Study of linear induction motor characteristics : the Mosebach model

DOT National Transportation Integrated Search

1976-05-31

This report covers the Mosebach theory of the double-sided linear induction motor, starting with the ideallized model and accompanying assumptions, and ending with relations for thrust, airgap power, and motor efficiency. Solutions of the magnetic in...

Study of linear induction motor characteristics : the Oberretl model

DOT National Transportation Integrated Search

1975-05-30

The Oberretl theory of the double-sided linear induction motor (LIM) is examined, starting with the idealized model and accompanying assumptions, and ending with relations for predicted thrust, airgap power, and motor efficiency. The effect of varyin...

Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

PubMed Central

Almedeij, Jaber

2012-01-01

Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984

Mathematical Modelling and the Learning Trajectory: Tools to Support the Teaching of Linear Algebra

ERIC Educational Resources Information Center

Cárcamo Bahamonde, Andrea Dorila; Fortuny Aymemí, Josep Maria; Gómez i Urgellés, Joan Vicenç

2017-01-01

In this article we present a didactic proposal for teaching linear algebra based on two compatible theoretical models: emergent models and mathematical modelling. This proposal begins with a problematic situation related to the creation and use of secure passwords, which leads students toward the construction of the concepts of spanning set and…

Theoretical and Empirical Comparisons between Two Models for Continuous Item Responses.

ERIC Educational Resources Information Center

Ferrando, Pere J.

2002-01-01

Analyzed the relations between two continuous response models intended for typical response items: the linear congeneric model and Samejima's continuous response model (CRM). Illustrated the relations described using an empirical example and assessed the relations through a simulation study. (SLD)

Application of a baseflow filter for evaluating model structure suitability of the IHACRES CMD

NASA Astrophysics Data System (ADS)

Kim, H. S.

2015-02-01

The main objective of this study was to assess the predictive uncertainty from the rainfall-runoff model structure coupling a conceptual module (non-linear module) with a metric transfer function module (linear module). The methodology was primarily based on the comparison between the outputs of the rainfall-runoff model and those from an alternative model approach. An alternative model approach was used to minimise uncertainties arising from data and the model structure. A baseflow filter was adopted to better understand deficiencies in the forms of the rainfall-runoff model by avoiding the uncertainties related to data and the model structure. The predictive uncertainty from the model structure was investigated for representative groups of catchments having similar hydrological response characteristics in the upper Murrumbidgee Catchment. In the assessment of model structure suitability, the consistency (or variability) of catchment response over time and space in model performance and parameter values has been investigated to detect problems related to the temporal and spatial variability of the model accuracy. The predictive error caused by model uncertainty was evaluated through analysis of the variability of the model performance and parameters. A graphical comparison of model residuals, effective rainfall estimates and hydrographs was used to determine a model's ability related to systematic model deviation between simulated and observed behaviours and general behavioural differences in the timing and magnitude of peak flows. The model's predictability was very sensitive to catchment response characteristics. The linear module performs reasonably well in the wetter catchments but has considerable difficulties when applied to the drier catchments where a hydrologic response is dominated by quick flow. The non-linear module has a potential limitation in its capacity to capture non-linear processes for converting observed rainfall into effective rainfall in both the wetter and drier catchments. The comparative study based on a better quantification of the accuracy and precision of hydrological modelling predictions yields a better understanding for the potential improvement of model deficiencies.

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.

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

PubMed

Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

2017-02-01

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

MAGDM linear-programming models with distinct uncertain preference structures.

PubMed

Xu, Zeshui S; Chen, Jian

2008-10-01

Group decision making with preference information on alternatives is an interesting and important research topic which has been receiving more and more attention in recent years. The purpose of this paper is to investigate multiple-attribute group decision-making (MAGDM) problems with distinct uncertain preference structures. We develop some linear-programming models for dealing with the MAGDM problems, where the information about attribute weights is incomplete, and the decision makers have their preferences on alternatives. The provided preference information can be represented in the following three distinct uncertain preference structures: 1) interval utility values; 2) interval fuzzy preference relations; and 3) interval multiplicative preference relations. We first establish some linear-programming models based on decision matrix and each of the distinct uncertain preference structures and, then, develop some linear-programming models to integrate all three structures of subjective uncertain preference information provided by the decision makers and the objective information depicted in the decision matrix. Furthermore, we propose a simple and straightforward approach in ranking and selecting the given alternatives. It is worth pointing out that the developed models can also be used to deal with the situations where the three distinct uncertain preference structures are reduced to the traditional ones, i.e., utility values, fuzzy preference relations, and multiplicative preference relations. Finally, we use a practical example to illustrate in detail the calculation process of the developed approach.

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

Linearly exact parallel closures for slab geometry

NASA Astrophysics Data System (ADS)

Ji, Jeong-Young; Held, Eric D.; Jhang, Hogun

2013-08-01

Parallel closures are obtained by solving a linearized kinetic equation with a model collision operator using the Fourier transform method. The closures expressed in wave number space are exact for time-dependent linear problems to within the limits of the model collision operator. In the adiabatic, collisionless limit, an inverse Fourier transform is performed to obtain integral (nonlocal) parallel closures in real space; parallel heat flow and viscosity closures for density, temperature, and flow velocity equations replace Braginskii's parallel closure relations, and parallel flow velocity and heat flow closures for density and temperature equations replace Spitzer's parallel transport relations. It is verified that the closures reproduce the exact linear response function of Hammett and Perkins [Phys. Rev. Lett. 64, 3019 (1990)] for Landau damping given a temperature gradient. In contrast to their approximate closures where the vanishing viscosity coefficient numerically gives an exact response, our closures relate the heat flow and nonvanishing viscosity to temperature and flow velocity (gradients).

Hierarchical Linear Modeling (HLM): An Introduction to Key Concepts within Cross-Sectional and Growth Modeling Frameworks. Technical Report #1308

ERIC Educational Resources Information Center

Anderson, Daniel

2012-01-01

This manuscript provides an overview of hierarchical linear modeling (HLM), as part of a series of papers covering topics relevant to consumers of educational research. HLM is tremendously flexible, allowing researchers to specify relations across multiple "levels" of the educational system (e.g., students, classrooms, schools, etc.).…

Modification of 2-D Time-Domain Shallow Water Wave Equation using Asymptotic Expansion Method

NASA Astrophysics Data System (ADS)

Khairuman, Teuku; Nasruddin, MN; Tulus; Ramli, Marwan

2018-01-01

Generally, research on the tsunami wave propagation model can be conducted by using a linear model of shallow water theory, where a non-linear side on high order is ignored. In line with research on the investigation of the tsunami waves, the Boussinesq equation model underwent a change aimed to obtain an improved quality of the dispersion relation and non-linearity by increasing the order to be higher. To solve non-linear sides at high order is used a asymptotic expansion method. This method can be used to solve non linear partial differential equations. In the present work, we found that this method needs much computational time and memory with the increase of the number of elements.

Multivariable control of the Space Shuttle Remote Manipulator System using linearization by state feedback. M.S. Thesis

NASA Technical Reports Server (NTRS)

Gettman, Chang-Ching LO

1993-01-01

This thesis develops and demonstrates an approach to nonlinear control system design using linearization by state feedback. The design provides improved transient response behavior allowing faster maneuvering of payloads by the SRMS. Modeling uncertainty is accounted for by using a second feedback loop designed around the feedback linearized dynamics. A classical feedback loop is developed to provide the easy implementation required for the relatively small on board computers. Feedback linearization also allows the use of higher bandwidth model based compensation in the outer loop, since it helps maintain stability in the presence of the nonlinearities typically neglected in model based designs.

Efficiency of energy conversion in model biological pumps. Optimization by linear nonequilibrium thermodynamic relations.

PubMed

Stucki, J W; Compiani, M; Caplan, S R

1983-09-01

Experimental investigations showed linear relations between flows and forces in some biological energy converters operating far from equilibrium. This observation cannot be understood on the basis of conventional nonequilibrium thermodynamics. Therefore, the efficiencies of a linear and a nonlinear mode of operation of an energy converter (a hypothetical redox-driven H+ pump) were compared. This comparison revealed that at physiological values of the forces and degrees of coupling (1) the force ratio permitting optimal efficiency was much higher in the linear than in the nonlinear mode and (2) the linear mode of operation was at least 10(6)-times more efficient that the nonlinear one. These observations suggest that the experimentally observed linear relations between flows and forces, particularly in the case of oxidative phosphorylation, may be due to a feedback regulation maintaining linear thermodynamic relations far from equilibrium. This regulation may have come about as the consequence of an evolutionary drive towards higher efficiency.

The "Chaos" Pattern in Piaget's Theory of Cognitive Development.

ERIC Educational Resources Information Center

Lindsay, Jean S.

Piaget's theory of the cognitive development of the child is related to the recently developed non-linear "chaos" model. The term "chaos" refers to the tendency of dynamical, non-linear systems toward irregular, sometimes unpredictable, deterministic behavior. Piaget identified this same pattern in his model of cognitive…

TENSOR DECOMPOSITIONS AND SPARSE LOG-LINEAR MODELS

PubMed Central

Johndrow, James E.; Bhattacharya, Anirban; Dunson, David B.

2017-01-01

Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. We derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions. PMID:29332971

Assessing Linearity in the Loudness Envelope of the Messa di Voce Singing Exercise Through Acoustic Signal Analysis.

PubMed

Yadav, Manuj; Cabrera, Densil; Kenny, Dianna T

2015-09-01

Messa di voce (MDV) is a singing exercise that involves sustaining a single pitch with a linear change in loudness from silence to maximum intensity (the crescendo part) and back to silence again (the decrescendo part), with time symmetry between the two parts. Previous studies have used the sound pressure level (SPL, in decibels) of a singer's voice to measure loudness, so as to assess the linearity of each part-an approach that has limitations due to loudness and SPL not being linearly related. This article studies the loudness envelope shapes of MDVs, comparing the SPL approach with approaches that are more closely related to human loudness perception. The MDVs were performed by a cohort of tertiary singing students, recorded six times (once per semester) over a period of 3 years. The loudness envelopes were derived for a typical audience listening position, and for listening to one's own singing, using three models: SPL, Stevens' power law-based model, and a computational loudness model. The effects on the envelope shape due to room acoustics (an important effect) and vibrato (minimal effect) were also considered. The results showed that the SPL model yielded a lower proportion of linear crescendi and decrescendi, compared with other models. The Stevens' power law-based model provided results similar to the more complicated computational loudness model. Longitudinally, there was no consistent trend in the shape of the MDV loudness envelope for the cohort although there were some individual singers who exhibited improvements in linearity. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

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…

Comparison of linear and nonlinear models for coherent hemodynamics spectroscopy (CHS)

NASA Astrophysics Data System (ADS)

Sassaroli, Angelo; Kainerstorfer, Jana; Fantini, Sergio

2015-03-01

A recently proposed linear time-invariant hemodynamic model for coherent hemodynamics spectroscopy1 (CHS) relates the tissue concentrations of oxy- and deoxy-hemoglobin (outputs of the system) to given dynamics of the tissue blood volume, blood flow and rate constant of oxygen diffusion (inputs of the system). This linear model was derived in the limit of "small" perturbations in blood flow velocity. We have extended this model to a more general model (which will be referred to as the nonlinear extension to the original model) that yields the time-dependent changes of oxy and deoxy-hemoglobin concentrations in response to arbitrary dynamic changes in capillary blood flow velocity. The nonlinear extension to the model relies on a general solution of the partial differential equation that governs the spatio-temporal behavior of oxygen saturation of hemoglobin in capillaries and venules on the basis of dynamic (or time resolved) blood transit time. We show preliminary results where the CHS spectra obtained from the linear and nonlinear models are compared to quantify the limits of applicability of the linear model.

Hamiltonian modelling of relative motion.

PubMed

Kasdin, N Jeremy; Gurfil, Pini

2004-05-01

This paper presents a Hamiltonian approach to modelling relative spacecraft motion based on derivation of canonical coordinates for the relative state-space dynamics. The Hamiltonian formulation facilitates the modelling of high-order terms and orbital perturbations while allowing us to obtain closed-form solutions to the relative motion problem. First, the Hamiltonian is partitioned into a linear term and a high-order term. The Hamilton-Jacobi equations are solved for the linear part by separation, and new constants for the relative motions are obtained, they are called epicyclic elements. The influence of higher order terms and perturbations, such as the oblateness of the Earth, are incorporated into the analysis by a variation of parameters procedure. Closed-form solutions for J(2-) and J(4-)invariant orbits and for periodic high-order unperturbed relative motion, in terms of the relative motion elements only, are obtained.

Nonlinear identification of the total baroreflex arc.

PubMed

Moslehpour, Mohsen; Kawada, Toru; Sunagawa, Kenji; Sugimachi, Masaru; Mukkamala, Ramakrishna

2015-12-15

The total baroreflex arc [the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP)] is known to exhibit nonlinear behaviors. However, few studies have quantitatively characterized its nonlinear dynamics. The aim of this study was to develop a nonlinear model of the sympathetically mediated total arc without assuming any model form. Normal rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned, the carotid sinus regions were isolated and attached to a servo-controlled piston pump, and the AP and sympathetic nerve activity (SNA) were measured. CSP was perturbed using a Gaussian white noise signal. A second-order Volterra model was developed by applying nonparametric identification to the measurements. The second-order kernel was mainly diagonal, but the diagonal differed in shape from the first-order kernel. Hence, a reduced second-order model was similarly developed comprising a linear dynamic system in parallel with a squaring system in cascade with a slower linear dynamic system. This "Uryson" model predicted AP changes 12% better (P < 0.01) than a linear model in response to new Gaussian white noise CSP. The model also predicted nonlinear behaviors, including thresholding and mean responses to CSP changes about the mean. Models of the neural arc (the system relating CSP to SNA) and peripheral arc (the system relating SNA to AP) were likewise developed and tested. However, these models of subsystems of the total arc showed approximately linear behaviors. In conclusion, the validated nonlinear model of the total arc revealed that the system takes on an Uryson structure. Copyright © 2015 the American Physiological Society.

Nonlinear identification of the total baroreflex arc

PubMed Central

Moslehpour, Mohsen; Kawada, Toru; Sunagawa, Kenji; Sugimachi, Masaru

2015-01-01

The total baroreflex arc [the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP)] is known to exhibit nonlinear behaviors. However, few studies have quantitatively characterized its nonlinear dynamics. The aim of this study was to develop a nonlinear model of the sympathetically mediated total arc without assuming any model form. Normal rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned, the carotid sinus regions were isolated and attached to a servo-controlled piston pump, and the AP and sympathetic nerve activity (SNA) were measured. CSP was perturbed using a Gaussian white noise signal. A second-order Volterra model was developed by applying nonparametric identification to the measurements. The second-order kernel was mainly diagonal, but the diagonal differed in shape from the first-order kernel. Hence, a reduced second-order model was similarly developed comprising a linear dynamic system in parallel with a squaring system in cascade with a slower linear dynamic system. This “Uryson” model predicted AP changes 12% better (P < 0.01) than a linear model in response to new Gaussian white noise CSP. The model also predicted nonlinear behaviors, including thresholding and mean responses to CSP changes about the mean. Models of the neural arc (the system relating CSP to SNA) and peripheral arc (the system relating SNA to AP) were likewise developed and tested. However, these models of subsystems of the total arc showed approximately linear behaviors. In conclusion, the validated nonlinear model of the total arc revealed that the system takes on an Uryson structure. PMID:26354845

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.

Longitudinal mathematics development of students with learning disabilities and students without disabilities: a comparison of linear, quadratic, and piecewise linear mixed effects models.

PubMed

Kohli, Nidhi; Sullivan, Amanda L; Sadeh, Shanna; Zopluoglu, Cengiz

2015-04-01

Effective instructional planning and intervening rely heavily on accurate understanding of students' growth, but relatively few researchers have examined mathematics achievement trajectories, particularly for students with special needs. We applied linear, quadratic, and piecewise linear mixed-effects models to identify the best-fitting model for mathematics development over elementary and middle school and to ascertain differences in growth trajectories of children with learning disabilities relative to their typically developing peers. The analytic sample of 2150 students was drawn from the Early Childhood Longitudinal Study - Kindergarten Cohort, a nationally representative sample of United States children who entered kindergarten in 1998. We first modeled students' mathematics growth via multiple mixed-effects models to determine the best fitting model of 9-year growth and then compared the trajectories of students with and without learning disabilities. Results indicate that the piecewise linear mixed-effects model captured best the functional form of students' mathematics trajectories. In addition, there were substantial achievement gaps between students with learning disabilities and students with no disabilities, and their trajectories differed such that students without disabilities progressed at a higher rate than their peers who had learning disabilities. The results underscore the need for further research to understand how to appropriately model students' mathematics trajectories and the need for attention to mathematics achievement gaps in policy. Copyright © 2015 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Modeling of non-ideal hard permanent magnets with an affine-linear model, illustrated for a bar and a horseshoe magnet

NASA Astrophysics Data System (ADS)

Glane, Sebastian; Reich, Felix A.; Müller, Wolfgang H.

2017-11-01

This study is dedicated to continuum-scale material modeling of isotropic permanent magnets. An affine-linear extension to the commonly used ideal hard model for permanent magnets is proposed, motivated, and detailed. In order to demonstrate the differences between these models, bar and horseshoe magnets are considered. The structure of the boundary value problem for the magnetic field and related solution techniques are discussed. For the ideal model, closed-form analytical solutions were obtained for both geometries. Magnetic fields of the boundary value problems for both models and differently shaped magnets were computed numerically by using the boundary element method. The results show that the character of the magnetic field is strongly influenced by the model that is used. Furthermore, it can be observed that the shape of an affine-linear magnet influences the near-field significantly. Qualitative comparisons with experiments suggest that both the ideal and the affine-linear models are relevant in practice, depending on the magnetic material employed. Mathematically speaking, the ideal magnetic model is a special case of the affine-linear one. Therefore, in applications where knowledge of the near-field is important, the affine-linear model can yield more accurate results—depending on the magnetic material.

Linear mixed-effects models to describe length-weight relationships for yellow croaker (Larimichthys Polyactis) along the north coast of China.

PubMed

Ma, Qiuyun; Jiao, Yan; Ren, Yiping

2017-01-01

In this study, length-weight relationships and relative condition factors were analyzed for Yellow Croaker (Larimichthys polyactis) along the north coast of China. Data covered six regions from north to south: Yellow River Estuary, Coastal Waters of Northern Shandong, Jiaozhou Bay, Coastal Waters of Qingdao, Haizhou Bay, and South Yellow Sea. In total 3,275 individuals were collected during six years (2008, 2011-2015). One generalized linear model, two simply linear models and nine linear mixed effect models that applied the effects from regions and/or years to coefficient a and/or the exponent b were studied and compared. Among these twelve models, the linear mixed effect model with random effects from both regions and years fit the data best, with lowest Akaike information criterion value and mean absolute error. In this model, the estimated a was 0.0192, with 95% confidence interval 0.0178~0.0308, and the estimated exponent b was 2.917 with 95% confidence interval 2.731~2.945. Estimates for a and b with the random effects in intercept and coefficient from Region and Year, ranged from 0.013 to 0.023 and from 2.835 to 3.017, respectively. Both regions and years had effects on parameters a and b, while the effects from years were shown to be much larger than those from regions. Except for Coastal Waters of Northern Shandong, a decreased from north to south. Condition factors relative to reference years of 1960, 1986, 2005, 2007, 2008~2009 and 2010 revealed that the body shape of Yellow Croaker became thinner in recent years. Furthermore relative condition factors varied among months, years, regions and length. The values of a and relative condition factors decreased, when the environmental pollution became worse, therefore, length-weight relationships could be an indicator for the environment quality. Results from this study provided basic description of current condition of Yellow Croaker along the north coast of China.

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…

A position-aware linear solid constitutive model for peridynamics

DOE PAGES

Mitchell, John A.; Silling, Stewart A.; Littlewood, David J.

2015-11-06

A position-aware linear solid (PALS) peridynamic constitutive model is proposed for isotropic elastic solids. The PALS model addresses problems that arise, in ordinary peridynamic material models such as the linear peridynamic solid (LPS), due to incomplete neighborhoods near the surface of a body. We improved model behavior in the vicinity of free surfaces through the application of two influence functions that correspond, respectively, to the volumetric and deviatoric parts of the deformation. Furthermore, the model is position-aware in that the influence functions vary over the body and reflect the proximity of each material point to free surfaces. Demonstration calculations onmore » simple benchmark problems show a sharp reduction in error relative to the LPS model.« less

A position-aware linear solid constitutive model for peridynamics

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

Mitchell, John A.; Silling, Stewart A.; Littlewood, David J.

A position-aware linear solid (PALS) peridynamic constitutive model is proposed for isotropic elastic solids. The PALS model addresses problems that arise, in ordinary peridynamic material models such as the linear peridynamic solid (LPS), due to incomplete neighborhoods near the surface of a body. We improved model behavior in the vicinity of free surfaces through the application of two influence functions that correspond, respectively, to the volumetric and deviatoric parts of the deformation. Furthermore, the model is position-aware in that the influence functions vary over the body and reflect the proximity of each material point to free surfaces. Demonstration calculations onmore » simple benchmark problems show a sharp reduction in error relative to the LPS model.« less

A Linearized Model for Flicker and Contrast Thresholds at Various Retinal Illuminances

NASA Technical Reports Server (NTRS)

Ahumada, Albert; Watson, Andrew

2015-01-01

We previously proposed a flicker visibility metric for bright displays, based on psychophysical data collected at a high mean luminance. Here we extend the metric to other mean luminances. This extension relies on a linear relation between log sensitivity and critical fusion frequency, and a linear relation between critical fusion frequency and log retina lilluminance. Consistent with our previous metric, the extended flicker visibility metric is measured in just-noticeable differences (JNDs).

Takagi-Sugeno-Kang fuzzy models of the rainfall-runoff transformation

NASA Astrophysics Data System (ADS)

Jacquin, A. P.; Shamseldin, A. Y.

2009-04-01

Fuzzy inference systems, or fuzzy models, are non-linear models that describe the relation between the inputs and the output of a real system using a set of fuzzy IF-THEN rules. This study deals with the application of Takagi-Sugeno-Kang type fuzzy models to the development of rainfall-runoff models operating on a daily basis, using a system based approach. The models proposed are classified in two types, each intended to account for different kinds of dominant non-linear effects in the rainfall-runoff relationship. Fuzzy models type 1 are intended to incorporate the effect of changes in the prevailing soil moisture content, while fuzzy models type 2 address the phenomenon of seasonality. Each model type consists of five fuzzy models of increasing complexity; the most complex fuzzy model of each model type includes all the model components found in the remaining fuzzy models of the respective type. The models developed are applied to data of six catchments from different geographical locations and sizes. Model performance is evaluated in terms of two measures of goodness of fit, namely the Nash-Sutcliffe criterion and the index of volumetric fit. The results of the fuzzy models are compared with those of the Simple Linear Model, the Linear Perturbation Model and the Nearest Neighbour Linear Perturbation Model, which use similar input information. Overall, the results of this study indicate that Takagi-Sugeno-Kang fuzzy models are a suitable alternative for modelling the rainfall-runoff relationship. However, it is also observed that increasing the complexity of the model structure does not necessarily produce an improvement in the performance of the fuzzy models. The relative importance of the different model components in determining the model performance is evaluated through sensitivity analysis of the model parameters in the accompanying study presented in this meeting. Acknowledgements: We would like to express our gratitude to Prof. Kieran M. O'Connor from the National University of Ireland, Galway, for providing the data used in this study.

Testing the Adaptation to Poverty-Related Stress Model: Predicting Psychopathology Symptoms in Families Facing Economic Hardship

ERIC Educational Resources Information Center

Wadsworth, Martha E.; Raviv, Tali; Santiago, Catherine DeCarlo; Etter, Erica M.

2011-01-01

This study tested the Adaptation to Poverty-related Stress Model and its proposed relations between poverty-related stress, effortful and involuntary stress responses, and symptoms of psychopathology in an ethnically diverse sample of low-income children and their parents. Prospective Hierarchical Linear Modeling analyses conducted with 98…

A phenomenological biological dose model for proton therapy based on linear energy transfer spectra.

PubMed

Rørvik, Eivind; Thörnqvist, Sara; Stokkevåg, Camilla H; Dahle, Tordis J; Fjaera, Lars Fredrik; Ytre-Hauge, Kristian S

2017-06-01

The relative biological effectiveness (RBE) of protons varies with the radiation quality, quantified by the linear energy transfer (LET). Most phenomenological models employ a linear dependency of the dose-averaged LET (LET d ) to calculate the biological dose. However, several experiments have indicated a possible non-linear trend. Our aim was to investigate if biological dose models including non-linear LET dependencies should be considered, by introducing a LET spectrum based dose model. The RBE-LET relationship was investigated by fitting of polynomials from 1st to 5th degree to a database of 85 data points from aerobic in vitro experiments. We included both unweighted and weighted regression, the latter taking into account experimental uncertainties. Statistical testing was performed to decide whether higher degree polynomials provided better fits to the data as compared to lower degrees. The newly developed models were compared to three published LET d based models for a simulated spread out Bragg peak (SOBP) scenario. The statistical analysis of the weighted regression analysis favored a non-linear RBE-LET relationship, with the quartic polynomial found to best represent the experimental data (P = 0.010). The results of the unweighted regression analysis were on the borderline of statistical significance for non-linear functions (P = 0.053), and with the current database a linear dependency could not be rejected. For the SOBP scenario, the weighted non-linear model estimated a similar mean RBE value (1.14) compared to the three established models (1.13-1.17). The unweighted model calculated a considerably higher RBE value (1.22). The analysis indicated that non-linear models could give a better representation of the RBE-LET relationship. However, this is not decisive, as inclusion of the experimental uncertainties in the regression analysis had a significant impact on the determination and ranking of the models. As differences between the models were observed for the SOBP scenario, both non-linear LET spectrum- and linear LET d based models should be further evaluated in clinically realistic scenarios. © 2017 American Association of Physicists in Medicine.

Modelling leaf photosynthetic and transpiration temperature-dependent responses in Vitis vinifera cv. Semillon grapevines growing in hot, irrigated vineyard conditions

PubMed Central

Greer, Dennis H.

2012-01-01

Background and aims Grapevines growing in Australia are often exposed to very high temperatures and the question of how the gas exchange processes adjust to these conditions is not well understood. The aim was to develop a model of photosynthesis and transpiration in relation to temperature to quantify the impact of the growing conditions on vine performance. Methodology Leaf gas exchange was measured along the grapevine shoots in accordance with their growth and development over several growing seasons. Using a general linear statistical modelling approach, photosynthesis and transpiration were modelled against leaf temperature separated into bands and the model parameters and coefficients applied to independent datasets to validate the model. Principal results Photosynthesis, transpiration and stomatal conductance varied along the shoot, with early emerging leaves having the highest rates, but these declined as later emerging leaves increased their gas exchange capacities in accordance with development. The general linear modelling approach applied to these data revealed that photosynthesis at each temperature was additively dependent on stomatal conductance, internal CO2 concentration and photon flux density. The temperature-dependent coefficients for these parameters applied to other datasets gave a predicted rate of photosynthesis that was linearly related to the measured rates, with a 1 : 1 slope. Temperature-dependent transpiration was multiplicatively related to stomatal conductance and the leaf to air vapour pressure deficit and applying the coefficients also showed a highly linear relationship, with a 1 : 1 slope between measured and modelled rates, when applied to independent datasets. Conclusions The models developed for the grapevines were relatively simple but accounted for much of the seasonal variation in photosynthesis and transpiration. The goodness of fit in each case demonstrated that explicitly selecting leaf temperature as a model parameter, rather than including temperature intrinsically as is usually done in more complex models, was warranted. PMID:22567220

A Kernel Embedding-Based Approach for Nonstationary Causal Model Inference.

