robust for a broad range of dynamical models. In the following Section the strengths of the algorithm are illustrated illustrated by inferring the parameters of the stochastic Lorenz system and comparing the results with those of earlier research. Next, we discuss a number of recent results in application to the development of the IHM for aerospace system. Firstly, we apply dynamical inference approach to a solution of classical three tank problems with mixed unknown continuous and binary parameters. The problem is considered in the context of ground support system for filling fuel tanks of liquid rocket motors. It is shown that the DI algorithm is well suited for successful solution of a hybrid version of this benchmark problem even in the presence of additional periodic and stochastic perturbation of unknown strength. Secondly, we illustrate our approach by its application to an analysis of the nozzle fault in a solid rocket motor (SRM). The internal ballistics of the SRM is modelled as a set of one-dimensional partial differential equations coupled to the dynamics of the propellant regression. In this example we are specifically focussed on the inference of discrete and continuous parameters of the nozzle blocking fault and on the possibility of an application of the DI algorithm to reducing the probability of "misses" of an on-board FD&P for SRM. In the next section re-contact problem caused by first stage/upper stage separation failure is discussed. The reaction forces imposed on the nozzle of the upper stage during the re-contact and their connection to the nozzle damage and to the thrust vector control (TVC) signal are obtained. It is shown that transient impact induced torquean be modelled as a response of an effective damped oscillator. A possible application of the DI algorithm to the inference of damage parameters and predicting fault dynamics ahead of time using the actuator signal is discussed. Finally, we formulate Bayesian inferential framework for development of the IHM system for in-flight structural health monitoring (SHM) of composite materials. We consider the signal generated by piezoelectric actuator mounted on composite structure generating elastic waves in it. The signal received by the sensor is than compared with the baseline signal. The possibility of damage inference is discussed in the context of development of the SHM.

Not Normal: the uncertainties of scientific measurements

NASA Astrophysics Data System (ADS)

Bailey, David C.

2017-01-01

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.

Not Normal: the uncertainties of scientific measurements

PubMed Central

2017-01-01

Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student’s t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply. PMID:28280557

Model-based damage evaluation of layered CFRP structures

NASA Astrophysics Data System (ADS)

Munoz, Rafael; Bochud, Nicolas; Rus, Guillermo; Peralta, Laura; Melchor, Juan; Chiachío, Juan; Chiachío, Manuel; Bond, Leonard J.

2015-03-01

An ultrasonic evaluation technique for damage identification of layered CFRP structures is presented. This approach relies on a model-based estimation procedure that combines experimental data and simulation of ultrasonic damage-propagation interactions. The CFPR structure, a [0/90]4s lay-up, has been tested in an immersion through transmission experiment, where a scan has been performed on a damaged specimen. Most ultrasonic techniques in industrial practice consider only a few features of the received signals, namely, time of flight, amplitude, attenuation, frequency contents, and so forth. In this case, once signals are captured, an algorithm is used to reconstruct the complete signal waveform and extract the unknown damage parameters by means of modeling procedures. A linear version of the data processing has been performed, where only Young modulus has been monitored and, in a second nonlinear version, the first order nonlinear coefficient β was incorporated to test the possibility of detection of early damage. The aforementioned physical simulation models are solved by the Transfer Matrix formalism, which has been extended from linear to nonlinear harmonic generation technique. The damage parameter search strategy is based on minimizing the mismatch between the captured and simulated signals in the time domain in an automated way using Genetic Algorithms. Processing all scanned locations, a C-scan of the parameter of each layer can be reconstructed, obtaining the information describing the state of each layer and each interface. Damage can be located and quantified in terms of changes in the selected parameter with a measurable extension. In the case of the nonlinear coefficient of first order, evidence of higher sensitivity to damage than imaging the linearly estimated Young Modulus is provided.

Accounting for Parameter Uncertainty in Complex Atmospheric Models, With an Application to Greenhouse Gas Emissions Evaluation

NASA Astrophysics Data System (ADS)

Swallow, B.; Rigby, M. L.; Rougier, J.; Manning, A.; Thomson, D.; Webster, H. N.; Lunt, M. F.; O'Doherty, S.

2016-12-01

In order to understand underlying processes governing environmental and physical phenomena, a complex mathematical model is usually required. However, there is an inherent uncertainty related to the parameterisation of unresolved processes in these simulators. Here, we focus on the specific problem of accounting for uncertainty in parameter values in an atmospheric chemical transport model. Systematic errors introduced by failing to account for these uncertainties have the potential to have a large effect on resulting estimates in unknown quantities of interest. One approach that is being increasingly used to address this issue is known as emulation, in which a large number of forward runs of the simulator are carried out, in order to approximate the response of the output to changes in parameters. However, due to the complexity of some models, it is often unfeasible to run large numbers of training runs that is usually required for full statistical emulators of the environmental processes. We therefore present a simplified model reduction method for approximating uncertainties in complex environmental simulators without the need for very large numbers of training runs. We illustrate the method through an application to the Met Office's atmospheric transport model NAME. We show how our parameter estimation framework can be incorporated into a hierarchical Bayesian inversion, and demonstrate the impact on estimates of UK methane emissions, using atmospheric mole fraction data. We conclude that accounting for uncertainties in the parameterisation of complex atmospheric models is vital if systematic errors are to be minimized and all relevant uncertainties accounted for. We also note that investigations of this nature can prove extremely useful in highlighting deficiencies in the simulator that might otherwise be missed.

On estimating the phase of periodic waveform in additive Gaussian noise, part 2

NASA Astrophysics Data System (ADS)

Rauch, L. L.

1984-11-01

Motivated by advances in signal processing technology that support more complex algorithms, a new look is taken at the problem of estimating the phase and other parameters of a periodic waveform in additive Gaussian noise. The general problem was introduced and the maximum a posteriori probability criterion with signal space interpretation was used to obtain the structures of optimum and some suboptimum phase estimators for known constant frequency and unknown constant phase with an a priori distribution. Optimal algorithms are obtained for some cases where the frequency is a parameterized function of time with the unknown parameters and phase having a joint a priori distribution. In the last section, the intrinsic and extrinsic geometry of hypersurfaces is introduced to provide insight to the estimation problem for the small noise and large noise cases.

On Estimating the Phase of Periodic Waveform in Additive Gaussian Noise, Part 2

NASA Technical Reports Server (NTRS)

Rauch, L. L.

1984-01-01

Motivated by advances in signal processing technology that support more complex algorithms, a new look is taken at the problem of estimating the phase and other parameters of a periodic waveform in additive Gaussian noise. The general problem was introduced and the maximum a posteriori probability criterion with signal space interpretation was used to obtain the structures of optimum and some suboptimum phase estimators for known constant frequency and unknown constant phase with an a priori distribution. Optimal algorithms are obtained for some cases where the frequency is a parameterized function of time with the unknown parameters and phase having a joint a priori distribution. In the last section, the intrinsic and extrinsic geometry of hypersurfaces is introduced to provide insight to the estimation problem for the small noise and large noise cases.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Applications

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1998-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

3D tomographic reconstruction using geometrical models

NASA Astrophysics Data System (ADS)

Battle, Xavier L.; Cunningham, Gregory S.; Hanson, Kenneth M.

1997-04-01

We address the issue of reconstructing an object of constant interior density in the context of 3D tomography where there is prior knowledge about the unknown shape. We explore the direct estimation of the parameters of a chosen geometrical model from a set of radiographic measurements, rather than performing operations (segmentation for example) on a reconstructed volume. The inverse problem is posed in the Bayesian framework. A triangulated surface describes the unknown shape and the reconstruction is computed with a maximum a posteriori (MAP) estimate. The adjoint differentiation technique computes the derivatives needed for the optimization of the model parameters. We demonstrate the usefulness of the approach and emphasize the techniques of designing forward and adjoint codes. We use the system response of the University of Arizona Fast SPECT imager to illustrate this method by reconstructing the shape of a heart phantom.

Global identifiability of linear compartmental models--a computer algebra algorithm.

PubMed

Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C

1998-01-01

A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.

Multilevel adaptive control of nonlinear interconnected systems.

PubMed

Motallebzadeh, Farzaneh; Ozgoli, Sadjaad; Momeni, Hamid Reza

2015-01-01

This paper presents an adaptive backstepping-based multilevel approach for the first time to control nonlinear interconnected systems with unknown parameters. The system consists of a nonlinear controller at the first level to neutralize the interaction terms, and some adaptive controllers at the second level, in which the gains are optimally tuned using genetic algorithm. The presented scheme can be used in systems with strong couplings where completely ignoring the interactions leads to problems in performance or stability. In order to test the suitability of the method, two case studies are provided: the uncertain double and triple coupled inverted pendulums connected by springs with unknown parameters. The simulation results show that the method is capable of controlling the system effectively, in both regulation and tracking tasks. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

A Multi-Resolution Nonlinear Mapping Technique for Design and Analysis Application

NASA Technical Reports Server (NTRS)

Phan, Minh Q.

1997-01-01

This report describes a nonlinear mapping technique where the unknown static or dynamic system is approximated by a sum of dimensionally increasing functions (one-dimensional curves, two-dimensional surfaces, etc.). These lower dimensional functions are synthesized from a set of multi-resolution basis functions, where the resolutions specify the level of details at which the nonlinear system is approximated. The basis functions also cause the parameter estimation step to become linear. This feature is taken advantage of to derive a systematic procedure to determine and eliminate basis functions that are less significant for the particular system under identification. The number of unknown parameters that must be estimated is thus reduced and compact models obtained. The lower dimensional functions (identified curves and surfaces) permit a kind of "visualization" into the complexity of the nonlinearity itself.

Fuzzy similarity measures for ultrasound tissue characterization

NASA Astrophysics Data System (ADS)

Emara, Salem M.; Badawi, Ahmed M.; Youssef, Abou-Bakr M.

1995-03-01

Computerized ultrasound tissue characterization has become an objective means for diagnosis of diseases. It is difficult to differentiate diffuse liver diseases, namely cirrhotic and fatty liver from a normal one, by visual inspection from the ultrasound images. The visual criteria for differentiating diffused diseases is rather confusing and highly dependent upon the sonographer's experience. The need for computerized tissue characterization is thus justified to quantitatively assist the sonographer for accurate differentiation and to minimize the degree of risk from erroneous interpretation. In this paper we used the fuzzy similarity measure as an approximate reasoning technique to find the maximum degree of matching between an unknown case defined by a feature vector and a family of prototypes (knowledge base). The feature vector used for the matching process contains 8 quantitative parameters (textural, acoustical, and speckle parameters) extracted from the ultrasound image. The steps done to match an unknown case with the family of prototypes (cirr, fatty, normal) are: Choosing the membership functions for each parameter, then obtaining the fuzzification matrix for the unknown case and the family of prototypes, then by the linguistic evaluation of two fuzzy quantities we obtain the similarity matrix, then by a simple aggregation method and the fuzzy integrals we obtain the degree of similarity. Finally, we find that the similarity measure results are comparable to the neural network classification techniques and it can be used in medical diagnosis to determine the pathology of the liver and to monitor the extent of the disease.

Physics-based forecasting of induced seismicity at Groningen gas field, the Netherlands

NASA Astrophysics Data System (ADS)

Dempsey, David; Suckale, Jenny

2017-08-01

Earthquakes induced by natural gas extraction from the Groningen reservoir, the Netherlands, put local communities at risk. Responsible operation of a reservoir whose gas reserves are of strategic importance to the country requires understanding of the link between extraction and earthquakes. We synthesize observations and a model for Groningen seismicity to produce forecasts for felt seismicity (M > 2.5) in the period February 2017 to 2024. Our model accounts for poroelastic earthquake triggering and rupture on the 325 largest reservoir faults, using an ensemble approach to model unknown heterogeneity and replicate earthquake statistics. We calculate probability distributions for key model parameters using a Bayesian method that incorporates the earthquake observations with a nonhomogeneous Poisson process. Our analysis indicates that the Groningen reservoir was not critically stressed prior to the start of production. Epistemic uncertainty and aleatoric uncertainty are incorporated into forecasts for three different future extraction scenarios. The largest expected earthquake was similar for all scenarios, with a 5% likelihood of exceeding M 4.0.

Fast estimate of Hartley entropy in image sharpening

NASA Astrophysics Data System (ADS)

Krbcová, Zuzana; Kukal, Jaromír.; Svihlik, Jan; Fliegel, Karel

2016-09-01

Two classes of linear IIR filters: Laplacian of Gaussian (LoG) and Difference of Gaussians (DoG) are frequently used as high pass filters for contextual vision and edge detection. They are also used for image sharpening when linearly combined with the original image. Resulting sharpening filters are radially symmetric in spatial and frequency domains. Our approach is based on the radial approximation of unknown optimal filter, which is designed as a weighted sum of Gaussian filters with various radii. The novel filter is designed for MRI image enhancement where the image intensity represents anatomical structure plus additive noise. We prefer the gradient norm of Hartley entropy of whole image intensity as a measure which has to be maximized for the best sharpening. The entropy estimation procedure is as fast as FFT included in the filter but this estimate is a continuous function of enhanced image intensities. Physically motivated heuristic is used for optimum sharpening filter design by its parameter tuning. Our approach is compared with Wiener filter on MRI images.

Fitting primitive shapes in point clouds: a practical approach to improve autonomous underwater grasp specification of unknown objects

NASA Astrophysics Data System (ADS)

Fornas, D.; Sales, J.; Peñalver, A.; Pérez, J.; Fernández, J. J.; Marín, R.; Sanz, P. J.

2016-03-01

This article presents research on the subject of autonomous underwater robot manipulation. Ongoing research in underwater robotics intends to increase the autonomy of intervention operations that require physical interaction in order to achieve social benefits in fields such as archaeology or biology that cannot afford the expenses of costly underwater operations using remote operated vehicles. Autonomous grasping is still a very challenging skill, especially in underwater environments, with highly unstructured scenarios, limited availability of sensors and adverse conditions that affect the robot perception and control systems. To tackle these issues, we propose the use of vision and segmentation techniques that aim to improve the specification of grasping operations on underwater primitive shaped objects. Several sources of stereo information are used to gather 3D information in order to obtain a model of the object. Using a RANSAC segmentation algorithm, the model parameters are estimated and a set of feasible grasps are computed. This approach is validated in both simulated and real underwater scenarios.

COMPETITIVE ABILITY IN MALE HOUSE MICE (Mus musculus): GENETIC INFLUENCES

PubMed Central

Cunningham, Christopher B.; Ruff, James S.; Chase, Kevin; Potts, Wayne K.; Carrier, David R.

2013-01-01

Conspecifics of many animal species physically compete to gain reproductive resources and thus fitness. Despite the importance of competitive ability across the animal kingdom, specific traits that influence or underpin competitive ability are poorly characterized. Here, we investigate whether there are genetic influences on competitive ability within male house mice. Additionally, we examined if litter demographics (litter size and litter sex ratio) influence competitive ability. We phenotyped two generations for a male s ability to possess a reproductive resource--a prime nesting site--using semi-natural enclosures with mixed sex groupings. We used the animal model coupled with an extensive pedigree to estimate several genetic parameters. Competitive ability was found to be highly heritable, but only displayed a moderate genetic correlation to body mass. Interestingly, litter sex ratio had a weak negative influence on competitive ability. Litter size had no significant influence on competitive ability. Our study also highlights how much remians unknown about the proximal causes of competitive ability. PMID:23291957

Apparent ambiguities in the post-Newtonian expansion for binary systems

NASA Astrophysics Data System (ADS)

Porto, Rafael A.; Rothstein, Ira Z.

2017-07-01

We discuss the source of the apparent ambiguities arising in the calculation of the dynamics of binary black holes within the post-Newtonian framework. Divergences appear in both the near and far zone calculations, and may be of either ultraviolet (UV) or infrared (IR) nature. The effective field theory (EFT) formalism elucidates the origin of the singularities which may introduce apparent ambiguities. In particular, the only (physical) "ambiguity parameters" that necessitate a matching calculation correspond to unknown finite size effects, which first appear at fifth post-Newtonian (5PN) order for nonspinning bodies. We demonstrate that the ambiguities linked to IR divergences in the near zone, that plague the recent derivations of the binding energy at 4PN order, both in the Arnowitt, Deser, and Misner (ADM) and "Fokker-action" approach, can be resolved by implementing the so-called zero-bin subtraction in the EFT framework. The procedure yields ambiguity-free results without the need of additional information beyond the PN expansion.

Oblique wave trapping by vertical permeable membrane barriers located near a wall

NASA Astrophysics Data System (ADS)

Koley, Santanu; Sahoo, Trilochan

2017-12-01

The effectiveness of a vertical partial flexible porous membrane wave barrier located near a rigid vertical impermeable seawall for trapping obliquely incident surface gravity waves are analyzed in water of uniform depth under the assumption of linear water wave theory and small amplitude membrane barrier response. From the general formulation of the submerged membrane barrier, results for bottom-standing and surface-piercing barriers are computed and analyzed in special cases. Using the eigenfunction expansion method, the boundary-value problems are converted into series relations and then the required unknowns are obtained using the least squares approximation method. Various physical quantities of interests like reflection coefficient, wave energy dissipation, wave forces acting on the membrane barrier and the seawall are computed and analyzed for different values of the wave and structural parameters. The study will be useful in the design of the membrane wave barrier for the creation of tranquility zone in the lee side of the barrier to protect the seawall.

Statistical modeling of optical attenuation measurements in continental fog conditions

NASA Astrophysics Data System (ADS)

Khan, Muhammad Saeed; Amin, Muhammad; Awan, Muhammad Saleem; Minhas, Abid Ali; Saleem, Jawad; Khan, Rahimdad

2017-03-01

Free-space optics is an innovative technology that uses atmosphere as a propagation medium to provide higher data rates. These links are heavily affected by atmospheric channel mainly because of fog and clouds that act to scatter and even block the modulated beam of light from reaching the receiver end, hence imposing severe attenuation. A comprehensive statistical study of the fog effects and deep physical understanding of the fog phenomena are very important for suggesting improvements (reliability and efficiency) in such communication systems. In this regard, 6-months real-time measured fog attenuation data are considered and statistically investigated. A detailed statistical analysis related to each fog event for that period is presented; the best probability density functions are selected on the basis of Akaike information criterion, while the estimates of unknown parameters are computed by maximum likelihood estimation technique. The results show that most fog attenuation events follow normal mixture distribution and some follow the Weibull distribution.

