User's manual for interactive LINEAR: A FORTRAN program to derive linear aircraft models

NASA Technical Reports Server (NTRS)

Antoniewicz, Robert F.; Duke, Eugene L.; Patterson, Brian P.

1988-01-01

An interactive FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models is documented in this report. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied linear or nonlinear aerodynamic model. The nonlinear equations of motion used are six-degree-of-freedom equations with stationary atmosphere and flat, nonrotating earth assumptions. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model.

Applying SDDP to very large hydro-economic models with a simplified formulation for irrigation: the case of the Tigris-Euphrates river basin.

NASA Astrophysics Data System (ADS)

Rougé, Charles; Tilmant, Amaury

2015-04-01

Stochastic dual dynamic programming (SDDP) is an optimization algorithm well-suited for the study of large-scale water resources systems comprising reservoirs - and hydropower plants - as well as irrigation nodes. It generates intertemporal allocation policies that balance the present and future marginal value of water while taking into account hydrological uncertainty. It is scalable, in the sense that the time and memory required for computation do not grow exponentially with the number of state variables. Still, this scalability relies on the sampling of a few relevant trajectories for the system, and the approximation of the future value of water through cuts -i.e., hyperplanes - at points along these trajectories. Therefore, the accuracy of this approximation arguably decreases as the number of state variables increases, and it is important not to have more than necessary. In previous formulations, SDDP had three types of state variables, namely storage in each reservoir, inflow at each node and water accumulated during the irrigation season for each crop at each node. We present a simplified formulation for irrigation that does not require using the latter type of state variable. It also requires only two decision variables for each irrigation site, where the previous formulation had four per crop - and there may be several crops at the same site. This reduction in decision variables effectively reduces computation time, since SDDP decomposes the stochastic, multiperiodic, non-linear maximization problem into a series of linear ones. The proposed formulation, while computationally simpler, is mathematically equivalent to the previous one, and therefore the model gives the same results. A corollary of this formulation is that marginal utility of water at an irrigation site is effectively related to consumption at that site, through a piecewise linear function representing the net benefits from irrigation. Last but not least, the proposed formulation can be extended to any type of consumptive use of water beyond irrigation, e.g., municipal, industrial, etc This slightly different version of SDDP is applied to a large portion of the Tigris-Euphrates river basin. It comprises 24 state variables representing storage in reservoirs, 28 hydrologic state variables, and 51 demand nodes. It is the largest yet to simultaneously consider hydropower and irrigation within the same river system, and the proposed formulation almost halves the number of state variables to be considered.

Continuous-variable phase estimation with unitary and random linear disturbance

NASA Astrophysics Data System (ADS)

Delgado de Souza, Douglas; Genoni, Marco G.; Kim, M. S.

2014-10-01

We address the problem of continuous-variable quantum phase estimation in the presence of linear disturbance at the Hamiltonian level by means of Gaussian probe states. In particular we discuss both unitary and random disturbance by considering the parameter which characterizes the unwanted linear term present in the Hamiltonian as fixed (unitary disturbance) or random with a given probability distribution (random disturbance). We derive the optimal input Gaussian states at fixed energy, maximizing the quantum Fisher information over the squeezing angle and the squeezing energy fraction, and we discuss the scaling of the quantum Fisher information in terms of the output number of photons, nout. We observe that, in the case of unitary disturbance, the optimal state is a squeezed vacuum state and the quadratic scaling is conserved. As regards the random disturbance, we observe that the optimal squeezing fraction may not be equal to one and, for any nonzero value of the noise parameter, the quantum Fisher information scales linearly with the average number of photons. Finally, we discuss the performance of homodyne measurement by comparing the achievable precision with the ultimate limit imposed by the quantum Cramér-Rao bound.

Non-linear feedback control of the p53 protein-mdm2 inhibitor system using the derivative-free non-linear Kalman filter.

PubMed

Rigatos, Gerasimos G

2016-06-01

It is proven that the model of the p53-mdm2 protein synthesis loop is a differentially flat one and using a diffeomorphism (change of state variables) that is proposed by differential flatness theory it is shown that the protein synthesis model can be transformed into the canonical (Brunovsky) form. This enables the design of a feedback control law that maintains the concentration of the p53 protein at the desirable levels. To estimate the non-measurable elements of the state vector describing the p53-mdm2 system dynamics, the derivative-free non-linear Kalman filter is used. Moreover, to compensate for modelling uncertainties and external disturbances that affect the p53-mdm2 system, the derivative-free non-linear Kalman filter is re-designed as a disturbance observer. The derivative-free non-linear Kalman filter consists of the Kalman filter recursion applied on the linearised equivalent of the protein synthesis model together with an inverse transformation based on differential flatness theory that enables to retrieve estimates for the state variables of the initial non-linear model. The proposed non-linear feedback control and perturbations compensation method for the p53-mdm2 system can result in more efficient chemotherapy schemes where the infusion of medication will be better administered.

The Theory of Thermodynamic Systems with Internal Variables of State: Necessary and Sufficient Conditions for Compliance with the Second Law of Thermodynamics

NASA Astrophysics Data System (ADS)

Shnip, A. I.

2018-01-01

Based on the entropy-free thermodynamic approach, a generalized theory of thermodynamic systems with internal variables of state is being developed. For the case of nonlinear thermodynamic systems with internal variables of state and linear relaxation, the necessary and sufficient conditions have been proved for fulfillment of the second law of thermodynamics in entropy-free formulation which, according to the basic theorem of the theory, are also necessary and sufficient for the existence of a thermodynamic potential. Moreover, relations of correspondence between thermodynamic systems with memory and systems with internal variables of state have been established, as well as some useful relations in the spaces of states of both types of systems.

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

Pisenti, N.; Gaebler, C. P. E.; Lynn, T. W.

Measuring an entangled state of two particles is crucial to many quantum communication protocols. Yet Bell-state distinguishability using a finite apparatus obeying linear evolution and local measurement is theoretically limited. We extend known bounds for Bell-state distinguishability in one and two variables to the general case of entanglement in n two-state variables. We show that at most 2{sup n+1}-1 classes out of 4{sup n} hyper-Bell states can be distinguished with one copy of the input state. With two copies, complete distinguishability is possible. We present optimal schemes in each case.

Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data.

PubMed

Terrill, Philip I; Wilson, Stephen J; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

2012-08-01

Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

Control logic to track the outputs of a command generator or randomly forced target

NASA Technical Reports Server (NTRS)

Trankle, T. L.; Bryson, A. E., Jr.

1977-01-01

A procedure is presented for synthesizing time-invariant control logic to cause the outputs of a linear plant to track the outputs of an unforced (or randomly forced) linear dynamic system. The control logic uses feed-forward of the reference system state variables and feedback of the plant state variables. The feed-forward gains are obtained from the solution of a linear algebraic matrix equation of the Liapunov type. The feedback gains are the usual regulator gains, determined to stabilize (or augment the stability of) the plant, possibly including integral control. The method is applied here to the design of control logic for a second-order servomechanism to follow a linearly increasing (ramp) signal, an unstable third-order system with two controls to track two separate ramp signals, and a sixth-order system with two controls to track a constant signal and an exponentially decreasing signal (aircraft landing-flare or glide-slope-capture with constant velocity).

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

Chen, Haixia; Zhang, Jing

We propose a scheme for continuous-variable quantum cloning of coherent states with phase-conjugate input modes using linear optics. The quantum cloning machine yields M identical optimal clones from N replicas of a coherent state and N replicas of its phase conjugate. This scheme can be straightforwardly implemented with the setups accessible at present since its optical implementation only employs simple linear optical elements and homodyne detection. Compared with the original scheme for continuous-variable quantum cloning with phase-conjugate input modes proposed by Cerf and Iblisdir [Phys. Rev. Lett. 87, 247903 (2001)], which utilized a nondegenerate optical parametric amplifier, our scheme losesmore » the output of phase-conjugate clones and is regarded as irreversible quantum cloning.« less

Estimation in Linear Systems Featuring Correlated Uncertain Observations Coming from Multiple Sensors

NASA Astrophysics Data System (ADS)

Caballero-Águila, R.; Hermoso-Carazo, A.; Linares-Pérez, J.

2009-08-01

In this paper, the state least-squares linear estimation problem from correlated uncertain observations coming from multiple sensors is addressed. It is assumed that, at each sensor, the state is measured in the presence of additive white noise and that the uncertainty in the observations is characterized by a set of Bernoulli random variables which are only correlated at consecutive time instants. Assuming that the statistical properties of such variables are not necessarily the same for all the sensors, a recursive filtering algorithm is proposed, and the performance of the estimators is illustrated by a numerical simulation example wherein a signal is estimated from correlated uncertain observations coming from two sensors with different uncertainty characteristics.

State-Space Formulation for Circuit Analysis

ERIC Educational Resources Information Center

Martinez-Marin, T.

2010-01-01

This paper presents a new state-space approach for temporal analysis of electrical circuits. The method systematically obtains the state-space formulation of nondegenerate linear networks without using concepts of topology. It employs nodal/mesh systematic analysis to reduce the number of undesired variables. This approach helps students to…

Individual tree diameter increment model for managed even-aged stands of ponderosa pine throughout the western United States using a multilevel linear mixed effects model

Treesearch

Fabian C.C. Uzoh; William W. Oliver

2008-01-01

A diameter increment model is developed and evaluated for individual trees of ponderosa pine throughout the species range in the United States using a multilevel linear mixed model. Stochastic variability is broken down among period, locale, plot, tree and within-tree components. Covariates acting at tree and stand level, as breast height diameter, density, site index...

Using state variables to model the response of tumour cells to radiation and heat: a novel multi-hit-repair approach.

PubMed

Scheidegger, Stephan; Fuchs, Hans U; Zaugg, Kathrin; Bodis, Stephan; Füchslin, Rudolf M

2013-01-01

In order to overcome the limitations of the linear-quadratic model and include synergistic effects of heat and radiation, a novel radiobiological model is proposed. The model is based on a chain of cell populations which are characterized by the number of radiation induced damages (hits). Cells can shift downward along the chain by collecting hits and upward by a repair process. The repair process is governed by a repair probability which depends upon state variables used for a simplistic description of the impact of heat and radiation upon repair proteins. Based on the parameters used, populations up to 4-5 hits are relevant for the calculation of the survival. The model describes intuitively the mathematical behaviour of apoptotic and nonapoptotic cell death. Linear-quadratic-linear behaviour of the logarithmic cell survival, fractionation, and (with one exception) the dose rate dependencies are described correctly. The model covers the time gap dependence of the synergistic cell killing due to combined application of heat and radiation, but further validation of the proposed approach based on experimental data is needed. However, the model offers a work bench for testing different biological concepts of damage induction, repair, and statistical approaches for calculating the variables of state.

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

NASA Technical Reports Server (NTRS)

Hill, R. E.

1987-01-01

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

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Transient times in linear metabolic pathways under constant affinity constraints.

PubMed

Lloréns, M; Nuño, J C; Montero, F

1997-10-15

In the early seventies, Easterby began the analytical study of transition times for linear reaction schemes [Easterby (1973) Biochim. Biophys. Acta 293, 552-558]. In this pioneer work and in subsequent papers, a state function (the transient time) was used to measure the period before the stationary state, for systems constrained to work under both constant and variable input flux, was reached. Despite the undoubted usefulness of this quantity to describe the time-dependent features of these kinds of systems, its application to the study of chemical reactions under other constraints is questionable. In the present work, a generalization of these magnitudes to linear metabolic pathways functioning under a constant-affinity constraint is carried out. It is proved that classical definitions of transient times do not reflect the actual properties of the transition to the steady state in systems evolving under this restriction. Alternatively, a more adequate framework for interpretation of the transient times for systems with both constant and variable input flux is suggested. Within this context, new definitions that reflect more accurately the transient characteristics of constant affinity systems are stated. Finally, the meaning of these transient times is discussed.

Estimation of streamflow, base flow, and nitrate-nitrogen loads in Iowa using multiple linear regression models

USGS Publications Warehouse

Schilling, K.E.; Wolter, C.F.

2005-01-01

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest. (JAWRA) (Copyright ?? 2005).

Procedures for generation and reduction of linear models of a turbofan engine

NASA Technical Reports Server (NTRS)

Seldner, K.; Cwynar, D. S.

1978-01-01

A real time hybrid simulation of the Pratt & Whitney F100-PW-F100 turbofan engine was used for linear-model generation. The linear models were used to analyze the effect of disturbances about an operating point on the dynamic performance of the engine. A procedure that disturbs, samples, and records the state and control variables was developed. For large systems, such as the F100 engine, the state vector is large and may contain high-frequency information not required for control. This, reducing the full-state to a reduced-order model may be a practicable approach to simplifying the control design. A reduction technique was developed to generate reduced-order models. Selected linear and nonlinear output responses to exhaust-nozzle area and main-burner fuel flow disturbances are presented for comparison.

Boosting multi-state models.

PubMed

Reulen, Holger; Kneib, Thomas

2016-04-01

One important goal in multi-state modelling is to explore information about conditional transition-type-specific hazard rate functions by estimating influencing effects of explanatory variables. This may be performed using single transition-type-specific models if these covariate effects are assumed to be different across transition-types. To investigate whether this assumption holds or whether one of the effects is equal across several transition-types (cross-transition-type effect), a combined model has to be applied, for instance with the use of a stratified partial likelihood formulation. Here, prior knowledge about the underlying covariate effect mechanisms is often sparse, especially about ineffectivenesses of transition-type-specific or cross-transition-type effects. As a consequence, data-driven variable selection is an important task: a large number of estimable effects has to be taken into account if joint modelling of all transition-types is performed. A related but subsequent task is model choice: is an effect satisfactory estimated assuming linearity, or is the true underlying nature strongly deviating from linearity? This article introduces component-wise Functional Gradient Descent Boosting (short boosting) for multi-state models, an approach performing unsupervised variable selection and model choice simultaneously within a single estimation run. We demonstrate that features and advantages in the application of boosting introduced and illustrated in classical regression scenarios remain present in the transfer to multi-state models. As a consequence, boosting provides an effective means to answer questions about ineffectiveness and non-linearity of single transition-type-specific or cross-transition-type effects.

Program for solution of ordinary differential equations

NASA Technical Reports Server (NTRS)

Sloate, H.

1973-01-01

A program for the solution of linear and nonlinear first order ordinary differential equations is described and user instructions are included. The program contains a new integration algorithm for the solution of initial value problems which is particularly efficient for the solution of differential equations with a wide range of eigenvalues. The program in its present form handles up to ten state variables, but expansion to handle up to fifty state variables is being investigated.

Regional robust stabilisation and domain-of-attraction estimation for MIMO uncertain nonlinear systems with input saturation

NASA Astrophysics Data System (ADS)

Azizi, S.; Torres, L. A. B.; Palhares, R. M.

2018-01-01

The regional robust stabilisation by means of linear time-invariant state feedback control for a class of uncertain MIMO nonlinear systems with parametric uncertainties and control input saturation is investigated. The nonlinear systems are described in a differential algebraic representation and the regional stability is handled considering the largest ellipsoidal domain-of-attraction (DOA) inside a given polytopic region in the state space. A novel set of sufficient Linear Matrix Inequality (LMI) conditions with new auxiliary decision variables are developed aiming to design less conservative linear state feedback controllers with corresponding larger DOAs, by considering the polytopic description of the saturated inputs. A few examples are presented showing favourable comparisons with recently published similar control design methodologies.

Geographical variation of cerebrovascular disease in New York State: the correlation with income

PubMed Central

Han, Daikwon; Carrow, Shannon S; Rogerson, Peter A; Munschauer, Frederick E

2005-01-01

Background Income is known to be associated with cerebrovascular disease; however, little is known about the more detailed relationship between cerebrovascular disease and income. We examined the hypothesis that the geographical distribution of cerebrovascular disease in New York State may be predicted by a nonlinear model using income as a surrogate socioeconomic risk factor. Results We used spatial clustering methods to identify areas with high and low prevalence of cerebrovascular disease at the ZIP code level after smoothing rates and correcting for edge effects; geographic locations of high and low clusters of cerebrovascular disease in New York State were identified with and without income adjustment. To examine effects of income, we calculated the excess number of cases using a non-linear regression with cerebrovascular disease rates taken as the dependent variable and income and income squared taken as independent variables. The resulting regression equation was: excess rate = 32.075 - 1.22*10-4(income) + 8.068*10-10(income2), and both income and income squared variables were significant at the 0.01 level. When income was included as a covariate in the non-linear regression, the number and size of clusters of high cerebrovascular disease prevalence decreased. Some 87 ZIP codes exceeded the critical value of the local statistic yielding a relative risk of 1.2. The majority of low cerebrovascular disease prevalence geographic clusters disappeared when the non-linear income effect was included. For linear regression, the excess rate of cerebrovascular disease falls with income; each $10,000 increase in median income of each ZIP code resulted in an average reduction of 3.83 observed cases. The significant nonlinear effect indicates a lessening of this income effect with increasing income. Conclusion Income is a non-linear predictor of excess cerebrovascular disease rates, with both low and high observed cerebrovascular disease rate areas associated with higher income. Income alone explains a significant amount of the geographical variance in cerebrovascular disease across New York State since both high and low clusters of cerebrovascular disease dissipate or disappear with income adjustment. Geographical modeling, including non-linear effects of income, may allow for better identification of other non-traditional risk factors. PMID:16242043

Suppression of chaos at slow variables by rapidly mixing fast dynamics through linear energy-preserving coupling

NASA Astrophysics Data System (ADS)

Abramov, R. V.

2011-12-01

Chaotic multiscale dynamical systems are common in many areas of science, one of the examples being the interaction of the low-frequency dynamics in the atmosphere with the fast turbulent weather dynamics. One of the key questions about chaotic multiscale systems is how the fast dynamics affects chaos at the slow variables, and, therefore, impacts uncertainty and predictability of the slow dynamics. Here we demonstrate that the linear slow-fast coupling with the total energy conservation property promotes the suppression of chaos at the slow variables through the rapid mixing at the fast variables, both theoretically and through numerical simulations. A suitable mathematical framework is developed, connecting the slow dynamics on the tangent subspaces to the infinite-time linear response of the mean state to a constant external forcing at the fast variables. Additionally, it is shown that the uncoupled dynamics for the slow variables may remain chaotic while the complete multiscale system loses chaos and becomes completely predictable at the slow variables through increasing chaos and turbulence at the fast variables. This result contradicts the common sense intuition, where, naturally, one would think that coupling a slow weakly chaotic system with another much faster and much stronger chaotic system would result in general increase of chaos at the slow variables.

A linear stepping endovascular intervention robot with variable stiffness and force sensing.

PubMed

He, Chengbin; Wang, Shuxin; Zuo, Siyang

2018-05-01

Robotic-assisted endovascular intervention surgery has attracted significant attention and interest in recent years. However, limited designs have focused on the variable stiffness mechanism of the catheter shaft. Flexible catheter needs to be partially switched to a rigid state that can hold its shape against external force to achieve a stable and effective insertion procedure. Furthermore, driving catheter in a similar way with manual procedures has the potential to make full use of the extensive experience from conventional catheter navigation. Besides driving method, force sensing is another significant factor for endovascular intervention. This paper presents a variable stiffness catheterization system that can provide stable and accurate endovascular intervention procedure with a linear stepping mechanism that has a similar operation mode to the conventional catheter navigation. A specially designed shape-memory polymer tube with water cooling structure is used to achieve variable stiffness of the catheter. Hence, four FBG sensors are attached to the catheter tip in order to monitor the tip contact force situation with temperature compensation. Experimental results show that the actuation unit is able to deliver linear and rotational motions. We have shown the feasibility of FBG force sensing to reduce the effect of temperature and detect the tip contact force. The designed catheter can change its stiffness partially, and the stiffness of the catheter can be remarkably increased in rigid state. Hence, in the rigid state, the catheter can hold its shape against a [Formula: see text] load. The prototype has also been validated with a vascular phantom, demonstrating the potential clinical value of the system. The proposed system provides important insights into the design of compact robotic-assisted catheter incorporating effective variable stiffness mechanism and real-time force sensing for intraoperative endovascular intervention.

Modeling of aircraft unsteady aerodynamic characteristics. Part 1: Postulated models

NASA Technical Reports Server (NTRS)

Klein, Vladislav; Noderer, Keith D.

1994-01-01

A short theoretical study of aircraft aerodynamic model equations with unsteady effects is presented. The aerodynamic forces and moments are expressed in terms of indicial functions or internal state variables. The first representation leads to aircraft integro-differential equations of motion; the second preserves the state-space form of the model equations. The formulations of unsteady aerodynamics is applied in two examples. The first example deals with a one-degree-of-freedom harmonic motion about one of the aircraft body axes. In the second example, the equations for longitudinal short-period motion are developed. In these examples, only linear aerodynamic terms are considered. The indicial functions are postulated as simple exponentials and the internal state variables are governed by linear, time-invariant, first-order differential equations. It is shown that both approaches to the modeling of unsteady aerodynamics lead to identical models.

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.

Analytical Solution of Steady State Equations for Chemical Reaction Networks with Bilinear Rate Laws

PubMed Central

Halász, Ádám M.; Lai, Hong-Jian; McCabe, Meghan M.; Radhakrishnan, Krishnan; Edwards, Jeremy S.

2014-01-01

True steady states are a rare occurrence in living organisms, yet their knowledge is essential for quasi-steady state approximations, multistability analysis, and other important tools in the investigation of chemical reaction networks (CRN) used to describe molecular processes on the cellular level. Here we present an approach that can provide closed form steady-state solutions to complex systems, resulting from CRN with binary reactions and mass-action rate laws. We map the nonlinear algebraic problem of finding steady states onto a linear problem in a higher dimensional space. We show that the linearized version of the steady state equations obeys the linear conservation laws of the original CRN. We identify two classes of problems for which complete, minimally parameterized solutions may be obtained using only the machinery of linear systems and a judicious choice of the variables used as free parameters. We exemplify our method, providing explicit formulae, on CRN describing signal initiation of two important types of RTK receptor-ligand systems, VEGF and EGF-ErbB1. PMID:24334389

Feedback linearization of singularly perturbed systems based on canonical similarity transformations

NASA Astrophysics Data System (ADS)

Kabanov, A. A.

2018-05-01

This paper discusses the problem of feedback linearization of a singularly perturbed system in a state-dependent coefficient form. The result is based on the introduction of a canonical similarity transformation. The transformation matrix is constructed from separate blocks for fast and slow part of an original singularly perturbed system. The transformed singular perturbed system has a linear canonical form that significantly simplifies a control design problem. Proposed similarity transformation allows accomplishing linearization of the system without considering the virtual output (as it is needed for normal form method), a technique of a transition from phase coordinates of the transformed system to state variables of the original system is simpler. The application of the proposed approach is illustrated through example.

Characterising non-linear dynamics in nocturnal breathing patterns of healthy infants using recurrence quantification analysis.

PubMed

Terrill, Philip I; Wilson, Stephen J; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

2013-05-01

Breathing dynamics vary between infant sleep states, and are likely to exhibit non-linear behaviour. This study applied the non-linear analytical tool recurrence quantification analysis (RQA) to 400 breath interval periods of REM and N-REM sleep, and then using an overlapping moving window. The RQA variables were different between sleep states, with REM radius 150% greater than N-REM radius, and REM laminarity 79% greater than N-REM laminarity. RQA allowed the observation of temporal variations in non-linear breathing dynamics across a night's sleep at 30s resolution, and provides a basis for quantifying changes in complex breathing dynamics with physiology and pathology. Copyright © 2013 Elsevier Ltd. All rights reserved.

On optimal control of linear systems in the presence of multiplicative noise

NASA Technical Reports Server (NTRS)

Joshi, S. M.

1976-01-01

This correspondence considers the problem of optimal regulator design for discrete time linear systems subjected to white state-dependent and control-dependent noise in addition to additive white noise in the input and the observations. A pseudo-deterministic problem is first defined in which multiplicative and additive input disturbances are present, but noise-free measurements of the complete state vector are available. This problem is solved via discrete dynamic programming. Next is formulated the problem in which the number of measurements is less than that of the state variables and the measurements are contaminated with state-dependent noise. The inseparability of control and estimation is brought into focus, and an 'enforced separation' solution is obtained via heuristic reasoning in which the control gains are shown to be the same as those in the pseudo-deterministic problem. An optimal linear state estimator is given in order to implement the controller.

RB Particle Filter Time Synchronization Algorithm Based on the DPM Model.

PubMed

Guo, Chunsheng; Shen, Jia; Sun, Yao; Ying, Na

2015-09-03

Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN) applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB) particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM) model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.

State Space Model with hidden variables for reconstruction of gene regulatory networks.

PubMed

Wu, Xi; Li, Peng; Wang, Nan; Gong, Ping; Perkins, Edward J; Deng, Youping; Zhang, Chaoyang

2011-01-01

State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray datasets. The hidden variables cannot be directly observed from experiments. How to determine the number of hidden variables has a significant impact on the accuracy of network inference. In this study, we used SSM to infer Gene regulatory networks (GRNs) from synthetic time series datasets, investigated Bayesian Information Criterion (BIC) and Principle Component Analysis (PCA) approaches to determining the number of hidden variables in SSM, and evaluated the performance of SSM in comparison with DBN. True GRNs and synthetic gene expression datasets were generated using GeneNetWeaver. Both DBN and linear SSM were used to infer GRNs from the synthetic datasets. The inferred networks were compared with the true networks. Our results show that inference precision varied with the number of hidden variables. For some regulatory networks, the inference precision of DBN was higher but SSM performed better in other cases. Although the overall performance of the two approaches is compatible, SSM is much faster and capable of inferring much larger networks than DBN. This study provides useful information in handling the hidden variables and improving the inference precision.

Universal quantum computation with temporal-mode bilayer square lattices

NASA Astrophysics Data System (ADS)

Alexander, Rafael N.; Yokoyama, Shota; Furusawa, Akira; Menicucci, Nicolas C.

2018-03-01

We propose an experimental design for universal continuous-variable quantum computation that incorporates recent innovations in linear-optics-based continuous-variable cluster state generation and cubic-phase gate teleportation. The first ingredient is a protocol for generating the bilayer-square-lattice cluster state (a universal resource state) with temporal modes of light. With this state, measurement-based implementation of Gaussian unitary gates requires only homodyne detection. Second, we describe a measurement device that implements an adaptive cubic-phase gate, up to a random phase-space displacement. It requires a two-step sequence of homodyne measurements and consumes a (non-Gaussian) cubic-phase state.

Commande de vol non lineaire d'un drone a voilure fixe par la methode du backstepping

NASA Astrophysics Data System (ADS)

Finoki, Edouard

This thesis describes the design of a non-linear controller for a UAV using the backstepping method. It is a fixed-wing UAV, the NexSTAR ARF from HobbicoRTM. The aim is to find the expressions of the aileron, the elevator, and the rudder deflection in order to command the flight path angle, the heading angle and the sideslip angle. Controlling the flight path angle allows a steady, climb or descent flight, controlling the heading cap allows to choose the heading and annul the sideslip angle allows an efficient flight. A good technical control has to ensure the stability of the system and provide optimal performances. Backstepping interlaces the choice of a Lyapunov function with the design of feedback control. This control technique works with the true non-linear model without any approximation. The procedure is to transform intermediate state variables into virtual inputs which will control other state variables. Advantages of this technique are its recursivity, its minimum control effort and its cascaded structure that allows dividing a high order system into several simpler lower order systems. To design this non-linear controller, a non-linear model of the UAV was used. Equations of motion are very accurate, aerodynamic coefficients result from interpolations between several essential variables in flight. The controller has been implemented in Matlab/Simulink and FlightGear.

Decoupling in linear time-varying multivariable systems

NASA Technical Reports Server (NTRS)

Sankaran, V.

1973-01-01

The necessary and sufficient conditions for the decoupling of an m-input, m-output, linear time varying dynamical system by state variable feedback is described. The class of feedback matrices which decouple the system are illustrated. Systems which do not satisfy these results are described and systems with disturbances are considered. Some examples are illustrated to clarify the results.

Microprocessor based implementation of attitude and shape control of large space structures

NASA Technical Reports Server (NTRS)

Reddy, A. S. S. R.

1984-01-01

The feasibility of off the shelf eight bit and 16 bit microprocessors to implement linear state variable feedback control laws and assessing the real time response to spacecraft dynamics is studied. The complexity of the dynamic model is described along with the appropriate software. An experimental setup of a beam, microprocessor system for implementing the control laws and the needed generalized software to implement any state variable feedback control system is included.

Evaluation of confidence intervals for a steady-state leaky aquifer model

USGS Publications Warehouse

Christensen, S.; Cooley, R.L.

1999-01-01

The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley [Vecchia, A.V. and Cooley, R.L., Water Resources Research, 1987, 23(7), 1237-1250] was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.The fact that dependent variables of groundwater models are generally nonlinear functions of model parameters is shown to be a potentially significant factor in calculating accurate confidence intervals for both model parameters and functions of the parameters, such as the values of dependent variables calculated by the model. The Lagrangian method of Vecchia and Cooley was used to calculate nonlinear Scheffe-type confidence intervals for the parameters and the simulated heads of a steady-state groundwater flow model covering 450 km2 of a leaky aquifer. The nonlinear confidence intervals are compared to corresponding linear intervals. As suggested by the significant nonlinearity of the regression model, linear confidence intervals are often not accurate. The commonly made assumption that widths of linear confidence intervals always underestimate the actual (nonlinear) widths was not correct. Results show that nonlinear effects can cause the nonlinear intervals to be asymmetric and either larger or smaller than the linear approximations. Prior information on transmissivities helps reduce the size of the confidence intervals, with the most notable effects occurring for the parameters on which there is prior information and for head values in parameter zones for which there is prior information on the parameters.

Impact of Preadmission Variables on USMLE Step 1 and Step 2 Performance

ERIC Educational Resources Information Center

Kleshinski, James; Khuder, Sadik A.; Shapiro, Joseph I.; Gold, Jeffrey P.

2009-01-01

Purpose: To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model. Method: Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of…

The Impact of ENSO on Extratropical Low Frequency Noise in Seasonal Forecasts

NASA Technical Reports Server (NTRS)

Schubert, Siegfried D.; Suarez, Max J.; Chang, Yehui; Branstator, Grant

2000-01-01

This study examines the uncertainty in forecasts of the January-February-March (JFM) mean extratropical circulation, and how that uncertainty is modulated by the El Nino/Southern Oscillation (ENSO). The analysis is based on ensembles of hindcasts made with an Atmospheric General Circulation Model (AGCM) forced with sea surface temperatures observed during; the 1983 El Nino and 1989 La Nina events. The AGCM produces pronounced interannual differences in the magnitude of the extratropical seasonal mean noise (intra-ensemble variability). The North Pacific, in particular, shows extensive regions where the 1989 seasonal mean noise kinetic energy (SKE), which is dominated by a "PNA-like" spatial structure, is more than twice that of the 1983 forecasts. The larger SKE in 1989 is associated with a larger than normal barotropic conversion of kinetic energy from the mean Pacific jet to the seasonal mean noise. The generation of SKE due to sub-monthly transients also shows substantial interannual differences, though these are much smaller than the differences in the mean flow conversions. An analysis of the Generation of monthly mean noise kinetic energy (NIKE) and its variability suggests that the seasonal mean noise is predominantly a statistical residue of variability resulting from dynamical processes operating on monthly and shorter times scales. A stochastically-forced barotropic model (linearized about the AGCM's 1983 and 1989 base states) is used to further assess the role of the basic state, submonthly transients, and tropical forcing, in modulating the uncertainties in the seasonal AGCM forecasts. When forced globally with spatially-white noise, the linear model generates much larger variance for the 1989 base state, consistent with the AGCM results. The extratropical variability for the 1989 base state is dominanted by a single eigenmode, and is strongly coupled with forcing over tropical western Pacific and the Indian Ocean, again consistent with the AGCM results. Linear calculations that include forcing from the AGCM variance of the tropical forcing and submonthly transients show a small impact on the variability over the Pacific/North American region compared with that of the base state differences.

Generation of linear dynamic models from a digital nonlinear simulation

NASA Technical Reports Server (NTRS)

Daniele, C. J.; Krosel, S. M.

1979-01-01

The results and methodology used to derive linear models from a nonlinear simulation are presented. It is shown that averaged positive and negative perturbations in the state variables can reduce numerical errors in finite difference, partial derivative approximations and, in the control inputs, can better approximate the system response in both directions about the operating point. Both explicit and implicit formulations are addressed. Linear models are derived for the F 100 engine, and comparisons of transients are made with the nonlinear simulation. The problem of startup transients in the nonlinear simulation in making these comparisons is addressed. Also, reduction of the linear models is investigated using the modal and normal techniques. Reduced-order models of the F 100 are derived and compared with the full-state models.

Thermal annealing induced the tunable optical properties of silver thin films with linear variable thickness

NASA Astrophysics Data System (ADS)

Hong, Ruijin; Shao, Wen; Ji, Jialin; Tao, Chunxian; Zhang, Dawei

2018-06-01

Silver thin films with linear variable thickness were deposited at room temperature. The corresponding tunability of optical properties and Raman scattering intensity were realized by thermal annealing process. With the thickness increasing, the topography of as-annealed silver thin films was observed to develop from discontinued nanospheres into continuous structure with a redshift of the surface plasmon resonance wavelength in visible region. Both the various nanosphere sizes and states of aggregation of as-annealed silver thin films contributed to significantly increasing the sensitivity of surface enhanced Raman scattering (SERS).

Flatness-based control and Kalman filtering for a continuous-time macroeconomic model

NASA Astrophysics Data System (ADS)

Rigatos, G.; Siano, P.; Ghosh, T.; Busawon, K.; Binns, R.

2017-11-01

The article proposes flatness-based control for a nonlinear macro-economic model of the UK economy. The differential flatness properties of the model are proven. This enables to introduce a transformation (diffeomorphism) of the system's state variables and to express the state-space description of the model in the linear canonical (Brunowsky) form in which both the feedback control and the state estimation problem can be solved. For the linearized equivalent model of the macroeconomic system, stabilizing feedback control can be achieved using pole placement methods. Moreover, to implement stabilizing feedback control of the system by measuring only a subset of its state vector elements the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied on the linearized equivalent model of the financial system and of an inverse transformation that is based again on differential flatness theory. The asymptotic stability properties of the control scheme are confirmed.

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.

PubMed

Brunton, Steven L; Brunton, Bingni W; Proctor, Joshua L; Kutz, J Nathan

2016-01-01

In this wIn this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control.ork, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control.

Dynamic Programming for Structured Continuous Markov Decision Problems

NASA Technical Reports Server (NTRS)

Dearden, Richard; Meuleau, Nicholas; Washington, Richard; Feng, Zhengzhu

2004-01-01

We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.

Suppression of chaos at slow variables by rapidly mixing fast dynamics

NASA Astrophysics Data System (ADS)

Abramov, R.

2012-04-01

One of the key questions about chaotic multiscale systems is how the fast dynamics affects chaos at the slow variables, and, therefore, impacts uncertainty and predictability of the slow dynamics. Here we demonstrate that the linear slow-fast coupling with the total energy conservation property promotes the suppression of chaos at the slow variables through the rapid mixing at the fast variables, both theoretically and through numerical simulations. A suitable mathematical framework is developed, connecting the slow dynamics on the tangent subspaces to the infinite-time linear response of the mean state to a constant external forcing at the fast variables. Additionally, it is shown that the uncoupled dynamics for the slow variables may remain chaotic while the complete multiscale system loses chaos and becomes completely predictable at the slow variables through increasing chaos and turbulence at the fast variables. This result contradicts the common sense intuition, where, naturally, one would think that coupling a slow weakly chaotic system with another much faster and much stronger mixing system would result in general increase of chaos at the slow variables.

Cost drivers and resource allocation in military health care systems.

PubMed

Fulton, Larry; Lasdon, Leon S; McDaniel, Reuben R

2007-03-01

This study illustrates the feasibility of incorporating technical efficiency considerations in the funding of military hospitals and identifies the primary drivers for hospital costs. Secondary data collected for 24 U.S.-based Army hospitals and medical centers for the years 2001 to 2003 are the basis for this analysis. Technical efficiency was measured by using data envelopment analysis; subsequently, efficiency estimates were included in logarithmic-linear cost models that specified cost as a function of volume, complexity, efficiency, time, and facility type. These logarithmic-linear models were compared against stochastic frontier analysis models. A parsimonious, three-variable, logarithmic-linear model composed of volume, complexity, and efficiency variables exhibited a strong linear relationship with observed costs (R(2) = 0.98). This model also proved reliable in forecasting (R(2) = 0.96). Based on our analysis, as much as $120 million might be reallocated to improve the United States-based Army hospital performance evaluated in this study.

A program for identification of linear systems

NASA Technical Reports Server (NTRS)

Buell, J.; Kalaba, R.; Ruspini, E.; Yakush, A.

1971-01-01

A program has been written for the identification of parameters in certain linear systems. These systems appear in biomedical problems, particularly in compartmental models of pharmacokinetics. The method presented here assumes that some of the state variables are regularly modified by jump conditions. This simulates administration of drugs following some prescribed drug regime. Parameters are identified by a least-square fit of the linear differential system to a set of experimental observations. The method is especially suited when the interval of observation of the system is very long.

Modified hyperbolic sine model for titanium dioxide-based memristive thin films

NASA Astrophysics Data System (ADS)

Abu Bakar, Raudah; Syahirah Kamarozaman, Nur; Fazlida Hanim Abdullah, Wan; Herman, Sukreen Hana

2018-03-01

Since the emergence of memristor as the newest fundamental circuit elements, studies on memristor modeling have been evolved. To date, the developed models were based on the linear model, linear ionic drift model using different window functions, tunnelling barrier model and hyperbolic-sine function based model. Although using hyperbolic-sine function model could predict the memristor electrical properties, the model was not well fitted to the experimental data. In order to improve the performance of the hyperbolic-sine function model, the state variable equation was modified. On the one hand, the addition of window function cannot provide an improved fitting. By multiplying the Yakopcic’s state variable model to Chang’s model on the other hand resulted in the closer agreement with the TiO2 thin film experimental data. The percentage error was approximately 2.15%.

Matrix completion by deep matrix factorization.

PubMed

Fan, Jicong; Cheng, Jieyu

2018-02-01

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Vibration Control Using a State Observer that Considers Disturbances of a Golf Swing Robot

NASA Astrophysics Data System (ADS)

Hoshino, Yohei; Kobayashi, Yukinori; Yamada, Gen

In this paper, optimal control of a golf swing robot that is used to evaluate the performance of golf clubs is described. The robot has two joints, a rigid link and a flexible link that is a golf club. A mathematical model of the golf club is derived by Hamilton’s principle in consideration of bending and torsional stiffness and in consideration of eccentricity of the center of gravity of the club head on the shaft axis. A linear quadratic regulator (LQR) that considers the vibration of the club shaft is used to stop the robot during the follow-through. Since the robot moves fast and has strong non-linearity, an ordinary state observer for a linear system cannot accurately estimate the states of the system. A state observer that considers disturbances accurately estimates the state variables that cannot be measured. The results of numerical simulation are compared with experimental results obtained by using a swing robot.

Kalman Filtering with Inequality Constraints for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2003-01-01

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This paper develops two analytic methods of incorporating state variable inequality constraints in the Kalman filter. The first method is a general technique of using hard constraints to enforce inequalities on the state variable estimates. The resultant filter is a combination of a standard Kalman filter and a quadratic programming problem. The second method uses soft constraints to estimate state variables that are known to vary slowly with time. (Soft constraints are constraints that are required to be approximately satisfied rather than exactly satisfied.) The incorporation of state variable constraints increases the computational effort of the filter but significantly improves its estimation accuracy. The improvement is proven theoretically and shown via simulation results. The use of the algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate health parameters. The turbofan engine model contains 16 state variables, 12 measurements, and 8 component health parameters. It is shown that the new algorithms provide improved performance in this example over unconstrained Kalman filtering.

Practical somewhat-secure quantum somewhat-homomorphic encryption with coherent states

NASA Astrophysics Data System (ADS)

Tan, Si-Hui; Ouyang, Yingkai; Rohde, Peter P.

2018-04-01

We present a scheme for implementing homomorphic encryption on coherent states encoded using phase-shift keys. The encryption operations require only rotations in phase space, which commute with computations in the code space performed via passive linear optics, and with generalized nonlinear phase operations that are polynomials of the photon-number operator in the code space. This encoding scheme can thus be applied to any computation with coherent-state inputs, and the computation proceeds via a combination of passive linear optics and generalized nonlinear phase operations. An example of such a computation is matrix multiplication, whereby a vector representing coherent-state amplitudes is multiplied by a matrix representing a linear optics network, yielding a new vector of coherent-state amplitudes. By finding an orthogonal partitioning of the support of our encoded states, we quantify the security of our scheme via the indistinguishability of the encrypted code words. While we focus on coherent-state encodings, we expect that this phase-key encoding technique could apply to any continuous-variable computation scheme where the phase-shift operator commutes with the computation.

Linear canonical transformations of coherent and squeezed states in the Wigner phase space. II - Quantitative analysis

NASA Technical Reports Server (NTRS)

Han, D.; Kim, Y. S.; Noz, Marilyn E.

1989-01-01

It is possible to calculate expectation values and transition probabilities from the Wigner phase-space distribution function. Based on the canonical transformation properties of the Wigner function, an algorithm is developed for calculating these quantities in quantum optics for coherent and squeezed states. It is shown that the expectation value of a dynamical variable can be written in terms of its vacuum expectation value of the canonically transformed variable. Parallel-axis theorems are established for the photon number and its variant. It is also shown that the transition probability between two squeezed states can be reduced to that of the transition from one squeezed state to vacuum.

Singularities of interference of three waves with different polarization states.

PubMed

Kurzynowski, Piotr; Woźniak, Władysław A; Zdunek, Marzena; Borwińska, Monika

2012-11-19

We presented the interference setup which can produce interesting two-dimensional patterns in polarization state of the resulting light wave emerging from the setup. The main element of our setup is the Wollaston prism which gives two plane, linearly polarized waves (eigenwaves of both Wollaston's wedges) with linearly changed phase difference between them (along the x-axis). The third wave coming from the second arm of proposed polarization interferometer is linearly or circularly polarized with linearly changed phase difference along the y-axis. The interference of three plane waves with different polarization states (LLL - linear-linear-linear or LLC - linear-linear-circular) and variable change difference produce two-dimensional light polarization and phase distributions with some characteristic points and lines which can be claimed to constitute singularities of different types. The aim of this article is to find all kind of these phase and polarization singularities as well as their classification. We postulated in our theoretical simulations and verified in our experiments different kinds of polarization singularities, depending on which polarization parameter was considered (the azimuth and ellipticity angles or the diagonal and phase angles). We also observed the phase singularities as well as the isolated zero intensity points which resulted from the polarization singularities when the proper analyzer was used at the end of the setup. The classification of all these singularities as well as their relationships were analyzed and described.

Development of a nearshore oscillating surge wave energy converter with variable geometry

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

Tom, N. M.; Lawson, M. J.; Yu, Y. H.

This paper presents an analysis of a novel wave energy converter concept that combines an oscillating surge wave energy converter (OSWEC) with control surfaces. The control surfaces allow for a variable device geometry that enables the hydrodynamic properties to be adapted with respect to structural loading, absorption range and power-take-off capability. The device geometry is adjusted on a sea state-to-sea state time scale and combined with wave-to-wave manipulation of the power take-off (PTO) to provide greater control over the capture efficiency, capacity factor, and design loads. This work begins with a sensitivity study of the hydrodynamic coefficients with respect tomore » device width, support structure thickness, and geometry. A linear frequency domain analysis is used to evaluate device performance in terms of absorbed power, foundation loads, and PTO torque. Previous OSWEC studies included nonlinear hydrodynamics, in response a nonlinear model that includes a quadratic viscous damping torque that was linearized via the Lorentz linearization. Inclusion of the quadratic viscous torque led to construction of an optimization problem that incorporated motion and PTO constraints. Results from this study found that, when transitioning from moderate-to-large sea states the novel OSWEC was capable of reducing structural loads while providing a near constant power output.« less

Continuous-variable quantum computing in optical time-frequency modes using quantum memories.

PubMed

Humphreys, Peter C; Kolthammer, W Steven; Nunn, Joshua; Barbieri, Marco; Datta, Animesh; Walmsley, Ian A

2014-09-26

We develop a scheme for time-frequency encoded continuous-variable cluster-state quantum computing using quantum memories. In particular, we propose a method to produce, manipulate, and measure two-dimensional cluster states in a single spatial mode by exploiting the intrinsic time-frequency selectivity of Raman quantum memories. Time-frequency encoding enables the scheme to be extremely compact, requiring a number of memories that are a linear function of only the number of different frequencies in which the computational state is encoded, independent of its temporal duration. We therefore show that quantum memories can be a powerful component for scalable photonic quantum information processing architectures.

Approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays: a robust stability problem.

PubMed

Pandiselvi, S; Raja, R; Cao, Jinde; Rajchakit, G; Ahmad, Bashir

2018-01-01

This work predominantly labels the problem of approximation of state variables for discrete-time stochastic genetic regulatory networks with leakage, distributed, and probabilistic measurement delays. Here we design a linear estimator in such a way that the absorption of mRNA and protein can be approximated via known measurement outputs. By utilizing a Lyapunov-Krasovskii functional and some stochastic analysis execution, we obtain the stability formula of the estimation error systems in the structure of linear matrix inequalities under which the estimation error dynamics is robustly exponentially stable. Further, the obtained conditions (in the form of LMIs) can be effortlessly solved by some available software packages. Moreover, the specific expression of the desired estimator is also shown in the main section. Finally, two mathematical illustrative examples are accorded to show the advantage of the proposed conceptual results.

A novel framework to simulating non-stationary, non-linear, non-Normal hydrological time series using Markov Switching Autoregressive Models

NASA Astrophysics Data System (ADS)

Birkel, C.; Paroli, R.; Spezia, L.; Tetzlaff, D.; Soulsby, C.

2012-12-01

In this paper we present a novel model framework using the class of Markov Switching Autoregressive Models (MSARMs) to examine catchments as complex stochastic systems that exhibit non-stationary, non-linear and non-Normal rainfall-runoff and solute dynamics. Hereby, MSARMs are pairs of stochastic processes, one observed and one unobserved, or hidden. We model the unobserved process as a finite state Markov chain and assume that the observed process, given the hidden Markov chain, is conditionally autoregressive, which means that the current observation depends on its recent past (system memory). The model is fully embedded in a Bayesian analysis based on Markov Chain Monte Carlo (MCMC) algorithms for model selection and uncertainty assessment. Hereby, the autoregressive order and the dimension of the hidden Markov chain state-space are essentially self-selected. The hidden states of the Markov chain represent unobserved levels of variability in the observed process that may result from complex interactions of hydroclimatic variability on the one hand and catchment characteristics affecting water and solute storage on the other. To deal with non-stationarity, additional meteorological and hydrological time series along with a periodic component can be included in the MSARMs as covariates. This extension allows identification of potential underlying drivers of temporal rainfall-runoff and solute dynamics. We applied the MSAR model framework to streamflow and conservative tracer (deuterium and oxygen-18) time series from an intensively monitored 2.3 km2 experimental catchment in eastern Scotland. Statistical time series analysis, in the form of MSARMs, suggested that the streamflow and isotope tracer time series are not controlled by simple linear rules. MSARMs showed that the dependence of current observations on past inputs observed by transport models often in form of the long-tailing of travel time and residence time distributions can be efficiently explained by non-stationarity either of the system input (climatic variability) and/or the complexity of catchment storage characteristics. The statistical model is also capable of reproducing short (event) and longer-term (inter-event) and wet and dry dynamical "hydrological states". These reflect the non-linear transport mechanisms of flow pathways induced by transient climatic and hydrological variables and modified by catchment characteristics. We conclude that MSARMs are a powerful tool to analyze the temporal dynamics of hydrological data, allowing for explicit integration of non-stationary, non-linear and non-Normal characteristics.

Analysis of control system responses for aircraft stability and efficient numerical techniques for real-time simulations

NASA Astrophysics Data System (ADS)

Stroe, Gabriela; Andrei, Irina-Carmen; Frunzulica, Florin

2017-01-01

The objectives of this paper are the study and the implementation of both aerodynamic and propulsion models, as linear interpolations using look-up tables in a database. The aerodynamic and propulsion dependencies on state and control variable have been described by analytic polynomial models. Some simplifying hypotheses were made in the development of the nonlinear aircraft simulations. The choice of a certain technique to use depends on the desired accuracy of the solution and the computational effort to be expended. Each nonlinear simulation includes the full nonlinear dynamics of the bare airframe, with a scaled direct connection from pilot inputs to control surface deflections to provide adequate pilot control. The engine power dynamic response was modeled with an additional state equation as first order lag in the actual power level response to commanded power level was computed as a function of throttle position. The number of control inputs and engine power states varied depending on the number of control surfaces and aircraft engines. The set of coupled, nonlinear, first-order ordinary differential equations that comprise the simulation model can be represented by the vector differential equation. A linear time-invariant (LTI) system representing aircraft dynamics for small perturbations about a reference trim condition is given by the state and output equations present. The gradients are obtained numerically by perturbing each state and control input independently and recording the changes in the trimmed state and output equations. This is done using the numerical technique of central finite differences, including the perturbations of the state and control variables. For a reference trim condition of straight and level flight, linearization results in two decoupled sets of linear, constant-coefficient differential equations for longitudinal and lateral / directional motion. The linearization is valid for small perturbations about the reference trim condition. Experimental aerodynamic and thrust data are used to model the applied aerodynamic and propulsion forces and moments for arbitrary states and controls. There is no closed form solution to such problems, so the equations must be solved using numerical integration. Techniques for solving this initial value problem for ordinary differential equations are employed to obtain approximate solutions at discrete points along the aircraft state trajectory.

Linear and nonlinear measures of fetal heart rate patterns evaluated on very short fetal magnetocardiograms.

PubMed

Moraes, Eder Rezende; Murta, Luiz Otavio; Baffa, Oswaldo; Wakai, Ronald T; Comani, Silvia

2012-10-01

We analyzed the effectiveness of linear short- and long-term variability time domain parameters, an index of sympatho-vagal balance (SDNN/RMSSD) and entropy in differentiating fetal heart rate patterns (fHRPs) on the fetal heart rate (fHR) series of 5, 3 and 2 min duration reconstructed from 46 fetal magnetocardiograms. Gestational age (GA) varied from 21 to 38 weeks. FHRPs were classified based on the fHR standard deviation. In sleep states, we observed that vagal influence increased with GA, and entropy significantly increased (decreased) with GA (SDNN/RMSSD), demonstrating that a prevalence of vagal activity with autonomous nervous system maturation may be associated with increased sleep state complexity. In active wakefulness, we observed a significant negative (positive) correlation of short-term (long-term) variability parameters with SDNN/RMSSD. ANOVA statistics demonstrated that long-term irregularity and standard deviation of normal-to-normal beat intervals (SDNN) best differentiated among fHRPs. Our results confirm that short- and long-term variability parameters are useful to differentiate between quiet and active states, and that entropy improves the characterization of sleep states. All measures differentiated fHRPs more effectively on very short HR series, as a result of the fMCG high temporal resolution and of the intrinsic timescales of the events that originate the different fHRPs.

Incorporating additional tree and environmental variables in a lodgepole pine stem profile model

Treesearch

John C. Byrne

1993-01-01

A new variable-form segmented stem profile model is developed for lodgepole pine (Pinus contorta) trees from the northern Rocky Mountains of the United States. I improved estimates of stem diameter by predicting two of the model coefficients with linear equations using a measure of tree form, defined as a ratio of dbh and total height. Additional improvements were...

Entanglement enhancement through multirail noise reduction for continuous-variable measurement-based quantum-information processing

NASA Astrophysics Data System (ADS)

Su, Yung-Chao; Wu, Shin-Tza

2017-09-01

We study theoretically the teleportation of a controlled-phase (cz) gate through measurement-based quantum-information processing for continuous-variable systems. We examine the degree of entanglement in the output modes of the teleported cz-gate for two classes of resource states: the canonical cluster states that are constructed via direct implementations of two-mode squeezing operations and the linear-optical version of cluster states which are built from linear-optical networks of beam splitters and phase shifters. In order to reduce the excess noise arising from finite-squeezed resource states, teleportation through resource states with different multirail designs will be considered and the enhancement of entanglement in the teleported cz gates will be analyzed. For multirail cluster with an arbitrary number of rails, we obtain analytical expressions for the entanglement in the output modes and analyze in detail the results for both classes of resource states. At the same time, we also show that for uniformly squeezed clusters the multirail noise reduction can be optimized when the excess noise is allocated uniformly to the rails. To facilitate the analysis, we develop a trick with manipulations of quadrature operators that can reveal rather efficiently the measurement sequence and corrective operations needed for the measurement-based gate teleportation, which will also be explained in detail.

Baroclinic instability with variable static stability - A design study for a spherical atmospheric model experiment. [for Spacelab flight

NASA Technical Reports Server (NTRS)

Giere, A. C.; Fowlis, W. W.

1980-01-01

The effect of a radially-variable, dielectric body force, analogous to gravity on baroclinic instability for the design of a spherical, synoptic-scale, atmospheric model experiment in a Spacelab flight is investigated. Exact solutions are examined for quasi-geostrophic baroclinic instability in which the rotational Froude number is a linear function of the height. Flow in a rotating rectilinear channel with a vertically variable body force without horizontal shear of the basic state is also discussed.

Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism

PubMed Central

Fleming, R.M.T.; Thiele, I.; Provan, G.; Nasheuer, H.P.

2010-01-01

The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in E. coli and compare favourably with in silico prediction by flux balance analysis. PMID:20230840

Moment-to-Moment BOLD Signal Variability Reflects Regional Changes in Neural Flexibility across the Lifespan.

PubMed

Nomi, Jason S; Bolt, Taylor S; Ezie, C E Chiemeka; Uddin, Lucina Q; Heller, Aaron S

2017-05-31

Variability of neuronal responses is thought to underlie flexible and optimal brain function. Because previous work investigating BOLD signal variability has been conducted within task-based fMRI contexts on adults and older individuals, very little is currently known regarding regional changes in spontaneous BOLD signal variability in the human brain across the lifespan. The current study used resting-state fMRI data from a large sample of male and female human participants covering a wide age range (6-85 years) across two different fMRI acquisition parameters (TR = 0.645 and 1.4 s). Variability in brain regions including a key node of the salience network (anterior insula) increased linearly across the lifespan across datasets. In contrast, variability in most other large-scale networks decreased linearly over the lifespan. These results demonstrate unique lifespan trajectories of BOLD variability related to specific regions of the brain and add to a growing literature demonstrating the importance of identifying normative trajectories of functional brain maturation. SIGNIFICANCE STATEMENT Although brain signal variability has traditionally been considered a source of unwanted noise, recent work demonstrates that variability in brain signals during task performance is related to brain maturation in old age as well as individual differences in behavioral performance. The current results demonstrate that intrinsic fluctuations in resting-state variability exhibit unique maturation trajectories in specific brain regions and systems, particularly those supporting salience detection. These results have implications for investigations of brain development and aging, as well as interpretations of brain function underlying behavioral changes across the lifespan. Copyright © 2017 the authors 0270-6474/17/375539-10$15.00/0.

[Completeness of notifications of violence perpetrated against adolescents in the State of Pernambuco, Brazil].

PubMed

Santos, Taciana Mirella Batista Dos; Cardoso, Mirian Domingos; Pitangui, Ana Carolina Rodarti; Santos, Yasmim Gabriella Cardoso; Paiva, Saul Martins; Melo, João Paulo Ramos; Silva, Lygia Maria Pereira

2016-12-01

The scope of this study was to analyze the trend of completeness of the data on violence perpetrated against adolescents registered in the State of Pernambuco between 2009 and 2012. This involved a cross-sectional survey of 5,259 adolescents, who were the victims of violence reported in SINAN-VIVA of the Pernambuco State Health Department. Simple linear regression was used to investigate the trend of completeness of the variables. The percentages of completeness were considered to be dependent variables (Y) and the number of years as independent variables (X). The results show a significant increase of 204% in the number of notifications. However, of the 34 variables analyzed, 27 (79.4%) showed a stationary trend, 6 (17.6%) a downward trend, and only one variable (2.9%) an upward trend. Completeness was considered 'Very Poor' for the variables: Education (47.3%), Full Address (21.3%), Occurrence Time (38%) and Use of Alcohol by the Attacker (47%). Therefore, despite the large increase in the number of notifications, data quality continued to be compromised, hampering a more realistic analysis of this group.