PubMed

Hu, Shoubo; Chen, Zhitang; Chan, Laiwan

2018-05-01

Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration. In this letter, we propose a kernel embedding-based approach, ENCI, for nonstationary causal model inference where data are collected from multiple domains with varying distributions. In ENCI, we transform the complicated relation of a cause-effect pair into a linear model of variables of which observations correspond to the kernel embeddings of the cause-and-effect distributions in different domains. In this way, we are able to estimate the causal direction by exploiting the causal asymmetry of the transformed linear model. Furthermore, we extend ENCI to causal graph discovery for multiple variables by transforming the relations among them into a linear nongaussian acyclic model. We show that by exploiting the nonstationarity of distributions, both cause-effect pairs and two kinds of causal graphs are identifiable under mild conditions. Experiments on synthetic and real-world data are conducted to justify the efficacy of ENCI over major existing methods.

SSME model, engine dynamic characteristics related to Pogo

NASA Technical Reports Server (NTRS)

1973-01-01

A linear model of the space shuttle main engine for use in Pogo studies was presented. A digital program is included from which engine transfer functions are determined relative to the engine operating level.

Investigation of metrics to assess vascular flow modifications for diverter device designs using hydrodynamics and angiographic studies

NASA Astrophysics Data System (ADS)

Ionita, Ciprian N.; Bednarek, Daniel R.; Rudin, Stephen

2012-03-01

Intracranial aneurysm treatment with flow diverters (FD) is a new minimally invasive approach, recently approved for use in human patients. Attempts to correlate the flow reduction observed in angiograms with a parameter related to the FD structure have not been totally successful. To find the proper parameter, we investigated four porous-media flow models. The models describing the relation between the pressure drop and flow velocity that are investigated include the capillary theory linear model (CTLM), the drag force linear model (DFLM), the simple quadratic model (SQM) and the modified quadratic model (MQM). Proportionality parameters are referred to as permeability for the linear models and resistance for the quadratic ones. A two stage experiment was performed. First, we verified flow model validity by placing six different stainless-steel meshes, resembling FD structures, in known flow conditions. The best flow model was used for the second stage, where six different FD's were inserted in aneurysm phantoms and flow modification was estimated using angiographically derived time density curves (TDC). Finally, TDC peak variation was compared with the FD parameter. Model validity experiments indicated errors of: 70% for the linear models, 26% for the SQM and 7% for the MQM. The resistance calculated according to the MQM model correlated well with the contrast flow reduction. Results indicate that resistance calculated according to MQM is appropriate to characterize the FD and could explain the flow modification observed in angiograms.

Fit Point-Wise AB Initio Calculation Potential Energies to a Multi-Dimension Long-Range Model

NASA Astrophysics Data System (ADS)

Zhai, Yu; Li, Hui; Le Roy, Robert J.

2016-06-01

A potential energy surface (PES) is a fundamental tool and source of understanding for theoretical spectroscopy and for dynamical simulations. Making correct assignments for high-resolution rovibrational spectra of floppy polyatomic and van der Waals molecules often relies heavily on predictions generated from a high quality ab initio potential energy surface. Moreover, having an effective analytic model to represent such surfaces can be as important as the ab initio results themselves. For the one-dimensional potentials of diatomic molecules, the most successful such model to date is arguably the ``Morse/Long-Range'' (MLR) function developed by R. J. Le Roy and coworkers. It is very flexible, is everywhere differentiable to all orders. It incorporates correct predicted long-range behaviour, extrapolates sensibly at both large and small distances, and two of its defining parameters are always the physically meaningful well depth {D}_e and equilibrium distance r_e. Extensions of this model, called the Multi-Dimension Morse/Long-Range (MD-MLR) function, linear molecule-linear molecule systems and atom-non-linear molecule system. have been applied successfully to atom-plus-linear molecule, linear molecule-linear molecule and atom-non-linear molecule systems. However, there are several technical challenges faced in modelling the interactions of general molecule-molecule systems, such as the absence of radial minima for some relative alignments, difficulties in fitting short-range potential energies, and challenges in determining relative-orientation dependent long-range coefficients. This talk will illustrate some of these challenges and describe our ongoing work in addressing them. Mol. Phys. 105, 663 (2007); J. Chem. Phys. 131, 204309 (2009); Mol. Phys. 109, 435 (2011). Phys. Chem. Chem. Phys. 10, 4128 (2008); J. Chem. Phys. 130, 144305 (2009) J. Chem. Phys. 132, 214309 (2010) J. Chem. Phys. 140, 214309 (2010)

A Closer Look at Charter Schools Using Hierarchical Linear Modeling. NCES 2006-460

ERIC Educational Resources Information Center

Braun, Henry; Jenkins, Frank; Grigg, Wendy

2006-01-01

Charter schools are a relatively new, but fast-growing, phenomenon in American public education. As such, they merit the attention of all parties interested in the education of the nation's youth. The present report comprises two separate analyses. The first is a "combined analysis" in which hierarchical linear models (HLMs) were…

Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland

NASA Technical Reports Server (NTRS)

Mcgwire, K.; Friedl, M.; Estes, J. E.

1993-01-01

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.

Alternative approaches to predicting methane emissions from dairy cows.

PubMed

Mills, J A N; Kebreab, E; Yates, C M; Crompton, L A; Cammell, S B; Dhanoa, M S; Agnew, R E; France, J

2003-12-01

Previous attempts to apply statistical models, which correlate nutrient intake with methane production, have been of limited value where predictions are obtained for nutrient intakes and diet types outside those used in model construction. Dynamic mechanistic models have proved more suitable for extrapolation, but they remain computationally expensive and are not applied easily in practical situations. The first objective of this research focused on employing conventional techniques to generate statistical models of methane production appropriate to United Kingdom dairy systems. The second objective was to evaluate these models and a model published previously using both United Kingdom and North American data sets. Thirdly, nonlinear models were considered as alternatives to the conventional linear regressions. The United Kingdom calorimetry data used to construct the linear models also were used to develop the three nonlinear alternatives that were all of modified Mitscherlich (monomolecular) form. Of the linear models tested, an equation from the literature proved most reliable across the full range of evaluation data (root mean square prediction error = 21.3%). However, the Mitscherlich models demonstrated the greatest degree of adaptability across diet types and intake level. The most successful model for simulating the independent data was a modified Mitscherlich equation with the steepness parameter set to represent dietary starch-to-ADF ratio (root mean square prediction error = 20.6%). However, when such data were unavailable, simpler Mitscherlich forms relating dry matter or metabolizable energy intake to methane production remained better alternatives relative to their linear counterparts.

Predicting musically induced emotions from physiological inputs: linear and neural network models.

PubMed

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

On the Validity of the Streaming Model for the Redshift-Space Correlation Function in the Linear Regime

NASA Astrophysics Data System (ADS)

Fisher, Karl B.

1995-08-01

The relation between the galaxy correlation functions in real-space and redshift-space is derived in the linear regime by an appropriate averaging of the joint probability distribution of density and velocity. The derivation recovers the familiar linear theory result on large scales but has the advantage of clearly revealing the dependence of the redshift distortions on the underlying peculiar velocity field; streaming motions give rise to distortions of θ(Ω0.6/b) while variations in the anisotropic velocity dispersion yield terms of order θ(Ω1.2/b2). This probabilistic derivation of the redshift-space correlation function is similar in spirit to the derivation of the commonly used "streaming" model, in which the distortions are given by a convolution of the real-space correlation function with a velocity distribution function. The streaming model is often used to model the redshift-space correlation function on small, highly nonlinear, scales. There have been claims in the literature, however, that the streaming model is not valid in the linear regime. Our analysis confirms this claim, but we show that the streaming model can be made consistent with linear theory provided that the model for the streaming has the functional form predicted by linear theory and that the velocity distribution is chosen to be a Gaussian with the correct linear theory dispersion.

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.

Modeling thermal sensation in a Mediterranean climate—a comparison of linear and ordinal models

NASA Astrophysics Data System (ADS)

Pantavou, Katerina; Lykoudis, Spyridon

2014-08-01

A simple thermo-physiological model of outdoor thermal sensation adjusted with psychological factors is developed aiming to predict thermal sensation in Mediterranean climates. Microclimatic measurements simultaneously with interviews on personal and psychological conditions were carried out in a square, a street canyon and a coastal location of the greater urban area of Athens, Greece. Multiple linear and ordinal regression were applied in order to estimate thermal sensation making allowance for all the recorded parameters or specific, empirically selected, subsets producing so-called extensive and empirical models, respectively. Meteorological, thermo-physiological and overall models - considering psychological factors as well - were developed. Predictions were improved when personal and psychological factors were taken into account as compared to meteorological models. The model based on ordinal regression reproduced extreme values of thermal sensation vote more adequately than the linear regression one, while the empirical model produced satisfactory results in relation to the extensive model. The effects of adaptation and expectation on thermal sensation vote were introduced in the models by means of the exposure time, season and preference related to air temperature and irradiation. The assessment of thermal sensation could be a useful criterion in decision making regarding public health, outdoor spaces planning and tourism.

The Elementary Operations of Human Vision Are Not Reducible to Template-Matching

PubMed Central

Neri, Peter

2015-01-01

It is generally acknowledged that biological vision presents nonlinear characteristics, yet linear filtering accounts of visual processing are ubiquitous. The template-matching operation implemented by the linear-nonlinear cascade (linear filter followed by static nonlinearity) is the most widely adopted computational tool in systems neuroscience. This simple model achieves remarkable explanatory power while retaining analytical tractability, potentially extending its reach to a wide range of systems and levels in sensory processing. The extent of its applicability to human behaviour, however, remains unclear. Because sensory stimuli possess multiple attributes (e.g. position, orientation, size), the issue of applicability may be asked by considering each attribute one at a time in relation to a family of linear-nonlinear models, or by considering all attributes collectively in relation to a specified implementation of the linear-nonlinear cascade. We demonstrate that human visual processing can operate under conditions that are indistinguishable from linear-nonlinear transduction with respect to substantially different stimulus attributes of a uniquely specified target signal with associated behavioural task. However, no specific implementation of a linear-nonlinear cascade is able to account for the entire collection of results across attributes; a satisfactory account at this level requires the introduction of a small gain-control circuit, resulting in a model that no longer belongs to the linear-nonlinear family. Our results inform and constrain efforts at obtaining and interpreting comprehensive characterizations of the human sensory process by demonstrating its inescapably nonlinear nature, even under conditions that have been painstakingly fine-tuned to facilitate template-matching behaviour and to produce results that, at some level of inspection, do conform to linear filtering predictions. They also suggest that compliance with linear transduction may be the targeted outcome of carefully crafted nonlinear circuits, rather than default behaviour exhibited by basic components. PMID:26556758

Large-scale linear programs in planning and prediction.

DOT National Transportation Integrated Search

2017-06-01

Large-scale linear programs are at the core of many traffic-related optimization problems in both planning and prediction. Moreover, many of these involve significant uncertainty, and hence are modeled using either chance constraints, or robust optim...

Linear Power-Flow Models in Multiphase Distribution Networks: Preprint

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

Bernstein, Andrey; Dall'Anese, Emiliano

This paper considers multiphase unbalanced distribution systems and develops approximate power-flow models where bus-voltages, line-currents, and powers at the point of common coupling are linearly related to the nodal net power injections. The linearization approach is grounded on a fixed-point interpretation of the AC power-flow equations, and it is applicable to distribution systems featuring (i) wye connections; (ii) ungrounded delta connections; (iii) a combination of wye-connected and delta-connected sources/loads; and, (iv) a combination of line-to-line and line-to-grounded-neutral devices at the secondary of distribution transformers. The proposed linear models can facilitate the development of computationally-affordable optimization and control applications -- frommore » advanced distribution management systems settings to online and distributed optimization routines. Performance of the proposed models is evaluated on different test feeders.« less

Longitudinal data analyses using linear mixed models in SPSS: concepts, procedures and illustrations.

PubMed

Shek, Daniel T L; Ma, Cecilia M S

2011-01-05

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented.

Longitudinal Data Analyses Using Linear Mixed Models in SPSS: Concepts, Procedures and Illustrations

PubMed Central

Shek, Daniel T. L.; Ma, Cecilia M. S.

2011-01-01

Although different methods are available for the analyses of longitudinal data, analyses based on generalized linear models (GLM) are criticized as violating the assumption of independence of observations. Alternatively, linear mixed models (LMM) are commonly used to understand changes in human behavior over time. In this paper, the basic concepts surrounding LMM (or hierarchical linear models) are outlined. Although SPSS is a statistical analyses package commonly used by researchers, documentation on LMM procedures in SPSS is not thorough or user friendly. With reference to this limitation, the related procedures for performing analyses based on LMM in SPSS are described. To demonstrate the application of LMM analyses in SPSS, findings based on six waves of data collected in the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) in Hong Kong are presented. PMID:21218263

Dynamical localization of coupled relativistic kicked rotors

NASA Astrophysics Data System (ADS)

Rozenbaum, Efim B.; Galitski, Victor

2017-02-01

A periodically driven rotor is a prototypical model that exhibits a transition to chaos in the classical regime and dynamical localization (related to Anderson localization) in the quantum regime. In a recent work [Phys. Rev. B 94, 085120 (2016), 10.1103/PhysRevB.94.085120], A. C. Keser et al. considered a many-body generalization of coupled quantum kicked rotors, and showed that in the special integrable linear case, dynamical localization survives interactions. By analogy with many-body localization, the phenomenon was dubbed dynamical many-body localization. In the present work, we study nonintegrable models of single and coupled quantum relativistic kicked rotors (QRKRs) that bridge the gap between the conventional quadratic rotors and the integrable linear models. For a single QRKR, we supplement the recent analysis of the angular-momentum-space dynamics with a study of the spin dynamics. Our analysis of two and three coupled QRKRs along with the proved localization in the many-body linear model indicate that dynamical localization exists in few-body systems. Moreover, the relation between QRKR and linear rotor models implies that dynamical many-body localization can exist in generic, nonintegrable many-body systems. And localization can generally result from a complicated interplay between Anderson mechanism and limiting integrability, since the many-body linear model is a high-angular-momentum limit of many-body QRKRs. We also analyze the dynamics of two coupled QRKRs in the highly unusual superballistic regime and find that the resonance conditions are relaxed due to interactions. Finally, we propose experimental realizations of the QRKR model in cold atoms in optical lattices.

Cosmological N -body simulations with generic hot dark matter

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

Brandbyge, Jacob; Hannestad, Steen, E-mail: jacobb@phys.au.dk, E-mail: sth@phys.au.dk

2017-10-01

We have calculated the non-linear effects of generic fermionic and bosonic hot dark matter components in cosmological N -body simulations. For sub-eV masses, the non-linear power spectrum suppression caused by thermal free-streaming resembles the one seen for massive neutrinos, whereas for masses larger than 1 eV, the non-linear relative suppression of power is smaller than in linear theory. We furthermore find that in the non-linear regime, one can map fermionic to bosonic models by performing a simple transformation.

Cosmological N-body simulations with generic hot dark matter

NASA Astrophysics Data System (ADS)

Brandbyge, Jacob; Hannestad, Steen

2017-10-01

We have calculated the non-linear effects of generic fermionic and bosonic hot dark matter components in cosmological N-body simulations. For sub-eV masses, the non-linear power spectrum suppression caused by thermal free-streaming resembles the one seen for massive neutrinos, whereas for masses larger than 1 eV, the non-linear relative suppression of power is smaller than in linear theory. We furthermore find that in the non-linear regime, one can map fermionic to bosonic models by performing a simple transformation.

Anomaly General Circulation Models.

NASA Astrophysics Data System (ADS)

Navarra, Antonio

The feasibility of the anomaly model is assessed using barotropic and baroclinic models. In the barotropic case, both a stationary and a time-dependent model has been formulated and constructed, whereas only the stationary, linear case is considered in the baroclinic case. Results from the barotropic model indicate that a relation between the stationary solution and the time-averaged non-linear solution exists. The stationary linear baroclinic solution can therefore be considered with some confidence. The linear baroclinic anomaly model poses a formidable mathematical problem because it is necessary to solve a gigantic linear system to obtain the solution. A new method to find solution of large linear system, based on a projection on the Krylov subspace is shown to be successful when applied to the linearized baroclinic anomaly model. The scheme consists of projecting the original linear system on the Krylov subspace, thereby reducing the dimensionality of the matrix to be inverted to obtain the solution. With an appropriate setting of the damping parameters, the iterative Krylov method reaches a solution even using a Krylov subspace ten times smaller than the original space of the problem. This generality allows the treatment of the important problem of linear waves in the atmosphere. A larger class (nonzonally symmetric) of basic states can now be treated for the baroclinic primitive equations. These problem leads to large unsymmetrical linear systems of order 10000 and more which can now be successfully tackled by the Krylov method. The (R7) linear anomaly model is used to investigate extensively the linear response to equatorial and mid-latitude prescribed heating. The results indicate that the solution is deeply affected by the presence of the stationary waves in the basic state. The instability of the asymmetric flows, first pointed out by Simmons et al. (1983), is active also in the baroclinic case. However, the presence of baroclinic processes modifies the dominant response. The most sensitive areas are identified; they correspond to north Japan, the Pole and Greenland regions. A limited set of higher resolution (R15) experiments indicate that this situation is still present and enhanced at higher resolution. The linear anomaly model is also applied to a realistic case. (Abstract shortened with permission of author.).

Closed-loop stability of linear quadratic optimal systems in the presence of modeling errors

NASA Technical Reports Server (NTRS)

Toda, M.; Patel, R.; Sridhar, B.

1976-01-01

The well-known stabilizing property of linear quadratic state feedback design is utilized to evaluate the robustness of a linear quadratic feedback design in the presence of modeling errors. Two general conditions are obtained for allowable modeling errors such that the resulting closed-loop system remains stable. One of these conditions is applied to obtain two more particular conditions which are readily applicable to practical situations where a designer has information on the bounds of modeling errors. Relations are established between the allowable parameter uncertainty and the weighting matrices of the quadratic performance index, thereby enabling the designer to select appropriate weighting matrices to attain a robust feedback design.

Acceleration of the direct reconstruction of linear parametric images using nested algorithms.

PubMed

Wang, Guobao; Qi, Jinyi

2010-03-07

Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.

An Application of the H-Function to Curve-Fitting and Density Estimation.

DTIC Science & Technology

1983-12-01

equations into a model that is linear in its coefficients. Nonlinear least squares estimation is a relatively new area developed to accomodate models which...to converge on a solution (10:9-10). For the simple linear model and when general assump- tions are made, the Gauss-Markov theorem states that the...distribution. For example, if the analyst wants to model the time between arrivals to a queue for a computer simulation, he infers the true probability

Modeling hardwood crown radii using circular data analysis

Treesearch

Paul F. Doruska; Hal O. Liechty; Douglas J. Marshall

2003-01-01

Cylindrical data are bivariate data composed of a linear and an angular component. One can use uniform, first-order (one maximum and one minimum) or second-order (two maxima and two minima) models to relate the linear component to the angular component. Crown radii can be treated as cylindrical data when the azimuths at which the radii are measured are also recorded....

Identification of linear system models and state estimators for controls

NASA Technical Reports Server (NTRS)

Chen, Chung-Wen

1992-01-01

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

Understanding Coupling of Global and Diffuse Solar Radiation with Climatic Variability

NASA Astrophysics Data System (ADS)

Hamdan, Lubna

Global solar radiation data is very important for wide variety of applications and scientific studies. However, this data is not readily available because of the cost of measuring equipment and the tedious maintenance and calibration requirements. Wide variety of models have been introduced by researchers to estimate and/or predict the global solar radiations and its components (direct and diffuse radiation) using other readily obtainable atmospheric parameters. The goal of this research is to understand the coupling of global and diffuse solar radiation with climatic variability, by investigating the relationships between these radiations and atmospheric parameters. For this purpose, we applied multilinear regression analysis on the data of National Solar Radiation Database 1991--2010 Update. The analysis showed that the main atmospheric parameters that affect the amount of global radiation received on earth's surface are cloud cover and relative humidity. Global radiation correlates negatively with both variables. Linear models are excellent approximations for the relationship between atmospheric parameters and global radiation. A linear model with the predictors total cloud cover, relative humidity, and extraterrestrial radiation is able to explain around 98% of the variability in global radiation. For diffuse radiation, the analysis showed that the main atmospheric parameters that affect the amount received on earth's surface are cloud cover and aerosol optical depth. Diffuse radiation correlates positively with both variables. Linear models are very good approximations for the relationship between atmospheric parameters and diffuse radiation. A linear model with the predictors total cloud cover, aerosol optical depth, and extraterrestrial radiation is able to explain around 91% of the variability in diffuse radiation. Prediction analysis showed that the linear models we fitted were able to predict diffuse radiation with efficiency of test adjusted R2 values equal to 0.93, using the data of total cloud cover, aerosol optical depth, relative humidity and extraterrestrial radiation. However, for prediction purposes, using nonlinear terms or nonlinear models might enhance the prediction of diffuse radiation.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

Spacecraft Constrained Maneuver Planning Using Positively Invariant Constraint Admissible Sets (Postprint)

DTIC Science & Technology

2013-08-14

Connectivity Graph; Graph Search; Bounded Disturbances; Linear Time-Varying (LTV); Clohessy - Wiltshire -Hill (CWH) 16. SECURITY CLASSIFICATION OF: 17...the linearization of the relative motion model given by the Hill- Clohessy - Wiltshire (CWH) equations is used [14]. A. Nonlinear equations of motion...equations can be used to describe the motion of the debris. B. Linearized HCW equations in discrete-time For δr << R, the linearized Hill- Clohessy

Real-time Adaptive Control Using Neural Generalized Predictive Control

NASA Technical Reports Server (NTRS)

Haley, Pam; Soloway, Don; Gold, Brian

1999-01-01

The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.

Features in visual search combine linearly

PubMed Central

Pramod, R. T.; Arun, S. P.

2014-01-01

Single features such as line orientation and length are known to guide visual search, but relatively little is known about how multiple features combine in search. To address this question, we investigated how search for targets differing in multiple features (intensity, length, orientation) from the distracters is related to searches for targets differing in each of the individual features. We tested race models (based on reaction times) and co-activation models (based on reciprocal of reaction times) for their ability to predict multiple feature searches. Multiple feature searches were best accounted for by a co-activation model in which feature information combined linearly (r = 0.95). This result agrees with the classic finding that these features are separable i.e., subjective dissimilarity ratings sum linearly. We then replicated the classical finding that the length and width of a rectangle are integral features—in other words, they combine nonlinearly in visual search. However, to our surprise, upon including aspect ratio as an additional feature, length and width combined linearly and this model outperformed all other models. Thus, length and width of a rectangle became separable when considered together with aspect ratio. This finding predicts that searches involving shapes with identical aspect ratio should be more difficult than searches where shapes differ in aspect ratio. We confirmed this prediction on a variety of shapes. We conclude that features in visual search co-activate linearly and demonstrate for the first time that aspect ratio is a novel feature that guides visual search. PMID:24715328

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

PubMed

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Parameterized Linear Longitudinal Airship Model

NASA Technical Reports Server (NTRS)

Kulczycki, Eric; Elfes, Alberto; Bayard, David; Quadrelli, Marco; Johnson, Joseph

2010-01-01

A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamics

Nonlinear Dynamic Models in Advanced Life Support

NASA Technical Reports Server (NTRS)

Jones, Harry

2002-01-01

To facilitate analysis, ALS systems are often assumed to be linear and time invariant, but they usually have important nonlinear and dynamic aspects. Nonlinear dynamic behavior can be caused by time varying inputs, changes in system parameters, nonlinear system functions, closed loop feedback delays, and limits on buffer storage or processing rates. Dynamic models are usually cataloged according to the number of state variables. The simplest dynamic models are linear, using only integration, multiplication, addition, and subtraction of the state variables. A general linear model with only two state variables can produce all the possible dynamic behavior of linear systems with many state variables, including stability, oscillation, or exponential growth and decay. Linear systems can be described using mathematical analysis. Nonlinear dynamics can be fully explored only by computer simulations of models. Unexpected behavior is produced by simple models having only two or three state variables with simple mathematical relations between them. Closed loop feedback delays are a major source of system instability. Exceeding limits on buffer storage or processing rates forces systems to change operating mode. Different equilibrium points may be reached from different initial conditions. Instead of one stable equilibrium point, the system may have several equilibrium points, oscillate at different frequencies, or even behave chaotically, depending on the system inputs and initial conditions. The frequency spectrum of an output oscillation may contain harmonics and the sums and differences of input frequencies, but it may also contain a stable limit cycle oscillation not related to input frequencies. We must investigate the nonlinear dynamic aspects of advanced life support systems to understand and counter undesirable behavior.

General job stress: a unidimensional measure and its non-linear relations with outcome variables.

PubMed

Yankelevich, Maya; Broadfoot, Alison; Gillespie, Jennifer Z; Gillespie, Michael A; Guidroz, Ashley

2012-04-01

This article aims to examine the non-linear relations between a general measure of job stress [Stress in General (SIG)] and two outcome variables: intentions to quit and job satisfaction. In so doing, we also re-examine the factor structure of the SIG and determine that, as a two-factor scale, it obscures non-linear relations with outcomes. Thus, in this research, we not only test for non-linear relations between stress and outcome variables but also present an updated version of the SIG scale. Using two distinct samples of working adults (sample 1, N = 589; sample 2, N = 4322), results indicate that a more parsimonious eight-item SIG has better model-data fit than the 15-item two-factor SIG and that the eight-item SIG has non-linear relations with job satisfaction and intentions to quit. Specifically, the revised SIG has an inverted curvilinear J-shaped relation with job satisfaction such that job satisfaction drops precipitously after a certain level of stress; the SIG has a J-shaped curvilinear relation with intentions to quit such that turnover intentions increase exponentially after a certain level of stress. Copyright © 2011 John Wiley & Sons, Ltd.

Modeling coding-sequence evolution within the context of residue solvent accessibility.

PubMed

Scherrer, Michael P; Meyer, Austin G; Wilke, Claus O

2012-09-12

Protein structure mediates site-specific patterns of sequence divergence. In particular, residues in the core of a protein (solvent-inaccessible residues) tend to be more evolutionarily conserved than residues on the surface (solvent-accessible residues). Here, we present a model of sequence evolution that explicitly accounts for the relative solvent accessibility of each residue in a protein. Our model is a variant of the Goldman-Yang 1994 (GY94) model in which all model parameters can be functions of the relative solvent accessibility (RSA) of a residue. We apply this model to a data set comprised of nearly 600 yeast genes, and find that an evolutionary-rate ratio ω that varies linearly with RSA provides a better model fit than an RSA-independent ω or an ω that is estimated separately in individual RSA bins. We further show that the branch length t and the transition-transverion ratio κ also vary with RSA. The RSA-dependent GY94 model performs better than an RSA-dependent Muse-Gaut 1994 (MG94) model in which the synonymous and non-synonymous rates individually are linear functions of RSA. Finally, protein core size affects the slope of the linear relationship between ω and RSA, and gene expression level affects both the intercept and the slope. Structure-aware models of sequence evolution provide a significantly better fit than traditional models that neglect structure. The linear relationship between ω and RSA implies that genes are better characterized by their ω slope and intercept than by just their mean ω.