Kinetic Monte Carlo simulations of ion-induced ripple formation: Dependence on flux, temperature, and defect concentration in the linear regime

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

Chason, E.; Chan, W. L.; Bharathi, M. S.

Low-energy ion bombardment produces spontaneous periodic structures (sputter ripples) on many surfaces. Continuum theories describe the pattern formation in terms of ion-surface interactions and surface relaxation kinetics, but many features of these models (such as defect concentration) are unknown or difficult to determine. In this work, we present results of kinetic Monte Carlo simulations that model surface evolution using discrete atomistic versions of the physical processes included in the continuum theories. From simulations over a range of parameters, we obtain the dependence of the ripple growth rate, wavelength, and velocity on the ion flux and temperature. The results are discussedmore » in terms of the thermally dependent concentration and diffusivity of ion-induced surface defects. We find that in the early stages of ripple formation the simulation results are surprisingly well described by the predictions of the continuum theory, in spite of simplifying approximations used in the continuum model.« less

Spin vectors of asteroids 21 Lutetia, 196 Philomela, 250 Bettina, 337 Devosa, and 804 Hispania

NASA Technical Reports Server (NTRS)

Michalowski, Tadeusz

1992-01-01

Such parameters as shape, orientation of spin axis, prograde or retrograde rotation are important for understanding the collisional evolution of asteroids since the primordial epochs of solar system history. These parameters remain unknown for most asteroids and poorly constrained for all but a few. This work presents results for five asteroids: 21, 196, 250, 337, and 804.

Advanced Aeroservoelastic Testing and Data Analysis (Les Essais Aeroservoelastiques et l’Analyse des Donnees).

DTIC Science & Technology

1995-11-01

network - based AFS concepts. Neural networks can addition of vanes in each engine exhaust for thrust provide...parameter estimation programs 19-11 8.6 Neural Network Based Methods unknown parameters of the postulated state space model Artificial neural network ...Forward Neural Network the network that the applicability of the recurrent neural and ii) Recurrent Neural Network [117-119]. network to

Decentralized adaptive neural control for high-order interconnected stochastic nonlinear time-delay systems with unknown system dynamics.

PubMed

Si, Wenjie; Dong, Xunde; Yang, Feifei

2018-03-01

This paper is concerned with the problem of decentralized adaptive backstepping state-feedback control for uncertain high-order large-scale stochastic nonlinear time-delay systems. For the control design of high-order large-scale nonlinear systems, only one adaptive parameter is constructed to overcome the over-parameterization, and neural networks are employed to cope with the difficulties raised by completely unknown system dynamics and stochastic disturbances. And then, the appropriate Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions of high-order large-scale systems for the first time. At last, on the basis of Lyapunov stability theory, the decentralized adaptive neural controller was developed, and it decreases the number of learning parameters. The actual controller can be designed so as to ensure that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges in the small neighborhood of zero. The simulation example is used to further show the validity of the design method. Copyright © 2018 Elsevier Ltd. All rights reserved.

The Universe Untangled; Modern physics for everyone

NASA Astrophysics Data System (ADS)

Pillitteri, Abigail

2017-04-01

Physics has always been a tricky subject for the general public. Millions are fascinated by the laws of the physical world, but there has been a lack of books written specifically for general readers. The Universe Untangled is for those who are curious; yet do not have an extensive mathematical background. It uses images, analogies and comprehensible language to cover popular topics of interest including the evolution of the Universe, fundamental forces, the nature of space and time, and the quest for knowing the unknown.

Fever of Unknown Origin in Childhood.

PubMed

Chusid, Michael J

2017-02-01

Childhood fever of unknown origin (FUO) is most often related to an underlying infection but can also be associated with a variety of neoplastic, rheumatologic, and inflammatory conditions. Repeated, focused reviews of patient history and physical examination are often helpful in suggesting a likely diagnosis. Diagnostic workup should be staged, usually leaving invasive testing for last. Advances in molecular genetic techniques have increased the importance of these assays in the diagnosis of FUO in children. Copyright © 2016 Elsevier Inc. All rights reserved.

A comparison between Gauss-Newton and Markov chain Monte Carlo basedmethods for inverting spectral induced polarization data for Cole-Coleparameters

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

Chen, Jinsong; Kemna, Andreas; Hubbard, Susan S.

2008-05-15

We develop a Bayesian model to invert spectral induced polarization (SIP) data for Cole-Cole parameters using Markov chain Monte Carlo (MCMC) sampling methods. We compare the performance of the MCMC based stochastic method with an iterative Gauss-Newton based deterministic method for Cole-Cole parameter estimation through inversion of synthetic and laboratory SIP data. The Gauss-Newton based method can provide an optimal solution for given objective functions under constraints, but the obtained optimal solution generally depends on the choice of initial values and the estimated uncertainty information is often inaccurate or insufficient. In contrast, the MCMC based inversion method provides extensive globalmore » information on unknown parameters, such as the marginal probability distribution functions, from which we can obtain better estimates and tighter uncertainty bounds of the parameters than with the deterministic method. Additionally, the results obtained with the MCMC method are independent of the choice of initial values. Because the MCMC based method does not explicitly offer single optimal solution for given objective functions, the deterministic and stochastic methods can complement each other. For example, the stochastic method can first be used to obtain the means of the unknown parameters by starting from an arbitrary set of initial values and the deterministic method can then be initiated using the means as starting values to obtain the optimal estimates of the Cole-Cole parameters.« less

Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data.

PubMed

Nagasaki, Masao; Yamaguchi, Rui; Yoshida, Ryo; Imoto, Seiya; Doi, Atsushi; Tamada, Yoshinori; Matsuno, Hiroshi; Miyano, Satoru; Higuchi, Tomoyuki

2006-01-01

We propose an automatic construction method of the hybrid functional Petri net as a simulation model of biological pathways. The problems we consider are how we choose the values of parameters and how we set the network structure. Usually, we tune these unknown factors empirically so that the simulation results are consistent with biological knowledge. Obviously, this approach has the limitation in the size of network of interest. To extend the capability of the simulation model, we propose the use of data assimilation approach that was originally established in the field of geophysical simulation science. We provide genomic data assimilation framework that establishes a link between our simulation model and observed data like microarray gene expression data by using a nonlinear state space model. A key idea of our genomic data assimilation is that the unknown parameters in simulation model are converted as the parameter of the state space model and the estimates are obtained as the maximum a posteriori estimators. In the parameter estimation process, the simulation model is used to generate the system model in the state space model. Such a formulation enables us to handle both the model construction and the parameter tuning within a framework of the Bayesian statistical inferences. In particular, the Bayesian approach provides us a way of controlling overfitting during the parameter estimations that is essential for constructing a reliable biological pathway. We demonstrate the effectiveness of our approach using synthetic data. As a result, parameter estimation using genomic data assimilation works very well and the network structure is suitably selected.

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain

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

Luo, Shaohua

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaosmore » of PMSM and show the effectiveness and robustness of the proposed method.« less

Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.

PubMed

Luo, Shaohua

2014-09-01

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

Physical and numerical studies of a fracture system model

NASA Astrophysics Data System (ADS)

Piggott, Andrew R.; Elsworth, Derek

1989-03-01

Physical and numerical studies of transient flow in a model of discretely fractured rock are presented. The physical model is a thermal analogue to fractured media flow consisting of idealized disc-shaped fractures. The numerical model is used to predict the behavior of the physical model. The use of different insulating materials to encase the physical model allows the effects of differing leakage magnitudes to be examined. A procedure for determining appropriate leakage parameters is documented. These parameters are used in forward analysis to predict the thermal response of the physical model. Knowledge of the leakage parameters and of the temporal variation of boundary conditions are shown to be essential to an accurate prediction. Favorable agreement is illustrated between numerical and physical results. The physical model provides a data source for the benchmarking of alternative numerical algorithms.

Identification of vehicle suspension parameters by design optimization

NASA Astrophysics Data System (ADS)

Tey, J. Y.; Ramli, R.; Kheng, C. W.; Chong, S. Y.; Abidin, M. A. Z.

2014-05-01

The design of a vehicle suspension system through simulation requires accurate representation of the design parameters. These parameters are usually difficult to measure or sometimes unavailable. This article proposes an efficient approach to identify the unknown parameters through optimization based on experimental results, where the covariance matrix adaptation-evolutionary strategy (CMA-es) is utilized to improve the simulation and experimental results against the kinematic and compliance tests. This speeds up the design and development cycle by recovering all the unknown data with respect to a set of kinematic measurements through a single optimization process. A case study employing a McPherson strut suspension system is modelled in a multi-body dynamic system. Three kinematic and compliance tests are examined, namely, vertical parallel wheel travel, opposite wheel travel and single wheel travel. The problem is formulated as a multi-objective optimization problem with 40 objectives and 49 design parameters. A hierarchical clustering method based on global sensitivity analysis is used to reduce the number of objectives to 30 by grouping correlated objectives together. Then, a dynamic summation of rank value is used as pseudo-objective functions to reformulate the multi-objective optimization to a single-objective optimization problem. The optimized results show a significant improvement in the correlation between the simulated model and the experimental model. Once accurate representation of the vehicle suspension model is achieved, further analysis, such as ride and handling performances, can be implemented for further optimization.

Performance Analysis for Channel Estimation With 1-Bit ADC and Unknown Quantization Threshold

NASA Astrophysics Data System (ADS)

Stein, Manuel S.; Bar, Shahar; Nossek, Josef A.; Tabrikian, Joseph

2018-05-01

In this work, the problem of signal parameter estimation from measurements acquired by a low-complexity analog-to-digital converter (ADC) with $1$-bit output resolution and an unknown quantization threshold is considered. Single-comparator ADCs are energy-efficient and can be operated at ultra-high sampling rates. For analysis of such systems, a fixed and known quantization threshold is usually assumed. In the symmetric case, i.e., zero hard-limiting offset, it is known that in the low signal-to-noise ratio (SNR) regime the signal processing performance degrades moderately by ${2}/{\\pi}$ ($-1.96$ dB) when comparing to an ideal $\\infty$-bit converter. Due to hardware imperfections, low-complexity $1$-bit ADCs will in practice exhibit an unknown threshold different from zero. Therefore, we study the accuracy which can be obtained with receive data processed by a hard-limiter with unknown quantization level by using asymptotically optimal channel estimation algorithms. To characterize the estimation performance of these nonlinear algorithms, we employ analytic error expressions for different setups while modeling the offset as a nuisance parameter. In the low SNR regime, we establish the necessary condition for a vanishing loss due to missing offset knowledge at the receiver. As an application, we consider the estimation of single-input single-output wireless channels with inter-symbol interference and validate our analysis by comparing the analytic and experimental performance of the studied estimation algorithms. Finally, we comment on the extension to multiple-input multiple-output channel models.

Adaptive Control Based Harvesting Strategy for a Predator-Prey Dynamical System.

PubMed

Sen, Moitri; Simha, Ashutosh; Raha, Soumyendu

2018-04-23

This paper deals with designing a harvesting control strategy for a predator-prey dynamical system, with parametric uncertainties and exogenous disturbances. A feedback control law for the harvesting rate of the predator is formulated such that the population dynamics is asymptotically stabilized at a positive operating point, while maintaining a positive, steady state harvesting rate. The hierarchical block strict feedback structure of the dynamics is exploited in designing a backstepping control law, based on Lyapunov theory. In order to account for unknown parameters, an adaptive control strategy has been proposed in which the control law depends on an adaptive variable which tracks the unknown parameter. Further, a switching component has been incorporated to robustify the control performance against bounded disturbances. Proofs have been provided to show that the proposed adaptive control strategy ensures asymptotic stability of the dynamics at a desired operating point, as well as exact parameter learning in the disturbance-free case and learning with bounded error in the disturbance prone case. The dynamics, with uncertainty in the death rate of the predator, subjected to a bounded disturbance has been simulated with the proposed control strategy.

Efficient Bayesian experimental design for contaminant source identification

NASA Astrophysics Data System (ADS)

Zhang, Jiangjiang; Zeng, Lingzao; Chen, Cheng; Chen, Dingjiang; Wu, Laosheng

2015-01-01

In this study, an efficient full Bayesian approach is developed for the optimal sampling well location design and source parameters identification of groundwater contaminants. An information measure, i.e., the relative entropy, is employed to quantify the information gain from concentration measurements in identifying unknown parameters. In this approach, the sampling locations that give the maximum expected relative entropy are selected as the optimal design. After the sampling locations are determined, a Bayesian approach based on Markov Chain Monte Carlo (MCMC) is used to estimate unknown parameters. In both the design and estimation, the contaminant transport equation is required to be solved many times to evaluate the likelihood. To reduce the computational burden, an interpolation method based on the adaptive sparse grid is utilized to construct a surrogate for the contaminant transport equation. The approximated likelihood can be evaluated directly from the surrogate, which greatly accelerates the design and estimation process. The accuracy and efficiency of our approach are demonstrated through numerical case studies. It is shown that the methods can be used to assist in both single sampling location and monitoring network design for contaminant source identifications in groundwater.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive control of nonlinear uncertain active suspension systems with prescribed performance.

PubMed

Huang, Yingbo; Na, Jing; Wu, Xing; Liu, Xiaoqin; Guo, Yu

2015-01-01

This paper proposes adaptive control designs for vehicle active suspension systems with unknown nonlinear dynamics (e.g., nonlinear spring and piece-wise linear damper dynamics). An adaptive control is first proposed to stabilize the vertical vehicle displacement and thus to improve the ride comfort and to guarantee other suspension requirements (e.g., road holding and suspension space limitation) concerning the vehicle safety and mechanical constraints. An augmented neural network is developed to online compensate for the unknown nonlinearities, and a novel adaptive law is developed to estimate both NN weights and uncertain model parameters (e.g., sprung mass), where the parameter estimation error is used as a leakage term superimposed on the classical adaptations. To further improve the control performance and simplify the parameter tuning, a prescribed performance function (PPF) characterizing the error convergence rate, maximum overshoot and steady-state error is used to propose another adaptive control. The stability for the closed-loop system is proved and particular performance requirements are analyzed. Simulations are included to illustrate the effectiveness of the proposed control schemes. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Longitudinal development of physical and performance parameters during biological maturation of young male swimmers.

PubMed

Lätt, Evelin; Jürimäe, Jaak; Haljaste, Kaja; Cicchella, Antonio; Purge, Priit; Jürimäe, Toivo

2009-02-01

The aim of the study was to examine the development of specific physical, physiological, and biomechanical parameters in 29 young male swimmers for whom measurements were made three times for two consecutive years. During the 400-m front-crawl swimming, the energy cost of swimming, and stroking parameters were assessed. Peak oxygen consumption (VO2 peak) was assessed by means of the backward-extrapolation technique recording VO2 during the first 20 sec. of recovery period after a maximal trial of 400-m distance. Swimming performance at different points of physical maturity was mainly related to the increases in body height and arm-span values from physical parameters, improvement in sport-specific VO2 peak value from physiological characteristics, and improvement in stroke indices on biomechanical parameters. In addition, biomechanical factors characterised best the 400-m swimming performance followed by physical and physiological factors during the 2-yr. study period for the young male swimmers.

Double density dynamics: realizing a joint distribution of a physical system and a parameter system

NASA Astrophysics Data System (ADS)

Fukuda, Ikuo; Moritsugu, Kei

2015-11-01

To perform a variety of types of molecular dynamics simulations, we created a deterministic method termed ‘double density dynamics’ (DDD), which realizes an arbitrary distribution for both physical variables and their associated parameters simultaneously. Specifically, we constructed an ordinary differential equation that has an invariant density relating to a joint distribution of the physical system and the parameter system. A generalized density function leads to a physical system that develops under nonequilibrium environment-describing superstatistics. The joint distribution density of the physical system and the parameter system appears as the Radon-Nikodym derivative of a distribution that is created by a scaled long-time average, generated from the flow of the differential equation under an ergodic assumption. The general mathematical framework is fully discussed to address the theoretical possibility of our method, and a numerical example representing a 1D harmonic oscillator is provided to validate the method being applied to the temperature parameters.

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.

Physical parameters for proton induced K-, L-, and M-shell ionization processes

NASA Astrophysics Data System (ADS)

Shehla; Puri, Sanjiv

2016-10-01

The proton induced atomic inner-shell ionization processes comprising radiative and non-radiative transitions are characterized by physical parameters, namely, the proton ionization cross sections, X-ray emission rates, fluorescence yields and Coster-Kronig (CK) transition probabilities. These parameters are required to calculate the K/L/M shell X-ray production (XRP) cross sections and relative X-ray intensity ratios, which in turn are required for different analytical applications. The current status of different physical parameters is presented in this report for use in various applications.

Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

PubMed

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.

Parameter estimation for lithium ion batteries

NASA Astrophysics Data System (ADS)

Santhanagopalan, Shriram

With an increase in the demand for lithium based batteries at the rate of about 7% per year, the amount of effort put into improving the performance of these batteries from both experimental and theoretical perspectives is increasing. There exist a number of mathematical models ranging from simple empirical models to complicated physics-based models to describe the processes leading to failure of these cells. The literature is also rife with experimental studies that characterize the various properties of the system in an attempt to improve the performance of lithium ion cells. However, very little has been done to quantify the experimental observations and relate these results to the existing mathematical models. In fact, the best of the physics based models in the literature show as much as 20% discrepancy when compared to experimental data. The reasons for such a big difference include, but are not limited to, numerical complexities involved in extracting parameters from experimental data and inconsistencies in interpreting directly measured values for the parameters. In this work, an attempt has been made to implement simplified models to extract parameter values that accurately characterize the performance of lithium ion cells. The validity of these models under a variety of experimental conditions is verified using a model discrimination procedure. Transport and kinetic properties are estimated using a non-linear estimation procedure. The initial state of charge inside each electrode is also maintained as an unknown parameter, since this value plays a significant role in accurately matching experimental charge/discharge curves with model predictions and is not readily known from experimental data. The second part of the dissertation focuses on parameters that change rapidly with time. For example, in the case of lithium ion batteries used in Hybrid Electric Vehicle (HEV) applications, the prediction of the State of Charge (SOC) of the cell under a variety of road conditions is important. An algorithm to predict the SOC in time intervals as small as 5 ms is of critical demand. In such cases, the conventional non-linear estimation procedure is not time-effective. There exist methodologies in the literature, such as those based on fuzzy logic; however, these techniques require a lot of computational storage space. Consequently, it is not possible to implement such techniques on a micro-chip for integration as a part of a real-time device. The Extended Kalman Filter (EKF) based approach presented in this work is a first step towards developing an efficient method to predict online, the State of Charge of a lithium ion cell based on an electrochemical model. The final part of the dissertation focuses on incorporating uncertainty in parameter values into electrochemical models using the polynomial chaos theory (PCT).