Solution of the finite Milne problem in stochastic media with RVT Technique

NASA Astrophysics Data System (ADS)

Slama, Howida; El-Bedwhey, Nabila A.; El-Depsy, Alia; Selim, Mustafa M.

2017-12-01

This paper presents the solution to the Milne problem in the steady state with isotropic scattering phase function. The properties of the medium are considered as stochastic ones with Gaussian or exponential distributions and hence the problem treated as a stochastic integro-differential equation. To get an explicit form for the radiant energy density, the linear extrapolation distance, reflectivity and transmissivity in the deterministic case the problem is solved using the Pomraning-Eddington method. The obtained solution is found to be dependent on the optical space variable and thickness of the medium which are considered as random variables. The random variable transformation (RVT) technique is used to find the first probability density function (1-PDF) of the solution process. Then the stochastic linear extrapolation distance, reflectivity and transmissivity are calculated. For illustration, numerical results with conclusions are provided.

On Matrices, Automata, and Double Counting

NASA Astrophysics Data System (ADS)

Beldiceanu, Nicolas; Carlsson, Mats; Flener, Pierre; Pearson, Justin

Matrix models are ubiquitous for constraint problems. Many such problems have a matrix of variables M, with the same constraint defined by a finite-state automaton A on each row of M and a global cardinality constraint gcc on each column of M. We give two methods for deriving, by double counting, necessary conditions on the cardinality variables of the gcc constraints from the automaton A. The first method yields linear necessary conditions and simple arithmetic constraints. The second method introduces the cardinality automaton, which abstracts the overall behaviour of all the row automata and can be encoded by a set of linear constraints. We evaluate the impact of our methods on a large set of nurse rostering problem instances.

Revisiting the Impact of NCLB High-Stakes School Accountability, Capacity, and Resources: State NAEP 1990-2009 Reading and Math Achievement Gaps and Trends

ERIC Educational Resources Information Center

Lee, Jaekyung; Reeves, Todd

2012-01-01

This study examines the impact of high-stakes school accountability, capacity, and resources under NCLB on reading and math achievement outcomes through comparative interrupted time-series analyses of 1990-2009 NAEP state assessment data. Through hierarchical linear modeling latent variable regression with inverse probability of treatment…

Decoupled and linear quadratic regulator control of a large, flexible space antenna with an observer in the control loop

NASA Technical Reports Server (NTRS)

Hamer, H. A.; Johnson, K. G.; Young, J. W.

1985-01-01

An analysis is performed to compare decoupled and linear quadratic regulator (LQR) procedures for the control of a large, flexible space antenna. Control objectives involve: (1) commanding changes in the rigid-body modes, (2) nulling initial disturbances in the rigid-body modes, or (3) nulling initial disturbances in the first three flexible modes. Control is achieved with two three-axis control-moment gyros located on the antenna column. Results are presented to illustrate various effects on control requirements for the two procedures. These effects include errors in the initial estimates of state variables, variations in the type, number, and location of sensors, and deletions of state-variable estimates for certain flexible modes after control activation. The advantages of incorporating a time lag in the control feedback are also illustrated. In addition, the effects of inoperative-control situations are analyzed with regard to control requirements and resultant modal responses. Comparisons are included which show the effects of perfect state feedback with no residual modes (ideal case). Time-history responses are presented to illustrate the various effects on the control procedures.

Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control

PubMed Central

Brunton, Steven L.; Brunton, Bingni W.; Proctor, Joshua L.; Kutz, J. Nathan

2016-01-01

In this work, we explore finite-dimensional linear representations of nonlinear dynamical systems by restricting the Koopman operator to an invariant subspace spanned by specially chosen observable functions. The Koopman operator is an infinite-dimensional linear operator that evolves functions of the state of a dynamical system. Dominant terms in the Koopman expansion are typically computed using dynamic mode decomposition (DMD). DMD uses linear measurements of the state variables, and it has recently been shown that this may be too restrictive for nonlinear systems. Choosing the right nonlinear observable functions to form an invariant subspace where it is possible to obtain linear reduced-order models, especially those that are useful for control, is an open challenge. Here, we investigate the choice of observable functions for Koopman analysis that enable the use of optimal linear control techniques on nonlinear problems. First, to include a cost on the state of the system, as in linear quadratic regulator (LQR) control, it is helpful to include these states in the observable subspace, as in DMD. However, we find that this is only possible when there is a single isolated fixed point, as systems with multiple fixed points or more complicated attractors are not globally topologically conjugate to a finite-dimensional linear system, and cannot be represented by a finite-dimensional linear Koopman subspace that includes the state. We then present a data-driven strategy to identify relevant observable functions for Koopman analysis by leveraging a new algorithm to determine relevant terms in a dynamical system by ℓ1-regularized regression of the data in a nonlinear function space; we also show how this algorithm is related to DMD. Finally, we demonstrate the usefulness of nonlinear observable subspaces in the design of Koopman operator optimal control laws for fully nonlinear systems using techniques from linear optimal control. PMID:26919740

Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.

PubMed

Mazandarani, Mehran; Pariz, Naser

2018-05-01

This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Linear versus non-linear measures of temporal variability in finger tapping and their relation to performance on open- versus closed-loop motor tasks: comparing standard deviations to Lyapunov exponents.

PubMed

Christman, Stephen D; Weaver, Ryan

2008-05-01

The nature of temporal variability during speeded finger tapping was examined using linear (standard deviation) and non-linear (Lyapunov exponent) measures. Experiment 1 found that right hand tapping was characterised by lower amounts of both linear and non-linear measures of variability than left hand tapping, and that linear and non-linear measures of variability were often negatively correlated with one another. Experiment 2 found that increased non-linear variability was associated with relatively enhanced performance on a closed-loop motor task (mirror tracing) and relatively impaired performance on an open-loop motor task (pointing in a dark room), especially for left hand performance. The potential uses and significance of measures of non-linear variability are discussed.

GWM-a ground-water management process for the U.S. Geological Survey modular ground-water model (MODFLOW-2000)

USGS Publications Warehouse

Ahlfeld, David P.; Barlow, Paul M.; Mulligan, Anne E.

2005-01-01

GWM is a Ground?Water Management Process for the U.S. Geological Survey modular three?dimensional ground?water model, MODFLOW?2000. GWM uses a response?matrix approach to solve several types of linear, nonlinear, and mixed?binary linear ground?water management formulations. Each management formulation consists of a set of decision variables, an objective function, and a set of constraints. Three types of decision variables are supported by GWM: flow?rate decision variables, which are withdrawal or injection rates at well sites; external decision variables, which are sources or sinks of water that are external to the flow model and do not directly affect the state variables of the simulated ground?water system (heads, streamflows, and so forth); and binary variables, which have values of 0 or 1 and are used to define the status of flow?rate or external decision variables. Flow?rate decision variables can represent wells that extend over one or more model cells and be active during one or more model stress periods; external variables also can be active during one or more stress periods. A single objective function is supported by GWM, which can be specified to either minimize or maximize the weighted sum of the three types of decision variables. Four types of constraints can be specified in a GWM formulation: upper and lower bounds on the flow?rate and external decision variables; linear summations of the three types of decision variables; hydraulic?head based constraints, including drawdowns, head differences, and head gradients; and streamflow and streamflow?depletion constraints. The Response Matrix Solution (RMS) Package of GWM uses the Ground?Water Flow Process of MODFLOW to calculate the change in head at each constraint location that results from a perturbation of a flow?rate variable; these changes are used to calculate the response coefficients. For linear management formulations, the resulting matrix of response coefficients is then combined with other components of the linear management formulation to form a complete linear formulation; the formulation is then solved by use of the simplex algorithm, which is incorporated into the RMS Package. Nonlinear formulations arise for simulated conditions that include water?table (unconfined) aquifers or head?dependent boundary conditions (such as streams, drains, or evapotranspiration from the water table). Nonlinear formulations are solved by sequential linear programming; that is, repeated linearization of the nonlinear features of the management problem. In this approach, response coefficients are recalculated for each iteration of the solution process. Mixed?binary linear (or mildly nonlinear) formulations are solved by use of the branch and bound algorithm, which is also incorporated into the RMS Package. Three sample problems are provided to demonstrate the use of GWM for typical ground?water flow management problems. These sample problems provide examples of how GWM input files are constructed to specify the decision variables, objective function, constraints, and solution process for a GWM run. The GWM Process runs with the MODFLOW?2000 Global and Ground?Water Flow Processes, but in its current form GWM cannot be used with the Observation, Sensitivity, Parameter?Estimation, or Ground?Water Transport Processes. The GWM Process is written with a modular structure so that new objective functions, constraint types, and solution algorithms can be added.

An automated procedure for calculating system matrices from perturbation data generated by an EAI Pacer and 100 hybrid computer system

NASA Technical Reports Server (NTRS)

Milner, E. J.; Krosel, S. M.

1977-01-01

Techniques are presented for determining the elements of the A, B, C, and D state variable matrices for systems simulated on an EAI Pacer 100 hybrid computer. An automated procedure systematically generates disturbance data necessary to linearize the simulation model and stores these data on a floppy disk. A separate digital program verifies this data, calculates the elements of the system matrices, and prints these matrices appropriately labeled. The partial derivatives forming the elements of the state variable matrices are approximated by finite difference calculations.

Meteorological Modes of Variability for Fine Particulate Matter (PM2.5) Air Quality in the United States: Implications for PM2.5 Sensitivity to Climate Change

EPA Science Inventory

We applied a multiple linear regression model to understand the relationships of PM_2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM_2.5 to climate change. We used 2004-2008 PM_2.5 observations fro...

Negative psychological aspects and survival in lung cancer patients.

PubMed

Nakaya, Naoki; Saito-Nakaya, Kumi; Akechi, Tatsuo; Kuriyama, Shinichi; Inagaki, Masatoshi; Kikuchi, Nobutaka; Nagai, Kanji; Tsugane, Shoichiro; Nishiwaki, Yutaka; Tsuji, Ichiro; Uchitomi, Yosuke

2008-05-01

We conducted a prospective cohort study in Japan to investigate associations between negative psychological aspects and cancer survival. Between July 1999 and July 2004, a total of 1178 lung cancer patients were enrolled. The questionnaire asked about socioeconomic variables, smoking status, clinical symptoms, and psychological aspects after diagnosis. Negative psychological aspects were assessed for the subscales of helplessness/hopelessness and depression. Clinical stage, performance status (PS), and histologic type were obtained from medical charts. The subjects were followed up until December 2004, and 686 had died. A Cox regression model was used to estimate the hazards ratio (HR) of all-cause mortality. After adjustment for socioeconomic variables and smoking status in addition to sex, age, and histologic type, both helplessness/hopelessness and depression subscales showed significant linear positive associations with the risk of mortality (p for trend<0.001 for both). However, after adjustment for clinical state variables in addition to sex, age, and histologic type, these significant linear positive associations were no longer observed (p for trend=0.41 and 0.26, respectively). Our data supported the hypothesis that the association between helplessness/hopelessness and depression and the risk of mortality among lung cancer patients was largely confounded by clinical state variables including clinical stage, PS, and clinical symptoms. (c) 2007 John Wiley & Sons, Ltd.

An iterative technique to stabilize a linear time invariant multivariable system with output feedback

NASA Technical Reports Server (NTRS)

Sankaran, V.

1974-01-01

An iterative procedure for determining the constant gain matrix that will stabilize a linear constant multivariable system using output feedback is described. The use of this procedure avoids the transformation of variables which is required in other procedures. For the case in which the product of the output and input vector dimensions is greater than the number of states of the plant, general solution is given. In the case in which the states exceed the product of input and output vector dimensions, a least square solution which may not be stable in all cases is presented. The results are illustrated with examples.

A nonlinear Kalman filtering approach to embedded control of turbocharged diesel engines

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Arsie, Ivan

2014-10-01

The development of efficient embedded control for turbocharged Diesel engines, requires the programming of elaborated nonlinear control and filtering methods. To this end, in this paper nonlinear control for turbocharged Diesel engines is developed with the use of Differential flatness theory and the Derivative-free nonlinear Kalman Filter. It is shown that the dynamic model of the turbocharged Diesel engine is differentially flat and admits dynamic feedback linearization. It is also shown that the dynamic model can be written in the linear Brunovsky canonical form for which a state feedback controller can be easily designed. To compensate for modeling errors and external disturbances the Derivative-free nonlinear Kalman Filter is used and redesigned as a disturbance observer. The filter consists of the Kalman Filter recursion on the linearized equivalent of the Diesel engine model and of an inverse transformation based on differential flatness theory which enables to obtain estimates for the state variables of the initial nonlinear model. Once the disturbances variables are identified it is possible to compensate them by including an additional control term in the feedback loop. The efficiency of the proposed control method is tested through simulation experiments.

No association of smoke-free ordinances with profits from bingo and charitable games in Massachusetts.

PubMed

Glantz, S A; Wilson-Loots, R

2003-12-01

Because it is widely played, claims that smoking restrictions will adversely affect bingo games is used as an argument against these policies. We used publicly available data from Massachusetts to assess the impact of 100% smoke-free ordinances on profits from bingo and other gambling sponsored by charitable organisations between 1985 and 2001. We conducted two analyses: (1) a general linear model implementation of a time series analysis with net profits (adjusted to 2001 dollars) as the dependent variable, and community (as a fixed effect), year, lagged net profits, and the length of time the ordinance had been in force as the independent variables; (2) multiple linear regression of total state profits against time, lagged profits, and the percentage of the entire state population in communities that allow charitable gaming but prohibit smoking. The general linear model analysis of data from individual communities showed that, while adjusted profits fell over time, this effect was not related to the presence of an ordinance. The analysis in terms of the fraction of the population living in communities with ordinances yielded the same result. Policymakers can implement smoke-free policies without concern that these policies will affect charitable gaming.

A comparison of two multi-variable integrator windup protection schemes

NASA Technical Reports Server (NTRS)

Mattern, Duane

1993-01-01

Two methods are examined for limit and integrator wind-up protection for multi-input, multi-output linear controllers subject to actuator constraints. The methods begin with an existing linear controller that satisfies the specifications for the nominal, small perturbation, linear model of the plant. The controllers are formulated to include an additional contribution to the state derivative calculations. The first method to be examined is the multi-variable version of the single-input, single-output, high gain, Conventional Anti-Windup (CAW) scheme. Except for the actuator limits, the CAW scheme is linear. The second scheme to be examined, denoted the Modified Anti-Windup (MAW) scheme, uses a scalar to modify the magnitude of the controller output vector while maintaining the vector direction. The calculation of the scalar modifier is a nonlinear function of the controller outputs and the actuator limits. In both cases the constrained actuator is tracked. These two integrator windup protection methods are demonstrated on a turbofan engine control system with five measurements, four control variables, and four actuators. The closed-loop responses of the two schemes are compared and contrasted during limit operation. The issue of maintaining the direction of the controller output vector using the Modified Anti-Windup scheme is discussed and the advantages and disadvantages of both of the IWP methods are presented.

Association between number of cell phone contracts and brain tumor incidence in nineteen U.S. States.

PubMed

Lehrer, Steven; Green, Sheryl; Stock, Richard G

2011-02-01

Some concern has arisen about adverse health effects of cell phones, especially the possibility that the low power microwave-frequency signal transmitted by the antennas on handsets might cause brain tumors or accelerate the growth of subclinical tumors. We analyzed data from the Statistical Report: Primary Brain Tumors in the United States, 2000-2004 and 2007 cell phone subscription data from the Governing State and Local Sourcebook. There was a significant correlation between number of cell phone subscriptions and brain tumors in nineteen US states (r = 0.950, P < 0.001). Because increased numbers of both cell phone subscriptions and brain tumors could be due solely to the fact that some states, such as New York, have much larger populations than other states, such as North Dakota, multiple linear regression was performed with number of brain tumors as the dependent variable, cell phone subscriptions, population, mean family income and mean age as independent variables. The effect of cell phone subscriptions was significant (P = 0.017), and independent of the effect of mean family income (P = 0.894), population (P = 0.003) and age (0.499). The very linear relationship between cell phone usage and brain tumor incidence is disturbing and certainly needs further epidemiological evaluation. In the meantime, it would be prudent to limit exposure to all sources of electro-magnetic radiation.

A variable structure approach to robust control of VTOL aircraft

NASA Technical Reports Server (NTRS)

Calise, A. J.; Kramer, F.

1982-01-01

This paper examines the application of variable structure control theory to the design of a flight control system for the AV-8A Harrier in a hover mode. The objective in variable structure design is to confine the motion to a subspace of the total state space. The motion in this subspace is insensitive to system parameter variations and external disturbances that lie in the range space of the control. A switching type of control law results from the design procedure. The control system was designed to track a vector velocity command defined in the body frame. For comparison purposes, a proportional controller was designed using optimal linear regulator theory. Both control designs were first evaluated for transient response performance using a linearized model, then a nonlinear simulation study of a hovering approach to landing was conducted. Wind turbulence was modeled using a 1052 destroyer class air wake model.

Shock and Vibration Control of a Golf-Swing Robot at Impacting the Ball

NASA Astrophysics Data System (ADS)

Hoshino, Yohei; Kobayashi, Yukinori

A golf swing robot is a kind of fast motion manipulator with a flexible link. A robot manipulator is greatly affected by Corioli's and centrifugal forces during fast motion. Nonlinearity due to these forces can have an adverse effect on the performance of feedback control. In the same way, ordinary state observers of a linear system cannot accurately estimate the states of nonlinear systems. This paper uses a state observer that considers disturbances to improve the performance of state estimation and feedback control. A mathematical model of the golf robot is derived by Hamilton's principle. A linear quadratic regulator (LQR) that considers the vibration of the club shaft is used to stop the robot during the follow-through action. The state observer that considers disturbances estimates accurate state variables when the disturbances due to Corioli's and centrifugal forces, and impact forces work on the robot. As a result, the performance of the state feedback control is improved. The study compares the results of the numerical simulations with experimental results.

Attitude estimation of earth orbiting satellites by decomposed linear recursive filters

NASA Technical Reports Server (NTRS)

Kou, S. R.

1975-01-01

Attitude estimation of earth orbiting satellites (including Large Space Telescope) subjected to environmental disturbances and noises was investigated. Modern control and estimation theory is used as a tool to design an efficient estimator for attitude estimation. Decomposed linear recursive filters for both continuous-time systems and discrete-time systems are derived. By using this accurate estimation of the attitude of spacecrafts, state variable feedback controller may be designed to achieve (or satisfy) high requirements of system performance.

Linear parameter varying representations for nonlinear control design

NASA Astrophysics Data System (ADS)

Carter, Lance Huntington

Linear parameter varying (LPV) systems are investigated as a framework for gain-scheduled control design and optimal hybrid control. An LPV system is defined as a linear system whose dynamics depend upon an a priori unknown but measurable exogenous parameter. A gain-scheduled autopilot design is presented for a bank-to-turn (BTT) missile. The method is novel in that the gain-scheduled design does not involve linearizations about operating points. Instead, the missile dynamics are brought to LPV form via a state transformation. This idea is applied to the design of a coupled longitudinal/lateral BTT missile autopilot. The pitch and yaw/roll dynamics are separately transformed to LPV form, where the cross axis states are treated as "exogenous" parameters. These are actually endogenous variables, so such a plant is called "quasi-LPV." Once in quasi-LPV form, a family of robust controllers using mu synthesis is designed for both the pitch and yaw/roll channels, using angle-of-attack and roll rate as the scheduling variables. The closed-loop time response is simulated using the original nonlinear model and also using perturbed aerodynamic coefficients. Modeling and control of engine idle speed is investigated using LPV methods. It is shown how generalized discrete nonlinear systems may be transformed into quasi-LPV form. A discrete nonlinear engine model is developed and expressed in quasi-LPV form with engine speed as the scheduling variable. An example control design is presented using linear quadratic methods. Simulations are shown comparing the LPV based controller performance to that using PID control. LPV representations are also shown to provide a setting for hybrid systems. A hybrid system is characterized by control inputs consisting of both analog signals and discrete actions. A solution is derived for the optimal control of hybrid systems with generalized cost functions. This is shown to be computationally intensive, so a suboptimal strategy is proposed that neglects a subset of possible parameter trajectories. A computational algorithm is constructed for this suboptimal solution applied to a class of linear non-quadratic cost functions.

A Tightly Coupled Non-Equilibrium Magneto-Hydrodynamic Model for Inductively Coupled RF Plasmas

DTIC Science & Technology

2016-02-29

development a tightly coupled magneto-hydrodynamic model for Inductively Coupled Radio- Frequency (RF) Plasmas. Non Local Thermodynamic Equilibrium (NLTE...for Inductively Coupled Radio-Frequency (RF) Plasmas. Non Local Thermodynamic Equilibrium (NLTE) effects are described based on a hybrid State-to-State... thermodynamic variable. This choice allows one to hide the non-linearity of the gas (total) thermal conductivity κ and can partially alle- 2 viate numerical

Comparative study of flare control laws. [optimal control of b-737 aircraft approach and landing

NASA Technical Reports Server (NTRS)

Nadkarni, A. A.; Breedlove, W. J., Jr.

1979-01-01

A digital 3-D automatic control law was developed to achieve an optimal transition of a B-737 aircraft between various initial glid slope conditions and the desired final touchdown condition. A discrete, time-invariant, optimal, closed-loop control law presented for a linear regulator problem, was extended to include a system being acted upon by a constant disturbance. Two forms of control laws were derived to solve this problem. One method utilized the feedback of integral states defined appropriately and augmented with the original system equations. The second method formulated the problem as a control variable constraint, and the control variables were augmented with the original system. The control variable constraint control law yielded a better performance compared to feedback control law for the integral states chosen.

Highway traffic estimation of improved precision using the derivative-free nonlinear Kalman Filter

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Siano, Pierluigi; Zervos, Nikolaos; Melkikh, Alexey

2015-12-01

The paper proves that the PDE dynamic model of the highway traffic is a differentially flat one and by applying spatial discretization its shows that the model's transformation into an equivalent linear canonical state-space form is possible. For the latter representation of the traffic's dynamics, state estimation is performed with the use of the Derivative-free nonlinear Kalman Filter. The proposed filter consists of the Kalman Filter recursion applied on the transformed state-space model of the highway traffic. Moreover, it makes use of an inverse transformation, based again on differential flatness theory which enables to obtain estimates of the state variables of the initial nonlinear PDE model. By avoiding approximate linearizations and the truncation of nonlinear terms from the PDE model of the traffic's dynamics the proposed filtering methods outperforms, in terms of accuracy, other nonlinear estimators such as the Extended Kalman Filter. The article's theoretical findings are confirmed through simulation experiments.

Locally optimal control under unknown dynamics with learnt cost function: application to industrial robot positioning

NASA Astrophysics Data System (ADS)

Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane; Nyiri, Eric

2017-01-01

Recent methods of Reinforcement Learning have enabled to solve difficult, high dimensional, robotic tasks under unknown dynamics using iterative Linear Quadratic Gaussian control theory. These algorithms are based on building a local time-varying linear model of the dynamics from data gathered through interaction with the environment. In such tasks, the cost function is often expressed directly in terms of the state and control variables so that it can be locally quadratized to run the algorithm. If the cost is expressed in terms of other variables, a model is required to compute the cost function from the variables manipulated. We propose a method to learn the cost function directly from the data, in the same way as for the dynamics. This way, the cost function can be defined in terms of any measurable quantity and thus can be chosen more appropriately for the task to be carried out. With our method, any sensor information can be used to design the cost function. We demonstrate the efficiency of this method through simulating, with the V-REP software, the learning of a Cartesian positioning task on several industrial robots with different characteristics. The robots are controlled in joint space and no model is provided a priori. Our results are compared with another model free technique, consisting in writing the cost function as a state variable.

Kalman Filter Constraint Tuning for Turbofan Engine Health Estimation

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2005-01-01

Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints are often neglected because they do not fit easily into the structure of the Kalman filter. Recently published work has shown a new method for incorporating state variable inequality constraints in the Kalman filter, which has been shown to generally improve the filter s estimation accuracy. However, the incorporation of inequality constraints poses some risk to the estimation accuracy as the Kalman filter is theoretically optimal. This paper proposes a way to tune the filter constraints so that the state estimates follow the unconstrained (theoretically optimal) filter when the confidence in the unconstrained filter is high. When confidence in the unconstrained filter is not so high, then we use our heuristic knowledge to constrain the state estimates. The confidence measure is based on the agreement of measurement residuals with their theoretical values. The algorithm is demonstrated on a linearized simulation of a turbofan engine to estimate engine health.

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

NASA Astrophysics Data System (ADS)

Nguyen, Hung T. T.; Galelli, Stefano

2018-03-01

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime-like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India.

PubMed

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K

2013-01-01

Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. CBRs were inversely related to literacy rates (slope parameter = -0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = -0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = -1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = -0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Female literacy is relatively highly important for both population stabilization and better infant health.

No association of smoke-free ordinances with profits from bingo and charitable games in Massachusetts

PubMed Central

Glantz, S; Wilson-Loots, R

2003-01-01

Background: Because it is widely played, claims that smoking restrictions will adversely affect bingo games is used as an argument against these policies. We used publicly available data from Massachusetts to assess the impact of 100% smoke-free ordinances on profits from bingo and other gambling sponsored by charitable organisations between 1985 and 2001. Methods: We conducted two analyses: (1) a general linear model implementation of a time series analysis with net profits (adjusted to 2001 dollars) as the dependent variable, and community (as a fixed effect), year, lagged net profits, and the length of time the ordinance had been in force as the independent variables; (2) multiple linear regression of total state profits against time, lagged profits, and the percentage of the entire state population in communities that allow charitable gaming but prohibit smoking. Results: The general linear model analysis of data from individual communities showed that, while adjusted profits fell over time, this effect was not related to the presence of an ordinance. The analysis in terms of the fraction of the population living in communities with ordinances yielded the same result. Conclusion: Policymakers can implement smoke-free policies without concern that these policies will affect charitable gaming. PMID:14660778

Nonlinear vs. linear biasing in Trp-cage folding simulations

NASA Astrophysics Data System (ADS)

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-01

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Nonlinear vs. linear biasing in Trp-cage folding simulations.

PubMed

Spiwok, Vojtěch; Oborský, Pavel; Pazúriková, Jana; Křenek, Aleš; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energy minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.

Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics.

PubMed

Cheng, Sen; Sabes, Philip N

2007-04-01

The sensorimotor calibration of visually guided reaching changes on a trial-to-trial basis in response to random shifts in the visual feedback of the hand. We show that a simple linear dynamical system is sufficient to model the dynamics of this adaptive process. In this model, an internal variable represents the current state of sensorimotor calibration. Changes in this state are driven by error feedback signals, which consist of the visually perceived reach error, the artificial shift in visual feedback, or both. Subjects correct for > or =20% of the error observed on each movement, despite being unaware of the visual shift. The state of adaptation is also driven by internal dynamics, consisting of a decay back to a baseline state and a "state noise" process. State noise includes any source of variability that directly affects the state of adaptation, such as variability in sensory feedback processing, the computations that drive learning, or the maintenance of the state. This noise is accumulated in the state across trials, creating temporal correlations in the sequence of reach errors. These correlations allow us to distinguish state noise from sensorimotor performance noise, which arises independently on each trial from random fluctuations in the sensorimotor pathway. We show that these two noise sources contribute comparably to the overall magnitude of movement variability. Finally, the dynamics of adaptation measured with random feedback shifts generalizes to the case of constant feedback shifts, allowing for a direct comparison of our results with more traditional blocked-exposure experiments.

Tracking Electroencephalographic Changes Using Distributions of Linear Models: Application to Propofol-Based Depth of Anesthesia Monitoring.

PubMed

Kuhlmann, Levin; Manton, Jonathan H; Heyse, Bjorn; Vereecke, Hugo E M; Lipping, Tarmo; Struys, Michel M R F; Liley, David T J

2017-04-01

Tracking brain states with electrophysiological measurements often relies on short-term averages of extracted features and this may not adequately capture the variability of brain dynamics. The objective is to assess the hypotheses that this can be overcome by tracking distributions of linear models using anesthesia data, and that anesthetic brain state tracking performance of linear models is comparable to that of a high performing depth of anesthesia monitoring feature. Individuals' brain states are classified by comparing the distribution of linear (auto-regressive moving average-ARMA) model parameters estimated from electroencephalographic (EEG) data obtained with a sliding window to distributions of linear model parameters for each brain state. The method is applied to frontal EEG data from 15 subjects undergoing propofol anesthesia and classified by the observers assessment of alertness/sedation (OAA/S) scale. Classification of the OAA/S score was performed using distributions of either ARMA parameters or the benchmark feature, Higuchi fractal dimension. The highest average testing sensitivity of 59% (chance sensitivity: 17%) was found for ARMA (2,1) models and Higuchi fractal dimension achieved 52%, however, no statistical difference was observed. For the same ARMA case, there was no statistical difference if medians are used instead of distributions (sensitivity: 56%). The model-based distribution approach is not necessarily more effective than a median/short-term average approach, however, it performs well compared with a distribution approach based on a high performing anesthesia monitoring measure. These techniques hold potential for anesthesia monitoring and may be generally applicable for tracking brain states.

Lidar-Based Estimates of Above-Ground Biomass in the Continental US and Mexico Using Ground, Airborne, and Satellite Observations

NASA Technical Reports Server (NTRS)

Nelson, Ross; Margolis, Hank; Montesano, Paul; Sun, Guoqing; Cook, Bruce; Corp, Larry; Andersen, Hans-Erik; DeJong, Ben; Pellat, Fernando Paz; Fickel, Thaddeus;

Dollars for lives: the effect of highway capital investments on traffic fatalities.

PubMed

Nguyen-Hoang, Phuong; Yeung, Ryan

2014-12-01

This study examines the effect of highway capital investments on highway fatalities. We used state-level data from the 48 contiguous states in the United States from 1968 through 2010 to estimate the effects on highway fatalities of capital expenditures and highway capital stock. We estimated these effects by controlling for a set of control variables together with state and year dummy variables and state-specific linear time trends. We found that capital expenditures and capital stock had significant and negative effects on highway fatalities. States faced with declines in gas tax revenues have already cut back drastically on spending on roads including on maintenance and capital outlay. If this trend continues, it may undermine traffic safety. While states and local governments are currently fiscally strained, it is important for them to continue investments in roadways to enhance traffic safety and, more significantly, to save lives. Copyright © 2014 National Safety Council and Elsevier Ltd. All rights reserved.

A kinetic approach to some quasi-linear laws of macroeconomics

NASA Astrophysics Data System (ADS)

Gligor, M.; Ignat, M.

2002-11-01

Some previous works have presented the data on wealth and income distributions in developed countries and have found that the great majority of population is described by an exponential distribution, which results in idea that the kinetic approach could be adequate to describe this empirical evidence. The aim of our paper is to extend this framework by developing a systematic kinetic approach of the socio-economic systems and to explain how linear laws, modelling correlations between macroeconomic variables, may arise in this context. Firstly we construct the Boltzmann kinetic equation for an idealised system composed by many individuals (workers, officers, business men, etc.), each of them getting a certain income and spending money for their needs. To each individual a certain time variable amount of money is associated this meaning him/her phase space coordinate. In this way the exponential distribution of money in a closed economy is explicitly found. The extension of this result, including states near the equilibrium, give us the possibility to take into account the regular increase of the total amount of money, according to the modern economic theories. The Kubo-Green-Onsager linear response theory leads us to a set of linear equations between some macroeconomic variables. Finally, the validity of such laws is discussed in relation with the time reversal symmetry and is tested empirically using some macroeconomic time series.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

Impacts analysis of car following models considering variable vehicular gap policies

NASA Astrophysics Data System (ADS)

Xin, Qi; Yang, Nan; Fu, Rui; Yu, Shaowei; Shi, Zhongke

2018-07-01

Due to the important roles playing in the vehicles' adaptive cruise control system, variable vehicular gap polices were employed to full velocity difference model (FVDM) to investigate the traffic flow properties. In this paper, two new car following models were put forward by taking constant time headway(CTH) policy and variable time headway(VTH) policy into optimal velocity function, separately. By steady state analysis of the new models, an equivalent optimal velocity function was defined. To determine the linear stable conditions of the new models, we introduce equivalent expressions of safe vehicular gap, and then apply small amplitude perturbation analysis and long terms of wave expansion techniques to obtain the new models' linear stable conditions. Additionally, the first order approximate solutions of the new models were drawn at the stable region, by transforming the models into typical Burger's partial differential equations with reductive perturbation method. The FVDM based numerical simulations indicate that the variable vehicular gap polices with proper parameters directly contribute to the improvement of the traffic flows' stability and the avoidance of the unstable traffic phenomena.

Quality-by-Design (QbD): An integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and process design space development.

PubMed

Wu, Huiquan; White, Maury; Khan, Mansoor A

2011-02-28

The aim of this work was to develop an integrated process analytical technology (PAT) approach for a dynamic pharmaceutical co-precipitation process characterization and design space development. A dynamic co-precipitation process by gradually introducing water to the ternary system of naproxen-Eudragit L100-alcohol was monitored at real-time in situ via Lasentec FBRM and PVM. 3D map of count-time-chord length revealed three distinguishable process stages: incubation, transition, and steady-state. The effects of high risk process variables (slurry temperature, stirring rate, and water addition rate) on both derived co-precipitation process rates and final chord-length-distribution were evaluated systematically using a 3(3) full factorial design. Critical process variables were identified via ANOVA for both transition and steady state. General linear models (GLM) were then used for parameter estimation for each critical variable. Clear trends about effects of each critical variable during transition and steady state were found by GLM and were interpreted using fundamental process principles and Nyvlt's transfer model. Neural network models were able to link process variables with response variables at transition and steady state with R(2) of 0.88-0.98. PVM images evidenced nucleation and crystal growth. Contour plots illustrated design space via critical process variables' ranges. It demonstrated the utility of integrated PAT approach for QbD development. Published by Elsevier B.V.

Climate patterns as predictors of amphibians species richness and indicators of potential stress

USGS Publications Warehouse

Battaglin, W.; Hay, L.; McCabe, G.; Nanjappa, P.; Gallant, Alisa L.

2005-01-01

Amphibians occupy a range of habitats throughout the world, but species richness is greatest in regions with moist, warm climates. We modeled the statistical relations of anuran and urodele species richness with mean annual climate for the conterminous United States, and compared the strength of these relations at national and regional levels. Model variables were calculated for county and subcounty mapping units, and included 40-year (1960-1999) annual mean and mean annual climate statistics, mapping unit average elevation, mapping unit land area, and estimates of anuran and urodele species richness. Climate data were derived from more than 7,500 first-order and cooperative meteorological stations and were interpolated to the mapping units using multiple linear regression models. Anuran and urodele species richness were calculated from the United States Geological Survey's Amphibian Research and Monitoring Initiative (ARMI) National Atlas for Amphibian Distributions. The national multivariate linear regression (MLR) model of anuran species richness had an adjusted coefficient of determination (R2) value of 0.64 and the national MLR model for urodele species richness had an R2 value of 0.45. Stratifying the United States by coarse-resolution ecological regions provided models for anUrans that ranged in R2 values from 0.15 to 0.78. Regional models for urodeles had R2 values. ranging from 0.27 to 0.74. In general, regional models for anurans were more strongly influenced by temperature variables, whereas precipitation variables had a larger influence on urodele models.

A guidance and navigation system for continuous low-thrust vehicles. M.S. Thesis

NASA Technical Reports Server (NTRS)

Jack-Chingtse, C.

1973-01-01

A midcourse guidance and navigation system for continuous low thrust vehicles was developed. The equinoctial elements are the state variables. Uncertainties are modelled statistically by random vector and stochastic processes. The motion of the vehicle and the measurements are described by nonlinear stochastic differential and difference equations respectively. A minimum time trajectory is defined; equations of motion and measurements are linearized about this trajectory. An exponential cost criterion is constructed and a linear feedback quidance law is derived. An extended Kalman filter is used for state estimation. A short mission using this system is simulated. It is indicated that this system is efficient for short missions, but longer missions require accurate trajectory and ground based measurements.

Rapid timing studies of black hole binaries in Optical and X-rays: correlated and non-linear variability

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

Gandhi, P.; Dhillon, V. S.; Durant, M.

2010-07-15

In a fast multi-wavelength timing study of black hole X-ray binaries (BHBs), we have discovered correlated optical and X-ray variability in the low/hard state of two sources: GX 339-4 and SWIFT J1753.5-0127. After XTE J1118+480, these are the only BHBs currently known to show rapid (sub-second) aperiodic optical flickering. Our simultaneous VLT/Ultracam and RXTE data reveal intriguing patterns with characteristic peaks, dips and lags down to very short timescales. Simple linear reprocessing models can be ruled out as the origin of the rapid, aperiodic optical power in both sources. A magnetic energy release model with fast interactions between the disk,more » jet and corona can explain the complex correlation patterns. We also show that in both the optical and X-ray light curves, the absolute source variability r.m.s. amplitude linearly increases with flux, and that the flares have a log-normal distribution. The implication is that variability at both wavelengths is not due to local fluctuations alone, but rather arises as a result of coupling of perturbations over a wide range of radii and timescales. These 'optical and X-ray rms-flux relations' thus provide new constraints to connect the outer and inner parts of the accretion flow, and the jet.« less

Using the Ridge Regression Procedures to Estimate the Multiple Linear Regression Coefficients

NASA Astrophysics Data System (ADS)

Gorgees, HazimMansoor; Mahdi, FatimahAssim

2018-05-01

This article concerns with comparing the performance of different types of ordinary ridge regression estimators that have been already proposed to estimate the regression parameters when the near exact linear relationships among the explanatory variables is presented. For this situations we employ the data obtained from tagi gas filling company during the period (2008-2010). The main result we reached is that the method based on the condition number performs better than other methods since it has smaller mean square error (MSE) than the other stated methods.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Successfully Transitioning to Linear Equations

ERIC Educational Resources Information Center

Colton, Connie; Smith, Wendy M.

2014-01-01

The Common Core State Standards for Mathematics (CCSSI 2010) asks students in as early as fourth grade to solve word problems using equations with variables. Equations studied at this level generate a single solution, such as the equation x + 10 = 25. For students in fifth grade, the Common Core standard for algebraic thinking expects them to…

Developing an Adequately Specified Model of State Level Student Achievement with Multilevel Data.

ERIC Educational Resources Information Center

Bernstein, Lawrence

Limitations of using linear, unilevel regression procedures in modeling student achievement are discussed. This study is a part of a broader study that is developing an empirically-based predictive model of variables associated with academic achievement from a multilevel perspective and examining the differences by which parameters are estimated…

Electronic Resource Expenditure and the Decline in Reference Transaction Statistics in Academic Libraries

ERIC Educational Resources Information Center

Dubnjakovic, Ana

2012-01-01

The current study investigates factors influencing increase in reference transactions in a typical week in academic libraries across the United States of America. Employing multiple regression analysis and general linear modeling, variables of interest from the "Academic Library Survey (ALS) 2006" survey (sample size 3960 academic libraries) were…

Thermospheric dynamics - A system theory approach

NASA Technical Reports Server (NTRS)

Codrescu, M.; Forbes, J. M.; Roble, R. G.

1990-01-01

A system theory approach to thermospheric modeling is developed, based upon a linearization method which is capable of preserving nonlinear features of a dynamical system. The method is tested using a large, nonlinear, time-varying system, namely the thermospheric general circulation model (TGCM) of the National Center for Atmospheric Research. In the linearized version an equivalent system, defined for one of the desired TGCM output variables, is characterized by a set of response functions that is constructed from corresponding quasi-steady state and unit sample response functions. The linearized version of the system runs on a personal computer and produces an approximation of the desired TGCM output field height profile at a given geographic location.

Digital program for solving the linear stochastic optimal control and estimation problem

NASA Technical Reports Server (NTRS)

Geyser, L. C.; Lehtinen, B.

1975-01-01

A computer program is described which solves the linear stochastic optimal control and estimation (LSOCE) problem by using a time-domain formulation. The LSOCE problem is defined as that of designing controls for a linear time-invariant system which is disturbed by white noise in such a way as to minimize a performance index which is quadratic in state and control variables. The LSOCE problem and solution are outlined; brief descriptions are given of the solution algorithms, and complete descriptions of each subroutine, including usage information and digital listings, are provided. A test case is included, as well as information on the IBM 7090-7094 DCS time and storage requirements.

Statistical structure of intrinsic climate variability under global warming

NASA Astrophysics Data System (ADS)

Zhu, Xiuhua; Bye, John; Fraedrich, Klaus

2017-04-01

Climate variability is often studied in terms of fluctuations with respect to the mean state, whereas the dependence between the mean and variability is rarely discussed. We propose a new climate metric to measure the relationship between means and standard deviations of annual surface temperature computed over non-overlapping 100-year segments. This metric is analyzed based on equilibrium simulations of the Max Planck Institute-Earth System Model (MPI-ESM): the last millennium climate (800-1799), the future climate projection following the A1B scenario (2100-2199), and the 3100-year unforced control simulation. A linear relationship is globally observed in the control simulation and thus termed intrinsic climate variability, which is most pronounced in the tropical region with negative regression slopes over the Pacific warm pool and positive slopes in the eastern tropical Pacific. It relates to asymmetric changes in temperature extremes and associates fluctuating climate means with increase or decrease in intensity and occurrence of both El Niño and La Niña events. In the future scenario period, the linear regression slopes largely retain their spatial structure with appreciable changes in intensity and geographical locations. Since intrinsic climate variability describes the internal rhythm of the climate system, it may serve as guidance for interpreting climate variability and climate change signals in the past and the future.

Maximally reliable Markov chains under energy constraints.

PubMed

Escola, Sean; Eisele, Michael; Miller, Kenneth; Paninski, Liam

2009-07-01

Signal-to-noise ratios in physical systems can be significantly degraded if the outputs of the systems are highly variable. Biological processes for which highly stereotyped signal generations are necessary features appear to have reduced their signal variabilities by employing multiple processing steps. To better understand why this multistep cascade structure might be desirable, we prove that the reliability of a signal generated by a multistate system with no memory (i.e., a Markov chain) is maximal if and only if the system topology is such that the process steps irreversibly through each state, with transition rates chosen such that an equal fraction of the total signal is generated in each state. Furthermore, our result indicates that by increasing the number of states, it is possible to arbitrarily increase the reliability of the system. In a physical system, however, an energy cost is associated with maintaining irreversible transitions, and this cost increases with the number of such transitions (i.e., the number of states). Thus, an infinite-length chain, which would be perfectly reliable, is infeasible. To model the effects of energy demands on the maximally reliable solution, we numerically optimize the topology under two distinct energy functions that penalize either irreversible transitions or incommunicability between states, respectively. In both cases, the solutions are essentially irreversible linear chains, but with upper bounds on the number of states set by the amount of available energy. We therefore conclude that a physical system for which signal reliability is important should employ a linear architecture, with the number of states (and thus the reliability) determined by the intrinsic energy constraints of the system.

The development and validation of a numerical integration method for non-linear viscoelastic modeling

PubMed Central

Ramo, Nicole L.; Puttlitz, Christian M.

2018-01-01

Compelling evidence that many biological soft tissues display both strain- and time-dependent behavior has led to the development of fully non-linear viscoelastic modeling techniques to represent the tissue’s mechanical response under dynamic conditions. Since the current stress state of a viscoelastic material is dependent on all previous loading events, numerical analyses are complicated by the requirement of computing and storing the stress at each step throughout the load history. This requirement quickly becomes computationally expensive, and in some cases intractable, for finite element models. Therefore, we have developed a strain-dependent numerical integration approach for capturing non-linear viscoelasticity that enables calculation of the current stress from a strain-dependent history state variable stored from the preceding time step only, which improves both fitting efficiency and computational tractability. This methodology was validated based on its ability to recover non-linear viscoelastic coefficients from simulated stress-relaxation (six strain levels) and dynamic cyclic (three frequencies) experimental stress-strain data. The model successfully fit each data set with average errors in recovered coefficients of 0.3% for stress-relaxation fits and 0.1% for cyclic. The results support the use of the presented methodology to develop linear or non-linear viscoelastic models from stress-relaxation or cyclic experimental data of biological soft tissues. PMID:29293558

Entanglement between total intensity and polarization for pairs of coherent states

NASA Astrophysics Data System (ADS)

Sanchidrián-Vaca, Carlos; Luis, Alfredo

2018-04-01

We examine entanglement between number and polarization, or number and relative phase, in pair coherent states and two-mode squeezed vacuum via linear entropy and covariance criteria. We consider the embedding of the two-mode Hilbert space in a larger space to get a well-defined factorization of the number-phase variables. This can be regarded as a kind of protoentanglement that can be extracted and converted into real particle entanglement via feasible experimental procedures. In particular this reveals interesting entanglement properties of pairs of coherent states.

Singular Perturbations and Time Scales in Modeling and Control of Dynamic Systems,

DTIC Science & Technology

1980-11-01

Madanic, "Closed-Loop Stackelberg Stategies for Singularly Perturbed Linear Quadratic Problem," IEEE Transactions on Automtic Control, Vol. AC-25, No...of the state variables. On the other hand the com- (15 damped high steqenc oscillalor mode cannsome o \\s/ del are not separable. In this ’ mixed ’ case...are found to be mixed and hence is not electromechanical Interactions and the single ma- suitable for direct state separation into a slow chine

Supporting second grade lower secondary school students’ understanding of linear equation system in two variables using ethnomathematics

NASA Astrophysics Data System (ADS)

Nursyahidah, F.; Saputro, B. A.; Rubowo, M. R.

2018-03-01

The aim of this research is to know the students’ understanding of linear equation system in two variables using Ethnomathematics and to acquire learning trajectory of linear equation system in two variables for the second grade of lower secondary school students. This research used methodology of design research that consists of three phases, there are preliminary design, teaching experiment, and retrospective analysis. Subject of this study is 28 second grade students of Sekolah Menengah Pertama (SMP) 37 Semarang. The result of this research shows that the students’ understanding in linear equation system in two variables can be stimulated by using Ethnomathematics in selling buying tradition in Peterongan traditional market in Central Java as a context. All of strategies and model that was applied by students and also their result discussion shows how construction and contribution of students can help them to understand concept of linear equation system in two variables. All the activities that were done by students produce learning trajectory to gain the goal of learning. Each steps of learning trajectory of students have an important role in understanding the concept from informal to the formal level. Learning trajectory using Ethnomathematics that is produced consist of watching video of selling buying activity in Peterongan traditional market to construct linear equation in two variables, determine the solution of linear equation in two variables, construct model of linear equation system in two variables from contextual problem, and solving a contextual problem related to linear equation system in two variables.

Prediction of Psilocybin Response in Healthy Volunteers

PubMed Central

Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X.

2012-01-01

Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin. PMID:22363492

Prediction of psilocybin response in healthy volunteers.

PubMed

Studerus, Erich; Gamma, Alex; Kometer, Michael; Vollenweider, Franz X

2012-01-01

Responses to hallucinogenic drugs, such as psilocybin, are believed to be critically dependent on the user's personality, current mood state, drug pre-experiences, expectancies, and social and environmental variables. However, little is known about the order of importance of these variables and their effect sizes in comparison to drug dose. Hence, this study investigated the effects of 24 predictor variables, including age, sex, education, personality traits, drug pre-experience, mental state before drug intake, experimental setting, and drug dose on the acute response to psilocybin. The analysis was based on the pooled data of 23 controlled experimental studies involving 409 psilocybin administrations to 261 healthy volunteers. Multiple linear mixed effects models were fitted for each of 15 response variables. Although drug dose was clearly the most important predictor for all measured response variables, several non-pharmacological variables significantly contributed to the effects of psilocybin. Specifically, having a high score in the personality trait of Absorption, being in an emotionally excitable and active state immediately before drug intake, and having experienced few psychological problems in past weeks were most strongly associated with pleasant and mystical-type experiences, whereas high Emotional Excitability, low age, and an experimental setting involving positron emission tomography most strongly predicted unpleasant and/or anxious reactions to psilocybin. The results confirm that non-pharmacological variables play an important role in the effects of psilocybin.

Correlating non-linear properties with spectral states of RXTE data: possible observational evidences for four different accretion modes around compact objects

NASA Astrophysics Data System (ADS)

Adegoke, Oluwashina; Dhang, Prasun; Mukhopadhyay, Banibrata; Ramadevi, M. C.; Bhattacharya, Debbijoy

2018-05-01

By analysing the time series of RXTE/PCA data, the non-linear variabilities of compact sources have been repeatedly established. Depending on the variation in temporal classes, compact sources exhibit different non-linear features. Sometimes they show low correlation/fractal dimension, but in other classes or intervals of time they exhibit stochastic nature. This could be because the accretion flow around a compact object is a non-linear general relativistic system involving magnetohydrodynamics. However, the more conventional way of addressing a compact source is the analysis of its spectral state. Therefore, the question arises: What is the connection of non-linearity to the underlying spectral properties of the flow when the non-linear properties are related to the associated transport mechanisms describing the geometry of the flow? This work is aimed at addressing this question. Based on the connection between observed spectral and non-linear (time series) properties of two X-ray binaries: GRS 1915+105 and Sco X-1, we attempt to diagnose the underlying accretion modes of the sources in terms of known accretion classes, namely, Keplerian disc, slim disc, advection dominated accretion flow and general advective accretion flow. We explore the possible transition of the sources from one accretion mode to others with time. We further argue that the accretion rate must play an important role in transition between these modes.

Hysteresis between coral reef calcification and the seawater aragonite saturation state

NASA Astrophysics Data System (ADS)

McMahon, Ashly; Santos, Isaac R.; Cyronak, Tyler; Eyre, Bradley D.

2013-09-01

predictions of how ocean acidification (OA) will affect coral reefs assume a linear functional relationship between the ambient seawater aragonite saturation state (Ωa) and net ecosystem calcification (NEC). We quantified NEC in a healthy coral reef lagoon in the Great Barrier Reef during different times of the day. Our observations revealed a diel hysteresis pattern in the NEC versus Ωa relationship, with peak NEC rates occurring before the Ωa peak and relatively steady nighttime NEC in spite of variable Ωa. Net ecosystem production had stronger correlations with NEC than light, temperature, nutrients, pH, and Ωa. The observed hysteresis may represent an overlooked challenge for predicting the effects of OA on coral reefs. If widespread, the hysteresis could prevent the use of a linear extrapolation to determine critical Ωa threshold levels required to shift coral reefs from a net calcifying to a net dissolving state.

Modeling the microstructurally dependent mechanical properties of poly(ester-urethane-urea)s.

PubMed

Warren, P Daniel; Sycks, Dalton G; McGrath, Dominic V; Vande Geest, Jonathan P

2013-12-01

Poly(ester-urethane-urea) (PEUU) is one of many synthetic biodegradable elastomers under scrutiny for biomedical and soft tissue applications. The goal of this study was to investigate the effect of the experimental parameters on mechanical properties of PEUUs following exposure to different degrading environments, similar to that of the human body, using linear regression, producing one predictive model. The model utilizes two independent variables of poly(caprolactone) (PCL) type and copolymer crystallinity to predict the dependent variable of maximum tangential modulus (MTM). Results indicate that comparisons between PCLs at different degradation states are statistically different (p < 0.0003), while the difference between experimental and predicted average MTM is statistically negligible (p < 0.02). The linear correlation between experimental and predicted MTM values is R(2) = 0.75. Copyright © 2013 Wiley Periodicals, Inc., a Wiley Company.

Linear Extended State Observer-Based Motion Synchronization Control for Hybrid Actuation System of More Electric Aircraft.

PubMed

Wang, Xingjian; Liao, Rui; Shi, Cun; Wang, Shaoping

2017-10-25

Moving towards the more electric aircraft (MEA), a hybrid actuator configuration provides an opportunity to introduce electromechanical actuator (EMA) into primary flight control. In the hybrid actuation system (HAS), an electro-hydraulic servo actuator (EHSA) and an EMA operate on the same control surface. In order to solve force fighting problem in HAS, this paper proposes a novel linear extended state observer (LESO)-based motion synchronization control method. To cope with the problem of unavailability of the state signals required by the motion synchronization controller, LESO is designed for EHSA and EMA to observe the state variables. Based on the observed states of LESO, motion synchronization controllers could enable EHSA and EMA to simultaneously track the desired motion trajectories. Additionally, nonlinearities, uncertainties and unknown disturbances as well as the coupling term between EHSA and EMA can be estimated and compensated by using the extended state of the proposed LESO. Finally, comparative simulation results indicate that the proposed LESO-based motion synchronization controller could reduce significant force fighting between EHSA and EMA.

Linear Extended State Observer-Based Motion Synchronization Control for Hybrid Actuation System of More Electric Aircraft

PubMed Central

Liao, Rui; Shi, Cun; Wang, Shaoping

2017-01-01

Moving towards the more electric aircraft (MEA), a hybrid actuator configuration provides an opportunity to introduce electromechanical actuator (EMA) into primary flight control. In the hybrid actuation system (HAS), an electro-hydraulic servo actuator (EHSA) and an EMA operate on the same control surface. In order to solve force fighting problem in HAS, this paper proposes a novel linear extended state observer (LESO)-based motion synchronization control method. To cope with the problem of unavailability of the state signals required by the motion synchronization controller, LESO is designed for EHSA and EMA to observe the state variables. Based on the observed states of LESO, motion synchronization controllers could enable EHSA and EMA to simultaneously track the desired motion trajectories. Additionally, nonlinearities, uncertainties and unknown disturbances as well as the coupling term between EHSA and EMA can be estimated and compensated by using the extended state of the proposed LESO. Finally, comparative simulation results indicate that the proposed LESO-based motion synchronization controller could reduce significant force fighting between EHSA and EMA. PMID:29068392

Differential Flatness and Cooperative Tracking in the Lorenz System

NASA Technical Reports Server (NTRS)

Crespo, Luis G.

2002-01-01

In this paper the control of the Lorenz system for both stabilization and tracking problems is studied via feedback linearization and differential flatness. By using the Rayleigh number as the control, only variable physically tunable, a barrier in the controllability of the system is incidentally imposed. This is reflected in the appearance of a singularity in the state transformation. Composite controllers that overcome this difficulty are designed and evaluated. The transition through the manifold defined by such a singularity is achieved by inducing a chaotic response within a boundary layer that contains it. Outside this region, a conventional feedback nonlinear control is applied. In this fashion, the authority of the control is enlarged to the whole. state space and the need for high control efforts is mitigated. In addition, the differential parametrization of the problem is used to track nonlinear functions of one state variable (single tracking) as well as several state variables (cooperative tracking). Control tasks that lead to integrable and non-integrable differential equations for the nominal flat output in steady-state are considered. In particular, a novel numerical strategy to deal with the non-integrable case is proposed. Numerical results validate very well the control design.

Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations

NASA Astrophysics Data System (ADS)

Wu, Hao; Nüske, Feliks; Paul, Fabian; Klus, Stefan; Koltai, Péter; Noé, Frank

2017-04-01

Markov state models (MSMs) and master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variational approach conformation dynamics/molecular kinetics (VAC) and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters. While it is known how to estimate MSMs from trajectories whose starting points are not sampled from an equilibrium ensemble, this has not yet been the case for TICA and the VAC. Previous estimates from short trajectories have been strongly biased and thus not variationally optimal. Here, we employ the Koopman operator theory and the ideas from dynamic mode decomposition to extend the VAC and TICA to non-equilibrium data. The main insight is that the VAC and TICA provide a coefficient matrix that we call Koopman model, as it approximates the underlying dynamical (Koopman) operator in conjunction with the basis set used. This Koopman model can be used to compute a stationary vector to reweight the data to equilibrium. From such a Koopman-reweighted sample, equilibrium expectation values and variationally optimal reversible Koopman models can be constructed even with short simulations. The Koopman model can be used to propagate densities, and its eigenvalue decomposition provides estimates of relaxation time scales and slow collective variables for dimension reduction. Koopman models are generalizations of Markov state models, TICA, and the linear VAC and allow molecular kinetics to be described without a cluster discretization.

Constitutive relations describing creep deformation for multi-axial time-dependent stress states

NASA Astrophysics Data System (ADS)

McCartney, L. N.

1981-02-01

A THEORY of primary and secondary creep deformation in metals is presented, which is based upon the concept of tensor internal state variables and the principles of continuum mechanics and thermodynamics. The theory is able to account for both multi-axial and time-dependent stress and strain states. The wellknown concepts of elastic, anelastic and plastic strains follow naturally from the theory. Homogeneous stress states are considered in detail and a simplified theory is derived by linearizing with respect to the internal state variables. It is demonstrated that the model can be developed in such a way that multi-axial constant-stress creep data can be presented as a single relationship between an equivalent stress and an equivalent strain. It is shown how the theory may be used to describe the multi-axial deformation of metals which are subjected to constant stress states. The multi-axial strain response to a general cyclic stress state is calculated. For uni-axial stress states, square-wave loading and a thermal fatigue stress cycle are analysed.