The Asian clam Corbicula fluminea as a biomonitor of trace element contamination: Accounting for different sources of variation using an hierarchical linear model

USGS Publications Warehouse

Shoults-Wilson, W. A.; Peterson, J.T.; Unrine, J.M.; Rickard, J.; Black, M.C.

2009-01-01

In the present study, specimens of the invasive clam, Corbicula fluminea, were collected above and below possible sources of potentially toxic trace elements (As, Cd, Cr, Cu, Hg, Pb, and Zn) in the Altamaha River system (Georgia, USA). Bioaccumulation of these elements was quantified, along with environmental (water and sediment) concentrations. Hierarchical linear models were used to account for variability in tissue concentrations related to environmental (site water chemistry and sediment characteristics) and individual (growth metrics) variables while identifying the strongest relations between these variables and trace element accumulation. The present study found significantly elevated concentrations of Cd, Cu, and Hg downstream of the outfall of kaolin-processing facilities, Zn downstream of a tire cording facility, and Cr downstream of both a nuclear power plant and a paper pulp mill. Models of the present study indicated that variation in trace element accumulation was linked to distance upstream from the estuary, dissolved oxygen, percentage of silt and clay in the sediment, elemental concentrations in sediment, shell length, and bivalve condition index. By explicitly modeling environmental variability, the Hierarchical linear modeling procedure allowed the identification of sites showing increased accumulation of trace elements that may have been caused by human activity. Hierarchical linear modeling is a useful tool for accounting for environmental and individual sources of variation in bioaccumulation studies. ?? 2009 SETAC.

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.

PubMed

Yang, James J; Williams, L Keoki; Buu, Anne

2017-08-24

A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

Accelerated Testing and Modeling of Potential-Induced Degradation as a Function of Temperature and Relative Humidity

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

Hacke, Peter; Spataru, Sergiu; Terwilliger, Kent

2015-06-14

An acceleration model based on the Peck equation was applied to power performance of crystalline silicon cell modules as a function of time and of temperature and humidity, the two main environmental stress factors that promote potential-induced degradation. This model was derived from module power degradation data obtained semi-continuously and statistically by in-situ dark current-voltage measurements in an environmental chamber. The modeling enables prediction of degradation rates and times as functions of temperature and humidity. Power degradation could be modeled linearly as a function of time to the second power; additionally, we found that coulombs transferred from the active cellmore » circuit to ground during the stress test is approximately linear with time. Therefore, the power loss could be linearized as a function of coulombs squared. With this result, we observed that when the module face was completely grounded with a condensed phase conductor, leakage current exceeded the anticipated corresponding degradation rate relative to the other tests performed in damp heat.« less

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.

Linear separability in superordinate natural language concepts.

PubMed

Ruts, Wim; Storms, Gert; Hampton, James

2004-01-01

Two experiments are reported in which linear separability was investigated in superordinate natural language concept pairs (e.g., toiletry-sewing gear). Representations of the exemplars of semantically related concept pairs were derived in two to five dimensions using multidimensional scaling (MDS) of similarities based on possession of the concept features. Next, category membership, obtained from an exemplar generation study (in Experiment 1) and from a forced-choice classification task (in Experiment 2) was predicted from the coordinates of the MDS representation using log linear analysis. The results showed that all natural kind concept pairs were perfectly linearly separable, whereas artifact concept pairs showed several violations. Clear linear separability of natural language concept pairs is in line with independent cue models. The violations in the artifact pairs, however, yield clear evidence against the independent cue models.

Adaptive Nonparametric Kinematic Modeling of Concentric Tube Robots.

PubMed

Fagogenis, Georgios; Bergeles, Christos; Dupont, Pierre E

2016-10-01

Concentric tube robots comprise telescopic precurved elastic tubes. The robot's tip and shape are controlled via relative tube motions, i.e. tube rotations and translations. Non-linear interactions between the tubes, e.g. friction and torsion, as well as uncertainty in the physical properties of the tubes themselves, e.g. the Young's modulus, curvature, or stiffness, hinder accurate kinematic modelling. In this paper, we present a machine-learning-based methodology for kinematic modelling of concentric tube robots and in situ model adaptation. Our approach is based on Locally Weighted Projection Regression (LWPR). The model comprises an ensemble of linear models, each of which locally approximates the original complex kinematic relation. LWPR can accommodate for model deviations by adjusting the respective local models at run-time, resulting in an adaptive kinematics framework. We evaluated our approach on data gathered from a three-tube robot, and report high accuracy across the robot's configuration space.

Local muscle oxygen consumption related to external and joint specific power.

PubMed

Skovereng, Knut; Ettema, Gertjan; van Beekvelt, Mireille

2016-02-01

The purpose of the present study was to examine the effects of external work rate on joint specific power and the relationship between knee extension power and vastus lateralis muscle oxygen consumption (mVO2). We measured kinematics and pedal forces and used inverse dynamics to calculate joint power for the hip, knee and ankle joints during an incremental cycling protocol performed by 21 recreational cyclists. Vastus lateralis mVO2 was estimated using near-infrared spectroscopy with an arterial occlusion. The main finding was a non-linear relationship between vastus lateralis mVO2 and external work rate that was characterised by an increase followed by a tendency for a levelling off (R(2)=0.99 and 0.94 for the quadratic and linear models respectively, p<0.05). When comparing 100W and 225W, there was a ∼43W increase in knee extension but still a ∼9% decrease in relative contribution of knee extension to external work rate resulting from a ∼47W increase in hip extension. When vastus lateralis mVO2 was related to knee extension power, the relationship was still non-linear (R(2)=0.99 and 0.97 for the quadratic and linear models respectively, p<0.05). These results demonstrate a non-linear response in mVO2 relative to a change in external work rate. Relating vastus lateralis mVO2 to knee extension power showed a better fit to a linear equation compared to external work rate, but it is not a straight line. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Self-Disclosure and Satisfaction in Marriage: The Relation Examined.

ERIC Educational Resources Information Center

Jorgensen, Stephen R.; Gaudy, Janis C.

1980-01-01

In tests of three models of self-disclosure and satisfaction in marriage, only the linear model achieved substantial support. Communication about relatively personal and intimate matters constitutes an important step in the process of need and goal fulfillment in marriage. (Author)

Development of non-linear models predicting daily fine particle concentrations using aerosol optical depth retrievals and ground-based measurements at a municipality in the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Gonçalves, Karen dos Santos; Winkler, Mirko S.; Benchimol-Barbosa, Paulo Roberto; de Hoogh, Kees; Artaxo, Paulo Eduardo; de Souza Hacon, Sandra; Schindler, Christian; Künzli, Nino

2018-07-01

Epidemiological studies generally use particulate matter measurements with diameter less 2.5 μm (PM2.5) from monitoring networks. Satellite aerosol optical depth (AOD) data has considerable potential in predicting PM2.5 concentrations, and thus provides an alternative method for producing knowledge regarding the level of pollution and its health impact in areas where no ground PM2.5 measurements are available. This is the case in the Brazilian Amazon rainforest region where forest fires are frequent sources of high pollution. In this study, we applied a non-linear model for predicting PM2.5 concentration from AOD retrievals using interaction terms between average temperature, relative humidity, sine, cosine of date in a period of 365,25 days and the square of the lagged relative residual. Regression performance statistics were tested comparing the goodness of fit and R2 based on results from linear regression and non-linear regression for six different models. The regression results for non-linear prediction showed the best performance, explaining on average 82% of the daily PM2.5 concentrations when considering the whole period studied. In the context of Amazonia, it was the first study predicting PM2.5 concentrations using the latest high-resolution AOD products also in combination with the testing of a non-linear model performance. Our results permitted a reliable prediction considering the AOD-PM2.5 relationship and set the basis for further investigations on air pollution impacts in the complex context of Brazilian Amazon Region.

Simultaneous determination of penicillin G salts by infrared spectroscopy: Evaluation of combining orthogonal signal correction with radial basis function-partial least squares regression

NASA Astrophysics Data System (ADS)

Talebpour, Zahra; Tavallaie, Roya; Ahmadi, Seyyed Hamid; Abdollahpour, Assem

2010-09-01

In this study, a new method for the simultaneous determination of penicillin G salts in pharmaceutical mixture via FT-IR spectroscopy combined with chemometrics was investigated. The mixture of penicillin G salts is a complex system due to similar analytical characteristics of components. Partial least squares (PLS) and radial basis function-partial least squares (RBF-PLS) were used to develop the linear and nonlinear relation between spectra and components, respectively. The orthogonal signal correction (OSC) preprocessing method was used to correct unexpected information, such as spectral overlapping and scattering effects. In order to compare the influence of OSC on PLS and RBF-PLS models, the optimal linear (PLS) and nonlinear (RBF-PLS) models based on conventional and OSC preprocessed spectra were established and compared. The obtained results demonstrated that OSC clearly enhanced the performance of both RBF-PLS and PLS calibration models. Also in the case of some nonlinear relation between spectra and component, OSC-RBF-PLS gave satisfactory results than OSC-PLS model which indicated that the OSC was helpful to remove extrinsic deviations from linearity without elimination of nonlinear information related to component. The chemometric models were tested on an external dataset and finally applied to the analysis commercialized injection product of penicillin G salts.

A model for managing sources of groundwater pollution

USGS Publications Warehouse

Gorelick, Steven M.

1982-01-01

The waste disposal capacity of a groundwater system can be maximized while maintaining water quality at specified locations by using a groundwater pollutant source management model that is based upon linear programing and numerical simulation. The decision variables of the management model are solute waste disposal rates at various facilities distributed over space. A concentration response matrix is used in the management model to describe transient solute transport and is developed using the U.S. Geological Survey solute transport simulation model. The management model was applied to a complex hypothetical groundwater system. Large-scale management models were formulated as dual linear programing problems to reduce numerical difficulties and computation time. Linear programing problems were solved using a numerically stable, available code. Optimal solutions to problems with successively longer management time horizons indicated that disposal schedules at some sites are relatively independent of the number of disposal periods. Optimal waste disposal schedules exhibited pulsing rather than constant disposal rates. Sensitivity analysis using parametric linear programing showed that a sharp reduction in total waste disposal potential occurs if disposal rates at any site are increased beyond their optimal values.

Complex messages regarding a thin ideal appearing in teenage girls' magazines from 1956 to 2005.

PubMed

Luff, Gina M; Gray, James J

2009-03-01

Seventeen and YM were assessed from 1956 through 2005 (n=312) to examine changes in the messages about thinness sent to teenage women. Trends were analyzed through an investigation of written, internal content focused on dieting, exercise, or both, while cover models were examined to explore fluctuations in body size. Pearson's Product correlations and weighted-least squares linear regression models were used to demonstrate changes over time. The frequency of written content related to exercise and combined plans increased in Seventeen, while a curvilinear relationship between time and content relating to dieting appeared. YM showed a linear increase in content related to dieting, exercise, and combined plans. Average cover model body size increased over time in YM while demonstrating no significant changes in Seventeen. Overall, more written messages about dieting and exercise appeared in teen's magazines in 2005 than before while the average cover model body size increased.

Metric versus observable operator representation, higher spin models

NASA Astrophysics Data System (ADS)

Fring, Andreas; Frith, Thomas

2018-02-01

We elaborate further on the metric representation that is obtained by transferring the time-dependence from a Hermitian Hamiltonian to the metric operator in a related non-Hermitian system. We provide further insight into the procedure on how to employ the time-dependent Dyson relation and the quasi-Hermiticity relation to solve time-dependent Hermitian Hamiltonian systems. By solving both equations separately we argue here that it is in general easier to solve the former. We solve the mutually related time-dependent Schrödinger equation for a Hermitian and non-Hermitian spin 1/2, 1 and 3/2 model with time-independent and time-dependent metric, respectively. In all models the overdetermined coupled system of equations for the Dyson map can be decoupled algebraic manipulations and reduces to simple linear differential equations and an equation that can be converted into the non-linear Ermakov-Pinney equation.

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

Devic, Slobodan; Tomic, Nada; Aldelaijan, Saad

Purpose: Despite numerous advantages of radiochromic film dosimeter (high spatial resolution, near tissue equivalence, low energy dependence) to measure a relative dose distribution with film, one needs to first measure an absolute dose (following previously established reference dosimetry protocol) and then convert measured absolute dose values into relative doses. In this work, we present result of our efforts to obtain a functional form that would linearize the inherently nonlinear dose-response curve of the radiochromic film dosimetry system. Methods: Functional form [{zeta}= (-1){center_dot}netOD{sup (2/3)}/ln(netOD)] was derived from calibration curves of various previously established radiochromic film dosimetry systems. In order to testmore » the invariance of the proposed functional form with respect to the film model used we tested it with three different GAFCHROMIC Trade-Mark-Sign film models (EBT, EBT2, and EBT3) irradiated to various doses and scanned on a same scanner. For one of the film models (EBT2), we tested the invariance of the functional form to the scanner model used by scanning irradiated film pieces with three different flatbed scanner models (Epson V700, 1680, and 10000XL). To test our hypothesis that the proposed functional argument linearizes the response of the radiochromic film dosimetry system, verification tests have been performed in clinical applications: percent depth dose measurements, IMRT quality assurance (QA), and brachytherapy QA. Results: Obtained R{sup 2} values indicate that the choice of the functional form of the new argument appropriately linearizes the dose response of the radiochromic film dosimetry system we used. The linear behavior was insensitive to both film model and flatbed scanner model used. Measured PDD values using the green channel response of the GAFCHROMIC Trade-Mark-Sign EBT3 film model are well within {+-}2% window of the local relative dose value when compared to the tabulated Cobalt-60 data. It was also found that criteria of 3%/3 mm for an IMRT QA plan and 3%/2 mm for a brachytherapy QA plan are passing 95% gamma function points. Conclusions: In this paper, we demonstrate the use of functional argument to linearize the inherently nonlinear response of a radiochromic film based reference dosimetry system. In this way, relative dosimetry can be conveniently performed using radiochromic film dosimetry system without the need of establishing calibration curve.« less

Application of linear regression analysis in accuracy assessment of rolling force calculations

NASA Astrophysics Data System (ADS)

Poliak, E. I.; Shim, M. K.; Kim, G. S.; Choo, W. Y.

1998-10-01

Efficient operation of the computational models employed in process control systems require periodical assessment of the accuracy of their predictions. Linear regression is proposed as a tool which allows separate systematic and random prediction errors from those related to measurements. A quantitative characteristic of the model predictive ability is introduced in addition to standard statistical tests for model adequacy. Rolling force calculations are considered as an example for the application. However, the outlined approach can be used to assess the performance of any computational model.

WE-H-BRA-01: BEST IN PHYSICS (THERAPY): Nano-Dosimetric Kinetic Model for Variable Relative Biological Effectiveness of Proton and Ion Beams

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

Abolfath, R; Bronk, L; Titt, U.

2016-06-15

Purpose: Recent clonogenic cell survival and γH2AX studies suggest proton relative biological effectiveness (RBE) may be a non-linear function of linear energy transfer (LET) in the distal edge of the Bragg peak and beyond. We sought to develop a multiscale model to account for non-linear response phenomena to aid in the optimization of intensity-modulated proton therapy. Methods: The model is based on first-principle simulations of proton track structures, including secondary ions, and an analytical derivation of the dependence on particle LET of the linear-quadratic (LQ) model parameters α and β. The derived formulas are an extension of the microdosimetric kineticmore » (MK) model that captures dissipative track structures and non-Poissonian distribution of DNA damage at the distal edge of the Bragg peak and beyond. Monte Carlo simulations were performed to confirm the non-linear dose-response characteristics arising from the non-Poisson distribution of initial DNA damage. Results: In contrast to low LET segments of the proton depth dose, from the beam entrance to the Bragg peak, strong deviations from non-dissipative track structures and Poisson distribution in the ionization events in the Bragg peak distal edge govern the non-linear cell response and result in the transformation α=(1+c-1 L) α-x+2(c-0 L+c-2 L^2 )(1+c-1 L) β-x and β=(1+c-1 L)^2 β-x. Here L is the charged particle LET, and c-0,c-1, and c-2 are functions of microscopic parameters and can be served as fitting parameters to the cell-survival data. In the low LET limit c-1, and c-2 are negligible hence the linear model proposed and used by Wilkins-Oelfke for the proton treatment planning system can be retrieved. The present model fits well the recent clonogenic survival data measured recently in our group in MDACC. Conclusion: The present hybrid method provides higher accuracy in calculating the RBE-weighted dose in the target and normal tissues.« less

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.

Venus Chasmata: A Lithospheric Stretching Model

NASA Technical Reports Server (NTRS)

Solomon, S. C.; Head, J. W.

1985-01-01

An outstanding problem for Venus is the characterization of its style of global tectonics, an issue intimately related to the dominant mechanism of lithospheric heat loss. Among the most spectacular and extensive of the major tectonic features on Venus are the chasmata, deep linear valleys generally interpreted to be the products of lithospheric extension and rifting. Systems of chasmata and related features can be traced along several tectonic zones up to 20,000 km in linear extent. A lithospheric stretching model was developed to explain the topographic characteristics of Venus chasmata and to constrain the physical properties of the Venus crust and lithosphere.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Bayesian assessment of the expected data impact on prediction confidence in optimal sampling design

NASA Astrophysics Data System (ADS)

Leube, P. C.; Geiges, A.; Nowak, W.

2012-02-01

Incorporating hydro(geo)logical data, such as head and tracer data, into stochastic models of (subsurface) flow and transport helps to reduce prediction uncertainty. Because of financial limitations for investigation campaigns, information needs toward modeling or prediction goals should be satisfied efficiently and rationally. Optimal design techniques find the best one among a set of investigation strategies. They optimize the expected impact of data on prediction confidence or related objectives prior to data collection. We introduce a new optimal design method, called PreDIA(gnosis) (Preposterior Data Impact Assessor). PreDIA derives the relevant probability distributions and measures of data utility within a fully Bayesian, generalized, flexible, and accurate framework. It extends the bootstrap filter (BF) and related frameworks to optimal design by marginalizing utility measures over the yet unknown data values. PreDIA is a strictly formal information-processing scheme free of linearizations. It works with arbitrary simulation tools, provides full flexibility concerning measurement types (linear, nonlinear, direct, indirect), allows for any desired task-driven formulations, and can account for various sources of uncertainty (e.g., heterogeneity, geostatistical assumptions, boundary conditions, measurement values, model structure uncertainty, a large class of model errors) via Bayesian geostatistics and model averaging. Existing methods fail to simultaneously provide these crucial advantages, which our method buys at relatively higher-computational costs. We demonstrate the applicability and advantages of PreDIA over conventional linearized methods in a synthetic example of subsurface transport. In the example, we show that informative data is often invisible for linearized methods that confuse zero correlation with statistical independence. Hence, PreDIA will often lead to substantially better sampling designs. Finally, we extend our example to specifically highlight the consideration of conceptual model uncertainty.

Path integral measure and triangulation independence in discrete gravity

NASA Astrophysics Data System (ADS)

Dittrich, Bianca; Steinhaus, Sebastian

2012-02-01

A path integral measure for gravity should also preserve the fundamental symmetry of general relativity, which is diffeomorphism symmetry. In previous work, we argued that a successful implementation of this symmetry into discrete quantum gravity models would imply discretization independence. We therefore consider the requirement of triangulation independence for the measure in (linearized) Regge calculus, which is a discrete model for quantum gravity, appearing in the semi-classical limit of spin foam models. To this end we develop a technique to evaluate the linearized Regge action associated to Pachner moves in 3D and 4D and show that it has a simple, factorized structure. We succeed in finding a local measure for 3D (linearized) Regge calculus that leads to triangulation independence. This measure factor coincides with the asymptotics of the Ponzano Regge Model, a 3D spin foam model for gravity. We furthermore discuss to which extent one can find a triangulation independent measure for 4D Regge calculus and how such a measure would be related to a quantum model for 4D flat space. To this end, we also determine the dependence of classical Regge calculus on the choice of triangulation in 3D and 4D.

A Generalization of Pythagoras's Theorem and Application to Explanations of Variance Contributions in Linear Models. Research Report. ETS RR-14-18

ERIC Educational Resources Information Center

Carlson, James E.

2014-01-01

Many aspects of the geometry of linear statistical models and least squares estimation are well known. Discussions of the geometry may be found in many sources. Some aspects of the geometry relating to the partitioning of variation that can be explained using a little-known theorem of Pappus and have not been discussed previously are the topic of…

Energy-balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1980-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Energy balance climate models

NASA Technical Reports Server (NTRS)

North, G. R.; Cahalan, R. F.; Coakley, J. A., Jr.

1981-01-01

An introductory survey of the global energy balance climate models is presented with an emphasis on analytical results. A sequence of increasingly complicated models involving ice cap and radiative feedback processes are solved, and the solutions and parameter sensitivities are studied. The model parameterizations are examined critically in light of many current uncertainties. A simple seasonal model is used to study the effects of changes in orbital elements on the temperature field. A linear stability theorem and a complete nonlinear stability analysis for the models are developed. Analytical solutions are also obtained for the linearized models driven by stochastic forcing elements. In this context the relation between natural fluctuation statistics and climate sensitivity is stressed.

Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

PubMed Central

2011-01-01

Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task. PMID:21867520

Modelling Students' Construction of Energy Models in Physics.

ERIC Educational Resources Information Center

Devi, Roshni; And Others

1996-01-01

Examines students' construction of experimentation models for physics theories in energy storage, transformation, and transfers involving electricity and mechanics. Student problem solving dialogs and artificial intelligence modeling of these processes is analyzed. Construction of models established relations between elements with linear causal…

A simple method for identifying parameter correlations in partially observed linear dynamic models.

PubMed

Li, Pu; Vu, Quoc Dong

2015-12-14

Parameter estimation represents one of the most significant challenges in systems biology. This is because biological models commonly contain a large number of parameters among which there may be functional interrelationships, thus leading to the problem of non-identifiability. Although identifiability analysis has been extensively studied by analytical as well as numerical approaches, systematic methods for remedying practically non-identifiable models have rarely been investigated. We propose a simple method for identifying pairwise correlations and higher order interrelationships of parameters in partially observed linear dynamic models. This is made by derivation of the output sensitivity matrix and analysis of the linear dependencies of its columns. Consequently, analytical relations between the identifiability of the model parameters and the initial conditions as well as the input functions can be achieved. In the case of structural non-identifiability, identifiable combinations can be obtained by solving the resulting homogenous linear equations. In the case of practical non-identifiability, experiment conditions (i.e. initial condition and constant control signals) can be provided which are necessary for remedying the non-identifiability and unique parameter estimation. It is noted that the approach does not consider noisy data. In this way, the practical non-identifiability issue, which is popular for linear biological models, can be remedied. Several linear compartment models including an insulin receptor dynamics model are taken to illustrate the application of the proposed approach. Both structural and practical identifiability of partially observed linear dynamic models can be clarified by the proposed method. The result of this method provides important information for experimental design to remedy the practical non-identifiability if applicable. The derivation of the method is straightforward and thus the algorithm can be easily implemented into a software packet.

CDP++.Italian: Modelling Sublexical and Supralexical Inconsistency in a Shallow Orthography

PubMed Central

Perry, Conrad; Ziegler, Johannes C.; Zorzi, Marco

2014-01-01

Most models of reading aloud have been constructed to explain data in relatively complex orthographies like English and French. Here, we created an Italian version of the Connectionist Dual Process Model of Reading Aloud (CDP++) to examine the extent to which the model could predict data in a language which has relatively simple orthography-phonology relationships but is relatively complex at a suprasegmental (word stress) level. We show that the model exhibits good quantitative performance and accounts for key phenomena observed in naming studies, including some apparently contradictory findings. These effects include stress regularity and stress consistency, both of which have been especially important in studies of word recognition and reading aloud in Italian. Overall, the results of the model compare favourably to an alternative connectionist model that can learn non-linear spelling-to-sound mappings. This suggests that CDP++ is currently the leading computational model of reading aloud in Italian, and that its simple linear learning mechanism adequately captures the statistical regularities of the spelling-to-sound mapping both at the segmental and supra-segmental levels. PMID:24740261

Analytical method to estimate waterflood performance

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

Cremonini, A.S.

A method to predict oil production resulting from the injection of immiscible fluids is described. The method is based on two models: one of them considers the vertical and displacement efficiencies, assuming unit areal efficiency and, therefore, a linear flow. It is a layered model without crossflow in which Buckley-Leveret`s displacement theory is used for each layer. The results obtained in the linear model are applied to a streamchannel model similar to the one used by Higgins and Leighton. In this way, areal efficiency is taken into account. The principal innovation is the possibility of applying different relative permeability curvesmore » to each layer. A numerical example in a five-spot pattern which uses relative permeability data obtained from reservoir core samples is presented.« less

A geometric approach to non-linear correlations with intrinsic scatter

NASA Astrophysics Data System (ADS)

Pihajoki, Pauli

2017-12-01

We propose a new mathematical model for n - k-dimensional non-linear correlations with intrinsic scatter in n-dimensional data. The model is based on Riemannian geometry and is naturally symmetric with respect to the measured variables and invariant under coordinate transformations. We combine the model with a Bayesian approach for estimating the parameters of the correlation relation and the intrinsic scatter. A side benefit of the approach is that censored and truncated data sets and independent, arbitrary measurement errors can be incorporated. We also derive analytic likelihoods for the typical astrophysical use case of linear relations in n-dimensional Euclidean space. We pay particular attention to the case of linear regression in two dimensions and compare our results to existing methods. Finally, we apply our methodology to the well-known MBH-σ correlation between the mass of a supermassive black hole in the centre of a galactic bulge and the corresponding bulge velocity dispersion. The main result of our analysis is that the most likely slope of this correlation is ∼6 for the data sets used, rather than the values in the range of ∼4-5 typically quoted in the literature for these data.

Dynamics of attitudes and genetic processes.

PubMed

Guastello, Stephen J; Guastello, Denise D

2008-01-01

Relatively new discoveries of a genetic component to attitudes have challenged the traditional viewpoint that attitudes are primarily learned ideas and behaviors. Attitudes that are regarded by respondents as "more important" tend to have greater genetic components to them, and tend to be more closely associated with authoritarianism. Nonlinear theories, nonetheless, have also been introduced to study attitude change. The objective of this study was to determine whether change in authoritarian attitudes across two generations would be more aptly described by a linear or a nonlinear model. Participants were 372 college students, their mothers, and their fathers who completed an attitude questionnaire. Results indicated that the nonlinear model (R2 = .09) was slightly better than the linear model (R2 = .08), but the two models offered very different forecasts for future generations of US society. The linear model projected a gradual and continuing bifurcation between authoritarians and non-authoritarians. The nonlinear model projected a stabilization of authoritarian attitudes.

Planck constant as spectral parameter in integrable systems and KZB equations

NASA Astrophysics Data System (ADS)

Levin, A.; Olshanetsky, M.; Zotov, A.