Improving flood forecasting capability of physically based distributed hydrological model by parameter optimization

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 used for Liuxihe model parameter optimization effectively, and could improve the model capability largely in catchment flood forecasting, thus proven that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for Liuxihe model catchment flood forcasting is 20 and 30, respectively.

Cognitive Load Imposed by Knobology May Adversely Affect Learners' Perception of Utility in Using Ultrasonography to Learn Physical Examination Skills, but Not Anatomy

ERIC Educational Resources Information Center

Jamniczky, Heather A.; McLaughlin, Kevin; Kaminska, Malgorzata E.; Raman, Maitreyi; Somayaji, Ranjani; Wright, Bruce; Ma, Irene W. Y.

2015-01-01

Ultrasonography is increasingly used for teaching anatomy and physical examination skills but its effect on cognitive load is unknown. This study aimed to determine ultrasound's perceived utility for learning, and to investigate the effect of cognitive load on its perceived utility. Consenting first-year medical students (n?=?137) completed…

A method for operative quantitative interpretation of multispectral images of biological tissues

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-10-01

A method for operative retrieval of spatial distributions of biophysical parameters of a biological tissue by using a multispectral image of it has been developed. The method is based on multiple regressions between linearly independent components of the diffuse reflection spectrum of the tissue and unknown parameters. Possibilities of the method are illustrated by an example of determining biophysical parameters of the skin (concentrations of melanin, hemoglobin and bilirubin, blood oxygenation, and scattering coefficient of the tissue). Examples of quantitative interpretation of the experimental data are presented.

A black box optimization approach to parameter estimation in a model for long/short term variations dynamics of commodity prices

NASA Astrophysics Data System (ADS)

De Santis, Alberto; Dellepiane, Umberto; Lucidi, Stefano

2012-11-01

In this paper we investigate the estimation problem for a model of the commodity prices. This model is a stochastic state space dynamical model and the problem unknowns are the state variables and the system parameters. Data are represented by the commodity spot prices, very seldom time series of Futures contracts are available for free. Both the system joint likelihood function (state variables and parameters) and the system marginal likelihood (the state variables are eliminated) function are addressed.

A nudging data assimilation algorithm for the identification of groundwater pumping

NASA Astrophysics Data System (ADS)

Cheng, Wei-Chen; Kendall, Donald R.; Putti, Mario; Yeh, William W.-G.

2009-08-01

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measured data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistent physical interpretation for pumping rate identification. The algorithm identifies the unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rates, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show an excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.

A nudging data assimilation algorithm for the identification of groundwater pumping

NASA Astrophysics Data System (ADS)

Cheng, W.; Kendall, D. R.; Putti, M.; Yeh, W. W.

2008-12-01

This study develops a nudging data assimilation algorithm for estimating unknown pumping from private wells in an aquifer system using measurement data of hydraulic head. The proposed algorithm treats the unknown pumping as an additional sink term in the governing equation of groundwater flow and provides a consistently physical interpretation for pumping rate identification. The algorithm identifies unknown pumping and, at the same time, reduces the forecast error in hydraulic heads. We apply the proposed algorithm to the Las Posas Groundwater Basin in southern California. We consider the following three pumping scenarios: constant pumping rate, spatially varying pumping rates, and temporally varying pumping rates. We also study the impact of head measurement errors on the proposed algorithm. In the case study, we seek to estimate the six unknown pumping rates from private wells using head measurements from four observation wells. The results show excellent rate of convergence for pumping estimation. The case study demonstrates the applicability, accuracy, and efficiency of the proposed data assimilation algorithm for the identification of unknown pumping in an aquifer system.

Optical absorption spectra of substitutional Co2+ ions in Mgx Cd1-x Se alloys

NASA Astrophysics Data System (ADS)

Jin, Moon-Seog; Kim, Chang-Dae; Jang, Kiwan; Park, Sang-An; Kim, Duck-Tae; Kim, Hyung-Gon; Kim, Wha-Tek

2006-09-01

Optical absorption spectra of substitutional Co2+ ions in Mgx Cd1-x Se alloys were investigated in the composition region of 0.0 x 0.4 and in the wavelength region of 300 to 2500 nm at 4.8 K and 290 K. We observed several absorption bands in the wavelength regions corresponding to the 4A2(4F) 4T1(4P) transition and the 4A2(4F) 4T1(4F) transition of Co2+ at a tetrahedral Td point symmetry point in the host crystals, as well as unknown absorption bands. The several absorption bands were analyzed in the framework of the crystal-field theory along with the second-order spin-orbit coupling. The unknown absorption bands were assigned as due to phonon-assisted absorption bands. We also investigated the variations of the crystal-field parameter Dq and the Racah parameter B with composition x in the Mgx Cd1-x Se system. The results showed that the crystal-field parameter (Dq ) increases, on the other hand, the Racah parameter (B ) decreases with increasing composition x, which may be connected with an increase in the covalency of the metal-ligand bond with increasing composition x in the Mgx Cd1-x Se system.

Information fusion methods based on physical laws.

PubMed

Rao, Nageswara S V; Reister, David B; Barhen, Jacob

2005-01-01

We consider systems whose parameters satisfy certain easily computable physical laws. Each parameter is directly measured by a number of sensors, or estimated using measurements, or both. The measurement process may introduce both systematic and random errors which may then propagate into the estimates. Furthermore, the actual parameter values are not known since every parameter is measured or estimated, which makes the existing sample-based fusion methods inapplicable. We propose a fusion method for combining the measurements and estimators based on the least violation of physical laws that relate the parameters. Under fairly general smoothness and nonsmoothness conditions on the physical laws, we show the asymptotic convergence of our method and also derive distribution-free performance bounds based on finite samples. For suitable choices of the fuser classes, we show that for each parameter the fused estimate is probabilistically at least as good as its best measurement as well as best estimate. We illustrate the effectiveness of this method for a practical problem of fusing well-log data in methane hydrate exploration.

Sensor System Fo4r Buried Waste Containment Sites

DOEpatents

Smith, Ann Marie; Gardner, Bradley M.; Kostelnik, Kevin M.; Partin, Judy K.; Lancaster, Gregory D.; Pfeifer, Mary Catherine

2003-11-18

A sensor system for a buried waste containment site having a bottom wall barrier and sidewall barriers, for containing hazardous waste. The sensor system includes one or more sensor devices disposed in one or more of the barriers for detecting a physical parameter either of the barrier itself or of the physical condition of the surrounding soils and buried waste, and for producing a signal representing the physical parameter detected. Also included is a signal processor for receiving signals produced by the sensor device and for developing information identifying the physical parameter detected, either for sounding an alarm, displaying a graphic representation of a physical parameter detected on a viewing screen and/or a hard copy printout. The sensor devices may be deployed in or adjacent the barriers at the same time the barriers are deployed and may be adapted to detect strain or cracking in the barriers, leakage of radiation through the barriers, the presence and leaking through the barriers of volatile organic compounds, or similar physical conditions.

Sensor system for buried waste containment sites

DOEpatents

Smith, Ann Marie; Gardner, Bradley M.; Kostelnik, Kevin M.; Partin, Judy K.; Lancaster, Gregory D.; Pfeifer, May Catherine

2000-01-01

A sensor system is disclosed for a buried waste containment site having a bottom wall barrier and/or sidewall barriers, for containing hazardous waste. The sensor system includes one or more sensor devices disposed in one or more of the barriers for detecting a physical parameter either of the barrier itself or of the physical condition of the surrounding soils and buried waste, and for producing a signal representing the physical parameter detected. Also included is a signal processor for receiving signals produced by the sensor device and for developing information identifying the physical parameter detected, either for sounding an alarm, displaying a graphic representation of a physical parameter detected on a viewing screen and/or a hard copy printout. The sensor devices may be deployed in or adjacent the barriers at the same time the barriers are deployed and may be adapted to detect strain or cracking in the barriers, leakage of radiation through the barriers, the presence and leaking through the barriers of volatile organic compounds, or similar physical conditions.

Sensor System Fo4r Buried Waste Containment Sites

DOEpatents

Smith, Ann Marie; Gardner, Bradley M.; Kostelnik, Kevin M.; Partin, Judy K.; Lancaster, Gregory D.; Pfeifer, Mary Catherine

2005-09-27

A sensor system for a buried waste containment site having a bottom wall barrier and/or sidewall barriers, for containing hazardous waste. The sensor system includes one or more sensor devices disposed in one or more of the barriers for detecting a physical parameter either of the barrier itself or of the physical condition of the surrounding soils and buried waste, and for producing a signal representing the physical parameter detected. Also included is a signal processor for receiving signals produced by the sensor device and for developing information identifying the physical parameter detected, either for sounding an alarm, displaying a graphic representation of a physical parameter detected on a viewing screen and/or a hard copy printout. The sensor devices may be deployed in or adjacent the barriers at the same time the barriers are deployed and may be adapted to detect strain or cracking in the barriers, leakage of radiation through the barriers, the presence and leaking through the barriers of volatile organic compounds, or similar physical conditions.

Image Restoration for Fluorescence Planar Imaging with Diffusion Model

PubMed Central

Gong, Yuzhu; Li, Yang

2017-01-01

Fluorescence planar imaging (FPI) is failure to capture high resolution images of deep fluorochromes due to photon diffusion. This paper presents an image restoration method to deal with this kind of blurring. The scheme of this method is conceived based on a reconstruction method in fluorescence molecular tomography (FMT) with diffusion model. A new unknown parameter is defined through introducing the first mean value theorem for definite integrals. System matrix converting this unknown parameter to the blurry image is constructed with the elements of depth conversion matrices related to a chosen plane named focal plane. Results of phantom and mouse experiments show that the proposed method is capable of reducing the blurring of FPI image caused by photon diffusion when the depth of focal plane is chosen within a proper interval around the true depth of fluorochrome. This method will be helpful to the estimation of the size of deep fluorochrome. PMID:29279843

78 FR 2433 - Notice of Inventory Completion: Fort Collins Museum of Discovery, Fort Collins, CO

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-11

... Discovery). Although specific provenience of the human remains is unknown, osteological analysis conducted by physical anthropologists and by independent forensic scientists determined that the remains are of...

Optimal estimation of parameters and states in stochastic time-varying systems with time delay

NASA Astrophysics Data System (ADS)

Torkamani, Shahab; Butcher, Eric A.

2013-08-01

In this study estimation of parameters and states in stochastic linear and nonlinear delay differential systems with time-varying coefficients and constant delay is explored. The approach consists of first employing a continuous time approximation to approximate the stochastic delay differential equation with a set of stochastic ordinary differential equations. Then the problem of parameter estimation in the resulting stochastic differential system is represented as an optimal filtering problem using a state augmentation technique. By adapting the extended Kalman-Bucy filter to the resulting system, the unknown parameters of the time-delayed system are estimated from noise-corrupted, possibly incomplete measurements of the states.

Walkability parameters, active transportation and objective physical activity: moderating and mediating effects of motor vehicle ownership in a cross-sectional study

PubMed Central

2012-01-01

Background Neighborhood walkability has been associated with physical activity in several studies. However, as environmental correlates of physical activity may be context specific, walkability parameters need to be investigated separately in various countries and contexts. Furthermore, the mechanisms by which walkability affects physical activity have been less investigated. Based on previous research, we hypothesized that vehicle ownership is a potential mediator. We investigated the associations between walkability parameters and physical activity, and the mediating and moderating effects of vehicle ownership on these associations in a large sample of Swedish adults. Methods Residential density, street connectivity and land use mix were assessed within polygon-based network buffers (using Geographic Information Systems) for 2,178 men and women. Time spent in moderate to vigorous physical activity was assessed by accelerometers, and walking and cycling for transportation were assessed by the International Physical Activity Questionnaire. Associations were examined by linear regression and adjusted for socio-demographic characteristics. The product of coefficients approach was used to investigate the mediating effect of vehicle ownership. Results Residential density and land use mix, but not street connectivity, were significantly associated with time spent in moderate to vigorous physical activity and walking for transportation. Cycling for transportation was not associated with any of the walkability parameters. Vehicle ownership mediated a significant proportion of the association between the walkability parameters and physical activity outcomes. For residential density, vehicle ownership mediated 25% of the association with moderate to vigorous physical activity and 20% of the association with the amount of walking for transportation. For land use mix, the corresponding proportions were 34% and 14%. Vehicle ownership did not moderate any of the associations between the walkability parameters and physical activity outcomes. Conclusions Residential density and land use mix were associated with time spent in moderate to vigorous physical activity and walking for transportation. Vehicle ownership was a mediator but not a moderator of these associations. The present findings may be useful for policy makers and city planners when designing neighborhoods that promote physical activity. PMID:23035633

Walkability parameters, active transportation and objective physical activity: moderating and mediating effects of motor vehicle ownership in a cross-sectional study.

PubMed

Eriksson, Ulf; Arvidsson, Daniel; Gebel, Klaus; Ohlsson, Henrik; Sundquist, Kristina

2012-10-05

Neighborhood walkability has been associated with physical activity in several studies. However, as environmental correlates of physical activity may be context specific, walkability parameters need to be investigated separately in various countries and contexts. Furthermore, the mechanisms by which walkability affects physical activity have been less investigated. Based on previous research, we hypothesized that vehicle ownership is a potential mediator. We investigated the associations between walkability parameters and physical activity, and the mediating and moderating effects of vehicle ownership on these associations in a large sample of Swedish adults. Residential density, street connectivity and land use mix were assessed within polygon-based network buffers (using Geographic Information Systems) for 2,178 men and women. Time spent in moderate to vigorous physical activity was assessed by accelerometers, and walking and cycling for transportation were assessed by the International Physical Activity Questionnaire. Associations were examined by linear regression and adjusted for socio-demographic characteristics. The product of coefficients approach was used to investigate the mediating effect of vehicle ownership. Residential density and land use mix, but not street connectivity, were significantly associated with time spent in moderate to vigorous physical activity and walking for transportation. Cycling for transportation was not associated with any of the walkability parameters. Vehicle ownership mediated a significant proportion of the association between the walkability parameters and physical activity outcomes. For residential density, vehicle ownership mediated 25% of the association with moderate to vigorous physical activity and 20% of the association with the amount of walking for transportation. For land use mix, the corresponding proportions were 34% and 14%. Vehicle ownership did not moderate any of the associations between the walkability parameters and physical activity outcomes. Residential density and land use mix were associated with time spent in moderate to vigorous physical activity and walking for transportation. Vehicle ownership was a mediator but not a moderator of these associations. The present findings may be useful for policy makers and city planners when designing neighborhoods that promote physical activity.

Comparative Study of Three Data Assimilation Methods for Ice Sheet Model Initialisation

NASA Astrophysics Data System (ADS)

Mosbeux, Cyrille; Gillet-Chaulet, Fabien; Gagliardini, Olivier

2015-04-01

The current global warming has direct consequences on ice-sheet mass loss contributing to sea level rise. This loss is generally driven by an acceleration of some coastal outlet glaciers and reproducing these mechanisms is one of the major issues in ice-sheet and ice flow modelling. The construction of an initial state, as close as possible to current observations, is required as a prerequisite before producing any reliable projection of the evolution of ice-sheets. For this step, inverse methods are often used to infer badly known or unknown parameters. For instance, the adjoint inverse method has been implemented and applied with success by different authors in different ice flow models in order to infer the basal drag [ Schafer et al., 2012; Gillet-chauletet al., 2012; Morlighem et al., 2010]. Others data fields, such as ice surface and bedrock topography, are easily measurable with more or less uncertainty but only locally along tracks and interpolated on finer model grid. All these approximations lead to errors on the data elevation model and give rise to an ill-posed problem inducing non-physical anomalies in flux divergence [Seroussi et al, 2011]. A solution to dissipate these divergences of flux is to conduct a surface relaxation step at the expense of the accuracy of the modelled surface [Gillet-Chaulet et al., 2012]. Other solutions, based on the inversion of ice thickness and basal drag were proposed [Perego et al., 2014; Pralong & Gudmundsson, 2011]. In this study, we create a twin experiment to compare three different assimilation algorithms based on inverse methods and nudging to constrain the bedrock friction and the bedrock elevation: (i) cyclic inversion of friction parameter and bedrock topography using adjoint method, (ii) cycles coupling inversion of friction parameter using adjoint method and nudging of bedrock topography, (iii) one step inversion of both parameters with adjoint method. The three methods show a clear improvement in parameters knowledge leading to a significant reduction of flux divergence of the model before forecasting.

On mantle heterogeneity and anisotropy as mapped by inversion of global surface wave data

NASA Astrophysics Data System (ADS)

Khan, A.; Boschi, L.; Connolly, J.; Deschamps, F.

2008-12-01

We jointly invert Love and Rayleigh wave dispersion curves for the Earth's mantle composition, thermal state, P and S wave anisotropy at different locations on the Earth, based on self-consistent thermodynamic calculations. The method consists of four parts: 1. The composition of the Earth is modeled by the chemical system CaO-FeO-MgO- Al2O3-SiO2. Given these parameters and a geotherm (also an unknown), we calculate stable mineral modes, elastic properties, bulk density at the prevailing physical conditions using Gibbs free energy minimisation. Voigt-Reuss-Hill averaging is subsequently emplouyed to compute radial isotropic P and S wave velocity profiles in the elastic limit. 2. Anisotropic P and S wave velocities are determined from the isotropic ones by employing the relations ξ=(Vsh/Vsv)2, φ = (Vpv/Vph)2, η=F/(2A-L), Vs=(2Vsv2+Vsh2)/3 and Vp=(Vpv2+4Vph2)/5. The former three parameters are the standard anisotropy parameters, that we also invert for. 4. From these radial profiles, i.e. of Vsv, Vsh, Vph, Vpv and ρ, sunthetic Love and Rayleigh wave dispersion curves are calculated. The dispersion curves, which comprise fundamental and overtones up to 5th (Love) and 6th (Rayleigh) order have been extracted from global surface wave velocity maps. Given the above scheme, the data are at each location are jointly inverted using a Markov Chain Monte Carlo algorithm, from which a range of compositions, temperatures and radial profiles of anisotropy parameters, fitting data within uncertainties, are obtained. Our method has several advantages over standard approaches, in that no scaling relationships between Vs and Vp and ρ and Vs have to be introduced, implying that the full sensitivity of Rayleigh and Love waves to the parameters Vs, Vp and ρ is accounted for. In this particular study we investigate 5 locations distributed across the globe and reveal mantle chemical and thermal differences at these locations.