Properties of Zero-Free Transfer Function Matrices

NASA Astrophysics Data System (ADS)

D. O. Anderson, Brian; Deistler, Manfred

Transfer functions of linear, time-invariant finite-dimensional systems with more outputs than inputs, as arise in factor analysis (for example in econometrics), have, for state-variable descriptions with generic entries in the relevant matrices, no finite zeros. This paper gives a number of characterizations of such systems (and indeed square discrete-time systems with no zeros), using state-variable, impulse response, and matrix-fraction descriptions. Key properties include the ability to recover the input values at any time from a bounded interval of output values, without any knowledge of an initial state, and an ability to verify the no-zero property in terms of a property of the impulse response coefficient matrices. Results are particularized to cases where the transfer function matrix in question may or may not have a zero at infinity or a zero at zero.

Female Literacy Rate is a Better Predictor of Birth Rate and Infant Mortality Rate in India

PubMed Central

Saurabh, Suman; Sarkar, Sonali; Pandey, Dhruv K.

2013-01-01

Background: Educated women are known to take informed reproductive and healthcare decisions. These result in population stabilization and better infant care reflected by lower birth rates and infant mortality rates (IMRs), respectively. Materials and Methods: Our objective was to study the relationship of male and female literacy rates with crude birth rates (CBRs) and IMRs of the states and union territories (UTs) of India. The data were analyzed using linear regression. CBR and IMR were taken as the dependent variables; while the overall literacy rates, male, and female literacy rates were the independent variables. Results: CBRs were inversely related to literacy rates (slope parameter = −0.402, P < 0.001). On multiple linear regression with male and female literacy rates, a significant inverse relationship emerged between female literacy rate and CBR (slope = −0.363, P < 0.001), while male literacy rate was not significantly related to CBR (P = 0.674). IMR of the states were also inversely related to their literacy rates (slope = −1.254, P < 0.001). Multiple linear regression revealed a significant inverse relationship between IMR and female literacy (slope = −0.816, P = 0.031), whereas male literacy rate was not significantly related (P = 0.630). Conclusion: Female literacy is relatively highly important for both population stabilization and better infant health. PMID:26664840

Temperature and field-dependent transport measurements in continuously tunable tantalum oxide memristors expose the dominant state variable

NASA Astrophysics Data System (ADS)

Graves, Catherine E.; Dávila, Noraica; Merced-Grafals, Emmanuelle J.; Lam, Si-Ty; Strachan, John Paul; Williams, R. Stanley

2017-03-01

Applications of memristor devices are quickly moving beyond computer memory to areas of analog and neuromorphic computation. These applications require the design of devices with different characteristics from binary memory, such as a large tunable range of conductance. A complete understanding of the conduction mechanisms and their corresponding state variable(s) is crucial for optimizing performance and designs in these applications. Here we present measurements of low bias I-V characteristics of 6 states in a Ta/ tantalum-oxide (TaOx)/Pt memristor spanning over 2 orders of magnitude in conductance and temperatures from 100 K to 500 K. Our measurements show that the 300 K device conduction is dominated by a temperature-insensitive current that varies with non-volatile memristor state, with an additional leakage contribution from a thermally-activated current channel that is nearly independent of the memristor state. We interpret these results with a parallel conduction model of Mott hopping and Schottky emission channels, fitting the voltage and temperature dependent experimental data for all memristor states with only two free parameters. The memristor conductance is linearly correlated with N, the density of electrons near EF participating in the Mott hopping conduction, revealing N to be the dominant state variable for low bias conduction in this system. Finally, we show that the Mott hopping sites can be ascribed to oxygen vacancies, where the local oxygen vacancy density responsible for critical hopping pathways controls the memristor conductance.

Controls/CFD Interdisciplinary Research Software Generates Low-Order Linear Models for Control Design From Steady-State CFD Results

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.

1997-01-01

The NASA Lewis Research Center is developing analytical methods and software tools to create a bridge between the controls and computational fluid dynamics (CFD) disciplines. Traditionally, control design engineers have used coarse nonlinear simulations to generate information for the design of new propulsion system controls. However, such traditional methods are not adequate for modeling the propulsion systems of complex, high-speed vehicles like the High Speed Civil Transport. To properly model the relevant flow physics of high-speed propulsion systems, one must use simulations based on CFD methods. Such CFD simulations have become useful tools for engineers that are designing propulsion system components. The analysis techniques and software being developed as part of this effort are an attempt to evolve CFD into a useful tool for control design as well. One major aspect of this research is the generation of linear models from steady-state CFD results. CFD simulations, often used during the design of high-speed inlets, yield high resolution operating point data. Under a NASA grant, the University of Akron has developed analytical techniques and software tools that use these data to generate linear models for control design. The resulting linear models have the same number of states as the original CFD simulation, so they are still very large and computationally cumbersome. Model reduction techniques have been successfully applied to reduce these large linear models by several orders of magnitude without significantly changing the dynamic response. The result is an accurate, easy to use, low-order linear model that takes less time to generate than those generated by traditional means. The development of methods for generating low-order linear models from steady-state CFD is most complete at the one-dimensional level, where software is available to generate models with different kinds of input and output variables. One-dimensional methods have been extended somewhat so that linear models can also be generated from two- and three-dimensional steady-state results. Standard techniques are adequate for reducing the order of one-dimensional CFD-based linear models. However, reduction of linear models based on two- and three-dimensional CFD results is complicated by very sparse, ill-conditioned matrices. Some novel approaches are being investigated to solve this problem.

Quantitative Tomography for Continuous Variable Quantum Systems

NASA Astrophysics Data System (ADS)

Landon-Cardinal, Olivier; Govia, Luke C. G.; Clerk, Aashish A.

2018-03-01

We present a continuous variable tomography scheme that reconstructs the Husimi Q function (Wigner function) by Lagrange interpolation, using measurements of the Q function (Wigner function) at the Padua points, conjectured to be optimal sampling points for two dimensional reconstruction. Our approach drastically reduces the number of measurements required compared to using equidistant points on a regular grid, although reanalysis of such experiments is possible. The reconstruction algorithm produces a reconstructed function with exponentially decreasing error and quasilinear runtime in the number of Padua points. Moreover, using the interpolating polynomial of the Q function, we present a technique to directly estimate the density matrix elements of the continuous variable state, with only a linear propagation of input measurement error. Furthermore, we derive a state-independent analytical bound on this error, such that our estimate of the density matrix is accompanied by a measure of its uncertainty.

Nonlinear vs. linear biasing in Trp-cage folding simulations

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

Spiwok, Vojtěch, E-mail: spiwokv@vscht.cz; Oborský, Pavel; Králová, Blanka

2015-03-21

Biased simulations have great potential for the study of slow processes, including protein folding. Atomic motions in molecules are nonlinear, which suggests that simulations with enhanced sampling of collective motions traced by nonlinear dimensionality reduction methods may perform better than linear ones. In this study, we compare an unbiased folding simulation of the Trp-cage miniprotein with metadynamics simulations using both linear (principle component analysis) and nonlinear (Isomap) low dimensional embeddings as collective variables. Folding of the mini-protein was successfully simulated in 200 ns simulation with linear biasing and non-linear motion biasing. The folded state was correctly predicted as the free energymore » minimum in both simulations. We found that the advantage of linear motion biasing is that it can sample a larger conformational space, whereas the advantage of nonlinear motion biasing lies in slightly better resolution of the resulting free energy surface. In terms of sampling efficiency, both methods are comparable.« less

Embedding of multidimensional time-dependent observations.

PubMed

Barnard, J P; Aldrich, C; Gerber, M

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Embedding of multidimensional time-dependent observations

NASA Astrophysics Data System (ADS)

Barnard, Jakobus P.; Aldrich, Chris; Gerber, Marius

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Analysis of Student Performance in Large-Enrollment Life Science Courses

ERIC Educational Resources Information Center

Creech, Leah Renee; Sweeder, Ryan D.

2012-01-01

This study examined the historical performance of students at Michigan State University in 12 life sciences courses over 13 yr to find variables impacting student success. Hierarchical linear modeling predicted 25.0-62.8% of the variance in students' grades in the courses analyzed. The primary predictor of a student's course grade was his or her…

Interresponse Time Structures in Variable-Ratio and Variable-Interval Schedules

ERIC Educational Resources Information Center

Bowers, Matthew T.; Hill, Jade; Palya, William L.

2008-01-01

The interresponse-time structures of pigeon key pecking were examined under variable-ratio, variable-interval, and variable-interval plus linear feedback schedules. Whereas the variable-ratio and variable-interval plus linear feedback schedules generally resulted in a distinct group of short interresponse times and a broad distribution of longer…

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Continuous-variable quantum-key-distribution protocols with a non-Gaussian modulation

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

Leverrier, Anthony; Grangier, Philippe; Laboratoire Charles Fabry, Institut d'Optique, CNRS, Univ. Paris-Sud, Campus Polytechnique, RD 128, F-91127 Palaiseau Cedex

2011-04-15

In this paper, we consider continuous-variable quantum-key-distribution (QKD) protocols which use non-Gaussian modulations. These specific modulation schemes are compatible with very efficient error-correction procedures, hence allowing the protocols to outperform previous protocols in terms of achievable range. In their simplest implementation, these protocols are secure for any linear quantum channels (hence against Gaussian attacks). We also show how the use of decoy states makes the protocols secure against arbitrary collective attacks, which implies their unconditional security in the asymptotic limit.

The Performance of A Sampled Data Delay Lock Loop Implemented with a Kalman Loop Filter.

DTIC Science & Technology

1980-01-01

que for analysis is computer simulation. Other techniques include state variable techniques and z-transform methods. Since the Kalman filter is linear...LOGIC NOT SHOWN Figure 2. Block diagram of the sampled data delay lock loop (SDDLL) Es A/ A 3/A/ Figure 3. Sampled error voltage ( Es ) as a function of...from a sum of two components. The first component is the previous filtered es - timate advanced one step forward by the state transition matrix. The 8

Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States

DTIC Science & Technology

2017-04-15

50 0 50 Singular Values Frequency (rad/s) S in g u la r V a lu e s ( d B ) controller . The non -output variables can be estimated by reliable linear...Contract # N00014-14-C-0004 Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States Progress Report...recovery of a VTOL UAV. There is a clear need for additional levels of stability and control augmentation and, ultimately, fully autonomous landing

Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

ERIC Educational Resources Information Center

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

2009-01-01

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

Dynamic Latent Trait Models with Mixed Hidden Markov Structure for Mixed Longitudinal Outcomes.

PubMed

Zhang, Yue; Berhane, Kiros

2016-01-01

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMM). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study (CHS) to jointly model questionnaire based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.

Linear and nonlinear pattern selection in Rayleigh-Benard stability problems

NASA Technical Reports Server (NTRS)

Davis, Sanford S.

1993-01-01

A new algorithm is introduced to compute finite-amplitude states using primitive variables for Rayleigh-Benard convection on relatively coarse meshes. The algorithm is based on a finite-difference matrix-splitting approach that separates all physical and dimensional effects into one-dimensional subsets. The nonlinear pattern selection process for steady convection in an air-filled square cavity with insulated side walls is investigated for Rayleigh numbers up to 20,000. The internalization of disturbances that evolve into coherent patterns is investigated and transient solutions from linear perturbation theory are compared with and contrasted to the full numerical simulations.

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

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

PubMed Central

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

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

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

PubMed

Rosenblatt, Marcus; Timmer, Jens; Kaschek, Daniel

2016-01-01

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

Linear and Nonlinear Dynamics of Heart Rate Variability are Correlated with Purpose in Life and Degree of Optimism in Anxiety Disorder Patients.

PubMed

Oh, Jihoon; Chae, Jeong-Ho

2018-04-01

Although heart rate variability (HRV) may be a crucial marker of mental health, how it is related to positive psychological factors (i.e. attitude to life and positive thinking) is largely unknown. Here we investigated the correlation of HRV linear and nonlinear dynamics with psychological scales that measured degree of optimism and happiness in patients with anxiety disorders. Results showed that low- to high-frequency HRV ratio (LF/HF) was increased and the HRV HF parameter was decreased in subjects who were more optimistic and who felt happier in daily living. Nonlinear analysis also showed that HRV dispersion and regulation were significantly correlated with the subjects' optimism and purpose in life. Our findings showed that HRV properties might be related to degree of optimistic perspectives on life and suggests that HRV markers of autonomic nervous system function could reflect positive human mind states.

An effective pseudospectral method for constraint dynamic optimisation problems with characteristic times

NASA Astrophysics Data System (ADS)

Xiao, Long; Liu, Xinggao; Ma, Liang; Zhang, Zeyin

2018-03-01

Dynamic optimisation problem with characteristic times, widely existing in many areas, is one of the frontiers and hotspots of dynamic optimisation researches. This paper considers a class of dynamic optimisation problems with constraints that depend on the interior points either fixed or variable, where a novel direct pseudospectral method using Legendre-Gauss (LG) collocation points for solving these problems is presented. The formula for the state at the terminal time of each subdomain is derived, which results in a linear combination of the state at the LG points in the subdomains so as to avoid the complex nonlinear integral. The sensitivities of the state at the collocation points with respect to the variable characteristic times are derived to improve the efficiency of the method. Three well-known characteristic time dynamic optimisation problems are solved and compared in detail among the reported literature methods. The research results show the effectiveness of the proposed method.

State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems

NASA Astrophysics Data System (ADS)

Auger-Méthé, Marie; Field, Chris; Albertsen, Christoffer M.; Derocher, Andrew E.; Lewis, Mark A.; Jonsen, Ian D.; Mills Flemming, Joanna

2016-05-01

State-space models (SSMs) are increasingly used in ecology to model time-series such as animal movement paths and population dynamics. This type of hierarchical model is often structured to account for two levels of variability: biological stochasticity and measurement error. SSMs are flexible. They can model linear and nonlinear processes using a variety of statistical distributions. Recent ecological SSMs are often complex, with a large number of parameters to estimate. Through a simulation study, we show that even simple linear Gaussian SSMs can suffer from parameter- and state-estimation problems. We demonstrate that these problems occur primarily when measurement error is larger than biological stochasticity, the condition that often drives ecologists to use SSMs. Using an animal movement example, we show how these estimation problems can affect ecological inference. Biased parameter estimates of a SSM describing the movement of polar bears (Ursus maritimus) result in overestimating their energy expenditure. We suggest potential solutions, but show that it often remains difficult to estimate parameters. While SSMs are powerful tools, they can give misleading results and we urge ecologists to assess whether the parameters can be estimated accurately before drawing ecological conclusions from their results.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient ( r ). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r 2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation ( y = a + bx ), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous.

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson's correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman's rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P < 0.05. A 95% confidence interval of the correlation coefficient can also be calculated for an idea of the correlation in the population. The value r2 denotes the proportion of the variability of the dependent variable y that can be attributed to its linear relation with the independent variable x and is called the coefficient of determination. Linear regression is a technique that attempts to link two correlated variables x and y in the form of a mathematical equation (y = a + bx), such that given the value of one variable the other may be predicted. In general, the method of least squares is applied to obtain the equation of the regression line. Correlation and linear regression analysis are based on certain assumptions pertaining to the data sets. If these assumptions are not met, misleading conclusions may be drawn. The first assumption is that of linear relationship between the two variables. A scatter plot is essential before embarking on any correlation-regression analysis to show that this is indeed the case. Outliers or clustering within data sets can distort the correlation coefficient value. Finally, it is vital to remember that though strong correlation can be a pointer toward causation, the two are not synonymous. PMID:27904175

Daily emotional states as reported by children and adolescents.

PubMed

Larson, R; Lampman-Petraitis, C

1989-10-01

Hour-to-hour emotional states reported by children, ages 9-15, were examined in order to evaluate the hypothesis that the onset of adolescence is associated with increased emotional variability. These youths carried electronic pagers for 1 week and filled out reports on their emotional states in response to signals received at random times. To evaluate possible age-related response sets, a subset of children was asked to use the same scales to rate the emotions shown in drawings of 6 faces. The expected relation between daily emotional variability and age was not found among the boys and was small among the girls. There was, however, a linear relation between age and average mood states, with the older participants reporting more dysphoric average states, especially more mildly negative states. An absence of age difference in the ratings of the faces indicated that this relation could not be attributed to age differences in response set. Thus, these findings provide little support for the hypothesis that the onset of adolescence is associated with increased emotionality but indicate significant alterations in everyday experience associated with this age period.

Income inequality and child maltreatment in the United States.

PubMed

Eckenrode, John; Smith, Elliott G; McCarthy, Margaret E; Dineen, Michael

2014-03-01

To examine the relation between county-level income inequality and rates of child maltreatment. Data on substantiated reports of child abuse and neglect from 2005 to 2009 were obtained from the National Child Abuse and Neglect Data System. County-level data on income inequality and children in poverty were obtained from the American Community Survey. Data for additional control variables were obtained from the American Community Survey and the Health Resources and Services Administration Area Resource File. The Gini coefficient was used as the measure of income inequality. Generalized additive models were estimated to explore linear and nonlinear relations among income inequality, poverty, and child maltreatment. In all models, state was included as a fixed effect to control for state-level differences in victim rates. Considerable variation in income inequality and child maltreatment rates was found across the 3142 US counties. Income inequality, as well as child poverty rate, was positively and significantly correlated with child maltreatment rates at the county level. Controlling for child poverty, demographic and economic control variables, and state-level variation in maltreatment rates, there was a significant linear effect of inequality on child maltreatment rates (P < .0001). This effect was stronger for counties with moderate to high levels of child poverty. Higher income inequality across US counties was significantly associated with higher county-level rates of child maltreatment. The findings contribute to the growing literature linking greater income inequality to a range of poor health and well-being outcomes in infants and children.

Early prediction of extreme stratospheric polar vortex states based on causal precursors

NASA Astrophysics Data System (ADS)

Kretschmer, Marlene; Runge, Jakob; Coumou, Dim

2017-08-01

Variability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low-frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response-guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r2 = 0.58), and our scheme correctly predicts 58% (46%) of extremely weak SPV states for lead times of 1-15 (16-30) days with false-alarm rates of only approximately 5%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long-lead predictions.

Complete convergence of randomly weighted END sequences and its application.

PubMed

Li, Penghua; Li, Xiaoqin; Wu, Kehan

2017-01-01

We investigate the complete convergence of partial sums of randomly weighted extended negatively dependent (END) random variables. Some results of complete moment convergence, complete convergence and the strong law of large numbers for this dependent structure are obtained. As an application, we study the convergence of the state observers of linear-time-invariant systems. Our results extend the corresponding earlier ones.

Mapping wildland fuels and forest structure for land management: a comparison of nearest neighbor imputation and other methods

Treesearch

Kenneth B. Pierce; Janet L. Ohmann; Michael C. Wimberly; Matthew J. Gregory; Jeremy S. Fried

2009-01-01

Land managers need consistent information about the geographic distribution of wildland fuels and forest structure over large areas to evaluate fire risk and plan fuel treatments. We compared spatial predictions for 12 fuel and forest structure variables across three regions in the western United States using gradient nearest neighbor (GNN) imputation, linear models (...

Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves

NASA Astrophysics Data System (ADS)

Shouval, Harel Z.; Agarwal, Animesh; Gavornik, Jeffrey P.

2013-04-01

Weber’s law, first characterized in the 19th century, states that errors estimating the magnitude of perceptual stimuli scale linearly with stimulus intensity. This linear relationship is found in most sensory modalities, generalizes to temporal interval estimation, and even applies to some abstract variables. Despite its generality and long experimental history, the neural basis of Weber’s law remains unknown. This work presents a simple theory explaining the conditions under which Weber’s law can result from neural variability and predicts that the tuning curves of neural populations which adhere to Weber’s law will have a log-power form with parameters that depend on spike-count statistics. The prevalence of Weber’s law suggests that it might be optimal in some sense. We examine this possibility, using variational calculus, and show that Weber’s law is optimal only when observed real-world variables exhibit power-law statistics with a specific exponent. Our theory explains how physiology gives rise to the behaviorally characterized Weber’s law and may represent a general governing principle relating perception to neural activity.

Modeling demand for public transit services in rural areas

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

Attaluri, P.; Seneviratne, P.N.; Javid, M.

1997-05-01

Accurate estimates of demand are critical for planning, designing, and operating public transit systems. Previous research has demonstrated that the expected demand in rural areas is a function of both demographic and transit system variables. Numerous models have been proposed to describe the relationship between the aforementioned variables. However, most of them are site specific and their validity over time and space is not reported or perhaps has not been tested. Moreover, input variables in some cases are extremely difficult to quantify. In this article, the estimation of demand using the generalized linear modeling technique is discussed. Two separate models,more » one for fixed-route and another for demand-responsive services, are presented. These models, calibrated with data from systems in nine different states, are used to demonstrate the appropriateness and validity of generalized linear models compared to the regression models. They explain over 70% of the variation in expected demand for fixed-route services and 60% of the variation in expected demand for demand-responsive services. It was found that the models are spatially transferable and that data for calibration are easily obtainable.« less

Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

NASA Astrophysics Data System (ADS)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

Findings regarding the relationships between sociodemographic, psychological, comorbidity factors, and functional status, in geriatric inpatients.

PubMed

Capisizu, Ana; Aurelian, Sorina; Zamfirescu, Andreea; Omer, Ioana; Haras, Monica; Ciobotaru, Camelia; Onose, Liliana; Spircu, Tiberiu; Onose, Gelu

2015-01-01

To assess the impact of socio-demographic and comorbidity factors, and quantified depressive symptoms on disability in inpatients. Observational cross-sectional study, including a number of 80 elderly (16 men, 64 women; mean age 72.48 years; standard deviation 9.95 years) admitted in the Geriatrics Clinic of "St. Luca" Hospital, Bucharest, between May-July, 2012. We used the Functional Independence Measure, Geriatric Depression Scale and an array of socio-demographic and poly-pathology parameters. Statistical analysis included Wilcoxon and Kruskal-Wallis tests for ordinal variables, linear bivariate correlations, general linear model analysis, ANOVA. FIM scores were negatively correlated with age (R=-0.301; 95%CI=-0.439 -0.163; p=0.007); GDS scores had a statistically significant negative correlation (R=-0.322; 95% CI=-0.324 -0.052; p=0.004) with FIM scores. A general linear model, including other variables (gender, age, provenance, matrimonial state, living conditions, education, respectively number of chronic illnesses) as factors, found living conditions (p=0.027) and the combination of matrimonial state and gender (p=0.004) to significantly influence FIM scores. ANOVA showed significant differences in FIM scores stratified by the number of chronic diseases (p=0.035). Our study objectified the negative impact of depression on functional status; interestingly, education had no influence on FIM scores; living conditions and a combination of matrimonial state and gender had an important impact: patients with living spouses showed better functional scores than divorced/widowers; the number of chronic diseases also affected the FIM scores: lower in patients with significant polypathology. These findings should be considered when designing geriatric rehabilitation programs, especially for home--including skilled--cares.

The evolution of the disc variability along the hard state of the black hole transient GX 339-4

NASA Astrophysics Data System (ADS)

De Marco, B.; Ponti, G.; Muñoz-Darias, T.; Nandra, K.

2015-12-01

We report on the analysis of hard-state power spectral density function (PSD) of GX 339-4 down to the soft X-ray band, where the disc significantly contributes to the total emission. At any luminosity probed, the disc in the hard state is intrinsically more variable than in the soft state. However, the fast decrease of disc variability as a function of luminosity, combined with the increase of disc intensity, causes a net drop of fractional variability at high luminosities and low energies, which reminds the well-known behaviour of disc-dominated energy bands in the soft state. The peak frequency of the high-frequency Lorentzian (likely corresponding to the high-frequency break seen in active galactic nuclei, AGN) scales with luminosity, but we do not find evidence for a linear scaling. In addition, we observe that this characteristic frequency is energy dependent. We find that the normalization of the PSD at the peak of the high-frequency Lorentzian decreases with luminosity at all energies, though in the soft band this trend is steeper. Together with the frequency shift, this yields quasi-constant high-frequency (5-20 Hz) fractional rms at high energies, with less than 10 per cent scatter. This reinforces previous claims suggesting that the high-frequency PSD solely scales with black hole mass. On the other hand, this constancy breaks down in the soft band (where the scatter increases to ˜30 per cent). This is a consequence of the additional contribution from the disc component, and resembles the behaviour of optical variability in AGN.

Characterizing the γ-ray long-term variability of PKS 2155-304 with H.E.S.S. and Fermi-LAT

NASA Astrophysics Data System (ADS)

H.E.S.S. Collaboration; Abdalla, H.; Abramowski, A.; Aharonian, F.; Ait Benkhali, F.; Akhperjanian, A. G.; Andersson, T.; Angüner, E. O.; Arrieta, M.; Aubert, P.; Backes, M.; Balzer, A.; Barnard, M.; Becherini, Y.; Becker Tjus, J.; Berge, D.; Bernhard, S.; Bernlöhr, K.; Blackwell, R.; Böttcher, M.; Boisson, C.; Bolmont, J.; Bordas, P.; Bregeon, J.; Brun, F.; Brun, P.; Bryan, M.; Bulik, T.; Capasso, M.; Carr, J.; Casanova, S.; Cerruti, M.; Chakraborty, N.; Chalme-Calvet, R.; Chaves, R. C. G.; Chen, A.; Chevalier, J.; Chrétien, M.; Colafrancesco, S.; Cologna, G.; Condon, B.; Conrad, J.; Cui, Y.; Davids, I. D.; Decock, J.; Degrange, B.; Deil, C.; Devin, J.; deWilt, P.; Dirson, L.; Djannati-Ataï, A.; Domainko, W.; Donath, A.; Drury, L. O.'C.; Dubus, G.; Dutson, K.; Dyks, J.; Edwards, T.; Egberts, K.; Eger, P.; Ernenwein, J.-P.; Eschbach, S.; Farnier, C.; Fegan, S.; Fernandes, M. V.; Fiasson, A.; Fontaine, G.; Förster, A.; Funk, S.; Füßling, M.; Gabici, S.; Gajdus, M.; Gallant, Y. A.; Garrigoux, T.; Giavitto, G.; Giebels, B.; Glicenstein, J. F.; Gottschall, D.; Goyal, A.; Grondin, M.-H.; Hadasch, D.; Hahn, J.; Haupt, M.; Hawkes, J.; Heinzelmann, G.; Henri, G.; Hermann, G.; Hervet, O.; Hinton, J. A.; Hofmann, W.; Hoischen, C.; Holler, M.; Horns, D.; Ivascenko, A.; Jacholkowska, A.; Jamrozy, M.; Janiak, M.; Jankowsky, D.; Jankowsky, F.; Jingo, M.; Jogler, T.; Jouvin, L.; Jung-Richardt, I.; Kastendieck, M. A.; Katarzyński, K.; Katz, U.; Kerszberg, D.; Khélifi, B.; Kieffer, M.; King, J.; Klepser, S.; Klochkov, D.; Kluźniak, W.; Kolitzus, D.; Komin, Nu.; Kosack, K.; Krakau, S.; Kraus, M.; Krayzel, F.; Krüger, P. P.; Laffon, H.; Lamanna, G.; Lau, J.; Lees, J.-P.; Lefaucheur, J.; Lefranc, V.; Lemière, A.; Lemoine-Goumard, M.; Lenain, J.-P.; Leser, E.; Lohse, T.; Lorentz, M.; Liu, R.; López-Coto, R.; Lypova, I.; Marandon, V.; Marcowith, A.; Mariaud, C.; Marx, R.; Maurin, G.; Maxted, N.; Mayer, M.; Meintjes, P. J.; Meyer, M.; Mitchell, A. M. W.; Moderski, R.; Mohamed, M.; Mohrmann, L.; Morå, K.; Moulin, E.; Murach, T.; de Naurois, M.; Niederwanger, F.; Niemiec, J.; Oakes, L.; O'Brien, P.; Odaka, H.; Öttl, S.; Ohm, S.; Ostrowski, M.; Oya, I.; Padovani, M.; Panter, M.; Parsons, R. D.; Pekeur, N. W.; Pelletier, G.; Perennes, C.; Petrucci, P.-O.; Peyaud, B.; Piel, Q.; Pita, S.; Poon, H.; Prokhorov, D.; Prokoph, H.; Pühlhofer, G.; Punch, M.; Quirrenbach, A.; Raab, S.; Reimer, A.; Reimer, O.; Renaud, M.; de los Reyes, R.; Rieger, F.; Romoli, C.; Rosier-Lees, S.; Rowell, G.; Rudak, B.; Rulten, C. B.; Sahakian, V.; Salek, D.; Sanchez, D. A.; Santangelo, A.; Sasaki, M.; Schlickeiser, R.; Schüssler, F.; Schulz, A.; Schwanke, U.; Schwemmer, S.; Settimo, M.; Seyffert, A. S.; Shafi, N.; Shilon, I.; Simoni, R.; Sol, H.; Spanier, F.; Spengler, G.; Spies, F.; Stawarz, Ł.; Steenkamp, R.; Stegmann, C.; Stinzing, F.; Stycz, K.; Sushch, I.; Tavernet, J.-P.; Tavernier, T.; Taylor, A. M.; Terrier, R.; Tibaldo, L.; Tiziani, D.; Tluczykont, M.; Trichard, C.; Tuffs, R.; Uchiyama, Y.; van der Walt, D. J.; van Eldik, C.; van Rensburg, C.; van Soelen, B.; Vasileiadis, G.; Veh, J.; Venter, C.; Viana, A.; Vincent, P.; Vink, J.; Voisin, F.; Völk, H. J.; Vuillaume, T.; Wadiasingh, Z.; Wagner, S. J.; Wagner, P.; Wagner, R. M.; White, R.; Wierzcholska, A.; Willmann, P.; Wörnlein, A.; Wouters, D.; Yang, R.; Zabalza, V.; Zaborov, D.; Zacharias, M.; Zdziarski, A. A.; Zech, A.; Zefi, F.; Ziegler, A.; Żywucka, N.

2017-02-01

Studying the temporal variability of BL Lac objects at the highest energies provides unique insights into the extreme physical processes occurring in relativistic jets and in the vicinity of super-massive black holes. To this end, the long-term variability of the BL Lac object PKS 2155-304 is analyzed in the high (HE, 100 MeV < E < 300 GeV) and very high energy (VHE, E > 200 GeV) γ-ray domain. Over the course of 9 yr of H.E.S.S. observations the VHE light curve in the quiescent state is consistent with a log-normal behavior. The VHE variability in this state is well described by flicker noise (power-spectral-density index ) on timescales larger than one day. An analysis of 5.5 yr of HE Fermi-LAT data gives consistent results (, on timescales larger than 10 days) compatible with the VHE findings. The HE and VHE power spectral densities show a scale invariance across the probed time ranges. A direct linear correlation between the VHE and HE fluxes could neither be excluded nor firmly established. These long-term-variability properties are discussed and compared to the red noise behavior (β 2) seen on shorter timescales during VHE-flaring states. The difference in power spectral noise behavior at VHE energies during quiescent and flaring states provides evidence that these states are influenced by different physical processes, while the compatibility of the HE and VHE long-term results is suggestive of a common physical link as it might be introduced by an underlying jet-disk connection.

The neural basis of attaining conscious awareness of sad mood.

PubMed

Smith, Ryan; Braden, B Blair; Chen, Kewei; Ponce, Francisco A; Lane, Richard D; Baxter, Leslie C

2015-09-01

The neural processes associated with becoming aware of sad mood are not fully understood. We examined the dynamic process of becoming aware of sad mood and recovery from sad mood. Sixteen healthy subjects underwent fMRI while participating in a sadness induction task designed to allow for variable mood induction times. Individualized regressors linearly modeled the time periods during the attainment of self-reported sad and baseline "neutral" mood states, and the validity of the linearity assumption was further tested using independent component analysis. During sadness induction the dorsomedial and ventrolateral prefrontal cortices, and anterior insula exhibited a linear increase in the blood oxygen level-dependent (BOLD) signal until subjects became aware of a sad mood and then a subsequent linear decrease as subjects transitioned from sadness back to the non-sadness baseline condition. These findings extend understanding of the neural basis of conscious emotional experience.

Expanding the occupational health methodology: A concatenated artificial neural network approach to model the burnout process in Chinese nurses.

PubMed

Ladstätter, Felix; Garrosa, Eva; Moreno-Jiménez, Bernardo; Ponsoda, Vicente; Reales Aviles, José Manuel; Dai, Junming

2016-01-01

Artificial neural networks are sophisticated modelling and prediction tools capable of extracting complex, non-linear relationships between predictor (input) and predicted (output) variables. This study explores this capacity by modelling non-linearities in the hardiness-modulated burnout process with a neural network. Specifically, two multi-layer feed-forward artificial neural networks are concatenated in an attempt to model the composite non-linear burnout process. Sensitivity analysis, a Monte Carlo-based global simulation technique, is then utilised to examine the first-order effects of the predictor variables on the burnout sub-dimensions and consequences. Results show that (1) this concatenated artificial neural network approach is feasible to model the burnout process, (2) sensitivity analysis is a prolific method to study the relative importance of predictor variables and (3) the relationships among variables involved in the development of burnout and its consequences are to different degrees non-linear. Many relationships among variables (e.g., stressors and strains) are not linear, yet researchers use linear methods such as Pearson correlation or linear regression to analyse these relationships. Artificial neural network analysis is an innovative method to analyse non-linear relationships and in combination with sensitivity analysis superior to linear methods.

Transfer of non-Gaussian quantum states of mechanical oscillator to light

NASA Astrophysics Data System (ADS)

Filip, Radim; Rakhubovsky, Andrey A.

2015-11-01

Non-Gaussian quantum states are key resources for quantum optics with continuous-variable oscillators. The non-Gaussian states can be deterministically prepared by a continuous evolution of the mechanical oscillator isolated in a nonlinear potential. We propose feasible and deterministic transfer of non-Gaussian quantum states of mechanical oscillators to a traveling light beam, using purely all-optical methods. The method relies on only basic feasible and high-quality elements of quantum optics: squeezed states of light, linear optics, homodyne detection, and electro-optical feedforward control of light. By this method, a wide range of novel non-Gaussian states of light can be produced in the future from the mechanical states of levitating particles in optical tweezers, including states necessary for the implementation of an important cubic phase gate.

Improvement of Storm Forecasts Using Gridded Bayesian Linear Regression for Northeast United States

NASA Astrophysics Data System (ADS)

Yang, J.; Astitha, M.; Schwartz, C. S.

2017-12-01

Bayesian linear regression (BLR) is a post-processing technique in which regression coefficients are derived and used to correct raw forecasts based on pairs of observation-model values. This study presents the development and application of a gridded Bayesian linear regression (GBLR) as a new post-processing technique to improve numerical weather prediction (NWP) of rain and wind storm forecasts over northeast United States. Ten controlled variables produced from ten ensemble members of the National Center for Atmospheric Research (NCAR) real-time prediction system are used for a GBLR model. In the GBLR framework, leave-one-storm-out cross-validation is utilized to study the performances of the post-processing technique in a database composed of 92 storms. To estimate the regression coefficients of the GBLR, optimization procedures that minimize the systematic and random error of predicted atmospheric variables (wind speed, precipitation, etc.) are implemented for the modeled-observed pairs of training storms. The regression coefficients calculated for meteorological stations of the National Weather Service are interpolated back to the model domain. An analysis of forecast improvements based on error reductions during the storms will demonstrate the value of GBLR approach. This presentation will also illustrate how the variances are optimized for the training partition in GBLR and discuss the verification strategy for grid points where no observations are available. The new post-processing technique is successful in improving wind speed and precipitation storm forecasts using past event-based data and has the potential to be implemented in real-time.

A non-modal analytical method to predict turbulent properties applied to the Hasegawa-Wakatani model

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

Friedman, B., E-mail: friedman11@llnl.gov; Lawrence Livermore National Laboratory, Livermore, California 94550; Carter, T. A.

2015-01-15

Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. We define such amore » non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. We test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.« less

A non-modal analytical method to predict turbulent properties applied to the Hasegawa-Wakatani model

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

Friedman, B.; Carter, T. A.

2015-01-15

Linear eigenmode analysis often fails to describe turbulence in model systems that have non-normal linear operators and thus nonorthogonal eigenmodes, which can cause fluctuations to transiently grow faster than expected from eigenmode analysis. When combined with energetically conservative nonlinear mode mixing, transient growth can lead to sustained turbulence even in the absence of eigenmode instability. Since linear operators ultimately provide the turbulent fluctuations with energy, it is useful to define a growth rate that takes into account non-modal effects, allowing for prediction of energy injection, transport levels, and possibly even turbulent onset in the subcritical regime. Here, we define suchmore » a non-modal growth rate using a relatively simple model of the statistical effect that the nonlinearities have on cross-phases and amplitude ratios of the system state variables. In particular, we model the nonlinearities as delta-function-like, periodic forces that randomize the state variables once every eddy turnover time. Furthermore, we estimate the eddy turnover time to be the inverse of the least stable eigenmode frequency or growth rate, which allows for prediction without nonlinear numerical simulation. Also, we test this procedure on the 2D and 3D Hasegawa-Wakatani model [A. Hasegawa and M. Wakatani, Phys. Rev. Lett. 50, 682 (1983)] and find that the non-modal growth rate is a good predictor of energy injection rates, especially in the strongly non-normal, fully developed turbulence regime.« less

Assessing the risk of bovine fasciolosis using linear regression analysis for the state of Rio Grande do Sul, Brazil.

PubMed

Silva, Ana Elisa Pereira; Freitas, Corina da Costa; Dutra, Luciano Vieira; Molento, Marcelo Beltrão

2016-02-15

Fasciola hepatica is the causative agent of fasciolosis, a disease that triggers a chronic inflammatory process in the liver affecting mainly ruminants and other animals including humans. In Brazil, F. hepatica occurs in larger numbers in the most Southern state of Rio Grande do Sul. The objective of this study was to estimate areas at risk using an eight-year (2002-2010) time series of climatic and environmental variables that best relate to the disease using a linear regression method to municipalities in the state of Rio Grande do Sul. The positivity index of the disease, which is the rate of infected animal per slaughtered animal, was divided into three risk classes: low, medium and high. The accuracy of the known sample classification on the confusion matrix for the low, medium and high rates produced by the estimated model presented values between 39 and 88% depending of the year. The regression analysis showed the importance of the time-based data for the construction of the model, considering the two variables of the previous year of the event (positivity index and maximum temperature). The generated data is important for epidemiological and parasite control studies mainly because F. hepatica is an infection that can last from months to years. Copyright © 2015 Elsevier B.V. All rights reserved.

Control of AUVs using differential flatness theory and the derivative-free nonlinear Kalman Filter

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Raffo, Guilerme

2015-12-01

The paper proposes nonlinear control and filtering for Autonomous Underwater Vessels (AUVs) based on differential flatness theory and on the use of the Derivative-free nonlinear Kalman Filter. First, it is shown that the 6-DOF dynamic model of the AUV is a differentially flat one. This enables its transformation into the linear canonical (Brunovsky) form and facilitates the design of a state feedback controller. A problem that has to be dealt with is the uncertainty about the parameters of the AUV's dynamic model, as well the external perturbations which affect its motion. To cope with this, it is proposed to use a disturbance observer which is based on the Derivative-free nonlinear Kalman Filter. The considered filtering method consists of the standard Kalman Filter recursion applied on the linearized model of the vessel and of an inverse transformation based on differential flatness theory, which enables to obtain estimates of the state variables of the initial nonlinear model of the vessel. The Kalman Filter-based disturbance observer performs simultaneous estimation of the non-measurable state variables of the AUV and of the perturbation terms that affect its dynamics. By estimating such disturbances, their compensation is also succeeded through suitable modification of the feedback control input. The efficiency of the proposed AUV control and estimation scheme is confirmed through simulation experiments.

Supporting Students' Understanding of Linear Equations with One Variable Using Algebra Tiles

ERIC Educational Resources Information Center

Saraswati, Sari; Putri, Ratu Ilma Indra; Somakim

2016-01-01

This research aimed to describe how algebra tiles can support students' understanding of linear equations with one variable. This article is a part of a larger research on learning design of linear equations with one variable using algebra tiles combined with balancing method. Therefore, it will merely discuss one activity focused on how students…

Silicon photonic transceiver circuit for high-speed polarization-based discrete variable quantum key distribution

DOE PAGES

Cai, Hong; Long, Christopher M.; DeRose, Christopher T.; ...

2017-01-01

We demonstrate a silicon photonic transceiver circuit for high-speed discrete variable quantum key distribution that employs a common structure for transmit and receive functions. The device is intended for use in polarization-based quantum cryptographic protocols, such as BB84. Our characterization indicates that the circuit can generate the four BB84 states (TE/TM/45°/135° linear polarizations) with >30 dB polarization extinction ratios and gigabit per second modulation speed, and is capable of decoding any polarization bases differing by 90° with high extinction ratios.

Silicon photonic transceiver circuit for high-speed polarization-based discrete variable quantum key distribution.

PubMed

Cai, Hong; Long, Christopher M; DeRose, Christopher T; Boynton, Nicholas; Urayama, Junji; Camacho, Ryan; Pomerene, Andrew; Starbuck, Andrew L; Trotter, Douglas C; Davids, Paul S; Lentine, Anthony L

2017-05-29

We demonstrate a silicon photonic transceiver circuit for high-speed discrete variable quantum key distribution that employs a common structure for transmit and receive functions. The device is intended for use in polarization-based quantum cryptographic protocols, such as BB84. Our characterization indicates that the circuit can generate the four BB84 states (TE/TM/45°/135° linear polarizations) with >30 dB polarization extinction ratios and gigabit per second modulation speed, and is capable of decoding any polarization bases differing by 90° with high extinction ratios.

Silicon photonic transceiver circuit for high-speed polarization-based discrete variable quantum key distribution

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

Cai, Hong; Long, Christopher M.; DeRose, Christopher T.

We demonstrate a silicon photonic transceiver circuit for high-speed discrete variable quantum key distribution that employs a common structure for transmit and receive functions. The device is intended for use in polarization-based quantum cryptographic protocols, such as BB84. Our characterization indicates that the circuit can generate the four BB84 states (TE/TM/45°/135° linear polarizations) with >30 dB polarization extinction ratios and gigabit per second modulation speed, and is capable of decoding any polarization bases differing by 90° with high extinction ratios.

Quantum square-well with logarithmic central spike

NASA Astrophysics Data System (ADS)

Znojil, Miloslav; Semorádová, Iveta

2018-01-01

Singular repulsive barrier V (x) = -gln(|x|) inside a square-well is interpreted and studied as a linear analog of the state-dependent interaction ℒeff(x) = -gln[ψ∗(x)ψ(x)] in nonlinear Schrödinger equation. In the linearized case, Rayleigh-Schrödinger perturbation theory is shown to provide a closed-form spectrum at sufficiently small g or after an amendment of the unperturbed Hamiltonian. At any spike strength g, the model remains solvable numerically, by the matching of wave functions. Analytically, the singularity is shown regularized via the change of variables x = expy which interchanges the roles of the asymptotic and central boundary conditions.

A FORTRAN program for the analysis of linear continuous and sample-data systems

NASA Technical Reports Server (NTRS)

Edwards, J. W.

1976-01-01

A FORTRAN digital computer program which performs the general analysis of linearized control systems is described. State variable techniques are used to analyze continuous, discrete, and sampled data systems. Analysis options include the calculation of system eigenvalues, transfer functions, root loci, root contours, frequency responses, power spectra, and transient responses for open- and closed-loop systems. A flexible data input format allows the user to define systems in a variety of representations. Data may be entered by inputing explicit data matrices or matrices constructed in user written subroutines, by specifying transfer function block diagrams, or by using a combination of these methods.

A recurrent neural network for solving bilevel linear programming problem.

PubMed

He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie; Huang, Junjian

2014-04-01

In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.

An analysis of hypercritical states in elastic and inelastic systems

NASA Astrophysics Data System (ADS)

Kowalczk, Maciej

The author raises a wide range of problems whose common characteristic is an analysis of hypercritical states in elastic and inelastic systems. the article consists of two basic parts. The first part primarily discusses problems of modelling hypercritical states, while the second analyzes numerical methods (so-called continuation methods) used to solve non-linear problems. The original approaches for modelling hypercritical states found in this article include the combination of plasticity theory and an energy condition for cracking, accounting for the variability and cyclical nature of the forms of fracture of a brittle material under a die, and the combination of plasticity theory and a simplified description of the phenomenon of localization along a discontinuity line. The author presents analytical solutions of three non-linear problems for systems made of elastic/brittle/plastic and elastic/ideally plastic materials. The author proceeds to discuss the analytical basics of continuation methods and analyzes the significance of the parameterization of non-linear problems, provides a method for selecting control parameters based on an analysis of the rank of a rectangular matrix of a uniform system of increment equations, and also provides a new method for selecting an equilibrium path originating from a bifurcation point. The author provides a general outline of continuation methods based on an analysis of the rank of a matrix of a corrective system of equations. The author supplements his theoretical solutions with numerical solutions of non-linear problems for rod systems and problems of the plastic disintegration of a notched rectangular plastic plate.

Decomposition of Time Scales in Linear Systems and Markovian Decision Processes.

DTIC Science & Technology

1980-11-01

this research. I, 3 iv U TABLE OF CONTENTS *Chapter Page *-1. INTRODUCTION .................................................. 1 2. EIGENSTRUCTTJRE...Components ..... o....... 16 2.4. Ordering of State Variables.. ......... ........ 20 2.5. Example - 8th Order Power System Model................ 22 3 ...results. In Chapter 3 we consider the time scale decomposition of singularly perturbed systems. For this problem (1.1) takes the form 12 + u (1.4) 2

Language Shift in the United States and Foreign-Born Older Mexican Heritage Individuals: Co-Ethnic Context for Language Resistance

ERIC Educational Resources Information Center

Siordia, Carlos; Diaz, Maria E.

2012-01-01

In this study, we investigate individual-level language shift in a population of Mexican origin Latinos/as aged 65 and up. By using data from the Hispanic Established Populations for the Epidemiologic Study of the Elderly, we investigate their English language use as the dependent variable in a hierarchical linear model. The microlevel independent…

Discrete Time McKean–Vlasov Control Problem: A Dynamic Programming Approach

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

Pham, Huyên, E-mail: pham@math.univ-paris-diderot.fr; Wei, Xiaoli, E-mail: tyswxl@gmail.com

We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal distribution as controlled state variable, and prove that dynamic programming principle holds in its general form. We apply our method for solving explicitly the mean-variance portfolio selection and the multivariate linear-quadratic McKean–Vlasov control problem.

An Analysis of Terrestrial and Aquatic Environmental Controls of Riverine Dissolved Organic Carbon in the Conterminous United States

DOE PAGES

Yang, Qichun; Zhang, Xuesong; Xu, Xingya; ...

2017-05-29

Riverine carbon cycling is an important, but insufficiently investigated component of the global carbon cycle. Analyses of environmental controls on riverine carbon cycling are critical for improved understanding of mechanisms regulating carbon processing and storage along the terrestrial-aquatic continuum. Here, we compile and analyze riverine dissolved organic carbon (DOC) concentration data from 1402 United States Geological Survey (USGS) gauge stations to examine the spatial variability and environmental controls of DOC concentrations in the United States (U.S.) surface waters. DOC concentrations exhibit high spatial variability, with an average of 6.42 ± 6.47 mg C/ L (Mean ± Standard Deviation). In general,more » high DOC concentrations occur in the Upper Mississippi River basin and the Southeastern U.S., while low concentrations are mainly distributed in the Western U.S. Single-factor analysis indicates that slope of drainage areas, wetlands, forests, percentage of first-order streams, and instream nutrients (such as nitrogen and phosphorus) pronouncedly influence DOC concentrations, but the explanatory power of each bivariate model is lower than 35%. Analyses based on the general multi-linear regression models suggest DOC concentrations are jointly impacted by multiple factors. Soil properties mainly show positive correlations with DOC concentrations; forest and shrub lands have positive correlations with DOC concentrations, but urban area and croplands demonstrate negative impacts; total instream phosphorus and dam density correlate positively with DOC concentrations. Notably, the relative importance of these environmental controls varies substantially across major U.S. water resource regions. In addition, DOC concentrations and environmental controls also show significant variability from small streams to large rivers, which may be caused by changing carbon sources and removal rates by river orders. In sum, our results reveal that general multi-linear regression analysis of twenty one terrestrial and aquatic environmental factors can partially explain (56%) the DOC concentration variation. In conclusion, this study highlights the complexity of the interactions among these environmental factors in determining DOC concentrations, thus calls for processes-based, non-linear methodologies to constrain uncertainties in riverine DOC cycling.« less

Information content of MOPITT CO profile retrievals: Temporal and geographical variability

NASA Astrophysics Data System (ADS)

Deeter, M. N.; Edwards, D. P.; Gille, J. C.; Worden, H. M.

2015-12-01

Satellite measurements of tropospheric carbon monoxide (CO) enable a wide array of applications including studies of air quality and pollution transport. The MOPITT (Measurements of Pollution in the Troposphere) instrument on the Earth Observing System Terra platform has been measuring CO concentrations globally since March 2000. As indicated by the Degrees of Freedom for Signal (DFS), the standard metric for trace-gas retrieval information content, MOPITT retrieval performance varies over a wide range. We show that both instrumental and geophysical effects yield significant geographical and temporal variability in MOPITT DFS values. Instrumental radiance uncertainties, which describe random errors (or "noise") in the calibrated radiances, vary over long time scales (e.g., months to years) and vary between the four detector elements of MOPITT's linear detector array. MOPITT retrieval performance depends on several factors including thermal contrast, fine-scale variability of surface properties, and CO loading. The relative importance of these various effects is highly variable, as demonstrated by analyses of monthly mean DFS values for the United States and the Amazon Basin. An understanding of the geographical and temporal variability of MOPITT retrieval performance is potentially valuable to data users seeking to limit the influence of the a priori through data filtering. To illustrate, it is demonstrated that calculated regional-average CO mixing ratios may be improved by excluding observations from a subset of pixels in MOPITT's linear detector array.

Correlation Between Fracture Network Properties and Stress Variability in Geological Media

NASA Astrophysics Data System (ADS)

Lei, Qinghua; Gao, Ke

2018-05-01

We quantitatively investigate the stress variability in fractured geological media under tectonic stresses. The fracture systems studied include synthetic fracture networks following power law length scaling and natural fracture patterns based on outcrop mapping. The stress field is derived from a finite-discrete element model, and its variability is analyzed using a set of mathematical formulations that honor the tensorial nature of stress data. We show that local stress perturbation, quantified by the Euclidean distance of a local stress tensor to the mean stress tensor, has a positive, linear correlation with local fracture intensity, defined as the total fracture length per unit area within a local sampling window. We also evaluate the stress dispersion of the entire stress field using the effective variance, that is, a scalar-valued measure of the overall stress variability. The results show that a well-connected fracture system under a critically stressed state exhibits strong local and global stress variabilities.

The primer vector in linear, relative-motion equations. [spacecraft trajectory optimization

NASA Technical Reports Server (NTRS)

1980-01-01

Primer vector theory is used in analyzing a set of linear, relative-motion equations - the Clohessy-Wiltshire equations - to determine the criteria and necessary conditions for an optimal, N-impulse trajectory. Since the state vector for these equations is defined in terms of a linear system of ordinary differential equations, all fundamental relations defining the solution of the state and costate equations, and the necessary conditions for optimality, can be expressed in terms of elementary functions. The analysis develops the analytical criteria for improving a solution by (1) moving any dependent or independent variable in the initial and/or final orbit, and (2) adding intermediate impulses. If these criteria are violated, the theory establishes a sufficient number of analytical equations. The subsequent satisfaction of these equations will result in the optimal position vectors and times of an N-impulse trajectory. The solution is examined for the specific boundary conditions of (1) fixed-end conditions, two-impulse, and time-open transfer; (2) an orbit-to-orbit transfer; and (3) a generalized rendezvous problem. A sequence of rendezvous problems is solved to illustrate the analysis and the computational procedure.

Wind turbine power tracking using an improved multimodel quadratic approach.

PubMed

Khezami, Nadhira; Benhadj Braiek, Naceur; Guillaud, Xavier

2010-07-01

In this paper, an improved multimodel optimal quadratic control structure for variable speed, pitch regulated wind turbines (operating at high wind speeds) is proposed in order to integrate high levels of wind power to actively provide a primary reserve for frequency control. On the basis of the nonlinear model of the studied plant, and taking into account the wind speed fluctuations, and the electrical power variation, a multimodel linear description is derived for the wind turbine, and is used for the synthesis of an optimal control law involving a state feedback, an integral action and an output reference model. This new control structure allows a rapid transition of the wind turbine generated power between different desired set values. This electrical power tracking is ensured with a high-performance behavior for all other state variables: turbine and generator rotational speeds and mechanical shaft torque; and smooth and adequate evolution of the control variables. 2010 ISA. Published by Elsevier Ltd. All rights reserved.

[Association between social deprivation and causes of mortality among elderly residents in the city of Recife, Pernambuco State, Brazil].

PubMed

Silva, Vanessa de Lima; Leal, Márcia Carréra Campos; Marino, Jacira Guiro; Marques, Ana Paula de Oliveira

2008-05-01

This paper aims to analyze mortality among elderly residents in the city of Recife, Pernambuco State, Brazil, and its association with social deprivation (hardship) in the year 2000. An ecological study was performed, and 94 neighborhoods and 5 social strata were analyzed. The independent variable consisted of a composite social deprivation indicator, obtained for each neighborhood and calculated through a scoring technique based on census variables: water supply, sewage, illiteracy, and head-of-household's years of schooling and income. The dependent variables were: mortality rate in individuals > 60 years of age and cause-specific mortality rates. The association was calculated by means of the Pearson correlation coefficient, linear regression, and mortality odds between social deprivation strata formed by grouping of neighborhoods according to the indicator's quintiles. The data show a statistically significant positive correlation between social deprivation and mortality in the elderly from pneumonia, protein-energy malnutrition, tuberculosis, diarrhea/gastroenteritis, and traffic accidents, and a negative correlation with deaths from bronchopulmonary and breast cancers.

Recurrence-plot-based measures of complexity and their application to heart-rate-variability data.

PubMed

Marwan, Norbert; Wessel, Niels; Meyerfeldt, Udo; Schirdewan, Alexander; Kurths, Jürgen

2002-08-01

The knowledge of transitions between regular, laminar or chaotic behaviors is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods that, however, require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart-rate-variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e., chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our measures to the heart-rate-variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.

A new polytopic approach for the unknown input functional observer design

NASA Astrophysics Data System (ADS)

Bezzaoucha, Souad; Voos, Holger; Darouach, Mohamed

2018-03-01

In this paper, a constructive procedure to design Functional Unknown Input Observers for nonlinear continuous time systems is proposed under the Polytopic Takagi-Sugeno framework. An equivalent representation for the nonlinear model is achieved using the sector nonlinearity transformation. Applying the Lyapunov theory and the ? attenuation, linear matrix inequalities conditions are deduced which are solved for feasibility to obtain the observer design matrices. To cope with the effect of unknown inputs, classical approach of decoupling the unknown input for the linear case is used. Both algebraic and solver-based solutions are proposed (relaxed conditions). Necessary and sufficient conditions for the existence of the functional polytopic observer are given. For both approaches, the general and particular cases (measurable premise variables, full state estimation with full and reduced order cases) are considered and it is shown that the proposed conditions correspond to the one presented for standard linear case. To illustrate the proposed theoretical results, detailed numerical simulations are presented for a Quadrotor Aerial Robots Landing and a Waste Water Treatment Plant. Both systems are highly nonlinear and represented in a T-S polytopic form with unmeasurable premise variables and unknown inputs.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Long-term forecasting of meteorological time series using Nonlinear Canonical Correlation Analysis (NLCCA)

NASA Astrophysics Data System (ADS)

Woldesellasse, H. T.; Marpu, P. R.; Ouarda, T.