2014-10-01

We construct special rational gl N Knizhnik-Zamolodchikov-Bernard (KZB) equations with Ñ punctures by deformation of the corresponding quantum gl N rational R-matrix. They have two parameters. The limit of the first one brings the model to the ordinary rational KZ equation. Another one is τ. At the level of classical mechanics the deformation parameter τ allows to extend the previously obtained modified Gaudin models to the modified Schlesinger systems. Next, we notice that the identities underlying generic (elliptic) KZB equations follow from some additional relations for the properly normalized R-matrices. The relations are noncommutative analogues of identities for (scalar) elliptic functions. The simplest one is the unitarity condition. The quadratic (in R matrices) relations are generated by noncommutative Fay identities. In particular, one can derive the quantum Yang-Baxter equations from the Fay identities. The cubic relations provide identities for the KZB equations as well as quadratic relations for the classical r-matrices which can be treated as halves of the classical Yang-Baxter equation. At last we discuss the R-matrix valued linear problems which provide gl Ñ CM models and Painlevé equations via the above mentioned identities. The role of the spectral parameter plays the Planck constant of the quantum R-matrix. When the quantum gl N R-matrix is scalar ( N = 1) the linear problem reproduces the Krichever's ansatz for the Lax matrices with spectral parameter for the gl Ñ CM models. The linear problems for the quantum CM models generalize the KZ equations in the same way as the Lax pairs with spectral parameter generalize those without it.

Valuing water resources in Switzerland using a hedonic price model

NASA Astrophysics Data System (ADS)

van Dijk, Diana; Siber, Rosi; Brouwer, Roy; Logar, Ivana; Sanadgol, Dorsa

2016-05-01

In this paper, linear and spatial hedonic price models are applied to the housing market in Switzerland, covering all 26 cantons in the country over the period 2005-2010. Besides structural house, neighborhood and socioeconomic characteristics, we include a wide variety of new environmental characteristics related to water to examine their role in explaining variation in sales prices. These include water abundance, different types of water bodies, the recreational function of water, and water disamenity. Significant spatial autocorrelation is found in the estimated models, as well as nonlinear effects for distances to the nearest lake and large river. Significant effects are furthermore found for water abundance and the distance to large rivers, but not to small rivers. Although in both linear and spatial models water related variables explain less than 1% of the price variation, the distance to the nearest bathing site has a larger marginal contribution than many neighborhood-related distance variables. The housing market shows to differentiate between different water related resources in terms of relative contribution to house prices, which could help the housing development industry make more geographically targeted planning activities.

Solar granulation and statistical crystallography: A modeling approach using size-shape relations

NASA Technical Reports Server (NTRS)

Noever, D. A.

1994-01-01

The irregular polygonal pattern of solar granulation is analyzed for size-shape relations using statistical crystallography. In contrast to previous work which has assumed perfectly hexagonal patterns for granulation, more realistic accounting of cell (granule) shapes reveals a broader basis for quantitative analysis. Several features emerge as noteworthy: (1) a linear correlation between number of cell-sides and neighboring shapes (called Aboav-Weaire's law); (2) a linear correlation between both average cell area and perimeter and the number of cell-sides (called Lewis's law and a perimeter law, respectively) and (3) a linear correlation between cell area and squared perimeter (called convolution index). This statistical picture of granulation is consistent with a finding of no correlation in cell shapes beyond nearest neighbors. A comparative calculation between existing model predictions taken from luminosity data and the present analysis shows substantial agreements for cell-size distributions. A model for understanding grain lifetimes is proposed which links convective times to cell shape using crystallographic results.

Fast radiative transfer models for retrieval of cloud properties in the back-scattering region: application to DSCOVR-EPIC sensor

NASA Astrophysics Data System (ADS)

Molina Garcia, Victor; Sasi, Sruthy; Efremenko, Dmitry; Doicu, Adrian; Loyola, Diego

2017-04-01

In this work, the requirements for the retrieval of cloud properties in the back-scattering region are described, and their application to the measurements taken by the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) is shown. Various radiative transfer models and their linearizations are implemented, and their advantages and issues are analyzed. As radiative transfer calculations in the back-scattering region are computationally time-consuming, several acceleration techniques are also studied. The radiative transfer models analyzed include the exact Discrete Ordinate method with Matrix Exponential (DOME), the Matrix Operator method with Matrix Exponential (MOME), and the approximate asymptotic and equivalent Lambertian cloud models. To reduce the computational cost of the line-by-line (LBL) calculations, the k-distribution method, the Principal Component Analysis (PCA) and a combination of the k-distribution method plus PCA are used. The linearized radiative transfer models for retrieval of cloud properties include the Linearized Discrete Ordinate method with Matrix Exponential (LDOME), the Linearized Matrix Operator method with Matrix Exponential (LMOME) and the Forward-Adjoint Discrete Ordinate method with Matrix Exponential (FADOME). These models were applied to the EPIC oxygen-A band absorption channel at 764 nm. It is shown that the approximate asymptotic and equivalent Lambertian cloud models give inaccurate results, so an offline processor for the retrieval of cloud properties in the back-scattering region requires the use of exact models such as DOME and MOME, which behave similarly. The combination of the k-distribution method plus PCA presents similar accuracy to the LBL calculations, but it is up to 360 times faster, and the relative errors for the computed radiances are less than 1.5% compared to the results when the exact phase function is used. Finally, the linearized models studied show similar behavior, with relative errors less than 1% for the radiance derivatives, but FADOME is 2 times faster than LDOME and 2.5 times faster than LMOME.

Plant uptake of elements in soil and pore water: field observations versus model assumptions.

PubMed

Raguž, Veronika; Jarsjö, Jerker; Grolander, Sara; Lindborg, Regina; Avila, Rodolfo

2013-09-15

Contaminant concentrations in various edible plant parts transfer hazardous substances from polluted areas to animals and humans. Thus, the accurate prediction of plant uptake of elements is of significant importance. The processes involved contain many interacting factors and are, as such, complex. In contrast, the most common way to currently quantify element transfer from soils into plants is relatively simple, using an empirical soil-to-plant transfer factor (TF). This practice is based on theoretical assumptions that have been previously shown to not generally be valid. Using field data on concentrations of 61 basic elements in spring barley, soil and pore water at four agricultural sites in mid-eastern Sweden, we quantify element-specific TFs. Our aim is to investigate to which extent observed element-specific uptake is consistent with TF model assumptions and to which extent TF's can be used to predict observed differences in concentrations between different plant parts (root, stem and ear). Results show that for most elements, plant-ear concentrations are not linearly related to bulk soil concentrations, which is congruent with previous studies. This behaviour violates a basic TF model assumption of linearity. However, substantially better linear correlations are found when weighted average element concentrations in whole plants are used for TF estimation. The highest number of linearly-behaving elements was found when relating average plant concentrations to soil pore-water concentrations. In contrast to other elements, essential elements (micronutrients and macronutrients) exhibited relatively small differences in concentration between different plant parts. Generally, the TF model was shown to work reasonably well for micronutrients, whereas it did not for macronutrients. The results also suggest that plant uptake of elements from sources other than the soil compartment (e.g. from air) may be non-negligible. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evidence of Non-Linear Associations between Frustration-Related Prefrontal Cortex Activation and the Normal:Abnormal Spectrum of Irritability in Young Children.

PubMed

Grabell, Adam S; Li, Yanwei; Barker, Jeff W; Wakschlag, Lauren S; Huppert, Theodore J; Perlman, Susan B

2018-01-01

Burgeoning interest in early childhood irritability has recently turned toward neuroimaging techniques to better understand normal versus abnormal irritability using dimensional methods. Current accounts largely assume a linear relationship between poor frustration management, an expression of irritability, and its underlying neural circuitry. However, the relationship between these constructs may not be linear (i.e., operate differently at varying points across the irritability spectrum), with implications for how early atypical irritability is identified and treated. Our goal was to examine how the association between frustration-related lateral prefrontal cortex (LPFC) activation and irritability differs across the dimensional spectrum of irritability by testing for non-linear associations. Children (N = 92; ages 3-7) ranging from virtually no irritability to the upper end of the clinical range completed a frustration induction task while we recorded LPFC hemoglobin levels using fNIRS. Children self-rated their emotions during the task and parents rated their child's level of irritability. Whereas a linear model showed no relationship between frustration-related LPFC activation and irritability, a quadratic model revealed frustration-related LPFC activation increased as parent-reported irritability scores increased within the normative range of irritability but decreased with increasing irritability in the severe range, with an apex at the 91st percentile. Complementarily, we found children's self-ratings of emotion during frustration related to concurrent LPFC activation as an inverted U function, such that children who reported mild distress had greater activation than peers reporting no or high distress. Results suggest children with relatively higher irritability who are unimpaired may possess well-developed LPFC support, a mechanism that drops out in the severe end of the irritability dimension. Findings suggest novel avenues for understanding the heterogeneity of early irritability and its clinical sequelae.

Linear summation of outputs in a balanced network model of motor cortex.

PubMed

Capaday, Charles; van Vreeswijk, Carl

2015-01-01

Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.

Nature of size effects in compact models of field effect transistors

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

Torkhov, N. A., E-mail: trkf@mail.ru; Scientific-Research Institute of Semiconductor Devices, Tomsk 634050; Tomsk State University of Control Systems and Radioelectronics, Tomsk 634050

Investigations have shown that in the local approximation (for sizes L < 100 μm), AlGaN/GaN high electron mobility transistor (HEMT) structures satisfy to all properties of chaotic systems and can be described in the language of fractal geometry of fractional dimensions. For such objects, values of their electrophysical characteristics depend on the linear sizes of the examined regions, which explain the presence of the so-called size effects—dependences of the electrophysical and instrumental characteristics on the linear sizes of the active elements of semiconductor devices. In the present work, a relationship has been established for the linear model parameters of themore » equivalent circuit elements of internal transistors with fractal geometry of the heteroepitaxial structure manifested through a dependence of its relative electrophysical characteristics on the linear sizes of the examined surface areas. For the HEMTs, this implies dependences of their relative static (A/mm, mA/V/mm, Ω/mm, etc.) and microwave characteristics (W/mm) on the width d of the sink-source channel and on the number of sections n that leads to a nonlinear dependence of the retrieved parameter values of equivalent circuit elements of linear internal transistor models on n and d. Thus, it has been demonstrated that the size effects in semiconductors determined by the fractal geometry must be taken into account when investigating the properties of semiconductor objects on the levels less than the local approximation limit and designing and manufacturing field effect transistors. In general, the suggested approach allows a complex of problems to be solved on designing, optimizing, and retrieving the parameters of equivalent circuits of linear and nonlinear models of not only field effect transistors but also any arbitrary semiconductor devices with nonlinear instrumental characteristics.« less

Gain scheduled linear quadratic control for quadcopter

NASA Astrophysics Data System (ADS)

Okasha, M.; Shah, J.; Fauzi, W.; Hanouf, Z.

2017-12-01

This study exploits the dynamics and control of quadcopters using Linear Quadratic Regulator (LQR) control approach. The quadcopter’s mathematical model is derived using the Newton-Euler method. It is a highly manoeuvrable, nonlinear, coupled with six degrees of freedom (DOF) model, which includes aerodynamics and detailed gyroscopic moments that are often ignored in many literatures. The linearized model is obtained and characterized by the heading angle (i.e. yaw angle) of the quadcopter. The adopted control approach utilizes LQR method to track several reference trajectories including circle and helix curves with significant variation in the yaw angle. The controller is modified to overcome difficulties related to the continuous changes in the operating points and eliminate chattering and discontinuity that is observed in the control input signal. Numerical non-linear simulations are performed using MATLAB and Simulink to illustrate to accuracy and effectiveness of the proposed controller.

ORACLS: A system for linear-quadratic-Gaussian control law design

NASA Technical Reports Server (NTRS)

Armstrong, E. S.

1978-01-01

A modern control theory design package (ORACLS) for constructing controllers and optimal filters for systems modeled by linear time-invariant differential or difference equations is described. Numerical linear-algebra procedures are used to implement the linear-quadratic-Gaussian (LQG) methodology of modern control theory. Algorithms are included for computing eigensystems of real matrices, the relative stability of a matrix, factored forms for nonnegative definite matrices, the solutions and least squares approximations to the solutions of certain linear matrix algebraic equations, the controllability properties of a linear time-invariant system, and the steady state covariance matrix of an open-loop stable system forced by white noise. Subroutines are provided for solving both the continuous and discrete optimal linear regulator problems with noise free measurements and the sampled-data optimal linear regulator problem. For measurement noise, duality theory and the optimal regulator algorithms are used to solve the continuous and discrete Kalman-Bucy filter problems. Subroutines are also included which give control laws causing the output of a system to track the output of a prescribed model.

Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Linear Analysis.

PubMed

Henley, B C; Shin, D C; Zhang, R; Marmarelis, V Z

Compartmental and data-based modeling of cerebral hemodynamics are alternative approaches that utilize distinct model forms and have been employed in the quantitative study of cerebral hemodynamics. This paper examines the relation between a compartmental equivalent-circuit and a data-based input-output model of dynamic cerebral autoregulation (DCA) and CO2-vasomotor reactivity (DVR). The compartmental model is constructed as an equivalent-circuit utilizing putative first principles and previously proposed hypothesis-based models. The linear input-output dynamics of this compartmental model are compared with data-based estimates of the DCA-DVR process. This comparative study indicates that there are some qualitative similarities between the two-input compartmental model and experimental results.

Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy.

PubMed

Huppert, Theodore J

2016-01-01

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low levels of light to measure changes in cerebral blood oxygenation levels. In the majority of NIRS functional brain studies, analysis of this data is based on a statistical comparison of hemodynamic levels between a baseline and task or between multiple task conditions by means of a linear regression model: the so-called general linear model. Although these methods are similar to their implementation in other fields, particularly for functional magnetic resonance imaging, the specific application of these methods in fNIRS research differs in several key ways related to the sources of noise and artifacts unique to fNIRS. In this brief communication, we discuss the application of linear regression models in fNIRS and the modifications needed to generalize these models in order to deal with structured (colored) noise due to systemic physiology and noise heteroscedasticity due to motion artifacts. The objective of this work is to present an overview of these noise properties in the context of the linear model as it applies to fNIRS data. This work is aimed at explaining these mathematical issues to the general fNIRS experimental researcher but is not intended to be a complete mathematical treatment of these concepts.

The Fisher-KPP problem with doubly nonlinear diffusion

NASA Astrophysics Data System (ADS)

Audrito, Alessandro; Vázquez, Juan Luis

2017-12-01

The famous Fisher-KPP reaction-diffusion model combines linear diffusion with the typical KPP reaction term, and appears in a number of relevant applications in biology and chemistry. It is remarkable as a mathematical model since it possesses a family of travelling waves that describe the asymptotic behaviour of a large class solutions 0 ≤ u (x , t) ≤ 1 of the problem posed in the real line. The existence of propagation waves with finite speed has been confirmed in some related models and disproved in others. We investigate here the corresponding theory when the linear diffusion is replaced by the "slow" doubly nonlinear diffusion and we find travelling waves that represent the wave propagation of more general solutions even when we extend the study to several space dimensions. A similar study is performed in the critical case that we call "pseudo-linear", i.e., when the operator is still nonlinear but has homogeneity one. With respect to the classical model and the "pseudo-linear" case, the "slow" travelling waves exhibit free boundaries.

Does competition improve financial stability of the banking sector in ASEAN countries? An empirical analysis.

PubMed

Noman, Abu Hanifa Md; Gee, Chan Sok; Isa, Che Ruhana

2017-01-01

This study examines the influence of competition on the financial stability of the commercial banks of Association of Southeast Asian Nation (ASEAN) over the 1990 to 2014 period. Panzar-Rosse H-statistic, Lerner index and Herfindahl-Hirschman Index (HHI) are used as measures of competition, while Z-score, non-performing loan (NPL) ratio and equity ratio are used as measures of financial stability. Two-step system Generalized Method of Moments (GMM) estimates demonstrate that competition measured by H-statistic is positively related to Z-score and equity ratio, and negatively related to non-performing loan ratio. Conversely, market power measured by Lerner index is negatively related to Z-score and equity ratio and positively related to NPL ratio. These results strongly support the competition-stability view for ASEAN banks. We also capture the non-linear relationship between competition and financial stability by incorporating a quadratic term of competition in our models. The results show that the coefficient of the quadratic term of H-statistic is negative for the Z-score model given a positive coefficient of the linear term in the same model. These results support the non-linear relationship between competition and financial stability of the banking sector. The study contains significant policy implications for improving the financial stability of the commercial banks.

Does competition improve financial stability of the banking sector in ASEAN countries? An empirical analysis

PubMed Central

Gee, Chan Sok; Isa, Che Ruhana

2017-01-01

This study examines the influence of competition on the financial stability of the commercial banks of Association of Southeast Asian Nation (ASEAN) over the 1990 to 2014 period. Panzar-Rosse H-statistic, Lerner index and Herfindahl-Hirschman Index (HHI) are used as measures of competition, while Z-score, non-performing loan (NPL) ratio and equity ratio are used as measures of financial stability. Two-step system Generalized Method of Moments (GMM) estimates demonstrate that competition measured by H-statistic is positively related to Z-score and equity ratio, and negatively related to non-performing loan ratio. Conversely, market power measured by Lerner index is negatively related to Z-score and equity ratio and positively related to NPL ratio. These results strongly support the competition-stability view for ASEAN banks. We also capture the non-linear relationship between competition and financial stability by incorporating a quadratic term of competition in our models. The results show that the coefficient of the quadratic term of H-statistic is negative for the Z-score model given a positive coefficient of the linear term in the same model. These results support the non-linear relationship between competition and financial stability of the banking sector. The study contains significant policy implications for improving the financial stability of the commercial banks. PMID:28486548

Nonlinear changes in brain activity during continuous word repetition: an event-related multiparametric functional MR imaging study.

PubMed

Hagenbeek, R E; Rombouts, S A R B; Veltman, D J; Van Strien, J W; Witter, M P; Scheltens, P; Barkhof, F

2007-10-01

Changes in brain activation as a function of continuous multiparametric word recognition have not been studied before by using functional MR imaging (fMRI), to our knowledge. Our aim was to identify linear changes in brain activation and, what is more interesting, nonlinear changes in brain activation as a function of extended word repetition. Fifteen healthy young right-handed individuals participated in this study. An event-related extended continuous word-recognition task with 30 target words was used to study the parametric effect of word recognition on brain activation. Word-recognition-related brain activation was studied as a function of 9 word repetitions. fMRI data were analyzed with a general linear model with regressors for linearly changing signal intensity and nonlinearly changing signal intensity, according to group average reaction time (RT) and individual RTs. A network generally associated with episodic memory recognition showed either constant or linearly decreasing brain activation as a function of word repetition. Furthermore, both anterior and posterior cingulate cortices and the left middle frontal gyrus followed the nonlinear curve of the group RT, whereas the anterior cingulate cortex was also associated with individual RT. Linear alteration in brain activation as a function of word repetition explained most changes in blood oxygen level-dependent signal intensity. Using a hierarchically orthogonalized model, we found evidence for nonlinear activation associated with both group and individual RTs.

Mended chiral symmetry and the linear sigma model in one-loop order

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

Scadron, M.D.

1992-02-28

In this paper it is shown that the linear {sigma}-model in one loop order in the chiral limit recovers meson masses m{sub {pi}} = 0, m{sub {sigma}} = 2m{sub qk} (NJL), m {sub {rho}} = {radical}2 g{sub {rho}}f{pi} (KSRF), along with couplings g{sigma}{pi}{pi} = m{sup 2}{sub {sigma}}/2f{pi}, g{rho}{pi}{pi} = g{sub {rho}} (VMD universality) and Weinberg's mended chiral symmetry decay width relation {Gamma}{sub {sigma}} = (9/2){Gamma}{sub {rho}}. The linear {sigma}-model combined quark and meson loops also properly predict the radiative decays {pi}{sup 0} {yields} 2{gamma} {yields} e{nu}{gamma} and {delta}{sup 0} (983) {yields} 2{gamma}.

Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

PubMed Central

Zhao, Xin; Cheung, Leo Wang-Kit

2007-01-01

Background Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more important for our understanding of diseases at genomic level. Although many machine learning methods have been developed and applied to the area of microarray gene expression data analysis, the majority of them are based on linear models, which however are not necessarily appropriate for the underlying connection between the target disease and its associated explanatory genes. Linear model based methods usually also bring in false positive significant features more easily. Furthermore, linear model based algorithms often involve calculating the inverse of a matrix that is possibly singular when the number of potentially important genes is relatively large. This leads to problems of numerical instability. To overcome these limitations, a few non-linear methods have recently been introduced to the area. Many of the existing non-linear methods have a couple of critical problems, the model selection problem and the model parameter tuning problem, that remain unsolved or even untouched. In general, a unified framework that allows model parameters of both linear and non-linear models to be easily tuned is always preferred in real-world applications. Kernel-induced learning methods form a class of approaches that show promising potentials to achieve this goal. Results A hierarchical statistical model named kernel-imbedded Gaussian process (KIGP) is developed under a unified Bayesian framework for binary disease classification problems using microarray gene expression data. In particular, based on a probit regression setting, an adaptive algorithm with a cascading structure is designed to find the appropriate kernel, to discover the potentially significant genes, and to make the optimal class prediction accordingly. A Gibbs sampler is built as the core of the algorithm to make Bayesian inferences. Simulation studies showed that, even without any knowledge of the underlying generative model, the KIGP performed very close to the theoretical Bayesian bound not only in the case with a linear Bayesian classifier but also in the case with a very non-linear Bayesian classifier. This sheds light on its broader usability to microarray data analysis problems, especially to those that linear methods work awkwardly. The KIGP was also applied to four published microarray datasets, and the results showed that the KIGP performed better than or at least as well as any of the referred state-of-the-art methods did in all of these cases. Conclusion Mathematically built on the kernel-induced feature space concept under a Bayesian framework, the KIGP method presented in this paper provides a unified machine learning approach to explore both the linear and the possibly non-linear underlying relationship between the target features of a given binary disease classification problem and the related explanatory gene expression data. More importantly, it incorporates the model parameter tuning into the framework. The model selection problem is addressed in the form of selecting a proper kernel type. The KIGP method also gives Bayesian probabilistic predictions for disease classification. These properties and features are beneficial to most real-world applications. The algorithm is naturally robust in numerical computation. The simulation studies and the published data studies demonstrated that the proposed KIGP performs satisfactorily and consistently. PMID:17328811

Calibrating Nonlinear Soil Material Properties for Seismic Analysis Using Soil Material Properties Intended for Linear Analysis

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

Spears, Robert Edward; Coleman, Justin Leigh

2015-08-01

Seismic analysis of nuclear structures is routinely performed using guidance provided in “Seismic Analysis of Safety-Related Nuclear Structures and Commentary (ASCE 4, 1998).” This document, which is currently under revision, provides detailed guidance on linear seismic soil-structure-interaction (SSI) analysis of nuclear structures. To accommodate the linear analysis, soil material properties are typically developed as shear modulus and damping ratio versus cyclic shear strain amplitude. A new Appendix in ASCE 4-2014 (draft) is being added to provide guidance for nonlinear time domain SSI analysis. To accommodate the nonlinear analysis, a more appropriate form of the soil material properties includes shear stressmore » and energy absorbed per cycle versus shear strain. Ideally, nonlinear soil model material properties would be established with soil testing appropriate for the nonlinear constitutive model being used. However, much of the soil testing done for SSI analysis is performed for use with linear analysis techniques. Consequently, a method is described in this paper that uses soil test data intended for linear analysis to develop nonlinear soil material properties. To produce nonlinear material properties that are equivalent to the linear material properties, the linear and nonlinear model hysteresis loops are considered. For equivalent material properties, the shear stress at peak shear strain and energy absorbed per cycle should match when comparing the linear and nonlinear model hysteresis loops. Consequently, nonlinear material properties are selected based on these criteria.« less

[Application of ordinary Kriging method in entomologic ecology].

PubMed

Zhang, Runjie; Zhou, Qiang; Chen, Cuixian; Wang, Shousong

2003-01-01

Geostatistics is a statistic method based on regional variables and using the tool of variogram to analyze the spatial structure and the patterns of organism. In simulating the variogram within a great range, though optimal simulation cannot be obtained, the simulation method of a dialogue between human and computer can be used to optimize the parameters of the spherical models. In this paper, the method mentioned above and the weighted polynomial regression were utilized to simulate the one-step spherical model, the two-step spherical model and linear function model, and the available nearby samples were used to draw on the ordinary Kriging procedure, which provided a best linear unbiased estimate of the constraint of the unbiased estimation. The sum of square deviation between the estimating and measuring values of varying theory models were figured out, and the relative graphs were shown. It was showed that the simulation based on the two-step spherical model was the best simulation, and the one-step spherical model was better than the linear function model.

Seismic behaviour of mountain belts controlled by plate convergence rate

NASA Astrophysics Data System (ADS)

Dal Zilio, Luca; van Dinther, Ylona; Gerya, Taras V.; Pranger, Casper C.

2018-01-01

The relative contribution of tectonic and kinematic processes to seismic behaviour of mountain belts is still controversial. To understand the partitioning between these processes we developed a model that simulates both tectonic and seismic processes in a continental collision setting. These 2D seismo-thermo-mechanical (STM) models obtain a Gutenberg-Richter frequency-magnitude distribution due to spontaneous events occurring throughout the orogen. Our simulations suggest that both the corresponding slope (b value) and maximum earthquake magnitude (MWmax) correlate linearly with plate convergence rate. By analyzing 1D rheological profiles and isotherm depths we demonstrate that plate convergence rate controls the brittle strength through a rheological feedback with temperature and strain rate. Faster convergence leads to cooler temperatures and also results in more larger seismogenic domains, thereby increasing both MWmax and the relative number of large earthquakes (decreasing b value). This mechanism also predicts a more seismogenic lower crust, which is confirmed by a transition from uni- to bi-modal hypocentre depth distributions in our models. This transition and a linear relation between convergence rate and b value and MWmax is supported by our comparison of earthquakes recorded across the Alps, Apennines, Zagros and Himalaya. These results imply that deformation in the Alps occurs in a more ductile manner compared to the Himalayas, thereby reducing its seismic hazard. Furthermore, a second set of experiments with higher temperature and different orogenic architecture shows the same linear relation with convergence rate, suggesting that large-scale tectonic structure plays a subordinate role. We thus propose that plate convergence rate, which also controls the average differential stress of the orogen and its linear relation to the b value, is the first-order parameter controlling seismic hazard of mountain belts.

Frequency-domain full-waveform inversion with non-linear descent directions

NASA Astrophysics Data System (ADS)

Geng, Yu; Pan, Wenyong; Innanen, Kristopher A.