Calibration of two complex ecosystem models with different likelihood functions

NASA Astrophysics Data System (ADS)

Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán

2014-05-01

The biosphere is a sensitive carbon reservoir. Terrestrial ecosystems were approximately carbon neutral during the past centuries, but they became net carbon sinks due to climate change induced environmental change and associated CO2 fertilization effect of the atmosphere. Model studies and measurements indicate that the biospheric carbon sink can saturate in the future due to ongoing climate change which can act as a positive feedback. Robustness of carbon cycle models is a key issue when trying to choose the appropriate model for decision support. The input parameters of the process-based models are decisive regarding the model output. At the same time there are several input parameters for which accurate values are hard to obtain directly from experiments or no local measurements are available. Due to the uncertainty associated with the unknown model parameters significant bias can be experienced if the model is used to simulate the carbon and nitrogen cycle components of different ecosystems. In order to improve model performance the unknown model parameters has to be estimated. We developed a multi-objective, two-step calibration method based on Bayesian approach in order to estimate the unknown parameters of PaSim and Biome-BGC models. Biome-BGC and PaSim are a widely used biogeochemical models that simulate the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems (in this research the developed version of Biome-BGC is used which is referred as BBGC MuSo). Both models were calibrated regardless the simulated processes and type of model parameters. The calibration procedure is based on the comparison of measured data with simulated results via calculating a likelihood function (degree of goodness-of-fit between simulated and measured data). In our research different likelihood function formulations were used in order to examine the effect of the different model goodness metric on calibration. The different likelihoods are different functions of RMSE (root mean squared error) weighted by measurement uncertainty: exponential / linear / quadratic / linear normalized by correlation. As a first calibration step sensitivity analysis was performed in order to select the influential parameters which have strong effect on the output data. In the second calibration step only the sensitive parameters were calibrated (optimal values and confidence intervals were calculated). In case of PaSim more parameters were found responsible for the 95% of the output data variance than is case of BBGC MuSo. Analysis of the results of the optimized models revealed that the exponential likelihood estimation proved to be the most robust (best model simulation with optimized parameter, highest confidence interval increase). The cross-validation of the model simulations can help in constraining the highly uncertain greenhouse gas budget of grasslands.

A critical literature review of focused electron beam induced deposition

NASA Astrophysics Data System (ADS)

van Dorp, W. F.; Hagen, C. W.

2008-10-01

An extensive review is given of the results from literature on electron beam induced deposition. Electron beam induced deposition is a complex process, where many and often mutually dependent factors are involved. The process has been studied by many over many years in many different experimental setups, so it is not surprising that there is a great variety of experimental results. To come to a better understanding of the process, it is important to see to which extent the experimental results are consistent with each other and with the existing model. All results from literature were categorized by sorting the data according to the specific parameter that was varied (current density, acceleration voltage, scan patterns, etc.). Each of these parameters can have an effect on the final deposit properties, such as the physical dimensions, the composition, the morphology, or the conductivity. For each parameter-property combination, the available data are discussed and (as far as possible) interpreted. By combining models for electron scattering in a solid, two different growth regimes, and electron beam induced heating, the majority of the experimental results were explained qualitatively. This indicates that the physical processes are well understood, although quantitatively speaking the models can still be improved. The review makes clear that several major issues remain. One issue encountered when interpreting results from literature is the lack of data. Often, important parameters (such as the local precursor pressure) are not reported, which can complicate interpretation of the results. Another issue is the fact that the cross section for electron induced dissociation is unknown. In a number of cases, a correlation between the vertical growth rate and the secondary electron yield was found, which suggests that the secondary electrons dominate the dissociation rather than the primary electrons. Conclusive evidence for this hypothesis has not been found. Finally, there is a limited understanding of the mechanism of electron induced precursor dissociation. In many cases, the deposit composition is not directly dependent on the stoichiometric composition of the precursor and the electron induced decomposition paths can be very different from those expected from calculations or thermal decomposition. The dissociation mechanism is one of the key factors determining the purity of the deposits and a better understanding of this process will help develop electron beam induced deposition into a viable nanofabrication technique.

Inertial parameter identification using contact force information for an unknown object captured by a space manipulator

NASA Astrophysics Data System (ADS)

Chu, Zhongyi; Ma, Ye; Hou, Yueyang; Wang, Fengwen

2017-02-01

This paper presents a novel identification method for the intact inertial parameters of an unknown object in space captured by a manipulator in a space robotic system. With strong dynamic and kinematic coupling existing in the robotic system, the inertial parameter identification of the unknown object is essential for the ideal control strategy based on changes in the attitude and trajectory of the space robot via capturing operations. Conventional studies merely refer to the principle and theory of identification, and an error analysis process of identification is deficient for a practical scenario. To solve this issue, an analysis of the effect of errors on identification is illustrated first, and the accumulation of measurement or estimation errors causing poor identification precision is demonstrated. Meanwhile, a modified identification equation incorporating the contact force, as well as the force/torque of the end-effector, is proposed to weaken the accumulation of errors and improve the identification accuracy. Furthermore, considering a severe disturbance condition caused by various measured noises, the hybrid immune algorithm, Recursive Least Squares and Affine Projection Sign Algorithm (RLS-APSA), is employed to decode the modified identification equation to ensure a stable identification property. Finally, to verify the validity of the proposed identification method, the co-simulation of ADAMS-MATLAB is implemented by multi-degree of freedom models of a space robotic system, and the numerical results show a precise and stable identification performance, which is able to guarantee the execution of aerospace operations and prevent failed control strategies.

Adaptive neural control for a class of nonlinear time-varying delay systems with unknown hysteresis.

PubMed

Liu, Zhi; Lai, Guanyu; Zhang, Yun; Chen, Xin; Chen, Chun Lung Philip

2014-12-01

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

Improved mapping of radio sources from VLBI data by least-square fit

NASA Technical Reports Server (NTRS)

Rodemich, E. R.

1985-01-01

A method is described for producing improved mapping of radio sources from Very Long Base Interferometry (VLBI) data. The method described is more direct than existing Fourier methods, is often more accurate, and runs at least as fast. The visibility data is modeled here, as in existing methods, as a function of the unknown brightness distribution and the unknown antenna gains and phases. These unknowns are chosen so that the resulting function values are as near as possible to the observed values. If researchers use the radio mapping source deviation to measure the closeness of this fit to the observed values, they are led to the problem of minimizing a certain function of all the unknown parameters. This minimization problem cannot be solved directly, but it can be attacked by iterative methods which we show converge automatically to the minimum with no user intervention. The resulting brightness distribution will furnish the best fit to the data among all brightness distributions of given resolution.

Adaptive Neural Networks Prescribed Performance Control Design for Switched Interconnected Uncertain Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-06-28

In this paper, an adaptive neural networks (NNs)-based decentralized control scheme with the prescribed performance is proposed for uncertain switched nonstrict-feedback interconnected nonlinear systems. It is assumed that nonlinear interconnected terms and nonlinear functions of the concerned systems are unknown, and also the switching signals are unknown and arbitrary. A linear state estimator is constructed to solve the problem of unmeasured states. The NNs are employed to approximate unknown interconnected terms and nonlinear functions. A new output feedback decentralized control scheme is developed by using the adaptive backstepping design technique. The control design problem of nonlinear interconnected switched systems with unknown switching signals can be solved by the proposed scheme, and only a tuning parameter is needed for each subsystem. The proposed scheme can ensure that all variables of the control systems are semi-globally uniformly ultimately bounded and the tracking errors converge to a small residual set with the prescribed performance bound. The effectiveness of the proposed control approach is verified by some simulation results.

Temporal gravity field modeling based on least square collocation with short-arc approach

NASA Astrophysics Data System (ADS)

ran, jiangjun; Zhong, Min; Xu, Houze; Liu, Chengshu; Tangdamrongsub, Natthachet

2014-05-01

After the launch of the Gravity Recovery And Climate Experiment (GRACE) in 2002, several research centers have attempted to produce the finest gravity model based on different approaches. In this study, we present an alternative approach to derive the Earth's gravity field, and two main objectives are discussed. Firstly, we seek the optimal method to estimate the accelerometer parameters, and secondly, we intend to recover the monthly gravity model based on least square collocation method. The method has been paid less attention compared to the least square adjustment method because of the massive computational resource's requirement. The positions of twin satellites are treated as pseudo-observations and unknown parameters at the same time. The variance covariance matrices of the pseudo-observations and the unknown parameters are valuable information to improve the accuracy of the estimated gravity solutions. Our analyses showed that introducing a drift parameter as an additional accelerometer parameter, compared to using only a bias parameter, leads to a significant improvement of our estimated monthly gravity field. The gravity errors outside the continents are significantly reduced based on the selected set of the accelerometer parameters. We introduced the improved gravity model namely the second version of Institute of Geodesy and Geophysics, Chinese Academy of Sciences (IGG-CAS 02). The accuracy of IGG-CAS 02 model is comparable to the gravity solutions computed from the Geoforschungszentrum (GFZ), the Center for Space Research (CSR) and the NASA Jet Propulsion Laboratory (JPL). In term of the equivalent water height, the correlation coefficients over the study regions (the Yangtze River valley, the Sahara desert, and the Amazon) among four gravity models are greater than 0.80.

The Inverse Problem for Confined Aquifer Flow: Identification and Estimation With Extensions

NASA Astrophysics Data System (ADS)

Loaiciga, Hugo A.; MariñO, Miguel A.

1987-01-01

The contributions of this work are twofold. First, a methodology for estimating the elements of parameter matrices in the governing equation of flow in a confined aquifer is developed. The estimation techniques for the distributed-parameter inverse problem pertain to linear least squares and generalized least squares methods. The linear relationship among the known heads and unknown parameters of the flow equation provides the background for developing criteria for determining the identifiability status of unknown parameters. Under conditions of exact or overidentification it is possible to develop statistically consistent parameter estimators and their asymptotic distributions. The estimation techniques, namely, two-stage least squares and three stage least squares, are applied to a specific groundwater inverse problem and compared between themselves and with an ordinary least squares estimator. The three-stage estimator provides the closer approximation to the actual parameter values, but it also shows relatively large standard errors as compared to the ordinary and two-stage estimators. The estimation techniques provide the parameter matrices required to simulate the unsteady groundwater flow equation. Second, a nonlinear maximum likelihood estimation approach to the inverse problem is presented. The statistical properties of maximum likelihood estimators are derived, and a procedure to construct confidence intervals and do hypothesis testing is given. The relative merits of the linear and maximum likelihood estimators are analyzed. Other topics relevant to the identification and estimation methodologies, i.e., a continuous-time solution to the flow equation, coping with noise-corrupted head measurements, and extension of the developed theory to nonlinear cases are also discussed. A simulation study is used to evaluate the methods developed in this study.

8. Photocopy of measured drawing (from the Iowa State University, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

8. Photocopy of measured drawing (from the Iowa State University, Physical Plant) Delineator unknown 1929; revised to 1943 MAINTENANCE PLANS, BASEMENT THROUGH THIRD FLOORS - Iowa State University, Morrill Hall, Morrill Road, Ames, Story County, IA

[Epidemiological characteristics of violence against women in the Federal District, Brazil, 2009-2012].

PubMed

Silva, Lídia Ester Lopes da; Oliveira, Maria Liz Cunha de

2016-01-01

to describe the epidemiological characteristics of cases of violence against women reported in the Federal District, Brazil, 2009-2012. this was a descriptive study of cases of violence against women aged 18- 59 registered on the National Notifiable Diseases System (Sinan). 1,924 cases of violence against women were registered, the perpetrators of which were identified as unknown (25.7%) or spouses (19.0%) of the victims; violence mainly occurred against women of brown skin color (25.0%) and in the domestic environment (38.5%); regarding violence type, physical violence (46.8%) by force (48.0%) stood out, whereby the genitals (15.7%) and the head (12.9%) were the most affected regions. physical violence in domestic environments by unknown aggressors was the main type of violence among the reported cases; shortcomings were identified in recording reported cases, showing the need to improve system quality and train health workers involved.

Statistical Physics Approaches to RNA Editing

NASA Astrophysics Data System (ADS)

Bundschuh, Ralf

2012-02-01

The central dogma of molecular Biology states that DNA is transcribed base by base into RNA which is in turn translated into proteins. However, some organisms edit their RNA before translation by inserting, deleting, or substituting individual or short stretches of bases. In many instances the mechanisms by which an organism recognizes the positions at which to edit or by which it performs the actual editing are unknown. One model system that stands out by its very high rate of on average one out of 25 bases being edited are the Myxomycetes, a class of slime molds. In this talk we will show how the computational methods and concepts from statistical Physics can be used to analyze DNA and protein sequence data to predict editing sites in these slime molds and to guide experiments that identified previously unknown types of editing as well as the complete set of editing events in the slime mold Physarum polycephalum.

Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields: Stochastic inversion of AVA and CSEM data

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

Chen, J.; Hoversten, G.M.

2011-09-15

Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electrical properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy tomore » derive statistical relationships among seismic and electrical attributes and reservoir parameters from distant borehole logs. In this study, we develop a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameter estimation using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters are carried out by lithotypes through Markov random fields. We apply the developed model to a synthetic case, which simulates a CO{sub 2} monitoring application. We derive statistical rock-physics relations from borehole logs at one location and estimate seismic P- and S-wave velocity ratio, acoustic impedance, density, electrical resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values shows that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.« less

The Primordial Inflation Explorer (PIXIE)

NASA Technical Reports Server (NTRS)

Kogut, Alan; Chluba, Jens; Fixsen, Dale J.; Meyer, Stephan; Spergel, David

2016-01-01

The Primordial Inflation Explorer is an Explorer-class mission to open new windows on the early universe through measurements of the polarization and absolute frequency spectrum of the cosmic microwave background. PIXIE will measure the gravitational-wave signature of primordial inflation through its distinctive imprint in linear polarization, and characterize the thermal history of the universe through precision measurements of distortions in the blackbody spectrum. PIXIE uses an innovative optical design to achieve background-limited sensitivity in 400 spectral channels spanning over 7 octaves in frequency from 30 GHz to 6 THz (1 cm to 50 micron wavelength). Multi-moded non-imaging optics feed a polarizing Fourier Transform Spectrometer to produce a set of interference fringes, proportional to the difference spectrum between orthogonal linear polarizations from the two input beams. Multiple levels of symmetry and signal modulation combine to reduce systematic errors to negligible levels. PIXIE will map the full sky in Stokes I, Q, and U parameters with angular resolution 2.6 degrees and sensitivity 70 nK per 1degree square pixel. The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r < 10(exp. -3) at 5 standard deviations. The PIXIE mission complements anticipated ground-based polarization measurements such as CMBS4, providing a cosmic-variance-limited determination of the large-scale E-mode signal to measure the optical depth, constrain models of reionization, and provide a firm detection of the neutrino mass (the last unknown parameter in the Standard Model of particle physics). In addition, PIXIE will measure the absolute frequency spectrum to characterize deviations from a blackbody with sensitivity 3 orders of magnitude beyond the seminal COBE/FIRAS limits. The sky cannot be black at this level; the expected results will constrain physical processes ranging from inflation to the nature of the first stars and the physical conditions within the interstellar medium of the Galaxy. We describe the PIXIE instrument and mission architecture required to measure the CMB to the limits imposed by astrophysical foregrounds.

Using Bayesian methods to predict climate impacts on groundwater availability and agricultural production in Punjab, India

NASA Astrophysics Data System (ADS)

Russo, T. A.; Devineni, N.; Lall, U.

2015-12-01

Lasting success of the Green Revolution in Punjab, India relies on continued availability of local water resources. Supplying primarily rice and wheat for the rest of India, Punjab supports crop irrigation with a canal system and groundwater, which is vastly over-exploited. The detailed data required to physically model future impacts on water supplies agricultural production is not readily available for this region, therefore we use Bayesian methods to estimate hydrologic properties and irrigation requirements for an under-constrained mass balance model. Using measured values of historical precipitation, total canal water delivery, crop yield, and water table elevation, we present a method using a Markov chain Monte Carlo (MCMC) algorithm to solve for a distribution of values for each unknown parameter in a conceptual mass balance model. Due to heterogeneity across the state, and the resolution of input data, we estimate model parameters at the district-scale using spatial pooling. The resulting model is used to predict the impact of precipitation change scenarios on groundwater availability under multiple cropping options. Predicted groundwater declines vary across the state, suggesting that crop selection and water management strategies should be determined at a local scale. This computational method can be applied in data-scarce regions across the world, where water resource management is required to resolve competition between food security and available resources in a changing climate.

A Representation of an Instantaneous Unit Hydrograph From Geomorphology

NASA Astrophysics Data System (ADS)

Gupta, Vijay K.; Waymire, Ed; Wang, C. T.

1980-10-01

The channel network and the overland flow regions in a river basin satisfy Horton's empirical geo-morphologic laws when ordered according to the Strahler ordering scheme. This setting is presently employed in a kinetic theoretic framework for obtaining an explicit mathematical representation for the instantaneous unit hydrograph (iuh) at the basin outlet. Two examples are developed which lead to explicit formulae for the iuh. These examples are formally analogous to the solutions that would result if a basin is represented in terms of linear reservoirs and channels, respectively, in series and in parallel. However, this analogy is only formal, and it does not carry through physically. All but one of the parameters appearing in the iuh formulae are obtained in terms of Horton's bifurcation ratio, stream length ratio, and stream area ratio. The one unknown parameter is obtained through specifying the basin mean lag time independently. Three basins from Illinois are selected to check the theoretical results with the observed direct surface runoff hydrographs. The theory provided excellent agreement for two basins with areas of the order of 1100 mi2 (1770 km2) but underestimates the peak flow for the smaller basin with 300-mi2 (483-km2) area. This relative lack of agreement for the smaller basin may be used to question the validity of the linearity assumption in the rainfall runoff transformation which is embedded in the above development.

Droplet size prediction in ultrasonic nebulization for non-oxide ceramic powder synthesis.