2016-12-01

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. A number of linear and nonlinear multivariate techniques has been used to predict the stochastic character of wind speed. A wind forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study Nonlinear Canonical Correlation Analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed and wind direction, of Abu Dhabi, UAE. The wind dataset was obtained from six ground stations. The first mode of NLCCA is capable of capturing the nonlinear mode of the climate indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the root mean square error (RMSE) and Mean absolute error (MAE). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behaviour of the dataset of variables than linear CCA model in terms of the correlation and root mean square error. Key words: Nonlinear Canonical Correlation Analysis (NLCCA), Canonical Correlation Analysis, Neural Network, Climate Indices, wind speed, wind direction

Control of variable speed variable pitch wind turbine based on a disturbance observer

NASA Astrophysics Data System (ADS)

Ren, Haijun; Lei, Xin

2017-11-01

In this paper, a novel sliding mode controller based on disturbance observer (DOB) to optimize the efficiency of variable speed variable pitch (VSVP) wind turbine is developed and analyzed. Due to the highly nonlinearity of the VSVP system, the model is linearly processed to obtain the state space model of the system. Then, a conventional sliding mode controller is designed and a DOB is added to estimate wind speed. The proposed control strategy can successfully deal with the random nature of wind speed, the nonlinearity of VSVP system, the uncertainty of parameters and external disturbance. Via adding the observer to the sliding mode controller, it can greatly reduce the chattering produced by the sliding mode switching gain. The simulation results show that the proposed control system has the effectiveness and robustness.

A duality approach for solving bounded linear programming problems with fuzzy variables based on ranking functions and its application in bounded transportation problems

NASA Astrophysics Data System (ADS)

Ebrahimnejad, Ali

2015-08-01

There are several methods, in the literature, for solving fuzzy variable linear programming problems (fuzzy linear programming in which the right-hand-side vectors and decision variables are represented by trapezoidal fuzzy numbers). In this paper, the shortcomings of some existing methods are pointed out and to overcome these shortcomings a new method based on the bounded dual simplex method is proposed to determine the fuzzy optimal solution of that kind of fuzzy variable linear programming problems in which some or all variables are restricted to lie within lower and upper bounds. To illustrate the proposed method, an application example is solved and the obtained results are given. The advantages of the proposed method over existing methods are discussed. Also, one application of this algorithm in solving bounded transportation problems with fuzzy supplies and demands is dealt with. The proposed method is easy to understand and to apply for determining the fuzzy optimal solution of bounded fuzzy variable linear programming problems occurring in real-life situations.

An analysis of approach navigation accuracy and guidance requirements for the grand tour mission to the outer planets

NASA Technical Reports Server (NTRS)

Jones, D. W.

1971-01-01

The navigation and guidance process for the Jupiter, Saturn and Uranus planetary encounter phases of the 1977 Grand Tour interior mission was simulated. Reference approach navigation accuracies were defined and the relative information content of the various observation types were evaluated. Reference encounter guidance requirements were defined, sensitivities to assumed simulation model parameters were determined and the adequacy of the linear estimation theory was assessed. A linear sequential estimator was used to provide an estimate of the augmented state vector, consisting of the six state variables of position and velocity plus the three components of a planet position bias. The guidance process was simulated using a nonspherical model of the execution errors. Computation algorithms which simulate the navigation and guidance process were derived from theory and implemented into two research-oriented computer programs, written in FORTRAN.

Finite-time mixed outer synchronization of complex networks with coupling time-varying delay.

PubMed

He, Ping; Ma, Shu-Hua; Fan, Tao

2012-12-01

This article is concerned with the problem of finite-time mixed outer synchronization (FMOS) of complex networks with coupling time-varying delay. FMOS is a recently developed generalized synchronization concept, i.e., in which different state variables of the corresponding nodes can evolve into finite-time complete synchronization, finite-time anti-synchronization, and even amplitude finite-time death simultaneously for an appropriate choice of the controller gain matrix. Some novel stability criteria for the synchronization between drive and response complex networks with coupling time-varying delay are derived using the Lyapunov stability theory and linear matrix inequalities. And a simple linear state feedback synchronization controller is designed as a result. Numerical simulations for two coupled networks of modified Chua's circuits are then provided to demonstrate the effectiveness and feasibility of the proposed complex networks control and synchronization schemes and then compared with the proposed results and the previous schemes for accuracy.

A guidance and navigation system for continuous low thrust vehicles. M.S. Thesis

NASA Technical Reports Server (NTRS)

Tse, C. J. C.

1973-01-01

A midcourse guidance and navigation system for continuous low thrust vehicles is described. A set of orbit elements, known as the equinoctial elements, are selected as the state variables. The uncertainties are modelled statistically by random vector and stochastic processes. The motion of the vehicle and the measurements are described by nonlinear stochastic differential and difference equations respectively. A minimum time nominal trajectory is defined and the equation of motion and the measurement equation are linearized about this nominal trajectory. An exponential cost criterion is constructed and a linear feedback guidance law is derived to control the thrusting direction of the engine. Using this guidance law, the vehicle will fly in a trajectory neighboring the nominal trajectory. The extended Kalman filter is used for state estimation. Finally a short mission using this system is simulated. The results indicate that this system is very efficient for short missions.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Immigration, acculturation, and risk factors for obesity and cardiovascular disease: a comparison between Latinos of Peruvian descent in Peru and in the United States.

PubMed

Lizarzaburu, Jesus L; Palinkas, Lawrence A

2002-01-01

To determine whether migration and acculturation was associated with risk factors for obesity and cardiovascular disease, whether this association is linear or curvilinear, and whether the socio-cultural context alters the association between obesity and cardiovascular disease and individual-level variables. Lima, Peru, San Diego and San Francisco, California. Ninety-two Peruvian residents of Lima and 83 Peruvian immigrant residents of California. total cholesterol, blood pressure, body mass index, waist-to-hip ratio. A significant linear association was found between migration and acculturation and alcohol consumption and total cholesterol in men and women, systolic (SBP) and diastolic (DBP) blood pressure and body mass index in men, and physical activity in women. Immigration/acculturation level was a significant independent predictor of total cholesterol. Age and body mass index were independent predictors of total cholesterol only in Peru. Sex was an independent predictor of DBP only in the United States. Body mass index was an in dependent predictor of DBP only in Peru. Household income was an independent predictor of SBP and DBP only in Peru and body mass index only in the United States, while level of education was inversely associated with body mass index only in Peru. Regular strenuous physical activity was an independent predictor of obesity measures only in the United States. The socio-cultural context alters the risk of obesity and cardiovascular disease associated with individual-level variables and accounts for gender and cross-national differences in the migration-illness association.

Batch-mode Reinforcement Learning for improved hydro-environmental systems management

NASA Astrophysics Data System (ADS)

Castelletti, A.; Galelli, S.; Restelli, M.; Soncini-Sessa, R.

2010-12-01

Despite the great progresses made in the last decades, the optimal management of hydro-environmental systems still remains a very active and challenging research area. The combination of multiple, often conflicting interests, high non-linearities of the physical processes and the management objectives, strong uncertainties in the inputs, and high dimensional state makes the problem challenging and intriguing. Stochastic Dynamic Programming (SDP) is one of the most suitable methods for designing (Pareto) optimal management policies preserving the original problem complexity. However, it suffers from a dual curse, which, de facto, prevents its practical application to even reasonably complex water systems. (i) Computational requirement grows exponentially with state and control dimension (Bellman's curse of dimensionality), so that SDP can not be used with water systems where the state vector includes more than few (2-3) units. (ii) An explicit model of each system's component is required (curse of modelling) to anticipate the effects of the system transitions, i.e. any information included into the SDP framework can only be either a state variable described by a dynamic model or a stochastic disturbance, independent in time, with the associated pdf. Any exogenous information that could effectively improve the system operation cannot be explicitly considered in taking the management decision, unless a dynamic model is identified for each additional information, thus adding to the problem complexity through the curse of dimensionality (additional state variables). To mitigate this dual curse, the combined use of batch-mode Reinforcement Learning (bRL) and Dynamic Model Reduction (DMR) techniques is explored in this study. bRL overcomes the curse of modelling by replacing explicit modelling with an external simulator and/or historical observations. The curse of dimensionality is averted using a functional approximation of the SDP value function based on proper non-linear regressors. DMR reduces the complexity and the associated computational requirements of non-linear distributed process based models, making them suitable for being included into optimization schemes. Results from real world applications of the approach are also presented, including reservoir operation with both quality and quantity targets.

Dynamic Alignment Models for Neural Coding

PubMed Central

Kollmorgen, Sepp; Hahnloser, Richard H. R.

2014-01-01

Recently, there have been remarkable advances in modeling the relationships between the sensory environment, neuronal responses, and behavior. However, most models cannot encompass variable stimulus-response relationships such as varying response latencies and state or context dependence of the neural code. Here, we consider response modeling as a dynamic alignment problem and model stimulus and response jointly by a mixed pair hidden Markov model (MPH). In MPHs, multiple stimulus-response relationships (e.g., receptive fields) are represented by different states or groups of states in a Markov chain. Each stimulus-response relationship features temporal flexibility, allowing modeling of variable response latencies, including noisy ones. We derive algorithms for learning of MPH parameters and for inference of spike response probabilities. We show that some linear-nonlinear Poisson cascade (LNP) models are a special case of MPHs. We demonstrate the efficiency and usefulness of MPHs in simulations of both jittered and switching spike responses to white noise and natural stimuli. Furthermore, we apply MPHs to extracellular single and multi-unit data recorded in cortical brain areas of singing birds to showcase a novel method for estimating response lag distributions. MPHs allow simultaneous estimation of receptive fields, latency statistics, and hidden state dynamics and so can help to uncover complex stimulus response relationships that are subject to variable timing and involve diverse neural codes. PMID:24625448

How Robust Is Linear Regression with Dummy Variables?

ERIC Educational Resources Information Center

Blankmeyer, Eric

2006-01-01

Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Indo-Pacific ENSO modes in a double-basin Zebiak-Cane model

NASA Astrophysics Data System (ADS)

Wieners, Claudia; de Ruijter, Will; Dijkstra, Henk

2016-04-01

We study Indo-Pacific interactions on ENSO timescales in a double-basin version of the Zebiak-Cane ENSO model, employing both time integrations and bifurcation analysis (continuation methods). The model contains two oceans (the Indian and Pacific Ocean) separated by a meridional wall. Interaction between the basins is possible via the atmosphere overlaying both basins. We focus on the effect of the Indian Ocean (both its mean state and its variability) on ENSO stability. In addition, inspired by analysis of observational data (Wieners et al, Coherent tropical Indo-Pacific interannual climate variability, in review), we investigate the effect of state-dependent atmospheric noise. Preliminary results include the following: 1) The background state of the Indian Ocean stabilises the Pacific ENSO (i.e. the Hopf bifurcation is shifted to higher values of the SST-atmosphere coupling), 2) the West Pacific cooling (warming) co-occurring with El Niño (La Niña) is essential to simulate the phase relations between Pacific and Indian SST anomalies, 3) a non-linear atmosphere is needed to simulate the effect of the Indian Ocean variability onto the Pacific ENSO that is suggested by observations.

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

Yang, Qichun; Zhang, Xuesong; Xu, Xingya

Riverine carbon cycling is an important, but insufficiently investigated component of the global carbon cycle. Analyses of environmental controls on riverine carbon cycling are critical for improved understanding of mechanisms regulating carbon processing and storage along the terrestrial-aquatic continuum. Here, we compile and analyze riverine dissolved organic carbon (DOC) concentration data from 1402 United States Geological Survey (USGS) gauge stations to examine the spatial variability and environmental controls of DOC concentrations in the United States (U.S.) surface waters. DOC concentrations exhibit high spatial variability, with an average of 6.42 ± 6.47 mg C/ L (Mean ± Standard Deviation). In general,more » high DOC concentrations occur in the Upper Mississippi River basin and the Southeastern U.S., while low concentrations are mainly distributed in the Western U.S. Single-factor analysis indicates that slope of drainage areas, wetlands, forests, percentage of first-order streams, and instream nutrients (such as nitrogen and phosphorus) pronouncedly influence DOC concentrations, but the explanatory power of each bivariate model is lower than 35%. Analyses based on the general multi-linear regression models suggest DOC concentrations are jointly impacted by multiple factors. Soil properties mainly show positive correlations with DOC concentrations; forest and shrub lands have positive correlations with DOC concentrations, but urban area and croplands demonstrate negative impacts; total instream phosphorus and dam density correlate positively with DOC concentrations. Notably, the relative importance of these environmental controls varies substantially across major U.S. water resource regions. In addition, DOC concentrations and environmental controls also show significant variability from small streams to large rivers, which may be caused by changing carbon sources and removal rates by river orders. In sum, our results reveal that general multi-linear regression analysis of twenty one terrestrial and aquatic environmental factors can partially explain (56%) the DOC concentration variation. In conclusion, this study highlights the complexity of the interactions among these environmental factors in determining DOC concentrations, thus calls for processes-based, non-linear methodologies to constrain uncertainties in riverine DOC cycling.« less

Change detection in the dynamics of an intracellular protein synthesis model using nonlinear Kalman filtering.

PubMed

Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel

2015-10-01

A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).

Multilevel Mixture Kalman Filter

NASA Astrophysics Data System (ADS)

Guo, Dong; Wang, Xiaodong; Chen, Rong

2004-12-01

The mixture Kalman filter is a general sequential Monte Carlo technique for conditional linear dynamic systems. It generates samples of some indicator variables recursively based on sequential importance sampling (SIS) and integrates out the linear and Gaussian state variables conditioned on these indicators. Due to the marginalization process, the complexity of the mixture Kalman filter is quite high if the dimension of the indicator sampling space is high. In this paper, we address this difficulty by developing a new Monte Carlo sampling scheme, namely, the multilevel mixture Kalman filter. The basic idea is to make use of the multilevel or hierarchical structure of the space from which the indicator variables take values. That is, we draw samples in a multilevel fashion, beginning with sampling from the highest-level sampling space and then draw samples from the associate subspace of the newly drawn samples in a lower-level sampling space, until reaching the desired sampling space. Such a multilevel sampling scheme can be used in conjunction with the delayed estimation method, such as the delayed-sample method, resulting in delayed multilevel mixture Kalman filter. Examples in wireless communication, specifically the coherent and noncoherent 16-QAM over flat-fading channels, are provided to demonstrate the performance of the proposed multilevel mixture Kalman filter.

Stokes polarimetry using analysis of the nonlinear voltage-retardance relationship for liquid-crystal variable retarders

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

López-Téllez, J. M., E-mail: jmlopez@comunidad.unam.mx; Bruce, N. C.

2014-03-15

We present a method for using liquid-crystal variable retarders (LCVR’s) with continually varying voltage to measure the Stokes vector of a light beam. The LCVR's are usually employed with fixed retardance values due to the nonlinear voltage-retardance behavior that they show. The nonlinear voltage-retardance relationship is first measured and then a linear fit of the known retardance terms to the detected signal is performed. We use known waveplates (half-wave and quarter-wave) as devices to provide controlled polarization states to the Stokes polarimeter and we use the measured Stokes parameters as functions of the orientation of the axes of the waveplatesmore » as an indication of the quality of the polarimeter. Results are compared to a Fourier analysis method that does not take into account the nonlinear voltage-retardance relationship and also to a Fourier analysis method that uses experimental voltage values to give a linear retardance function with time. Also, we present results of simulations for comparison.« less

Delay compensation in integrated communication and control systems. I - Conceptual development and analysis

NASA Technical Reports Server (NTRS)

Luck, Rogelio; Ray, Asok

1990-01-01

A procedure for compensating for the effects of distributed network-induced delays in integrated communication and control systems (ICCS) is proposed. The problem of analyzing systems with time-varying and possibly stochastic delays could be circumvented by use of a deterministic observer which is designed to perform under certain restrictive but realistic assumptions. The proposed delay-compensation algorithm is based on a deterministic state estimator and a linear state-variable-feedback control law. The deterministic observer can be replaced by a stochastic observer without any structural modifications of the delay compensation algorithm. However, if a feedforward-feedback control law is chosen instead of the state-variable feedback control law, the observer must be modified as a conventional nondelayed system would be. Under these circumstances, the delay compensation algorithm would be accordingly changed. The separation principle of the classical Luenberger observer holds true for the proposed delay compensator. The algorithm is suitable for ICCS in advanced aircraft, spacecraft, manufacturing automation, and chemical process applications.

Estimation of Subjective Mental Work Load Level with Heart Rate Variability by Tolerance to Driver's Mental Load

NASA Astrophysics Data System (ADS)

Yokoi, Toshiyuki; Itoh, Michimasa; Oguri, Koji

Most of the traffic accidents have been caused by inappropriate driver's mental state. Therefore, driver monitoring is one of the most important challenges to prevent traffic accidents. Some studies for evaluating the driver's mental state while driving have been reported; however driver's mental state should be estimated in real-time in the future. This paper proposes a way to estimate quantitatively driver's mental workload using heart rate variability. It is assumed that the tolerance to driver's mental workload is different depending on the individual. Therefore, we classify people based on their individual tolerance to mental workload. Our estimation method is multiple linear regression analysis, and we compare it to NASA-TLX which is used as the evaluation method of subjective mental workload. As a result, the coefficient of correlation improved from 0.83 to 0.91, and the standard deviation of error also improved. Therefore, our proposed method demonstrated the possibility to estimate mental workload.

Application of low-dimensional techniques for closed-loop control of turbulent flows

NASA Astrophysics Data System (ADS)

Ausseur, Julie

The groundwork for an advanced closed-loop control of separated shear layer flows is laid out in this document. The experimental testbed for the present investigation is the turbulent flow over a NACA-4412 model airfoil tested in the Syracuse University subsonic wind tunnel at Re=135,000. The specified control objective is to delay separation - or stall - by constantly keeping the flow attached to the surface of the wing. The proper orthogonal decomposition (POD) is shown to he a valuable tool to provide a low-dimensional estimate of the flow state and the first POD expansion coefficient is proposed to he used as the control variable. Other reduced-order techniques such as the modified linear and quadratic stochastic measurement methods (mLSM, mQSM) are applied to reduce the complexity of the flow field and their ability to accurately estimate the flow state from surface pressure measurements alone is examined. A simple proportional feedback control is successfully implemented in real-time using these tools and flow separation is efficiently delayed by over 3 degrees angle of attack. To further improve the quality of the flow state estimate, the implementation of a Kalman filter is foreseen, in which the knowledge of the flow dynamics is added to the computation of the control variable to correct for the potential measurement errors. To this aim, a reduced-order model (ROM) of the flow is developed using the least-squares method to obtain the coefficients of the POD/Galerkin projection of the Navier-Stokes equations from experimental data. To build the training ensemble needed in this experimental procedure, the spectral mLSM is performed to generate time-resolved series of POD expansion coefficients from which temporal derivatives are computed. This technique, which is applied to independent PIV velocity snapshots and time-resolved surface measurements, is able to retrieve the rational temporal evolution of the flow physics in the entire 2-D measurement area. The quality of the spectral measurements is confirmed by the results from both the linear and quadratic dynamical systems. The preliminary results from the linear ROM strengthens the motivation for future control implementation of a linear Kalman filter in this flow.

Some Classes of Imperfect Information Finite State-Space Stochastic Games with Finite-Dimensional Solutions

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

McEneaney, William M.

2004-08-15

Stochastic games under imperfect information are typically computationally intractable even in the discrete-time/discrete-state case considered here. We consider a problem where one player has perfect information.A function of a conditional probability distribution is proposed as an information state.In the problem form here, the payoff is only a function of the terminal state of the system,and the initial information state is either linear ora sum of max-plus delta functions.When the initial information state belongs to these classes, its propagation is finite-dimensional.The state feedback value function is also finite-dimensional,and obtained via dynamic programming,but has a nonstandard form due to the necessity ofmore » an expanded state variable.Under a saddle point assumption,Certainty Equivalence is obtained and the proposed function is indeed an information state.« less

Improving the maximum transmission distance of continuous-variable quantum key distribution using a noiseless amplifier

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

Blandino, Rémi; Etesse, Jean; Grangier, Philippe

2014-12-04

We show that the maximum transmission distance of continuous-variable quantum key distribution in presence of a Gaussian noisy lossy channel can be arbitrarily increased using a heralded noiseless linear amplifier. We explicitly consider a protocol using amplitude and phase modulated coherent states with reverse reconciliation. Assuming that the secret key rate drops to zero for a line transmittance T{sub lim}, we find that a noiseless amplifier with amplitude gain g can improve this value to T{sub lim}/g{sup 2}, corresponding to an increase in distance proportional to log g. We also show that the tolerance against noise is increased.

Analysis of the thermal comfort model in an environment of metal mechanical branch.

PubMed

Pinto, N M; Xavier, A A P; do Amaral, Regiane T

2012-01-01

This study aims to identify the correlation between the Predicted Mean Vote (PMV) with the thermal sensation (S) of 55 employees, establishing a linear multiple regression equation. The measurement of environmental variables followed established standards. The survey was conducted in a metal industry located in Ponta Grossa of the State of Parana in Brazil. It was applied the physical model of thermal comfort to the environmental variables and also to the subjective data on the thermal sensations of employees. The survey was conducted from May to November, 2010, with 48 measurements. This study will serve as the basis for a dissertation consisting of 72 measurements.

Linear quadratic optimization for positive LTI system

NASA Astrophysics Data System (ADS)

Muhafzan, Yenti, Syafrida Wirma; Zulakmal

2017-05-01

Nowaday the linear quadratic optimization subject to positive linear time invariant (LTI) system constitute an interesting study considering it can become a mathematical model of variety of real problem whose variables have to nonnegative and trajectories generated by these variables must be nonnegative. In this paper we propose a method to generate an optimal control of linear quadratic optimization subject to positive linear time invariant (LTI) system. A sufficient condition that guarantee the existence of such optimal control is discussed.

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

B-737 Linear Autoland Simulink Model

NASA Technical Reports Server (NTRS)

Belcastro, Celeste (Technical Monitor); Hogge, Edward F.

2004-01-01

The Linear Autoland Simulink model was created to be a modular test environment for testing of control system components in commercial aircraft. The input variables, physical laws, and referenced frames used are summarized. The state space theory underlying the model is surveyed and the location of the control actuators described. The equations used to realize the Dryden gust model to simulate winds and gusts are derived. A description of the pseudo-random number generation method used in the wind gust model is included. The longitudinal autopilot, lateral autopilot, automatic throttle autopilot, engine model and automatic trim devices are considered as subsystems. The experience in converting the Airlabs FORTRAN aircraft control system simulation to a graphical simulation tool (Matlab/Simulink) is described.

A 4-cylinder Stirling engine computer program with dynamic energy equations

NASA Technical Reports Server (NTRS)

Daniele, C. J.; Lorenzo, C. F.

1983-01-01

A computer program for simulating the steady state and transient performance of a four cylinder Stirling engine is presented. The thermodynamic model includes both continuity and energy equations and linear momentum terms (flow resistance). Each working space between the pistons is broken into seven control volumes. Drive dynamics and vehicle load effects are included. The model contains 70 state variables. Also included in the model are piston rod seal leakage effects. The computer program includes a model of a hydrogen supply system, from which hydrogen may be added to the system to accelerate the engine. Flow charts are provided.

A Time Integration Algorithm Based on the State Transition Matrix for Structures with Time Varying and Nonlinear Properties

NASA Technical Reports Server (NTRS)

Bartels, Robert E.

2003-01-01

A variable order method of integrating the structural dynamics equations that is based on the state transition matrix has been developed. The method has been evaluated for linear time variant and nonlinear systems of equations. When the time variation of the system can be modeled exactly by a polynomial it produces nearly exact solutions for a wide range of time step sizes. Solutions of a model nonlinear dynamic response exhibiting chaotic behavior have been computed. Accuracy of the method has been demonstrated by comparison with solutions obtained by established methods.

Linear Relationship between Resilience, Learning Approaches, and Coping Strategies to Predict Achievement in Undergraduate Students

PubMed Central

de la Fuente, Jesús; Fernández-Cabezas, María; Cambil, Matilde; Vera, Manuel M.; González-Torres, Maria Carmen; Artuch-Garde, Raquel

2017-01-01

The aim of the present research was to analyze the linear relationship between resilience (meta-motivational variable), learning approaches (meta-cognitive variables), strategies for coping with academic stress (meta-emotional variable) and academic achievement, necessary in the context of university academic stress. A total of 656 students from a southern university in Spain completed different questionnaires: a resiliency scale, a coping strategies scale, and a study process questionnaire. Correlations and structural modeling were used for data analyses. There was a positive and significant linear association showing a relationship of association and prediction of resilience to the deep learning approach, and problem-centered coping strategies. In a complementary way, these variables positively and significantly predicted the academic achievement of university students. These results enabled a linear relationship of association and consistent and differential prediction to be established among the variables studied. Implications for future research are set out. PMID:28713298

Heart rate variability in normal and pathological sleep.

PubMed

Tobaldini, Eleonora; Nobili, Lino; Strada, Silvia; Casali, Karina R; Braghiroli, Alberto; Montano, Nicola

2013-10-16

Sleep is a physiological process involving different biological systems, from molecular to organ level; its integrity is essential for maintaining health and homeostasis in human beings. Although in the past sleep has been considered a state of quiet, experimental and clinical evidences suggest a noteworthy activation of different biological systems during sleep. A key role is played by the autonomic nervous system (ANS), whose modulation regulates cardiovascular functions during sleep onset and different sleep stages. Therefore, an interest on the evaluation of autonomic cardiovascular control in health and disease is growing by means of linear and non-linear heart rate variability (HRV) analyses. The application of classical tools for ANS analysis, such as HRV during physiological sleep, showed that the rapid eye movement (REM) stage is characterized by a likely sympathetic predominance associated with a vagal withdrawal, while the opposite trend is observed during non-REM sleep. More recently, the use of non-linear tools, such as entropy-derived indices, have provided new insight on the cardiac autonomic regulation, revealing for instance changes in the cardiovascular complexity during REM sleep, supporting the hypothesis of a reduced capability of the cardiovascular system to deal with stress challenges. Interestingly, different HRV tools have been applied to characterize autonomic cardiac control in different pathological conditions, from neurological sleep disorders to sleep disordered breathing (SDB). In summary, linear and non-linear analysis of HRV are reliable approaches to assess changes of autonomic cardiac modulation during sleep both in health and diseases. The use of these tools could provide important information of clinical and prognostic relevance.

Identifying Slow Molecular Motions in Complex Chemical Reactions.

PubMed

Piccini, GiovanniMaria; Polino, Daniela; Parrinello, Michele

2017-09-07

We have studied the cyclization reaction of deprotonated 4-chloro-1-butanethiol to tetrahydrothiophene by means of well-tempered metadynamics. To properly select the collective variables, we used the recently proposed variational approach to conformational dynamics within the framework of metadyanmics. This allowed us to select the appropriate linear combinations from a set of collective variables representing the slow degrees of freedom that best describe the slow modes of the reaction. We performed our calculations at three different temperatures, namely, 300, 350, and 400 K. We show that the choice of such collective variables allows one to easily interpret the complex free-energy surface of such a reaction by univocal identification of the conformers belonging to reactants and product states playing a fundamental role in the reaction mechanism.

Very highly excited vibrational states of LiCN using a discrete variable representation

NASA Astrophysics Data System (ADS)

Henderson, James R.; Tennyson, Jonathan

Calculations are presented for the lowest 900 vibrational (J = 0) states of the LiCN floppy system for a two dimensional potential energy surface (rCN frozen). Most of these states lie well above the barrier separating the two linear isomers of the molecule and the point where the classical dynamics of the system becomes chaotic. Analysis of the wavefunctions of individual states in the high energy region shows that while most have an irregular nodal structure, a significant number of states appear regular - corresponding to solutions of standard, 'mode localized' hamiltonians. Motions corresponding in zero-order to Li-CN and Li-NC normal modes as well as free rotor states are identified. The distribution of level spacings is also studied and yields results in good agreement with those obtained by analysing nodal structures.

A finite element solution algorithm for the Navier-Stokes equations

NASA Technical Reports Server (NTRS)

Baker, A. J.

1974-01-01

A finite element solution algorithm is established for the two-dimensional Navier-Stokes equations governing the steady-state kinematics and thermodynamics of a variable viscosity, compressible multiple-species fluid. For an incompressible fluid, the motion may be transient as well. The primitive dependent variables are replaced by a vorticity-streamfunction description valid in domains spanned by rectangular, cylindrical and spherical coordinate systems. Use of derived variables provides a uniformly elliptic partial differential equation description for the Navier-Stokes system, and for which the finite element algorithm is established. Explicit non-linearity is accepted by the theory, since no psuedo-variational principles are employed, and there is no requirement for either computational mesh or solution domain closure regularity. Boundary condition constraints on the normal flux and tangential distribution of all computational variables, as well as velocity, are routinely piecewise enforceable on domain closure segments arbitrarily oriented with respect to a global reference frame.

Observer synthesis for a class of Takagi-Sugeno descriptor system with unmeasurable premise variable. Application to fault diagnosis

NASA Astrophysics Data System (ADS)

López-Estrada, F. R.; Astorga-Zaragoza, C. M.; Theilliol, D.; Ponsart, J. C.; Valencia-Palomo, G.; Torres, L.

2017-12-01

This paper proposes a methodology to design a Takagi-Sugeno (TS) descriptor observer for a class of TS descriptor systems. Unlike the popular approach that considers measurable premise variables, this paper considers the premise variables depending on unmeasurable vectors, e.g. the system states. This consideration covers a large class of nonlinear systems and represents a real challenge for the observer synthesis. Sufficient conditions to guarantee robustness against the unmeasurable premise variables and asymptotic convergence of the TS descriptor observer are obtained based on the H∞ approach together with the Lyapunov method. As a result, the designing conditions are given in terms of linear matrix inequalities (LMIs). In addition, sensor fault detection and isolation are performed by means of a generalised observer bank. Two numerical experiments, an electrical circuit and a rolling disc system, are presented in order to illustrate the effectiveness of the proposed method.

Regularized linearization for quantum nonlinear optical cavities: application to degenerate optical parametric oscillators.

PubMed

Navarrete-Benlloch, Carlos; Roldán, Eugenio; Chang, Yue; Shi, Tao

2014-10-06

Nonlinear optical cavities are crucial both in classical and quantum optics; in particular, nowadays optical parametric oscillators are one of the most versatile and tunable sources of coherent light, as well as the sources of the highest quality quantum-correlated light in the continuous variable regime. Being nonlinear systems, they can be driven through critical points in which a solution ceases to exist in favour of a new one, and it is close to these points where quantum correlations are the strongest. The simplest description of such systems consists in writing the quantum fields as the classical part plus some quantum fluctuations, linearizing then the dynamical equations with respect to the latter; however, such an approach breaks down close to critical points, where it provides unphysical predictions such as infinite photon numbers. On the other hand, techniques going beyond the simple linear description become too complicated especially regarding the evaluation of two-time correlators, which are of major importance to compute observables outside the cavity. In this article we provide a regularized linear description of nonlinear cavities, that is, a linearization procedure yielding physical results, taking the degenerate optical parametric oscillator as the guiding example. The method, which we call self-consistent linearization, is shown to be equivalent to a general Gaussian ansatz for the state of the system, and we compare its predictions with those obtained with available exact (or quasi-exact) methods. Apart from its operational value, we believe that our work is valuable also from a fundamental point of view, especially in connection to the question of how far linearized or Gaussian theories can be pushed to describe nonlinear dissipative systems which have access to non-Gaussian states.

Using Log Linear Analysis for Categorical Family Variables.

ERIC Educational Resources Information Center

Moen, Phyllis

The Goodman technique of log linear analysis is ideal for family research, because it is designed for categorical (non-quantitative) variables. Variables are dichotomized (for example, married/divorced, childless/with children) or otherwise categorized (for example, level of permissiveness, life cycle stage). Contingency tables are then…

Changes in the Structure and Propagation of the MJO with Increasing CO2

NASA Technical Reports Server (NTRS)

Adames, Angel F.; Kim, Daehyun; Sobel, Adam H.; Del Genio, Anthony; Wu, Jingbo

2017-01-01

Changes in the Madden-Julian Oscillation (MJO) with increasing CO2 concentrations are examined using the Goddard Institute for Space Studies Global Climate Model (GCM). Four simulations performed with fixed CO2 concentrations of 0.5, 1, 2 and 4 times pre-industrial levels using the GCM coupled with a mixed layer ocean model are analyzed in terms of the basic state, rainfall and moisture variability, and the structure and propagation of the MJO.The GCM simulates basic state changes associated with increasing CO2 that are consistent with results from earlier studies: column water vapor increases at approximately 7.1% K(exp -1), precipitation also increases but at a lower rate (approximately 3% K(exp -1)), and column relative humidity shows little change. Moisture and rainfall variability intensify with warming. Total moisture and rainfall variability increases at a rate that is similar to that of the mean state change. The intensification is faster in the MJO-related anomalies than in the total anomalies, though the ratio of the MJO band variability to its westward counterpart increases at a much slower rate. On the basis of linear regression analysis and space-time spectral analysis, it is found that the MJO exhibits faster eastward propagation, faster westward energy dispersion, a larger zonal scale and deeper vertical structure in warmer climates.

Unfolding dimension and the search for functional markers in the human electroencephalogram

NASA Astrophysics Data System (ADS)

Dünki, Rudolf M.; Schmid, Gary Bruno

1998-02-01

A biparametric approach to dimensional analysis in terms of a so-called ``unfolding dimension'' is introduced to explore the extent to which the human EEG can be described by stable features characteristic of an individual despite the well-known problems of intraindividual variability. Our analysis comprises an EEG data set recorded from healthy individuals over a time span of 5 years. The outcome is shown to be comparable to advanced linear methods of spectral analysis with regard to intraindividual specificity and stability over time. Such linear methods have not yet proven to be specific to the EEG of different brain states. Thus we have also investigated the specificity of our biparametric approach by comparing the mental states schizophrenic psychosis and remission, i.e., illness versus full recovery. A difference between EEG in psychosis and remission became apparent within recordings taken at rest with eyes closed and no stimulated or requested mental activity. Hence our approach distinguishes these functional brain states even in the absence of an active or intentional stimulus. This sheds a different light upon theories of schizophrenia as an information-processing disturbance of the brain.

Sensor fault diagnosis of singular delayed LPV systems with inexact parameters: an uncertain system approach

NASA Astrophysics Data System (ADS)

Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc

2018-01-01

In this paper, sensor fault diagnosis of a singular delayed linear parameter varying (LPV) system is considered. In the considered system, the model matrices are dependent on some parameters which are real-time measurable. The case of inexact parameter measurements is considered which is close to real situations. Fault diagnosis in this system is achieved via fault estimation. For this purpose, an augmented system is created by including sensor faults as additional system states. Then, an unknown input observer (UIO) is designed which estimates both the system states and the faults in the presence of measurement noise, disturbances and uncertainty induced by inexact measured parameters. Error dynamics and the original system constitute an uncertain system due to inconsistencies between real and measured values of the parameters. Then, the robust estimation of the system states and the faults are achieved with H∞ performance and formulated with a set of linear matrix inequalities (LMIs). The designed UIO is also applicable for fault diagnosis of singular delayed LPV systems with unmeasurable scheduling variables. The efficiency of the proposed approach is illustrated with an example.

Derivation of Nationally Consistent Indices Representing Urban Intensity Within and Across Nine Metropolitan Areas of the Conterminous United States

USGS Publications Warehouse

Cuffney, Thomas F.; Falcone, James A.

2009-01-01

Two nationally consistent multimetric indices of urban intensity were developed to support studies of the effects of urbanization on streams in nine metropolitan areas of the conterminous United States: Atlanta, Georgia; Birmingham, Alabama; Boston, Massachusetts; Dallas-Fort Worth, Texas; Denver, Colorado; Milwaukee-Green Bay, Wisconsin; Portland, Oregon; Raleigh, North Carolina; and Salt Lake City, Utah. These studies were conducted as a part of the U.S. Geological Survey's National Water-Quality Assessment Program. These urban intensity indices were used to define gradients of urbanization and to interpret biological, physical, and chemical changes along these gradients. Ninety census, land-cover, and infrastructure variables obtained from nationally available databases were evaluated. Only variables that exhibited a strong and consistent linear relation with 2000 population density were considered for use in the indices. Housing-unit density (HUDEN), percentage of basin area in developed land (P_NLCD1_2), and road density (ROADDEN) were selected as the best representatives of census, land-cover, and infrastructure variables. The metropolitan area national urban intensity index (MA-NUII) was scaled to represent urban intensity within each metropolitan area and ranged from 0 (little or no urban) to 100 (maximum urban) for sites within each metropolitan area. The national urban intensity index (NUII) was scaled to represent urban intensity across all nine metropolitan areas and ranged from 0 to 100 for all sites. The rates at which HUDEN, P_NLCD1_2, and ROADDEN changed with changes in population density varied among metropolitan areas. Therefore, these variables were adjusted to obtain a more uniform rate of response across metropolitan areas in the derivation of the NUII. The NUII indicated that maximum levels of urban intensity occurred in the West and Midwest rather than in the East primarily because small inner-city streams in eastern metropolitan areas are buried and converted to storm drains or sewers and because of higher density development in the Western and Central United States. The national indices (MA-NUII, NUII) were compared to indices that were derived independently for each metropolitan area (MA-UII) based on variables that were of local interest. The MA-UIIs, which were based on 5 to 40 variables, tended to overestimate urban intensity relative to the national indices particularly when the MA-UII was composed of large numbers of variables that were not linearly related to population density as in Denver, Dallas-Fort Worth, and Milwaukee-Green Bay.

Impact of climate variability on various Rabi crops over Northwest India

NASA Astrophysics Data System (ADS)

Nageswararao, M. M.; Dhekale, B. S.; Mohanty, U. C.

2018-01-01

The Indian agriculture with its two prominent cropping seasons [summer ( Kharif) and winter ( Rabi)] is the mainstay of the rural economy. Northwest India (NWI) is an important region for the cultivation of Rabi crops grown during the period from October to April. In the present study, state wise impact analysis is carried out to ascertain the influence of climate indices Nino3.4 region Sea Surface Temperature (SST), Southern Oscillation Index (SOI), Arctic Oscillation (AO), North Atlantic Oscillation (NAO) and local precipitation, soil moisture, minimum ( T min), maximum ( T max) and mean ( T mean) temperatures on different Rabi crops (wheat, gram, rapeseed-mustard, oilseeds, and total Rabi food grains) over NWI during the years 1966-2011. To study the impact of climate variability on different Rabi crops, firstly, the influence of technology on the productivity of these crops has been removed by using linear function, as linear trend has noticed in all the time series. Correlation analysis provides an indication of the influence of local precipitation, soil moisture, T min, T max and T mean and some of its potential predictors (Nino3.4 region SST, SOI, AO, and NAO) on the productivity of different Rabi crops. Overall impact analysis indicates that the productivity of different Rabi crops in most of the places of NWI is most likely influenced by variability in local temperatures. Moreover, Nino3.4 region SST (SOI) positively (negatively) affects the productivity of gram, rapeseed-mustard, and total Rabi oilseeds in most of the states. The results of this study are useful in determining the strategies for increasing sustainable production through better agronomic practices.

Learning to Estimate Dynamical State with Probabilistic Population Codes.

PubMed

Makin, Joseph G; Dichter, Benjamin K; Sabes, Philip N

2015-11-01

Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH)-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Learning to Estimate Dynamical State with Probabilistic Population Codes

PubMed Central

Sabes, Philip N.

2015-01-01

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states. PMID:26540152

A multi-criteria index for ecological evaluation of tropical agriculture in southeastern Mexico.

PubMed

Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette

2014-01-01

The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts' evaluation.

A Multi-Criteria Index for Ecological Evaluation of Tropical Agriculture in Southeastern Mexico

PubMed Central

Huerta, Esperanza; Kampichler, Christian; Ochoa-Gaona, Susana; De Jong, Ben; Hernandez-Daumas, Salvador; Geissen, Violette

2014-01-01

The aim of this study was to generate an easy to use index to evaluate the ecological state of agricultural land from a sustainability perspective. We selected environmental indicators, such as the use of organic soil amendments (green manure) versus chemical fertilizers, plant biodiversity (including crop associations), variables which characterize soil conservation of conventional agricultural systems, pesticide use, method and frequency of tillage. We monitored the ecological state of 52 agricultural plots to test the performance of the index. The variables were hierarchically aggregated with simple mathematical algorithms, if-then rules, and rule-based fuzzy models, yielding the final multi-criteria index with values from 0 (worst) to 1 (best conditions). We validated the model through independent evaluation by experts, and we obtained a linear regression with an r2 = 0.61 (p = 2.4e-06, d.f. = 49) between index output and the experts’ evaluation. PMID:25405980

Nonlinear model predictive control for chemical looping process

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

Joshi, Abhinaya; Lei, Hao; Lou, Xinsheng

A control system for optimizing a chemical looping ("CL") plant includes a reduced order mathematical model ("ROM") that is designed by eliminating mathematical terms that have minimal effect on the outcome. A non-linear optimizer provides various inputs to the ROM and monitors the outputs to determine the optimum inputs that are then provided to the CL plant. An estimator estimates the values of various internal state variables of the CL plant. The system has one structure adapted to control a CL plant that only provides pressure measurements in the CL loops A and B, a second structure adapted to amore » CL plant that provides pressure measurements and solid levels in both loops A, and B, and a third structure adapted to control a CL plant that provides full information on internal state variables. A final structure provides a neural network NMPC controller to control operation of loops A and B.« less

Well-balanced schemes for the Euler equations with gravitation: Conservative formulation using global fluxes

NASA Astrophysics Data System (ADS)

Chertock, Alina; Cui, Shumo; Kurganov, Alexander; Özcan, Şeyma Nur; Tadmor, Eitan

2018-04-01

We develop a second-order well-balanced central-upwind scheme for the compressible Euler equations with gravitational source term. Here, we advocate a new paradigm based on a purely conservative reformulation of the equations using global fluxes. The proposed scheme is capable of exactly preserving steady-state solutions expressed in terms of a nonlocal equilibrium variable. A crucial step in the construction of the second-order scheme is a well-balanced piecewise linear reconstruction of equilibrium variables combined with a well-balanced central-upwind evolution in time, which is adapted to reduce the amount of numerical viscosity when the flow is at (near) steady-state regime. We show the performance of our newly developed central-upwind scheme and demonstrate importance of perfect balance between the fluxes and gravitational forces in a series of one- and two-dimensional examples.

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

Zhai Zehui; Guo Juan; College of Physics and Electronics Engineering, Shanxi University, Taiyuan 030006

We propose an asymmetric quantum cloning scheme. Based on the proposal and experiment by Andersen et al. [Phys. Rev. Lett. 94, 240503 (2005)], we generalize it to two asymmetric cases: quantum cloning with asymmetry between output clones and between quadrature variables. These optical implementations also employ linear elements and homodyne detection only. Finally, we also compare the utility of symmetric and asymmetric cloning in an analysis of a squeezed-state quantum key distribution protocol and find that the asymmetric one is more advantageous.

Final Report for Dynamic Models for Causal Analysis of Panel Data. Models for Change in Quantitative Variables, Part I Deterministic Models. Part II, Chapter 3.

ERIC Educational Resources Information Center

Hannan, Michael T.

This document is part of a series of chapters described in SO 011 759. Addressing the question of effective models to measure change and the change process, the author suggests that linear structural equation systems may be viewed as steady state outcomes of continuous-change models and have rich sociological grounding. Two interpretations of the…

Aircraft model prototypes which have specified handling-quality time histories

NASA Technical Reports Server (NTRS)

Johnson, S. H.

1976-01-01

Several techniques for obtaining linear constant-coefficient airplane models from specified handling-quality time histories are discussed. One technique, the pseudodata method, solves the basic problem, yields specified eigenvalues, and accommodates state-variable transfer-function zero suppression. The method is fully illustrated for a fourth-order stability-axis small-motion model with three lateral handling-quality time histories specified. The FORTRAN program which obtains and verifies the model is included and fully documented.

A FORTRAN technique for correlating a circular environmental variable with a linear physiological variable in the sugar maple.

PubMed

Pease, J M; Morselli, M F

1987-01-01

This paper deals with a computer program adapted to a statistical method for analyzing an unlimited quantity of binary recorded data of an independent circular variable (e.g. wind direction), and a linear variable (e.g. maple sap flow volume). Circular variables cannot be statistically analyzed with linear methods, unless they have been transformed. The program calculates a critical quantity, the acrophase angle (PHI, phi o). The technique is adapted from original mathematics [1] and is written in Fortran 77 for easier conversion between computer networks. Correlation analysis can be performed following the program or regression which, because of the circular nature of the independent variable, becomes periodic regression. The technique was tested on a file of approximately 4050 data pairs.

Mover Position Detection for PMTLM Based on Linear Hall Sensors through EKF Processing

PubMed Central

Yan, Leyang; Zhang, Hui; Ye, Peiqing

2017-01-01

Accurate mover position is vital for a permanent magnet tubular linear motor (PMTLM) control system. In this paper, two linear Hall sensors are utilized to detect the mover position. However, Hall sensor signals contain third-order harmonics, creating errors in mover position detection. To filter out the third-order harmonics, a signal processing method based on the extended Kalman filter (EKF) is presented. The limitation of conventional processing method is first analyzed, and then EKF is adopted to detect the mover position. In the EKF model, the amplitude of the fundamental component and the percentage of the harmonic component are taken as state variables, and they can be estimated based solely on the measured sensor signals. Then, the harmonic component can be calculated and eliminated. The proposed method has the advantages of faster convergence, better stability and higher accuracy. Finally, experimental results validate the effectiveness and superiority of the proposed method. PMID:28383505

Information Processing Capacity of Dynamical Systems

NASA Astrophysics Data System (ADS)

Dambre, Joni; Verstraeten, David; Schrauwen, Benjamin; Massar, Serge

2012-07-01

Many dynamical systems, both natural and artificial, are stimulated by time dependent external signals, somehow processing the information contained therein. We demonstrate how to quantify the different modes in which information can be processed by such systems and combine them to define the computational capacity of a dynamical system. This is bounded by the number of linearly independent state variables of the dynamical system, equaling it if the system obeys the fading memory condition. It can be interpreted as the total number of linearly independent functions of its stimuli the system can compute. Our theory combines concepts from machine learning (reservoir computing), system modeling, stochastic processes, and functional analysis. We illustrate our theory by numerical simulations for the logistic map, a recurrent neural network, and a two-dimensional reaction diffusion system, uncovering universal trade-offs between the non-linearity of the computation and the system's short-term memory.

Information Processing Capacity of Dynamical Systems

PubMed Central

Dambre, Joni; Verstraeten, David; Schrauwen, Benjamin; Massar, Serge

2012-01-01

Many dynamical systems, both natural and artificial, are stimulated by time dependent external signals, somehow processing the information contained therein. We demonstrate how to quantify the different modes in which information can be processed by such systems and combine them to define the computational capacity of a dynamical system. This is bounded by the number of linearly independent state variables of the dynamical system, equaling it if the system obeys the fading memory condition. It can be interpreted as the total number of linearly independent functions of its stimuli the system can compute. Our theory combines concepts from machine learning (reservoir computing), system modeling, stochastic processes, and functional analysis. We illustrate our theory by numerical simulations for the logistic map, a recurrent neural network, and a two-dimensional reaction diffusion system, uncovering universal trade-offs between the non-linearity of the computation and the system's short-term memory. PMID:22816038

Characterization of the Infrared/X-ray sub-second variability for the black-hole transient GX 339-4

NASA Astrophysics Data System (ADS)

Vincentelli, F. M.; Casella, P.; Maccarone, T. J.; Uttley, P.; Gandhi, P.; Belloni, T.; De Marco, B.; Russell, D. M.; Stella, L.; O'Brien, K.

2018-03-01

We present a detailed analysis of the X-ray/IR fast variability of the Black-Hole Transient GX 339-4 during its low/hard state in August 2008. Thanks to simultaneous high time-resolution observations made with the VLT and RXTE, we performed the first characterisation of the sub-second variability in the near-infrared band - and of its correlation with the X-rays - for a low-mass X-ray binary, using both time- and frequency-domain techniques. We found a power-law correlation between the X-ray and infrared fluxes when measured on timescales of 16 seconds, with a marginally variable slope, steeper than the one found on timescales of days at similar flux levels. We suggest the variable slope - if confirmed - could be due to the infrared flux being a non-constant combination of both optically thin and optically thick synchrotron emission from the jet, as a result of a variable self-absorption break. From cross spectral analysis we found an approximately constant infrared time lag of ≈0.1s, and a very high coherence of ˜90 per cent on timescales of tens of seconds, slowly decreasing toward higher frequencies. Finally, we report on the first detection of a linear rms-flux relation in the emission from a low-mass X-ray binary jet, on timescales where little correlation is found between the X-rays and the jet emission itself. This suggests that either the inflow variations and jet IR emission are coupled by a non-linear or time-variable transform, or that the IR rms-flux relation is not transferred from the inflow to the jet, but is an intrinsic property of emission processes in the jet.

Characterization of the infrared/X-ray subsecond variability for the black hole transient GX 339-4

NASA Astrophysics Data System (ADS)

Vincentelli, F. M.; Casella, P.; Maccarone, T. J.; Uttley, P.; Gandhi, P.; Belloni, T.; De Marco, B.; Russell, D. M.; Stella, L.; O'Brien, K.

2018-07-01

We present a detailed analysis of the X-ray/IR fast variability of the Black-Hole Transient GX 339-4 during its low/hard state in 2008 August. Thanks to simultaneous high time resolution observations made with the VLT and RXTE, we performed the first characterization of the subsecond variability in the near-infrared band - and of its correlation with the X-rays - for a low-mass X-ray binary, using both time- and frequency-domain techniques. We found a power-law correlation between the X-ray and infrared fluxes when measured on time-scales of 16 s, with a marginally variable slope, steeper than the one found on time-scales of days at similar flux levels. We suggest the variable slope - if confirmed - could be due to the infrared flux being a non-constant combination of both optically thin and optically thick synchrotron emission from the jet, as a result of a variable self-absorption break. From cross spectral analysis, we found an approximately constant infrared time lag of ≈0.1 s, and a very high coherence of ˜90 per cent on time-scales of tens of seconds, slowly decreasing towards higher frequencies. Finally, we report on the first detection of a linear rms-flux relation in the emission from a low-mass X-ray binary jet, on time-scales where little correlation is found between the X-rays and the jet emission itself. This suggests that either the inflow variations and jet IR emission are coupled by a non-linear or time-variable transform, or that the IR rms-flux relation is not transferred from the inflow to the jet, but is an intrinsic property of emission processes in the jet.

Application of Local Linear Embedding to Nonlinear Exploratory Latent Structure Analysis

ERIC Educational Resources Information Center

Wang, Haonan; Iyer, Hari

2007-01-01

In this paper we discuss the use of a recent dimension reduction technique called Locally Linear Embedding, introduced by Roweis and Saul, for performing an exploratory latent structure analysis. The coordinate variables from the locally linear embedding describing the manifold on which the data reside serve as the latent variable scores. We…

How Does Variability in Aragonite Saturation Proxies Impact Our Estimates of the Intensity and Duration of Exposure to Aragonite Corrosive Conditions in a Coastal Upwelling System?

NASA Astrophysics Data System (ADS)

Abell, J. T.; Jacobsen, J.; Bjorkstedt, E.

2016-02-01

Determining aragonite saturation state (Ω) in seawater requires measurement of two parameters of the carbonate system: most commonly dissolved inorganic carbon (DIC) and total alkalinity (TA). The routine measurement of DIC and TA is not always possible on frequently repeated hydrographic lines or at moored-time series that collect hydrographic data at short time intervals. In such cases a proxy can be developed that relates the saturation state as derived from one time or infrequent DIC and TA measurements (Ωmeas) to more frequently measured parameters such as dissolved oxygen (DO) and temperature (Temp). These proxies are generally based on best-fit parameterizations that utilize references values of DO and Temp and adjust linear coefficients until the error between the proxy-derived saturation state (Ωproxy) and Ωmeas is minimized. Proxies have been used to infer Ω from moored hydrographic sensors and gliders which routinely collect DO and Temp data but do not include carbonate parameter measurements. Proxies can also calculate Ω in regional oceanographic models which do not explicitly include carbonate parameters. Here we examine the variability and accuracy of Ωproxy along a near-shore hydrographic line and a moored-time series stations at Trinidad Head, CA. The saturation state is determined using proxies from different coastal regions of the California Current Large Marine Ecosystem and from different years of sampling along the hydrographic line. We then calculate the variability and error associated with the use of different proxy coefficients, the sensitivity to reference values and the inclusion of additional variables. We demonstrate how this variability affects estimates of the intensity and duration of exposure to aragonite corrosive conditions on the near-shore shelf and in the water column.

Photonic multipartite entanglement conversion using nonlocal operations

NASA Astrophysics Data System (ADS)

Tashima, T.; Tame, M. S.; Özdemir, Ş. K.; Nori, F.; Koashi, M.; Weinfurter, H.

2016-11-01

We propose a simple setup for the conversion of multipartite entangled states in a quantum network with restricted access. The scheme uses nonlocal operations to enable the preparation of states that are inequivalent under local operations and classical communication, but most importantly does not require full access to the states. It is based on a flexible linear optical conversion gate that uses photons, which are ideally suited for distributed quantum computation and quantum communication in extended networks. In order to show the basic working principles of the gate, we focus on converting a four-qubit entangled cluster state to other locally inequivalent four-qubit states, such as the Greenberger-Horne-Zeilinger and symmetric Dicke states. We also show how the gate can be incorporated into extended graph state networks and can be used to generate variable entanglement and quantum correlations without entanglement but nonvanishing quantum discord.

Reduced-order dynamic output feedback control of uncertain discrete-time Markov jump linear systems

NASA Astrophysics Data System (ADS)

Morais, Cecília F.; Braga, Márcio F.; Oliveira, Ricardo C. L. F.; Peres, Pedro L. D.

2017-11-01

This paper deals with the problem of designing reduced-order robust dynamic output feedback controllers for discrete-time Markov jump linear systems (MJLS) with polytopic state space matrices and uncertain transition probabilities. Starting from a full order, mode-dependent and polynomially parameter-dependent dynamic output feedback controller, sufficient linear matrix inequality based conditions are provided for the existence of a robust reduced-order dynamic output feedback stabilising controller with complete, partial or none mode dependency assuring an upper bound to the ? or the ? norm of the closed-loop system. The main advantage of the proposed method when compared to the existing approaches is the fact that the dynamic controllers are exclusively expressed in terms of the decision variables of the problem. In other words, the matrices that define the controller realisation do not depend explicitly on the state space matrices associated with the modes of the MJLS. As a consequence, the method is specially suitable to handle order reduction or cluster availability constraints in the context of ? or ? dynamic output feedback control of discrete-time MJLS. Additionally, as illustrated by means of numerical examples, the proposed approach can provide less conservative results than other conditions in the literature.

Gross Motor Skills and Cardiometabolic Risk in Children: A Mediation Analysis.

PubMed

Burns, Ryan D; Brusseau, Timothy A; Fu, You; Hannon, James C

2017-04-01

The purpose of this study was to examine the linear relationship between gross motor skills and cardiometabolic risk, with aerobic fitness as a mediator variable, in low-income children from the United States. Participants were a convenience sample of 224 children (mean ± SD age = 9.1 ± 1.1 yr; 129 girls and 95 boys) recruited from five low-income elementary schools from the Mountain West Region of the United States. Gross motor skills were assessed using the Test for Gross Motor Development, 3rd Edition. Gross motor skills were analyzed using a locomotor skill, a ball skill, and a total gross motor skill score. Aerobic fitness was assessed using the Progressive Aerobic Cardiovascular Endurance Run that was administered during physical education class. A continuous and age- and sex-adjusted metabolic syndrome score (MetS) was calculated from health and blood marker measurements collected in a fasted state before school hours. Total effects, average direct effects, and indirect effects (average causal mediation effect) were calculated using a bootstrap mediation analysis method via a linear regression algorithm. The average causal mediation effect of gross locomotor skills on MetS scores, using aerobic fitness as the mediator variable, was statistically significant (β = -0.055, 95% confidence interval = -0.097 to -0.021, P = 0.003). The model explained approximately 17.5% of the total variance in MetS with approximately 43.7% of the relationship between locomotor skills and MetS mediated through aerobic fitness. Ball skills did not significantly relate with cardiometabolic risk. There is a significant relationship between gross locomotor skills and cardiometabolic risk that is partially mediated through aerobic fitness in a sample of low-income children from the United States.

Determining vehicle operating speed and lateral position along horizontal curves using linear mixed-effects models.