2018-05-01

Full-waveform inversion (FWI) is a highly non-linear inverse problem, normally solved iteratively, with each iteration involving an update constructed through linear operations on the residuals. Incorporating a flexible degree of non-linearity within each update may have important consequences for convergence rates, determination of low model wavenumbers and discrimination of parameters. We examine one approach for doing so, wherein higher order scattering terms are included within the sensitivity kernel during the construction of the descent direction, adjusting it away from that of the standard Gauss-Newton approach. These scattering terms are naturally admitted when we construct the sensitivity kernel by varying not the current but the to-be-updated model at each iteration. Linear and/or non-linear inverse scattering methodologies allow these additional sensitivity contributions to be computed from the current data residuals within any given update. We show that in the presence of pre-critical reflection data, the error in a second-order non-linear update to a background of s0 is, in our scheme, proportional to at most (Δs/s0)3 in the actual parameter jump Δs causing the reflection. In contrast, the error in a standard Gauss-Newton FWI update is proportional to (Δs/s0)2. For numerical implementation of more complex cases, we introduce a non-linear frequency-domain scheme, with an inner and an outer loop. A perturbation is determined from the data residuals within the inner loop, and a descent direction based on the resulting non-linear sensitivity kernel is computed in the outer loop. We examine the response of this non-linear FWI using acoustic single-parameter synthetics derived from the Marmousi model. The inverted results vary depending on data frequency ranges and initial models, but we conclude that the non-linear FWI has the capability to generate high-resolution model estimates in both shallow and deep regions, and to converge rapidly, relative to a benchmark FWI approach involving the standard gradient.

Estimating cosmic velocity fields from density fields and tidal tensors

NASA Astrophysics Data System (ADS)

Kitaura, Francisco-Shu; Angulo, Raul E.; Hoffman, Yehuda; Gottlöber, Stefan

2012-10-01

In this work we investigate the non-linear and non-local relation between cosmological density and peculiar velocity fields. Our goal is to provide an algorithm for the reconstruction of the non-linear velocity field from the fully non-linear density. We find that including the gravitational tidal field tensor using second-order Lagrangian perturbation theory based upon an estimate of the linear component of the non-linear density field significantly improves the estimate of the cosmic flow in comparison to linear theory not only in the low density, but also and more dramatically in the high-density regions. In particular we test two estimates of the linear component: the lognormal model and the iterative Lagrangian linearization. The present approach relies on a rigorous higher order Lagrangian perturbation theory analysis which incorporates a non-local relation. It does not require additional fitting from simulations being in this sense parameter free, it is independent of statistical-geometrical optimization and it is straightforward and efficient to compute. The method is demonstrated to yield an unbiased estimator of the velocity field on scales ≳5 h-1 Mpc with closely Gaussian distributed errors. Moreover, the statistics of the divergence of the peculiar velocity field is extremely well recovered showing a good agreement with the true one from N-body simulations. The typical errors of about 10 km s-1 (1σ confidence intervals) are reduced by more than 80 per cent with respect to linear theory in the scale range between 5 and 10 h-1 Mpc in high-density regions (δ > 2). We also find that iterative Lagrangian linearization is significantly superior in the low-density regime with respect to the lognormal model.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly

PubMed Central

2013-01-01

Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042

Assessing variance components in multilevel linear models using approximate Bayes factors: A case study of ethnic disparities in birthweight

PubMed Central

Saville, Benjamin R.; Herring, Amy H.; Kaufman, Jay S.

2013-01-01

Racial/ethnic disparities in birthweight are a large source of differential morbidity and mortality worldwide and have remained largely unexplained in epidemiologic models. We assess the impact of maternal ancestry and census tract residence on infant birth weights in New York City and the modifying effects of race and nativity by incorporating random effects in a multilevel linear model. Evaluating the significance of these predictors involves the test of whether the variances of the random effects are equal to zero. This is problematic because the null hypothesis lies on the boundary of the parameter space. We generalize an approach for assessing random effects in the two-level linear model to a broader class of multilevel linear models by scaling the random effects to the residual variance and introducing parameters that control the relative contribution of the random effects. After integrating over the random effects and variance components, the resulting integrals needed to calculate the Bayes factor can be efficiently approximated with Laplace’s method. PMID:24082430

Simulation Research on Vehicle Active Suspension Controller Based on G1 Method

NASA Astrophysics Data System (ADS)

Li, Gen; Li, Hang; Zhang, Shuaiyang; Luo, Qiuhui

2017-09-01

Based on the order relation analysis method (G1 method), the optimal linear controller of vehicle active suspension is designed. The system of the main and passive suspension of the single wheel vehicle is modeled and the system input signal model is determined. Secondly, the system motion state space equation is established by the kinetic knowledge and the optimal linear controller design is completed with the optimal control theory. The weighting coefficient of the performance index coefficients of the main passive suspension is determined by the relational analysis method. Finally, the model is simulated in Simulink. The simulation results show that: the optimal weight value is determined by using the sequence relation analysis method under the condition of given road conditions, and the vehicle acceleration, suspension stroke and tire motion displacement are optimized to improve the comprehensive performance of the vehicle, and the active control is controlled within the requirements.

From Feynman rules to conserved quantum numbers, I

NASA Astrophysics Data System (ADS)

Nogueira, P.

2017-05-01

In the context of Quantum Field Theory (QFT) there is often the need to find sets of graph-like diagrams (the so-called Feynman diagrams) for a given physical model. If negative, the answer to the related problem 'Are there any diagrams with this set of external fields?' may settle certain physical questions at once. Here the latter problem is formulated in terms of a system of linear diophantine equations derived from the Lagrangian density, from which necessary conditions for the existence of the required diagrams may be obtained. Those conditions are equalities that look like either linear diophantine equations or linear modular (i.e. congruence) equations, and may be found by means of fairly simple algorithms that involve integer computations. The diophantine equations so obtained represent (particle) number conservation rules, and are related to the conserved (additive) quantum numbers that may be assigned to the fields of the model.

Modelling the spatio-temporal modulation response of ganglion cells with difference-of-Gaussians receptive fields: relation to photoreceptor response kinetics.

PubMed

Donner, K; Hemilä, S

1996-01-01

Difference-of-Gaussians (DOG) models for the receptive fields of retinal ganglion cells accurately predict linear responses to both periodic stimuli (typically moving sinusoidal gratings) and aperiodic stimuli (typically circular fields presented as square-wave pulses). While the relation of spatial organization to retinal anatomy has received considerable attention, temporal characteristics have been only loosely connected to retinal physiology. Here we integrate realistic photoreceptor response waveforms into the DOG model to clarify how far a single set of physiological parameters predict temporal aspects of linear responses to both periodic and aperiodic stimuli. Traditional filter-cascade models provide a useful first-order approximation of the single-photon response in photoreceptors. The absolute time scale of these, plus a time for retinal transmission, here construed as a fixed delay, are obtained from flash/step data. Using these values, we find that the DOG model predicts the main features of both the amplitude and phase response of linear cat ganglion cells to sinusoidal flicker. Where the simplest model formulation fails, it serves to reveal additional mechanisms. Unforeseen facts are the attenuation of low temporal frequencies even in pure center-type responses, and the phase advance of the response relative to the stimulus at low frequencies. Neither can be explained by any experimentally documented cone response waveform, but both would be explained by signal differentiation, e.g. in the retinal transmission pathway, as demonstrated at least in turtle retina.

GVE-Based Dynamics and Control for Formation Flying Spacecraft

NASA Technical Reports Server (NTRS)

Breger, Louis; How, Jonathan P.

2004-01-01

Formation flying is an enabling technology for many future space missions. This paper presents extensions to the equations of relative motion expressed in Keplerian orbital elements, including new initialization techniques for general formation configurations. A new linear time-varying form of the equations of relative motion is developed from Gauss Variational Equations and used in a model predictive controller. The linearizing assumptions for these equations are shown to be consistent with typical formation flying scenarios. Several linear, convex initialization techniques are presented, as well as a general, decentralized method for coordinating a tetrahedral formation using differential orbital elements. Control methods are validated using a commercial numerical propagator.

Energy Level Alignment at the Interface between Linear-Structured Benzenediamine Molecules and Au(111) Surface

NASA Astrophysics Data System (ADS)

Li, Guo; Rangel, Tonatiuh; Liu, Zhenfei; Cooper, Valentino; Neaton, Jeffrey

Using density functional theory with model self-energy corrections, we calculate the adsorption energetics and geometry, and the energy level alignment of benzenediamine (BDA) molecules adsorbed on Au(111) surfaces. Our calculations show that linear structures of BDA, stabilized via hydrogen bonds between amine groups, are energetically more favorable than monomeric phases. Moreover, our self-energy-corrected calculations of energy level alignment show that the highest occupied molecular orbital energy of the BDA linear structure is deeper relative to the Fermi level relative to the isolated monomer and agrees well with the values measured with photoemission spectroscopy. This work supported by DOE.

Probabilistic structural analysis by extremum methods

NASA Technical Reports Server (NTRS)

Nafday, Avinash M.

1990-01-01

The objective is to demonstrate discrete extremum methods of structural analysis as a tool for structural system reliability evaluation. Specifically, linear and multiobjective linear programming models for analysis of rigid plastic frames under proportional and multiparametric loadings, respectively, are considered. Kinematic and static approaches for analysis form a primal-dual pair in each of these models and have a polyhedral format. Duality relations link extreme points and hyperplanes of these polyhedra and lead naturally to dual methods for system reliability evaluation.

Endoreversible quantum heat engines in the linear response regime.

PubMed

Wang, Honghui; He, Jizhou; Wang, Jianhui

2017-07-01

We analyze general models of quantum heat engines operating a cycle of two adiabatic and two isothermal processes. We use the quantum master equation for a system to describe heat transfer current during a thermodynamic process in contact with a heat reservoir, with no use of phenomenological thermal conduction. We apply the endoreversibility description to such engine models working in the linear response regime and derive expressions of the efficiency and the power. By analyzing the entropy production rate along a single cycle, we identify the thermodynamic flux and force that a linear relation connects. From maximizing the power output, we find that such heat engines satisfy the tight-coupling condition and the efficiency at maximum power agrees with the Curzon-Ahlborn efficiency known as the upper bound in the linear response regime.

Testing concordance of instrumental variable effects in generalized linear models with application to Mendelian randomization

PubMed Central

Dai, James Y.; Chan, Kwun Chuen Gary; Hsu, Li

2014-01-01

Instrumental variable regression is one way to overcome unmeasured confounding and estimate causal effect in observational studies. Built on structural mean models, there has been considerale work recently developed for consistent estimation of causal relative risk and causal odds ratio. Such models can sometimes suffer from identification issues for weak instruments. This hampered the applicability of Mendelian randomization analysis in genetic epidemiology. When there are multiple genetic variants available as instrumental variables, and causal effect is defined in a generalized linear model in the presence of unmeasured confounders, we propose to test concordance between instrumental variable effects on the intermediate exposure and instrumental variable effects on the disease outcome, as a means to test the causal effect. We show that a class of generalized least squares estimators provide valid and consistent tests of causality. For causal effect of a continuous exposure on a dichotomous outcome in logistic models, the proposed estimators are shown to be asymptotically conservative. When the disease outcome is rare, such estimators are consistent due to the log-linear approximation of the logistic function. Optimality of such estimators relative to the well-known two-stage least squares estimator and the double-logistic structural mean model is further discussed. PMID:24863158

Immittance Data Validation by Kramers‐Kronig Relations – Derivation and Implications

PubMed Central

2017-01-01

Abstract Explicitly based on causality, linearity (superposition) and stability (time invariance) and implicit on continuity (consistency), finiteness (convergence) and uniqueness (single valuedness) in the time domain, Kramers‐Kronig (KK) integral transform (KKT) relations for immittances are derived as pure mathematical constructs in the complex frequency domain using the two‐sided (bilateral) Laplace integral transform (LT) reduced to the Fourier domain for sufficiently rapid exponential decaying, bounded immittances. Novel anti KK relations are also derived to distinguish LTI (linear, time invariant) systems from non‐linear, unstable and acausal systems. All relations can be used to test KK transformability on the LTI principles of linearity, stability and causality of measured and model data by Fourier transform (FT) in immittance spectroscopy (IS). Also, integral transform relations are provided to estimate (conjugate) immittances at zero and infinite frequency particularly useful to normalise data and compare data. Also, important implications for IS are presented and suggestions for consistent data analysis are made which generally apply likewise to complex valued quantities in many fields of engineering and natural sciences. PMID:29577007

Northwest Laboratory for Integrated Systems, University of Washington, Semiannual Technical Report Number 1, July 1-November 8, 1991

DTIC Science & Technology

1991-11-08

only simple bounds on delays but also relate the delays in linear inequalities so that tradeoffs are apparent. We model circuits as communicating...set of linear inequalities constraining the variables. These relations provide synthesis tools with information about tradeoffs between circuit delays...available to express the original circuit as a graph of elementary gates and then cover the graph’s fanout-free trees with collections of three-input

Estimating trajectories of energy intake through childhood and adolescence using linear-spline multilevel models.

PubMed

Anderson, Emma L; Tilling, Kate; Fraser, Abigail; Macdonald-Wallis, Corrie; Emmett, Pauline; Cribb, Victoria; Northstone, Kate; Lawlor, Debbie A; Howe, Laura D

2013-07-01

Methods for the assessment of changes in dietary intake across the life course are underdeveloped. We demonstrate the use of linear-spline multilevel models to summarize energy-intake trajectories through childhood and adolescence and their application as exposures, outcomes, or mediators. The Avon Longitudinal Study of Parents and Children assessed children's dietary intake several times between ages 3 and 13 years, using both food frequency questionnaires (FFQs) and 3-day food diaries. We estimated energy-intake trajectories for 12,032 children using linear-spline multilevel models. We then assessed the associations of these trajectories with maternal body mass index (BMI), and later offspring BMI, and also their role in mediating the relation between maternal and offspring BMIs. Models estimated average and individual energy intake at 3 years, and linear changes in energy intake from age 3 to 7 years and from age 7 to 13 years. By including the exposure (in this example, maternal BMI) in the multilevel model, we were able to estimate the average energy-intake trajectories across levels of the exposure. When energy-intake trajectories are the exposure for a later outcome (in this case offspring BMI) or a mediator (between maternal and offspring BMI), results were similar, whether using a two-step process (exporting individual-level intercepts and slopes from multilevel models and using these in linear regression/path analysis), or a single-step process (multivariate multilevel models). Trajectories were similar when FFQs and food diaries were assessed either separately, or when combined into one model. Linear-spline multilevel models provide useful summaries of trajectories of dietary intake that can be used as an exposure, outcome, or mediator.

Linear summation of outputs in a balanced network model of motor cortex

PubMed Central

Capaday, Charles; van Vreeswijk, Carl

2015-01-01

Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis. PMID:26097452

Non-linear stochastic growth rates and redshift space distortions

DOE PAGES

Jennings, Elise; Jennings, David

2015-04-09

The linear growth rate is commonly defined through a simple deterministic relation between the velocity divergence and the matter overdensity in the linear regime. We introduce a formalism that extends this to a non-linear, stochastic relation between θ = ∇ ∙ v(x,t)/aH and δ. This provides a new phenomenological approach that examines the conditional mean , together with the fluctuations of θ around this mean. We also measure these stochastic components using N-body simulations and find they are non-negative and increase with decreasing scale from ~10 per cent at k < 0.2 h Mpc -1 to 25 per cent atmore » k ~ 0.45 h Mpc -1 at z = 0. Both the stochastic relation and non-linearity are more pronounced for haloes, M ≤ 5 × 10 12 M ⊙ h -1, compared to the dark matter at z = 0 and 1. Non-linear growth effects manifest themselves as a rotation of the mean away from the linear theory prediction -f LTδ, where f LT is the linear growth rate. This rotation increases with wavenumber, k, and we show that it can be well-described by second-order Lagrangian perturbation theory (2LPT) fork < 0.1 h Mpc -1. Furthermore, the stochasticity in the θ – δ relation is not so simply described by 2LPT, and we discuss its impact on measurements of f LT from two-point statistics in redshift space. Furthermore, given that the relationship between δ and θ is stochastic and non-linear, this will have implications for the interpretation and precision of f LT extracted using models which assume a linear, deterministic expression.« less

A Bayesian approach for estimating under-reported dengue incidence with a focus on non-linear associations between climate and dengue in Dhaka, Bangladesh.

PubMed

Sharmin, Sifat; Glass, Kathryn; Viennet, Elvina; Harley, David

2018-04-01

Determining the relation between climate and dengue incidence is challenging due to under-reporting of disease and consequent biased incidence estimates. Non-linear associations between climate and incidence compound this. Here, we introduce a modelling framework to estimate dengue incidence from passive surveillance data while incorporating non-linear climate effects. We estimated the true number of cases per month using a Bayesian generalised linear model, developed in stages to adjust for under-reporting. A semi-parametric thin-plate spline approach was used to quantify non-linear climate effects. The approach was applied to data collected from the national dengue surveillance system of Bangladesh. The model estimated that only 2.8% (95% credible interval 2.7-2.8) of all cases in the capital Dhaka were reported through passive case reporting. The optimal mean monthly temperature for dengue transmission is 29℃ and average monthly rainfall above 15 mm decreases transmission. Our approach provides an estimate of true incidence and an understanding of the effects of temperature and rainfall on dengue transmission in Dhaka, Bangladesh.

Energetic consequences of mechanical loads.

PubMed

Loiselle, D S; Crampin, E J; Niederer, S A; Smith, N P; Barclay, C J

2008-01-01

In this brief review, we have focussed largely on the well-established, but essentially phenomenological, linear relationship between the energy expenditure of the heart (commonly assessed as the oxygen consumed per beat, oxygen consumption (VO2)) and the pressure-volume-area (PVA, the sum of pressure-volume work and a specified 'potential energy' term). We raise concerns regarding the propriety of ignoring work done during 'passive' ventricular enlargement during diastole as well as the work done against series elasticity during systole. We question the common assumption that the rate of basal metabolism is independent of ventricular volume, given the equally well-established Feng- or stretch-effect. Admittedly, each of these issues is more of conceptual than of quantitative import. We point out that the linearity of the enthalpy-PVA relation is now so well established that observed deviations from linearity are often ignored. Given that a one-dimensional equivalent of the linear VO2-PVA relation exists in papillary muscles, it seems clear that the phenomenon arises at the cellular level, rather than being a property of the intact heart. This leads us to discussion of the classes of crossbridge models that can be applied to the study of cardiac energetics. An admittedly superficial examination of the historical role played by Hooke's Law in theories of muscle contraction foreshadows deeper consideration of the thermodynamic constraints that must, in our opinion, guide the development of any mathematical model. We conclude that a satisfying understanding of the origin of the enthalpy-PVA relation awaits the development of such a model.

surrkick: Black-hole kicks from numerical-relativity surrogate models

NASA Astrophysics Data System (ADS)

Gerosa, Davide; Hébert, François; Stein, Leo C.

2018-04-01

surrkick quickly and reliably extract recoils imparted to generic, precessing, black hole binaries. It uses a numerical-relativity surrogate model to obtain the gravitational waveform given a set of binary parameters, and from this waveform directly integrates the gravitational-wave linear momentum flux. This entirely bypasses the need of fitting formulae which are typically used to model black-hole recoils in astrophysical contexts.

Regional variability among nonlinear chlorophyll-phosphorus relationships in lakes

USGS Publications Warehouse

Filstrup, Christopher T.; Wagner, Tyler; Soranno, Patricia A.; Stanley, Emily H.; Stow, Craig A.; Webster, Katherine E.; Downing, John A.

2014-01-01

The relationship between chlorophyll a (Chl a) and total phosphorus (TP) is a fundamental relationship in lakes that reflects multiple aspects of ecosystem function and is also used in the regulation and management of inland waters. The exact form of this relationship has substantial implications on its meaning and its use. We assembled a spatially extensive data set to examine whether nonlinear models are a better fit for Chl a—TP relationships than traditional log-linear models, whether there were regional differences in the form of the relationships, and, if so, which regional factors were related to these differences. We analyzed a data set from 2105 temperate lakes across 35 ecoregions by fitting and comparing two different nonlinear models and one log-linear model. The two nonlinear models fit the data better than the log-linear model. In addition, the parameters for the best-fitting model varied among regions: the maximum and lower Chl aasymptotes were positively and negatively related to percent regional pasture land use, respectively, and the rate at which chlorophyll increased with TP was negatively related to percent regional wetland cover. Lakes in regions with more pasture fields had higher maximum chlorophyll concentrations at high TP concentrations but lower minimum chlorophyll concentrations at low TP concentrations. Lakes in regions with less wetland cover showed a steeper Chl a—TP relationship than wetland-rich regions. Interpretation of Chl a—TP relationships depends on regional differences, and theory and management based on a monolithic relationship may be inaccurate.

Modeling the stress dependence of Barkhausen phenomena for stress axis linear and noncollinear with applied magnetic field (abstract)

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

Sablik, M.J.; Augustyniak, B.; Chmielewski, M.

1996-04-01

The almost linear dependence of the maximum Barkhausen noise signal amplitude on stress has made it a tool for nondestructive evaluation of residual stress. Recently, a model has been developed to account for the stress dependence of the Barkhausen noise signal. The model uses the development of Alessandro {ital et} {ital al}. who use coupled Langevin equations to derive an expression for the Barkhausen noise power spectrum. The model joins this expression to the magnetomechanical hysteresis model of Sablik {ital et} {ital al}., obtaining both a hysteretic and stress-dependent result for the magnetic-field-dependent Barkhausen noise envelope and obtaining specifically themore » almost linear stress dependence of the Barkhausen noise maximum experimentally. In this paper, we extend the model to derive the angular dependence observed by Kwun of the Barkhausen noise amplitude when stress axis is taken at different angles relative to magnetic field. We also apply the model to the experimental observation that in XC10 French steel, there is an apparent almost linear correlation with stress of hysteresis loss and of the integral of the Barkhausen noise signal over applied field {ital H}. Further, the two quantities, Barkhausen noise integral and hysteresis loss, are linearly correlated with each other. The model shows how that behavior is to be expected for the measured steel because of its sharply rising hysteresis curve. {copyright} {ital 1996 American Institute of Physics.}« less

Complexity and Productivity Differentiation Models of Metallogenic Indicator Elements in Rocks and Supergene Media Around Daijiazhuang Pb-Zn Deposit in Dangchang County, Gansu Province

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

He, Jin-zhong, E-mail: viewsino@163.com; Yao, Shu-zhen; Zhang, Zhong-ping

2013-03-15

With the help of complexity indices, we quantitatively studied multifractals, frequency distributions, and linear and nonlinear characteristics of geochemical data for exploration of the Daijiazhuang Pb-Zn deposit. Furthermore, we derived productivity differentiation models of elements from thermodynamics and self-organized criticality of metallogenic systems. With respect to frequency distributions and multifractals, only Zn in rocks and most elements except Sb in secondary media, which had been derived mainly from weathering and alluviation, exhibit nonlinear distributions. The relations of productivity to concentrations of metallogenic elements and paragenic elements in rocks and those of elements strongly leached in secondary media can be seenmore » as linear addition of exponential functions with a characteristic weak chaos. The relations of associated elements such as Mo, Sb, and Hg in rocks and other elements in secondary media can be expressed as an exponential function, and the relations of one-phase self-organized geological or metallogenic processes can be represented by a power function, each representing secondary chaos or strong chaos. For secondary media, exploration data of most elements should be processed using nonlinear mathematical methods or should be transformed to linear distributions before processing using linear mathematical methods.« less

On the Statistical Errors of RADAR Location Sensor Networks with Built-In Wi-Fi Gaussian Linear Fingerprints

PubMed Central

Zhou, Mu; Xu, Yu Bin; Ma, Lin; Tian, Shuo

2012-01-01

The expected errors of RADAR sensor networks with linear probabilistic location fingerprints inside buildings with varying Wi-Fi Gaussian strength are discussed. As far as we know, the statistical errors of equal and unequal-weighted RADAR networks have been suggested as a better way to evaluate the behavior of different system parameters and the deployment of reference points (RPs). However, up to now, there is still not enough related work on the relations between the statistical errors, system parameters, number and interval of the RPs, let alone calculating the correlated analytical expressions of concern. Therefore, in response to this compelling problem, under a simple linear distribution model, much attention will be paid to the mathematical relations of the linear expected errors, number of neighbors, number and interval of RPs, parameters in logarithmic attenuation model and variations of radio signal strength (RSS) at the test point (TP) with the purpose of constructing more practical and reliable RADAR location sensor networks (RLSNs) and also guaranteeing the accuracy requirements for the location based services in future ubiquitous context-awareness environments. Moreover, the numerical results and some real experimental evaluations of the error theories addressed in this paper will also be presented for our future extended analysis. PMID:22737027

On the statistical errors of RADAR location sensor networks with built-in Wi-Fi Gaussian linear fingerprints.

PubMed

Zhou, Mu; Xu, Yu Bin; Ma, Lin; Tian, Shuo

2012-01-01

The expected errors of RADAR sensor networks with linear probabilistic location fingerprints inside buildings with varying Wi-Fi Gaussian strength are discussed. As far as we know, the statistical errors of equal and unequal-weighted RADAR networks have been suggested as a better way to evaluate the behavior of different system parameters and the deployment of reference points (RPs). However, up to now, there is still not enough related work on the relations between the statistical errors, system parameters, number and interval of the RPs, let alone calculating the correlated analytical expressions of concern. Therefore, in response to this compelling problem, under a simple linear distribution model, much attention will be paid to the mathematical relations of the linear expected errors, number of neighbors, number and interval of RPs, parameters in logarithmic attenuation model and variations of radio signal strength (RSS) at the test point (TP) with the purpose of constructing more practical and reliable RADAR location sensor networks (RLSNs) and also guaranteeing the accuracy requirements for the location based services in future ubiquitous context-awareness environments. Moreover, the numerical results and some real experimental evaluations of the error theories addressed in this paper will also be presented for our future extended analysis.

Removing an intersubject variance component in a general linear model improves multiway factoring of event-related spectral perturbations in group EEG studies.

PubMed

Spence, Jeffrey S; Brier, Matthew R; Hart, John; Ferree, Thomas C

2013-03-01

Linear statistical models are used very effectively to assess task-related differences in EEG power spectral analyses. Mixed models, in particular, accommodate more than one variance component in a multisubject study, where many trials of each condition of interest are measured on each subject. Generally, intra- and intersubject variances are both important to determine correct standard errors for inference on functions of model parameters, but it is often assumed that intersubject variance is the most important consideration in a group study. In this article, we show that, under common assumptions, estimates of some functions of model parameters, including estimates of task-related differences, are properly tested relative to the intrasubject variance component only. A substantial gain in statistical power can arise from the proper separation of variance components when there is more than one source of variability. We first develop this result analytically, then show how it benefits a multiway factoring of spectral, spatial, and temporal components from EEG data acquired in a group of healthy subjects performing a well-studied response inhibition task. Copyright © 2011 Wiley Periodicals, Inc.

Relating Stellar Cycle Periods to Dynamo Calculations

NASA Technical Reports Server (NTRS)

Tobias, S. M.

1998-01-01

Stellar magnetic activity in slowly rotating stars is often cyclic, with the period of the magnetic cycle depending critically on the rotation rate and the convective turnover time of the star. Here we show that the interpretation of this law from dynamo models is not a simple task. It is demonstrated that the period is (unsurprisingly) sensitive to the precise type of non-linearity employed. Moreover the calculation of the wave-speed of plane-wave solutions does not (as was previously supposed) give an indication of the magnetic period in a more realistic dynamo model, as the changes in length-scale of solutions are not easily captured by this approach. Progress can be made, however, by considering a realistic two-dimensional model, in which the radial length-scale of waves is included. We show that it is possible in this case to derive a more robust relation between cycle period and dynamo number. For all the non-linearities considered in the most realistic model, the magnetic cycle period is a decreasing function of IDI (the amplitude of the dynamo number). However, discriminating between different non-linearities is difficult in this case and care must therefore be taken before advancing explanations for the magnetic periods of stars.