PubMed

Muñoz, Mariana; Goutier, Simon; Foucaud, Sylvie; Mariaux, Gilles; Poirier, Thierry

2018-03-01

Spray pyrolysis process has been used for the synthesis of non-oxide ceramic powders from liquid precursors in the Si/C/N system. Particles with a high thermal stability and with variable composition and size distribution have been obtained. In this process, the mechanisms involved in precursor decomposition and gas phase recombination of species are still unknown. The final aim of this work consists in improving the whole process comprehension by an experimental/modelling approach that helps to connect the synthesized particles characteristics to the precursor properties and process operating parameters. It includes the following steps: aerosol formation by a piezoelectric nebulizer, its transport and the chemical-physical phenomena involved in the reaction processes. This paper focuses on the aerosol characterization to understand the relationship between the liquid precursor properties and the liquid droplet diameter distribution. Liquids with properties close to the precursor of interest (hexamethyldisilazane) have been used. Experiments have been performed using a shadowgraphy technique to determine the drop size distribution of the aerosol. For all operating parameters of the nebulizer device and liquids used, bimodal droplet size distributions have been obtained. Correlations proposed in the literature for the droplet size prediction by ultrasonic nebulization were used and adapted to the specific nebulizer device used in this study, showing rather good agreement with experimental values. Copyright © 2017 Elsevier B.V. All rights reserved.

The Stereotype-Matching Effect: Greater Influence on Functioning When Age Stereotypes Correspond to Outcomes

PubMed Central

Levy, Becca R.; Leifheit-Limson, Erica

2009-01-01

Older individuals assimilate, and are targeted by, contradictory positive and negative age stereotypes. It was unknown whether the influence of stereotype valence is stronger when the stereotype content corresponds to the outcome domain. We randomly assigned older individuals to either positive-cognitive, negative-cognitive, positive-physical, or negative-physical subliminal-age-stereotype groups and assessed cognitive and physical outcomes. As predicted, when the age stereotypes corresponded to the outcome domains, their valence had a significantly greater impact on cognitive and physical performance. This suggests that if a match occurs, it is more likely to generate expectations that become self-fulfilling prophecies. PMID:19290757

Deletion of the SHOX gene in patients with short stature of unknown cause.

PubMed

Morizio, E; Stuppia, L; Gatta, V; Fantasia, D; Guanciali Franchi, P; Rinaldi, M M; Scarano, G; Concolino, D; Giannotti, A; Verrotti, A; Chiarelli, F; Calabrese, G; Palka, G

2003-06-15

A fluorescence in situ hybridization (FISH) study was performed in 56 patients with short stature of unknown cause in order to establish the role of deletion of the SHOX gene in this population. FISH analysis was carried out on metaphase spreads and interphase lymphocytes from blood smears using a probe specific for the SHOX gene. Deletion of SHOX was found in four patients (7.1%). No skeletal abnormalities were detected in these patients either at the physical examination or at X-rays of the upper and lower limbs. Present results indicate that SHOX plays an important role also in short stature of unknown cause, and FISH analysis appears as an easy, appropriate, and inexpensive method for the detection of SHOX deletion. Copyright 2003 Wiley-Liss, Inc.

Physical activity enhances metabolic fitness independently of cardiorespiratory fitness in marathon runners.

PubMed

Laye, M J; Nielsen, M B; Hansen, L S; Knudsen, T; Pedersen, B K

2015-01-01

High levels of cardiovascular fitness (CRF) and physical activity (PA) are associated with decreased mortality and risk to develop metabolic diseases. The independent contributions of CRF and PA to metabolic disease risk factors are unknown. We tested the hypothesis that runners who run consistently >50 km/wk and/or >2 marathons/yr for the last 5 years have superior metabolic fitness compared to matched sedentary subjects (CRF, age, gender, and BMI). Case-control recruitment of 31 pairs of runner-sedentary subjects identified 10 matched pairs with similar VO2max (mL/min/kg) (similar-VO2max). The similar-VO2max group was compared with a group of age, gender, and BMI matched pairs who had the largest difference in VO2max (different-VO2max). Primary outcomes that defined metabolic fitness including insulin response to an oral glucose tolerance test, fasting lipids, and fasting insulin were superior in runners versus sedentary controls despite similar VO2max. Furthermore, performance (velocity at VO2max, running economy), improved exercise metabolism (lactate threshold), and skeletal muscle levels of mitochondrial proteins were superior in runners versus sedentary controls with similar VO2max. In conclusion subjects with a high amount of PA have more positive metabolic health parameters independent of CRF. PA is thus a good marker against metabolic diseases.

Physical Activity Enhances Metabolic Fitness Independently of Cardiorespiratory Fitness in Marathon Runners

PubMed Central

Laye, M. J.; Nielsen, M. B.; Hansen, L. S.; Knudsen, T.; Pedersen, B. K.

2015-01-01

High levels of cardiovascular fitness (CRF) and physical activity (PA) are associated with decreased mortality and risk to develop metabolic diseases. The independent contributions of CRF and PA to metabolic disease risk factors are unknown. We tested the hypothesis that runners who run consistently >50 km/wk and/or >2 marathons/yr for the last 5 years have superior metabolic fitness compared to matched sedentary subjects (CRF, age, gender, and BMI). Case-control recruitment of 31 pairs of runner-sedentary subjects identified 10 matched pairs with similar VO2max (mL/min/kg) (similar-VO2max). The similar-VO2max group was compared with a group of age, gender, and BMI matched pairs who had the largest difference in VO2max (different-VO2max). Primary outcomes that defined metabolic fitness including insulin response to an oral glucose tolerance test, fasting lipids, and fasting insulin were superior in runners versus sedentary controls despite similar VO2max. Furthermore, performance (velocity at VO2max, running economy), improved exercise metabolism (lactate threshold), and skeletal muscle levels of mitochondrial proteins were superior in runners versus sedentary controls with similar VO2max. In conclusion subjects with a high amount of PA have more positive metabolic health parameters independent of CRF. PA is thus a good marker against metabolic diseases. PMID:25821340

A Measurement of the Parity-Violating Asymmetry in Aluminum and its Contribution to a Measurement of the Proton's Weak Charge

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

Magee, Joshua Allen

2016-05-01

The Q_weak experiment, which ran at the Thomas Jefferson National Accelerator Facility, made a precision measurement of the proton's weak charge, Q^p_W. The weak charge is extracted via a measurement of the parity-violating asymmetry in elastic electron-proton scattering from hydrogen at low momentum transfer (Q^2=0.025 GeV^2). This result is directly related to the electroweak mixing angle, sin^2(Theta_W), a fundamental parameter in the Standard Model of particle physics. This provides a precision test sensitive to new, as yet unknown, fundamental physics. This dissertation focuses on two central corrections to the Q_weak measurement: the target window contribution and sub-percent determination of themore » electron beam polarization. The aluminum target windows contribute approximately 30% of the measured asymmetry. Removal of this background requires precise measurements of both the elastic electron-aluminum scattering rate and its parity-violating asymmetry. The results reported here are the most precise measurement of the Q_weak target dilution and asymmetry to date. The parity-violating asymmetry for the aluminum alloy was found to be 1.6174 +/- 0.0704 (stat.) +/- 0.0113 (sys.) parts-per-million. The first sub-percent precision polarization measurements made from the Hall C Moller polarimeter are also reported, with systematic uncertainties of 0.84%.« less

Management of physical health in patients with schizophrenia: practical recommendations.

PubMed

Heald, A; Montejo, A L; Millar, H; De Hert, M; McCrae, J; Correll, C U

2010-06-01

Improved physical health care is a pressing need for patients with schizophrenia. It can be achieved by means of a multidisciplinary team led by the psychiatrist. Key priorities should include: selection of antipsychotic therapy with a low risk of weight gain and metabolic adverse effects; routine assessment, recording and longitudinal tracking of key physical health parameters, ideally by electronic spreadsheets; and intervention to control CVD risk following the same principles as for the general population. A few simple tools to assess and record key physical parameters, combined with lifestyle intervention and pharmacological treatment as indicated, could significantly improve physical outcomes. Effective implementation of strategies to optimise physical health parameters in patients with severe enduring mental illness requires engagement and communication between psychiatrists and primary care in most health settings. Copyright (c) 2010 Elsevier Masson SAS. All rights reserved.

A Bayesian Retrieval of Greenland Ice Sheet Internal Temperature from Ultra-wideband Software-defined Microwave Radiometer (UWBRAD) Measurements

NASA Astrophysics Data System (ADS)

Duan, Y.; Durand, M. T.; Jezek, K. C.; Yardim, C.; Bringer, A.; Aksoy, M.; Johnson, J. T.

2017-12-01

The ultra-wideband software-defined microwave radiometer (UWBRAD) is designed to provide ice sheet internal temperature product via measuring low frequency microwave emission. Twelve channels ranging from 0.5 to 2.0 GHz are covered by the instrument. A Greenland air-borne demonstration was demonstrated in September 2016, provided first demonstration of Ultra-wideband radiometer observations of geophysical scenes, including ice sheets. Another flight is planned for September 2017 for acquiring measurements in central ice sheet. A Bayesian framework is designed to retrieve the ice sheet internal temperature from simulated UWBRAD brightness temperature (Tb) measurements over Greenland flight path with limited prior information of the ground. A 1-D heat-flow model, the Robin Model, was used to model the ice sheet internal temperature profile with ground information. Synthetic UWBRAD Tb observations was generated via the partially coherent radiation transfer model, which utilizes the Robin model temperature profile and an exponential fit of ice density from Borehole measurement as input, and corrupted with noise. The effective surface temperature, geothermal heat flux, the variance of upper layer ice density, and the variance of fine scale density variation at deeper ice sheet were treated as unknown variables within the retrieval framework. Each parameter is defined with its possible range and set to be uniformly distributed. The Markov Chain Monte Carlo (MCMC) approach is applied to make the unknown parameters randomly walk in the parameter space. We investigate whether the variables can be improved over priors using the MCMC approach and contribute to the temperature retrieval theoretically. UWBRAD measurements near camp century from 2016 was also treated with the MCMC to examine the framework with scattering effect. The fine scale density fluctuation is an important parameter. It is the most sensitive yet highly unknown parameter in the estimation framework. Including the fine scale density fluctuation greatly improved the retrieval results. The ice sheet vertical temperature profile, especially the 10m temperature, can be well retrieved via the MCMC process. Future retrieval work will apply the Bayesian approach to UWBRAD airborne measurements.

Acoustic impedance properties of seafloor sediments off the coast of Southeastern Hainan, South China Sea

NASA Astrophysics Data System (ADS)

Hou, Zhengyu; Chen, Zhong; Wang, Jingqiang; Zheng, Xufeng; Yan, Wen; Tian, Yuhang; Luo, Yun

2018-04-01

Geoacoustic parameters are essential inputs to sediment wave propagation theories and are vital to underwater acoustic environment and explorations of the sea bottom. In this study, 21 seafloor sediment samples were collected off the coast of southeastern Hainan in the South China Sea. The sound speed was measured using a portable WSD-3 digital sonic instrument and the coaxial differential distance measurement method. Based on the measured sound speed and physical properties, the acoustic impedance and the pore-water-independent index of impedance (IOI) were calculated in this study. Similar to the sound speed, the IOI values are closely related to the sediment physical properties and change gradually from the northwest to the southeast. The relations between IOI and physical properties were studied and compared to the relations between the sound speed and physical properties. IOI is better correlated to physical properties than sound speed. This study also uses an error norm method to analyze the sensitivity of IOI to the physical parameters in the double-parameter equations and finds that the most influential physical parameters are as follows: wet bulk density > porosity > clay content > mean particle size.

Physics issues of gamma ray burst emissions

NASA Technical Reports Server (NTRS)

Liang, Edison

1987-01-01

The critical physics issues in the interpretation of gamma-ray-burst spectra are reviewed. An attempt is made to define the emission-region parameter space satisfying the maximum number of observational and theoretical constraints. Also discussed are the physical mechanisms responsible for the bursts that are most consistent with the above parameter space.

Physical activity is not related to semen quality in young healthy men

PubMed Central

Mínguez-Alarcón, Lidia; Chavarro, Jorge E; Mendiola, Jaime; Gaskins, Audrey J; Torres-Cantero, Alberto M

2015-01-01

Objective To study the relation of physical activity with semen quality among healthy young men from Spain. Design Cross-sectional study. Setting University and college campuses of Murcia Region, Spain. Patients Healthy young men with untested fertility (n=215). Intervention A physical examination, blood and semen samples, and completion of a questionnaire. Main outcomes measure Semen quality parameters. Results Physical activity was not related to semen quality parameters. The adjusted percentage differences (95% confidence interval) in semen parameters comparing men in the top quartile of moderate to vigorous physical activity (≥9.5h/wk) to men in the bottom quartile (≤3h/wk) were 4.3% (−30.2, 38.9) for total sperm count, 7.2% (−30.6, 45.1) for sperm concentration, −2.42% (−6.53, 1.69) for sperm motility, and 12.6% (−12.0, 37.2) for sperm morphology. Conclusion In contrast to previous research among athletes, these data suggest that physical activity is not deleterious to testicular function, as captured by semen quality parameters in this population of healthy young men in Spain. PMID:25064411

Physical activity is not related to semen quality in young healthy men.

PubMed

Mínguez-Alarcón, Lidia; Chavarro, Jorge E; Mendiola, Jaime; Gaskins, Audrey J; Torres-Cantero, Alberto M

2014-10-01

To study the relationship of physical activity with semen quality among healthy young men from Spain. Cross-sectional study. University and college campuses of Murcia Region, Spain. Healthy young men with untested fertility (n = 215). A physical examination, blood and semen samples, and completion of a questionnaire. Semen quality parameters. Physical activity was not related to semen quality parameters. The adjusted percentage differences (95% confidence interval) in semen parameters comparing men in the top quartile of moderate-to-vigorous physical activity (≥9.5 h/wk) with men in the bottom quartile (≤3 h/wk) were 4.3% (-30.2%, 38.9%) for total sperm count, 7.2% (-30.6%, 45.1%) for sperm concentration, -2.42% (-6.53%, 1.69%) for sperm motility, and 12.6% (-12.0%, 37.2%) for sperm morphology. In contrast to previous research among athletes, these data suggest that physical activity is not deleterious to testicular function, as captured by semen quality parameters in this population of healthy young men in Spain. Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

ESTIMATION OF CHEMICAL SPECIFIC PARAMETERS WITHIN PHYSIOLOGICALLY BASED PHARMACOKINETIC/PHARMACODYNAMIC MODELS

EPA Science Inventory

While relationships between chemical structure and observed properties or activities (QSAR - quantitative structure activity relationship) can be used to predict the behavior of unknown chemicals, this method is semiempirical in nature relying on high quality experimental data to...

Astronomical, physical, and meteorological parameters for planetary atmospheres

NASA Technical Reports Server (NTRS)

Allison, Michael; Travis, Larry D.

1986-01-01

A newly compiled table of astronomical, physical, and meteorological parameters for planetary atmospheres is presented. Formulae and explanatory notes for their application and a complete listing of sources are also given.

Optimal critic learning for robot control in time-varying environments.

PubMed

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

Quantum Hamiltonian identification from measurement time traces.

PubMed

Zhang, Jun; Sarovar, Mohan

2014-08-22

Precise identification of parameters governing quantum processes is a critical task for quantum information and communication technologies. In this Letter, we consider a setting where system evolution is determined by a parametrized Hamiltonian, and the task is to estimate these parameters from temporal records of a restricted set of system observables (time traces). Based on the notion of system realization from linear systems theory, we develop a constructive algorithm that provides estimates of the unknown parameters directly from these time traces. We illustrate the algorithm and its robustness to measurement noise by applying it to a one-dimensional spin chain model with variable couplings.

Bayesian source tracking via focalization and marginalization in an uncertain Mediterranean Sea environment.

PubMed

Dosso, Stan E; Wilmut, Michael J; Nielsen, Peter L

2010-07-01

This paper applies Bayesian source tracking in an uncertain environment to Mediterranean Sea data, and investigates the resulting tracks and track uncertainties as a function of data information content (number of data time-segments, number of frequencies, and signal-to-noise ratio) and of prior information (environmental uncertainties and source-velocity constraints). To track low-level sources, acoustic data recorded for multiple time segments (corresponding to multiple source positions along the track) are inverted simultaneously. Environmental uncertainty is addressed by including unknown water-column and seabed properties as nuisance parameters in an augmented inversion. Two approaches are considered: Focalization-tracking maximizes the posterior probability density (PPD) over the unknown source and environmental parameters. Marginalization-tracking integrates the PPD over environmental parameters to obtain a sequence of joint marginal probability distributions over source coordinates, from which the most-probable track and track uncertainties can be extracted. Both approaches apply track constraints on the maximum allowable vertical and radial source velocity. The two approaches are applied for towed-source acoustic data recorded at a vertical line array at a shallow-water test site in the Mediterranean Sea where previous geoacoustic studies have been carried out.

Scattering of electromagnetic plane wave from a perfect electric conducting strip placed at interface of topological insulator-chiral medium

NASA Astrophysics Data System (ADS)

Shoukat, Sobia; Naqvi, Qaisar A.

2016-12-01

In this manuscript, scattering from a perfect electric conducting strip located at planar interface of topological insulator (TI)-chiral medium is investigated using the Kobayashi Potential method. Longitudinal components of electric and magnetic vector potential in terms of unknown weighting function are considered. Use of related set of boundary conditions yields two algebraic equations and four dual integral equations (DIEs). Integrand of two DIEs are expanded in terms of the characteristic functions with expansion coefficients which must satisfy, simultaneously, the discontinuous property of the Weber-Schafheitlin integrals, required edge and boundary conditions. The resulting expressions are then combined with algebraic equations to express the weighting function in terms of expansion coefficients, these expansion coefficients are then substituted in remaining DIEs. The projection is applied using the Jacobi polynomials. This treatment yields matrix equation for expansion coefficients which is solved numerically. These unknown expansion coefficients are used to find the scattered field. The far zone scattering width is investigated with respect to different parameters of the geometry, i.e, chirality of chiral medium, angle of incidence, size of the strip. Significant effects of different parameters including TI parameter on the scattering width are noted.

Characterisation of the physico-mechanical parameters of MSW.