PubMed

Fitzsimmons, Eric J; Kvam, Vanessa; Souleyrette, Reginald R; Nambisan, Shashi S; Bonett, Douglas G

2013-01-01

Despite recent improvements in highway safety in the United States, serious crashes on curves remain a significant problem. To assist in better understanding causal factors leading to this problem, this article presents and demonstrates a methodology for collection and analysis of vehicle trajectory and speed data for rural and urban curves using Z-configured road tubes. For a large number of vehicle observations at 2 horizontal curves located in Dexter and Ames, Iowa, the article develops vehicle speed and lateral position prediction models for multiple points along these curves. Linear mixed-effects models were used to predict vehicle lateral position and speed along the curves as explained by operational, vehicle, and environmental variables. Behavior was visually represented for an identified subset of "risky" drivers. Linear mixed-effect regression models provided the means to predict vehicle speed and lateral position while taking into account repeated observations of the same vehicle along horizontal curves. Speed and lateral position at point of entry were observed to influence trajectory and speed profiles. Rural horizontal curve site models are presented that indicate that the following variables were significant and influenced both vehicle speed and lateral position: time of day, direction of travel (inside or outside lane), and type of vehicle.

Correspondence between EQ-5D health state classifications and EQ VAS scores.

PubMed

Whynes, David K

2008-11-07

The EQ-5D health-related quality of life instrument comprises a health state classification followed by a health evaluation using a visual analogue scale (VAS). The EQ-5D has been employed frequently in economic evaluations, yet the relationship between the two parts of the instrument remains ill-understood. In this paper, we examine the correspondence between VAS scores and health state classifications for a large sample, and identify variables which contribute to determining the VAS scores independently of the health states as classified. A UK trial of management of low-grade abnormalities detected on screening for cervical pre-cancer (TOMBOLA) provided EQ-5D data for over 3,000 women. Information on distress and multi-dimensional health locus of control had been collected using other instruments. A linear regression model was fitted, with VAS score as the dependent variable. Independent variables comprised EQ-5D health state classifications, distress, locus of control, and socio-demographic characteristics. Equivalent EQ-5D and distress data, collected at twelve months, were available for over 2,000 of the women, enabling us to predict changes in VAS score over time from changes in EQ-5D classification and distress. In addition to EQ-5D health state classification, VAS score was influenced by the subject's perceived locus of control, and by her age, educational attainment, ethnic origin and smoking behaviour. Although the EQ-5D classification includes a distress dimension, the independent measure of distress was an additional determinant of VAS score. Changes in VAS score over time were explained by changes in both EQ-5D severities and distress. Women allocated to the experimental management arm of the trial reported an increase in VAS score, independently of any changes in health state and distress. In this sample, EQ VAS scores were predictable from the EQ-5D health state classification, although there also existed other group variables which contributed systematically and independently towards determining such scores. These variables comprised psychological disposition, socio-demographic factors such as age and education, clinically-important distress, and the clinical intervention itself. ISRCTN34841617.

Bayesian dynamical systems modelling in the social sciences.

PubMed

Ranganathan, Shyam; Spaiser, Viktoria; Mann, Richard P; Sumpter, David J T

2014-01-01

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.

Variability of activity patterns across mood disorders and time of day.

PubMed

Krane-Gartiser, Karoline; Vaaler, Arne E; Fasmer, Ole Bernt; Sørensen, Kjetil; Morken, Gunnar; Scott, Jan

2017-12-19

Few actigraphy studies in mood disorders have simultaneously included unipolar (UP) and bipolar (BD) depression or BD mixed states as a separate subgroup from mania. This study compared objectively measured activity in UP, BD depression, mania and mixed states and examined if patterns differed according to time of day and/or diagnostic group. Eighty -eight acutely admitted inpatients with mood disorders (52 UP; 18 mania; 12 BD depression; 6 mixed states) underwent 24 hours of actigraphy monitoring. Non-parametric analyses were used to compare median activity level over 24 h (counts per minute), two time series (64-min periods of continuous motor activity) in the morning and evening, and variability in activity across and within groups. There was no between-group difference in 24-h median level of activity, but significant differences emerged between BD depression compared to mania in the active morning period, and between UP and mania and mixed states in the active evening period. Within-group analyses revealed that UP cases showed several significant changes between morning and evening activity, with fewer changes in the BD groups. Mean activity over 24 hours has limited utility in differentiating UP and BD. In contrast, analysis of non-linear variability measures of activity at different times of day could help objectively distinguish between mood disorder subgroups. ClinicalTrials.gov Identifier: NCT01415323 , first registration July 6, 2011.

Comparison of Classifiers for Decoding Sensory and Cognitive Information from Prefrontal Neuronal Populations

PubMed Central

Astrand, Elaine; Enel, Pierre; Ibos, Guilhem; Dominey, Peter Ford; Baraduc, Pierre; Ben Hamed, Suliann

2014-01-01

Decoding neuronal information is important in neuroscience, both as a basic means to understand how neuronal activity is related to cerebral function and as a processing stage in driving neuroprosthetic effectors. Here, we compare the readout performance of six commonly used classifiers at decoding two different variables encoded by the spiking activity of the non-human primate frontal eye fields (FEF): the spatial position of a visual cue, and the instructed orientation of the animal's attention. While the first variable is exogenously driven by the environment, the second variable corresponds to the interpretation of the instruction conveyed by the cue; it is endogenously driven and corresponds to the output of internal cognitive operations performed on the visual attributes of the cue. These two variables were decoded using either a regularized optimal linear estimator in its explicit formulation, an optimal linear artificial neural network estimator, a non-linear artificial neural network estimator, a non-linear naïve Bayesian estimator, a non-linear Reservoir recurrent network classifier or a non-linear Support Vector Machine classifier. Our results suggest that endogenous information such as the orientation of attention can be decoded from the FEF with the same accuracy as exogenous visual information. All classifiers did not behave equally in the face of population size and heterogeneity, the available training and testing trials, the subject's behavior and the temporal structure of the variable of interest. In most situations, the regularized optimal linear estimator and the non-linear Support Vector Machine classifiers outperformed the other tested decoders. PMID:24466019

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

NASA Astrophysics Data System (ADS)

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

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

Variable-energy drift-tube linear accelerator

DOEpatents

Swenson, Donald A.; Boyd, Jr., Thomas J.; Potter, James M.; Stovall, James E.

1984-01-01

A linear accelerator system includes a plurality of post-coupled drift-tubes wherein each post coupler is bistably positionable to either of two positions which result in different field distributions. With binary control over a plurality of post couplers, a significant accumlative effect in the resulting field distribution is achieved yielding a variable-energy drift-tube linear accelerator.

Variable-energy drift-tube linear accelerator

DOEpatents

Swenson, D.A.; Boyd, T.J. Jr.; Potter, J.M.; Stovall, J.E.

A linear accelerator system includes a plurality of post-coupled drift-tubes wherein each post coupler is bistably positionable to either of two positions which result in different field distributions. With binary control over a plurality of post couplers, a significant accumlative effect in the resulting field distribution is achieved yielding a variable-energy drift-tube linear accelerator.

Variables Predicting Foreign Language Reading Comprehension and Vocabulary Acquisition in a Linear Hypermedia Environment

ERIC Educational Resources Information Center

Akbulut, Yavuz

2007-01-01

Factors predicting vocabulary learning and reading comprehension of advanced language learners of English in a linear multimedia text were investigated in the current study. Predictor variables of interest were multimedia type, reading proficiency, learning styles, topic interest and background knowledge about the topic. The outcome variables of…

Graph-cut based discrete-valued image reconstruction.

PubMed

Tuysuzoglu, Ahmet; Karl, W Clem; Stojanovic, Ivana; Castañòn, David; Ünlü, M Selim

2015-05-01

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

Self-organizing biochemical cycle in dynamic feedback with soil structure

NASA Astrophysics Data System (ADS)

Vasilyeva, Nadezda; Vladimirov, Artem; Smirnov, Alexander; Matveev, Sergey; Tyrtyshnikov, Evgeniy; Yudina, Anna; Milanovskiy, Evgeniy; Shein, Evgeniy

2016-04-01

In the present study we perform bifurcation analysis of a physically-based mathematical model of self-organized structures in soil (Vasilyeva et al., 2015). The state variables in this model included microbial biomass, two organic matter types, oxygen, carbon dioxide, water content and capillary pore size. According to our previous experimental studies, organic matter affinity to water is an important property affecting soil structure. Therefore, organic matter wettability was taken as principle distinction between organic matter types in this model. It considers general known biological feedbacks with soil physical properties formulated as a system of parabolic type non-linear partial differential equations with elements of discrete modeling for water and pore formation. The model shows complex behavior, involving emergence of temporal and spatial irregular auto-oscillations from initially homogeneous distributions. The energy of external impact on a system was defined by a constant oxygen level on the boundary. Non-linear as opposed to linear oxygen diffusion gives possibility of modeling anaerobic micro-zones formation (organic matter conservation mechanism). For the current study we also introduced population competition of three different types of microorganisms according to their mobility/feeding (diffusive, moving and fungal growth). The strongly non-linear system was solved and parameterized by time-optimized algorithm combining explicit and implicit (matrix form of Thomas algorithm) methods considering the time for execution of the evaluated time-step according to accuracy control. The integral flux of the CO2 state variable was used as a macroscopic parameter to describe system as a whole and validation was carried out on temperature series of moisture dependence for soil heterotrophic respiration data. Thus, soil heterotrophic respiration can be naturally modeled as an integral result of complex dynamics on microscale, arising from biological processes formulated as a sum of state variables products, with no need to introduce any saturation functions, such as Mikhaelis-Menten type kinetics, inside the model. Analyzed dynamic soil model is being further developed to describe soil structure formation and its effect on organic matter decomposition at macro-scale, to predict changes with external perturbations. To link micro- and macro-scales we additionally model soil particles aggregation process. The results from local biochemical soil organic matter cycle serve as inputs to aggregation process, while the output aggregate size distributions define physical properties in the soil profile, these in turn serve as dynamic parameters in local biochemical cycles. The additional formulation is a system of non-linear ordinary differential equations, including Smoluchowski-type equations for aggregation and reaction kinetics equations for coagulation/adsorption/adhesion processes. Vasilyeva N.A., Ingtem J.G., Silaev D.A. Nonlinear dynamical model of microbial growth in soil medium. Computational Mathematics and Modeling, vol. 49, p.31-44, 2015 (in Russian). English version is expected in corresponding vol.27, issue 2, 2016.

Tangent linear superparameterization of convection in a 10 layer global atmosphere with calibrated climatology

DOE PAGES

Kelly, Patrick; Mapes, Brian; Hu, I-Kuan; ...

2017-04-03

This study describes a new intermediate global atmosphere model in which synoptic and planetary dynamics including the advection of water vapor are explicit, the time mean flow is centered near a realistic state through the calibration of time-independent 3D forcings, and temporal anomalies of convective tendencies of heat and moisture in each column are represented as a linear matrix acting on the anomalous temperature and moisture profiles in the GCM. This matrix was devised from Kuang’s [2010] linear response function (LRF) of a cooled cyclic convection-permitting model (CCPM) with 256 km periodic domain and 1km mesh, measured around an equilibriummore » state with a mean rainrate of 3.5 mm/d. The goal of this effort was to cleanly test the role of convection’s free-tropospheric moisture sensitivity in tropical waves, without incurring large changes of mean climate that confuse the interpretation of experiments with entrainment rates in the convection schemes of full-physics GCMs. As the sensitivity to free tropospheric moisture (columns 12-20 of the matrix, representing sensitivity to humidity above 900 hPa altitude) is multiplied by a factor ranging from 0 to 2, the model’s variability ranges from: (1) moderately strong convectively coupled waves with speeds near 20 m s -1; to (0) weak waves, but still slowed by convective coupling; to (2) wave variability that is greater in amplitude as the water vapor field plays an increasingly important role. Longitudinal structure in the model’s time-mean tropical flow is not fully realistic, and does change significantly with matrix edits, disappointing initial hopes that the Madden-Julian oscillation would be well simulated in the control and could be convincingly decomposed, but further work could improve this class of models.« less

Tangent linear superparameterization of convection in a 10 layer global atmosphere with calibrated climatology

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

Kelly, Patrick; Mapes, Brian; Hu, I-Kuan

This study describes a new intermediate global atmosphere model in which synoptic and planetary dynamics including the advection of water vapor are explicit, the time mean flow is centered near a realistic state through the calibration of time-independent 3D forcings, and temporal anomalies of convective tendencies of heat and moisture in each column are represented as a linear matrix acting on the anomalous temperature and moisture profiles in the GCM. This matrix was devised from Kuang’s [2010] linear response function (LRF) of a cooled cyclic convection-permitting model (CCPM) with 256 km periodic domain and 1km mesh, measured around an equilibriummore » state with a mean rainrate of 3.5 mm/d. The goal of this effort was to cleanly test the role of convection’s free-tropospheric moisture sensitivity in tropical waves, without incurring large changes of mean climate that confuse the interpretation of experiments with entrainment rates in the convection schemes of full-physics GCMs. As the sensitivity to free tropospheric moisture (columns 12-20 of the matrix, representing sensitivity to humidity above 900 hPa altitude) is multiplied by a factor ranging from 0 to 2, the model’s variability ranges from: (1) moderately strong convectively coupled waves with speeds near 20 m s -1; to (0) weak waves, but still slowed by convective coupling; to (2) wave variability that is greater in amplitude as the water vapor field plays an increasingly important role. Longitudinal structure in the model’s time-mean tropical flow is not fully realistic, and does change significantly with matrix edits, disappointing initial hopes that the Madden-Julian oscillation would be well simulated in the control and could be convincingly decomposed, but further work could improve this class of models.« less

Cosmological perturbations of a perfect fluid and noncommutative variables

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

De Felice, Antonio; Gerard, Jean-Marc; Suyama, Teruaki

2010-03-15

We describe the linear cosmological perturbations of a perfect fluid at the level of an action, providing thus an alternative to the standard approach based only on the equations of motion. This action is suited not only to perfect fluids with a barotropic equation of state, but also to those for which the pressure depends on two thermodynamical variables. By quantizing the system we find that (1) some perturbation fields exhibit a noncommutativity quite analogous to the one observed for a charged particle moving in a strong magnetic field, (2) local curvature and pressure perturbations cannot be measured simultaneously, (3)more » ghosts appear if the null energy condition is violated.« less

Solving the Problem of Bending of Multiply Connected Plates with Elastic Inclusions

NASA Astrophysics Data System (ADS)

Kaloerov, S. A.; Koshkin, A. A.

2017-11-01

This paper describes a method for determining the strain state of a thin anisotropic plate with elastic arbitrarily arranged elliptical inclusions. Complex potentials are used to reduce the problem to determining functions of generalized complex variables, which, in turn, comes down to an overdetermined system of linear algebraic equations, solved by singular expansions. This paper presents the results of numerical calculations that helped establish the influence of rigidity of elastic inclusions, distances between inclusions, and their geometric characteristics on the bending moments occurring in the plate. It is found that the specific properties of distribution of moments near the apexes of linear elastic inclusions, characterized by moment intensity coefficients, occur only in the case of sufficiently rigid and elastic inclusions.

Detection of orbital angular momentum using a photonic integrated circuit.

PubMed

Rui, Guanghao; Gu, Bing; Cui, Yiping; Zhan, Qiwen

2016-06-20

Orbital angular momentum (OAM) state of photons offer an attractive additional degree of freedom that has found a variety of applications. Measurement of OAM state, which is a critical task of these applications, demands photonic integrated devices for improved fidelity, miniaturization, and reconfiguration. Here we report the design of a silicon-integrated OAM receiver that is capable of detecting distinct and variable OAM states. Furthermore, the reconfiguration capability of the detector is achieved by applying voltage to the GeSe film to form gratings with alternate states. The resonant wavelength for arbitrary OAM state is demonstrated to be tunable in a quasi-linear manner through adjusting the duty cycle of the gratings. This work provides a viable approach for the realization of a compact integrated OAM detection device with enhanced functionality that may find important applications in optical communications and information processing with OAM states.

Geometric characterization of separability and entanglement in pure Gaussian states by single-mode unitary operations

NASA Astrophysics Data System (ADS)

Adesso, Gerardo; Giampaolo, Salvatore M.; Illuminati, Fabrizio

2007-10-01

We present a geometric approach to the characterization of separability and entanglement in pure Gaussian states of an arbitrary number of modes. The analysis is performed adapting to continuous variables a formalism based on single subsystem unitary transformations that has been recently introduced to characterize separability and entanglement in pure states of qubits and qutrits [S. M. Giampaolo and F. Illuminati, Phys. Rev. A 76, 042301 (2007)]. In analogy with the finite-dimensional case, we demonstrate that the 1×M bipartite entanglement of a multimode pure Gaussian state can be quantified by the minimum squared Euclidean distance between the state itself and the set of states obtained by transforming it via suitable local symplectic (unitary) operations. This minimum distance, corresponding to a , uniquely determined, extremal local operation, defines an entanglement monotone equivalent to the entropy of entanglement, and amenable to direct experimental measurement with linear optical schemes.

Do bioclimate variables improve performance of climate envelope models?

USGS Publications Warehouse

Watling, James I.; Romañach, Stephanie S.; Bucklin, David N.; Speroterra, Carolina; Brandt, Laura A.; Pearlstine, Leonard G.; Mazzotti, Frank J.

2012-01-01

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.

A linear programming model for protein inference problem in shotgun proteomics.

PubMed

Huang, Ting; He, Zengyou

2012-11-15

Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/. zyhe@dlut.edu.cn. Supplementary data are available at Bioinformatics Online.

Prediction and experimental observation of damage dependent damping in laminated composite beams

NASA Technical Reports Server (NTRS)

Allen, D. H.; Harris, C. E.; Highsmith, A. L.

1987-01-01

The equations of motion are developed for laminated composite beams with load-induced matrix cracking. The damage is accounted for by utilizing internal state variables. The net result of these variables on the field equations is the introduction of both enhanced damping, and degraded stiffness. Both quantities are history dependent and spatially variable, thus resulting in nonlinear equations of motion. It is explained briefly how these equations may be quasi-linearized for laminated polymeric composites under certain types of structural loading. The coupled heat conduction equation is developed, and it is shown that an enhanced Zener damping effect is produced by the introduction of microstructural damage. The resulting equations are utilized to demonstrate how damage dependent material properties may be obtained from dynamic experiments. Finaly, experimental results are compared to model predictions for several composite layups.

Robust high-precision attitude control for flexible spacecraft with improved mixed H2/H∞ control strategy under poles assignment constraint

NASA Astrophysics Data System (ADS)

Liu, Chuang; Ye, Dong; Shi, Keke; Sun, Zhaowei

2017-07-01

A novel improved mixed H2/H∞ control technique combined with poles assignment theory is presented to achieve attitude stabilization and vibration suppression simultaneously for flexible spacecraft in this paper. The flexible spacecraft dynamics system is described and transformed into corresponding state space form. Based on linear matrix inequalities (LMIs) scheme and poles assignment theory, the improved mixed H2/H∞ controller does not restrict the equivalence of the two Lyapunov variables involved in H2 and H∞ performance, which can reduce conservatives compared with traditional mixed H2/H∞ controller. Moreover, it can eliminate the coupling of Lyapunov matrix variables and system matrices by introducing slack variable that provides additional degree of freedom. Several simulations are performed to demonstrate the effectiveness and feasibility of the proposed method in this paper.

A Blocked Linear Method for Optimizing Large Parameter Sets in Variational Monte Carlo

DOE PAGES

Zhao, Luning; Neuscamman, Eric

2017-05-17

We present a modification to variational Monte Carlo’s linear method optimization scheme that addresses a critical memory bottleneck while maintaining compatibility with both the traditional ground state variational principle and our recently-introduced variational principle for excited states. For wave function ansatzes with tens of thousands of variables, our modification reduces the required memory per parallel process from tens of gigabytes to hundreds of megabytes, making the methodology a much better fit for modern supercomputer architectures in which data communication and per-process memory consumption are primary concerns. We verify the efficacy of the new optimization scheme in small molecule tests involvingmore » both the Hilbert space Jastrow antisymmetric geminal power ansatz and real space multi-Slater Jastrow expansions. Satisfied with its performance, we have added the optimizer to the QMCPACK software package, with which we demonstrate on a hydrogen ring a prototype approach for making systematically convergent, non-perturbative predictions of Mott-insulators’ optical band gaps.« less

Effects of stress on emotional memory in patients with Alzheimer's disease and in healthy elderly.

PubMed

Gómez-Gallego, María; Gómez-García, Juan

2017-12-14

We aimed at examining the relation between stress markers (cortisol levels and state anxiety) with memory for emotional information in AD patients and in healthy elderly. Baseline and changes in stress markers during memory testing were assessed in a sample of 98 elderly (46 mild-to-moderate Alzheimer's disease patients and 52 controls) recruited from dementia day centers and adult day centers, respectively. Salivary cortisol, state anxiety, and measures of immediate recall and delayed recognition using the International Affective Pictures System. Patients' performance in memory tasks was not associated with either cortisol levels or anxiety. In controls, quadratic and linear associations were found between cortisol and immediate recall scores (total and bias, respectively). Besides, quadratic and linear associations were observed between anxiety and delayed recognition scores (total and bias, respectively). The emotional memory of patients with Alzheimer´s disease is not related to stress markers as healthy older adults' is. Future studies that include moderating variables are needed to explain the lack of association.

A Kinetic Approach to Propagation and Stability of Detonation Waves

NASA Astrophysics Data System (ADS)

Monaco, R.; Bianchi, M. Pandolfi; Soares, A. J.

2008-12-01

The problem of the steady propagation and linear stability of a detonation wave is formulated in the kinetic frame for a quaternary gas mixture in which a reversible bimolecular reaction takes place. The reactive Euler equations and related Rankine-Hugoniot conditions are deduced from the mesoscopic description of the process. The steady propagation problem is solved for a Zeldovich, von Neuman and Doering (ZND) wave, providing the detonation profiles and the wave thickness for different overdrive degrees. The one-dimensional stability of such detonation wave is then studied in terms of an initial value problem coupled with an acoustic radiation condition at the equilibrium final state. The stability equations and their initial data are deduced from the linearized reactive Euler equations and related Rankine-Hugoniot conditions through a normal mode analysis referred to the complex disturbances of the steady state variables. Some numerical simulations for an elementary reaction of the hydrogen-oxygen chain are proposed in order to describe the time and space evolution of the instabilities induced by the shock front perturbation.

Two-Component Noncollinear Time-Dependent Spin Density Functional Theory for Excited State Calculations.

PubMed

Egidi, Franco; Sun, Shichao; Goings, Joshua J; Scalmani, Giovanni; Frisch, Michael J; Li, Xiaosong

2017-06-13

We present a linear response formalism for the description of the electronic excitations of a noncollinear reference defined via Kohn-Sham spin density functional methods. A set of auxiliary variables, defined using the density and noncollinear magnetization density vector, allows the generalization of spin density functional kernels commonly used in collinear DFT to noncollinear cases, including local density, GGA, meta-GGA and hybrid functionals. Working equations and derivations of functional second derivatives with respect to the noncollinear density, required in the linear response noncollinear TDDFT formalism, are presented in this work. This formalism takes all components of the spin magnetization into account independent of the type of reference state (open or closed shell). As a result, the method introduced here is able to afford a nonzero local xc torque on the spin magnetization while still satisfying the zero-torque theorem globally. The formalism is applied to a few test cases using the variational exact-two-component reference including spin-orbit coupling to illustrate the capabilities of the method.

The risk-adjusted vision beyond casemix (DRG) funding in Australia. International lessons in high complexity and capitation.

PubMed

Antioch, Kathryn M; Walsh, Michael K

2004-06-01

Hospitals throughout the world using funding based on diagnosis-related groups (DRG) have incurred substantial budgetary deficits, despite high efficiency. We identify the limitations of DRG funding that lack risk (severity) adjustment for State-wide referral services. Methods to risk adjust DRGs are instructive. The average price in casemix funding in the Australian State of Victoria is policy based, not benchmarked. Average cost weights are too low for high-complexity DRGs relating to State-wide referral services such as heart and lung transplantation and trauma. Risk-adjusted specified grants (RASG) are required for five high-complexity respiratory, cardiology and stroke DRGs incurring annual deficits of $3.6 million due to high casemix complexity and government under-funding despite high efficiency. Five stepwise linear regressions for each DRG excluded non-significant variables and assessed heteroskedasticity and multicollinearlity. Cost per patient was the dependent variable. Significant independent variables were age, length-of-stay outliers, number of disease types, diagnoses, procedures and emergency status. Diagnosis and procedure severity markers were identified. The methodology and the work of the State-wide Risk Adjustment Working Group can facilitate risk adjustment of DRGs State-wide and for Treasury negotiations for expenditure growth. The Alfred Hospital previously negotiated RASG of $14 million over 5 years for three trauma and chronic DRGs. Some chronic diseases require risk-adjusted capitation funding models for Australian Health Maintenance Organizations as an alternative to casemix funding. The use of Diagnostic Cost Groups can facilitate State and Federal government reform via new population-based risk adjusted funding models that measure health need.

A Numerical Scheme for Ordinary Differential Equations Having Time Varying and Nonlinear Coefficients Based on the State Transition Matrix

NASA Technical Reports Server (NTRS)

Bartels, Robert E.

2002-01-01

A variable order method of integrating initial value ordinary differential equations that is based on the state transition matrix has been developed. The method has been evaluated for linear time variant and nonlinear systems of equations. While it is more complex than most other methods, it produces exact solutions at arbitrary time step size when the time variation of the system can be modeled exactly by a polynomial. Solutions to several nonlinear problems exhibiting chaotic behavior have been computed. Accuracy of the method has been demonstrated by comparison with an exact solution and with solutions obtained by established methods.

Fall in homicides in the City of São Paulo: an exploratory analysis of possible determinants

PubMed Central

Peres, Maria Fernanda Tourinho; de Almeida, Juliana Feliciano; Vicentin, Diego; Cerda, Magdalena; Cardia, Nancy; Adorno, Sérgio

2012-01-01

Throughout the first decade of the 2000s the homicide mortality rate (HMR) showed a significant reduction in the state and the city of São Paulo (MSP). The aim of this study is to describe the trend of HMR, socio-demographic indicators, and the investment in social and public security, and to analyze the correlation between HMR and independent variables in the MSP between 1996 and 2008. An exploratory time series ecological study was conducted. The following variables were included: HMR per 100,000 inhabitants, socio-demographic indicators, and investments in social and public security. The moving-averages for all variables were calculated and trends were analyzed through Simple Linear Regression models. Annual percentage changes, the average annual change and periodic percentage changes were calculated for all variables, and the associations between annual percentage changes were tested by Spearman’s correlation analysis. Correlations were found for the proportion of youth in the population (r = 0.69), unemployment rate (r = 0.60), State budget for education and culture (r = 0.87) and health and sanitation (r = 0.56), municipal (r = 0.68) and State (r = 0.53) budget for Public Security, firearms seized (r = 0.69) and the incarceration rate (r = 0.71). The results allow us to support the hypothesis that demographic changes, acceleration of the economy, in particular the fall in unemployment, investment in social policies and changes in public security policies act synergistically to reduce HMR in São Paulo. Complex models of analysis, incorporating the joint action of different potential explanatory variables, should be developed. PMID:22218669

Sleep variability and cardiac autonomic modulation in adolescents – Penn State Child Cohort (PSCC) study

PubMed Central

Rodríguez-Colón, Sol M.; He, Fan; Bixler, Edward O.; Fernandez-Mendoza, Julio; Vgontzas, Alexandros N.; Calhoun, Susan; Zheng, Zhi-Jie; Liao, Duanping

2015-01-01

Objective To investigate the effects of objectively measured habitual sleep patterns on cardiac autonomic modulation (CAM) in a population-based sample of adolescents. Methods We used data from 421 adolescents who completed the follow-up examination in the Penn State Children Cohort study. CAM was assessed by heart rate (HR) variability (HRV) analysis of beat-to-beat normal R-R intervals from a 39-h electrocardiogram, on a 30-min basis. The HRV indices included frequency domain (HF, LF, and LF/HF ratio), and time domain (SDNN, RMSSD, and heart rate or HR) variables. Actigraphy was used for seven consecutive nights to estimate nightly sleep duration and time in bed. The seven-night mean (SD) of sleep duration and sleep efficiency were used to represent sleep duration, duration variability, sleep efficiency, and efficiency variability, respectively. HF and LF were log-transformed for statistical analysis. Linear mixed-effect models were used to analyze the association between sleep patterns and CAM. Results After adjusting for major confounders, increased sleep duration variability and efficiency variability were significantly associated with lower HRV and higher HR during the 39-h, as well as separated by daytime and nighttime. For instance, a 1-h increase in sleep duration variability is associated with −0.14(0.04), −0.12(0.06), and −0.16(0.05) ms2 decrease in total, daytime, and nighttime HF, respectively. No associations were found between sleep duration, or sleep efficiency and HRV. Conclusion Higher habitual sleep duration variability and efficiency variability are associated with lower HRV and higher HR, suggesting that an irregular sleep pattern has an adverse impact on CAM, even in healthy adolescents. PMID:25555635

Quantifying the Contribution of Wind-Driven Linear Response to the Seasonal and Interannual Variability of Amoc Volume Transports Across 26.5ºN

NASA Astrophysics Data System (ADS)

Shimizu, K.; von Storch, J. S.; Haak, H.; Nakayama, K.; Marotzke, J.

2014-12-01

Surface wind stress is considered to be an important forcing of the seasonal and interannual variability of Atlantic Meridional Overturning Circulation (AMOC) volume transports. A recent study showed that even linear response to wind forcing captures observed features of the mean seasonal cycle. However, the study did not assess the contribution of wind-driven linear response in realistic conditions against the RAPID/MOCHA array observation or Ocean General Circulation Model (OGCM) simulations, because it applied a linear two-layer model to the Atlantic assuming constant upper layer thickness and density difference across the interface. Here, we quantify the contribution of wind-driven linear response to the seasonal and interannual variability of AMOC transports by comparing wind-driven linear simulations under realistic continuous stratification against the RAPID observation and OCGM (MPI-OM) simulations with 0.4º resolution (TP04) and 0.1º resolution (STORM). All the linear and MPI-OM simulations capture more than 60% of the variance in the observed mean seasonal cycle of the Upper Mid-Ocean (UMO) and Florida Strait (FS) transports, two components of the upper branch of the AMOC. The linear and TP04 simulations also capture 25-40% of the variance in the observed transport time series between Apr 2004 and Oct 2012; the STORM simulation does not capture the observed variance because of the stochastic signal in both datasets. Comparison of half-overlapping 12-month-long segments reveals some periods when the linear and TP04 simulations capture 40-60% of the observed variance, as well as other periods when the simulations capture only 0-20% of the variance. These results show that wind-driven linear response is a major contributor to the seasonal and interannual variability of the UMO and FS transports, and that its contribution varies in an interannual timescale, probably due to the variability of stochastic processes.

Influence of electromagnetic interference on implanted cardiac arrhythmia devices in and around a magnetically levitated linear motor car.

PubMed

Fukuta, Motoyuki; Mizutani, Noboru; Waseda, Katsuhisa

2005-01-01

This study was designed to determine the susceptibility of implanted cardiac arrhythmia devices to electromagnetic interference in and around a magnetically levitated linear motor car [High-Speed Surface Transport (HSST)]. During the study, cardiac devices were connected to a phantom model that had similar characteristics to the human body. Three pacemakers from three manufacturers and one implantable cardioverter-defibrillator (ICD) were evaluated in and around the magnetically levitated vehicle. The system is based on a normal conductive system levitated by the attractive force of magnets and propelled by a linear induction motor without wheels. The magnetic field strength at 40 cm from the vehicle in the nonlevitating state was 0.12 mT and that during levitation was 0.20 mT. The magnetic and electric field strengths on a seat close to the variable voltage/variable frequency inverter while the vehicle was moving and at rest were 0.13 mT, 2.95 V/m and 0.04 mT, 0.36 V/m, respectively. Data recorded on a seat close to the reactor while the vehicle was moving and at rest were 0.09 mT, 2.45 V/m and 0.05 mT, 1.46 V/m, respectively. Measured magnetic and electric field strengths both inside and outside the linear motor car were too low to result in device inactivation. No sensing, pacing, or arrhythmic interactions were noted with any pacemaker or ICD programmed in either bipolar and unipolar configurations. In conclusion, our data suggest that a permanent programming change or a device failure is unlikely to occur and that the linear motor car system is probably safe for patients with one of the four implanted cardiac arrhythmia devices used in this study under the conditions tested.

A Partitioning and Bounded Variable Algorithm for Linear Programming

ERIC Educational Resources Information Center

Sheskin, Theodore J.

2006-01-01

An interesting new partitioning and bounded variable algorithm (PBVA) is proposed for solving linear programming problems. The PBVA is a variant of the simplex algorithm which uses a modified form of the simplex method followed by the dual simplex method for bounded variables. In contrast to the two-phase method and the big M method, the PBVA does…

Unambiguous discrimination between linearly dependent equidistant states with multiple copies

NASA Astrophysics Data System (ADS)

Zhang, Wen-Hai; Ren, Gang

2018-07-01

Linearly independent quantum states can be unambiguously discriminated, but linearly dependent ones cannot. For linearly dependent quantum states, however, if C copies of the single states are available, then they may form linearly independent states, and can be unambiguously discriminated. We consider unambiguous discrimination among N = D + 1 linearly dependent states given that C copies are available and that the single copies span a D-dimensional space with equal inner products. The maximum unambiguous discrimination probability is derived for all C with equal a priori probabilities. For this classification of the linearly dependent equidistant states, our result shows that if C is even then adding a further copy fails to increase the maximum discrimination probability.

Multi-Wavelength, Multi-Beam, and Polarization-Sensitive Laser Transmitter for Surface Mapping

NASA Technical Reports Server (NTRS)

Yu, Anthony W.; Ramos-Izquierdo, Luis; Harding, David; Huss, Tim

2011-01-01

A multi-beam, multi-color, polarized laser transmitter has been developed for mapping applications. It uses commercial off-the-shelf components for a lowcost approach for a ruggedized laser suitable for field deployment. The laser transmitter design is capable of delivering dual wavelengths, multiple beams on each wavelength with equal (or variable) intensities per beam, and a welldefined state of polarization. This laser transmitter has been flown on several airborne campaigns for the Slope Imaging Multi-Polarization Photon Counting Lidar (SIMPL) instrument, and at the time of this reporting is at a technology readiness level of between 5 and 6. The laser is a 1,064-nm microchip high-repetition-rate laser emitting energy of about 8 microjoules per pulse. The beam was frequency-doubled to 532 nm using a KTP (KTiOPO4) nonlinear crystal [other nonlinear crystals such as LBO (LiB3O5) or periodically poled lithium niobiate can be used as well, depending on the conversion efficiency requirements], and the conversion efficiency was approximately 30 percent. The KTP was under temperature control using a thermoelectric cooler and a feedback monitoring thermistor. The dual-wavelength beams were then spectrally separated and each color went through its own optical path, which consisted of a beam-shaping lens, quarterwave plate (QWP), and a birefringent crystal (in this case, a calcite crystal, but others such as vanadate can be used). The QWP and calcite crystal set was used to convert the laser beams from a linearly polarized state to circularly polarized light, which when injected into a calcite crystal, will spatially separate the circularly polarized light into the two linear polarized components. The spatial separation of the two linearly polarized components is determined by the length of the crystal. A second set of QWP and calcite then further separated the two beams into four. Additional sets of QWP and calcite can be used to further split the beams into multiple orders of two. The spatially separated beams had alternating linearly polarization states; a half-wave plate (HWP) array was then made to rotate the alternating states of A multi-beam, multi-color, polarized laser transmitter has been developed for mapping applications. It uses commercial off-the-shelf components for a lowcost approach for a ruggedized laser suitable for field deployment. The laser transmitter design is capable of delivering dual wavelengths, multiple beams on each wavelength with equal (or variable) intensities per beam, and a welldefined state of polarization. This laser transmitter has been flown on several airborne campaigns for the Slope Imaging Multi-Polarization Photon Counting Lidar (SIMPL) instrument, and at the time of this reporting is at a technology readiness level of between 5 and 6. The laser is a 1,064-nm microchip high-repetition-rate laser emitting energy of about 8 microjoules per pulse. The beam was frequency-doubled to 532 nm using a KTP (KTiOPO4) nonlinear crystal [other nonlinear crystals such as LBO (LiB3O5) or periodically poled lithium niobiate can be used as well, depending on the conversion efficiency requirements], and the conversion efficiency was approximately 30 percent. The KTP was under temperature control using a thermoelectric cooler and a feedback monitoring thermistor. The dual-wavelength beams were then spectrally separated and each color went through its own optical path, which consisted of a beam-shaping lens, quarterwave plate (QWP), and a birefringent crystal (in this case, a calcite crystal, but others such as vanadate can be used). The QWP and calcite crystal set was used to convert the laser beams from a linearly polarized state to circularly polarized light, which when injected into a calcite crystal, will spatially separate the circularly polarized light into the two linear polarized components. The spatial separation of the two linearly polarized components is determined by the length of the crystal. A cond set of QWP and calcite then further separated the two beams into four. Additional sets of QWP and calcite can be used to further split the beams into multiple orders of two. The spatially separated beams had alternating linearly polarization states; a half-wave plate (HWP) array was then made to rotate the alternating states of

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

A new large initial condition ensemble to assess avoided impacts in a climate mitigation scenario

NASA Astrophysics Data System (ADS)

Sanderson, B. M.; Tebaldi, C.; Knutti, R.; Oleson, K. W.

2014-12-01

It has recently been demonstrated that when considering timescales of up to 50 years, natural variability may play an equal role to anthropogenic forcing on subcontinental trends for a variety of climate indicators. Thus, for many questions assessing climate impacts on such time and spatial scales, it has become clear that a significant number of ensemble members may be required to produce robust statistics (and especially so for extreme events). However, large ensemble experiments to date have considered the role of variability in a single scenario, leaving uncertain the relationship between the forced climate trajectory and the variability about that path. To address this issue, we present a new, publicly available, 15 member initial condition ensemble of 21st century climate projections for the RCP 4.5 scenario using the CESM1.1 Earth System Model, which we propose as a companion project to the existing 40 member CESM large ensemble which uses the higher greenhouse gas emission future of RCP8.5. This provides a valuable data set for assessing what societal and ecological impacts might be avoided through a moderate mitigation strategy in contrast to a fossil fuel intensive future. We present some early analyses of these combined ensembles to assess to what degree the climate variability can be considered to combine linearly with the underlying forced response. In regions where there is no detectable relationship between the mean state and the variability about the mean trajectory, then linear assumptions can be trivially exploited to utilize a single ensemble or control simulation to characterize the variability in any scenario of interest. We highlight regions where there is a detectable nonlinearity in extreme event frequency, how far in the future they will be manifested and propose mechanisms to account for these effects.

Temporal variability of spectro-temporal receptive fields in the anesthetized auditory cortex.

PubMed

Meyer, Arne F; Diepenbrock, Jan-Philipp; Ohl, Frank W; Anemüller, Jörn

2014-01-01

Temporal variability of neuronal response characteristics during sensory stimulation is a ubiquitous phenomenon that may reflect processes such as stimulus-driven adaptation, top-down modulation or spontaneous fluctuations. It poses a challenge to functional characterization methods such as the receptive field, since these often assume stationarity. We propose a novel method for estimation of sensory neurons' receptive fields that extends the classic static linear receptive field model to the time-varying case. Here, the long-term estimate of the static receptive field serves as the mean of a probabilistic prior distribution from which the short-term temporally localized receptive field may deviate stochastically with time-varying standard deviation. The derived corresponding generalized linear model permits robust characterization of temporal variability in receptive field structure also for highly non-Gaussian stimulus ensembles. We computed and analyzed short-term auditory spectro-temporal receptive field (STRF) estimates with characteristic temporal resolution 5-30 s based on model simulations and responses from in total 60 single-unit recordings in anesthetized Mongolian gerbil auditory midbrain and cortex. Stimulation was performed with short (100 ms) overlapping frequency-modulated tones. Results demonstrate identification of time-varying STRFs, with obtained predictive model likelihoods exceeding those from baseline static STRF estimation. Quantitative characterization of STRF variability reveals a higher degree thereof in auditory cortex compared to midbrain. Cluster analysis indicates that significant deviations from the long-term static STRF are brief, but reliably estimated. We hypothesize that the observed variability more likely reflects spontaneous or state-dependent internal fluctuations that interact with stimulus-induced processing, rather than experimental or stimulus design.

Key-Generation Algorithms for Linear Piece In Hand Matrix Method

NASA Astrophysics Data System (ADS)

Tadaki, Kohtaro; Tsujii, Shigeo

The linear Piece In Hand (PH, for short) matrix method with random variables was proposed in our former work. It is a general prescription which can be applicable to any type of multivariate public-key cryptosystems for the purpose of enhancing their security. Actually, we showed, in an experimental manner, that the linear PH matrix method with random variables can certainly enhance the security of HFE against the Gröbner basis attack, where HFE is one of the major variants of multivariate public-key cryptosystems. In 1998 Patarin, Goubin, and Courtois introduced the plus method as a general prescription which aims to enhance the security of any given MPKC, just like the linear PH matrix method with random variables. In this paper we prove the equivalence between the plus method and the primitive linear PH matrix method, which is introduced by our previous work to explain the notion of the PH matrix method in general in an illustrative manner and not for a practical use to enhance the security of any given MPKC. Based on this equivalence, we show that the linear PH matrix method with random variables has the substantial advantage over the plus method with respect to the security enhancement. In the linear PH matrix method with random variables, the three matrices, including the PH matrix, play a central role in the secret-key and public-key. In this paper, we clarify how to generate these matrices and thus present two probabilistic polynomial-time algorithms to generate these matrices. In particular, the second one has a concise form, and is obtained as a byproduct of the proof of the equivalence between the plus method and the primitive linear PH matrix method.

Continuous-variable entanglement distillation of non-Gaussian mixed states

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

Dong Ruifang; Lassen, Mikael; Department of Physics, Technical University of Denmark, Building 309, DK-2800 Lyngby

2010-07-15

Many different quantum-information communication protocols such as teleportation, dense coding, and entanglement-based quantum key distribution are based on the faithful transmission of entanglement between distant location in an optical network. The distribution of entanglement in such a network is, however, hampered by loss and noise that is inherent in all practical quantum channels. Thus, to enable faithful transmission one must resort to the protocol of entanglement distillation. In this paper we present a detailed theoretical analysis and an experimental realization of continuous variable entanglement distillation in a channel that is inflicted by different kinds of non-Gaussian noise. The continuous variablemore » entangled states are generated by exploiting the third order nonlinearity in optical fibers, and the states are sent through a free-space laboratory channel in which the losses are altered to simulate a free-space atmospheric channel with varying losses. We use linear optical components, homodyne measurements, and classical communication to distill the entanglement, and we find that by using this method the entanglement can be probabilistically increased for some specific non-Gaussian noise channels.« less

Identifying Bearing Rotodynamic Coefficients Using an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Miller, Brad A.; Howard, Samuel A.

2008-01-01

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter's performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

Investigating parameters participating in the infant respiratory control system attractor.

PubMed

Terrill, Philip I; Wilson, Stephen J; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

2008-01-01

Theoretically, any participating parameter in a non-linear system represents the dynamics of the whole system. Taken's time delay embedding theory provides the fundamental basis for allowing non-linear analysis to be performed on physiological, time-series data. In practice, only one measurable parameter is required to be measured to convey an accurate representation of the system dynamics. In this paper, the infant respiratory control system is represented using three variables-a digitally sampled respiratory inductive plethysmography waveform, and the derived parameters tidal volume and inter-breath interval time series data. For 14 healthy infants, these data streams were analysed using recurrence plot analysis across one night of sleep. The measured attractor size of these variables followed the same qualitative trends across the nights study. Results suggest that the attractor size measures of the derived IBI and tidal volume are representative surrogates for the raw respiratory waveform. The extent to which the relative attractor sizes of IBI and tidal volume remain constant through changing sleep state could potentially be used to quantify pathology, or maturation of breathing control.

Cotton-type and joint invariants for linear elliptic systems.

PubMed

Aslam, A; Mahomed, F M

2013-01-01

Cotton-type invariants for a subclass of a system of two linear elliptic equations, obtainable from a complex base linear elliptic equation, are derived both by spliting of the corresponding complex Cotton invariants of the base complex equation and from the Laplace-type invariants of the system of linear hyperbolic equations equivalent to the system of linear elliptic equations via linear complex transformations of the independent variables. It is shown that Cotton-type invariants derived from these two approaches are identical. Furthermore, Cotton-type and joint invariants for a general system of two linear elliptic equations are also obtained from the Laplace-type and joint invariants for a system of two linear hyperbolic equations equivalent to the system of linear elliptic equations by complex changes of the independent variables. Examples are presented to illustrate the results.

Cotton-Type and Joint Invariants for Linear Elliptic Systems

PubMed Central

Aslam, A.; Mahomed, F. M.

2013-01-01

Cotton-type invariants for a subclass of a system of two linear elliptic equations, obtainable from a complex base linear elliptic equation, are derived both by spliting of the corresponding complex Cotton invariants of the base complex equation and from the Laplace-type invariants of the system of linear hyperbolic equations equivalent to the system of linear elliptic equations via linear complex transformations of the independent variables. It is shown that Cotton-type invariants derived from these two approaches are identical. Furthermore, Cotton-type and joint invariants for a general system of two linear elliptic equations are also obtained from the Laplace-type and joint invariants for a system of two linear hyperbolic equations equivalent to the system of linear elliptic equations by complex changes of the independent variables. Examples are presented to illustrate the results. PMID:24453871

A linear and nonlinear study of Mira

NASA Astrophysics Data System (ADS)

Cox, A. N.; Ostlie, D. A.

1993-12-01

Both linear and nonlinear calculations of the 331 day, long period variable star Mira have been undertaken to see what radial pulsation mode is naturally selected. Models are similar to those considered in the linear nonadiabatic stellar pulsation study of Ostlie and Cox (1986). Models are considered with masses near one solar mass, luminosities between 4000 and 5000 solar luminosities, and effective temperatures of approximately 3000 K. These models have fundamental mode periods that closely match the pulsation period of Mira. The equation of state for the stellar material is given by the Stellingwerf (1975ab) procedure, and the opacity is obtained from a fit by Cahn that matches the low temperature molecular absorption data for the poplulation I Ross-Aller 1 mixture calculated from the Los Alamos Astrophysical Opacity Library. For the linear study, the Cox, Brownlee, and Eilers (1966) approximation is used for the linear theory variation of the convection luminosity. For the nonlinear work, the method described by Ostlie (1990) and Cox (1990) is followed. Results showing internal details of the radial fundamental and first overtone modes behavior in linear theory are presented. Preliminary radial fundamental mode nonlinear calculations are discussed. The very tentative conclusion is that neither the fundamental or first overtone mode is excluded from being the actual observed one.

The Role of Dysfunctional Myths in a Decision-Making Process under Bounded Rationality: A Complex Dynamical Systems Perspective.

PubMed

Stamovlasis, Dimitrios; Vaiopoulou, Julie

2017-07-01

The present study examines the factors influencing a decision-making process, with specific focus on the role of dysfunctional myths (DM). DM are thoughts or beliefs that are rather irrational, however influential to people's decisions. In this paper a decision-making process regarding the career choice of university students majoring in natural sciences and education (N=496) is examined by analyzing survey data taken via Career Decision Making Difficulties Questionnaire (CDDQ). The difficulty of making the choice and the certainty about one's decision were the state variables, while the independent variables were factors related to the lack of information or knowledge needed, which actually reflect a bounded rationality. Cusp catastrophe analysis, based on both least squares and maximum likelihood procedures, showed that the nonlinear models predicting the two state variables were superior to linear alternatives. Factors related to lack of knowledge about the steps involved in the process of career decision-making, lack of information about the various occupations, lack of information about self and lack of motivation acted as asymmetry, while dysfunctional myths acted as bifurcation factor for both state variables. The catastrophe model, grounded in empirical data, revealed a unique role for DM and a better interpretation within the context of complexity and the notion of bounded rationality. The analysis opens the nonlinear dynamical systems (NDS) perspective in studying decision-making processes. Theoretical and practical implications are discussed.

Rank-based estimation in the {ell}1-regularized partly linear model for censored outcomes with application to integrated analyses of clinical predictors and gene expression data.

PubMed

Johnson, Brent A

2009-10-01

We consider estimation and variable selection in the partial linear model for censored data. The partial linear model for censored data is a direct extension of the accelerated failure time model, the latter of which is a very important alternative model to the proportional hazards model. We extend rank-based lasso-type estimators to a model that may contain nonlinear effects. Variable selection in such partial linear model has direct application to high-dimensional survival analyses that attempt to adjust for clinical predictors. In the microarray setting, previous methods can adjust for other clinical predictors by assuming that clinical and gene expression data enter the model linearly in the same fashion. Here, we select important variables after adjusting for prognostic clinical variables but the clinical effects are assumed nonlinear. Our estimator is based on stratification and can be extended naturally to account for multiple nonlinear effects. We illustrate the utility of our method through simulation studies and application to the Wisconsin prognostic breast cancer data set.

Free piston variable-stroke linear-alternator generator

DOEpatents

Haaland, Carsten M.

1998-01-01

A free-piston variable stroke linear-alternator AC power generator for a combustion engine. An alternator mechanism and oscillator system generates AC current. The oscillation system includes two oscillation devices each having a combustion cylinder and a flying turnbuckle. The flying turnbuckle moves in accordance with the oscillation device. The alternator system is a linear alternator coupled between the two oscillation devices by a slotted connecting rod.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Examining for any impact of climate change on the association between seasonality and hospitalization for mania.

PubMed

Parker, Gordon B; Hadzi-Pavlovic, Dusan; Graham, Rebecca K

2017-01-15

Studies have established higher rates of hospitalization for mania in spring and summer and posit various explanatory climatic variables. As the earth's climate is changing, we pursue whether this is reflected in the yearly seasonal variation in hospitalizations for mania. This would be indicated by the presence of secular changes in both the hospitalization seasonal pattern and climatic variables, and associations between both variable sets. Data were obtained for 21,882 individuals hospitalized to psychiatric hospitals in the Australian state of New South Wales (NSW) over a 14-year period (2000-2014) with ICD-diagnosed mania - and with NSW population figures and salient climatic variables collected for the same period. Regression analyses were conducted to examine the predictive value of climate variables on hospital admissions. Data quantified a peak for manic admissions in spring of the southern hemisphere, in the months of October and November. There was a significant linear increase in manic admissions (0.5%/year) over the 14-year time period, with significant variation across years. In terms of climatic variables, there was a significant linear trend over the interval for solar radiation, although the trend indicated a decrease rather than an increase. Seasonal variation in admissions was most closely associated with two climate variables - evaporation in the current month and temperature in the previous month. Hospitalization rates do not necessarily provide an accurate estimate of the onset of manic episodes and findings may be limited to the southern hemisphere, or New South Wales. While overall findings do not support the hypothesis that climate change is leading to a higher seasonal impact for manic hospital admissions in the southern hemisphere, analyses identified two climate/weather variables - evaporation and temperature - that may account for the yearly spring excess. Copyright © 2016 Elsevier B.V. All rights reserved.

Relationship Between Motor Variability, Accuracy, and Ball Speed in the Tennis Serve

PubMed Central

Antúnez, Ruperto Menayo; Hernández, Francisco Javier Moreno; García, Juan Pedro Fuentes; Vaíllo, Raúl Reina; Arroyo, Jesús Sebastián Damas

2012-01-01

The main objective of this study was to analyze the motor variability in the performance of the tennis serve and its relationship to performance outcome. Seventeen male tennis players took part in the research, and they performed 20 serves. Linear and non-linear variability during the hand movement was measured by 3D Motion Tracking. Ball speed was recorded with a sports radar gun and the ball bounces were video recorded to calculate accuracy. The results showed a relationship between the amount of variability and its non-linear structure found in performance of movement and the outcome of the serve. The study also found that movement predictability correlates with performance. An increase in the amount of movement variability could affect the tennis serve performance in a negative way by reducing speed and accuracy of the ball. PMID:23486998

Hybrid Solution of Stochastic Optimal Control Problems Using Gauss Pseudospectral Method and Generalized Polynomial Chaos Algorithms

DTIC Science & Technology

2012-03-01

0-486-41183-4. 15. Brown , Robert G. and Patrick Y. C. Hwang . Introduction to Random Signals and Applied Kalman Filtering. Wiley, New York, 1996. ISBN...stability and perfor- mance criteria. In the 1960’s, Kalman introduced the Linear Quadratic Regulator (LQR) method using an integral performance index...feedback of the state variables and was able to apply this method to time-varying and Multi-Input Multi-Output (MIMO) systems. Kalman further showed

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

Luo, Yousong, E-mail: yousong.luo@rmit.edu.au

This paper deals with a class of optimal control problems governed by an initial-boundary value problem of a parabolic equation. The case of semi-linear boundary control is studied where the control is applied to the system via the Wentzell boundary condition. The differentiability of the state variable with respect to the control is established and hence a necessary condition is derived for the optimal solution in the case of both unconstrained and constrained problems. The condition is also sufficient for the unconstrained convex problems. A second order condition is also derived.

A Stationary North-Finding Scheme for an Azimuth Rotational IMU Utilizing a Linear State Equality Constraint

PubMed Central

Yu, Huapeng; Zhu, Hai; Gao, Dayuan; Yu, Meng; Wu, Wenqi

2015-01-01

The Kalman filter (KF) has always been used to improve north-finding performance under practical conditions. By analyzing the characteristics of the azimuth rotational inertial measurement unit (ARIMU) on a stationary base, a linear state equality constraint for the conventional KF used in the fine north-finding filtering phase is derived. Then, a constrained KF using the state equality constraint is proposed and studied in depth. Estimation behaviors of the concerned navigation errors when implementing the conventional KF scheme and the constrained KF scheme during stationary north-finding are investigated analytically by the stochastic observability approach, which can provide explicit formulations of the navigation errors with influencing variables. Finally, multiple practical experimental tests at a fixed position are done on a postulate system to compare the stationary north-finding performance of the two filtering schemes. In conclusion, this study has successfully extended the utilization of the stochastic observability approach for analytic descriptions of estimation behaviors of the concerned navigation errors, and the constrained KF scheme has demonstrated its superiority over the conventional KF scheme for ARIMU stationary north-finding both theoretically and practically. PMID:25688588

Assessing Factors Contributing to Cyanobacteria Harmful Algal Blooms in U.S. Lakes

NASA Astrophysics Data System (ADS)

Salls, W. B.; Iiames, J. S., Jr.; Lunetta, R. S.; Mehaffey, M.; Schaeffer, B. A.

2017-12-01

Cyanobacteria Harmful Algal Blooms (CHABs) in inland lakes have emerged as a major threat to water quality from both ecological and public health standpoints. Understanding the factors and processes driving CHAB occurrence is important in order to properly manage ensuring more favorable water quality outcomes. High water temperatures and nutrient loadings are known drivers of CHABs; however, the contribution of landscape variables and their interactions with these drivers remains relatively unstudied at a regional or national scale. This study assesses upstream landscape variables that may contribute to or obstruct/delay nutrient loadings to freshwater systems in several hundred inland lakes in the Upper Mid-western and Northeastern United States. We employ multiple linear regression and random forest modeling to determine which variables contribute most strongly to CHAB occurrence. This lakeshed-based approach will rank the impact of each landscape variable on cyanobacteria levels derived from satellite remotely sensed data from the Medium Resolution Imaging Spectrometer (MERIS) sensor for the 2011 bloom season (July - October).

Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2017-10-01

We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space. Copyright © 2017 Elsevier B.V. All rights reserved.

Linear and angular control of circular walking in healthy older adults and subjects with cerebellar ataxia.

PubMed

Goodworth, Adam D; Paquette, Caroline; Jones, Geoffrey Melvill; Block, Edward W; Fletcher, William A; Hu, Bin; Horak, Fay B

2012-05-01

Linear and angular control of trunk and leg motion during curvilinear navigation was investigated in subjects with cerebellar ataxia and age-matched control subjects. Subjects walked with eyes open around a 1.2-m circle. The relationship of linear to angular motion was quantified by determining the ratios of trunk linear velocity to trunk angular velocity and foot linear position to foot angular position. Errors in walking radius (the ratio of linear to angular motion) also were quantified continuously during the circular walk. Relative variability of linear and angular measures was compared using coefficients of variation (CoV). Patterns of variability were compared using power spectral analysis for the trunk and auto-covariance analysis for the feet. Errors in radius were significantly increased in patients with cerebellar damage as compared to controls. Cerebellar subjects had significantly larger CoV of feet and trunk in angular, but not linear, motion. Control subjects also showed larger CoV in angular compared to linear motion of the feet and trunk. Angular and linear components of stepping differed in that angular, but not linear, foot placement had a negative correlation from one stride to the next. Thus, walking in a circle was associated with more, and a different type of, variability in angular compared to linear motion. Results are consistent with increased difficulty of, and role of the cerebellum in, control of angular trunk and foot motion for curvilinear locomotion.