A non-linear induced polarization effect on transient electromagnetic soundings

NASA Astrophysics Data System (ADS)

Hallbauer-Zadorozhnaya, Valeriya Yu.; Santarato, Giovanni; Abu Zeid, Nasser; Bignardi, Samuel

2016-10-01

In a TEM survey conducted for characterizing the subsurface for geothermal purposes, a strong induced polarization effect was recorded in all collected data. Surprisingly, anomalous decay curves were obtained in part of the sites, whose shape depended on the repetition frequency of the exciting square waveform, i.e. on current pulse length. The Cole-Cole model, besides being not directly related to physical parameters of rocks, was found inappropriate to model the observed distortion, due to induced polarization, because this model is linear, i.e. it cannot fit any dependence on current pulse. This phenomenon was investigated and explained as due to the presence of membrane polarization linked to constrictivity of (fresh) water-saturated pores. An algorithm for mathematical modeling of TEM data was then developed to fit this behavior. The case history is then discussed: 1D inversion, which accommodates non-linear effects, produced models that agree quite satisfactorily with resistivity and chargeability models obtained by an electrical resistivity tomography carried out for comparison.

Computational study of nonlinear plasma waves. [plasma simulation model applied to electrostatic waves in collisionless plasma

NASA Technical Reports Server (NTRS)

Matsuda, Y.

1974-01-01

A low-noise plasma simulation model is developed and applied to a series of linear and nonlinear problems associated with electrostatic wave propagation in a one-dimensional, collisionless, Maxwellian plasma, in the absence of magnetic field. It is demonstrated that use of the hybrid simulation model allows economical studies to be carried out in both the linear and nonlinear regimes with better quantitative results, for comparable computing time, than can be obtained by conventional particle simulation models, or direct solution of the Vlasov equation. The characteristics of the hybrid simulation model itself are first investigated, and it is shown to be capable of verifying the theoretical linear dispersion relation at wave energy levels as low as .000001 of the plasma thermal energy. Having established the validity of the hybrid simulation model, it is then used to study the nonlinear dynamics of monochromatic wave, sideband instability due to trapped particles, and satellite growth.

Measures and models for angular correlation and angular-linear correlation. [correlation of random variables

NASA Technical Reports Server (NTRS)

Johnson, R. A.; Wehrly, T.

1976-01-01

Population models for dependence between two angular measurements and for dependence between an angular and a linear observation are proposed. The method of canonical correlations first leads to new population and sample measures of dependence in this latter situation. An example relating wind direction to the level of a pollutant is given. Next, applied to pairs of angular measurements, the method yields previously proposed sample measures in some special cases and a new sample measure in general.

Dynamic Modeling and Very Short-term Prediction of Wind Power Output Using Box-Cox Transformation

NASA Astrophysics Data System (ADS)

Urata, Kengo; Inoue, Masaki; Murayama, Dai; Adachi, Shuichi

2016-09-01

We propose a statistical modeling method of wind power output for very short-term prediction. The modeling method with a nonlinear model has cascade structure composed of two parts. One is a linear dynamic part that is driven by a Gaussian white noise and described by an autoregressive model. The other is a nonlinear static part that is driven by the output of the linear part. This nonlinear part is designed for output distribution matching: we shape the distribution of the model output to match with that of the wind power output. The constructed model is utilized for one-step ahead prediction of the wind power output. Furthermore, we study the relation between the prediction accuracy and the prediction horizon.

Quantification of frequency-components contributions to the discharge of a karst spring

NASA Astrophysics Data System (ADS)

Taver, V.; Johannet, A.; Vinches, M.; Borrell, V.; Pistre, S.; Bertin, D.

2013-12-01

Karst aquifers represent important underground resources for water supplies, providing it to 25% of the population. Nevertheless such systems are currently underexploited because of their heterogeneity and complexity, which make work fields and physical measurements expensive, and frequently not representative of the whole aquifer. The systemic paradigm appears thus at a complementary approach to study and model karst aquifers in the framework of non-linear system analysis. Its input and output signals, namely rainfalls and discharge contain information about the function performed by the physical process. Therefore, improvement of knowledge about the karst system can be provided using time series analysis, for example Fourier analysis or orthogonal decomposition [1]. Another level of analysis consists in building non-linear models to identify rainfall/discharge relation, component by component [2]. In this context, this communication proposes to use neural networks to first model the rainfall-runoff relation using frequency components, and second to analyze the models, using the KnoX method [3], in order to quantify the importance of each component. Two different neural models were designed: (i) the recurrent model which implements a non-linear recurrent model fed by rainfalls, ETP and previous estimated discharge, (ii) the feed-forward model which implements a non-linear static model fed by rainfalls, ETP and previous observed discharges. The first model is known to better represent the rainfall-runoff relation; the second one to better predict the discharge based on previous discharge observations. KnoX method is based on a variable selection method, which simply considers values of parameters after the training without taking into account the non-linear behavior of the model during functioning. An amelioration of the KnoX method, is thus proposed in order to overcome this inadequacy. The proposed method, leads thus to both a hierarchization and a quantification of the input variables, here the frequency components, over output signal. Applied to the Lez karst aquifer, the combination of frequency decomposition and knowledge extraction improves knowledge on hydrological behavior. Both models and both extraction methods were applied and assessed using a fictitious reference model. Discussion is proposed in order to analyze efficiency of the methods compared to in situ measurements and tracing. [1] D. Labat et al. 'Rainfall-runoff relations for karst springs. Part II: continuous wavelet and discrete orthogonal multiresolution' In J of Hydrology, Vol. 238, 2000, pp. 149-178. [2] A. Johannet et al. 'Prediction of Lez Spring Discharge (Southern France) by Neural Networks using Orthogonal Wavelet Decomposition'.IJCNN Proceedings Brisbane 2012. [3] L. Kong A Siou et al. 'Modélisation hydrodynamique des karsts par réseaux de neurones : Comment dépasser la boîte noire. (Karst hydrodynamic modelling using artificial neural networks: how to surpass the black box ?)'. Proceedings of the 9th conference on limestone hydrogeology,2011 Besançon, France.

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

Ko, L.F.

Calculations for the two-point correlation functions in the scaling limit for two statistical models are presented. In Part I, the Ising model with a linear defect is studied for T < T/sub c/ and T > T/sub c/. The transfer matrix method of Onsager and Kaufman is used. The energy-density correlation is given by functions related to the modified Bessel functions. The dispersion expansion for the spin-spin correlation functions are derived. The dominant behavior for large separations at T not equal to T/sub c/ is extracted. It is shown that these expansions lead to systems of Fredholm integral equations. Inmore » Part II, the electric correlation function of the eight-vertex model for T < T/sub c/ is studied. The eight vertex model decouples to two independent Ising models when the four spin coupling vanishes. To first order in the four-spin coupling, the electric correlation function is related to a three-point function of the Ising model. This relation is systematically investigated and the full dispersion expansion (to first order in four-spin coupling) is obtained. The results is a new kind of structure which, unlike those of many solvable models, is apparently not expressible in terms of linear integral equations.« less

Quantifying parametric uncertainty in the Rothermel model

Treesearch

S. Goodrick

2008-01-01

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spreadmodel (implemented in software such as fire spread models in the United States. This model consists of a non-linear system of equations that relates environmentalvariables (input parameter groups...

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

ERIC Educational Resources Information Center

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

LEOPARD: A grid-based dispersion relation solver for arbitrary gyrotropic distributions

NASA Astrophysics Data System (ADS)

Astfalk, Patrick; Jenko, Frank

2017-01-01

Particle velocity distributions measured in collisionless space plasmas often show strong deviations from idealized model distributions. Despite this observational evidence, linear wave analysis in space plasma environments such as the solar wind or Earth's magnetosphere is still mainly carried out using dispersion relation solvers based on Maxwellians or other parametric models. To enable a more realistic analysis, we present the new grid-based kinetic dispersion relation solver LEOPARD (Linear Electromagnetic Oscillations in Plasmas with Arbitrary Rotationally-symmetric Distributions) which no longer requires prescribed model distributions but allows for arbitrary gyrotropic distribution functions. In this work, we discuss the underlying numerical scheme of the code and we show a few exemplary benchmarks. Furthermore, we demonstrate a first application of LEOPARD to ion distribution data obtained from hybrid simulations. In particular, we show that in the saturation stage of the parallel fire hose instability, the deformation of the initial bi-Maxwellian distribution invalidates the use of standard dispersion relation solvers. A linear solver based on bi-Maxwellians predicts further growth even after saturation, while LEOPARD correctly indicates vanishing growth rates. We also discuss how this complies with former studies on the validity of quasilinear theory for the resonant fire hose. In the end, we briefly comment on the role of LEOPARD in directly analyzing spacecraft data, and we refer to an upcoming paper which demonstrates a first application of that kind.

Development of control strategies for safe microburst penetration: A progress report

NASA Technical Reports Server (NTRS)

Psiaki, Mark L.

1987-01-01

A single-engine, propeller-driven, general-aviation model was incorporated into the nonlinear simulation and into the linear analysis of root loci and frequency response. Full-scale wind tunnel data provided its aerodynamic model, and the thrust model included the airspeed dependent effects of power and propeller efficiency. Also, the parameters of the Jet Transport model were changed to correspond more closely to the Boeing 727. In order to study their effects on steady-state repsonse to vertical wind inputs, altitude and total specific energy (air-relative and inertial) feedback capabilities were added to the nonlinear and linear models. Multiloop system design goals were defined. Attempts were made to develop controllers which achieved these goals.

Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation.

PubMed

Cole, Marc; Eikenberry, Steffen; Kato, Takahide; Sandler, Roman A; Yamashiro, Stanley M; Marmarelis, Vasilis Z

2017-03-01

A nonparametric model of smooth muscle tension response to electrical stimulation was estimated using the Laguerre expansion technique of nonlinear system kernel estimation. The experimental data consisted of force responses of smooth muscle to energy-matched alternating single pulse and burst current stimuli. The burst stimuli led to at least a 10-fold increase in peak force in smooth muscle from Mytilus edulis, despite the constant energy constraint. A linear model did not fit the data. However, a second-order model fit the data accurately, so the higher-order models were not required to fit the data. Results showed that smooth muscle force response is not linearly related to the stimulation power.

Linear Instability Analysis of non-uniform Bubbly Mixing layer with Two-Fluid model

NASA Astrophysics Data System (ADS)

Sharma, Subash; Chetty, Krishna; Lopez de Bertodano, Martin

We examine the inviscid instability of a non-uniform adiabatic bubbly shear layer with a Two-Fluid model. The Two-Fluid model is made well-posed with the closure relations for interfacial forces. First, a characteristic analysis is carried out to study the well posedness of the model over range of void fraction with interfacial forces for virtual mass, interfacial drag, interfacial pressure. A dispersion analysis then allow us to obtain growth rate and wavelength. Then, the well-posed two-fluid model is solved using CFD to validate the results obtained with the linear stability analysis. The effect of the void fraction and the distribution profile on stability is analyzed.

A conformal approach for the analysis of the non-linear stability of radiation cosmologies

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

Luebbe, Christian, E-mail: c.luebbe@ucl.ac.uk; Department of Mathematics, University of Leicester, University Road, LE1 8RH; Valiente Kroon, Juan Antonio, E-mail: j.a.valiente-kroon@qmul.ac.uk

2013-01-15

The conformal Einstein equations for a trace-free (radiation) perfect fluid are derived in terms of the Levi-Civita connection of a conformally rescaled metric. These equations are used to provide a non-linear stability result for de Sitter-like trace-free (radiation) perfect fluid Friedman-Lemaitre-Robertson-Walker cosmological models. The solutions thus obtained exist globally towards the future and are future geodesically complete. - Highlights: Black-Right-Pointing-Pointer We study the Einstein-Euler system in General Relativity using conformal methods. Black-Right-Pointing-Pointer We analyze the structural properties of the associated evolution equations. Black-Right-Pointing-Pointer We establish the non-linear stability of pure radiation cosmological models.

A simple and exploratory way to determine the mean-variance relationship in generalized linear models.

PubMed

Tsou, Tsung-Shan

2007-03-30

This paper introduces an exploratory way to determine how variance relates to the mean in generalized linear models. This novel method employs the robust likelihood technique introduced by Royall and Tsou.A urinary data set collected by Ginsberg et al. and the fabric data set analysed by Lee and Nelder are considered to demonstrate the applicability and simplicity of the proposed technique. Application of the proposed method could easily reveal a mean-variance relationship that would generally be left unnoticed, or that would require more complex modelling to detect. Copyright (c) 2006 John Wiley & Sons, Ltd.

Using the nonlinear aquifer storage-discharge relationship to simulate the base flow of glacier- and snowmelt-dominated basins in northwest China

NASA Astrophysics Data System (ADS)

Gan, R.; Luo, Y.

2013-09-01

Base flow is an important component in hydrological modeling. This process is usually modeled by using the linear aquifer storage-discharge relation approach, although the outflow from groundwater aquifers is nonlinear. To identify the accuracy of base flow estimates in rivers dominated by snowmelt and/or glacier melt in arid and cold northwestern China, a nonlinear storage-discharge relationship for use in SWAT (Soil Water Assessment Tool) modeling was developed and applied to the Manas River basin in the Tian Shan Mountains. Linear reservoir models and a digital filter program were used for comparisons. Meanwhile, numerical analysis of recession curves from 78 river gauge stations revealed variation in the parameters of the nonlinear relationship. It was found that the nonlinear reservoir model can improve the streamflow simulation, especially for low-flow period. The higher Nash-Sutcliffe efficiency, logarithmic efficiency, and volumetric efficiency, and lower percent bias were obtained when compared to the one-linear reservoir approach. The parameter b of the aquifer storage-discharge function varied mostly between 0.0 and 0.1, which is much smaller than the suggested value of 0.5. The coefficient a of the function is related to catchment properties, primarily the basin and glacier areas.

LINEAR LATTICE AND TRAJECTORY RECONSTRUCTION AND CORRECTION AT FAST LINEAR ACCELERATOR

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

Romanov, A.; Edstrom, D.; Halavanau, A.

2017-07-16

The low energy part of the FAST linear accelerator based on 1.3 GHz superconducting RF cavities was successfully commissioned [1]. During commissioning, beam based model dependent methods were used to correct linear lattice and trajectory. Lattice correction algorithm is based on analysis of beam shape from profile monitors and trajectory responses to dipole correctors. Trajectory responses to field gradient variations in quadrupoles and phase variations in superconducting RF cavities were used to correct bunch offsets in quadrupoles and accelerating cavities relative to their magnetic axes. Details of used methods and experimental results are presented.

Helping Students Assess the Relative Importance of Different Intermolecular Interactions

ERIC Educational Resources Information Center

Jasien, Paul G.

2008-01-01

A semi-quantitative model has been developed to estimate the relative effects of dispersion, dipole-dipole interactions, and H-bonding on the normal boiling points ("T[subscript b]") for a subset of simple organic systems. The model is based upon a statistical analysis using multiple linear regression on a series of straight-chain organic…

Changing Mental Representations Using Related Physical Models: The Effects of Analyzing Number Lines on Learner Internal Scale of Numerical Magnitude

ERIC Educational Resources Information Center

Bengtson, Barbara J.

2013-01-01

Understanding the linear relationship of numbers is essential for doing practical and abstract mathematics throughout education and everyday life. There is evidence that number line activities increase learners' number sense, improving the linearity of mental number line representations (Siegler & Ramani, 2009). Mental representations of…

Quasi-likelihood generalized linear regression analysis of fatality risk data

DOT National Transportation Integrated Search

2009-01-01

Transportation-related fatality risks is a function of many interacting human, vehicle, and environmental factors. Statisitcally valid analysis of such data is challenged both by the complexity of plausable structural models relating fatality rates t...

A Review on Inertia and Linear Friction Welding of Ni-Based Superalloys

NASA Astrophysics Data System (ADS)

Chamanfar, Ahmad; Jahazi, Mohammad; Cormier, Jonathan

2015-04-01

Inertia and linear friction welding are being increasingly used for near-net-shape manufacturing of high-value materials in aerospace and power generation gas turbines because of providing a better quality joint and offering many advantages over conventional fusion welding and mechanical joining techniques. In this paper, the published works up-to-date on inertia and linear friction welding of Ni-based superalloys are reviewed with the objective to make clarifications on discrepancies and uncertainties reported in literature regarding issues related to these two friction welding processes as well as microstructure, texture, and mechanical properties of the Ni-based superalloy weldments. Initially, the chemical composition and microstructure of Ni-based superalloys that contribute to the quality of the joint are reviewed briefly. Then, problems related to fusion welding of these alloys are addressed with due consideration of inertia and linear friction welding as alternative techniques. The fundamentals of inertia and linear friction welding processes are analyzed next with emphasis on the bonding mechanisms and evolution of temperature and strain rate across the weld interface. Microstructural features, texture development, residual stresses, and mechanical properties of similar and dissimilar polycrystalline and single crystal Ni-based superalloy weldments are discussed next. Then, application of inertia and linear friction welding for joining Ni-based superalloys and related advantages over fusion welding, mechanical joining, and machining are explained briefly. Finally, present scientific and technological challenges facing inertia and linear friction welding of Ni-based superalloys including those related to modeling of these processes are addressed.

Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon

USGS Publications Warehouse

Hall, Robert O.; Yackulic, Charles B.; Kennedy, Theodore A.; Yard, Michael D.; Rosi-Marshall, Emma J.; Voichick, Nicholas; Behn, Kathrine E.

2015-01-01

Dams and river regulation greatly alter the downstream environment for gross primary production (GPP) because of changes in water clarity, flow, and temperature regimes. We estimated reach-scale GPP in five locations of the regulated Colorado River in Grand Canyon using an open channel model of dissolved oxygen. Benthic GPP dominates in Grand Canyon due to fast transport times and low pelagic algal biomass. In one location, we used a 738 days time series of GPP to identify the relative contribution of different physical controls of GPP. We developed both linear and semimechanistic time series models that account for unmeasured temporal covariance due to factors such as algal biomass dynamics. GPP varied from 0 g O2 m−2 d−1 to 3.0 g O2 m−2 d−1 with a relatively low annual average of 0.8 g O2 m−2d−1. Semimechanistic models fit the data better than linear models and demonstrated that variation in turbidity primarily controlled GPP. Lower solar insolation during winter and from cloud cover lowered GPP much further. Hydropeaking lowered GPP but only during turbid conditions. Using the best model and parameter values, the model accurately predicted seasonal estimates of GPP at 3 of 4 upriver sites and outperformed the linear model at all sites; discrepancies were likely from higher algal biomass at upstream sites. This modeling approach can predict how changes in physical controls will affect relative rates of GPP throughout the 385 km segment of the Colorado River in Grand Canyon and can be easily applied to other streams and rivers.

Frequency analysis via the method of moment functionals

NASA Technical Reports Server (NTRS)

Pearson, A. E.; Pan, J. Q.

1990-01-01

Several variants are presented of a linear-in-parameters least squares formulation for determining the transfer function of a stable linear system at specified frequencies given a finite set of Fourier series coefficients calculated from transient nonstationary input-output data. The basis of the technique is Shinbrot's classical method of moment functionals using complex Fourier based modulating functions to convert a differential equation model on a finite time interval into an algebraic equation which depends linearly on frequency-related parameters.

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Application of linear logic to simulation

NASA Astrophysics Data System (ADS)

Clarke, Thomas L.

1998-08-01

Linear logic, since its introduction by Girard in 1987 has proven expressive and powerful. Linear logic has provided natural encodings of Turing machines, Petri nets and other computational models. Linear logic is also capable of naturally modeling resource dependent aspects of reasoning. The distinguishing characteristic of linear logic is that it accounts for resources; two instances of the same variable are considered differently from a single instance. Linear logic thus must obey a form of the linear superposition principle. A proportion can be reasoned with only once, unless a special operator is applied. Informally, linear logic distinguishes two kinds of conjunction, two kinds of disjunction, and also introduces a modal storage operator that explicitly indicates propositions that can be reused. This paper discuses the application of linear logic to simulation. A wide variety of logics have been developed; in addition to classical logic, there are fuzzy logics, affine logics, quantum logics, etc. All of these have found application in simulations of one sort or another. The special characteristics of linear logic and its benefits for simulation will be discussed. Of particular interest is a connection that can be made between linear logic and simulated dynamics by using the concept of Lie algebras and Lie groups. Lie groups provide the connection between the exponential modal storage operators of linear logic and the eigen functions of dynamic differential operators. Particularly suggestive are possible relations between complexity result for linear logic and non-computability results for dynamical systems.

Analysis of calibration data for the uranium active neutron coincidence counting collar with attention to errors in the measured neutron coincidence rate

DOE PAGES

Croft, Stephen; Burr, Thomas Lee; Favalli, Andrea; ...

2015-12-10

We report that the declared linear density of 238U and 235U in fresh low enriched uranium light water reactor fuel assemblies can be verified for nuclear safeguards purposes using a neutron coincidence counter collar in passive and active mode, respectively. The active mode calibration of the Uranium Neutron Collar – Light water reactor fuel (UNCL) instrument is normally performed using a non-linear fitting technique. The fitting technique relates the measured neutron coincidence rate (the predictor) to the linear density of 235U (the response) in order to estimate model parameters of the nonlinear Padé equation, which traditionally is used to modelmore » the calibration data. Alternatively, following a simple data transformation, the fitting can also be performed using standard linear fitting methods. This paper compares performance of the nonlinear technique to the linear technique, using a range of possible error variance magnitudes in the measured neutron coincidence rate. We develop the required formalism and then apply the traditional (nonlinear) and alternative approaches (linear) to the same experimental and corresponding simulated representative datasets. Lastly, we find that, in this context, because of the magnitude of the errors in the predictor, it is preferable not to transform to a linear model, and it is preferable not to adjust for the errors in the predictor when inferring the model parameters« less

An improved null model for assessing the net effects of multiple stressors on communities.

PubMed

Thompson, Patrick L; MacLennan, Megan M; Vinebrooke, Rolf D

2018-01-01

Ecological stressors (i.e., environmental factors outside their normal range of variation) can mediate each other through their interactions, leading to unexpected combined effects on communities. Determining whether the net effect of stressors is ecologically surprising requires comparing their cumulative impact to a null model that represents the linear combination of their individual effects (i.e., an additive expectation). However, we show that standard additive and multiplicative null models that base their predictions on the effects of single stressors on community properties (e.g., species richness or biomass) do not provide this linear expectation, leading to incorrect interpretations of antagonistic and synergistic responses by communities. We present an alternative, the compositional null model, which instead bases its predictions on the effects of stressors on individual species, and then aggregates them to the community level. Simulations demonstrate the improved ability of the compositional null model to accurately provide a linear expectation of the net effect of stressors. We simulate the response of communities to paired stressors that affect species in a purely additive fashion and compare the relative abilities of the compositional null model and two standard community property null models (additive and multiplicative) to predict these linear changes in species richness and community biomass across different combinations (both positive, negative, or opposite) and intensities of stressors. The compositional model predicts the linear effects of multiple stressors under almost all scenarios, allowing for proper classification of net effects, whereas the standard null models do not. Our findings suggest that current estimates of the prevalence of ecological surprises on communities based on community property null models are unreliable, and should be improved by integrating the responses of individual species to the community level as does our compositional null model. © 2017 John Wiley & Sons Ltd.

Biological effects and equivalent doses in radiotherapy: A software solution

PubMed Central

Voyant, Cyril; Julian, Daniel; Roustit, Rudy; Biffi, Katia; Lantieri, Céline

2013-01-01

Background The limits of TDF (time, dose, and fractionation) and linear quadratic models have been known for a long time. Medical physicists and physicians are required to provide fast and reliable interpretations regarding delivered doses or any future prescriptions relating to treatment changes. Aim We, therefore, propose a calculation interface under the GNU license to be used for equivalent doses, biological doses, and normal tumor complication probability (Lyman model). Materials and methods The methodology used draws from several sources: the linear-quadratic-linear model of Astrahan, the repopulation effects of Dale, and the prediction of multi-fractionated treatments of Thames. Results and conclusions The results are obtained from an algorithm that minimizes an ad-hoc cost function, and then compared to an equivalent dose computed using standard calculators in seven French radiotherapy centers. PMID:24936319

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

NASA Astrophysics Data System (ADS)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both heteroscedastic and homoscedastic data. Conversely, samples related to different methods produce discrepant results, due to the presence of (still undetected) systematic errors, which implies no definitive statement can be made at present. A comparison is also made between different expressions of regression line slope and intercept variance estimators, where fractional discrepancies are found to be not exceeding a few percent, which grows up to about 20% in the presence of large dispersion data. An extension of the formalism to structural models is left to a forthcoming paper.

Within crown variation in the relationship between foliage biomass and sapwood area in jack pine.

PubMed

Schneider, Robert; Berninger, Frank; Ung, Chhun-Huor; Mäkelä, Annikki; Swift, D Edwin; Zhang, S Y

2011-01-01

The relationship between sapwood area and foliage biomass is the basis for a lot of research on eco-phyisology. In this paper, foliage biomass change between two consecutive whorls is studied, using different variations in the pipe model theory. Linear and non-linear mixed-effect models relating foliage differences to sapwood area increments were tested to take into account whorl location, with the best fit statistics supporting the non-linear formulation. The estimated value of the exponent is 0.5130, which is significantly different from 1, the expected value given by the pipe model theory. When applied to crown stem sapwood taper, the model indicates that foliage biomass distribution influences the foliage biomass to sapwood area at crown base ratio. This result is interpreted as being the consequence of differences in the turnover rates of sapwood and foliage. More importantly, the model explains previously reported trends in jack pine sapwood area at crown base to tree foliage biomass ratio.

A Novel Nonlinear Piezoelectric Energy Harvesting System Based on Linear-Element Coupling: Design, Modeling and Dynamic Analysis.

PubMed

Zhou, Shengxi; Yan, Bo; Inman, Daniel J

2018-05-09

This paper presents a novel nonlinear piezoelectric energy harvesting system which consists of linear piezoelectric energy harvesters connected by linear springs. In principle, the presented nonlinear system can improve broadband energy harvesting efficiency where magnets are forbidden. The linear spring inevitably produces the nonlinear spring force on the connected harvesters, because of the geometrical relationship and the time-varying relative displacement between two adjacent harvesters. Therefore, the presented nonlinear system has strong nonlinear characteristics. A theoretical model of the presented nonlinear system is deduced, based on Euler-Bernoulli beam theory, Kirchhoff’s law, piezoelectric theory and the relevant geometrical relationship. The energy harvesting enhancement of the presented nonlinear system (when n = 2, 3) is numerically verified by comparing with its linear counterparts. In the case study, the output power area of the presented nonlinear system with two and three energy harvesters is 268.8% and 339.8% of their linear counterparts, respectively. In addition, the nonlinear dynamic response characteristics are analyzed via bifurcation diagrams, Poincare maps of the phase trajectory, and the spectrum of the output voltage.

Incorporating inductances in tissue-scale models of cardiac electrophysiology

NASA Astrophysics Data System (ADS)

Rossi, Simone; Griffith, Boyce E.