PubMed

Stoltz, Guillaume; Gourc, Jean-Pierre; Oxarango, Laurent

2010-01-01

Following the basics of soil mechanics, the physico-mechanical behaviour of municipal solid waste (MSW) can be defined through constitutive relationships which are expressed with respect to three physical parameters: the dry density, the porosity and the gravimetric liquid content. In order to take into account the complexity of MSW (grain size distribution and heterogeneity larger than for conventional soils), a special oedometer was designed to carry out laboratory experiments. This apparatus allowed a coupled measurement of physical parameters for MSW settlement under stress. The studied material was a typical sample of fresh MSW from a French landfill. The relevant physical parameters were measured using a gas pycnometer. Moreover, the compressibility of MSW was studied with respect to the initial gravimetric liquid content. Proposed methods to assess the set of three physical parameters allow a relevant understanding of the physico-mechanical behaviour of MSW under compression, specifically, the evolution of the limit liquid content. The present method can be extended to any type of MSW. 2010 Elsevier Ltd. All rights reserved.

Associations between the lower esophageal sphincter function and the level of physical activity.

PubMed

Waśko-Czopnik, Dorota; Jóźków, Paweł; Dunajska, Katarzyna; Mędraś, Marek; Paradowski, Leszek

2013-01-01

Gastroesophageal reflux disease (GERD) is a very frequent and multifactorial disease. It has been found that GERD is associated with obesity, smoking, esophagitis, diet and lifestyle. Physical activity is among the factors involved in the occurrence of GERD. The aim of the study was to evaluate the associations between the different parameters of lower esophageal pressure (LES) and the level of everyday physical activity in patients with GERD. The authors examined 100 consecutive patients who underwent manometry and pH-metry because of symptoms suggesting GERD. Physical activity was assessed by means of the International Physical Activity Questionnaire (IPAQ). In accordance with IPAQ categorical scoring, the authors divided the studied subjects into 3 groups according to their level of physical activity. The investigation comprised 59 men and 41 women, with the mean age 49 ± 14 years. The authors analyzed the relationships between the LES parameters (pressure, total LES length and HPZ length) and physical activity. The authors did not find any significant correlations between the studied parameters and the amount of physical activity. The authors also did not observe any association between the LES pressure and the level of physical activity. The subgroups distinguished on the basis of LESP did not differ as to the amount of everyday physical activity as well. Although most data indicates that intense exercise exacerbates GERD symptoms, the authors did not find any associations between LES parameters and physical activity. In view of the present results maintaining the recommended level of everyday physical activity does not interfere with the mechanisms of GERD.

Theropod courtship: large scale physical evidence of display arenas and avian-like scrape ceremony behaviour by Cretaceous dinosaurs.

PubMed

Lockley, Martin G; McCrea, Richard T; Buckley, Lisa G; Lim, Jong Deock; Matthews, Neffra A; Breithaupt, Brent H; Houck, Karen J; Gierliński, Gerard D; Surmik, Dawid; Kim, Kyung Soo; Xing, Lida; Kong, Dal Yong; Cart, Ken; Martin, Jason; Hadden, Glade

2016-01-07

Relationships between non-avian theropod dinosaurs and extant and fossil birds are a major focus of current paleobiological research. Despite extensive phylogenetic and morphological support, behavioural evidence is mostly ambiguous and does not usually fossilize. Thus, inferences that dinosaurs, especially theropods displayed behaviour analogous to modern birds are intriguing but speculative. Here we present extensive and geographically widespread physical evidence of substrate scraping behavior by large theropods considered as compelling evidence of "display arenas" or leks, and consistent with "nest scrape display" behaviour among many extant ground-nesting birds. Large scrapes, up to 2 m in diameter, occur abundantly at several Cretaceous sites in Colorado. They constitute a previously unknown category of large dinosaurian trace fossil, inferred to fill gaps in our understanding of early phases in the breeding cycle of theropods. The trace makers were probably lekking species that were seasonally active at large display arena sites. Such scrapes indicate stereotypical avian behaviour hitherto unknown among Cretaceous theropods, and most likely associated with terrirorial activity in the breeding season. The scrapes most probably occur near nesting colonies, as yet unknown or no longer preserved in the immediate study areas. Thus, they provide clues to paleoenvironments where such nesting sites occurred.

Theropod courtship: large scale physical evidence of display arenas and avian-like scrape ceremony behaviour by Cretaceous dinosaurs

NASA Astrophysics Data System (ADS)

Lockley, Martin G.; McCrea, Richard T.; Buckley, Lisa G.; Deock Lim, Jong; Matthews, Neffra A.; Breithaupt, Brent H.; Houck, Karen J.; Gierliński, Gerard D.; Surmik, Dawid; Soo Kim, Kyung; Xing, Lida; Yong Kong, Dal; Cart, Ken; Martin, Jason; Hadden, Glade

2016-01-01

Relationships between non-avian theropod dinosaurs and extant and fossil birds are a major focus of current paleobiological research. Despite extensive phylogenetic and morphological support, behavioural evidence is mostly ambiguous and does not usually fossilize. Thus, inferences that dinosaurs, especially theropods displayed behaviour analogous to modern birds are intriguing but speculative. Here we present extensive and geographically widespread physical evidence of substrate scraping behavior by large theropods considered as compelling evidence of “display arenas” or leks, and consistent with “nest scrape display” behaviour among many extant ground-nesting birds. Large scrapes, up to 2 m in diameter, occur abundantly at several Cretaceous sites in Colorado. They constitute a previously unknown category of large dinosaurian trace fossil, inferred to fill gaps in our understanding of early phases in the breeding cycle of theropods. The trace makers were probably lekking species that were seasonally active at large display arena sites. Such scrapes indicate stereotypical avian behaviour hitherto unknown among Cretaceous theropods, and most likely associated with terrirorial activity in the breeding season. The scrapes most probably occur near nesting colonies, as yet unknown or no longer preserved in the immediate study areas. Thus, they provide clues to paleoenvironments where such nesting sites occurred.

Theropod courtship: large scale physical evidence of display arenas and avian-like scrape ceremony behaviour by Cretaceous dinosaurs

PubMed Central

Lockley, Martin G.; McCrea, Richard T.; Buckley, Lisa G.; Deock Lim, Jong; Matthews, Neffra A.; Breithaupt, Brent H.; Houck, Karen J.; Gierliński, Gerard D.; Surmik, Dawid; Soo Kim, Kyung; Xing, Lida; Yong Kong, Dal; Cart, Ken; Martin, Jason; Hadden, Glade

2016-01-01

Relationships between non-avian theropod dinosaurs and extant and fossil birds are a major focus of current paleobiological research. Despite extensive phylogenetic and morphological support, behavioural evidence is mostly ambiguous and does not usually fossilize. Thus, inferences that dinosaurs, especially theropods displayed behaviour analogous to modern birds are intriguing but speculative. Here we present extensive and geographically widespread physical evidence of substrate scraping behavior by large theropods considered as compelling evidence of “display arenas” or leks, and consistent with “nest scrape display” behaviour among many extant ground-nesting birds. Large scrapes, up to 2 m in diameter, occur abundantly at several Cretaceous sites in Colorado. They constitute a previously unknown category of large dinosaurian trace fossil, inferred to fill gaps in our understanding of early phases in the breeding cycle of theropods. The trace makers were probably lekking species that were seasonally active at large display arena sites. Such scrapes indicate stereotypical avian behaviour hitherto unknown among Cretaceous theropods, and most likely associated with terrirorial activity in the breeding season. The scrapes most probably occur near nesting colonies, as yet unknown or no longer preserved in the immediate study areas. Thus, they provide clues to paleoenvironments where such nesting sites occurred. PMID:26741567

Multimodal, high-dimensional, model-based, Bayesian inverse problems with applications in biomechanics

NASA Astrophysics Data System (ADS)

Franck, I. M.; Koutsourelakis, P. S.

2017-01-01

This paper is concerned with the numerical solution of model-based, Bayesian inverse problems. We are particularly interested in cases where the cost of each likelihood evaluation (forward-model call) is expensive and the number of unknown (latent) variables is high. This is the setting in many problems in computational physics where forward models with nonlinear PDEs are used and the parameters to be calibrated involve spatio-temporarily varying coefficients, which upon discretization give rise to a high-dimensional vector of unknowns. One of the consequences of the well-documented ill-posedness of inverse problems is the possibility of multiple solutions. While such information is contained in the posterior density in Bayesian formulations, the discovery of a single mode, let alone multiple, poses a formidable computational task. The goal of the present paper is two-fold. On one hand, we propose approximate, adaptive inference strategies using mixture densities to capture multi-modal posteriors. On the other, we extend our work in [1] with regard to effective dimensionality reduction techniques that reveal low-dimensional subspaces where the posterior variance is mostly concentrated. We validate the proposed model by employing Importance Sampling which confirms that the bias introduced is small and can be efficiently corrected if the analyst wishes to do so. We demonstrate the performance of the proposed strategy in nonlinear elastography where the identification of the mechanical properties of biological materials can inform non-invasive, medical diagnosis. The discovery of multiple modes (solutions) in such problems is critical in achieving the diagnostic objectives.

Computer network defense through radial wave functions

NASA Astrophysics Data System (ADS)

Malloy, Ian J.

The purpose of this research is to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has been devastating to geopolitical regions in that they are severely difficult for a civilian to avoid triggering given the unknown position of a landmine. Thus, the importance of understanding a logic bomb is relevant and has corollaries to quantum mechanics as well. The research synthesizes quantum logic phase shifts in certain respects using the Dynamic Data Exchange protocol in software written for this work, as well as a C-NOT gate applied to a virtual quantum circuit environment by implementing a Quantum Fourier Transform. The research focus applies the principles of coherence and entanglement from quantum physics, the concept of expert systems in artificial intelligence, principles of prime number based cryptography with trapdoor functions, and modeling radio wave propagation against an event from unknown parameters. This comes as a program relying on the artificial intelligence concept of an expert system in conjunction with trigger events for a trapdoor function relying on infinite recursion, as well as system mechanics for elliptic curve cryptography along orbital angular momenta. Here trapdoor both denotes the form of cipher, as well as the implied relationship to logic bombs.

The use of minimal spanning trees in particle physics

DOE PAGES

Rainbolt, J. Lovelace; Schmitt, M.

2017-02-14

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

The use of minimal spanning trees in particle physics

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

Rainbolt, J. Lovelace; Schmitt, M.

Minimal spanning trees (MSTs) have been used in cosmology and astronomy to distinguish distributions of points in a multi-dimensional space. They are essentially unknown in particle physics, however. We briefly define MSTs and illustrate their properties through a series of examples. We show how they might be applied to study a typical event sample from a collider experiment and conclude that MSTs may prove useful in distinguishing different classes of events.

Physical Stress, Consumer Control, and New Theory in Ecology.

PubMed

Silliman, Brian R; He, Qiang

2018-05-22

Consumer-prey interactions form the foundation of food webs and are affected by the physical environment. Multiple foundational theories in ecology [e.g., the environmental stress model (ESM), the stress-gradient hypothesis (SGH), and ecosystem resilience theory] assume increased physical stress dampens top-down control of prey. In the large majority of empirical studies, however, physical stress either does not affect or amplifies consumer control. Additive and synergistic impacts of physical stress on consumer control appear more common, for example, for herbivory versus predation, and for warm- versus cold-blooded consumers. Predictability in how physical stress affects consumer control, however, remains largely unknown. We expand classical theories in ecology so that their assumption about physical stress-consumer control relationships can be inclusive of what primarily occurs in nature. Copyright © 2018. Published by Elsevier Ltd.

A novel sensitivity-based method for damage detection of structures under unknown periodic excitations

NASA Astrophysics Data System (ADS)

Naseralavi, S. S.; Salajegheh, E.; Fadaee, M. J.; Salajegheh, J.

2014-06-01

This paper presents a technique for damage detection in structures under unknown periodic excitations using the transient displacement response. The method is capable of identifying the damage parameters without finding the input excitations. We first define the concept of displacement space as a linear space in which each point represents displacements of structure under an excitation and initial condition. Roughly speaking, the method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering this novel geometrical viewpoint, an equation called kernel parallelization equation (KPE) is derived for damage detection under unknown periodic excitations and a sensitivity-based algorithm for solving KPE is proposed accordingly. The method is evaluated via three case studies under periodic excitations, which confirm the efficiency of the proposed method.

Object-Image Correspondence for Algebraic Curves under Projections

NASA Astrophysics Data System (ADS)

Burdis, Joseph M.; Kogan, Irina A.; Hong, Hoon

2013-03-01

We present a novel algorithm for deciding whether a given planar curve is an image of a given spatial curve, obtained by a central or a parallel projection with unknown parameters. The motivation comes from the problem of establishing a correspondence between an object and an image, taken by a camera with unknown position and parameters. A straightforward approach to this problem consists of setting up a system of conditions on the projection parameters and then checking whether or not this system has a solution. The computational advantage of the algorithm presented here, in comparison to algorithms based on the straightforward approach, lies in a significant reduction of a number of real parameters that need to be eliminated in order to establish existence or non-existence of a projection that maps a given spatial curve to a given planar curve. Our algorithm is based on projection criteria that reduce the projection problem to a certain modification of the equivalence p! roblem of planar curves under affine and projective transformations. To solve the latter problem we make an algebraic adaptation of signature construction that has been used to solve the equivalence problems for smooth curves. We introduce a notion of a classifying set of rational differential invariants and produce explicit formulas for such invariants for the actions of the projective and the affine groups on the plane.

Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

2014-10-01

A commonly encountered problem in numerous areas of applications is to estimate the unknown coefficients of a dynamical system from direct or indirect observations at discrete times of some of the components of the state vector. A related problem is to estimate unobserved components of the state. An egregious example of such a problem is provided by metabolic models, in which the numerous model parameters and the concentrations of the metabolites in tissue are to be estimated from concentration data in the blood. A popular method for addressing similar questions in stochastic and turbulent dynamics is the ensemble Kalman filter (EnKF), a particle-based filtering method that generalizes classical Kalman filtering. In this work, we adapt the EnKF algorithm for deterministic systems in which the numerical approximation error is interpreted as a stochastic drift with variance based on classical error estimates of numerical integrators. This approach, which is particularly suitable for stiff systems where the stiffness may depend on the parameters, allows us to effectively exploit the parallel nature of particle methods. Moreover, we demonstrate how spatial prior information about the state vector, which helps the stability of the computed solution, can be incorporated into the filter. The viability of the approach is shown by computed examples, including a metabolic system modeling an ischemic episode in skeletal muscle, with a high number of unknown parameters.

Overview of Icing Physics Relevant to Scaling

NASA Technical Reports Server (NTRS)

Anderson, David N.; Tsao, Jen-Ching

2005-01-01

An understanding of icing physics is required for the development of both scaling methods and ice-accretion prediction codes. This paper gives an overview of our present understanding of the important physical processes and the associated similarity parameters that determine the shape of Appendix C ice accretions. For many years it has been recognized that ice accretion processes depend on flow effects over the model, on droplet trajectories, on the rate of water collection and time of exposure, and, for glaze ice, on a heat balance. For scaling applications, equations describing these events have been based on analyses at the stagnation line of the model and have resulted in the identification of several non-dimensional similarity parameters. The parameters include the modified inertia parameter of the water drop, the accumulation parameter and the freezing fraction. Other parameters dealing with the leading edge heat balance have also been used for convenience. By equating scale expressions for these parameters to the values to be simulated a set of equations is produced which can be solved for the scale test conditions. Studies in the past few years have shown that at least one parameter in addition to those mentioned above is needed to describe surface-water effects, and some of the traditional parameters may not be as significant as once thought. Insight into the importance of each parameter, and the physical processes it represents, can be made by viewing whether ice shapes change, and the extent of the change, when each parameter is varied. Experimental evidence is presented to establish the importance of each of the traditionally used parameters and to identify the possible form of a new similarity parameter to be used for scaling.

The concept of physical surface in nuclear matter

NASA Astrophysics Data System (ADS)

Mazilu, Nicolae; Agop, Maricel

2015-02-01

The main point of a physical definition of surface forces in the matter in general, especially in the nuclear matter, is that the curvature of surfaces and its variation should be physically defined. The forces are therefore just the vehicles of introducing physics. The problem of mathematical definition of a surface in term of the curvature parameters thus naturally occurs. The present work addresses this problem in terms of the asymptotic directions of a surface in a point. A physical meaning of these parameters is given, first in terms of inertial forces, then in terms of a differential theory of colors, whereby the space of curvature parameters is identified with the color space. The work concludes with an image of the evolution of a local portion of a surface.

Four-parameter potential box with inverse square singular boundaries

NASA Astrophysics Data System (ADS)

Alhaidari, A. D.; Taiwo, T. J.

2018-03-01

Using the Tridiagonal Representation Approach (TRA), we obtain solutions (energy spectrum and corresponding wavefunctions) for a four-parameter potential box with inverse square singularity at the boundaries. It could be utilized in physical applications to replace the widely used one-parameter infinite square potential well (ISPW). The four parameters of the potential provide an added flexibility over the one-parameter ISPW to control the physical features of the system. The two potential parameters that give the singularity strength at the boundaries are naturally constrained to avoid the inherent quantum anomalies associated with the inverse square potential.

The Multiverse and Particle Physics

NASA Astrophysics Data System (ADS)

Donoghue, John F.

2016-10-01

The possibility of fundamental theories with very many ground states, each with different physical parameters, changes the way that we approach the major questions of particle physics. Most importantly, it raises the possibility that these different parameters could be realized in different domains in the larger universe. In this review, I survey the motivations for the multiverse and the impact of the idea of the multiverse on the search for new physics beyond the Standard Model.

Physical Modeling of Activation Energy in Organic Semiconductor Devices based on Energy and Momentum Conservations

PubMed Central

Mao, Ling-Feng; Ning, H.; Hu, Changjun; Lu, Zhaolin; Wang, Gaofeng

2016-01-01

Field effect mobility in an organic device is determined by the activation energy. A new physical model of the activation energy is proposed by virtue of the energy and momentum conservation equations. The dependencies of the activation energy on the gate voltage and the drain voltage, which were observed in the experiments in the previous independent literature, can be well explained using the proposed model. Moreover, the expression in the proposed model, which has clear physical meanings in all parameters, can have the same mathematical form as the well-known Meyer-Neldel relation, which lacks of clear physical meanings in some of its parameters since it is a phenomenological model. Thus it not only describes a physical mechanism but also offers a possibility to design the next generation of high-performance optoelectronics and integrated flexible circuits by optimizing device physical parameter. PMID:27103586

Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization

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 that the improved PSO algorithm could be used for the Liuxihe model parameter optimization effectively and could improve the model capability largely in catchment flood forecasting, thus proving that parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological models. It also has been found that the appropriate particle number and the maximum evolution number of PSO algorithm used for the Liuxihe model catchment flood forecasting are 20 and 30 respectively.