Instrumental Variable Analysis with a Nonlinear Exposure–Outcome Relationship

PubMed Central

Davies, Neil M.; Thompson, Simon G.

2014-01-01

Background: Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure–outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. Methods: Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure–outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. Results: Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure–outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. Conclusions: Nonlinear exposure–outcome relations should not be a barrier to instrumental variable analyses. When the exposure–outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure–outcome relation can be estimated. PMID:25166881

A Fully Associative, Non-Linear Kinematic, Unified Viscoplastic Model for Titanium Based Matrices

NASA Technical Reports Server (NTRS)

Arnold, S. M.; Saleeb, A. F.; Castelli, M. G.

1994-01-01

Specific forms for both the Gibb's and complementary dissipation potentials are chosen such that a complete (i.e., fully associative) potential based multiaxial unified viscoplastic model is obtained. This model possesses one tensorial internal state variable that is associated with dislocation substructure, with an evolutionary law that has nonlinear kinematic hardening and both thermal and strain induced recovery mechanisms. A unique aspect of the present model is the inclusion of non-linear hardening through the use of a compliance operator, derived from the Gibb's potential, in the evolution law for the back stress. This non-linear tensorial operator is significant in that it allows both the flow and evolutionary laws to be fully associative (and therefore easily integrated) and greatly influences the multiaxial response under non-proportional loading paths. In addition to this nonlinear compliance operator, a new consistent, potential preserving, internal strain unloading criterion has been introduced to prevent abnormalities in the predicted stress-strain curves, which are present with nonlinear hardening formulations, during unloading and reversed loading of the external variables. Specification of an experimental program for the complete determination of the material functions and parameters for characterizing a metallic matrix, e.g., TIMETAL 21S, is given. The experiments utilized are tensile, creep, and step creep tests. Finally, a comparison of this model and a commonly used Bodner-Partom model is made on the basis of predictive accuracy and numerical efficiency.

PSC algorithm description

NASA Technical Reports Server (NTRS)

Nobbs, Steven G.

1995-01-01

An overview of the performance seeking control (PSC) algorithm and details of the important components of the algorithm are given. The onboard propulsion system models, the linear programming optimization, and engine control interface are described. The PSC algorithm receives input from various computers on the aircraft including the digital flight computer, digital engine control, and electronic inlet control. The PSC algorithm contains compact models of the propulsion system including the inlet, engine, and nozzle. The models compute propulsion system parameters, such as inlet drag and fan stall margin, which are not directly measurable in flight. The compact models also compute sensitivities of the propulsion system parameters to change in control variables. The engine model consists of a linear steady state variable model (SSVM) and a nonlinear model. The SSVM is updated with efficiency factors calculated in the engine model update logic, or Kalman filter. The efficiency factors are used to adjust the SSVM to match the actual engine. The propulsion system models are mathematically integrated to form an overall propulsion system model. The propulsion system model is then optimized using a linear programming optimization scheme. The goal of the optimization is determined from the selected PSC mode of operation. The resulting trims are used to compute a new operating point about which the optimization process is repeated. This process is continued until an overall (global) optimum is reached before applying the trims to the controllers.

Free piston variable-stroke linear-alternator generator

DOEpatents

Haaland, C.M.

1998-12-15

A free-piston variable stroke linear-alternator AC power generator for a combustion engine is described. An alternator mechanism and oscillator system generates AC current. The oscillation system includes two oscillation devices each having a combustion cylinder and a flying turnbuckle. The flying turnbuckle moves in accordance with the oscillation device. The alternator system is a linear alternator coupled between the two oscillation devices by a slotted connecting rod. 8 figs.

Unscented Kalman Filter for Brain-Machine Interfaces

PubMed Central

Li, Zheng; O'Doherty, Joseph E.; Hanson, Timothy L.; Lebedev, Mikhail A.; Henriquez, Craig S.; Nicolelis, Miguel A. L.

2009-01-01

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation. PMID:19603074

Probabilistic quantum cloning of a subset of linearly dependent states

NASA Astrophysics Data System (ADS)

Rui, Pinshu; Zhang, Wen; Liao, Yanlin; Zhang, Ziyun

2018-02-01

It is well known that a quantum state, secretly chosen from a certain set, can be probabilistically cloned with positive cloning efficiencies if and only if all the states in the set are linearly independent. In this paper, we focus on probabilistic quantum cloning of a subset of linearly dependent states. We show that a linearly-independent subset of linearly-dependent quantum states {| Ψ 1⟩,| Ψ 2⟩,…,| Ψ n ⟩} can be probabilistically cloned if and only if any state in the subset cannot be expressed as a linear superposition of the other states in the set {| Ψ 1⟩,| Ψ 2⟩,…,| Ψ n ⟩}. The optimal cloning efficiencies are also investigated.

Spatial vs. individual variability with inheritance in a stochastic Lotka-Volterra system

NASA Astrophysics Data System (ADS)

Dobramysl, Ulrich; Tauber, Uwe C.

2012-02-01

We investigate a stochastic spatial Lotka-Volterra predator-prey model with randomized interaction rates that are either affixed to the lattice sites and quenched, and / or specific to individuals in either population. In the latter situation, we include rate inheritance with mutations from the particles' progenitors. Thus we arrive at a simple model for competitive evolution with environmental variability and selection pressure. We employ Monte Carlo simulations in zero and two dimensions to study the time evolution of both species' densities and their interaction rate distributions. The predator and prey concentrations in the ensuing steady states depend crucially on the environmental variability, whereas the temporal evolution of the individualized rate distributions leads to largely neutral optimization. Contrary to, e.g., linear gene expression models, this system does not experience fixation at extreme values. An approximate description of the resulting data is achieved by means of an effective master equation approach for the interaction rate distribution.

Feedback controlled optics with wavefront compensation

NASA Technical Reports Server (NTRS)

Breckenridge, William G. (Inventor); Redding, David C. (Inventor)

1993-01-01

The sensitivity model of a complex optical system obtained by linear ray tracing is used to compute a control gain matrix by imposing the mathematical condition for minimizing the total wavefront error at the optical system's exit pupil. The most recent deformations or error states of the controlled segments or optical surfaces of the system are then assembled as an error vector, and the error vector is transformed by the control gain matrix to produce the exact control variables which will minimize the total wavefront error at the exit pupil of the optical system. These exact control variables are then applied to the actuators controlling the various optical surfaces in the system causing the immediate reduction in total wavefront error observed at the exit pupil of the optical system.

tICA-Metadynamics: Accelerating Metadynamics by Using Kinetically Selected Collective Variables.

PubMed

M Sultan, Mohammad; Pande, Vijay S

2017-06-13

Metadynamics is a powerful enhanced molecular dynamics sampling method that accelerates simulations by adding history-dependent multidimensional Gaussians along selective collective variables (CVs). In practice, choosing a small number of slow CVs remains challenging due to the inherent high dimensionality of biophysical systems. Here we show that time-structure based independent component analysis (tICA), a recent advance in Markov state model literature, can be used to identify a set of variationally optimal slow coordinates for use as CVs for Metadynamics. We show that linear and nonlinear tICA-Metadynamics can complement existing MD studies by explicitly sampling the system's slowest modes and can even drive transitions along the slowest modes even when no such transitions are observed in unbiased simulations.

An Improved Search Approach for Solving Non-Convex Mixed-Integer Non Linear Programming Problems

NASA Astrophysics Data System (ADS)

Sitopu, Joni Wilson; Mawengkang, Herman; Syafitri Lubis, Riri

2018-01-01

The nonlinear mathematical programming problem addressed in this paper has a structure characterized by a subset of variables restricted to assume discrete values, which are linear and separable from the continuous variables. The strategy of releasing nonbasic variables from their bounds, combined with the “active constraint” method, has been developed. This strategy is used to force the appropriate non-integer basic variables to move to their neighbourhood integer points. Successful implementation of these algorithms was achieved on various test problems.

Cancer family caregiver depression: are religion-related variables important?

PubMed

Williams, Anna-Leila; Dixon, Jane; Feinn, Richard; McCorkle, Ruth

2015-07-01

Prevalence estimates for clinical depression among cancer family caregivers (CFC) range upwards to 39%. Research inconsistently reports risk for CFC depressive symptoms when evaluating age, gender, ethnicity, or length of time as caregiver. The discrepant findings, coupled with emerging literature indicating religiosity may mitigate depression in some populations, led us to investigate religion-related variables to help predict CFC depressive symptoms. We conducted a cross-sectional study of 150 CFC. Explanatory variables included age, gender, spousal status, length of time as caregiver, attendance at religious services, and prayer. The outcome variable was the Center for Epidemiological Studies Depression Scale score. Compared with large national and state datasets, our sample has lower representation of individuals with no religious affiliation (10.7% vs. 16.1% national, p = 0.07 and 23.0% state, p = 0.001), higher rate of attendance at religious services (81.3% vs. 67.2% national, p < 0.001 and 30.0% state, p < 0.001), and higher rate of prayer (65.3% vs. 42.9% national, p < 0.001; no state data available). In unadjusted and adjusted models, prayer is not significantly associated with caregiver depressive symptoms or clinically significant depressive symptomology. Attendance at religious services is associated with depressive symptoms (p = 0.004) with an inversely linear trend (p = 0.002). The significant inverse association between attendance at religious services and depressive symptoms, despite no association between prayer and depressive symptoms, indicates that social or other factors may accompany attendance at religious services and contribute to the association. Clinicians can consider supporting a CFC's attendance at religious services as a potential preventive measure for depressive symptoms. Copyright © 2014 John Wiley & Sons, Ltd.

Comment on "Scaling regimes and linear/nonlinear responses of last millennium climate to volcanic and solar forcing" by S. Lovejoy and C. Varotsos (2016)

NASA Astrophysics Data System (ADS)

Rypdal, Kristoffer; Rypdal, Martin

2016-07-01

Lovejoy and Varotsos (2016) (L&V) analyse the temperature response to solar, volcanic, and solar plus volcanic forcing in the Zebiak-Cane (ZC) model, and to solar and solar plus volcanic forcing in the Goddard Institute for Space Studies (GISS) E2-R model. By using a simple wavelet filtering technique they conclude that the responses in the ZC model combine subadditively on timescales from 50 to 1000 years. Nonlinear response on shorter timescales is claimed by analysis of intermittencies in the forcing and the temperature signal for both models. The analysis of additivity in the ZC model suffers from a confusing presentation of results based on an invalid approximation, and from ignoring the effect of internal variability. We present tests without this approximation which are not able to detect nonlinearity in the response, even without accounting for internal variability. We also demonstrate that internal variability will appear as subadditivity if it is not accounted for. L&V's analysis of intermittencies is based on a mathematical result stating that the intermittencies of forcing and response are the same if the response is linear. We argue that there are at least three different factors that may invalidate the application of this result for these data. It is valid only for a power-law response function; it assumes power-law scaling of structure functions of forcing as well as temperature signal; and the internal variability, which is strong at least on the short timescales, will exert an influence on temperature intermittence which is independent of the forcing. We demonstrate by a synthetic example that the differences in intermittencies observed by L&V easily can be accounted for by these effects under the assumption of a linear response. Our conclusion is that the analysis performed by L&V does not present valid evidence for a detectable nonlinear response in the global temperature in these climate models.

Magnetic susceptibility of Inconel alloys 718, 625, and 600 at cryogenic temperatures

NASA Technical Reports Server (NTRS)

Goldberg, Ira B.; Mitchell, Michael R.; Murphy, Allan R.; Goldfarb, Ronald B.; Loughran, Robert J.

1990-01-01

After a hydrogen fuel bleed valve problem on the Discovery Space Shuttle was traced to the strong magnetization of Inconel 718 in the armature of the linear variable differential transformer near liquid hydrogen temperatures, the ac magnetic susceptibility of three samples of Inconel 718 of slightly different compositions, one sample of Inconel 625, and on sample of Inconel 600 were measured as a function of temperature. Inconel 718 alloys are found to exhibit a spin glass state below 16 K. Inconel 600 exhibits three different magnetic phases, the lowest-temperature state (below 6 K) being somewhat similar to that of Inconel 718. The magnetic states of the Inconel alloys and their magnetic susceptibilities appear to be strongly dependent on the exact composition of the alloy.

A variable capacitance based modeling and power capability predicting method for ultracapacitor

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

DOE PAGES

Ma, Ding; Yang, Laurence; Fleming, Ronan M. T.; ...

2017-01-18

Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of ourmore » linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.« less

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

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

Ma, Ding; Yang, Laurence; Fleming, Ronan M. T.

Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of ourmore » linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.« less

Computational modeling of unsteady third-grade fluid flow over a vertical cylinder: A study of heat transfer visualization

NASA Astrophysics Data System (ADS)

Reddy, G. Janardhana; Hiremath, Ashwini; Kumar, Mahesh

2018-03-01

The present paper aims to investigate the effect of Prandtl number for unsteady third-grade fluid flow over a uniformly heated vertical cylinder using Bejan's heat function concept. The mathematical model of this problem is given by highly time-dependent non-linear coupled equations and are resolved by an efficient unconditionally stable implicit scheme. The time histories of average values of momentum and heat transport coefficients as well as the steady-state flow variables are displayed graphically for distinct values of non-dimensional control parameters arising in the system. As the non-dimensional parameter value gets amplified, the time taken for the fluid flow variables to attain the time-independent state is decreasing. The dimensionless heat function values are closely associated with an overall rate of heat transfer. Thermal energy transfer visualization implies that the heat function contours are compact in the neighborhood of the leading edge of the hot cylindrical wall. It is noticed that the deviations of flow-field variables from the hot wall for a non-Newtonian third-grade fluid flow are significant compared to the usual Newtonian fluid flow.

Assessment of unconstrained cerebrovascular reactivity marker for large age-range FMRI studies.

PubMed

Kannurpatti, Sridhar S; Motes, Michael A; Biswal, Bharat B; Rypma, Bart

2014-01-01

Breath hold (BH), a commonly used task to measure cerebrovascular reactivity (CVR) in fMRI studies varies in outcome among individuals due to subject-physiology and/or BH-inspiration/expiration differences (i.e., performance). In prior age-related fMRI studies, smaller task-related BOLD response variability is observed among younger than older individuals. Also, a linear CVR versus task relationship exists in younger individuals which maybe useful to test the accuracy of CVR responses in older groups. Hence we hypothesized that subject-related physiological and/or BH differences, if present, may compromise CVR versus task linearity in older individuals. To test the hypothesis, empirical BH versus task relationships from motor and cognitive areas were obtained in younger (mean age = 26 years) and older (mean age = 58 years) human subjects. BH versus task linearity was observed only in the younger group, confirming our hypothesis. Further analysis indicated BH responses and its variability to be similar in both younger and older groups, suggesting that BH may not accurately represent CVR in a large age range. Using the resting state fluctuation of amplitude (RSFA) as an unconstrained alternative to BH, subject-wise correspondence between BH and RSFA was tested. Correlation between BH versus RSFA was significant within the motor but was not significant in the cognitive areas in the younger and was completely disrupted in both areas in the older subjects indicating that BH responses are constrained by subject-related physiology and/or performance-related differences. Contrasting BH to task, RSFA-task relationships were independent of age accompanied by age-related increases in CVR variability as measured by RSFA, not observed with BH. Together the results obtained indicate that RSFA accurately represents CVR in any age range avoiding multiple and yet unknown physiologic and task-related pitfalls of BH.

Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm

NASA Astrophysics Data System (ADS)

Makin, Joseph G.; O'Doherty, Joseph E.; Cardoso, Mariana M. B.; Sabes, Philip N.

2018-04-01

Objective. The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of a machine, archetypically a robotic arm or a cursor. The standard procedure treats neural activity—vectors of spike counts in small temporal windows—as noisy observations of the kinematic state (position, velocity, acceleration) of the fingertip. Inferring the state from the observations then takes the form of a dynamical filter, typically some variant on Kalman’s (KF). The KF, however, although fairly robust in practice, is optimal only when the relationships between variables are linear and the noise is Gaussian, conditions usually violated in practice. Approach. To overcome these limitations we introduce a new filter, the ‘recurrent exponential-family harmonium’ (rEFH), that models the spike counts explicitly as Poisson-distributed, and allows for arbitrary nonlinear dynamics and observation models. Furthermore, the model underlying the filter is acquired through unsupervised learning, which allows temporal correlations in spike counts to be explained by latent dynamics that do not necessarily correspond to the kinematic state of the fingertip. Main results. We test the rEFH on offline reconstruction of the kinematics of reaches in the plane. The rEFH outperforms the standard, as well as three other state-of-the-art, decoders, across three monkeys, two different tasks, most kinematic variables, and a range of bin widths, amounts of training data, and numbers of neurons. Significance. Our algorithm establishes a new state of the art for offline decoding of reaches—in particular, for fingertip velocities, the variable used for control in most online decoders.

Lack of linear correlation between dynamic and steady-state cerebral autoregulation.

PubMed

de Jong, Daan L K; Tarumi, Takashi; Liu, Jie; Zhang, Rong; Claassen, Jurgen A H R

2017-08-15

For correct application and interpretation of cerebral autoregulation (CA) measurements in research and in clinical care, it is essential to understand differences and similarities between dynamic and steady-state CA. The present study found no correlation between dynamic and steady-state CA indices in healthy older adults. There was variability between individuals in all (steady-state and dynamic) autoregulatory indices, ranging from low (almost absent) to highly efficient CA in this healthy population. These findings challenge the assumption that assessment of a single CA parameter or a single set of parameters can be generalized to overall CA functioning. Therefore, depending on specific research purposes, the choice for either steady-state or dynamic measures or both should be weighed carefully. The present study aimed to investigate the relationship between dynamic (dCA) and steady-state cerebral autoregulation (sCA). In 28 healthy older adults, sCA was quantified by a linear regression slope of proportionate (%) changes in cerebrovascular resistance (CVR) in response to proportionate (%) changes in mean blood pressure (BP) induced by stepwise sodium nitroprusside (SNP) and phenylephrine (PhE) infusion. Cerebral blood flow (CBF) was measured at the internal carotid artery (ICA) and vertebral artery (VA) and CBF velocity at the middle cerebral artery (MCA). With CVR = BP/CBF, Slope-CVR ICA , Slope-CVR VA and Slope-CVRi MCA were derived. dCA was assessed (i) in supine rest, analysed with transfer function analysis (gain and phase) and autoregulatory index (ARI) fit from spontaneous oscillations (ARI Baseline ), and (ii) with transient changes in BP using a bolus injection of SNP (ARI SNP ) and PhE (ARI PhE ). Comparison of sCA and dCA parameters (using Pearson's r for continuous and Spearman's ρ for ordinal parameters) demonstrated a lack of linear correlations between sCA and dCA measures. However, comparisons of parameters within dCA and within sCA were correlated. For sCA slope-CVR VA with Slope-CVRi MCA (r = 0.45, P < 0.03); for dCA ARI SNP with ARI PhE (ρ = 0.50, P = 0.03), ARI Baseline (ρ = 0.57, P = 0.03) and Phase LF (ρ = 0.48, P = 0.03); and for Gain VLF with Gain LF (r = 0.51, P = 0.01). By contrast to the commonly held assumption based on an earlier study, there were no linear correlations between sCA and dCA. As an additional observation, there was strong inter-individual variability, both in dCA and sCA, in this healthy group of elderly, in a range from low to high CA efficiency. © 2017 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.

Using directed information for influence discovery in interconnected dynamical systems

NASA Astrophysics Data System (ADS)

Rao, Arvind; Hero, Alfred O.; States, David J.; Engel, James Douglas

2008-08-01

Structure discovery in non-linear dynamical systems is an important and challenging problem that arises in various applications such as computational neuroscience, econometrics, and biological network discovery. Each of these systems have multiple interacting variables and the key problem is the inference of the underlying structure of the systems (which variables are connected to which others) based on the output observations (such as multiple time trajectories of the variables). Since such applications demand the inference of directed relationships among variables in these non-linear systems, current methods that have a linear assumption on structure or yield undirected variable dependencies are insufficient. Hence, in this work, we present a methodology for structure discovery using an information-theoretic metric called directed time information (DTI). Using both synthetic dynamical systems as well as true biological datasets (kidney development and T-cell data), we demonstrate the utility of DTI in such problems.

Development of a new linearly variable edge filter (LVEF)-based compact slit-less mini-spectrometer

NASA Astrophysics Data System (ADS)

Mahmoud, Khaled; Park, Seongchong; Lee, Dong-Hoon

2018-02-01

This paper presents the development of a compact charge-coupled detector (CCD) spectrometer. We describe the design, concept and characterization of VNIR linear variable edge filter (LVEF)- based mini-spectrometer. The new instrument has been realized for operation in the 300 nm to 850 nm wavelength range. The instrument consists of a linear variable edge filter in front of CCD array. Low-size, light-weight and low-cost could be achieved using the linearly variable filters with no need to use any moving parts for wavelength selection as in the case of commercial spectrometers available in the market. This overview discusses the main components characteristics, the main concept with the main advantages and limitations reported. Experimental characteristics of the LVEFs are described. The mathematical approach to get the position-dependent slit function of the presented prototype spectrometer and its numerical de-convolution solution for a spectrum reconstruction is described. The performance of our prototype instrument is demonstrated by measuring the spectrum of a reference light source.

EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems

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

Zhou, Yuyang; Zhang, Qichun; Wang, Hong

In this paper, a novel control algorithm is presented to enhance the performance of tracking property for a class of non-linear dynamic stochastic systems with unmeasurable variables. To minimize the entropy of tracking errors without changing the existing closed loop with PI controller, the enhanced performance loop is constructed based on the state estimation by extended Kalman Filter and the new controller is designed by full state feedback following this presented control algorithm. Besides, the conditions are obtained for the stability analysis in the mean square sense. In the end, the comparative simulation results are given to illustrate the effectivenessmore » of proposed control algorithm.« less

Ride comfort control in large flexible aircraft. M.S. Thesis

NASA Technical Reports Server (NTRS)

Warren, M. E.

1971-01-01

The problem of ameliorating the discomfort of passengers on a large air transport subject to flight disturbances is examined. The longitudinal dynamics of the aircraft, including effects of body flexing, are developed in terms of linear, constant coefficient differential equations in state variables. A cost functional, penalizing the rigid body displacements and flexure accelerations over the surface of the aircraft is formulated as a quadratic form. The resulting control problem, to minimize the cost subject to the state equation constraints, is of a class whose solutions are well known. The feedback gains for the optimal controller are calculated digitally, and the resulting autopilot is simulated on an analog computer and its performance evaluated.

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

PubMed

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

2015-01-15

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

Smooth muscle contraction: mechanochemical formulation for homogeneous finite strains.

PubMed

Stålhand, J; Klarbring, A; Holzapfel, G A

2008-01-01

Chemical kinetics of smooth muscle contraction affect mechanical properties of organs that function under finite strains. In an effort to gain further insight into organ physiology, we formulate a mechanochemical finite strain model by considering the interaction between mechanical and biochemical components of cell function during activation. We propose a new constitutive framework and use a mechanochemical device that consists of two parallel elements: (i) spring for the cell stiffness; (ii) contractile element for the sarcomere. We use a multiplicative decomposition of cell elongation into filament contraction and cross-bridge deformation, and suggest that the free energy be a function of stretches, four variables (free unphosphorylated myosin, phosphorylated cross-bridges, phosphorylated and dephosphorylated cross-bridges attached to actin), chemical state variable driven by Ca2+-concentration, and temperature. The derived constitutive laws are thermodynamically consistent. Assuming isothermal conditions, we specialize the mechanical phase such that we recover the linear model of Yang et al. [2003a. The myogenic response in isolated rat cerebrovascular arteries: smooth muscle cell. Med. Eng. Phys. 25, 691-709]. The chemical phase is also specialized so that the linearized chemical evolution law leads to the four-state model of Hai and Murphy [1988. Cross-bridge phosphorylation and regulation of latch state in smooth muscle. Am. J. Physiol. 254, C99-C106]. One numerical example shows typical mechanochemical effects and the efficiency of the proposed approach. We discuss related parameter identification, and illustrate the dependence of muscle contraction (Ca2+-concentration) on active stress and related stretch. Mechanochemical models of this kind serve the mathematical basis for analyzing coupled processes such as the dependency of tissue properties on the chemical kinetics of smooth muscle.

Causal relationship model between variables using linear regression to improve professional commitment of lecturer

NASA Astrophysics Data System (ADS)

Setyaningsih, S.

2017-01-01

The main element to build a leading university requires lecturer commitment in a professional manner. Commitment is measured through willpower, loyalty, pride, loyalty, and integrity as a professional lecturer. A total of 135 from 337 university lecturers were sampled to collect data. Data were analyzed using validity and reliability test and multiple linear regression. Many studies have found a link on the commitment of lecturers, but the basic cause of the causal relationship is generally neglected. These results indicate that the professional commitment of lecturers affected by variables empowerment, academic culture, and trust. The relationship model between variables is composed of three substructures. The first substructure consists of endogenous variables professional commitment and exogenous three variables, namely the academic culture, empowerment and trust, as well as residue variable ɛ y . The second substructure consists of one endogenous variable that is trust and two exogenous variables, namely empowerment and academic culture and the residue variable ɛ 3. The third substructure consists of one endogenous variable, namely the academic culture and exogenous variables, namely empowerment as well as residue variable ɛ 2. Multiple linear regression was used in the path model for each substructure. The results showed that the hypothesis has been proved and these findings provide empirical evidence that increasing the variables will have an impact on increasing the professional commitment of the lecturers.

Cryptic chytridiomycosis linked to climate and genetic variation in amphibian populations of the southeastern United States

PubMed Central

Hoffman, Eric A.; Tye, Matthew R.; Hether, Tyler D.; Savage, Anna E.

2017-01-01

North American amphibians have recently been impacted by two major emerging pathogens, the fungus Batrachochytrium dendrobatidis (Bd) and iridoviruses in the genus Ranavirus (Rv). Environmental factors and host genetics may play important roles in disease dynamics, but few studies incorporate both of these components into their analyses. Here, we investigated the role of environmental and genetic factors in driving Bd and Rv infection prevalence and severity in a biodiversity hot spot, the southeastern United States. We used quantitative PCR to characterize Bd and Rv dynamics in natural populations of three amphibian species: Notophthalmus perstriatus, Hyla squirella and Pseudacris ornata. We combined pathogen data, genetic diversity metrics generated from neutral markers, and environmental variables into general linear models to evaluate how these factors impact infectious disease dynamics. Occurrence, prevalence and intensity of Bd and Rv varied across species and populations, but only one species, Pseudacris ornata, harbored high Bd intensities in the majority of sampled populations. Genetic diversity and climate variables both predicted Bd prevalence, whereas climatic variables alone predicted infection intensity. We conclude that Bd is more abundant in the southeastern United States than previously thought and that genetic and environmental factors are both important for predicting amphibian pathogen dynamics. Incorporating both genetic and environmental information into conservation plans for amphibians is necessary for the development of more effective management strategies to mitigate the impact of emerging infectious diseases. PMID:28448517

Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect

NASA Astrophysics Data System (ADS)

Li, Lianfa; Wu, Anna H.; Cheng, Iona; Chen, Jiu-Chiuan; Wu, Jun

2017-10-01

Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available.

Sparse 4D TomoSAR imaging in the presence of non-linear deformation

NASA Astrophysics Data System (ADS)

Khwaja, Ahmed Shaharyar; ćetin, Müjdat

2018-04-01

In this paper, we present a sparse four-dimensional tomographic synthetic aperture radar (4D TomoSAR) imaging scheme that can estimate elevation and linear as well as non-linear seasonal deformation rates of scatterers using the interferometric phase. Unlike existing sparse processing techniques that use fixed dictionaries based on a linear deformation model, we use a variable dictionary for the non-linear deformation in the form of seasonal sinusoidal deformation, in addition to the fixed dictionary for the linear deformation. We estimate the amplitude of the sinusoidal deformation using an optimization method and create the variable dictionary using the estimated amplitude. We show preliminary results using simulated data that demonstrate the soundness of our proposed technique for sparse 4D TomoSAR imaging in the presence of non-linear deformation.

Progression of Mortality due to Diseases of the Circulatory System and Human Development Index in Rio de Janeiro Municipalities

PubMed Central

Soares, Gabriel Porto; Klein, Carlos Henrique; Silva, Nelson Albuquerque de Souza e; de Oliveira, Glaucia Maria Moraes

2016-01-01

Background Diseases of the circulatory system (DCS) are the major cause of death in Brazil and worldwide. Objective To correlate the compensated and adjusted mortality rates due to DCS in the Rio de Janeiro State municipalities between 1979 and 2010 with the Human Development Index (HDI) from 1970 onwards. Methods Population and death data were obtained in DATASUS/MS database. Mortality rates due to ischemic heart diseases (IHD), cerebrovascular diseases (CBVD) and DCS adjusted by using the direct method and compensated for ill-defined causes. The HDI data were obtained at the Brazilian Institute of Applied Research in Economics. The mortality rates and HDI values were correlated by estimating Pearson linear coefficients. The correlation coefficients between the mortality rates of census years 1991, 2000 and 2010 and HDI data of census years 1970, 1980 and 1991 were calculated with discrepancy of two demographic censuses. The linear regression coefficients were estimated with disease as the dependent variable and HDI as the independent variable. Results In recent decades, there was a reduction in mortality due to DCS in all Rio de Janeiro State municipalities, mainly because of the decline in mortality due to CBVD, which was preceded by an elevation in HDI. There was a strong correlation between the socioeconomic indicator and mortality rates. Conclusion The HDI progression showed a strong correlation with the decline in mortality due to DCS, signaling to the relevance of improvements in life conditions. PMID:27849263

Progression of Mortality due to Diseases of the Circulatory System and Human Development Index in Rio de Janeiro Municipalities.

PubMed

Soares, Gabriel Porto; Klein, Carlos Henrique; Silva, Nelson Albuquerque de Souza E; Oliveira, Glaucia Maria Moraes de

2016-10-01

Diseases of the circulatory system (DCS) are the major cause of death in Brazil and worldwide. To correlate the compensated and adjusted mortality rates due to DCS in the Rio de Janeiro State municipalities between 1979 and 2010 with the Human Development Index (HDI) from 1970 onwards. Population and death data were obtained in DATASUS/MS database. Mortality rates due to ischemic heart diseases (IHD), cerebrovascular diseases (CBVD) and DCS adjusted by using the direct method and compensated for ill-defined causes. The HDI data were obtained at the Brazilian Institute of Applied Research in Economics. The mortality rates and HDI values were correlated by estimating Pearson linear coefficients. The correlation coefficients between the mortality rates of census years 1991, 2000 and 2010 and HDI data of census years 1970, 1980 and 1991 were calculated with discrepancy of two demographic censuses. The linear regression coefficients were estimated with disease as the dependent variable and HDI as the independent variable. In recent decades, there was a reduction in mortality due to DCS in all Rio de Janeiro State municipalities, mainly because of the decline in mortality due to CBVD, which was preceded by an elevation in HDI. There was a strong correlation between the socioeconomic indicator and mortality rates. The HDI progression showed a strong correlation with the decline in mortality due to DCS, signaling to the relevance of improvements in life conditions.

Catastrophizing, state anxiety, anger, and depressive symptoms do not correlate with disability when variations of trait anxiety are taken into account. a study of chronic low back pain patients treated in Spanish pain units [NCT00360802].

PubMed

Moix, Jenny; Kovacs, Francisco M; Martín, Andrés; Plana, María N; Royuela, Ana

2011-07-01

To assess the influence of pain severity, catastrophizing, anger, anxiety, and depression on nonspecific low back pain (LBP)-related disability in Spanish patients with chronic LBP. Study Design.  Cross-sectional correlation between psychological variables and disability. Methods.  One hundred twenty-three patients treated for chronic LBP in pain units within nine Spanish National Health Service Hospitals, in eight cities, were included in this study. Intensity of LBP and pain referred to the leg, disability, catastrophizing, anger, state anxiety, trait anxiety, and depression were assessed through previously validated questionnaires. The association of disability with these variables, as well as gender, age, academic level, work status, and use of antidepressants, was analyzed through linear regression models. Correlations between LBP, referred pain, disability, catastrophizing, anger, state anxiety, trait anxiety, and depression were significant, except for the ones between anger and LBP and between anger and referred pain. The multivariate regression model showed that when variations of trait anxiety were taken into account, the association of the other psychological variables with disability was no longer significant. The final model explained 49% of the variability of disability. Standardized coefficients were 0.452 for trait anxiety, 0.362 for intensity of LBP, 0.253 for failed back surgery, and -0.140 for higher academic level. Among Spanish chronic LBP patients treated at pain units, the correlation of catastrophizing, state anxiety, anger, and depression with disability ceases to be significant when variations of trait anxiety are taken into account. Further studies with LBP patients should determine whether anxiety trait mediates the effects of the other variables, explore its prognostic value, and assess the therapeutic effect of reducing it. Wiley Periodicals, Inc.

Van der Waals equation of state revisited: importance of the dispersion correction.

PubMed

de Visser, Sam P

2011-04-28

One of the most basic equations of state describing nonideal gases and liquids is the van der Waals equation of state, and as a consequence, it is generally taught in most first year undergraduate chemistry courses. In this work, we show that the constants a and b in the van der Waals equation of state are linearly proportional to the polarizability volume of the molecules in a gas or liquid. Using this information, a new thermodynamic one-parameter equation of state is derived that contains experimentally measurable variables and physics constants only. This is the first equation of state apart from the Ideal Gas Law that contains experimentally measurable variables and physics constants only, and as such, it may be a very useful and practical equation for the description of dilute gases and liquids. The modified van der Waals equation of state describes pV as the sum of repulsive and attractive intermolecular interaction energies that are represented by an exponential repulsion function between the electron clouds of the molecules and a London dispersion component, respectively. The newly derived equation of state is tested against experimental data for several gas and liquid examples, and the agreement is satisfactory. The description of the equation of state as a one-parameter function also has implications on other thermodynamic functions, such as critical parameters, virial coefficients, and isothermal compressibilities. Using our modified van der Waals equation of state, we show that all of these properties are a function of the molecular polarizability volume. Correlations of experimental data confirm the derived proportionalities.

Heart rate variability based on risk stratification for type 2 diabetes mellitus.

PubMed

Silva-E-Oliveira, Julia; Amélio, Pâmela Marina; Abranches, Isabela Lopes Laguardia; Damasceno, Dênis Derly; Furtado, Fabianne

2017-01-01

To evaluate heart rate variability among adults with different risk levels for type 2 diabetes mellitus. The risk for type 2 diabetes mellitus was assessed in 130 participants (89 females) based on the questionnaire Finnish Diabetes Risk Score and was classified as low risk (n=26), slightly elevated risk (n=41), moderate risk (n=27) and high risk (n=32). To measure heart rate variability, a heart-rate monitor Polar S810i® was employed to obtain RR series for each individual, at rest, for 5 minutes, followed by analysis of linear and nonlinear indexes. The groups at higher risk of type 2 diabetes mellitus had significantly lower linear and nonlinear heart rate variability indexes. The individuals at high risk for type 2 diabetes mellitus have lower heart rate variability. Avaliar a variabilidade da frequência cardíaca em adultos com diferentes níveis de risco para diabetes mellitus tipo 2. O grau de risco para diabetes mellitus tipo 2 de 130 participantes (41 homens) foi avaliado pelo questionário Finnish Diabetes Risk Score. Os participantes foram classificados em baixo risco (n=26), risco levemente elevado (n=41), risco moderado (n=27) e alto risco (n=32). Para medir a variabilidade da frequência cardíaca, utilizou-se o frequencímetro Polar S810i® para obter séries de intervalo RR para cada indivíduo, em repouso, durante 5 minutos; posteriormente, realizou-se análise por meio de índices lineares e não-lineares. O grupo com maior risco para diabetes mellitus tipo 2 teve uma diminuição significante nos índices lineares e não-lineares da variabilidade da frequência cardíaca. Os resultados apontam que indivíduos com risco alto para diabetes mellitus tipo 2 tem menor variabilidade da frequência cardíaca. To evaluate heart rate variability among adults with different risk levels for type 2 diabetes mellitus. The risk for type 2 diabetes mellitus was assessed in 130 participants (89 females) based on the questionnaire Finnish Diabetes Risk Score and was classified as low risk (n=26), slightly elevated risk (n=41), moderate risk (n=27) and high risk (n=32). To measure heart rate variability, a heart-rate monitor Polar S810i® was employed to obtain RR series for each individual, at rest, for 5 minutes, followed by analysis of linear and nonlinear indexes. The groups at higher risk of type 2 diabetes mellitus had significantly lower linear and nonlinear heart rate variability indexes. The individuals at high risk for type 2 diabetes mellitus have lower heart rate variability.

RatCar system for estimating locomotion states using neural signals with parameter monitoring: Vehicle-formed brain-machine interfaces for rat.

PubMed

Fukayama, Osamu; Taniguchi, Noriyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

2008-01-01

An online brain-machine interface (BMI) in the form of a small vehicle, the 'RatCar,' has been developed. A rat had neural electrodes implanted in its primary motor cortex and basal ganglia regions to continuously record neural signals. Then, a linear state space model represents a correlation between the recorded neural signals and locomotion states (i.e., moving velocity and azimuthal variances) of the rat. The model parameters were set so as to minimize estimation errors, and the locomotion states were estimated from neural firing rates using a Kalman filter algorithm. The results showed a small oscillation to achieve smooth control of the vehicle in spite of fluctuating firing rates with noises applied to the model. Major variation of the model variables converged in a first 30 seconds of the experiments and lasted for the entire one hour session.

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

NASA Technical Reports Server (NTRS)

Tiffany, Sherwood H.; Karpel, Mordechay

1989-01-01

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

Earthquake Cycle Simulations with Rate-and-State Friction and Linear and Nonlinear Viscoelasticity

NASA Astrophysics Data System (ADS)

Allison, K. L.; Dunham, E. M.

2016-12-01

We have implemented a parallel code that simultaneously models both rate-and-state friction on a strike-slip fault and off-fault viscoelastic deformation throughout the earthquake cycle in 2D. Because we allow fault slip to evolve with a rate-and-state friction law and do not impose the depth of the brittle-to-ductile transition, we are able to address: the physical processes limiting the depth of large ruptures (with hazard implications); the degree of strain localization with depth; the relative partitioning of fault slip and viscous deformation in the brittle-to-ductile transition zone; and the relative contributions of afterslip and viscous flow to postseismic surface deformation. The method uses a discretization that accommodates variable off-fault material properties, depth-dependent frictional properties, and linear and nonlinear viscoelastic rheologies. All phases of the earthquake cycle are modeled, allowing the model to spontaneously generate earthquakes, and to capture afterslip and postseismic viscous flow. We compare the effects of a linear Maxwell rheology, often used in geodetic models, with those of a nonlinear power law rheology, which laboratory data indicates more accurately represents the lower crust and upper mantle. The viscosity of the Maxwell rheology is set by power law rheological parameters with an assumed a geotherm and strain rate, producing a viscosity that exponentially decays with depth and is constant in time. In contrast, the power law rheology will evolve an effective viscosity that is a function of the temperature profile and the stress state, and therefore varies both spatially and temporally. We will also integrate the energy equation for the thermomechanical problem, capturing frictional heat generation on the fault and off-fault viscous shear heating, and allowing these in turn to alter the effective viscosity.

Geometric characterization of separability and entanglement in pure Gaussian states by single-mode unitary operations

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

Adesso, Gerardo; CNR-INFM Coherentia, Naples; CNISM, Unita di Salerno, Salerno

2007-10-15

We present a geometric approach to the characterization of separability and entanglement in pure Gaussian states of an arbitrary number of modes. The analysis is performed adapting to continuous variables a formalism based on single subsystem unitary transformations that has been recently introduced to characterize separability and entanglement in pure states of qubits and qutrits [S. M. Giampaolo and F. Illuminati, Phys. Rev. A 76, 042301 (2007)]. In analogy with the finite-dimensional case, we demonstrate that the 1xM bipartite entanglement of a multimode pure Gaussian state can be quantified by the minimum squared Euclidean distance between the state itself andmore » the set of states obtained by transforming it via suitable local symplectic (unitary) operations. This minimum distance, corresponding to a, uniquely determined, extremal local operation, defines an entanglement monotone equivalent to the entropy of entanglement, and amenable to direct experimental measurement with linear optical schemes.« less

Evaluating the ecological association of casino industry economic development on community health status: a natural experiment in the Mississippi delta region.

PubMed

Honoré, Peggy A; Simoes, Eduardo J; Moonesinghe, Ramal; Wang, Xueyuan; Brown, Lovetta

2007-01-01

Objectives of this study were to examine for associations of casino industry economic development on improving community health status and funding for public health services in two counties in the Mississippi Delta Region of the United States. An ecological approach was used to evaluate whether two counties with casino gaming had improved health status and public health funding in comparison with two noncasino counties in the same region with similar social, racial, and ethic backgrounds. Variables readily available from state health department records were used to develop a logic model for guiding analytical work. A linear regression model was built using a stepwise approach and hierarchical regression principles with many dependent variables and a set of fixed and nonfixed independent variables. County-level data for 23 variables over an 11-year period were used. Overall, this study found a lack of association between the presence of a casino and desirable health outcomes or funding for public health services. Changes in the environment were made to promote health by utilizing gaming revenues to build state-of-the-art community health and wellness centers and sports facilities. However, significant increases in funding for local public health services were not found in either of the counties with casinos. These findings are relevant for policy makers when debating economic development strategies. Analysis similar to this should be combined with other routine public health assessments after implementation of development strategies to increase knowledge of health outcome trends and shifts in socioeconomic position that may be expected to accrue from economic development projects.

Effects of climate on numbers of northern prairie wetlands

USGS Publications Warehouse

Larson, Diane L.

1995-01-01

The amount of water held in individual wetland basins depends not only on local climate patterns but also on groundwater flow regime, soil permeability, and basin size. Most wetland basins in the northern prairies hold water in some years and are dry in others. To assess the potential effect of climate change on the number of wetland basins holding water in a given year, one must first determine how much of the variability in number of wet basins is accounted for by climatic variables. I used multiple linear regression to examine the relationship between climate variables and percentage of wet basins throughout the Prairie Pothole Region of Canada and the United States. The region was divided into three areas: parkland, Canadian grassland, and United States grassland (i.e., North Dakota and South Dakota). The models - which included variables for spring and fall temperature, yearly precipitation, the previous year's count of wet basins, and for grassland areas, the previous fall precipitation - accounted for 63 to 65% of the variation in the number of wet basins. I then explored the sensitivities of the models to changes in temperature and precipitation, as might be associated with increased greenhouse gas concentrations. Parkland wetlands are shown to be much more vulnerable to increased temperatures than are wetlands in either Canadian or United States grasslands. Sensitivity to increased precipitation did not vary geographically. These results have implications for waterfowl and other wildlife populations that depend on availability of wetlands in the parklands for breeding or during periods of drought in the southern grasslands.

Linear solvation energy relationships: "rule of thumb" for estimation of variable values

USGS Publications Warehouse

Hickey, James P.; Passino-Reader, Dora R.

1991-01-01

For the linear solvation energy relationship (LSER), values are listed for each of the variables (Vi/100, π*, &betam, αm) for fundamental organic structures and functional groups. We give the guidelines to estimate LSER variable values quickly for a vast array of possible organic compounds such as those found in the environment. The difficulty in generating these variables has greatly discouraged the application of this quantitative structure-activity relationship (QSAR) method. This paper present the first compilation of molecular functional group values together with a utilitarian set of the LSER variable estimation rules. The availability of these variable values and rules should facilitate widespread application of LSER for hazard evaluation of environmental contaminants.

Algebraic Functions of H-Functions with Specific Dependency Structure.

DTIC Science & Technology

1984-05-01

a study of its characteristic function. Such analysis is reproduced in books by Springer (17), Anderson (23), Feller (34,35), Mood and Graybill (52...following linearity property for expectations of jointly distributed random variables is derived. r 1 Theorem 1.1: If X and Y are real random variables...appear in American Journal of Mathematical and Management Science. 13. Mathai, A.M., and R.K. Saxena, "On linear combinations of stochastic variables

Study of the (1 + 1) D Long Wavelength Steady States of the Bénard Problem For Ultrathin Films

NASA Astrophysics Data System (ADS)

Zhou, Chengzhe; Troian, Sandra

We investigate the stationary states of the (1 + 1) D equation ht +h3hxxx +h2γx (h) x = 0 for thin films of thickness h (x , t) where x is the spatial variable and t is time. The variable γ (h) , denotes the surface tension along the gas/liquid interface of the slender bilayer confined between two substrates enforcing thermal conduction within the gap. Equilibrium solutions include flat films, droplets, trenches/ridges and positive periodic steady states (PPSS), the latter conveniently parameterized by a generalized interfacial pressure and the global extremum in shape. We derive perturbative solutions describing PPSS shapes near the stability threshold including their minimal period, average height and free energy. Weakly nonlinear analysis confirms that flat films always undergo a supercritical unstable pitch-fork bifurcation. Globally, our numerical simulations indicate at most one non-trivial PPSS per given period and volume. The free energy of droplet states is also always lower than the relevant corresponding PPSS, suggesting that initial flat films tend to redistribute mass into droplet-like configurations. By solving the linearized eigenvalue problem, we also confirm the unstable nature of PPSS solutions far from the stability threshold.

Application of modern control theory to the design of optimum aircraft controllers

NASA Technical Reports Server (NTRS)

Power, L. J.

1973-01-01

The synthesis procedure presented is based on the solution of the output regulator problem of linear optimal control theory for time-invariant systems. By this technique, solution of the matrix Riccati equation leads to a constant linear feedback control law for an output regulator which will maintain a plant in a particular equilibrium condition in the presence of impulse disturbances. Two simple algorithms are presented that can be used in an automatic synthesis procedure for the design of maneuverable output regulators requiring only selected state variables for feedback. The first algorithm is for the construction of optimal feedforward control laws that can be superimposed upon a Kalman output regulator and that will drive the output of a plant to a desired constant value on command. The second algorithm is for the construction of optimal Luenberger observers that can be used to obtain feedback control laws for the output regulator requiring measurement of only part of the state vector. This algorithm constructs observers which have minimum response time under the constraint that the magnitude of the gains in the observer filter be less than some arbitrary limit.

Effect of 24-h continuous rotigotine treatment on stationary and non-stationary locomotion in de novo patients with Parkinson disease in an open-label uncontrolled study.

PubMed

Serrao, Mariano; Ranavolo, Alberto; Conte, Carmela; Davassi, Chiara; Mari, Silvia; Fasano, Alfonso; Chini, Giorgia; Coppola, Gianluca; Draicchio, Francesco; Pierelli, Francesco

2015-11-01

The aim of this study was to investigate the effect of a rotigotine transdermal patch on stationary and non-stationary locomotion in de novo Parkinson disease (PD) patients in an open-label uncontrolled study. A 3-D gait analysis system was used to investigate four different locomotor tasks: steady-state linear walking, gait initiation, gait termination and 180°-turning. A series of gait variables were measured for each locomotor task. PD patients who received rotigotine treatment (4-8 mg) displayed: (1) increased step length, gait speed, cadence and arm oscillations, and reduced double support duration and step asymmetry during steady-state linear gait; (2) increased initial step length during gait initiation; (3) increased final step length and gait speed, and decreased stability index during gait termination; (4) decreased duration of turning and head-pelvis delays during 180°-turning. The main finding that emerges from the present study is that the dopamine agonist rotigotine can improve various aspects of gait in de novo PD patients.

Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples

NASA Astrophysics Data System (ADS)

Gao, Yuan; Ma, Jiayi; Yuille, Alan L.

2017-05-01

This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., additive nuisance variables such as bad lighting, wearing of glasses) and non-linear (i.e., non-additive pixel-wise nuisance variables such as expression changes). The small number of labeled examples means that it is hard to remove these nuisance variables between the training and testing faces to obtain good recognition performance. To address the problem we propose a method called Semi-Supervised Sparse Representation based Classification (S$^3$RC). This is based on recent work on sparsity where faces are represented in terms of two dictionaries: a gallery dictionary consisting of one or more examples of each person, and a variation dictionary representing linear nuisance variables (e.g., different lighting conditions, different glasses). The main idea is that (i) we use the variation dictionary to characterize the linear nuisance variables via the sparsity framework, then (ii) prototype face images are estimated as a gallery dictionary via a Gaussian Mixture Model (GMM), with mixed labeled and unlabeled samples in a semi-supervised manner, to deal with the non-linear nuisance variations between labeled and unlabeled samples. We have done experiments with insufficient labeled samples, even when there is only a single labeled sample per person. Our results on the AR, Multi-PIE, CAS-PEAL, and LFW databases demonstrate that the proposed method is able to deliver significantly improved performance over existing methods.

Multifactor valuation models of energy futures and options on futures

NASA Astrophysics Data System (ADS)

Bertus, Mark J.

The intent of this dissertation is to investigate continuous time pricing models for commodity derivative contracts that consider mean reversion. The motivation for pricing commodity futures and option on futures contracts leads to improved practical risk management techniques in markets where uncertainty is increasing. In the dissertation closed-form solutions to mean reverting one-factor, two-factor, three-factor Brownian motions are developed for futures contracts. These solutions are obtained through risk neutral pricing methods that yield tractable expressions for futures prices, which are linear in the state variables, hence making them attractive for estimation. These functions, however, are expressed in terms of latent variables (i.e. spot prices, convenience yield) which complicate the estimation of the futures pricing equation. To address this complication a discussion on Dynamic factor analysis is given. This procedure documents latent variables using a Kalman filter and illustrations show how this technique may be used for the analysis. In addition, to the futures contracts closed form solutions for two option models are obtained. Solutions to the one- and two-factor models are tailored solutions of the Black-Scholes pricing model. Furthermore, since these contracts are written on the futures contracts, they too are influenced by the same underlying parameters of the state variables used to price the futures contracts. To conclude, the analysis finishes with an investigation of commodity futures options that incorporate random discrete jumps.

VASP- VARIABLE DIMENSION AUTOMATIC SYNTHESIS PROGRAM

NASA Technical Reports Server (NTRS)

White, J. S.

1994-01-01

VASP is a variable dimension Fortran version of the Automatic Synthesis Program, ASP. The program is used to implement Kalman filtering and control theory. Basically, it consists of 31 subprograms for solving most modern control problems in linear, time-variant (or time-invariant) control systems. These subprograms include operations of matrix algebra, computation of the exponential of a matrix and its convolution integral, and the solution of the matrix Riccati equation. The user calls these subprograms by means of a FORTRAN main program, and so can easily obtain solutions to most general problems of extremization of a quadratic functional of the state of the linear dynamical system. Particularly, these problems include the synthesis of the Kalman filter gains and the optimal feedback gains for minimization of a quadratic performance index. VASP, as an outgrowth of the Automatic Synthesis Program, has the following improvements: more versatile programming language; more convenient input/output format; some new subprograms which consolidate certain groups of statements that are often repeated; and variable dimensioning. The pertinent difference between the two programs is that VASP has variable dimensioning and more efficient storage. The documentation for the VASP program contains a VASP dictionary and example problems. The dictionary contains a description of each subroutine and instructions on its use. The example problems include dynamic response, optimal control gain, solution of the sampled data matrix Riccati equation, matrix decomposition, and a pseudo-inverse of a matrix. This program is written in FORTRAN IV and has been implemented on the IBM 360. The VASP program was developed in 1971.

Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects.

PubMed

Melillo, Paolo; Jovic, Alan; De Luca, Nicola; Pecchia, Leandro

2015-08-01

Accidental falls are a major problem of later life. Different technologies to predict falls have been investigated, but with limited success, mainly because of low specificity due to a high false positive rate. This Letter presents an automatic classifier based on heart rate variability (HRV) analysis with the goal to identify fallers automatically. HRV was used in this study as it is considered a good estimator of autonomic nervous system (ANS) states, which are responsible, among other things, for human balance control. Nominal 24 h electrocardiogram recordings from 168 cardiac patients (age 72 ± 8 years, 60 female), of which 47 were fallers, were investigated. Linear and nonlinear HRV properties were analysed in 30 min excerpts. Different data mining approaches were adopted and their performances were compared with a subject-based receiver operating characteristic analysis. The best performance was achieved by a hybrid algorithm, RUSBoost, integrated with feature selection method based on principal component analysis, which achieved satisfactory specificity and accuracy (80 and 72%, respectively), but low sensitivity (51%). These results suggested that ANS states causing falls could be reliably detected, but also that not all the falls were due to ANS states.

Effects of guided breath exercise on complex behaviour of heart rate dynamics.

PubMed

Tavares, Bruna S; de Paula Vidigal, Giovanna; Garner, David M; Raimundo, Rodrigo D; de Abreu, Luiz Carlos; Valenti, Vitor E

2017-11-01

Cardiac autonomic regulation is influenced by changes in respiratory rate, which has been demonstrated by linear analysis of heart rate variability (HRV). Conversely, the complex behaviour is not well defined for HRV during this physiological state. In this sense, Higuchi Fractal Dimension is applied directly to the time series. It analyses the fractal dimension of discrete time sequences and is simpler and faster than correlation dimension and many other classical measures derived from chaos theory. We investigated chaotic behaviour of heart rate dynamics during guided breath exercises. We investigated 21 healthy male volunteers aged between 18 and 30 years. HRV was analysed 10 min before and 10 min during guided breath exercises. HRV was analysed in the time and frequency domain for linear analysis and through HFD for non-linear analysis. Linear analysis indicated that SDNN, pNN50, RMSSD, LF, HF and LF/HF increased during guided breath exercises. HFD analysis illustrated that between K max 20 to K max 120 intervals, was enhanced during guided breath exercises. Guided breath exercises acutely increased chaotic behaviour of HRV measured by HFD. © 2016 Scandinavian Society of Clinical Physiology and Nuclear Medicine. Published by John Wiley & Sons Ltd.

Hybrid Methods in Quantum Information

NASA Astrophysics Data System (ADS)

Marshall, Kevin

Today, the potential power of quantum information processing comes as no surprise to physicist or science-fiction writer alike. However, the grand promises of this field remain unrealized, despite significant strides forward, due to the inherent difficulties of manipulating quantum systems. Simply put, it turns out that it is incredibly difficult to interact, in a controllable way, with the quantum realm when we seem to live our day to day lives in a classical world. In an effort to solve this challenge, people are exploring a variety of different physical platforms, each with their strengths and weaknesses, in hopes of developing new experimental methods that one day might allow us to control a quantum system. One path forward rests in combining different quantum systems in novel ways to exploit the benefits of different systems while circumventing their respective weaknesses. In particular, quantum systems come in two different flavours: either discrete-variable systems or continuous-variable ones. The field of hybrid quantum information seeks to combine these systems, in clever ways, to help overcome the challenges blocking the path between what is theoretically possible and what is achievable in a laboratory. In this thesis we explore four topics in the context of hybrid methods in quantum information, in an effort to contribute to the resolution of existing challenges and to stimulate new avenues of research. First, we explore the manipulation of a continuous-variable quantum system consisting of phonons in a linear chain of trapped ions where we use the discretized internal levels to mediate interactions. Using our proposed interaction we are able to implement, for example, the acoustic equivalent of a beam splitter with modest experimental resources. Next we propose an experimentally feasible implementation of the cubic phase gate, a primitive non-Gaussian gate required for universal continuous-variable quantum computation, based off sequential photon subtraction. We then discuss the notion of embedding a finite dimensional state into a continuous-variable system, and propose a method of performing quantum computations on encrypted continuous-variable states. This protocol allows for a client, of limited quantum ability, to outsource a computation while hiding their information. Next, we discuss the possibility of performing universal quantum computation on discrete-variable logical states encoded in mixed continuous-variable quantum states. Finally, we present an account of open problems related to our results, and possible future avenues of research.

Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter

NASA Technical Reports Server (NTRS)

Miller, Brad A.; Howard, Samuel A.

2008-01-01

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter s performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

State-Dependent Pseudo-Linear Filter for Spacecraft Attitude and Rate Estimation

NASA Technical Reports Server (NTRS)

Bar-Itzhack, Itzhack Y.; Harman, Richard R.