2017-09-01

In standard models of cardiac electrophysiology, including the bidomain and monodomain models, local perturbations can propagate at infinite speed. We address this unrealistic property by developing a hyperbolic bidomain model that is based on a generalization of Ohm's law with a Cattaneo-type model for the fluxes. Further, we obtain a hyperbolic monodomain model in the case that the intracellular and extracellular conductivity tensors have the same anisotropy ratio. In one spatial dimension, the hyperbolic monodomain model is equivalent to a cable model that includes axial inductances, and the relaxation times of the Cattaneo fluxes are strictly related to these inductances. A purely linear analysis shows that the inductances are negligible, but models of cardiac electrophysiology are highly nonlinear, and linear predictions may not capture the fully nonlinear dynamics. In fact, contrary to the linear analysis, we show that for simple nonlinear ionic models, an increase in conduction velocity is obtained for small and moderate values of the relaxation time. A similar behavior is also demonstrated with biophysically detailed ionic models. Using the Fenton-Karma model along with a low-order finite element spatial discretization, we numerically analyze differences between the standard monodomain model and the hyperbolic monodomain model. In a simple benchmark test, we show that the propagation of the action potential is strongly influenced by the alignment of the fibers with respect to the mesh in both the parabolic and hyperbolic models when using relatively coarse spatial discretizations. Accurate predictions of the conduction velocity require computational mesh spacings on the order of a single cardiac cell. We also compare the two formulations in the case of spiral break up and atrial fibrillation in an anatomically detailed model of the left atrium, and we examine the effect of intracellular and extracellular inductances on the virtual electrode phenomenon.

Spacecraft nonlinear control

NASA Technical Reports Server (NTRS)

Sheen, Jyh-Jong; Bishop, Robert H.

1992-01-01

The feedback linearization technique is applied to the problem of spacecraft attitude control and momentum management with control moment gyros (CMGs). The feedback linearization consists of a coordinate transformation, which transforms the system to a companion form, and a nonlinear feedback control law to cancel the nonlinear dynamics resulting in a linear equivalent model. Pole placement techniques are then used to place the closed-loop poles. The coordinate transformation proposed here evolves from three output functions of relative degree four, three, and two, respectively. The nonlinear feedback control law is presented. Stability in a neighborhood of a controllable torque equilibrium attitude (TEA) is guaranteed and this fact is demonstrated by the simulation results. An investigation of the nonlinear control law shows that singularities exist in the state space outside the neighborhood of the controllable TEA. The nonlinear control law is simplified by a standard linearization technique and it is shown that the linearized nonlinear controller provides a natural way to select control gains for the multiple-input, multiple-output system. Simulation results using the linearized nonlinear controller show good performance relative to the nonlinear controller in the neighborhood of the TEA.

Linear model describing three components of flow in karst aquifers using 18O data

USGS Publications Warehouse

Long, Andrew J.; Putnam, L.D.

2004-01-01

The stable isotope of oxygen, 18O, is used as a naturally occurring ground-water tracer. Time-series data for ??18O are analyzed to model the distinct responses and relative proportions of the conduit, intermediate, and diffuse flow components in karst aquifers. This analysis also describes mathematically the dynamics of the transient fluid interchange between conduits and diffusive networks. Conduit and intermediate flow are described by linear-systems methods, whereas diffuse flow is described by mass-balance methods. An automated optimization process estimates parameters of lognormal, Pearson type III, and gamma distributions, which are used as transfer functions in linear-systems analysis. Diffuse flow and mixing parameters also are estimated by these optimization methods. Results indicate the relative proximity of a well to a main conduit flowpath and can help to predict the movement and residence times of potential contaminants. The three-component linear model is applied to five wells, which respond to changes in the isotopic composition of point recharge water from a sinking stream in the Madison aquifer in the Black Hills of South Dakota. Flow velocities as much as 540 m/d and system memories of as much as 71 years are estimated by this method. Also, the mean, median, and standard deviation of traveltimes; time to peak response; and the relative fraction of flow for each of the three components are determined for these wells. This analysis infers that flow may branch apart and rejoin as a result of an anastomotic (or channeled) karst network.

Polarization of stacking fault related luminescence in GaN nanorods

NASA Astrophysics Data System (ADS)

Pozina, G.; Forsberg, M.; Serban, E. A.; Hsiao, C.-L.; Junaid, M.; Birch, J.; Kaliteevski, M. A.

2017-01-01

Linear polarization properties of light emission are presented for GaN nanorods (NRs) grown along [0001] direction on Si(111) substrates by direct-current magnetron sputter epitaxy. The near band gap photoluminescence (PL) measured at low temperature for a single NR demonstrated an excitonic line at ˜3.48 eV and the stacking faults (SFs) related transition at ˜3.43 eV. The SF related emission is linear polarized in direction perpendicular to the NR growth axis in contrast to a non-polarized excitonic PL. The results are explained in the frame of the model describing basal plane SFs as polymorphic heterostructure of type II, where anisotropy of chemical bonds at the interfaces between zinc blende and wurtzite GaN subjected to in-built electric field is responsible for linear polarization parallel to the interface planes.

Magnetotransport in a Model of a Disordered Strange Metal

NASA Astrophysics Data System (ADS)

Patel, Aavishkar A.; McGreevy, John; Arovas, Daniel P.; Sachdev, Subir

2018-04-01

Despite much theoretical effort, there is no complete theory of the "strange" metal state of the high temperature superconductors, and its linear-in-temperature T resistivity. Recent experiments showing an unexpected linear-in-field B magnetoresistivity have deepened the puzzle. We propose a simple model of itinerant electrons, interacting via random couplings, with electrons localized on a lattice of "quantum dots" or "islands." This model is solvable in a particular large-N limit and can reproduce observed behavior. The key feature of our model is that the electrons in each quantum dot are described by a Sachdev-Ye-Kitaev model describing electrons without quasiparticle excitations. For a particular choice of the interaction between the itinerant and localized electrons, this model realizes a controlled description of a diffusive marginal-Fermi liquid (MFL) without momentum conservation, which has a linear-in-T resistivity and a T ln T specific heat as T →0 . By tuning the strength of this interaction relative to the bandwidth of the itinerant electrons, we can additionally obtain a finite-T crossover to a fully incoherent regime that also has a linear-in-T resistivity. We describe the magnetotransport properties of this model and show that the MFL regime has conductivities that scale as a function of B /T ; however, the magnetoresistance saturates at large B . We then consider a macroscopically disordered sample with domains of such MFLs with varying densities of electrons and islands. Using an effective-medium approximation, we obtain a macroscopic electrical resistance that scales linearly in the magnetic field B applied perpendicular to the plane of the sample, at large B . The resistance also scales linearly in T at small B , and as T f (B /T ) at intermediate B . We consider implications for recent experiments reporting linear transverse magnetoresistance in the strange metal phases of the pnictides and cuprates.

Quasi-linear versus potential-based formulations of force-flux relations and the GENERIC for irreversible processes: comparisons and examples

NASA Astrophysics Data System (ADS)

Hütter, Markus; Svendsen, Bob

2013-11-01

An essential part in modeling out-of-equilibrium dynamics is the formulation of irreversible dynamics. In the latter, the major task consists in specifying the relations between thermodynamic forces and fluxes. In the literature, mainly two distinct approaches are used for the specification of force-flux relations. On the one hand, quasi-linear relations are employed, which are based on the physics of transport processes and fluctuation-dissipation theorems (de Groot and Mazur in Non-equilibrium thermodynamics, North Holland, Amsterdam, 1962, Lifshitz and Pitaevskii in Physical kinetics. Volume 10, Landau and Lifshitz series on theoretical physics, Pergamon Press, Oxford, 1981). On the other hand, force-flux relations are also often represented in potential form with the help of a dissipation potential (Šilhavý in The mechanics and thermodynamics of continuous media, Springer, Berlin, 1997). We address the question of how these two approaches are related. The main result of this presentation states that the class of models formulated by quasi-linear relations is larger than what can be described in a potential-based formulation. While the relation between the two methods is shown in general terms, it is demonstrated also with the help of three examples. The finding that quasi-linear force-flux relations are more general than dissipation-based ones also has ramifications for the general equation for non-equilibrium reversible-irreversible coupling (GENERIC: e.g., Grmela and Öttinger in Phys Rev E 56:6620-6632, 6633-6655, 1997, Öttinger in Beyond equilibrium thermodynamics, Wiley Interscience Publishers, Hoboken, 2005). This framework has been formulated and used in two different forms, namely a quasi-linear (Öttinger and Grmela in Phys Rev E 56:6633-6655, 1997, Öttinger in Beyond equilibrium thermodynamics, Wiley Interscience Publishers, Hoboken, 2005) and a dissipation potential-based (Grmela in Adv Chem Eng 39:75-129, 2010, Grmela in J Non-Newton Fluid Mech 165:980-986, 2010, Mielke in Continuum Mech Therm 23:233-256, 2011) form, respectively, relating the irreversible evolution to the entropy gradient. It is found that also in the case of GENERIC, the quasi-linear representation encompasses a wider class of phenomena as compared to the dissipation-based formulation. Furthermore, it is found that a potential exists for the irreversible part of the GENERIC if and only if one does for the underlying force-flux relations.

A polymer, random walk model for the size-distribution of large DNA fragments after high linear energy transfer radiation

NASA Technical Reports Server (NTRS)

Ponomarev, A. L.; Brenner, D.; Hlatky, L. R.; Sachs, R. K.

2000-01-01

DNA double-strand breaks (DSBs) produced by densely ionizing radiation are not located randomly in the genome: recent data indicate DSB clustering along chromosomes. Stochastic DSB clustering at large scales, from > 100 Mbp down to < 0.01 Mbp, is modeled using computer simulations and analytic equations. A random-walk, coarse-grained polymer model for chromatin is combined with a simple track structure model in Monte Carlo software called DNAbreak and is applied to data on alpha-particle irradiation of V-79 cells. The chromatin model neglects molecular details but systematically incorporates an increase in average spatial separation between two DNA loci as the number of base-pairs between the loci increases. Fragment-size distributions obtained using DNAbreak match data on large fragments about as well as distributions previously obtained with a less mechanistic approach. Dose-response relations, linear at small doses of high linear energy transfer (LET) radiation, are obtained. They are found to be non-linear when the dose becomes so large that there is a significant probability of overlapping or close juxtaposition, along one chromosome, for different DSB clusters from different tracks. The non-linearity is more evident for large fragments than for small. The DNAbreak results furnish an example of the RLC (randomly located clusters) analytic formalism, which generalizes the broken-stick fragment-size distribution of the random-breakage model that is often applied to low-LET data.

Time-delay control of a magnetic levitated linear positioning system

NASA Technical Reports Server (NTRS)

Tarn, J. H.; Juang, K. Y.; Lin, C. E.

1994-01-01

In this paper, a high accuracy linear positioning system with a linear force actuator and magnetic levitation is proposed. By locating a permanently magnetized rod inside a current-carrying solenoid, the axial force is achieved by the boundary effect of magnet poles and utilized to power the linear motion, while the force for levitation is governed by Ampere's Law supplied with the same solenoid. With the levitation in a radial direction, there is hardly any friction between the rod and the solenoid. The high speed motion can hence be achieved. Besides, the axial force acting on the rod is a smooth function of rod position, so the system can provide nanometer resolution linear positioning to the molecule size. Since the force-position relation is highly nonlinear, and the mathematical model is derived according to some assumptions, such as the equivalent solenoid of the permanently magnetized rod, so there exists unknown dynamics in practical application. Thus 'robustness' is an important issue in controller design. Meanwhile the load effect reacts directly on the servo system without transmission elements, so the capability of 'disturbance rejection; is also required. With the above consideration, a time-delay control scheme is chosen and applied. By comparing the input-output relation and the mathematical model, the time-delay controller calculates an estimation of unmodeled dynamics and disturbances and then composes the desired compensation into the system. Effectiveness of the linear positioning system and control scheme are illustrated with simulation results.

Finite linear diffusion model for design of overcharge protection for rechargeable lithium batteries

NASA Technical Reports Server (NTRS)

Narayanan, S. R.; Surampudi, S.; Attia, A. I.

1991-01-01

The overcharge condition in secondary lithium batteries employing redox additives for overcharge protection has been theoretically analyzed in terms of a finite linear diffusion model. The analysis leads to expressions relating the steady-state overcharge current density and cell voltage to the concentration, diffusion coefficient, standard reduction potential of the redox couple, and interelectrode distance. The model permits the estimation of the maximum permissible overcharge rate for any chosen set of system conditions. The model has been experimentally verified using 1,1-prime-dimethylferrocene as a redox additive. The theoretical results may be exploited in the design and optimization of overcharge protection by the redox additive approach.

Transmogrifying fuzzy vortices

NASA Astrophysics Data System (ADS)

Murugan, Jeff; Millner, Antony

2004-04-01

We show that the construction of vortex solitons of the noncommutative abelian-Higgs model can be extended to a critically coupled gauged linear sigma model with Fayet-Illiopolous D-terms. Like its commutative counterpart, this fuzzy linear sigma model has a rich spectrum of BPS solutions. We offer an explicit construction of the degree-k static semilocal vortex and study in some detail the infinite coupling limit in which it descends to a degree-k Bbb CBbb PkN instanton. This relation between the fuzzy vortex and noncommutative lump is used to suggest an interpretation of the noncommutative sigma model soliton as tilted D-strings stretched between an NS5-brane and a stack of D3-branes in type-IIB superstring theory.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

Assessing the performance of eight real-time updating models and procedures for the Brosna River

NASA Astrophysics Data System (ADS)

Goswami, M.; O'Connor, K. M.; Bhattarai, K. P.; Shamseldin, A. Y.

2005-10-01

The flow forecasting performance of eight updating models, incorporated in the Galway River Flow Modelling and Forecasting System (GFMFS), was assessed using daily data (rainfall, evaporation and discharge) of the Irish Brosna catchment (1207 km2), considering their one to six days lead-time discharge forecasts. The Perfect Forecast of Input over the Forecast Lead-time scenario was adopted, where required, in place of actual rainfall forecasts. The eight updating models were: (i) the standard linear Auto-Regressive (AR) model, applied to the forecast errors (residuals) of a simulation (non-updating) rainfall-runoff model; (ii) the Neural Network Updating (NNU) model, also using such residuals as input; (iii) the Linear Transfer Function (LTF) model, applied to the simulated and the recently observed discharges; (iv) the Non-linear Auto-Regressive eXogenous-Input Model (NARXM), also a neural network-type structure, but having wide options of using recently observed values of one or more of the three data series, together with non-updated simulated outflows, as inputs; (v) the Parametric Simple Linear Model (PSLM), of LTF-type, using recent rainfall and observed discharge data; (vi) the Parametric Linear perturbation Model (PLPM), also of LTF-type, using recent rainfall and observed discharge data, (vii) n-AR, an AR model applied to the observed discharge series only, as a naïve updating model; and (viii) n-NARXM, a naive form of the NARXM, using only the observed discharge data, excluding exogenous inputs. The five GFMFS simulation (non-updating) models used were the non-parametric and parametric forms of the Simple Linear Model and of the Linear Perturbation Model, the Linearly-Varying Gain Factor Model, the Artificial Neural Network Model, and the conceptual Soil Moisture Accounting and Routing (SMAR) model. As the SMAR model performance was found to be the best among these models, in terms of the Nash-Sutcliffe R2 value, both in calibration and in verification, the simulated outflows of this model only were selected for the subsequent exercise of producing updated discharge forecasts. All the eight forms of updating models for producing lead-time discharge forecasts were found to be capable of producing relatively good lead-1 (1-day ahead) forecasts, with R2 values almost 90% or above. However, for higher lead time forecasts, only three updating models, viz., NARXM, LTF, and NNU, were found to be suitable, with lead-6 values of R2 about 90% or higher. Graphical comparisons were made of the lead-time forecasts for the two largest floods, one in the calibration period and the other in the verification period.

A polynomial based model for cell fate prediction in human diseases.

PubMed

Ma, Lichun; Zheng, Jie

2017-12-21

Cell fate regulation directly affects tissue homeostasis and human health. Research on cell fate decision sheds light on key regulators, facilitates understanding the mechanisms, and suggests novel strategies to treat human diseases that are related to abnormal cell development. In this study, we proposed a polynomial based model to predict cell fate. This model was derived from Taylor series. As a case study, gene expression data of pancreatic cells were adopted to test and verify the model. As numerous features (genes) are available, we employed two kinds of feature selection methods, i.e. correlation based and apoptosis pathway based. Then polynomials of different degrees were used to refine the cell fate prediction function. 10-fold cross-validation was carried out to evaluate the performance of our model. In addition, we analyzed the stability of the resultant cell fate prediction model by evaluating the ranges of the parameters, as well as assessing the variances of the predicted values at randomly selected points. Results show that, within both the two considered gene selection methods, the prediction accuracies of polynomials of different degrees show little differences. Interestingly, the linear polynomial (degree 1 polynomial) is more stable than others. When comparing the linear polynomials based on the two gene selection methods, it shows that although the accuracy of the linear polynomial that uses correlation analysis outcomes is a little higher (achieves 86.62%), the one within genes of the apoptosis pathway is much more stable. Considering both the prediction accuracy and the stability of polynomial models of different degrees, the linear model is a preferred choice for cell fate prediction with gene expression data of pancreatic cells. The presented cell fate prediction model can be extended to other cells, which may be important for basic research as well as clinical study of cell development related diseases.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Investigating Sustainability Impacts of Bioenergy Usage Within the Eisenwurzen Region in Austria

NASA Astrophysics Data System (ADS)

Putzhuber, F.; Hasenauer, H.

2009-04-01

Within the past few years sustainability and bioenergy usage become a key term in emphasizing the relationship between economic progress and the protection of the environment. One key difficulty is the definition of criteria and indicators for assessing sustainability issues and their change over time. This work introduces methods to create linear parametric models of the sustainable impact issues relevant in the establishment of new bio-energetic heating systems. Our application example is the Eisenwurzen region in Austria. The total area covers 5743 km km² and includes 99 municipalities. A total of 11 impact issues covering the economic, social and environmental areas are proposed for developing the linear parametric models. The indicator selection for deriving the impact issues is based on public official data from 68 indicators, as well as stakeholder interviews and the impact assessment framework. In total we obtained 415 variables from the 99 municipalities to create the 68 indicators for the Local Administration Unit 2 (LAU2) over the last (if available) 25 years. The 68 indicators are on a relative scale to address the size differences of the municipalities. The idea of the analysis is to create linear models which derive 11 defined impact issues related to the establishment of new bio-energetic heating systems. Each analysis follows a strict statistical procedure based on (i) independent indicator selection, (ii) remove indicators with higher VIF value grater then 6, (iii) remove indicators with α higher than 0,05, (iv) possible linear transformation, (v) remove the non-significant indicators (p-value >0,05), (vi) model valuation, (vii) remove the out-lines plots and (viii) test of the normality distribution of the residual with a Kolmogorov- Smirnov test. The results suggest that for the 11 sustainable impact issues 21 of the 68 indicators are significant drives. The models revealed that it is possible to create tools for assessing impact issues in a municipality level. In this case impact issues related to bio-energy usages on a rural mountain region.

Directed electromagnetic wave propagation in 1D metamaterial: Projecting operators method

NASA Astrophysics Data System (ADS)

Ampilogov, Dmitrii; Leble, Sergey

2016-07-01

We consider a boundary problem for 1D electrodynamics modeling of a pulse propagation in a metamaterial medium. We build and apply projecting operators to a Maxwell system in time domain that allows to split the linear propagation problem to directed waves for a material relations with general dispersion. Matrix elements of the projectors act as convolution integral operators. For a weak nonlinearity we generalize the linear results still for arbitrary dispersion and derive the system of interacting right/left waves with combined (hybrid) amplitudes. The result is specified for the popular metamaterial model with Drude formula for both permittivity and permeability coefficients. We also discuss and investigate stationary solutions of the system related to some boundary regimes.

A model for compression-weakening materials and the elastic fields due to contractile cells

NASA Astrophysics Data System (ADS)

Rosakis, Phoebus; Notbohm, Jacob; Ravichandran, Guruswami

2015-12-01

We construct a homogeneous, nonlinear elastic constitutive law that models aspects of the mechanical behavior of inhomogeneous fibrin networks. Fibers in such networks buckle when in compression. We model this as a loss of stiffness in compression in the stress-strain relations of the homogeneous constitutive model. Problems that model a contracting biological cell in a finite matrix are solved. It is found that matrix displacements and stresses induced by cell contraction decay slower (with distance from the cell) in a compression weakening material than linear elasticity would predict. This points toward a mechanism for long-range cell mechanosensing. In contrast, an expanding cell would induce displacements that decay faster than in a linear elastic matrix.

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

On Markov parameters in system identification

NASA Technical Reports Server (NTRS)

Phan, Minh; Juang, Jer-Nan; Longman, Richard W.

1991-01-01

A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest.

Atomic temporal interval relations in branching time: calculation and application

NASA Astrophysics Data System (ADS)

Anger, Frank D.; Ladkin, Peter B.; Rodriguez, Rita V.

1991-03-01

A practical method of reasoning about intervals in a branching-time model which is dense, unbounded, future-branching, without rejoining branches is presented. The discussion is based on heuristic constraint- propagation techniques using the relation algebra of binary temporal relations among the intervals over the branching-time model. This technique has been applied with success to models of intervals over linear time by Allen and others, and is of cubic-time complexity. To extend it to branding-time models, it is necessary to calculate compositions of the relations; thus, the table of compositions for the 'atomic' relations is computed, enabling the rapid determination of the composition of arbitrary relations, expressed as disjunctions or unions of the atomic relations.

Genetic programming over context-free languages with linear constraints for the knapsack problem: first results.

PubMed

Bruhn, Peter; Geyer-Schulz, Andreas

2002-01-01

In this paper, we introduce genetic programming over context-free languages with linear constraints for combinatorial optimization, apply this method to several variants of the multidimensional knapsack problem, and discuss its performance relative to Michalewicz's genetic algorithm with penalty functions. With respect to Michalewicz's approach, we demonstrate that genetic programming over context-free languages with linear constraints improves convergence. A final result is that genetic programming over context-free languages with linear constraints is ideally suited to modeling complementarities between items in a knapsack problem: The more complementarities in the problem, the stronger the performance in comparison to its competitors.

Statistical Modelling of Temperature and Moisture Uptake of Biochars Exposed to Selected Relative Humidity of Air.

PubMed

Bastistella, Luciane; Rousset, Patrick; Aviz, Antonio; Caldeira-Pires, Armando; Humbert, Gilles; Nogueira, Manoel

2018-02-09

New experimental techniques, as well as modern variants on known methods, have recently been employed to investigate the fundamental reactions underlying the oxidation of biochar. The purpose of this paper was to experimentally and statistically study how the relative humidity of air, mass, and particle size of four biochars influenced the adsorption of water and the increase in temperature. A random factorial design was employed using the intuitive statistical software Xlstat. A simple linear regression model and an analysis of variance with a pairwise comparison were performed. The experimental study was carried out on the wood of Quercus pubescens , Cyclobalanopsis glauca , Trigonostemon huangmosun , and Bambusa vulgaris , and involved five relative humidity conditions (22, 43, 75, 84, and 90%), two mass samples (0.1 and 1 g), and two particle sizes (powder and piece). Two response variables including water adsorption and temperature increase were analyzed and discussed. The temperature did not increase linearly with the adsorption of water. Temperature was modeled by nine explanatory variables, while water adsorption was modeled by eight. Five variables, including factors and their interactions, were found to be common to the two models. Sample mass and relative humidity influenced the two qualitative variables, while particle size and biochar type only influenced the temperature.

Development and Validation of Linear Alternator Models for the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Metscher, Jonathan F.; Lewandowski, Edward

2014-01-01

Two models of the linear alternator of the Advanced Stirling Convertor (ASC) have been developed using the Sage 1-D modeling software package. The first model relates the piston motion to electric current by means of a motor constant. The second uses electromagnetic model components to model the magnetic circuit of the alternator. The models are tuned and validated using test data and compared against each other. Results show both models can be tuned to achieve results within 7% of ASC test data under normal operating conditions. Using Sage enables the creation of a complete ASC model to be developed and simulations completed quickly compared to more complex multi-dimensional models. These models allow for better insight into overall Stirling convertor performance, aid with Stirling power system modeling, and in the future support NASA mission planning for Stirling-based power systems.

Development and Validation of Linear Alternator Models for the Advanced Stirling Convertor

NASA Technical Reports Server (NTRS)

Metscher, Jonathan F.; Lewandowski, Edward J.

2015-01-01

Two models of the linear alternator of the Advanced Stirling Convertor (ASC) have been developed using the Sage 1-D modeling software package. The first model relates the piston motion to electric current by means of a motor constant. The second uses electromagnetic model components to model the magnetic circuit of the alternator. The models are tuned and validated using test data and also compared against each other. Results show both models can be tuned to achieve results within 7 of ASC test data under normal operating conditions. Using Sage enables the creation of a complete ASC model to be developed and simulations completed quickly compared to more complex multi-dimensional models. These models allow for better insight into overall Stirling convertor performance, aid with Stirling power system modeling, and in the future support NASA mission planning for Stirling-based power systems.

Gravitational field of static p -branes in linearized ghost-free gravity

NASA Astrophysics Data System (ADS)

Boos, Jens; Frolov, Valeri P.; Zelnikov, Andrei

2018-04-01

We study the gravitational field of static p -branes in D -dimensional Minkowski space in the framework of linearized ghost-free (GF) gravity. The concrete models of GF gravity we consider are parametrized by the nonlocal form factors exp (-□/μ2) and exp (□2/μ4) , where μ-1 is the scale of nonlocality. We show that the singular behavior of the gravitational field of p -branes in general relativity is cured by short-range modifications introduced by the nonlocalities, and we derive exact expressions of the regularized gravitational fields, whose geometry can be written as a warped metric. For large distances compared to the scale of nonlocality, μ r →∞ , our solutions approach those found in linearized general relativity.

Surface wave and linear operating mode of a plasma antenna

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

Bogachev, N. N., E-mail: bgniknik@yandex.ru; Bogdankevich, I. L.; Gusein-zade, N. G.

The relation between the propagation conditions of a surface electromagnetic wave along a finiteradius plasma cylinder and the linear operating mode of a plasma antenna is investigated. The solution to the dispersion relation for a surface wave propagating along a finite-radius plasma cylinder is analyzed for weakly and strongly collisional plasmas. Computer simulations of an asymmetrical plasma dipole antenna are performed using the KARAT code, wherein the dielectric properties of plasma are described in terms of the Drude model. The plasma parameters corresponding to the linear operating mode of a plasma antenna are determined. It is demonstrated that the characteristicsmore » of the plasma antenna in this mode are close to those of an analogous metal antenna.« less

Quantile Regression in the Study of Developmental Sciences

PubMed Central

Petscher, Yaacov; Logan, Jessica A. R.

2014-01-01

Linear regression analysis is one of the most common techniques applied in developmental research, but only allows for an estimate of the average relations between the predictor(s) and the outcome. This study describes quantile regression, which provides estimates of the relations between the predictor(s) and outcome, but across multiple points of the outcome’s distribution. Using data from the High School and Beyond and U.S. Sustained Effects Study databases, quantile regression is demonstrated and contrasted with linear regression when considering models with: (a) one continuous predictor, (b) one dichotomous predictor, (c) a continuous and a dichotomous predictor, and (d) a longitudinal application. Results from each example exhibited the differential inferences which may be drawn using linear or quantile regression. PMID:24329596

Is high relative humidity associated with childhood hand, foot, and mouth disease in rural and urban areas?