EFFICIENT MODEL-FITTING AND MODEL-COMPARISON FOR HIGH-DIMENSIONAL BAYESIAN GEOSTATISTICAL MODELS. (R826887)

EPA Science Inventory

Geostatistical models are appropriate for spatially distributed data measured at irregularly spaced locations. We propose an efficient Markov chain Monte Carlo (MCMC) algorithm for fitting Bayesian geostatistical models with substantial numbers of unknown parameters to sizable...

Bootstrap Methods: A Very Leisurely Look.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Winstead, Wayland H.

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…

Characterizing scale- and location-dependent correlation of water retention parameters with soil physical properties using wavelet techniques.

PubMed

Shu, Qiaosheng; Liu, Zuoxin; Si, Bingcheng

2008-01-01

Understanding the correlation between soil hydraulic parameters and soil physical properties is a prerequisite for the prediction of soil hydraulic properties from soil physical properties. The objective of this study was to examine the scale- and location-dependent correlation between two water retention parameters (alpha and n) in the van Genuchten (1980) function and soil physical properties (sand content, bulk density [Bd], and organic carbon content) using wavelet techniques. Soil samples were collected from a transect from Fuxin, China. Soil water retention curves were measured, and the van Genuchten parameters were obtained through curve fitting. Wavelet coherency analysis was used to elucidate the location- and scale-dependent relationships between these parameters and soil physical properties. Results showed that the wavelet coherence between alpha and sand content was significantly different from red noise at small scales (8-20 m) and from a distance of 30 to 470 m. Their wavelet phase spectrum was predominantly out of phase, indicating negative correlation between these two variables. The strong negative correlation between alpha and Bd existed mainly at medium scales (30-80 m). However, parameter n had a strong positive correlation only with Bd at scales between 20 and 80 m. Neither of the two retention parameters had significant wavelet coherency with organic carbon content. These results suggested that location-dependent scale analyses are necessary to improve the performance for soil water retention characteristic predictions.

Multivariate Statistical Analysis of Cigarette Design Feature Influence on ISO TNCO Yields.

PubMed

Agnew-Heard, Kimberly A; Lancaster, Vicki A; Bravo, Roberto; Watson, Clifford; Walters, Matthew J; Holman, Matthew R

2016-06-20

The aim of this study is to explore how differences in cigarette physical design parameters influence tar, nicotine, and carbon monoxide (TNCO) yields in mainstream smoke (MSS) using the International Organization of Standardization (ISO) smoking regimen. Standardized smoking methods were used to evaluate 50 U.S. domestic brand cigarettes and a reference cigarette representing a range of TNCO yields in MSS collected from linear smoking machines using a nonintense smoking regimen. Multivariate statistical methods were used to form clusters of cigarettes based on their ISO TNCO yields and then to explore the relationship between the ISO generated TNCO yields and the nine cigarette physical design parameters between and within each cluster simultaneously. The ISO generated TNCO yields in MSS are 1.1-17.0 mg tar/cigarette, 0.1-2.2 mg nicotine/cigarette, and 1.6-17.3 mg CO/cigarette. Cluster analysis divided the 51 cigarettes into five discrete clusters based on their ISO TNCO yields. No one physical parameter dominated across all clusters. Predicting ISO machine generated TNCO yields based on these nine physical design parameters is complex due to the correlation among and between the nine physical design parameters and TNCO yields. From these analyses, it is estimated that approximately 20% of the variability in the ISO generated TNCO yields comes from other parameters (e.g., filter material, filter type, inclusion of expanded or reconstituted tobacco, and tobacco blend composition, along with differences in tobacco leaf origin and stalk positions and added ingredients). A future article will examine the influence of these physical design parameters on TNCO yields under a Canadian Intense (CI) smoking regimen. Together, these papers will provide a more robust picture of the design features that contribute to TNCO exposure across the range of real world smoking patterns.

MoCha: Molecular Characterization of Unknown Pathways.

PubMed

Lobo, Daniel; Hammelman, Jennifer; Levin, Michael

2016-04-01

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

Geometrically constrained kinematic global navigation satellite systems positioning: Implementation and performance

NASA Astrophysics Data System (ADS)

Asgari, Jamal; Mohammadloo, Tannaz H.; Amiri-Simkooei, Ali Reza

2015-09-01

GNSS kinematic techniques are capable of providing precise coordinates in extremely short observation time-span. These methods usually determine the coordinates of an unknown station with respect to a reference one. To enhance the precision, accuracy, reliability and integrity of the estimated unknown parameters, GNSS kinematic equations are to be augmented by possible constraints. Such constraints could be derived from the geometric relation of the receiver positions in motion. This contribution presents the formulation of the constrained kinematic global navigation satellite systems positioning. Constraints effectively restrict the definition domain of the unknown parameters from the three-dimensional space to a subspace defined by the equation of motion. To test the concept of the constrained kinematic positioning method, the equation of a circle is employed as a constraint. A device capable of moving on a circle was made and the observations from 11 positions on the circle were analyzed. Relative positioning was conducted by considering the center of the circle as the reference station. The equation of the receiver's motion was rewritten in the ECEF coordinates system. A special attention is drawn onto how a constraint is applied to kinematic positioning. Implementing the constraint in the positioning process provides much more precise results compared to the unconstrained case. This has been verified based on the results obtained from the covariance matrix of the estimated parameters and the empirical results using kinematic positioning samples as well. The theoretical standard deviations of the horizontal components are reduced by a factor ranging from 1.24 to 2.64. The improvement on the empirical standard deviation of the horizontal components ranges from 1.08 to 2.2.

Theoretical Advances in Sequential Data Assimilation for the Atmosphere and Oceans

NASA Astrophysics Data System (ADS)

Ghil, M.

2007-05-01

We concentrate here on two aspects of advanced Kalman--filter-related methods: (i) the stability of the forecast- assimilation cycle, and (ii) parameter estimation for the coupled ocean-atmosphere system. The nonlinear stability of a prediction-assimilation system guarantees the uniqueness of the sequentially estimated solutions in the presence of partial and inaccurate observations, distributed in space and time; this stability is shown to be a necessary condition for the convergence of the state estimates to the true evolution of the turbulent flow. The stability properties of the governing nonlinear equations and of several data assimilation systems are studied by computing the spectrum of the associated Lyapunov exponents. These ideas are applied to a simple and an intermediate model of atmospheric variability and we show that the degree of stabilization depends on the type and distribution of the observations, as well as on the data assimilation method. These results represent joint work with A. Carrassi, A. Trevisan and F. Uboldi. Much is known by now about the main physical mechanisms that give rise to and modulate the El-Nino/Southern- Oscillation (ENSO), but the values of several parameters that enter these mechanisms are an important unknown. We apply Extended Kalman Filtering (EKF) for both model state and parameter estimation in an intermediate, nonlinear, coupled ocean-atmosphere model of ENSO. Model behavior is very sensitive to two key parameters: (a) "mu", the ocean-atmosphere coupling coefficient between the sea-surface temperature (SST) and wind stress anomalies; and (b) "delta-s", the surface-layer coefficient. Previous work has shown that "delta- s" determines the period of the model's self-sustained oscillation, while "mu' measures the degree of nonlinearity. Depending on the values of these parameters, the spatio-temporal pattern of model solutions is either that of a delayed oscillator or of a westward propagating mode. Assimilation of SST data from the NCEP- NCAR Reanalysis-2 shows that the parameters can vary on fairly short time scales and switch between values that approximate the two distinct modes of ENSO behavior. Rapid adjustments of these parameters occur, in particular, during strong ENSO events. Ways to apply EKF parameter estimation efficiently to state-of-the-art coupled ocean-atmosphere GCMs will be discussed. These results arise from joint work with D. Kondrashov and C.-j. Sun.

On how to avoid input and structural uncertainties corrupt the inference of hydrological parameters using a Bayesian framework

NASA Astrophysics Data System (ADS)

Hernández, Mario R.; Francés, Félix

2015-04-01

One phase of the hydrological models implementation process, significantly contributing to the hydrological predictions uncertainty, is the calibration phase in which values of the unknown model parameters are tuned by optimizing an objective function. An unsuitable error model (e.g. Standard Least Squares or SLS) introduces noise into the estimation of the parameters. The main sources of this noise are the input errors and the hydrological model structural deficiencies. Thus, the biased calibrated parameters cause the divergence model phenomenon, where the errors variance of the (spatially and temporally) forecasted flows far exceeds the errors variance in the fitting period, and provoke the loss of part or all of the physical meaning of the modeled processes. In other words, yielding a calibrated hydrological model which works well, but not for the right reasons. Besides, an unsuitable error model yields a non-reliable predictive uncertainty assessment. Hence, with the aim of prevent all these undesirable effects, this research focuses on the Bayesian joint inference (BJI) of both the hydrological and error model parameters, considering a general additive (GA) error model that allows for correlation, non-stationarity (in variance and bias) and non-normality of model residuals. As hydrological model, it has been used a conceptual distributed model called TETIS, with a particular split structure of the effective model parameters. Bayesian inference has been performed with the aid of a Markov Chain Monte Carlo (MCMC) algorithm called Dream-ZS. MCMC algorithm quantifies the uncertainty of the hydrological and error model parameters by getting the joint posterior probability distribution, conditioned on the observed flows. The BJI methodology is a very powerful and reliable tool, but it must be used correctly this is, if non-stationarity in errors variance and bias is modeled, the Total Laws must be taken into account. The results of this research show that the application of BJI with a GA error model outperforms the hydrological parameters robustness (diminishing the divergence model phenomenon) and improves the reliability of the streamflow predictive distribution, in respect of the results of a bad error model as SLS. Finally, the most likely prediction in a validation period, for both BJI+GA and SLS error models shows a similar performance.

Low-profile wireless passive resonators for sensing

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

Gong, Xun; An, Linan

A resonator for sensing a physical or an environmental parameter includes a support having a top surface that provides a ground plane, and a polymer-derived ceramic (PDC) element positioned on the top surface including a PDC layer, and a metal patch on the PDC layer. The metal patch is electrically isolated from all surrounding structure, and the resonator has a resonant frequency that changes as a function of the physical or environmental parameter. A system for wirelessly sensing a physical or environmental parameter includes at least one resonator and a wireless RF reader located remotely from the resonator for transmittingmore » a wide-band RF interrogation signal that excites the resonator. The wireless RF reader detects a sensing signal retransmitted by the resonator and includes a processor for determining the physical or environmental parameter at the location of the resonator from the sensing signal.« less

Estimating Mass Properties of Dinosaurs Using Laser Imaging and 3D Computer Modelling

PubMed Central

Bates, Karl T.; Manning, Phillip L.; Hodgetts, David; Sellers, William I.

2009-01-01

Body mass reconstructions of extinct vertebrates are most robust when complete to near-complete skeletons allow the reconstruction of either physical or digital models. Digital models are most efficient in terms of time and cost, and provide the facility to infinitely modify model properties non-destructively, such that sensitivity analyses can be conducted to quantify the effect of the many unknown parameters involved in reconstructions of extinct animals. In this study we use laser scanning (LiDAR) and computer modelling methods to create a range of 3D mass models of five specimens of non-avian dinosaur; two near-complete specimens of Tyrannosaurus rex, the most complete specimens of Acrocanthosaurus atokensis and Strutiomimum sedens, and a near-complete skeleton of a sub-adult Edmontosaurus annectens. LiDAR scanning allows a full mounted skeleton to be imaged resulting in a detailed 3D model in which each bone retains its spatial position and articulation. This provides a high resolution skeletal framework around which the body cavity and internal organs such as lungs and air sacs can be reconstructed. This has allowed calculation of body segment masses, centres of mass and moments or inertia for each animal. However, any soft tissue reconstruction of an extinct taxon inevitably represents a best estimate model with an unknown level of accuracy. We have therefore conducted an extensive sensitivity analysis in which the volumes of body segments and respiratory organs were varied in an attempt to constrain the likely maximum plausible range of mass parameters for each animal. Our results provide wide ranges in actual mass and inertial values, emphasizing the high level of uncertainty inevitable in such reconstructions. However, our sensitivity analysis consistently places the centre of mass well below and in front of hip joint in each animal, regardless of the chosen combination of body and respiratory structure volumes. These results emphasize that future biomechanical assessments of extinct taxa should be preceded by a detailed investigation of the plausible range of mass properties, in which sensitivity analyses are used to identify a suite of possible values to be tested as inputs in analytical models. PMID:19225569

Estimating mass properties of dinosaurs using laser imaging and 3D computer modelling.

PubMed

Bates, Karl T; Manning, Phillip L; Hodgetts, David; Sellers, William I

2009-01-01

Body mass reconstructions of extinct vertebrates are most robust when complete to near-complete skeletons allow the reconstruction of either physical or digital models. Digital models are most efficient in terms of time and cost, and provide the facility to infinitely modify model properties non-destructively, such that sensitivity analyses can be conducted to quantify the effect of the many unknown parameters involved in reconstructions of extinct animals. In this study we use laser scanning (LiDAR) and computer modelling methods to create a range of 3D mass models of five specimens of non-avian dinosaur; two near-complete specimens of Tyrannosaurus rex, the most complete specimens of Acrocanthosaurus atokensis and Strutiomimum sedens, and a near-complete skeleton of a sub-adult Edmontosaurus annectens. LiDAR scanning allows a full mounted skeleton to be imaged resulting in a detailed 3D model in which each bone retains its spatial position and articulation. This provides a high resolution skeletal framework around which the body cavity and internal organs such as lungs and air sacs can be reconstructed. This has allowed calculation of body segment masses, centres of mass and moments or inertia for each animal. However, any soft tissue reconstruction of an extinct taxon inevitably represents a best estimate model with an unknown level of accuracy. We have therefore conducted an extensive sensitivity analysis in which the volumes of body segments and respiratory organs were varied in an attempt to constrain the likely maximum plausible range of mass parameters for each animal. Our results provide wide ranges in actual mass and inertial values, emphasizing the high level of uncertainty inevitable in such reconstructions. However, our sensitivity analysis consistently places the centre of mass well below and in front of hip joint in each animal, regardless of the chosen combination of body and respiratory structure volumes. These results emphasize that future biomechanical assessments of extinct taxa should be preceded by a detailed investigation of the plausible range of mass properties, in which sensitivity analyses are used to identify a suite of possible values to be tested as inputs in analytical models.

Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach.

PubMed

Park, Eun Sug; Hopke, Philip K; Oh, Man-Suk; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford H

2014-07-01

There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Comparison of parameters of bone profile and homocysteine in physically active and non-active postmenopausal females.

PubMed

Tariq, Sundus; Lone, Khalid Parvez; Tariq, Saba

2016-01-01

Optimal physical activity is important in attaining a peak bone mass. Physically active women have better bone mineral density and reduce fracture risk as compared to females living a sedentary life. The objective of this study was to compare parameters of bone profile and serum homocysteine levels in physically active and non-active postmenopausal females. In this cross sectional study postmenopausal females between 50-70 years of age were recruited and divided into two groups: Physically inactive (n=133) performing light physical activity and Physically active (n=34) performing moderate physical activity. Physical activity (in metabolic equivalents), bone mineral density and serum homocysteine levels were assessed. Spearman's rho correlation was applied to observe correlations. Two independent sample t test and Mann Whitney U test were applied to compare groups. P-value ≤ 0.05 was taken statistically significant. Parameters of bone profile were significantly higher and serum homocysteine levels were significantly lower in postmenopausal females performing moderate physical activity as compared to females performing light physical activity. Homocysteine was not significantly related to T-score and Z-score in both groups. Improving physical activity could be beneficial for improving the quality of bone, decreasing fracture risk and decreasing serum homocysteine levels.

Optimization of Biomathematical Model Predictions for Cognitive Performance Impairment in Individuals: Accounting for Unknown Traits and Uncertain States in Homeostatic and Circadian Processes

PubMed Central

Van Dongen, Hans P. A.; Mott, Christopher G.; Huang, Jen-Kuang; Mollicone, Daniel J.; McKenzie, Frederic D.; Dinges, David F.

2007-01-01

Current biomathematical models of fatigue and performance do not accurately predict cognitive performance for individuals with a priori unknown degrees of trait vulnerability to sleep loss, do not predict performance reliably when initial conditions are uncertain, and do not yield statistically valid estimates of prediction accuracy. These limitations diminish their usefulness for predicting the performance of individuals in operational environments. To overcome these 3 limitations, a novel modeling approach was developed, based on the expansion of a statistical technique called Bayesian forecasting. The expanded Bayesian forecasting procedure was implemented in the two-process model of sleep regulation, which has been used to predict performance on the basis of the combination of a sleep homeostatic process and a circadian process. Employing the two-process model with the Bayesian forecasting procedure to predict performance for individual subjects in the face of unknown traits and uncertain states entailed subject-specific optimization of 3 trait parameters (homeostatic build-up rate, circadian amplitude, and basal performance level) and 2 initial state parameters (initial homeostatic state and circadian phase angle). Prior information about the distribution of the trait parameters in the population at large was extracted from psychomotor vigilance test (PVT) performance measurements in 10 subjects who had participated in a laboratory experiment with 88 h of total sleep deprivation. The PVT performance data of 3 additional subjects in this experiment were set aside beforehand for use in prospective computer simulations. The simulations involved updating the subject-specific model parameters every time the next performance measurement became available, and then predicting performance 24 h ahead. Comparison of the predictions to the subjects' actual data revealed that as more data became available for the individuals at hand, the performance predictions became increasingly more accurate and had progressively smaller 95% confidence intervals, as the model parameters converged efficiently to those that best characterized each individual. Even when more challenging simulations were run (mimicking a change in the initial homeostatic state; simulating the data to be sparse), the predictions were still considerably more accurate than would have been achieved by the two-process model alone. Although the work described here is still limited to periods of consolidated wakefulness with stable circadian rhythms, the results obtained thus far indicate that the Bayesian forecasting procedure can successfully overcome some of the major outstanding challenges for biomathematical prediction of cognitive performance in operational settings. Citation: Van Dongen HPA; Mott CG; Huang JK; Mollicone DJ; McKenzie FD; Dinges DF. Optimization of biomathematical model predictions for cognitive performance impairment in individuals: accounting for unknown traits and uncertain states in homeostatic and circadian processes. SLEEP 2007;30(9):1129-1143. PMID:17910385

Hardy's test as a device-independent dimension witness

NASA Astrophysics Data System (ADS)

Mukherjee, Amit; Roy, Arup; Bhattacharya, Some Sankar; Das, Subhadipa; Gazi, Md. Rajjak; Banik, Manik

2015-08-01

Knowing the dimension of an unknown physical system has practical relevance, as dimensionality plays an important role in various information theoretic tasks. In this work we show that a modified version of Hardy's argument, which reveals the contradiction of quantum theory with local realism, turns out to be useful for inspecting the minimal subsystem dimension of an unknown correlated quantum system. The use of Hardy's test in this task has a novel advantage: the subsystem dimension can be determined without knowing the detailed functioning of the experimental devices; i.e., Hardy's test suffices to be a device-independent dimension witness.