2001-01-01

This paper presents the development and performance of a special algorithm for estimating the attitude and angular rate of a spacecraft. The algorithm is a pseudo-linear Kalman filter, which is an ordinary linear Kalman filter that operates on a linear model whose matrices are current state estimate dependent. The nonlinear rotational dynamics equation of the spacecraft is presented in the state space as a state-dependent linear system. Two types of measurements are considered. One type is a measurement of the quaternion of rotation, which is obtained from a newly introduced star tracker based apparatus. The other type of measurement is that of vectors, which permits the use of a variety of vector measuring sensors like sun sensors and magnetometers. While quaternion measurements are related linearly to the state vector, vector measurements constitute a nonlinear function of the state vector. Therefore, in this paper, a state-dependent linear measurement equation is developed for the vector measurement case. The state-dependent pseudo linear filter is applied to simulated spacecraft rotations and adequate estimates of the spacecraft attitude and rate are obtained for the case of quaternion measurements as well as of vector measurements.

Improved finite element methodology for integrated thermal structural analysis

NASA Technical Reports Server (NTRS)

Dechaumphai, P.; Thornton, E. A.

1982-01-01

An integrated thermal-structural finite element approach for efficient coupling of thermal and structural analysis is presented. New thermal finite elements which yield exact nodal and element temperatures for one dimensional linear steady state heat transfer problems are developed. A nodeless variable formulation is used to establish improved thermal finite elements for one dimensional nonlinear transient and two dimensional linear transient heat transfer problems. The thermal finite elements provide detailed temperature distributions without using additional element nodes and permit a common discretization with lower order congruent structural finite elements. The accuracy of the integrated approach is evaluated by comparisons with analytical solutions and conventional finite element thermal structural analyses for a number of academic and more realistic problems. Results indicate that the approach provides a significant improvement in the accuracy and efficiency of thermal stress analysis for structures with complex temperature distributions.

Macroweather Predictions and Climate Projections using Scaling and Historical Observations

NASA Astrophysics Data System (ADS)

Hébert, R.; Lovejoy, S.; Del Rio Amador, L.

2017-12-01

There are two fundamental time scales that are pertinent to decadal forecasts and multidecadal projections. The first is the lifetime of planetary scale structures, about 10 days (equal to the deterministic predictability limit), and the second is - in the anthropocene - the scale at which the forced anthropogenic variability exceeds the internal variability (around 16 - 18 years). These two time scales define three regimes of variability: weather, macroweather and climate that are respectively characterized by increasing, decreasing and then increasing varibility with scale.We discuss how macroweather temperature variability can be skilfully predicted to its theoretical stochastic predictability limits by exploiting its long-range memory with the Stochastic Seasonal and Interannual Prediction System (StocSIPS). At multi-decadal timescales, the temperature response to forcing is approximately linear and this can be exploited to make projections with a Green's function, or Climate Response Function (CRF). To make the problem tractable, we exploit the temporal scaling symmetry and restrict our attention to global mean forcing and temperature response using a scaling CRF characterized by the scaling exponent H and an inner scale of linearity τ. An aerosol linear scaling factor α and a non-linear volcanic damping exponent ν were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference using historical data and these allow us to analytically calculate a median (and likely 66% range) for the transient climate response, and for the equilibrium climate sensitivity: 1.6K ([1.5,1.8]K) and 2.4K ([1.9,3.4]K) respectively. Aerosol forcing typically has large uncertainty and we find a modern (2005) forcing very likely range (90%) of [-1.0, -0.3] Wm-2 with median at -0.7 Wm-2. Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to Representative Concentration Pathway (RCP) 2.6 for which the probability to remain under 1.5 K is 48%. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability. This underscores that over the next century, the state of the environment will be strongly influenced by past, present and future economical policies.

Control performance of a road vehicle with four independent single-wheel electric motors and steer-by-wire system

NASA Astrophysics Data System (ADS)

Weiskircher, Thomas; Müller, Steffen

2012-01-01

This article presents a motion controller for a road vehicle equipped with a steer-by-wire system and four independent electric rim-mounted drives. The motion controller separates the control law from the specific actuator setup by the usage of virtual global control variables acting on the vehicle centre of gravity. A control allocation algorithm distributes the virtual control variables to the available actuators. An approximation of the real actuator dynamics is used to analyse the performance of different motion controller types in the linear and nonlinear driving regions. In addition, a vehicle state observer consisting of a traction force observer and an unscented Kalman filter is discussed to analyse the control behaviour in the case of a real sensor setup.

Design of permanent magnet eddy current brake for a small scaled electromagnetic launch model

NASA Astrophysics Data System (ADS)

Zhou, Shigui; Yu, Haitao; Hu, Minqiang; Huang, Lei

2012-04-01

A variable pole-pitch double-sided permanent magnet (PM) linear eddy current brake (LECB) is proposed for a small scaled electromagnetic launch model. A two-dimensional (2D) analytical steady state model is presented for the double-sided PM-LECB, and the expression for the braking force is derived. Based on the analytical model, the material and eddy current skin effect of the conducting plate are analyzed. Moreover, a variable pole-pitch double-sided PM-LECB is proposed for the effective braking of the moving plate. In addition, the braking force is predicted by finite element (FE) analysis, and the simulated results are in good agreement with the analytical model. Finally, a prototype is presented to test the braking profile for validation of the proposed design.

Experimental demonstration of nonbilocal quantum correlations.

PubMed

Saunders, Dylan J; Bennet, Adam J; Branciard, Cyril; Pryde, Geoff J

2017-04-01

Quantum mechanics admits correlations that cannot be explained by local realistic models. The most studied models are the standard local hidden variable models, which satisfy the well-known Bell inequalities. To date, most works have focused on bipartite entangled systems. We consider correlations between three parties connected via two independent entangled states. We investigate the new type of so-called "bilocal" models, which correspondingly involve two independent hidden variables. These models describe scenarios that naturally arise in quantum networks, where several independent entanglement sources are used. Using photonic qubits, we build such a linear three-node quantum network and demonstrate nonbilocal correlations by violating a Bell-like inequality tailored for bilocal models. Furthermore, we show that the demonstration of nonbilocality is more noise-tolerant than that of standard Bell nonlocality in our three-party quantum network.

Worst case encoder-decoder policies for a communication system in the presence of an unknown probabilistic jammer

NASA Astrophysics Data System (ADS)

Cascio, David M.

1988-05-01

States of nature or observed data are often stochastically modelled as Gaussian random variables. At times it is desirable to transmit this information from a source to a destination with minimal distortion. Complicating this objective is the possible presence of an adversary attempting to disrupt this communication. In this report, solutions are provided to a class of minimax and maximin decision problems, which involve the transmission of a Gaussian random variable over a communications channel corrupted by both additive Gaussian noise and probabilistic jamming noise. The jamming noise is termed probabilistic in the sense that with nonzero probability 1-P, the jamming noise is prevented from corrupting the channel. We shall seek to obtain optimal linear encoder-decoder policies which minimize given quadratic distortion measures.

Human and biophysical influences on fire occurrence in the United States

USGS Publications Warehouse

Hawbaker, Todd J.; Radeloff, Volker C.; Stewart, Susan I.; Hammer, Roger B.; Keuler, Nicholas S.; Clayton, Murray K.

2013-01-01

National-scale analyses of fire occurrence are needed to prioritize fire policy and management activities across the United States. However, the drivers of national-scale patterns of fire occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of fire occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative influence of human, vegetation, climate, and topographic variables on fire occurrence in the United States, as measured by MODIS active fire detections collected between 2000 and 2006. We constructed models for all fires and for large fires only and generated predictive maps to quantify fire occurrence probabilities. Areas with high fire occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-fire occurrence were generally lower, but hot spots existed in the western and south-central United States The probability of fire occurrence is a critical component of fire risk assessments, in addition to vegetation type, fire behavior, and the values at risk. Many of the hot spots we identified have extensive development in the wildland–urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all fires and large fires and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also significant variables in most ecoregions. If recent housing growth trends and fire occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wildfires with the ecological benefits of fire.

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

M. P. Jensen; Toto, T.

Standard Atmospheric Radiation Measurement (ARM) Climate Research Facility sounding files provide atmospheric state data in one dimension of increasing time and height per sonde launch. Many applications require a quick estimate of the atmospheric state at higher time resolution. The INTERPOLATEDSONDE (i.e., Interpolated Sounding) Value-Added Product (VAP) transforms sounding data into continuous daily files on a fixed time-height grid, at 1-minute time resolution, on 332 levels, from the surface up to a limit of approximately 40 km. The grid extends that high so the full height of soundings can be captured; however, most soundings terminate at an altitude between 25more » and 30 km, above which no data is provided. Between soundings, the VAP linearly interpolates atmospheric state variables in time for each height level. In addition, INTERPOLATEDSONDE provides relative humidity scaled to microwave radiometer (MWR) observations.« less

Exponential Boundary Observers for Pressurized Water Pipe

NASA Astrophysics Data System (ADS)

Hermine Som, Idellette Judith; Cocquempot, Vincent; Aitouche, Abdel

2015-11-01

This paper deals with state estimation on a pressurized water pipe modeled by nonlinear coupled distributed hyperbolic equations for non-conservative laws with three known boundary measures. Our objective is to estimate the fourth boundary variable, which will be useful for leakage detection. Two approaches are studied. Firstly, the distributed hyperbolic equations are discretized through a finite-difference scheme. By using the Lipschitz property of the nonlinear term and a Lyapunov function, the exponential stability of the estimation error is proven by solving Linear Matrix Inequalities (LMIs). Secondly, the distributed hyperbolic system is preserved for state estimation. After state transformations, a Luenberger-like PDE boundary observer based on backstepping mathematical tools is proposed. An exponential Lyapunov function is used to prove the stability of the resulted estimation error. The performance of the two observers are shown on a water pipe prototype simulated example.

Analysis of complex neural circuits with nonlinear multidimensional hidden state models

PubMed Central

Friedman, Alexander; Slocum, Joshua F.; Tyulmankov, Danil; Gibb, Leif G.; Altshuler, Alex; Ruangwises, Suthee; Shi, Qinru; Toro Arana, Sebastian E.; Beck, Dirk W.; Sholes, Jacquelyn E. C.; Graybiel, Ann M.

2016-01-01

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain. PMID:27222584

The Multifaceted Variable Approach: Selection of Method in Solving Simple Linear Equations

ERIC Educational Resources Information Center

Tahir, Salma; Cavanagh, Michael

2010-01-01

This paper presents a comparison of the solution strategies used by two groups of Year 8 students as they solved linear equations. The experimental group studied algebra following a multifaceted variable approach, while the comparison group used a traditional approach. Students in the experimental group employed different solution strategies,…

Stability of Nonlinear Principal Components Analysis: An Empirical Study Using the Balanced Bootstrap

ERIC Educational Resources Information Center

Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Kooij, Anita J.

2007-01-01

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate…

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…

Common pitfalls in statistical analysis: Linear regression analysis

PubMed Central

Aggarwal, Rakesh; Ranganathan, Priya

2017-01-01

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis. PMID:28447022

An analytical approach to top predator interference on the dynamics of a food chain model

NASA Astrophysics Data System (ADS)

Senthamarai, R.; Vijayalakshmi, T.

2018-04-01

In this paper, a nonlinear mathematical model is proposed and analyzed to study of top predator interference on the dynamics of a food chain model. The mathematical model is formulated using the system of non-linear ordinary differential equations. In this model, there are three state dimensionless variables, viz, size of prey population x, size of intermediate predator y and size of top predator population z. The analytical results are compared with the numerical simulation using MATLAB software and satisfactory results are noticed.

Water-quality trend analysis and sampling design for the Devils Lake Basin, North Dakota, January 1965 through September 2003

USGS Publications Warehouse

Ryberg, Karen R.; Vecchia, Aldo V.

2006-01-01

This report presents the results of a study conducted by the U.S. Geological Survey, in cooperation with the North Dakota State Water Commission, the Devils Lake Basin Joint Water Resource Board, and the Red River Joint Water Resource District, to analyze historical water-quality trends in three dissolved major ions, three nutrients, and one dissolved trace element for eight stations in the Devils Lake Basin in North Dakota and to develop an efficient sampling design to monitor the future trends. A multiple-regression model was used to detect and remove streamflow-related variability in constituent concentrations. To separate the natural variability in concentration as a result of variability in streamflow from the variability in concentration as a result of other factors, the base-10 logarithm of daily streamflow was divided into four components-a 5-year streamflow anomaly, an annual streamflow anomaly, a seasonal streamflow anomaly, and a daily streamflow anomaly. The constituent concentrations then were adjusted for streamflow-related variability by removing the 5-year, annual, seasonal, and daily variability. Constituents used for the water-quality trend analysis were evaluated for a step trend to examine the effect of Channel A on water quality in the basin and a linear trend to detect gradual changes with time from January 1980 through September 2003. The fitted upward linear trends for dissolved calcium concentrations during 1980-2003 for two stations were significant. The fitted step trends for dissolved sulfate concentrations for three stations were positive and similar in magnitude. Of the three upward trends, one was significant. The fitted step trends for dissolved chloride concentrations were positive but insignificant. The fitted linear trends for the upstream stations were small and insignificant, but three of the downward trends that occurred during 1980-2003 for the remaining stations were significant. The fitted upward linear trends for dissolved nitrite plus nitrate as nitrogen concentrations during 1987-2003 for two stations were significant. However, concentrations during recent years appear to be lower than those for the 1970s and early 1980s but higher than those for the late 1980s and early 1990s. The fitted downward linear trend for dissolved ammonia concentrations for one station was significant. The fitted linear trends for total phosphorus concentrations for two stations were significant. Upward trends for total phosphorus concentrations occurred from the late 1980s to 2003 for most stations, but a small and insignificant downward trend occurred for one station. Continued monitoring will be needed to determine if the recent trend toward higher dissolved nitrite plus nitrate as nitrogen and total phosphorus concentrations continues in the future. For continued monitoring of water-quality trends in the upper Devils Lake Basin, an efficient sampling design consists of five major-ion, nutrient, and trace-element samples per year at three existing stream stations and at three existing lake stations. This sampling design requires the collection of 15 stream samples and 15 lake samples per year rather than 16 stream samples and 20 lake samples per year as in the 1992-2003 program. Thus, the design would result in a program that is less costly and more efficient than the 1992-2003 program but that still would provide the data needed to monitor water-quality trends in the Devils Lake Basin.

A new chaotic oscillator with free control

NASA Astrophysics Data System (ADS)

Li, Chunbiao; Sprott, Julien Clinton; Akgul, Akif; Iu, Herbert H. C.; Zhao, Yibo

2017-08-01

A novel chaotic system is explored in which all terms are quadratic except for a linear function. The slope of the linear function rescales the amplitude and frequency of the variables linearly while its zero intercept allows offset boosting for one of the variables. Therefore, a free-controlled chaotic oscillation can be obtained with any desired amplitude, frequency, and offset by an easy modification of the linear function. When implemented as an electronic circuit, the corresponding chaotic signal can be controlled by two independent potentiometers, which is convenient for constructing a chaos-based application system. To the best of our knowledge, this class of chaotic oscillators has never been reported.

Variance approach for multi-objective linear programming with fuzzy random of objective function coefficients

NASA Astrophysics Data System (ADS)

Indarsih, Indrati, Ch. Rini

2016-02-01

In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.

Estimating linear effects in ANOVA designs: the easy way.

PubMed

Pinhas, Michal; Tzelgov, Joseph; Ganor-Stern, Dana

2012-09-01

Research in cognitive science has documented numerous phenomena that are approximated by linear relationships. In the domain of numerical cognition, the use of linear regression for estimating linear effects (e.g., distance and SNARC effects) became common following Fias, Brysbaert, Geypens, and d'Ydewalle's (1996) study on the SNARC effect. While their work has become the model for analyzing linear effects in the field, it requires statistical analysis of individual participants and does not provide measures of the proportions of variability accounted for (cf. Lorch & Myers, 1990). In the present methodological note, using both the distance and SNARC effects as examples, we demonstrate how linear effects can be estimated in a simple way within the framework of repeated measures analysis of variance. This method allows for estimating effect sizes in terms of both slope and proportions of variability accounted for. Finally, we show that our method can easily be extended to estimate linear interaction effects, not just linear effects calculated as main effects.

Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes.

PubMed

Pey, Jon; Rubio, Angel; Theodoropoulos, Constantinos; Cascante, Marta; Planes, Francisco J

2012-07-01

Constraints-based modeling is an emergent area in Systems Biology that includes an increasing set of methods for the analysis of metabolic networks. In order to refine its predictions, the development of novel methods integrating high-throughput experimental data is currently a key challenge in the field. In this paper, we present a novel set of constraints that integrate tracer-based metabolomics data from Isotope Labeling Experiments and metabolic fluxes in a linear fashion. These constraints are based on Elementary Carbon Modes (ECMs), a recently developed concept that generalizes Elementary Flux Modes at the carbon level. To illustrate the effect of our ECMs-based constraints, a Flux Variability Analysis approach was applied to a previously published metabolic network involving the main pathways in the metabolism of glucose. The addition of our ECMs-based constraints substantially reduced the under-determination resulting from a standard application of Flux Variability Analysis, which shows a clear progress over the state of the art. In addition, our approach is adjusted to deal with combinatorial explosion of ECMs in genome-scale metabolic networks. This extension was applied to infer the maximum biosynthetic capacity of non-essential amino acids in human metabolism. Finally, as linearity is the hallmark of our approach, its importance is discussed at a methodological, computational and theoretical level and illustrated with a practical application in the field of Isotope Labeling Experiments. Copyright © 2012 Elsevier Inc. All rights reserved.

Optimal clinical trial design based on a dichotomous Markov-chain mixed-effect sleep model.

PubMed

Steven Ernest, C; Nyberg, Joakim; Karlsson, Mats O; Hooker, Andrew C

2014-12-01

D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.

Comparing and improving proper orthogonal decomposition (POD) to reduce the complexity of groundwater models

NASA Astrophysics Data System (ADS)

Gosses, Moritz; Nowak, Wolfgang; Wöhling, Thomas

2017-04-01

Physically-based modeling is a wide-spread tool in understanding and management of natural systems. With the high complexity of many such models and the huge amount of model runs necessary for parameter estimation and uncertainty analysis, overall run times can be prohibitively long even on modern computer systems. An encouraging strategy to tackle this problem are model reduction methods. In this contribution, we compare different proper orthogonal decomposition (POD, Siade et al. (2010)) methods and their potential applications to groundwater models. The POD method performs a singular value decomposition on system states as simulated by the complex (e.g., PDE-based) groundwater model taken at several time-steps, so-called snapshots. The singular vectors with the highest information content resulting from this decomposition are then used as a basis for projection of the system of model equations onto a subspace of much lower dimensionality than the original complex model, thereby greatly reducing complexity and accelerating run times. In its original form, this method is only applicable to linear problems. Many real-world groundwater models are non-linear, tough. These non-linearities are introduced either through model structure (unconfined aquifers) or boundary conditions (certain Cauchy boundaries, like rivers with variable connection to the groundwater table). To date, applications of POD focused on groundwater models simulating pumping tests in confined aquifers with constant head boundaries. In contrast, POD model reduction either greatly looses accuracy or does not significantly reduce model run time if the above-mentioned non-linearities are introduced. We have also found that variable Dirichlet boundaries are problematic for POD model reduction. An extension to the POD method, called POD-DEIM, has been developed for non-linear groundwater models by Stanko et al. (2016). This method uses spatial interpolation points to build the equation system in the reduced model space, thereby allowing the recalculation of system matrices at every time-step necessary for non-linear models while retaining the speed of the reduced model. This makes POD-DEIM applicable for groundwater models simulating unconfined aquifers. However, in our analysis, the method struggled to reproduce variable river boundaries accurately and gave no advantage for variable Dirichlet boundaries compared to the original POD method. We have developed another extension for POD that targets to address these remaining problems by performing a second POD operation on the model matrix on the left-hand side of the equation. The method aims to at least reproduce the accuracy of the other methods where they are applicable while outperforming them for setups with changing river boundaries or variable Dirichlet boundaries. We compared the new extension with original POD and POD-DEIM for different combinations of model structures and boundary conditions. The new method shows the potential of POD extensions for applications to non-linear groundwater systems and complex boundary conditions that go beyond the current, relatively limited range of applications. References: Siade, A. J., Putti, M., and Yeh, W. W.-G. (2010). Snapshot selection for groundwater model reduction using proper orthogonal decomposition. Water Resour. Res., 46(8):W08539. Stanko, Z. P., Boyce, S. E., and Yeh, W. W.-G. (2016). Nonlinear model reduction of unconfined groundwater flow using pod and deim. Advances in Water Resources, 97:130 - 143.

Engineering double-well potentials with variable-width annular Josephson tunnel junctions

NASA Astrophysics Data System (ADS)

Monaco, Roberto

2016-11-01

Long Josephson tunnel junctions are non-linear transmission lines that allow propagation of current vortices (fluxons) and electromagnetic waves and are used in various applications within superconductive electronics. Recently, the Josephson vortex has been proposed as a new superconducting qubit. We describe a simple method to create a double-well potential for an individual fluxon trapped in a long elliptic annular Josephson tunnel junction characterized by an intrinsic non-uniform width. The distance between the potential wells and the height of the inter-well potential barrier are controlled by the strength of an in-plane magnetic field. The manipulation of the vortex states can be achieved by applying a proper current ramp across the junction. The read-out of the state is accomplished by measuring the vortex depinning current in a small magnetic field. An accurate one-dimensional sine-Gordon model for this strongly non-linear system is presented, from which we calculate the position-dependent fluxon rest-mass, its Hamiltonian density and the corresponding trajectories in the phase space. We examine the dependence of the potential properties on the annulus eccentricity and its electrical parameters and address the requirements for observing quantum-mechanical effects, as discrete energy levels and tunneling, in this two-state system.

Parameterizing sorption isotherms using a hybrid global-local fitting procedure.

PubMed

Matott, L Shawn; Singh, Anshuman; Rabideau, Alan J

2017-05-01

Predictive modeling of the transport and remediation of groundwater contaminants requires an accurate description of the sorption process, which is usually provided by fitting an isotherm model to site-specific laboratory data. Commonly used calibration procedures, listed in order of increasing sophistication, include: trial-and-error, linearization, non-linear regression, global search, and hybrid global-local search. Given the considerable variability in fitting procedures applied in published isotherm studies, we investigated the importance of algorithm selection through a series of numerical experiments involving 13 previously published sorption datasets. These datasets, considered representative of state-of-the-art for isotherm experiments, had been previously analyzed using trial-and-error, linearization, or non-linear regression methods. The isotherm expressions were re-fit using a 3-stage hybrid global-local search procedure (i.e. global search using particle swarm optimization followed by Powell's derivative free local search method and Gauss-Marquardt-Levenberg non-linear regression). The re-fitted expressions were then compared to previously published fits in terms of the optimized weighted sum of squared residuals (WSSR) fitness function, the final estimated parameters, and the influence on contaminant transport predictions - where easily computed concentration-dependent contaminant retardation factors served as a surrogate measure of likely transport behavior. Results suggest that many of the previously published calibrated isotherm parameter sets were local minima. In some cases, the updated hybrid global-local search yielded order-of-magnitude reductions in the fitness function. In particular, of the candidate isotherms, the Polanyi-type models were most likely to benefit from the use of the hybrid fitting procedure. In some cases, improvements in fitness function were associated with slight (<10%) changes in parameter values, but in other cases significant (>50%) changes in parameter values were noted. Despite these differences, the influence of isotherm misspecification on contaminant transport predictions was quite variable and difficult to predict from inspection of the isotherms. Copyright © 2017 Elsevier B.V. All rights reserved.

Computing anticipatory systems with incursion and hyperincursion

NASA Astrophysics Data System (ADS)

Dubois, Daniel M.

1998-07-01

An anticipatory system is a system which contains a model of itself and/or of its environment in view of computing its present state as a function of the prediction of the model. With the concepts of incursion and hyperincursion, anticipatory discrete systems can be modelled, simulated and controlled. By definition an incursion, an inclusive or implicit recursion, can be written as: x(t+1)=F[…,x(t-1),x(t),x(t+1),…] where the value of a variable x(t+1) at time t+1 is a function of this variable at past, present and future times. This is an extension of recursion. Hyperincursion is an incursion with multiple solutions. For example, chaos in the Pearl-Verhulst map model: x(t+1)=a.x(t).[1-x(t)] is controlled by the following anticipatory incursive model: x(t+1)=a.x(t).[1-x(t+1)] which corresponds to the differential anticipatory equation: dx(t)/dt=a.x(t).[1-x(t+1)]-x(t). The main part of this paper deals with the discretisation of differential equation systems of linear and non-linear oscillators. The non-linear oscillator is based on the Lotka-Volterra equations model. The discretisation is made by incursion. The incursive discrete equation system gives the same stability condition than the original differential equations without numerical instabilities. The linearisation of the incursive discrete non-linear Lotka-Volterra equation system gives rise to the classical harmonic oscillator. The incursive discretisation of the linear oscillator is similar to define backward and forward discrete derivatives. A generalized complex derivative is then considered and applied to the harmonic oscillator. Non-locality seems to be a property of anticipatory systems. With some mathematical assumption, the Schrödinger quantum equation is derived for a particle in a uniform potential. Finally an hyperincursive system is given in the case of a neural stack memory.

Finite state modeling of aeroelastic systems

NASA Technical Reports Server (NTRS)

Vepa, R.

1977-01-01

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

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

PubMed Central

Song, Yong-Ze; Yang, Hong-Lei; Peng, Jun-Huan; Song, Yi-Rong; Sun, Qian; Li, Yuan

2015-01-01

Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi'an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi'an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. PMID:26540446

Effects of Expressive Writing Effects on Disgust and Anxiety in a Subsequent Dissection

NASA Astrophysics Data System (ADS)

Randler, Christoph; Wüst-Ackermann, Peter; im Kampe, Viola Otte; Meyer-Ahrens, Inga H.; Tempel, Benjamin J.; Vollmer, Christian

2015-10-01

Emotions influence motivation and achievement, but negative emotions have rarely been assessed in science education. In this study, we assessed the influence of two different expressive writing assignments on disgust and anxiety in university students prior to the dissection of a trout. We randomly assigned students to one of two expressive writing tasks and measured specific state disgust and state anxiety after writing and after the dissection. Specific state disgust was measured a third time after 3 weeks. One writing task was concerned with the dissection, and the other was related to behavioral experiments with mice. We used two general linear models with repeated measures. In the first model, specific state disgust (pre, post, and follow-up) was used as the dependent repeated measure and experimental group as the independent variable. In the second model, state anxiety was used as the dependent repeated measure (pre, post) with experimental group as the independent variable. The repeated testing showed a highly significant effect of experimental group on the repeated measures of disgust. Writing about worries and emotions concerning the dissection leads to higher disgust scores compared to writing about mice. These higher scores persisted even 3 weeks later in the follow-up test. Concerning anxiety, there was a clear influence of the repeated measure of state anxiety, but anxiety was not influenced by the experimental group. We suggest that positive writing should be used in educational contexts to reduce disgust.

A Probabilistic Approach for Real-Time Volcano Surveillance

NASA Astrophysics Data System (ADS)

Cannavo, F.; Cannata, A.; Cassisi, C.; Di Grazia, G.; Maronno, P.; Montalto, P.; Prestifilippo, M.; Privitera, E.; Gambino, S.; Coltelli, M.

2016-12-01

Continuous evaluation of the state of potentially dangerous volcanos plays a key role for civil protection purposes. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the coupling of highly non-linear and complex volcanic dynamic processes leads to measurable effects that can show a large variety of different behaviors. Moreover, due to intrinsic uncertainties and possible failures in some recorded data, the volcano state needs to be expressed in probabilistic terms, thus making the fast volcano state assessment sometimes impracticable for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, we present a probabilistic graphical model to estimate automatically the ongoing volcano state from all the available different kind of measurements. The model consists of a Bayesian network able to represent a set of variables and their conditional dependencies via a directed acyclic graph. The model variables are both the measurements and the possible states of the volcano through the time. The model output is an estimation of the probability distribution of the feasible volcano states. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision making purposes.

Kinetics of human aging: I. Rates of senescence between ages 30 and 70 years in healthy people.

PubMed

Sehl, M E; Yates, F E

2001-05-01

A calculation of loss rates is reported for human structural and functional variables from a substantially larger data set than has been previously studied. Data were collected for healthy, nonsmoking human subjects of both sexes from a literature search of cross-sectional, longitudinal, and cross-sequential studies. The number of studies analyzed was 469, and the total number of subjects was 54,274. A linear model provided a fit of the data, for each variable, that was not significantly different from the best polynomial fit. Therefore, linear loss rates (as a percent decline per year from the reference value at age 30) were calculated for 445 variables from 13 organ systems, and additionally for 24 variables even more integrative, such as maximum oxygen consumption and exercise performance, that express effects of multiple contributing variables and systems. The frequency distribution of the 13 individual system linear loss rates (as percent loss per year) for a very healthy population has roughly a unimodal, right-skewed shape, with mean 0.65, median 0.5, and variance 0.32. (The actual underlying distribution could be a truncated Gaussian, an exponential, Poisson, gamma or some other). The linear estimates of loss rates were clustered between 0% and 2% per year for variables from most organ systems, with exceptions being the endocrine, thermoregulatory, and gastrointestinal systems, for which wider ranges (up to approximately 3% per year) of loss rates were found. We suggest that this set of linear losses over time, observed in healthy individuals between ages (approximately) 30 to 70 years, exposes the underlying kinetics of human senescence, independent of effects of substantial disease.

Relationship between health services, socioeconomic variables and inadequate weight gain among Brazilian children.

PubMed Central

de Souza, A. C.; Peterson, K. E.; Cufino, E.; Gardner, J.; Craveiro, M. V.; Ascherio, A.

1999-01-01

This ecological analysis assessed the relative contribution of behavioural, health services and socioeconomic variables to inadequate weight gain in infants (0-11 months) and children (12-23 months) in 140 municipalities in the State of Ceara, north-east Brazil. To assess the total effect of selected variables, we fitted three unique sets of multivariate linear regression models to the prevalence of inadequate weight gain in infants and in children. The final predictive models included variables from the three sets. Findings showed that participation in growth monitoring and urbanization were inversely and significantly associated with the prevalence of inadequate weight gain in infants, accounting for 38.3% of the variation. Female illiteracy rate, participation in growth monitoring and degree of urbanization were all positively associated with prevalence of inadequate weight gain in children. Together, these factors explained 25.6% of the variation. Our results suggest that efforts to reduce the average municipality-specific female illiteracy rate, in combination with participation in growth monitoring, may be effective in reducing municipality-level prevalence of inadequate weight gain in infants and children in Ceara. PMID:10612885

Relationship between health services, socioeconomic variables and inadequate weight gain among Brazilian children.

PubMed

de Souza, A C; Peterson, K E; Cufino, E; Gardner, J; Craveiro, M V; Ascherio, A

1999-01-01

This ecological analysis assessed the relative contribution of behavioural, health services and socioeconomic variables to inadequate weight gain in infants (0-11 months) and children (12-23 months) in 140 municipalities in the State of Ceara, north-east Brazil. To assess the total effect of selected variables, we fitted three unique sets of multivariate linear regression models to the prevalence of inadequate weight gain in infants and in children. The final predictive models included variables from the three sets. Findings showed that participation in growth monitoring and urbanization were inversely and significantly associated with the prevalence of inadequate weight gain in infants, accounting for 38.3% of the variation. Female illiteracy rate, participation in growth monitoring and degree of urbanization were all positively associated with prevalence of inadequate weight gain in children. Together, these factors explained 25.6% of the variation. Our results suggest that efforts to reduce the average municipality-specific female illiteracy rate, in combination with participation in growth monitoring, may be effective in reducing municipality-level prevalence of inadequate weight gain in infants and children in Ceara.

Un formalisme de systemes a sauts pour la recirculation optimale des casses dans une machine a papier

NASA Astrophysics Data System (ADS)

Khanbaghi, Maryam

Increasing closure of white water circuits is making mill productivity and quality of paper produced increasingly affected by the occurrence of paper breaks. In this thesis the main objective is the development of white water and broke recirculation policies. The thesis consists of three main parts, respectively corresponding to the synthesis of a statistical model of paper breaks in a paper mill, the basic mathematical setup for the formulation of white water and broke recirculation policies in the mill as a jump linear quadratic regulation problem, and finally the tuning of the control law based on first passage-time theory, and its extension to the case of control sensitive paper break rates. More specifically, in the first part a statistical model of paper machine breaks is developed. We start from the hypothesis that the breaks process is a Markov chain with three states: the first state is the operational one, while the two others are associated with the general types of paper-breaks that can take place in the mill (wet breaks and dry breaks). The Markovian hypothesis is empirically validated. We also establish how paper-break rates are correlated with machine speed and broke recirculation ratio. Subsequently, we show how the obtained Markov chain model of paper-breaks can be used to formulate a machine operating speed parameter optimization problem. In the second part, upon recognizing that paper breaks can be modelled as a Markov chain type of process which, when interacting with the continuous mill dynamics, yields a jump Markov model, jump linear theory is proposed as a means of constructing white water and broke recirculation strategies which minimize process variability. Reduced process variability comes at the expense of relatively large swings in white water and broke tanks level. Since the linear design does not specifically account for constraints on the state-space, under the resulting law, damaging events of tank overflow or emptiness can occur. A heuristic simulation-based approach is proposed to choose the performance measure design parameters to keep the mean time between incidents of fluid in broke and white water tanks either overflowing, or reaching dangerously low levels, sufficiently long. In the third part, a methodology, mainly founded on the first passage-time theory of stochastic processes, is proposed to choose the performance measure design parameters to limit process variability while accounting for the possibility of undesirable tank overflows or tank emptiness. The heart of the approach is an approximation technique for evaluating mean first passage-times of the controlled tanks levels. This technique appears to have an applicability which largely exceeds the problem area it was designed for. Furthermore, the introduction of control sensitive break rates and the analysis of the ensuing control problem are presented. This is to account for the experimentally observed increase in breaks concomitant with flow rate variability.

[Relations between biomedical variables: mathematical analysis or linear algebra?].

PubMed

Hucher, M; Berlie, J; Brunet, M

1977-01-01

The authors, after a short reminder of one pattern's structure, stress on the possible double approach of relations uniting the variables of this pattern: use of fonctions, what is within the mathematical analysis sphere, use of linear algebra profiting by matricial calculation's development and automatiosation. They precise the respective interests on these methods, their bounds and the imperatives for utilization, according to the kind of variables, of data, and the objective for work, understanding phenomenons or helping towards decision.

[Variable selection methods combined with local linear embedding theory used for optimization of near infrared spectral quantitative models].

PubMed

Hao, Yong; Sun, Xu-Dong; Yang, Qiang

2012-12-01

Variables selection strategy combined with local linear embedding (LLE) was introduced for the analysis of complex samples by using near infrared spectroscopy (NIRS). Three methods include Monte Carlo uninformation variable elimination (MCUVE), successive projections algorithm (SPA) and MCUVE connected with SPA were used for eliminating redundancy spectral variables. Partial least squares regression (PLSR) and LLE-PLSR were used for modeling complex samples. The results shown that MCUVE can both extract effective informative variables and improve the precision of models. Compared with PLSR models, LLE-PLSR models can achieve more accurate analysis results. MCUVE combined with LLE-PLSR is an effective modeling method for NIRS quantitative analysis.

Non-Linear Relationship between Economic Growth and CO2 Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models

PubMed Central

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-01-01

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO2 emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO2 emissions is significantly higher than those of GDPpc and Es on per capita CO2 emissions. PMID:29236083

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Non-Linear Relationship between Economic Growth and CO₂ Emissions in China: An Empirical Study Based on Panel Smooth Transition Regression Models.

PubMed

Wang, Zheng-Xin; Hao, Peng; Yao, Pei-Yi

2017-12-13

The non-linear relationship between provincial economic growth and carbon emissions is investigated by using panel smooth transition regression (PSTR) models. The research indicates that, on the condition of separately taking Gross Domestic Product per capita (GDPpc), energy structure (Es), and urbanisation level (Ul) as transition variables, three models all reject the null hypothesis of a linear relationship, i.e., a non-linear relationship exists. The results show that the three models all contain only one transition function but different numbers of location parameters. The model taking GDPpc as the transition variable has two location parameters, while the other two models separately considering Es and Ul as the transition variables both contain one location parameter. The three models applied in the study all favourably describe the non-linear relationship between economic growth and CO₂ emissions in China. It also can be seen that the conversion rate of the influence of Ul on per capita CO₂ emissions is significantly higher than those of GDPpc and Es on per capita CO₂ emissions.

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Non-Linear Approach in Kinesiology Should Be Preferred to the Linear--A Case of Basketball.

PubMed

Trninić, Marko; Jeličić, Mario; Papić, Vladan

2015-07-01

In kinesiology, medicine, biology and psychology, in which research focus is on dynamical self-organized systems, complex connections exist between variables. Non-linear nature of complex systems has been discussed and explained by the example of non-linear anthropometric predictors of performance in basketball. Previous studies interpreted relations between anthropometric features and measures of effectiveness in basketball by (a) using linear correlation models, and by (b) including all basketball athletes in the same sample of participants regardless of their playing position. In this paper the significance and character of linear and non-linear relations between simple anthropometric predictors (AP) and performance criteria consisting of situation-related measures of effectiveness (SE) in basketball were determined and evaluated. The sample of participants consisted of top-level junior basketball players divided in three groups according to their playing time (8 minutes and more per game) and playing position: guards (N = 42), forwards (N = 26) and centers (N = 40). Linear (general model) and non-linear (general model) regression models were calculated simultaneously and separately for each group. The conclusion is viable: non-linear regressions are frequently superior to linear correlations when interpreting actual association logic among research variables.

Ecosystem response to climatic variables - air temperature and precipitation: How can these variables alter plant productions in C4-grass dominant ecosystem?

NASA Astrophysics Data System (ADS)

Jung, C. G.; Jiang, L.; Luo, Y.

2017-12-01

Understanding net primary production (NPP) response to the key climatic variables, temperature and precipitation, is essential since the response could be represented by one of future consequences from ecosystem responses. Under future climatic warming, fluctuating precipitation is expected. In addition, NPP solely could not explain whole ecosystem response; therefore, not only NPP, but also above- and below-ground NPP (ANPP and BNPP, respectively) need to be examined. This examination needs to include how the plant productions response along temperature and precipitation gradients. Several studies have examined the response of NPP against each of single climatic variable, but understanding the response of ANPP and BNPP to the multiple variables is notably poor. In this study, we used the plant productions data (NPP, ANPP, and BNPP) with climatic variables, i.e., air temperature and precipitation, from 1999 to 2015 under warming and clipping treatments (mimicking hay-harvesting) in C4-grass dominant ecosystem located in central Oklahoma, United States. Firstly, we examined the nonlinear relationships with the climatic variables for NPP, ANPP and BNPP; and then predicted possible responses in the temperature - precipitation space by using a linear mixed effect model. Nonlinearities of NPP, ANPP and BNPP to the climatic variables have been found to show unimodal curves, and nonlinear models have better goodness of fit as shown lower Akaike information criterion (AIC) than linear models. Optimum condition for NPP is represented at high temperature and precipitation level whereas BNPP is maximized at moderate precipitation levels while ANPP has same range of NPP's optimum condition. Clipping significantly reduced ANPP while there was no clipping effect on NPP and BNPP. Furthermore, inclining NPP and ANPP have shown in a range from moderate to high precipitation level with increasing temperature while inclining pattern for BNPP was observed in moderate precipitation level. Overall, the C4-grass dominant ecosystem has a potential for considerable increases in NPP in hotter and wetter conditions as shown a range from moderate to high temperature and precipitation levels; ANPP has peaked at the high temperature and precipitation level, but maximum BNPP needs moderate precipitation level and high temperature.

A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.

2012-01-01

A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…

Missing Data Treatments at the Second Level of Hierarchical Linear Models

ERIC Educational Resources Information Center

St. Clair, Suzanne W.

2011-01-01

The current study evaluated the performance of traditional versus modern MDTs in the estimation of fixed-effects and variance components for data missing at the second level of an hierarchical linear model (HLM) model across 24 different study conditions. Variables manipulated in the analysis included, (a) number of Level-2 variables with missing…

New insight on intergenerational attachment from a relationship-based analysis.

PubMed

Bailey, Heidi N; Tarabulsy, George M; Moran, Greg; Pederson, David R; Bento, Sandi

2017-05-01

Research on attachment transmission has focused on variable-centered analyses, where hypotheses are tested by examining linear associations between variables. The purpose of this study was to apply a relationship-centered approach to data analysis, where adult states of mind, maternal sensitivity, and infant attachment were conceived as being three components of a single, intergenerational relationship. These variables were assessed in 90 adolescent and 99 adult mother-infant dyads when infants were 12 months old. Initial variable-centered analyses replicated the frequently observed associations between these three core attachment variables. Relationship-based, latent class analyses then revealed that the most common pattern among young mother dyads featured maternal unresolved trauma, insensitive interactive behavior, and disorganized infant attachment (61%), whereas the most prevalent adult mother dyad relationship pattern involved maternal autonomy, sensitive maternal behavior, and secure infant attachment (59%). Three less prevalent relationship patterns were also observed. Moderation analyses revealed that the adolescent-adult mother distinction differentiated between secure and disorganized intergenerational relationship patterns, whereas experience of traumatic events distinguished between disorganized and avoidant patterns. Finally, socioeconomic status distinguished between avoidant and secure patterns. Results emphasize the value of a relationship-based approach, adding an angle of understanding to the study of attachment transmission.

Hydroclimatic and landscape controls on phosphorus loads to hypereutrophic Upper Klamath Lake, Oregon, United States

NASA Astrophysics Data System (ADS)

Records, R.; Fassnacht, S. R.; Arabi, M.; Duffy, W. G.

2014-12-01

Elevated total phosphorus (P) loading into Upper Klamath Lake, southern Oregon, United States has caused hypereutrophic conditions impacting endangered lake fish species. Increases in P loading have been attributed to land use changes, such as timber harvest and wetland drainage. The contribution of P to Upper Klamath Lake has been estimated from each major tributary, yet little research has explored what land use or other variables have most influence on P loading within the tributaries. In addition, previous work has shown a range of potential hydroclimatic shifts by the 2040s, with potential to alter P loading mechanisms. In this study, we use statistical methods including principle component analysis and multiple linear regression to determine what hydroclimatic and landscape variables best explain flow-weighted P concentration in the Sprague River, one of three main tributaries to Upper Klamath Lake. Identification of key variables affecting P loading has direct implications for management decisions in the Upper Klamath River Basin. Increases in P loading related to sediment loading are due to bank and upslope erosion. The former is more prevalent in areas of historic channel alteration and cattle grazing, while the latter is more dominant in areas of heavy timber harvesting and more precipitation as rain.

Associations between Depressive State and Impaired Higher-Level Functional Capacity in the Elderly with Long-Term Care Requirements.

PubMed

Ogata, Soshiro; Hayashi, Chisato; Sugiura, Keiko; Hayakawa, Kazuo

2015-01-01

Depressive state has been reported to be significantly associated with higher-level functional capacity among community-dwelling elderly. However, few studies have investigated the associations among people with long-term care requirements. We aimed to investigate the associations between depressive state and higher-level functional capacity and obtain marginal odds ratios using propensity score analyses in people with long-term care requirements. We conducted a cross-sectional study based on participants aged ≥ 65 years (n = 545) who were community dwelling and used outpatient care services for long-term preventive care. We measured higher-level functional capacity, depressive state, and possible confounders. Then, we estimated the marginal odds ratios (i.e., the change in odds of impaired higher-level functional capacity if all versus no participants were exposed to depressive state) by logistic models using generalized linear models with the inverse probability of treatment weighting (IPTW) for propensity score and design-based standard errors. Depressive state was used as the exposure variable and higher-level functional capacity as the outcome variable. The all absolute standardized differences after the IPTW using the propensity scores were < 10% which indicated negligible differences in the mean or prevalence of the covariates between non-depressive state and depressive state. The marginal odds ratios were estimated by the logistic models with IPTW using the propensity scores. The marginal odds ratios were 2.17 (95%CI: 1.13-4.19) for men and 2.57 (95%CI: 1.26-5.26) for women. Prevention of depressive state may contribute to not only depressive state but also higher-level functional capacity.

Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

ERIC Educational Resources Information Center

Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

2013-01-01

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

Estimation of aquifer radionuclide concentrations by postprocessing of conservative tracer model results

NASA Astrophysics Data System (ADS)

Gedeon, M.; Vandersteen, K.; Rogiers, B.

2012-04-01

Radionuclide concentrations in aquifers represent an important indicator in estimating the impact of a planned surface disposal for low and medium level short-lived radioactive waste in Belgium, developed by the Belgian Agency for Radioactive Waste and Enriched Fissile Materials (ONDRAF/NIRAS), who also coordinates and leads the corresponding research. Estimating aquifer concentrations for individual radionuclides represents a computational challenge because (a) different retardation values are applied to different hydrogeologic units and (b) sequential decay reactions with radionuclides of various sorption characteristics cause long computational times until a steady-state is reached. The presented work proposes a methodology reducing substantially the computational effort by postprocessing the results of a prior non-reactive tracer simulation. These advective transport results represent the steady-state concentration - source flux ratio and the break-through time at each modelling cell. These two variables are further used to estimate the individual radionuclide concentrations by (a) scaling the steady-state concentrations to the source fluxes of individual radionuclides; (b) applying the radioactive decay and ingrowth in a decay chain; (c) scaling the travel time by the retardation factor and (d) applying linear sorption. While all steps except (b) require solving simple linear equations, applying ingrowth of individual radionuclides in decay chains requires solving the differential Bateman equation. This equation needs to be solved once for a unit radionuclide activity at all arrival times found in the numerical grid. The ratios between the parent nuclide activity and the progeny activities are then used in the postprocessing. Results are presented for discrete points and examples of radioactive plume maps are given. These results compare well to the results achieved using a full numerical simulation including the respective chemical reaction processes. Although the proposed method represents a fast way to estimate the radionuclide concentrations without performing timely challenging simulations, its applicability has some limits. The radionuclide source needs to be assumed constant during the period of achieving a steady-state in the model. Otherwise, the source variability of individual radionuclides needs to be modelled using a numerical simulation. However, such a situation only occurs in cases of source variability in a period until steady-state is reached and such a simulation takes a relatively short time. The proposed method enables an effective estimation of individual radionuclide concentrations in the frame of performance assessment of a radioactive waste disposal. Reducing the calculation time to a minimum enables performing sensitivity and uncertainty analyses, testing alternative models, etc. thus enhancing the overall quality of the modelling analysis.

Body temperature variability (Part 1): a review of the history of body temperature and its variability due to site selection, biological rhythms, fitness, and aging.

PubMed

Kelly, Greg

2006-12-01

Body temperature is a complex, non-linear data point, subject to many sources of internal and external variation. While these sources of variation significantly complicate interpretation of temperature data, disregarding knowledge in favor of oversimplifying complex issues would represent a significant departure from practicing evidence-based medicine. Part 1 of this review outlines the historical work of Wunderlich on temperature and the origins of the concept that a healthy normal temperature is 98.6 degrees F (37.0 degrees C). Wunderlich's findings and methodology are reviewed and his results are contrasted with findings from modern clinical thermometry. Endogenous sources of temperature variability, including variations caused by site of measurement, circadian, menstrual, and annual biological rhythms, fitness, and aging are discussed. Part 2 will review the effects of exogenous masking agents - external factors in the environment, diet, or lifestyle that can influence body temperature, as well as temperature findings in disease states.

Language Shift in the United States and Foreign-Born Older Mexican Heritage Individuals: Co-ethnic Context for Language Resistance

PubMed Central

Siordia, Carlos; Díaz, María E.

2014-01-01

In this study, we investigate individual-level language shift in a population of Mexican origin Latinos/as aged 65 and up. By using data from the Hispanic Established Populations for the Epidemiologic Study of the Elderly, we investigate their English language use as the dependent variable in a hierarchical linear model. The microlevel independent continuous variable is their level of contact with “Anglos”; the macrolevel continuous independent variable is the percentage of Mexicans in tract of residence. After accounting for their generational status, other microlevel social and health covariates, and tract-level attributes, we found a direct relationship between contact with Anglos and a “shift” toward more English language use, where as co-ethnic concentration increases, the influence of contact with Anglos decreases. We frame this article with a discussion on language shifting, and explain how co-ethnic concentration may provide the resources for engaging in a language resistance. PMID:25104874

Control Augmented Structural Synthesis

NASA Technical Reports Server (NTRS)

Lust, Robert V.; Schmit, Lucien A.

1988-01-01

A methodology for control augmented structural synthesis is proposed for a class of structures which can be modeled as an assemblage of frame and/or truss elements. It is assumed that both the plant (structure) and the active control system dynamics can be adequately represented with a linear model. The structural sizing variables, active control system feedback gains and nonstructural lumped masses are treated simultaneously as independent design variables. Design constraints are imposed on static and dynamic displacements, static stresses, actuator forces and natural frequencies to ensure acceptable system behavior. Multiple static and dynamic loading conditions are considered. Side constraints imposed on the design variables protect against the generation of unrealizable designs. While the proposed approach is fundamentally more general, here the methodology is developed and demonstrated for the case where: (1) the dynamic loading is harmonic and thus the steady state response is of primary interest; (2) direct output feedback is used for the control system model; and (3) the actuators and sensors are collocated.

Dynamic physiological modeling for functional diffuse optical tomography

PubMed Central

Diamond, Solomon Gilbert; Huppert, Theodore J.; Kolehmainen, Ville; Franceschini, Maria Angela; Kaipio, Jari P.; Arridge, Simon R.; Boas, David A.

2009-01-01

Diffuse optical tomography (DOT) is a noninvasive imaging technology that is sensitive to local concentration changes in oxy- and deoxyhemoglobin. When applied to functional neuroimaging, DOT measures hemodynamics in the scalp and brain that reflect competing metabolic demands and cardiovascular dynamics. The diffuse nature of near-infrared photon migration in tissue and the multitude of physiological systems that affect hemodynamics motivate the use of anatomical and physiological models to improve estimates of the functional hemodynamic response. In this paper, we present a linear state-space model for DOT analysis that models the physiological fluctuations present in the data with either static or dynamic estimation. We demonstrate the approach by using auxiliary measurements of blood pressure variability and heart rate variability as inputs to model the background physiology in DOT data. We evaluate the improvements accorded by modeling this physiology on ten human subjects with simulated functional hemodynamic responses added to the baseline physiology. Adding physiological modeling with a static estimator significantly improved estimates of the simulated functional response, and further significant improvements were achieved with a dynamic Kalman filter estimator (paired t tests, n = 10, P < 0.05). These results suggest that physiological modeling can improve DOT analysis. The further improvement with the Kalman filter encourages continued research into dynamic linear modeling of the physiology present in DOT. Cardiovascular dynamics also affect the blood-oxygen-dependent (BOLD) signal in functional magnetic resonance imaging (fMRI). This state-space approach to DOT analysis could be extended to BOLD fMRI analysis, multimodal studies and real-time analysis. PMID:16242967

Deterministic quantum teleportation of photonic quantum bits by a hybrid technique.

PubMed

Takeda, Shuntaro; Mizuta, Takahiro; Fuwa, Maria; van Loock, Peter; Furusawa, Akira

2013-08-15

Quantum teleportation allows for the transfer of arbitrary unknown quantum states from a sender to a spatially distant receiver, provided that the two parties share an entangled state and can communicate classically. It is the essence of many sophisticated protocols for quantum communication and computation. Photons are an optimal choice for carrying information in the form of 'flying qubits', but the teleportation of photonic quantum bits (qubits) has been limited by experimental inefficiencies and restrictions. Main disadvantages include the fundamentally probabilistic nature of linear-optics Bell measurements, as well as the need either to destroy the teleported qubit or attenuate the input qubit when the detectors do not resolve photon numbers. Here we experimentally realize fully deterministic quantum teleportation of photonic qubits without post-selection. The key step is to make use of a hybrid technique involving continuous-variable teleportation of a discrete-variable, photonic qubit. When the receiver's feedforward gain is optimally tuned, the continuous-variable teleporter acts as a pure loss channel, and the input dual-rail-encoded qubit, based on a single photon, represents a quantum error detection code against photon loss and hence remains completely intact for most teleportation events. This allows for a faithful qubit transfer even with imperfect continuous-variable entangled states: for four qubits the overall transfer fidelities range from 0.79 to 0.82 and all of them exceed the classical limit of teleportation. Furthermore, even for a relatively low level of the entanglement, qubits are teleported much more efficiently than in previous experiments, albeit post-selectively (taking into account only the qubit subspaces), and with a fidelity comparable to the previously reported values.

Resveratrol food supplements: a survey on the role of individual consumer characteristics in predicting the attitudes and adoption intentions of US American and Danish respondents.

PubMed

Aschemann-Witzel, Jessica; Grunert, Klaus G

2015-02-10

Consumers increasingly choose food supplements in addition to their diet. Research on supplement users finds they are likely to be female, older and well-educated; Furthermore, supplement users are often characterised as being especially health-oriented, an observation which is termed the 'inverse supplement hypothesis'. However, results are dependent on the substance in question. Little is known so far about botanicals in general, and more specifically, little is known about resveratrol. The psychographic variables of food supplement users are yet relatively underexplored. By comparing US and Danish respondents, we aimed to identify whether sociodemographic variables, health status, health beliefs and behaviour and interest in food aspects specifically relevant to resveratrol (e.g., naturalness, indulgence, and Mediterranean food) explain favourable attitudes and adoption intentions toward resveratrol supplements. A survey sent to a representative online panel in the United States and Denmark was analysed using linear regression. We find that sociodemographic variables contribute little to explaining favourable attitudes toward and adoption intentions of resveratrol supplements, except for the negative association with higher education in the United States. The inverse supplement hypothesis was not confirmed. Belief in the favourable health effects of resveratrol and usage of complementary and alternative medicine positively affect attitudes and adoption intention. An interest in the indulgence dimension of food explains positive attitudes in the United States and adoption intentions in both countries. The results indicate that potential consumers of resveratrol supplements are identified by their usage of complementary and alternative medicine, rather than by sociodemographic variables. They are not characterised by especially healthy behaviours, which contradicts the inverse supplement hypothesis. Instead, potential consumers of resveratrol supplements may be characterised by their focus on the indulgence dimension of food.

Improving Forecasts Through Realistic Uncertainty Estimates: A Novel Data Driven Method for Model Uncertainty Quantification in Data Assimilation

NASA Astrophysics Data System (ADS)

Pathiraja, S. D.; Moradkhani, H.; Marshall, L. A.; Sharma, A.; Geenens, G.

2016-12-01

Effective combination of model simulations and observations through Data Assimilation (DA) depends heavily on uncertainty characterisation. Many traditional methods for quantifying model uncertainty in DA require some level of subjectivity (by way of tuning parameters or by assuming Gaussian statistics). Furthermore, the focus is typically on only estimating the first and second moments. We propose a data-driven methodology to estimate the full distributional form of model uncertainty, i.e. the transition density p(xt|xt-1). All sources of uncertainty associated with the model simulations are considered collectively, without needing to devise stochastic perturbations for individual components (such as model input, parameter and structural uncertainty). A training period is used to derive the distribution of errors in observed variables conditioned on hidden states. Errors in hidden states are estimated from the conditional distribution of observed variables using non-linear optimization. The theory behind the framework and case study applications are discussed in detail. Results demonstrate improved predictions and more realistic uncertainty bounds compared to a standard perturbation approach.

Extraction of Lateral-Directional Stability and Control Derivatives for the Basic F-18 Aircraft at High Angles of Attack

NASA Technical Reports Server (NTRS)

Iliff, Kenneth W.; Wang, Kon-Sheng Charles

1997-01-01

The results of parameter identification to determine the lateral-directional stability and control derivatives of an F-18 research aircraft in its basic hardware and software configuration are presented. The derivatives are estimated from dynamic flight data using a specialized identification program developed at NASA Dryden Flight Research Center. The formulation uses the linearized aircraft equations of motions in their continuous/discrete form and a maximum likelihood estimator that accounts for both state and measurement noise. State noise is used to model the uncommanded forcing function caused by unsteady aerodynamics, such as separated and vortical flows, over the aircraft. The derivatives are plotted as functions of angle of attack between 3 deg and 47 deg and compared with wind-tunnel predictions. The quality of the derivative estimates obtained by parameter identification is somewhat degraded because the maneuvers were flown with the aircraft's control augmentation system engaged, which introduced relatively high correlations between the control variables and response variables as a result of control motions from the feedback control system.

Characterization of depressive States in bipolar patients using wearable textile technology and instantaneous heart rate variability assessment.