Radiation Hydrodynamical Models for Type I Superluminous Supernovae: Constraints on Progenitors and Explosion Mechanisms

NASA Astrophysics Data System (ADS)

Nomoto, Ken&'Ichi; Tolstov, Alexey; Sorokina, Elena; Blinnikov, Sergei; Bersten, Melina; Suzuki, Tomoharu

2017-11-01

The physical origin of Type-I (hydrogen-less) superluminous supernovae (SLSNe-I), whose luminosities are 10 to 500 times higher than normal core-collapse supernovae, remains still unknown. Thanks to their brightness, SLSNe-I would be useful probes of distant Universe. For the power source of the light curves of SLSNe-I, radioactive-decays, magnetars, and circumstellar interactions have been proposed, although no definitive conclusions have been reached yet. Since most of light curve studies have been based on simplified semi-analytic models, we have constructed multi-color light curve models by means of detailed radiation hydrodynamical calculations for various mass of stars including very massive ones and large amount of mass loss. We compare the rising time, peak luminosity, width, and decline rate of the model light curves with observations of SLSNe-I and obtain constraints on their progenitors and explosion mechanisms. We particularly pay attention to the recently reported double peaks of the light curves. We discuss how to discriminate three models, relevant models parameters, their evolutionary origins, and implications for the early evolution of the Universe.

Investigating physical field effects on the size-dependent dynamic behavior of inhomogeneous nanoscale plates

NASA Astrophysics Data System (ADS)

Ebrahimi, Farzad; Reza Barati, Mohammad

2017-02-01

This article investigates the thermo-mechanical vibration frequencies of magneto-electro-thermo-elastic functionally graded (METE-FG) nanoplates in the framework of refined four-unknown shear deformation plate theory. The present nanoplate is subjected to various kinds of thermal loads with uniform, linear and nonlinear distributions. The nonlinear distribution is considered as heat conduction and sinusoidal temperature rise. The present refined theory captures the influences of shear deformations without the need for shear correction factors. Thermo-magneto-electro-elastic coefficients of the FG nanoplate vary gradually along the thickness according to the power-law form. The scale coefficient is taken into consideration implementing the nonlocal elasticity of Eringen. The governing equations are derived through Hamilton's principle and are solved analytically. The frequency response is compared with those of previously published data. The obtained results are presented for the thermo-mechanical vibrations of the FG nanobeams to investigate the effects of material graduation, nonlocal parameter, mode number, slenderness ratio and thermal loading in detail. The present study is associated to aerospace, mechanical and nuclear engineering structures which are under thermal loads.

The mirage effect to probe the adsorption of organic molecules on the surface of the mass standards

NASA Astrophysics Data System (ADS)

Taillade, F.; Silva, M. Z.; Lepoutre, F.; Lecollinet, M.; Pinot, P.

2000-05-01

Among all the basic SI units, the mass unit is the only one to be defined in terms of a material standard: a prototype called K. All the industrial countries possess their own standards which were compared to the K during the last international comparison showing that unknown evolution occurs, but the adsorption-desorption of cleaning products plays a relatively important role. A few years ago, several laboratories in the U.S.A., Germany, and France reported interesting results of photothermal measurements to detect desorption at normal temperature and pressure (NTP). This paper presents a mirage set-up built to detect the film of condensable gasses on metallic surfaces at NTP conditions. In order to quantify these measurements, an inverse method has been developed to determine the adsorption isotherm involved in the physical process of adsorption-desorption and the linked parameters such as absorbability, type of adsorption, and differential heat of adsorption. The results will be discussed to imagine possible tracks to reduce the instabilities of the standards in the future and for possible new definitions of standards built with silicon.

Nuclear Masses in the A=80 Region of Nuclei

NASA Astrophysics Data System (ADS)

Cuka, N.; Gadala-Maria, A.; Aprahamian, A.

1996-05-01

Nucleosynthesis in explosive hydrogen burning at high temperatures above 8x10^8 K is characterized by the rp-process. A recent study^1 of the reaction flow and their associated time scales showed that the reaction path may in fact proceed well beyond the A=80 region of nuclei. An accurate simulation of the nucleosynthesis and energy generation of this process strongly depends on reliable nuclear physics input parameters such as masses, lifetimes, and reaction rates. We have extended the use of the simple P-parametrization^2,3 that had been applied to the characterization of the structure contributions to the nuclear masses in the actinides to include the A=80 region. The results will be presented along with predictions of masses for presently unknown masses of nuclei along the rp-process path. ^1 R. Wallace and S. Woosley, Ap. J. Suppl. 45, 389 (81). ^2 R. F. Casten, D.S. Brenner and P.E. Haustein, Phys. Rev. Lett. 58, 658 (87). ^3 P. Haustein, D.S. Brenner and R.F. Casten, Phys. Rev. C 38, 467 (88).

Investigating the origin of acoustic attenuation in liquid foams.

PubMed

Pierre, Juliette; Gaulon, Camille; Derec, Caroline; Elias, Florence; Leroy, Valentin

2017-08-01

Liquid foams are known to be highly efficient to absorb acoustic waves but the origin of the sound dissipation remains unknown. In this paper, we present low frequency (0.5-4kHz) experimental results measured with an impedance tube and we confront the recorded attenuations with a simple model that considers the foam as a concentrate bubbly liquid. In order to identify the influence of the different parameters constituting the foams we probe samples with different gases, and various liquid fractions and bubble size distributions. We demonstrate that the intrinsic acoustic attenuation in the liquid foam is due to both thermal and viscous losses. The physical mechanism of the viscous term is not elucidated but the microscopic effective viscosity evidenced here can be described by a phenomenological law scaling with the bubble size and the gas density. In our experimental configuration a third dissipation term occurs. It comes from the viscous friction on the wall of the impedance tube and it is well described by the Kirchhoff law considering the macroscopic effective viscosity classically measured in rheology experiments.

Wind gusts and plant aeroelasticity effects on the aerodynamics of pollen shedding: a hypothetical turbulence-initiated wind-pollination mechanism.

PubMed

Urzay, Javier; Llewellyn Smith, Stefan G; Thompson, Elinor; Glover, Beverley J

2009-08-21

Plant reproduction depends on pollen dispersal. For anemophilous (wind-pollinated) species, such as grasses and many trees, shedding pollen from the anther must be accomplished by physical mechanisms. The unknown nature of this process has led to its description as the 'paradox of pollen liberation'. A simple scaling analysis, supported by experimental measurements on typical wind-pollinated plant species, is used to estimate the suitability of previous resolutions of this paradox based on wind-gust aerodynamic models of fungal-spore liberation. According to this scaling analysis, the steady Stokes drag force is found to be large enough to liberate anemophilous pollen grains, and unsteady boundary-layer forces produced by wind gusts are found to be mostly ineffective since the ratio of the characteristic viscous time scale to the inertial time scale of acceleration of the wind stream is a small parameter for typical anemophilous species. A hypothetical model of a stochastic aeroelastic mechanism, initiated by the atmospheric turbulence typical of the micrometeorological conditions in the vicinity of the plant, is proposed to contribute to wind pollination.

Supermassive black holes do not correlate with dark matter haloes of galaxies.

PubMed

Kormendy, John; Bender, Ralf

2011-01-20

Supermassive black holes have been detected in all galaxies that contain bulge components when the galaxies observed were close enough that the searches were feasible. Together with the observation that bigger black holes live in bigger bulges, this has led to the belief that black-hole growth and bulge formation regulate each other. That is, black holes and bulges coevolve. Therefore, reports of a similar correlation between black holes and the dark matter haloes in which visible galaxies are embedded have profound implications. Dark matter is likely to be non-baryonic, so these reports suggest that unknown, exotic physics controls black-hole growth. Here we show, in part on the basis of recent measurements of bulgeless galaxies, that there is almost no correlation between dark matter and parameters that measure black holes unless the galaxy also contains a bulge. We conclude that black holes do not correlate directly with dark matter. They do not correlate with galaxy disks, either. Therefore, black holes coevolve only with bulges. This simplifies the puzzle of their coevolution by focusing attention on purely baryonic processes in the galaxy mergers that make bulges.

Spider web and silk performance landscapes across nutrient space

PubMed Central

Blamires, Sean J.; Tseng, Yi-Hsuan; Wu, Chung-Lin; Toft, Søren; Raubenheimer, David; Tso, I.-Min

2016-01-01

Predators have been shown to alter their foraging as a regulatory response to recent feeding history, but it remains unknown whether trap building predators modulate their traps similarly as a regulatory strategy. Here we fed the orb web spider Nephila pilipes either live crickets, dead crickets with webs stimulated by flies, or dead crickets without web stimulation, over 21 days to enforce spiders to differentially extract nutrients from a single prey source. In addition to the nutrients extracted we measured web architectures, silk tensile properties, silk amino acid compositions, and web tension after each feeding round. We then plotted web and silk “performance landscapes” across nutrient space. The landscapes had multiple peaks and troughs for each web and silk performance parameter. The findings suggest that N. pilipes plastically adjusts the chemical and physical properties of their web and silk in accordance with its nutritional history. Our study expands the application of the geometric framework foraging model to include a type of predatory trap. Whether it can be applied to other predatory traps requires further testing. PMID:27216252

A Systematic Approach to Sensor Selection for Aircraft Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Garg, Sanjay

2009-01-01

A systematic approach for selecting an optimal suite of sensors for on-board aircraft gas turbine engine health estimation is presented. The methodology optimally chooses the engine sensor suite and the model tuning parameter vector to minimize the Kalman filter mean squared estimation error in the engine s health parameters or other unmeasured engine outputs. This technique specifically addresses the underdetermined estimation problem where there are more unknown system health parameters representing degradation than available sensor measurements. This paper presents the theoretical estimation error equations, and describes the optimization approach that is applied to select the sensors and model tuning parameters to minimize these errors. Two different model tuning parameter vector selection approaches are evaluated: the conventional approach of selecting a subset of health parameters to serve as the tuning parameters, and an alternative approach that selects tuning parameters as a linear combination of all health parameters. Results from the application of the technique to an aircraft engine simulation are presented, and compared to those from an alternative sensor selection strategy.

Parameter studies of sediments in the Storegga Slide region

NASA Astrophysics Data System (ADS)

Yang, S. L.; Kvalstad, T.; Solheim, A.; Forsberg, C. F.

2006-09-01

Based on classification tests, oedometer tests, fall-cone tests and triaxial tests, physical and mechanical properties of sediments in the Storegga Slide region were analysed to assess parameter interrelationships. The data show good relationships between a number of physical and mechanical parameters. Goodness of fit between compression index and various physical parameters can be improved by multiple regression analysis. The interclay void ratio and liquidity index correlate well with the undrained shear strength of clay. Sediments with higher water content, liquid limit, activity, interclay void ratio, plasticity index and liquidity index showed higher compression index and/or lower undrained shear strength. Some relationships between parameters were tested by using data from two other sites south of the Storegga Slide. A better understanding of properties of sediments in regions such as that of the Storegga Slide can be obtained through this approach.

ON IDENTIFIABILITY OF NONLINEAR ODE MODELS AND APPLICATIONS IN VIRAL DYNAMICS

PubMed Central

MIAO, HONGYU; XIA, XIAOHUA; PERELSON, ALAN S.; WU, HULIN

2011-01-01

Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice. PMID:21785515

Distributed adaptive asymptotically consensus tracking control of uncertain Euler-Lagrange systems under directed graph condition.

PubMed

Wang, Wei; Wen, Changyun; Huang, Jiangshuai; Fan, Huijin

2017-11-01

In this paper, a backstepping based distributed adaptive control scheme is proposed for multiple uncertain Euler-Lagrange systems under directed graph condition. The common desired trajectory is allowed totally unknown by part of the subsystems and the linearly parameterized trajectory model assumed in currently available results is no longer needed. To compensate the effects due to unknown trajectory information, a smooth function of consensus errors and certain positive integrable functions are introduced in designing virtual control inputs. Besides, to overcome the difficulty of completely counteracting the coupling terms of distributed consensus errors and parameter estimation errors in the presence of asymmetric Laplacian matrix, extra information transmission of local parameter estimates are introduced among linked subsystem and adaptive gain technique is adopted to generate distributed torque inputs. It is shown that with the proposed distributed adaptive control scheme, global uniform boundedness of all the closed-loop signals and asymptotically output consensus tracking can be achieved. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Backstepping Design of Adaptive Neural Fault-Tolerant Control for MIMO Nonlinear Systems.

PubMed

Gao, Hui; Song, Yongduan; Wen, Changyun

In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.

ESTIMATION OF PHYSICAL PROPERTIES AND CHEMICAL REACTIVITY PARAMETERS OF ORGANIC COMPOUNDS

EPA Science Inventory

The computer program SPARC (Sparc Performs Automated Reasoning in Chemistry)has been under development for several years to estimate physical properties and chemical reactivity parameters of organic compounds strictly from molecular structure. SPARC uses computational algorithms ...

Mapping land water and energy balance relations through conditional sampling of remote sensing estimates of atmospheric forcing and surface states

NASA Astrophysics Data System (ADS)

Farhadi, Leila; Entekhabi, Dara; Salvucci, Guido

2016-04-01

In this study, we develop and apply a mapping estimation capability for key unknown parameters that link the surface water and energy balance equations. The method is applied to the Gourma region in West Africa. The accuracy of the estimation method at point scale was previously examined using flux tower data. In this study, the capability is scaled to be applicable with remotely sensed data products and hence allow mapping. Parameters of the system are estimated through a process that links atmospheric forcing (precipitation and incident radiation), surface states, and unknown parameters. Based on conditional averaging of land surface temperature and moisture states, respectively, a single objective function is posed that measures moisture and temperature-dependent errors solely in terms of observed forcings and surface states. This objective function is minimized with respect to parameters to identify evapotranspiration and drainage models and estimate water and energy balance flux components. The uncertainty of the estimated parameters (and associated statistical confidence limits) is obtained through the inverse of Hessian of the objective function, which is an approximation of the covariance matrix. This calibration-free method is applied to the mesoscale region of Gourma in West Africa using multiplatform remote sensing data. The retrievals are verified against tower-flux field site data and physiographic characteristics of the region. The focus is to find the functional form of the evaporative fraction dependence on soil moisture, a key closure function for surface and subsurface heat and moisture dynamics, using remote sensing data.

Application of Ensemble Kalman Filter in Power System State Tracking and Sensitivity

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

Li, Yulan; Huang, Zhenyu; Zhou, Ning

2012-05-01

Ensemble Kalman Filter (EnKF) is proposed to track dynamic states of generators. The algorithm of EnKF and its application to generator state tracking are presented in detail. The accuracy and sensitivity of the method are analyzed with respect to initial state errors, measurement noise, unknown fault locations, time steps and parameter errors. It is demonstrated through simulation studies that even with some errors in the parameters, the developed EnKF can effectively track generator dynamic states using disturbance data.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Approximation-Based Adaptive Neural Tracking Control of Nonlinear MIMO Unknown Time-Varying Delay Systems With Full State Constraints.

PubMed

Li, Da-Peng; Li, Dong-Juan; Liu, Yan-Jun; Tong, Shaocheng; Chen, C L Philip

2017-10-01

This paper deals with the tracking control problem for a class of nonlinear multiple input multiple output unknown time-varying delay systems with full state constraints. To overcome the challenges which cause by the appearances of the unknown time-varying delays and full-state constraints simultaneously in the systems, an adaptive control method is presented for such systems for the first time. The appropriate Lyapunov-Krasovskii functions and a separation technique are employed to eliminate the effect of unknown time-varying delays. The barrier Lyapunov functions are employed to prevent the violation of the full state constraints. The singular problems are dealt with by introducing the signal function. Finally, it is proven that the proposed method can both guarantee the good tracking performance of the systems output, all states are remained in the constrained interval and all the closed-loop signals are bounded in the design process based on choosing appropriate design parameters. The practicability of the proposed control technique is demonstrated by a simulation study in this paper.

Assessing uncertainty and sensitivity of model parameterizations and parameters in WRF affecting simulated surface fluxes and land-atmosphere coupling over the Amazon region

NASA Astrophysics Data System (ADS)

Qian, Y.; Wang, C.; Huang, M.; Berg, L. K.; Duan, Q.; Feng, Z.; Shrivastava, M. B.; Shin, H. H.; Hong, S. Y.

2016-12-01

This study aims to quantify the relative importance and uncertainties of different physical processes and parameters in affecting simulated surface fluxes and land-atmosphere coupling strength over the Amazon region. We used two-legged coupling metrics, which include both terrestrial (soil moisture to surface fluxes) and atmospheric (surface fluxes to atmospheric state or precipitation) legs, to diagnose the land-atmosphere interaction and coupling strength. Observations made using the Department of Energy's Atmospheric Radiation Measurement (ARM) Mobile Facility during the GoAmazon field campaign together with satellite and reanalysis data are used to evaluate model performance. To quantify the uncertainty in physical parameterizations, we performed a 120 member ensemble of simulations with the WRF model using a stratified experimental design including 6 cloud microphysics, 3 convection, 6 PBL and surface layer, and 3 land surface schemes. A multiple-way analysis of variance approach is used to quantitatively analyze the inter- and intra-group (scheme) means and variances. To quantify parameter sensitivity, we conducted an additional 256 WRF simulations in which an efficient sampling algorithm is used to explore the multiple-dimensional parameter space. Three uncertainty quantification approaches are applied for sensitivity analysis (SA) of multiple variables of interest to 20 selected parameters in YSU PBL and MM5 surface layer schemes. Results show consistent parameter sensitivity across different SA methods. We found that 5 out of 20 parameters contribute more than 90% total variance, and first-order effects dominate comparing to the interaction effects. Results of this uncertainty quantification study serve as guidance for better understanding the roles of different physical processes in land-atmosphere interactions, quantifying model uncertainties from various sources such as physical processes, parameters and structural errors, and providing insights for improving the model physics parameterizations.