PubMed

Valenza, Gaetano; Citi, Luca; Gentili, Claudio; Lanata, Antonio; Scilingo, Enzo Pasquale; Barbieri, Riccardo

2015-01-01

The analysis of cognitive and autonomic responses to emotionally relevant stimuli could provide a viable solution for the automatic recognition of different mood states, both in normal and pathological conditions. In this study, we present a methodological application describing a novel system based on wearable textile technology and instantaneous nonlinear heart rate variability assessment, able to characterize the autonomic status of bipolar patients by considering only electrocardiogram recordings. As a proof of this concept, our study presents results obtained from eight bipolar patients during their normal daily activities and being elicited according to a specific emotional protocol through the presentation of emotionally relevant pictures. Linear and nonlinear features were computed using a novel point-process-based nonlinear autoregressive integrative model and compared with traditional algorithmic methods. The estimated indices were used as the input of a multilayer perceptron to discriminate the depressive from the euthymic status. Results show that our system achieves much higher accuracy than the traditional techniques. Moreover, the inclusion of instantaneous higher order spectra features significantly improves the accuracy in successfully recognizing depression from euthymia.

Constructing Compact Takagi-Sugeno Rule Systems: Identification of Complex Interactions in Epidemiological Data

PubMed Central

Zhou, Shang-Ming; Lyons, Ronan A.; Brophy, Sinead; Gravenor, Mike B.

2012-01-01

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data. PMID:23272108

Obesity and severe obesity forecasts through 2030.

PubMed

Finkelstein, Eric A; Khavjou, Olga A; Thompson, Hope; Trogdon, Justin G; Pan, Liping; Sherry, Bettylou; Dietz, William

2012-06-01

Previous efforts to forecast future trends in obesity applied linear forecasts assuming that the rise in obesity would continue unabated. However, evidence suggests that obesity prevalence may be leveling off. This study presents estimates of adult obesity and severe obesity prevalence through 2030 based on nonlinear regression models. The forecasted results are then used to simulate the savings that could be achieved through modestly successful obesity prevention efforts. The study was conducted in 2009-2010 and used data from the 1990 through 2008 Behavioral Risk Factor Surveillance System (BRFSS). The analysis sample included nonpregnant adults aged ≥ 18 years. The individual-level BRFSS variables were supplemented with state-level variables from the U.S. Bureau of Labor Statistics, the American Chamber of Commerce Research Association, and the Census of Retail Trade. Future obesity and severe obesity prevalence were estimated through regression modeling by projecting trends in explanatory variables expected to influence obesity prevalence. Linear time trend forecasts suggest that by 2030, 51% of the population will be obese. The model estimates a much lower obesity prevalence of 42% and severe obesity prevalence of 11%. If obesity were to remain at 2010 levels, the combined savings in medical expenditures over the next 2 decades would be $549.5 billion. The study estimates a 33% increase in obesity prevalence and a 130% increase in severe obesity prevalence over the next 2 decades. If these forecasts prove accurate, this will further hinder efforts for healthcare cost containment. Copyright © 2012 Elsevier Inc. All rights reserved.

Constructing compact Takagi-Sugeno rule systems: identification of complex interactions in epidemiological data.

PubMed

Zhou, Shang-Ming; Lyons, Ronan A; Brophy, Sinead; Gravenor, Mike B

2012-01-01

The Takagi-Sugeno (TS) fuzzy rule system is a widely used data mining technique, and is of particular use in the identification of non-linear interactions between variables. However the number of rules increases dramatically when applied to high dimensional data sets (the curse of dimensionality). Few robust methods are available to identify important rules while removing redundant ones, and this results in limited applicability in fields such as epidemiology or bioinformatics where the interaction of many variables must be considered. Here, we develop a new parsimonious TS rule system. We propose three statistics: R, L, and ω-values, to rank the importance of each TS rule, and a forward selection procedure to construct a final model. We use our method to predict how key components of childhood deprivation combine to influence educational achievement outcome. We show that a parsimonious TS model can be constructed, based on a small subset of rules, that provides an accurate description of the relationship between deprivation indices and educational outcomes. The selected rules shed light on the synergistic relationships between the variables, and reveal that the effect of targeting specific domains of deprivation is crucially dependent on the state of the other domains. Policy decisions need to incorporate these interactions, and deprivation indices should not be considered in isolation. The TS rule system provides a basis for such decision making, and has wide applicability for the identification of non-linear interactions in complex biomedical data.

Linear variability of gait according to socioeconomic status in elderly

PubMed Central

2016-01-01

Aim: To evaluate the linear variability of comfortable gait according to socioeconomic status in community-dwelling elderly. Method: For this cross-sectional observational study 63 self- functioning elderly were categorized according to the socioeconomic level on medium-low (n= 33, age 69.0 ± 5.0 years) and medium-high (n= 30, age 71.0 ± 6.0 years). Each participant was asked to perform comfortable gait speed for 3 min on an 40 meters elliptical circuit, recording in video five strides which were transformed into frames, determining the minimum foot clearance, maximum foot clearance and stride length. The intra-group linear variability was calculated by the coefficient of variation in percent. Results: The trajectory parameters variability is not different according to socioeconomic status with a 30% (range= 15-55%) for the minimum foot clearance and 6% (range= 3-8%) in maximum foot clearance. Meanwhile, the stride length consistently was more variable in the medium-low socioeconomic status for the overall sample (p= 0.004), female (p= 0.041) and male gender (p= 0.007), with values near 4% ​​(range = 2.5-5.0%) in the medium-low and 2% (range = 1.5-3.5%) in the medium-high. Conclusions: The intra-group linear variability is consistently higher and within reference parameters for stride length during comfortable gait for elderly belonging to medium-low socioeconomic status. This might be indicative of greater complexity and consequent motor adaptability. PMID:27546931

Semilinear programming: applications and implementation

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

Mohan, S.

Semilinear programming is a method of solving optimization problems with linear constraints where the non-negativity restrictions on the variables are dropped and the objective function coefficients can take on different values depending on whether the variable is positive or negative. The simplex method for linear programming is modified in this thesis to solve general semilinear and piecewise linear programs efficiently without having to transform them into equivalent standard linear programs. Several models in widely different areas of optimization such as production smoothing, facility locations, goal programming and L/sub 1/ estimation are presented first to demonstrate the compact formulation that arisesmore » when such problems are formulated as semilinear programs. A code SLP is constructed using the semilinear programming techniques. Problems in aggregate planning and L/sub 1/ estimation are solved using SLP and equivalent linear programs using a linear programming simplex code. Comparisons of CPU times and number iterations indicate SLP to be far superior. The semilinear programming techniques are extended to piecewise linear programming in the implementation of the code PLP. Piecewise linear models in aggregate planning are solved using PLP and equivalent standard linear programs using a simple upper bounded linear programming code SUBLP.« less

Applying the Expectancy-Value Model to understand health values.

PubMed

Zhang, Xu-Hao; Xie, Feng; Wee, Hwee-Lin; Thumboo, Julian; Li, Shu-Chuen

2008-03-01

Expectancy-Value Model (EVM) is the most structured model in psychology to predict attitudes by measuring attitudinal attributes (AAs) and relevant external variables. Because health value could be categorized as attitude, we aimed to apply EVM to explore its usefulness in explaining variances in health values and investigate underlying factors. Focus group discussion was carried out to identify the most common and significant AAs toward 5 different health states (coded as 11111, 11121, 21221, 32323, and 33333 in EuroQol Five-Dimension (EQ-5D) descriptive system). AAs were measured in a sum of multiplications of subjective probability (expectancy) and perceived value of attributes with 7-point Likert scales. Health values were measured using visual analog scales (VAS, range 0-1). External variables (age, sex, ethnicity, education, housing, marital status, and concurrent chronic diseases) were also incorporated into survey questionnaire distributed by convenience sampling among eligible respondents. Univariate analyses were used to identify external variables causing significant differences in VAS. Multiple linear regression model (MLR) and hierarchical regression model were used to investigate the explanatory power of AAs and possible significant external variable(s) separately or in combination, for each individual health state and a mixed scenario of five states, respectively. Four AAs were identified, namely, "worsening your quality of life in terms of health" (WQoL), "adding a burden to your family" (BTF), "making you less independent" (MLI) and "unable to work or study" (UWS). Data were analyzed based on 232 respondents (mean [SD] age: 27.7 [15.07] years, 49.1% female). Health values varied significantly across 5 health states, ranging from 0.12 (33333) to 0.97 (11111). With no significant external variables identified, EVM explained up to 62% of the variances in health values across 5 health states. The explanatory power of 4 AAs were found to be between 13% and 28% in separate MLR models (P < 0.05). When data were analyzed for each health state, variances in health values became small and explanatory power of EVM was reduced to a range between 8% and 23%. EVM was useful in explaining variances of health values and predicting important factors. Its power to explain small variances might be restricted due to limitations of 7-point Likert scale to measure AAs accurately. With further improvement and validation of a compatible continuous scale for more accurate measurement, EVM is expected to explain health values to a larger extent.

Hybrid Reynolds-Averaged/Large Eddy Simulation of a Cavity Flameholder; Assessment of Modeling Sensitivities

NASA Technical Reports Server (NTRS)

Baurle, R. A.

2015-01-01

Steady-state and scale-resolving simulations have been performed for flow in and around a model scramjet combustor flameholder. The cases simulated corresponded to those used to examine this flowfield experimentally using particle image velocimetry. A variety of turbulence models were used for the steady-state Reynolds-averaged simulations which included both linear and non-linear eddy viscosity models. The scale-resolving simulations used a hybrid Reynolds-averaged / large eddy simulation strategy that is designed to be a large eddy simulation everywhere except in the inner portion (log layer and below) of the boundary layer. Hence, this formulation can be regarded as a wall-modeled large eddy simulation. This effort was undertaken to formally assess the performance of the hybrid Reynolds-averaged / large eddy simulation modeling approach in a flowfield of interest to the scramjet research community. The numerical errors were quantified for both the steady-state and scale-resolving simulations prior to making any claims of predictive accuracy relative to the measurements. The steady-state Reynolds-averaged results showed a high degree of variability when comparing the predictions obtained from each turbulence model, with the non-linear eddy viscosity model (an explicit algebraic stress model) providing the most accurate prediction of the measured values. The hybrid Reynolds-averaged/large eddy simulation results were carefully scrutinized to ensure that even the coarsest grid had an acceptable level of resolution for large eddy simulation, and that the time-averaged statistics were acceptably accurate. The autocorrelation and its Fourier transform were the primary tools used for this assessment. The statistics extracted from the hybrid simulation strategy proved to be more accurate than the Reynolds-averaged results obtained using the linear eddy viscosity models. However, there was no predictive improvement noted over the results obtained from the explicit Reynolds stress model. Fortunately, the numerical error assessment at most of the axial stations used to compare with measurements clearly indicated that the scale-resolving simulations were improving (i.e. approaching the measured values) as the grid was refined. Hence, unlike a Reynolds-averaged simulation, the hybrid approach provides a mechanism to the end-user for reducing model-form errors.

A Solution Space for a System of Null-State Partial Differential Equations: Part 3

NASA Astrophysics Data System (ADS)

Flores, Steven M.; Kleban, Peter

2015-01-01

This article is the third of four that completely and rigorously characterize a solution space for a homogeneous system of 2 N + 3 linear partial differential equations (PDEs) in 2 N variables that arises in conformal field theory (CFT) and multiple Schramm-Löwner evolution (SLE κ ). The system comprises 2 N null-state equations and three conformal Ward identities that govern CFT correlation functions of 2 N one-leg boundary operators. In the first two articles (Flores and Kleban, in Commun Math Phys, arXiv:1212.2301, 2012; Commun Math Phys, arXiv:1404.0035, 2014), we use methods of analysis and linear algebra to prove that dim , with C N the Nth Catalan number. Extending these results, we prove in this article that dim and entirely consists of (real-valued) solutions constructed with the CFT Coulomb gas (contour integral) formalism. In order to prove this claim, we show that a certain set of C N such solutions is linearly independent. Because the formulas for these solutions are complicated, we prove linear independence indirectly. We use the linear injective map of Lemma 15 in Flores and Kleban (Commun Math Phys, arXiv:1212.2301, 2012) to send each solution of the mentioned set to a vector in , whose components we find as inner products of elements in a Temperley-Lieb algebra. We gather these vectors together as columns of a symmetric matrix, with the form of a meander matrix. If the determinant of this matrix does not vanish, then the set of C N Coulomb gas solutions is linearly independent. And if this determinant does vanish, then we construct an alternative set of C N Coulomb gas solutions and follow a similar procedure to show that this set is linearly independent. The latter situation is closely related to CFT minimal models. We emphasize that, although the system of PDEs arises in CFT in away that is typically non-rigorous, our treatment of this system here and in Flores and Kleban (Commun Math Phys, arXiv:1212.2301, 2012; Commun Math Phys, arXiv:1404.0035, 2014; Commun Math Phys, arXiv:1405.2747, 2014) is completely rigorous.

Corrections to the Eckhaus' stability criterion for one-dimensional stationary structures

NASA Astrophysics Data System (ADS)

Malomed, B. A.; Staroselsky, I. E.; Konstantinov, A. B.

1989-01-01

Two amendments to the well-known Eckhaus' stability criterion for small-amplitude non-linear structures generated by weak instability of a spatially uniform state of a non-equilibrium one-dimensional system against small perturbations with finite wavelengths are obtained. Firstly, we evaluate small corrections to the main Eckhaus' term which, on the contrary so that term, do not have a universal form. Comparison of those non-universal corrections with experimental or numerical results gives a possibility to select a more relevant form of an effective nonlinear evolution equation. In particular, the comparison with such results for convective rolls and Taylor vortices gives arguments in favor of the Swift-Hohenberg equation. Secondly, we derive an analog of the Eckhaus criterion for systems degenerate in the sense that in an expansion of their non-linear parts in powers of dynamical variables, the second and third degree terms are absent.

Nonlinear self-sustained structures and fronts in spatially developing wake flows

NASA Astrophysics Data System (ADS)

Pier, Benoît; Huerre, Patrick

2001-05-01

A family of slowly spatially developing wakes with variable pressure gradient is numerically demonstrated to sustain a synchronized finite-amplitude vortex street tuned at a well-defined frequency. This oscillating state is shown to be described by a steep global mode exhibiting a sharp Dee Langer-type front at the streamwise station of marginal absolute instability. The front acts as a wavemaker which sends out nonlinear travelling waves in the downstream direction, the global frequency being imposed by the real absolute frequency prevailing at the front station. The nonlinear travelling waves are determined to be governed by the local nonlinear dispersion relation resulting from a temporal evolution problem on a local wake profile considered as parallel. Although the vortex street is fully nonlinear, its frequency is dictated by a purely linear marginal absolute instability criterion applied to the local linear dispersion relation.

Directionality volatility in electroencephalogram time series

NASA Astrophysics Data System (ADS)

Mansor, Mahayaudin M.; Green, David A.; Metcalfe, Andrew V.

2016-06-01

We compare time series of electroencephalograms (EEGs) from healthy volunteers with EEGs from subjects diagnosed with epilepsy. The EEG time series from the healthy group are recorded during awake state with their eyes open and eyes closed, and the records from subjects with epilepsy are taken from three different recording regions of pre-surgical diagnosis: hippocampal, epileptogenic and seizure zone. The comparisons for these 5 categories are in terms of deviations from linear time series models with constant variance Gaussian white noise error inputs. One feature investigated is directionality, and how this can be modelled by either non-linear threshold autoregressive models or non-Gaussian errors. A second feature is volatility, which is modelled by Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) processes. Other features include the proportion of variability accounted for by time series models, and the skewness and the kurtosis of the residuals. The results suggest these comparisons may have diagnostic potential for epilepsy and provide early warning of seizures.

Stability and complexity of small random linear systems

NASA Astrophysics Data System (ADS)

Hastings, Harold

2010-03-01

We explore the stability of the small random linear systems, typically involving 10-20 variables, motivated by dynamics of the world trade network and the US and Canadian power grid. This report was prepared as an account of work sponsored by an agency of the US Government. Neither the US Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the US Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the US Government or any agency thereof.

A Method for Modeling the Intrinsic Dynamics of Intraindividual Variability: Recovering the Parameters of Simulated Oscillators in Multi-Wave Panel Data.

ERIC Educational Resources Information Center

Boker, Steven M.; Nesselroade, John R.

2002-01-01

Examined two methods for fitting models of intrinsic dynamics to intraindividual variability data by testing these techniques' behavior in equations through simulation studies. Among the main results is the demonstration that a local linear approximation of derivatives can accurately recover the parameters of a simulated linear oscillator, with…

Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

ERIC Educational Resources Information Center

Vidal, Sherry

Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

An Exploration of a Quantitative Reasoning Instructional Approach to Linear Equations in Two Variables with Community College Students

ERIC Educational Resources Information Center

Belue, Paul T.; Cavey, Laurie Overman; Kinzel, Margaret T.

2017-01-01

In this exploratory study, we examined the effects of a quantitative reasoning instructional approach to linear equations in two variables on community college students' conceptual understanding, procedural fluency, and reasoning ability. This was done in comparison to the use of a traditional procedural approach for instruction on the same topic.…

Social inequality, lifestyles and health - a non-linear canonical correlation analysis based on the approach of Pierre Bourdieu.

PubMed

Grosse Frie, Kirstin; Janssen, Christian

2009-01-01

Based on the theoretical and empirical approach of Pierre Bourdieu, a multivariate non-linear method is introduced as an alternative way to analyse the complex relationships between social determinants and health. The analysis is based on face-to-face interviews with 695 randomly selected respondents aged 30 to 59. Variables regarding socio-economic status, life circumstances, lifestyles, health-related behaviour and health were chosen for the analysis. In order to determine whether the respondents can be differentiated and described based on these variables, a non-linear canonical correlation analysis (OVERALS) was performed. The results can be described on three dimensions; Eigenvalues add up to the fit of 1.444, which can be interpreted as approximately 50 % of explained variance. The three-dimensional space illustrates correspondences between variables and provides a framework for interpretation based on latent dimensions, which can be described by age, education, income and gender. Using non-linear canonical correlation analysis, health characteristics can be analysed in conjunction with socio-economic conditions and lifestyles. Based on Bourdieus theoretical approach, the complex correlations between these variables can be more substantially interpreted and presented.

Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method.

PubMed

Jiang, Yuan; He, Yunxiao; Zhang, Heping

LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This paper proposes an extension of LASSO, namely, prior LASSO (pLASSO), to incorporate that prior information into penalized generalized linear models. The goal is achieved by adding in the LASSO criterion function an additional measure of the discrepancy between the prior information and the model. For linear regression, the whole solution path of the pLASSO estimator can be found with a procedure similar to the Least Angle Regression (LARS). Asymptotic theories and simulation results show that pLASSO provides significant improvement over LASSO when the prior information is relatively accurate. When the prior information is less reliable, pLASSO shows great robustness to the misspecification. We illustrate the application of pLASSO using a real data set from a genome-wide association study.

Steady-state and transitional aerodynamic characteristics of a wing in simulated heavy rain

NASA Technical Reports Server (NTRS)

Campbell, Bryan A.; Bezos, Gaudy M.

1989-01-01

The steady-state and transient effects of simulated heavy rain on the subsonic aerodynamic characteristics of a wing model were determined in the Langley 14- by 22-Foot Subsonic Tunnel. The 1.29 foot chord wing was comprised of a NACA 23015 airfoil and had an aspect ratio of 6.10. Data were obtained while test variables of liquid water content, angle of attack, and trailing edge flap angle were parametrically varied at dynamic pressures of 10, 30, and 50 psf (i.e., Reynolds numbers of .76x10(6), 1.31x10(6), and 1.69x10(6)). The experimental results showed reductions in lift and increases in drag when in the simulated rain environment. Accompanying this was a reduction of the stall angle of attack by approximately 4 deg. The transient aerodynamic performance during transition from dry to wet steady-state conditions varied between a linear and a nonlinear transition.

Influence of intrinsic decoherence on tripartite entanglement and bipartite fidelity of polar molecules in pendular states

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

Han, Jia-Xing; Hu, Yuan; Jin, Yu

An array of ultracold polar molecules trapped in an external electric field is regarded as a promising carrier of quantum information. Under the action of this field, molecules are compelled to undergo pendular oscillations by the Stark effect. Particular attention has been paid to the influence of intrinsic decoherence on the model of linear polar molecular pendular states, thereby we evaluate the tripartite entanglement with negativity, as well as fidelity of bipartite quantum systems for input and output signals using electric dipole moments of polar molecules as qubits. According to this study, we consider three typical initial states for bothmore » systems, respectively, and investigate the temporal evolution with variable values of the external field intensity, the intrinsic decoherence factor, and the dipole-dipole interaction. Thus, we demonstrate the sound selection of these three main parameters to obtain the best entanglement degree and fidelity.« less

Influence of intrinsic decoherence on tripartite entanglement and bipartite fidelity of polar molecules in pendular states.

PubMed

Han, Jia-Xing; Hu, Yuan; Jin, Yu; Zhang, Guo-Feng

2016-04-07

An array of ultracold polar molecules trapped in an external electric field is regarded as a promising carrier of quantum information. Under the action of this field, molecules are compelled to undergo pendular oscillations by the Stark effect. Particular attention has been paid to the influence of intrinsic decoherence on the model of linear polar molecular pendular states, thereby we evaluate the tripartite entanglement with negativity, as well as fidelity of bipartite quantum systems for input and output signals using electric dipole moments of polar molecules as qubits. According to this study, we consider three typical initial states for both systems, respectively, and investigate the temporal evolution with variable values of the external field intensity, the intrinsic decoherence factor, and the dipole-dipole interaction. Thus, we demonstrate the sound selection of these three main parameters to obtain the best entanglement degree and fidelity.

Enhanced model of gear transmission dynamics for condition monitoring applications: Effects of torque, friction and bearing clearance

NASA Astrophysics Data System (ADS)

Fernandez-del-Rincon, A.; Garcia, P.; Diez-Ibarbia, A.; de-Juan, A.; Iglesias, M.; Viadero, F.

2017-02-01

Gear transmissions remain as one of the most complex mechanical systems from the point of view of noise and vibration behavior. Research on gear modeling leading to the obtaining of models capable of accurately reproduce the dynamic behavior of real gear transmissions has spread out the last decades. Most of these models, although useful for design stages, often include simplifications that impede their application for condition monitoring purposes. Trying to filling this gap, the model presented in this paper allows us to simulate gear transmission dynamics including most of these features usually neglected by the state of the art models. This work presents a model capable of considering simultaneously the internal excitations due to the variable meshing stiffness (including the coupling among successive tooth pairs in contact, the non-linearity linked with the contacts between surfaces and the dissipative effects), and those excitations consequence of the bearing variable compliance (including clearances or pre-loads). The model can also simulate gear dynamics in a realistic torque dependent scenario. The proposed model combines a hybrid formulation for calculation of meshing forces with a non-linear variable compliance approach for bearings. Meshing forces are obtained by means of a double approach which combines numerical and analytical aspects. The methodology used provides a detailed description of the meshing forces, allowing their calculation even when gear center distance is modified due to shaft and bearing flexibilities, which are unavoidable in real transmissions. On the other hand, forces at bearing level were obtained considering a variable number of supporting rolling elements, depending on the applied load and clearances. Both formulations have been developed and applied to the simulation of the vibration of a sample transmission, focusing the attention on the transmitted load, friction meshing forces and bearing preloads.

Spectral Behavior of a Linearized Land-Atmosphere Model: Applications to Hydrometeorology

NASA Astrophysics Data System (ADS)

Gentine, P.; Entekhabi, D.; Polcher, J.

2008-12-01

The present study develops an improved version of the linearized land-atmosphere model first introduced by Lettau (1951). This model is used to investigate the spectral response of land-surface variables to a daily forcing of incoming radiation at the land-surface. An analytical solution of the problem is found in the form of temporal Fourier series and gives the atmospheric boundary-layer and soil profiles of state variables (potential temperature, specific humidity, sensible and latent heat fluxes). Moreover the spectral dependency of surface variables is expressed as function of land-surface parameters (friction velocity, vegetation height, aerodynamic resistance, stomatal conductance). This original approach has several advantages: First, the model only requires little data to work and perform well: only time series of incoming radiation at the land-surface, mean specific humidity and temperature at any given height are required. These inputs being widely available over the globe, the model can easily be run and tested under various conditions. The model will also help analysing the diurnal shape and frequency dependency of surface variables and soil-ABL profiles. In particular, a strong emphasis is being placed on the explanation and prediction of Evaporative Fraction (EF) and Bowen Ratio diurnal shapes. EF is shown to remain a diurnal constant under restricting conditions: fair and dry weather, with strong solar radiation and no clouds. Moreover, the EF pseudo-constancy value is found and given as function of surface parameters, such as aerodynamic resistance and stomatal conductance. Then, application of the model for the conception of remote-sensing tools, according to the temporal resolution of the sensor, will also be discussed. Finally, possible extensions and improvement of the model will be discussed.

Stratification Pattern of Static and Scale-Invariant Dynamic Measures of Heartbeat Fluctuations Across Sleep Stages in Young and Elderly

PubMed Central

Schmitt, Daniel T.; Stein, Phyllis K.; Ivanov, Plamen Ch.

2010-01-01

Cardiac dynamics exhibit complex variability characterized by scale-invariant and nonlinear temporal organization related to the mechanism of neuroautonomic control, which changes with physiologic states and pathologic conditions. Changes in sleep regulation during sleep stages are also related to fluctuations in autonomic nervous activity. However, the interaction between sleep regulation and cardiac autonomic control remains not well understood. Even less is known how this interaction changes with age, as aspects of both cardiac dynamics and sleep regulation differ in healthy elderly compared to young subjects. We hypothesize that because of the neuroautonomic responsiveness in young subjects, fractal and nonlinear features of cardiac dynamics exhibit a pronounced stratification pattern across sleep stages, while in elderly these features will remain unchanged due to age-related loss of cardiac variability and decline of neuroautonomic responsiveness. We analyze the variability and the temporal fractal organization of heartbeat fluctuations across sleep stages in both young and elderly. We find that independent linear and nonlinear measures of cardiac control consistently exhibit the same ordering in their values across sleep stages, forming a robust stratification pattern. Despite changes in sleep architecture and reduced heart rate variability in elderly subjects, this stratification surprisingly does not break down with advanced age. Moreover, the difference between sleep stages for some linear, fractal, and nonlinear measures exceeds the difference between young and elderly, suggesting that the effect of sleep regulation on cardiac dynamics is significantly stronger than the effect of healthy aging. Quantifying changes in this stratification pattern may provide insights into how alterations in sleep regulation contribute to increased cardiac risk. PMID:19203874

Optimal output fast feedback in two-time scale control of flexible arms

NASA Technical Reports Server (NTRS)

Siciliano, B.; Calise, A. J.; Jonnalagadda, V. R. P.

1986-01-01

Control of lightweight flexible arms moving along predefined paths can be successfully synthesized on the basis of a two-time scale approach. A model following control can be designed for the reduced order slow subsystem. The fast subsystem is a linear system in which the slow variables act as parameters. The flexible fast variables which model the deflections of the arm along the trajectory can be sensed through strain gage measurements. For full state feedback design the derivatives of the deflections need to be estimated. The main contribution of this work is the design of an output feedback controller which includes a fixed order dynamic compensator, based on a recent convergent numerical algorithm for calculating LQ optimal gains. The design procedure is tested by means of simulation results for the one link flexible arm prototype in the laboratory.

Experimental demonstration of nonbilocal quantum correlations

PubMed Central

Saunders, Dylan J.; Bennet, Adam J.; Branciard, Cyril; Pryde, Geoff J.

2017-01-01

Quantum mechanics admits correlations that cannot be explained by local realistic models. The most studied models are the standard local hidden variable models, which satisfy the well-known Bell inequalities. To date, most works have focused on bipartite entangled systems. We consider correlations between three parties connected via two independent entangled states. We investigate the new type of so-called “bilocal” models, which correspondingly involve two independent hidden variables. These models describe scenarios that naturally arise in quantum networks, where several independent entanglement sources are used. Using photonic qubits, we build such a linear three-node quantum network and demonstrate nonbilocal correlations by violating a Bell-like inequality tailored for bilocal models. Furthermore, we show that the demonstration of nonbilocality is more noise-tolerant than that of standard Bell nonlocality in our three-party quantum network. PMID:28508045

Improvements In Ball-Screw Linear Actuators

NASA Technical Reports Server (NTRS)

Iskenderian, Theodore; Joffe, Benjamin; Summers, Robert

1996-01-01

Report describes modifications of design of type of ball-screw linear actuator driven by dc motor, with linear-displacement feedback via linear variable-differential transformer (LVDT). Actuators used to position spacecraft engines to direct thrust. Modifications directed toward ensuring reliable and predictable operation during planned 12-year cruise and interval of hard use at end of cruise.

Introducing Linear Functions: An Alternative Statistical Approach

ERIC Educational Resources Information Center

Nolan, Caroline; Herbert, Sandra

2015-01-01

The introduction of linear functions is the turning point where many students decide if mathematics is useful or not. This means the role of parameters and variables in linear functions could be considered to be "threshold concepts". There is recognition that linear functions can be taught in context through the exploration of linear…

Rate-compatible protograph LDPC code families with linear minimum distance

NASA Technical Reports Server (NTRS)

Divsalar, Dariush (Inventor); Dolinar, Jr., Samuel J (Inventor); Jones, Christopher R. (Inventor)

2012-01-01

Digital communication coding methods are shown, which generate certain types of low-density parity-check (LDPC) codes built from protographs. A first method creates protographs having the linear minimum distance property and comprising at least one variable node with degree less than 3. A second method creates families of protographs of different rates, all having the linear minimum distance property, and structurally identical for all rates except for a rate-dependent designation of certain variable nodes as transmitted or non-transmitted. A third method creates families of protographs of different rates, all having the linear minimum distance property, and structurally identical for all rates except for a rate-dependent designation of the status of certain variable nodes as non-transmitted or set to zero. LDPC codes built from the protographs created by these methods can simultaneously have low error floors and low iterative decoding thresholds, and families of such codes of different rates can be decoded efficiently using a common decoding architecture.

Bimodal behavior of post-measured entropy and one-way quantum deficit for two-qubit X states

NASA Astrophysics Data System (ADS)

Yurischev, Mikhail A.

2018-01-01

A method for calculating the one-way quantum deficit is developed. It involves a careful study of post-measured entropy shapes. We discovered that in some regions of X-state space the post-measured entropy \\tilde{S} as a function of measurement angle θ \\in [0,π /2] exhibits a bimodal behavior inside the open interval (0,π /2), i.e., it has two interior extrema: one minimum and one maximum. Furthermore, cases are found when the interior minimum of such a bimodal function \\tilde{S}(θ ) is less than that one at the endpoint θ =0 or π /2. This leads to the formation of a boundary between the phases of one-way quantum deficit via finite jumps of optimal measured angle from the endpoint to the interior minimum. Phase diagram is built up for a two-parameter family of X states. The subregions with variable optimal measured angle are around 1% of the total region, with their relative linear sizes achieving 17.5%, and the fidelity between the states of those subregions can be reduced to F=0.968. In addition, a correction to the one-way deficit due to the interior minimum can achieve 2.3%. Such conditions are favorable to detect the subregions with variable optimal measured angle of one-way quantum deficit in an experiment.

Universal Barenco quantum gates via a tunable noncollinear interaction

NASA Astrophysics Data System (ADS)

Shi, Xiao-Feng

2018-03-01

The Barenco gate (B ) is a type of two-qubit quantum gate based on which alone universal quantum computation can be achieved. Each B is characterized by three angles (α , θ , and ϕ ), though it works in a two-qubit Hilbert space. Here we design B via a noncollinear interaction V | r1r2>< r1r3|+H .c . , where | ri> is a state that can be excited from a qubit state and V is adjustable. We present two protocols for B . The first (second) protocol consists of two (six) pulses and one (two) wait period(s), where the former causes rotations between qubit states and excited states, and the latter induces gate transformation via the noncollinear interaction. In the first protocol, the variable ϕ can be tuned by varying the phases of external controls, and the other two variables α and θ , tunable via adjustment of the wait duration, have a linear dependence on each other. Meanwhile, the first protocol can give rise to cnot and controlled-y gates. In the second protocol, α ,θ , and ϕ can be varied by changing the interaction amplitudes and wait durations, and the latter two are dependent on α nonlinearly. Both protocols can also lead to another universal gate when {α ,ϕ }={1 /4 ,1 /2 }π with appropriate parameters. Implementation of these universal gates is analyzed based on the van der Waals interaction of neutral Rydberg atoms.

Unemployment and mortality among Finnish men, 1981-5.

PubMed Central

Martikainen, P T

1990-01-01

OBJECTIVE--To ascertain whether, after controlling for several relevant background variables simultaneously, unemployment is related to mortality and to assess whether this relation is causal or whether unhealthy people are more likely to become unemployed. DESIGN--Prospective study of mortality in Finland during 1981-5 based on 1980 census data on 30-54 year old wage earner men and with particular attention to unemployment in the year before the census. SETTING--Research project at the University of Helsinki. SUBJECTS--All wage earner men in Finland aged 30-54 at the 1980 census. MAIN OUTCOME MEASURES--Causes of death during 1981-5 and duration of unemployment in the year before the census. Background variables controlled for were age, socioeconomic state, marital state, and health. The data were analysed by log linear regression models. RESULTS--During the study period 1981-5, which covered almost 2.7 million person years, there were 9810 deaths. After controlling for all background variables relative total mortality among unemployed versus employed men was 1.93 (95% confidence interval 1.82 to 2.05). The excess mortality was highest in accidental and violent causes of death (relative mortality 2.51; 95% confidence interval 2.28 to 2.76). For circulatory diseases the relative death rate was 1.54 (95% confidence interval 1.40 to 1.70), but among neoplasms only lung cancer was associated with excess mortality. Selection for unemployment based on age, socioeconomic state, and marital state was evident but no such selection was detected based on health. Effects of unemployment on mortality were more pronounced with increasing duration of unemployment. CONCLUSIONS--The relative excess mortality of unemployed men in Finland cannot fully be explained by demographic, social, and health variables preceding unemployment. Unemployment therefore seems to have an independent causal effect on male mortality. Further studies are needed to elucidate the mechanisms between unemployment and mortality. PMID:2282395

Experimental tree removal in tallgrass prairie: variable responses of flora and fauna along a woody cover gradient.

PubMed

Alford, Aaron L; Hellgren, Eric C; Limb, Ryan; Engle, David M

2012-04-01

Woody plant encroachment is a worldwide phenomenon in grassland and savanna systems whose consequence is often the development of an alternate woodland state. Theoretically, an alternate state may be associated with changes in system state variables (e.g., species composition) or abiotic parameter shifts (e.g., nutrient availability). When state-variable changes are cumulative, such as in woody plant encroachment, the probability of parameter shifts increases as system feedbacks intensify over time. Using a Before-After Control-Impact (BACI) design, we studied eight pairs of grassland sites undergoing various levels of eastern redcedar (Juniperus virginiana) encroachment to determine whether responses of flora and fauna to experimental redcedar removal differed according to the level of pretreatment redcedar cover. In the first year after removal, herbaceous plant species diversity and evenness, woody plant evenness, and invertebrate family richness increased linearly with pretreatment redcedar cover, whereas increases in small-mammal diversity and evenness were described by logarithmic trends. In contrast, increases in woody plant diversity and total biomass of terrestrial invertebrates were accentuated at levels of higher pretreatment cover. Tree removal also shifted small-mammal species composition toward a more grassland-associated assemblage. During the second year postremoval, increases in herbaceous plant diversity followed a polynomial trend, but increases in most other metrics did not vary along the pretreatment cover gradient. These changes were accompanied by extremely high growing-season precipitation, which may have homogenized floral and faunal responses to removal. Our results demonstrate that tree removal increases important community metrics among grassland flora and fauna within two years, with some responses to removal being strongly influenced by the stage of initial encroachment and modulated by climatic variability. Our results underscore the importance of decisive management for reversing the effects of woody plant encroachment in imperiled grassland ecosystems.

Photoswitchable gas permeation membranes based on azobenzene-doped liquid crystals II. Permeation-switching characterization under variable volume and variable pressure conditions

NASA Astrophysics Data System (ADS)

Glowacki, E.; Hunt, K.; Abud, D.; Marshall, K. L.

2010-08-01

Stimuli-responsive gas permeation membranes hold substantial potential for industrial processes as well as in analytical and screening applications. Such "smart" membrane systems, although prevalent in liquid mass-transfer manipulations, have yet to be realized for gas applications. We report our progress in developing gas permeation membranes in which liquid crystalline (LC) phases afford the active region of permeation. To achieve rapid and reversible switching between LC and isotropic permeation states, we harnessed the photomechanical action of mesogenic azobenzene dyes that can produce isothermal nematic-isotropic transitions. Both polymeric and low-molecular-weight LC materials were tested. Three different dye-doped LC mixtures with mesogenic azo dyes were infused into commercially available track-etched porous membranes with regular cylindrical pores (0.4 to 10.0 μm). Photoinduced isothermal phase changes in the imbibed material produced large and fully reversible changes in the permeability of the membrane to nitrogen with 5 s of irradiation at 2 mW/cm2. Using two measurement tools constructed in-house, the permeability of the photoswitched membranes was determined by both variable-pressure and variable-volume methods. Both the LC and photogenerated isotropic states demonstrate a linear permeability/pressure (ideal sorption) relationship, with up to a 16-fold difference in their permeability coefficients. Liquid crystal compositions can be chosen such that the LC phase is more permeable than the isotropic-or vice versa. This approach is the first system offering reversible tunable gas permeation membranes.

Deep Wavelet Scattering for Quantum Energy Regression

NASA Astrophysics Data System (ADS)

Hirn, Matthew

Physical functionals are usually computed as solutions of variational problems or from solutions of partial differential equations, which may require huge computations for complex systems. Quantum chemistry calculations of ground state molecular energies is such an example. Indeed, if x is a quantum molecular state, then the ground state energy E0 (x) is the minimum eigenvalue solution of the time independent Schrödinger Equation, which is computationally intensive for large systems. Machine learning algorithms do not simulate the physical system but estimate solutions by interpolating values provided by a training set of known examples {(xi ,E0 (xi) } i <= n . However, precise interpolations may require a number of examples that is exponential in the system dimension, and are thus intractable. This curse of dimensionality may be circumvented by computing interpolations in smaller approximation spaces, which take advantage of physical invariants. Linear regressions of E0 over a dictionary Φ ={ϕk } k compute an approximation E 0 as: E 0 (x) =∑kwkϕk (x) , where the weights {wk } k are selected to minimize the error between E0 and E 0 on the training set. The key to such a regression approach then lies in the design of the dictionary Φ. It must be intricate enough to capture the essential variability of E0 (x) over the molecular states x of interest, while simple enough so that evaluation of Φ (x) is significantly less intensive than a direct quantum mechanical computation (or approximation) of E0 (x) . In this talk we present a novel dictionary Φ for the regression of quantum mechanical energies based on the scattering transform of an intermediate, approximate electron density representation ρx of the state x. The scattering transform has the architecture of a deep convolutional network, composed of an alternating sequence of linear filters and nonlinear maps. Whereas in many deep learning tasks the linear filters are learned from the training data, here the physical properties of E0 (invariance to isometric transformations of the state x, stable to deformations of x) are leveraged to design a collection of linear filters ρx *ψλ for an appropriate wavelet ψ. These linear filters are composed with the nonlinear modulus operator, and the process is iterated upon so that at each layer stable, invariant features are extracted: ϕk (x) = ∥ | | ρx *ψλ1 | * ψλ2 | * ... *ψλm ∥ , k = (λ1 , ... ,λm) , m = 1 , 2 , ... The scattering transform thus encodes not only interactions at multiple scales (in the first layer, m = 1), but also features that encode complex phenomena resulting from a cascade of interactions across scales (in subsequent layers, m >= 2). Numerical experiments give state of the art accuracy over data bases of organic molecules, while theoretical results guarantee performance for the component of the ground state energy resulting from Coulombic interactions. Supported by the ERC InvariantClass 320959 Grant.

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Variable-Delay Polarization Modulators for Cryogenic Millimeter-Wave Applications

NASA Technical Reports Server (NTRS)

Chuss, D. T.; Eimer, J. R.; Fixsen, D. J.; Hinderks, J.; Kogut, A. J.; Lazear, J.; Mirel, P.; Switzer, E.; Voellmer, G. M.; Wollack, E. J..

2014-01-01

We describe the design, construction, and initial validation of the variable-delay polarization modulator (VPM) designed for the PIPER cosmic microwave background polarimeter. The VPM modulates between linear and circular polarization by introducing a variable phase delay between orthogonal linear polarizations. Each VPM has a diameter of 39 cm and is engineered to operate in a cryogenic environment (1.5 K). We describe the mechanical design and performance of the kinematic double-blade flexure and drive mechanism along with the construction of the high precision wire grid polarizers.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.

Steady-state pharmacokinetics of isotretinoin and its 4-oxo metabolite: implications for fetal safety.

PubMed

Nulman, I; Berkovitch, M; Klein, J; Pastuszak, A; Lester, R S; Shear, N; Koren, G

1998-10-01

Isotretinoin is the most potent human teratogen on the market. Women for whom contraception fails may conceive during or soon after discontinuing isotretinoin therapy, making its elimination kinetics a crucial determinant of fetal safety. The steady-state pharmacokinetics of isotretinoin and its major 4-oxo metabolite were studied in 16 adult patients treated for acne who were receiving doses that ranged from 0.47 to 1.7 mg/kg daily. This is the first study of the pharmacokinetics of isotretinoin in women of childbearing age (n = 11). The clinical efficacy and tolerability of isotretinoin was investigated, and the correlation between these data and steady-state serum concentrations of isotretinoin was tested. The concentration-time data best fitted a two-compartment open model with linear elimination. There was no correlation between efficacy and tolerability of isotretinoin and steady-state serum concentrations. There was no correlation between dose of isotretinoin and steady-state concentration, due to the large variability in apparent clearance. Values for elimination half-life (t1/2) of isotretinoin and its metabolite were 29+/-40 hours and 22+/-10 hours, respectively. These data suggest a longer elimination t1/2 of the parent drug than previously reported. This is probably due to the longer sampling time used in this study (as long as 28 days). This study suggests that a greater variability exists in the safe time after discontinuation of the drug for onset of conception.

Detection of quantum steering in multipartite continuous-variable Greenberger-Horne-Zeilinger-like states

NASA Astrophysics Data System (ADS)

Wang, Meng; Xiang, Yu; He, Qiongyi; Gong, Qihuang

2015-01-01

The multipartite entangled state has drawn broad attention for both foundations of quantum mechanics and applications in quantum information processing. Here, we study the spatially separated N -partite continuous-variable Greenberger-Horne-Zeilinger-like states, which can be produced by a linear optical network with squeezed light and N -1 beamsplitters. We investigate the properties of multipartite Einstein-Podolsky-Rosen steering possessed by those states, and find that the steering of a given quantum mode is allowed when not less than half of the modes within the states take part in the steering group. This is certified by the detection of the correlation between position and momentum quadratures of the steered mode and a combination of quadratures of other modes inside the steering group. The steering is evidenced by the high correlation where the steering group can infer the quadratures of the steered mode to high precision, i.e., below the quantum limit for the position and momentum quadratures of the steered quantum mode. We also examine the influence of inefficiency on the multipartite steering, and derive the threshold of the loss tolerance. Furthermore, we discuss the collective N -partite steering induced by the asymmetric loss on beams, which exists when a given quantum mode can only be steered by all the remaining N -1 modes collaboratively. The present multipartite steering correlation may have potential applications in certain quantum information tasks where the issue of trust is important, such as one-sided device-independent quantum secret sharing.

Empirical Assessment of Spatial Prediction Methods for Location Cost Adjustment Factors

PubMed Central

Migliaccio, Giovanni C.; Guindani, Michele; D'Incognito, Maria; Zhang, Linlin

2014-01-01

In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables. PMID:25018582

A linear-encoding model explains the variability of the target morphology in regeneration

PubMed Central

Lobo, Daniel; Solano, Mauricio; Bubenik, George A.; Levin, Michael

2014-01-01

A fundamental assumption of today's molecular genetics paradigm is that complex morphology emerges from the combined activity of low-level processes involving proteins and nucleic acids. An inherent characteristic of such nonlinear encodings is the difficulty of creating the genetic and epigenetic information that will produce a given self-assembling complex morphology. This ‘inverse problem’ is vital not only for understanding the evolution, development and regeneration of bodyplans, but also for synthetic biology efforts that seek to engineer biological shapes. Importantly, the regenerative mechanisms in deer antlers, planarian worms and fiddler crabs can solve an inverse problem: their target morphology can be altered specifically and stably by injuries in particular locations. Here, we discuss the class of models that use pre-specified morphological goal states and propose the existence of a linear encoding of the target morphology, making the inverse problem easy for these organisms to solve. Indeed, many model organisms such as Drosophila, hydra and Xenopus also develop according to nonlinear encodings producing linear encodings of their final morphologies. We propose the development of testable models of regeneration regulation that combine emergence with a top-down specification of shape by linear encodings of target morphology, driving transformative applications in biomedicine and synthetic bioengineering. PMID:24402915

Effects of variables upon pyrotechnically induced shock response spectra

NASA Technical Reports Server (NTRS)

Smith, J. L.

1986-01-01

Throughout the aerospace industry, large variations of 50 percent (6 dB) or more are continually noted for linear shaped charge (LSC) generated shock response spectra (SRS) from flight data (from the exact same location on different flights) and from plate tests (side by side measurements on the same test). A research program was developed to investigate causes of these large SRS variations. A series of ball drop calibration tests to verify calibration of accelerometers and a series of plate tests to investigate charge and assembly variables were performed. The resulting data were analyzed to determine if and to what degree manufacturing and assembly variables, distance from the shock source, data acquisition instrumentation, and shock energy propagation affect the SRS. LSC variables consisted of coreload, standoff, and apex angle. The assembly variable was the torque on the LSC holder. Other variables were distance from source of accelerometers, accelerometer mounting methods, and joint effects. Results indicated that LSC variables did not affect SRS as long as the plate was severed. Accelerometers mounted on mounting blocks showed significantly lower levels above 5000 Hz. Lap joints did not affect SRS levels. The test plate was mounted in an almost free-free state; therefore, distance from the source did not affect the SRS. Several varieties and brands of accelerometers were used, and all but one demonstrated very large variations in SRS.

Density dependence and climate effects in Rocky Mountain elk: an application of regression with instrumental variables for population time series with sampling error.

PubMed

Creel, Scott; Creel, Michael

2009-11-01

1. Sampling error in annual estimates of population size creates two widely recognized problems for the analysis of population growth. First, if sampling error is mistakenly treated as process error, one obtains inflated estimates of the variation in true population trajectories (Staples, Taper & Dennis 2004). Second, treating sampling error as process error is thought to overestimate the importance of density dependence in population growth (Viljugrein et al. 2005; Dennis et al. 2006). 2. In ecology, state-space models are used to account for sampling error when estimating the effects of density and other variables on population growth (Staples et al. 2004; Dennis et al. 2006). In econometrics, regression with instrumental variables is a well-established method that addresses the problem of correlation between regressors and the error term, but requires fewer assumptions than state-space models (Davidson & MacKinnon 1993; Cameron & Trivedi 2005). 3. We used instrumental variables to account for sampling error and fit a generalized linear model to 472 annual observations of population size for 35 Elk Management Units in Montana, from 1928 to 2004. We compared this model with state-space models fit with the likelihood function of Dennis et al. (2006). We discuss the general advantages and disadvantages of each method. Briefly, regression with instrumental variables is valid with fewer distributional assumptions, but state-space models are more efficient when their distributional assumptions are met. 4. Both methods found that population growth was negatively related to population density and winter snow accumulation. Summer rainfall and wolf (Canis lupus) presence had much weaker effects on elk (Cervus elaphus) dynamics [though limitation by wolves is strong in some elk populations with well-established wolf populations (Creel et al. 2007; Creel & Christianson 2008)]. 5. Coupled with predictions for Montana from global and regional climate models, our results predict a substantial reduction in the limiting effect of snow accumulation on Montana elk populations in the coming decades. If other limiting factors do not operate with greater force, population growth rates would increase substantially.

What controls springtime fine dust variability in the western United States? Implications for air quality and public health risks under future climate change.

NASA Astrophysics Data System (ADS)

Achakulwisut, P.; Mickley, L. J.; Shen, L.; Anenberg, S.

2017-12-01

Studies suggest that deposition of atmospheric dust and its surface concentrations have recently been increasing in the Southwest, and a key concern is that climate change will impact dust mobilization and transport across the western United States. Here we diagnose the dominant meteorological factors driving observed fine dust interannual variability in the western United States during 2002-2015 in March-May, and investigate the implications of our results for future dust levels. Empirical orthogonal function analysis suggests that for each spring month, 50-61% of present-day variance in fine dust can be explained by either a coherent pattern of co-variability across the West or a dipole Northwest-Southwest pattern. We identify the key drivers of fine dust variability to be regional precipitation, temperature, and soil moisture, which in turn are influenced at least in part by large-scale changes in sea surface temperature and/or atmospheric circulation patterns. Fluctuations in the trans-Pacific transport of Asian dust also contribute to fine dust variability in March and April. We find that March fine dust concentrations have increased from 2002 to 2015 in Southwest regions (0.09 ± 0.07 μg m-3 a-1). Multiple linear regression analysis suggests that these increases are associated with: (1) regionally drier and warmer conditions driven by constructive interference between El Niño Southern-Oscillation and Pacific Decadal Oscillation; (2) soil moisture reductions in areas spanning the North American deserts; and (3) stronger trans-Pacific transport. We then use observed drought-sensitivities to project future changes in annual mean fine dust during the late-21st century (2076-2095), using bias-corrected downscaled meteorological outputs from 23 CMIP5 models following two Representative Concentration Pathways (RCP2.6 and RCP8.5). Together with projections of future population and baseline incidence rates, and results from epidemiological studies of health risks due to PM2.5 exposure, we estimate the excess premature mortality and morbidity associated with projected changes in annual mean fine dust in the southwest United States.

Nonlocal effects on the polarization state of a photon, induced by distant absorbers

NASA Technical Reports Server (NTRS)

Ryff, Luis Carlos B.

1994-01-01

A variant of a Franson's two-photon correlation experiment is discussed, in which the linear polarization state of one of the photons depends on the path followed in the interferometer. It is shown that although the path difference is greater than the coherence length, the photon can be found in a polarization state represented by the superposition of the polarization states associated to the paths when there is coincident detection. Since the photons, produced via parametric down-conversion, are fairly well localized in space and time, the situation in which one of the photons is detected before the other can reach the interferometer raises an intriguing point: it seems that in some cases the second photon would have to be described by two wave packets simultaneously. Unlike previous experiments, in which nonlocal effects were induced by means of polarizers of phase shifters, in the proposed experiment nonlocal effects can be induced by means of variable absorbers.

Nonlinear predictive control of a boiler-turbine unit: A state-space approach with successive on-line model linearisation and quadratic optimisation.

PubMed

Ławryńczuk, Maciej

2017-03-01

This paper details development of a Model Predictive Control (MPC) algorithm for a boiler-turbine unit, which is a nonlinear multiple-input multiple-output process. The control objective is to follow set-point changes imposed on two state (output) variables and to satisfy constraints imposed on three inputs and one output. In order to obtain a computationally efficient control scheme, the state-space model is successively linearised on-line for the current operating point and used for prediction. In consequence, the future control policy is easily calculated from a quadratic optimisation problem. For state estimation the extended Kalman filter is used. It is demonstrated that the MPC strategy based on constant linear models does not work satisfactorily for the boiler-turbine unit whereas the discussed algorithm with on-line successive model linearisation gives practically the same trajectories as the truly nonlinear MPC controller with nonlinear optimisation repeated at each sampling instant. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Are dark energy models with variable EoS parameter w compatible with the late inhomogeneous Universe?

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

Akarsu, Özgür; Bouhmadi-López, Mariam; Brilenkov, Maxim

We study the late-time evolution of the Universe where dark energy (DE) is presented by a barotropic fluid on top of cold dark matter (CDM) . We also take into account the radiation content of the Universe. Here by the late stage of the evolution we refer to the epoch where CDM is already clustered into inhomogeneously distributed discrete structures (galaxies, groups and clusters of galaxies). Under this condition the mechanical approach is an adequate tool to study the Universe deep inside the cell of uniformity. More precisely, we study scalar perturbations of the FLRW metric due to inhomogeneities ofmore » CDM as well as fluctuations of radiation and DE. For an arbitrary equation of state for DE we obtain a system of equations for the scalar perturbations within the mechanical approach. First, in the case of a constant DE equation of state parameter w, we demonstrate that our method singles out the cosmological constant as the only viable dark energy candidate. Then, we apply our approach to variable equation of state parameters in the form of three different linear parametrizations of w, e.g., the Chevallier-Polarski-Linder perfect fluid model. We conclude that all these models are incompatible with the theory of scalar perturbations in the late Universe.« less

Differential flatness properties and multivariable adaptive control of ovarian system dynamics

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos

2016-12-01

The ovarian system exhibits nonlinear dynamics which is modeled by a set of coupled nonlinear differential equations. The paper proposes adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be described as differential functions of the flat output. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. The learning procedure for these estimators is determined by the requirement the first derivative of the closed-loop's Lyapunov function to be a negative one. Moreover, Lyapunov stability analysis shows that H-infinity tracking performance is succeeded for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations. The efficiency of the proposed adaptive fuzzy control scheme is confirmed through simulation experiments.

Second order sliding mode control for a quadrotor UAV.

PubMed

Zheng, En-Hui; Xiong, Jing-Jing; Luo, Ji-Liang

2014-07-01

A method based on second order sliding mode control (2-SMC) is proposed to design controllers for a small quadrotor UAV. For the switching sliding manifold design, the selection of the coefficients of the switching sliding manifold is in general a sophisticated issue because the coefficients are nonlinear. In this work, in order to perform the position and attitude tracking control of the quadrotor perfectly, the dynamical model of the quadrotor is divided into two subsystems, i.e., a fully actuated subsystem and an underactuated subsystem. For the former, a sliding manifold is defined by combining the position and velocity tracking errors of one state variable, i.e., the sliding manifold has two coefficients. For the latter, a sliding manifold is constructed via a linear combination of position and velocity tracking errors of two state variables, i.e., the sliding manifold has four coefficients. In order to further obtain the nonlinear coefficients of the sliding manifold, Hurwitz stability analysis is used to the solving process. In addition, the flight controllers are derived by using Lyapunov theory, which guarantees that all system state trajectories reach and stay on the sliding surfaces. Extensive simulation results are given to illustrate the effectiveness of the proposed control method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Old and New Ideas for Data Screening and Assumption Testing for Exploratory and Confirmatory Factor Analysis

PubMed Central

Flora, David B.; LaBrish, Cathy; Chalmers, R. Philip

2011-01-01

We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables. PMID:22403561

Aspects of porosity prediction using multivariate linear regression

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

Byrnes, A.P.; Wilson, M.D.

1991-03-01

Highly accurate multiple linear regression models have been developed for sandstones of diverse compositions. Porosity reduction or enhancement processes are controlled by the fundamental variables, Pressure (P), Temperature (T), Time (t), and Composition (X), where composition includes mineralogy, size, sorting, fluid composition, etc. The multiple linear regression equation, of which all linear porosity prediction models are subsets, takes the generalized form: Porosity = C{sub 0} + C{sub 1}(P) + C{sub 2}(T) + C{sub 3}(X) + C{sub 4}(t) + C{sub 5}(PT) + C{sub 6}(PX) + C{sub 7}(Pt) + C{sub 8}(TX) + C{sub 9}(Tt) + C{sub 10}(Xt) + C{sub 11}(PTX) + C{submore » 12}(PXt) + C{sub 13}(PTt) + C{sub 14}(TXt) + C{sub 15}(PTXt). The first four primary variables are often interactive, thus requiring terms involving two or more primary variables (the form shown implies interaction and not necessarily multiplication). The final terms used may also involve simple mathematic transforms such as log X, e{sup T}, X{sup 2}, or more complex transformations such as the Time-Temperature Index (TTI). The X term in the equation above represents a suite of compositional variable and, therefore, a fully expanded equation may include a series of terms incorporating these variables. Numerous published bivariate porosity prediction models involving P (or depth) or Tt (TTI) are effective to a degree, largely because of the high degree of colinearity between p and TTI. However, all such bivariate models ignore the unique contributions of P and Tt, as well as various X terms. These simpler models become poor predictors in regions where colinear relations change, were important variables have been ignored, or where the database does not include a sufficient range or weight distribution for the critical variables.« less