PubMed

Yang, H; Wu, J; Cheng, J; Wang, X; Wen, L; Li, K; Su, H

2017-01-01

To examine the relationship between relative humidity and childhood hand, foot and mouth disease (HFMD) in Hefei, China, and to explore whether the effect is different between urban and rural areas. Retrospective ecological study. A Poisson generalized linear model combined with a distributed lag non-linear model was used to examine the relationship between relative humidity and childhood HFMD in a temperate Chinese city during 2010-2012. The effect of relative humidity on childhood HFMD increased above a humidity of 84%, with a 0.34% (95% CI: 0.23%-0.45%) increase of childhood HFMD per 1% increment of relative humidity. Notably, urban children, male children, and children aged 0-4 years appeared to be more vulnerable to the effect of relative humidity on HFMD. This article study indicates that high relative humidity may trigger childhood HFMD in a temperate area, Hefei, particularly for those who are young and from urban areas. Copyright © 2015 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Relating retinal nerve fiber thickness to behavioral sensitivity in patients with glaucoma: application of a linear model.

PubMed

Hood, Donald C

2007-05-01

Glaucoma causes damage to the retinal ganglion cells and their axons, and this damage can be detected with both structural and functional tests. The purpose of this study was to better understand the relationship between a structural measure of retinal nerve fiber layer (RNFL) and the most common functional test, behavioral sensitivity with static automated perimetry (SAP). First, a linear model, previously shown to describe the relationship between local visual evoked potentials and SAP sensitivity, was modified to predict the change in RNFL as measured by optical coherence tomography. Second, previous work by others was shown to be consistent with this model.

Monotonic non-linear transformations as a tool to investigate age-related effects on brain white matter integrity: A Box-Cox investigation.

PubMed

Morozova, Maria; Koschutnig, Karl; Klein, Elise; Wood, Guilherme

2016-01-15

Non-linear effects of age on white matter integrity are ubiquitous in the brain and indicate that these effects are more pronounced in certain brain regions at specific ages. Box-Cox analysis is a technique to increase the log-likelihood of linear relationships between variables by means of monotonic non-linear transformations. Here we employ Box-Cox transformations to flexibly and parsimoniously determine the degree of non-linearity of age-related effects on white matter integrity by means of model comparisons using a voxel-wise approach. Analysis of white matter integrity in a sample of adults between 20 and 89years of age (n=88) revealed that considerable portions of the white matter in the corpus callosum, cerebellum, pallidum, brainstem, superior occipito-frontal fascicle and optic radiation show non-linear effects of age. Global analyses revealed an increase in the average non-linearity from fractional anisotropy to radial diffusivity, axial diffusivity, and mean diffusivity. These results suggest that Box-Cox transformations are a useful and flexible tool to investigate more complex non-linear effects of age on white matter integrity and extend the functionality of the Box-Cox analysis in neuroimaging. Copyright © 2015 Elsevier Inc. All rights reserved.

Linear time series modeling of GPS-derived TEC observations over the Indo-Thailand region

NASA Astrophysics Data System (ADS)

Suraj, Puram Sai; Kumar Dabbakuti, J. R. K.; Chowdhary, V. Rajesh; Tripathi, Nitin K.; Ratnam, D. Venkata

2017-12-01

This paper proposes a linear time series model to represent the climatology of the ionosphere and to investigate the characteristics of hourly averaged total electron content (TEC). The GPS-TEC observation data at the Bengaluru international global navigation satellite system (GNSS) service (IGS) station (geographic 13.02°N , 77.57°E ; geomagnetic latitude 4.4°N ) have been utilized for processing the TEC data during an extended period (2009-2016) in the 24{th} solar cycle. Solar flux F10.7p index, geomagnetic Ap index, and periodic oscillation factors have been considered to construct a linear TEC model. It is evident from the results that solar activity effect on TEC is high. It reaches the maximum value (˜ 40 TECU) during the high solar activity (HSA) year (2014) and minimum value (˜ 15 TECU) during the low solar activity (LSA) year (2009). The larger magnitudes of semiannual variations are observed during the HSA periods. The geomagnetic effect on TEC is relatively low, with the highest being ˜ 4 TECU (March 2015). The magnitude of periodic variations can be seen more significantly during HSA periods (2013-2015) and less during LSA periods (2009-2011). The correlation coefficient of 0.89 between the observations and model-based estimations has been found. The RMSE between the observed TEC and model TEC values is 4.0 TECU (linear model) and 4.21 TECU (IRI2016 Model). Further, the linear TEC model has been validated at different latitudes over the northern low-latitude region. The solar component (F10.7p index) value decreases with an increase in latitude. The magnitudes of the periodic component become less significant with the increase in latitude. The influence of geomagnetic component becomes less significant at Lucknow GNSS station (26.76°N, 80.88°E) when compared to other GNSS stations. The hourly averaged TEC values have been considered and ionospheric features are well recovered with linear TEC model.

Model cerebellar granule cells can faithfully transmit modulated firing rate signals

PubMed Central

Rössert, Christian; Solinas, Sergio; D'Angelo, Egidio; Dean, Paul; Porrill, John

2014-01-01

A crucial assumption of many high-level system models of the cerebellum is that information in the granular layer is encoded in a linear manner. However, granule cells are known for their non-linear and resonant synaptic and intrinsic properties that could potentially impede linear signal transmission. In this modeling study we analyse how electrophysiological granule cell properties and spike sampling influence information coded by firing rate modulation, assuming no signal-related, i.e., uncorrelated inhibitory feedback (open-loop mode). A detailed one-compartment granule cell model was excited in simulation by either direct current or mossy-fiber synaptic inputs. Vestibular signals were represented as tonic inputs to the flocculus modulated at frequencies up to 20 Hz (approximate upper frequency limit of vestibular-ocular reflex, VOR). Model outputs were assessed using estimates of both the transfer function, and the fidelity of input-signal reconstruction measured as variance-accounted-for. The detailed granule cell model with realistic mossy-fiber synaptic inputs could transmit information faithfully and linearly in the frequency range of the vestibular-ocular reflex. This was achieved most simply if the model neurons had a firing rate at least twice the highest required frequency of modulation, but lower rates were also adequate provided a population of neurons was utilized, especially in combination with push-pull coding. The exact number of neurons required for faithful transmission depended on the precise values of firing rate and noise. The model neurons were also able to combine excitatory and inhibitory signals linearly, and could be replaced by a simpler (modified) integrate-and-fire neuron in the case of high tonic firing rates. These findings suggest that granule cells can in principle code modulated firing-rate inputs in a linear manner, and are thus consistent with the high-level adaptive-filter model of the cerebellar microcircuit. PMID:25352777

Closed-form solution for static pull-in voltage of electrostatically actuated clamped-clamped micro/nano beams under the effect of fringing field and van der Waals force

NASA Astrophysics Data System (ADS)

Bhojawala, V. M.; Vakharia, D. P.

2017-12-01

This investigation provides an accurate prediction of static pull-in voltage for clamped-clamped micro/nano beams based on distributed model. The Euler-Bernoulli beam theory is used adapting geometric non-linearity of beam, internal (residual) stress, van der Waals force, distributed electrostatic force and fringing field effects for deriving governing differential equation. The Galerkin discretisation method is used to make reduced-order model of the governing differential equation. A regime plot is presented in the current work for determining the number of modes required in reduced-order model to obtain completely converged pull-in voltage for micro/nano beams. A closed-form relation is developed based on the relationship obtained from curve fitting of pull-in instability plots and subsequent non-linear regression for the proposed relation. The output of regression analysis provides Chi-square (χ 2) tolerance value equals to 1  ×  10-9, adjusted R-square value equals to 0.999 29 and P-value equals to zero, these statistical parameters indicate the convergence of non-linear fit, accuracy of fitted data and significance of the proposed model respectively. The closed-form equation is validated using available data of experimental and numerical results. The relative maximum error of 4.08% in comparison to several available experimental and numerical data proves the reliability of the proposed closed-form equation.

Discovery of the linear region of Near Infrared Diffuse Reflectance spectra using the Kubelka-Munk theory

NASA Astrophysics Data System (ADS)

Dai, Shengyun; Pan, Xiaoning; Ma, Lijuan; Huang, Xingguo; Du, Chenzhao; Qiao, Yanjiang; Wu, Zhisheng

2018-05-01

Particle size is of great importance for the quantitative model of the NIR diffuse reflectance. In this paper, the effect of sample particle size on the measurement of harpagoside in Radix Scrophulariae powder by near infrared diffuse (NIR) reflectance spectroscopy was explored. High-performance liquid chromatography (HPLC) was employed as a reference method to construct the quantitative particle size model. Several spectral preprocessing methods were compared, and particle size models obtained by different preprocessing methods for establishing the partial least-squares (PLS) models of harpagoside. Data showed that the particle size distribution of 125-150 μm for Radix Scrophulariae exhibited the best prediction ability with R2pre=0.9513, RMSEP=0.1029 mg·g-1, and RPD = 4.78. For the hybrid granularity calibration model, the particle size distribution of 90-180 μm exhibited the best prediction ability with R2pre=0.8919, RMSEP=0.1632 mg·g-1, and RPD = 3.09. Furthermore, the Kubelka-Munk theory was used to relate the absorption coefficient k (concentration-dependent) and scatter coefficient s (particle size-dependent). The scatter coefficient s was calculated based on the Kubelka-Munk theory to study the changes of s after being mathematically preprocessed. A linear relationship was observed between k/s and absorption A within a certain range and the value for k/s was greater than 4. According to this relationship, the model was more accurately constructed with the particle size distribution of 90-180 μm when s was kept constant or in a small linear region. This region provided a good reference for the linear modeling of diffuse reflectance spectroscopy. To establish a diffuse reflectance NIR model, further accurate assessment should be obtained in advance for a precise linear model.

Personalized Medicine Enrichment Design for DHA Supplementation Clinical Trial.

PubMed

Lei, Yang; Mayo, Matthew S; Carlson, Susan E; Gajewski, Byron J

2017-03-01

Personalized medicine aims to match patient subpopulation to the most beneficial treatment. The purpose of this study is to design a prospective clinical trial in which we hope to achieve the highest level of confirmation in identifying and making treatment recommendations for subgroups, when the risk levels in the control arm can be ordered. This study was motivated by our goal to identify subgroups in a DHA (docosahexaenoic acid) supplementation trial to reduce preterm birth (gestational age<37 weeks) rate. We performed a meta-analysis to obtain informative prior distributions and simulated operating characteristics to ensure that overall Type I error rate was close to 0.05 in designs with three different models: independent, hierarchical, and dynamic linear models. We performed simulations and sensitivity analysis to examine the subgroup power of models and compared results to a chi-square test. We performed simulations under two hypotheses: a large overall treatment effect and a small overall treatment effect. Within each hypothesis, we designed three different subgroup effects scenarios where resulting subgroup rates are linear, flat, or nonlinear. When the resulting subgroup rates are linear or flat, dynamic linear model appeared to be the most powerful method to identify the subgroups with a treatment effect. It also outperformed other methods when resulting subgroup rates are nonlinear and the overall treatment effect is big. When the resulting subgroup rates are nonlinear and the overall treatment effect is small, hierarchical model and chi-square test did better. Compared to independent and hierarchical models, dynamic linear model tends to be relatively robust and powerful when the control arm has ordinal risk subgroups.

SU-F-J-138: An Extension of PCA-Based Respiratory Deformation Modeling Via Multi-Linear Decomposition

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

Iliopoulos, AS; Sun, X; Pitsianis, N

Purpose: To address and lift the limited degree of freedom (DoF) of globally bilinear motion components such as those based on principal components analysis (PCA), for encoding and modeling volumetric deformation motion. Methods: We provide a systematic approach to obtaining a multi-linear decomposition (MLD) and associated motion model from deformation vector field (DVF) data. We had previously introduced MLD for capturing multi-way relationships between DVF variables, without being restricted by the bilinear component format of PCA-based models. PCA-based modeling is commonly used for encoding patient-specific deformation as per planning 4D-CT images, and aiding on-board motion estimation during radiotherapy. However, themore » bilinear space-time decomposition inherently limits the DoF of such models by the small number of respiratory phases. While this limit is not reached in model studies using analytical or digital phantoms with low-rank motion, it compromises modeling power in the presence of relative motion, asymmetries and hysteresis, etc, which are often observed in patient data. Specifically, a low-DoF model will spuriously couple incoherent motion components, compromising its adaptability to on-board deformation changes. By the multi-linear format of extracted motion components, MLD-based models can encode higher-DoF deformation structure. Results: We conduct mathematical and experimental comparisons between PCA- and MLD-based models. A set of temporally-sampled analytical trajectories provides a synthetic, high-rank DVF; trajectories correspond to respiratory and cardiac motion factors, including different relative frequencies and spatial variations. Additionally, a digital XCAT phantom is used to simulate a lung lesion deforming incoherently with respect to the body, which adheres to a simple respiratory trend. In both cases, coupling of incoherent motion components due to a low model DoF is clearly demonstrated. Conclusion: Multi-linear decomposition can enable decoupling of distinct motion factors in high-rank DVF measurements. This may improve motion model expressiveness and adaptability to on-board deformation, aiding model-based image reconstruction for target verification. NIH Grant No. R01-184173.« less

A Maximum Likelihood Approach for Multisample Nonlinear Structural Equation Models with Missing Continuous and Dichotomous Data

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2006-01-01

Structural equation models are widely appreciated in social-psychological research and other behavioral research to model relations between latent constructs and manifest variables and to control for measurement error. Most applications of SEMs are based on fully observed continuous normal data and models with a linear structural equation.…

Constrained Maximum Likelihood Estimation for Two-Level Mean and Covariance Structure Models

ERIC Educational Resources Information Center

Bentler, Peter M.; Liang, Jiajuan; Tang, Man-Lai; Yuan, Ke-Hai

2011-01-01

Maximum likelihood is commonly used for the estimation of model parameters in the analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in…

Dynamical Analysis in the Mathematical Modelling of Human Blood Glucose

ERIC Educational Resources Information Center

Bae, Saebyok; Kang, Byungmin

2012-01-01

We want to apply the geometrical method to a dynamical system of human blood glucose. Due to the educational importance of model building, we show a relatively general modelling process using observational facts. Next, two models of some concrete forms are analysed in the phase plane by means of linear stability, phase portrait and vector…

Spatially explicit estimates of N2 O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management.

PubMed

Gerber, James S; Carlson, Kimberly M; Makowski, David; Mueller, Nathaniel D; Garcia de Cortazar-Atauri, Iñaki; Havlík, Petr; Herrero, Mario; Launay, Marie; O'Connell, Christine S; Smith, Pete; West, Paul C

2016-10-01

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N2 O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N2 O emissions at the country scale by aggregating all crops, under the assumption that N2 O emissions are linearly related to N application. However, field studies and meta-analyses indicate a nonlinear relationship, in which N2 O emissions are relatively greater at higher N application rates. Here, we apply a super-linear emissions response model to crop-specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N2 O emissions from croplands. We estimate 0.66 Tg of N2 O-N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N2 O emissions range from 20% to 40% lower throughout sub-Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N2 O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N2 O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high-resolution N application data are critical to support accurate N2 O emissions estimates. © 2016 John Wiley & Sons Ltd.

Linear models for assessing mechanisms of sperm competition: the trouble with transformations.

PubMed

Eggert, Anne-Katrin; Reinhardt, Klaus; Sakaluk, Scott K

2003-01-01

Although sperm competition is a pervasive selective force shaping the reproductive tactics of males, the mechanisms underlying different patterns of sperm precedence remain obscure. Parker et al. (1990) developed a series of linear models designed to identify two of the more basic mechanisms: sperm lotteries and sperm displacement; the models can be tested experimentally by manipulating the relative numbers of sperm transferred by rival males and determining the paternity of offspring. Here we show that tests of the model derived for sperm lotteries can result in misleading inferences about the underlying mechanism of sperm precedence because the required inverse transformations may lead to a violation of fundamental assumptions of linear regression. We show that this problem can be remedied by reformulating the model using the actual numbers of offspring sired by each male, and log-transforming both sides of the resultant equation. Reassessment of data from a previous study (Sakaluk and Eggert 1996) using the corrected version of the model revealed that we should not have excluded a simple sperm lottery as a possible mechanism of sperm competition in decorated crickets, Gryllodes sigillatus.

A linear solvation energy relationship model of organic chemical partitioning to dissolved organic carbon.

PubMed

Kipka, Undine; Di Toro, Dominic M

2011-09-01

Predicting the association of contaminants with both particulate and dissolved organic matter is critical in determining the fate and bioavailability of chemicals in environmental risk assessment. To date, the association of a contaminant to particulate organic matter is considered in many multimedia transport models, but the effect of dissolved organic matter is typically ignored due to a lack of either reliable models or experimental data. The partition coefficient to dissolved organic carbon (K(DOC)) may be used to estimate the fraction of a contaminant that is associated with dissolved organic matter. Models relating K(DOC) to the octanol-water partition coefficient (K(OW)) have not been successful for many types of dissolved organic carbon in the environment. Instead, linear solvation energy relationships are proposed to model the association of chemicals with dissolved organic matter. However, more chemically diverse K(DOC) data are needed to produce a more robust model. For humic acid dissolved organic carbon, the linear solvation energy relationship predicts log K(DOC) with a root mean square error of 0.43. Copyright © 2011 SETAC.

A Well-Posed, Objective and Dynamic Two-Fluid Model

NASA Astrophysics Data System (ADS)

Chetty, Krishna; Vaidheeswaran, Avinash; Sharma, Subash; Clausse, Alejandro; Lopez de Bertodano, Martin

The transition from dispersed to clustered bubbly flows due to wake entrainment is analyzed with a well-posed and objective one-dimensional (1-D) Two-Fluid Model, derived from variational principles. Modeling the wake entrainment force using the variational technique requires formulation of the inertial coupling coefficient, which defines the kinetic coupling between the phases. The kinetic coupling between a pair of bubbles and the liquid is obtained from potential flow over two-spheres and the results are validated by comparing the virtual mass coefficients with existing literature. The two-body interaction kinetic coupling is then extended to a lumped parameter model for viscous flow over two cylindrical bubbles, to get the Two-Fluid Model for wake entrainment. Linear stability analyses comprising the characteristics and the dispersion relation and non-linear numerical simulations are performed with the 1-D variational Two-Fluid Model to demonstrate the wake entrainment instability leading to clustering of bubbles. Finally, the wavelengths, amplitudes and propagation velocities of the void waves from non-linear simulations are compared with the experimental data.

A Comparison of the CHILD and Landlab Computational Landscape Evolution Models and Examples of Best Practices in Numerical Modeling of Surface Processes

NASA Astrophysics Data System (ADS)

Gasparini, N. M.; Hobley, D. E. J.; Tucker, G. E.; Istanbulluoglu, E.; Adams, J. M.; Nudurupati, S. S.; Hutton, E. W. H.

2014-12-01

Computational models are important tools that can be used to quantitatively understand the evolution of real landscapes. Commonalities exist among most landscape evolution models, although they are also idiosyncratic, in that they are coded in different languages, require different input values, and are designed to tackle a unique set of questions. These differences can make applying a landscape evolution model challenging, especially for novice programmers. In this study, we compare and contrast two landscape evolution models that are designed to tackle similar questions, but the actual model designs are quite different. The first model, CHILD, is over a decade-old and is relatively well-tested, well-developed and well-used. It is coded in C++, operates on an irregular grid and was designed more with function rather than user-experience in mind. In contrast, the second model, Landlab, is relatively new and was designed to be accessible to a wide range of scientists, including those who have not previously used or developed a numerical model. Landlab is coded in Python, a relatively easy language for the non-proficient programmer, and has the ability to model landscapes described on both regular and irregular grids. We present landscape simulations from both modeling platforms. Our goal is to illustrate best practices for implementing a new process module in a landscape evolution model, and therefore the simulations are applicable regardless of the modeling platform. We contrast differences and highlight similarities between the use of the two models, including setting-up the model and input file for different evolutionary scenarios, computational time, and model output. Whenever possible, we compare model output with analytical solutions and illustrate the effects, or lack thereof, of a uniform vs. non-uniform grid. Our simulations focus on implementing a single process, including detachment-limited or transport-limited fluvial bedrock incision and linear or non-linear diffusion of material on hillslopes. We also illustrate the steps necessary to couple processes together, for example, detachment-limited fluvial bedrock incision with linear diffusion on hillslopes. Trade-offs exist between the two modeling platforms, and these are primarily in speed and ease-of-use.

Linear regression in astronomy. II

NASA Technical Reports Server (NTRS)

Feigelson, Eric D.; Babu, Gutti J.

1992-01-01

A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.

Fuzzy branching temporal logic.

PubMed

Moon, Seong-ick; Lee, Kwang H; Lee, Doheon

2004-04-01

Intelligent systems require a systematic way to represent and handle temporal information containing uncertainty. In particular, a logical framework is needed that can represent uncertain temporal information and its relationships with logical formulae. Fuzzy linear temporal logic (FLTL), a generalization of propositional linear temporal logic (PLTL) with fuzzy temporal events and fuzzy temporal states defined on a linear time model, was previously proposed for this purpose. However, many systems are best represented by branching time models in which each state can have more than one possible future path. In this paper, fuzzy branching temporal logic (FBTL) is proposed to address this problem. FBTL adopts and generalizes concurrent tree logic (CTL*), which is a classical branching temporal logic. The temporal model of FBTL is capable of representing fuzzy temporal events and fuzzy temporal states, and the order relation among them is represented as a directed graph. The utility of FBTL is demonstrated using a fuzzy job shop scheduling problem as an example.

A performance model for GPUs with caches

DOE PAGES

Dao, Thanh Tuan; Kim, Jungwon; Seo, Sangmin; ...

2014-06-24

To exploit the abundant computational power of the world's fastest supercomputers, an even workload distribution to the typically heterogeneous compute devices is necessary. While relatively accurate performance models exist for conventional CPUs, accurate performance estimation models for modern GPUs do not exist. This paper presents two accurate models for modern GPUs: a sampling-based linear model, and a model based on machine-learning (ML) techniques which improves the accuracy of the linear model and is applicable to modern GPUs with and without caches. We first construct the sampling-based linear model to predict the runtime of an arbitrary OpenCL kernel. Based on anmore » analysis of NVIDIA GPUs' scheduling policies we determine the earliest sampling points that allow an accurate estimation. The linear model cannot capture well the significant effects that memory coalescing or caching as implemented in modern GPUs have on performance. We therefore propose a model based on ML techniques that takes several compiler-generated statistics about the kernel as well as the GPU's hardware performance counters as additional inputs to obtain a more accurate runtime performance estimation for modern GPUs. We demonstrate the effectiveness and broad applicability of the model by applying it to three different NVIDIA GPU architectures and one AMD GPU architecture. On an extensive set of OpenCL benchmarks, on average, the proposed model estimates the runtime performance with less than 7 percent error for a second-generation GTX 280 with no on-chip caches and less than 5 percent for the Fermi-based GTX 580 with hardware caches. On the Kepler-based GTX 680, the linear model has an error of less than 10 percent. On an AMD GPU architecture, Radeon HD 6970, the model estimates with 8 percent of error rates. As a result, the proposed technique outperforms existing models by a factor of 5 to 6 in terms of accuracy.« less

Is long-term exposure to traffic pollution associated with mortality? A small-area study in London.

PubMed

Halonen, Jaana I; Blangiardo, Marta; Toledano, Mireille B; Fecht, Daniela; Gulliver, John; Ghosh, Rebecca; Anderson, H Ross; Beevers, Sean D; Dajnak, David; Kelly, Frank J; Wilkinson, Paul; Tonne, Cathryn

2016-01-01

Long-term exposure to primary traffic pollutants may be harmful for health but few studies have investigated effects on mortality. We examined associations for six primary traffic pollutants with all-cause and cause-specific mortality in 2003-2010 at small-area level using linear and piecewise linear Poisson regression models. In linear models most pollutants showed negative or null association with all-cause, cardiovascular or respiratory mortality. In the piecewise models we observed positive associations in the lowest exposure range (e.g. relative risk (RR) for all-cause mortality 1.07 (95% credible interval (CI) = 1.00-1.15) per 0.15 μg/m(3) increase in exhaust related primary particulate matter ≤2.5 μm (PM2.5)) whereas associations in the highest exposure range were negative (corresponding RR 0.93, 95% CI: 0.91-0.96). Overall, there was only weak evidence of positive associations with mortality. That we found the strongest positive associations in the lowest exposure group may reflect residual confounding by unmeasured confounders that varies by exposure group. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Quantum description of light propagation in generalized media

NASA Astrophysics Data System (ADS)

Häyrynen, Teppo; Oksanen, Jani

2016-02-01

Linear quantum input-output relation based models are widely applied to describe the light propagation in a lossy medium. The details of the interaction and the associated added noise depend on whether the device is configured to operate as an amplifier or an attenuator. Using the traveling wave (TW) approach, we generalize the linear material model to simultaneously account for both the emission and absorption processes and to have point-wise defined noise field statistics and intensity dependent interaction strengths. Thus, our approach describes the quantum input-output relations of linear media with net attenuation, amplification or transparency without pre-selection of the operation point. The TW approach is then applied to investigate materials at thermal equilibrium, inverted materials, the transparency limit where losses are compensated, and the saturating amplifiers. We also apply the approach to investigate media in nonuniform states which can be e.g. consequences of a temperature gradient over the medium or a position dependent inversion of the amplifier. Furthermore, by using the generalized model we investigate devices with intensity dependent interactions and show how an initial thermal field transforms to a field having coherent statistics due to gain saturation.

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.

Can a minimalist model of wind forced baroclinic Rossby waves produce reasonable results?

NASA Astrophysics Data System (ADS)

Watanabe, Wandrey B.; Polito, Paulo S.; da Silveira, Ilson C. A.

2016-04-01

The linear theory predicts that Rossby waves are the large scale mechanism of adjustment to perturbations of the geophysical fluid. Satellite measurements of sea level anomaly (SLA) provided sturdy evidence of the existence of these waves. Recent studies suggest that the variability in the altimeter records is mostly due to mesoscale nonlinear eddies and challenges the original interpretation of westward propagating features as Rossby waves. The objective of this work is to test whether a classic linear dynamic model is a reasonable explanation for the observed SLA. A linear-reduced gravity non-dispersive Rossby wave model is used to estimate the SLA forced by direct and remote wind stress. Correlations between model results and observations are up to 0.88. The best agreement is in the tropical region of all ocean basins. These correlations decrease towards insignificance in mid-latitudes. The relative contributions of eastern boundary (remote) forcing and local wind forcing in the generation of Rossby waves are also estimated and suggest that the main wave forming mechanism is the remote forcing. Results suggest that linear long baroclinic Rossby wave dynamics explain a significant part of the SLA annual variability at least in the tropical oceans.

A General Multidimensional Model for the Measurement of Cultural Differences.

ERIC Educational Resources Information Center

Olmedo, Esteban L.; Martinez, Sergio R.

A multidimensional model for measuring cultural differences (MCD) based on factor analytic theory and techniques is proposed. The model assumes that a cultural space may be defined by means of a relatively small number of orthogonal dimensions which are linear combinations of a much larger number of cultural variables. Once a suitable,…

Trajectories of Infants' Biobehavioral Development: Timing and Rate of A-Not-B Performance Gains and EEG Maturation.

PubMed

MacNeill, Leigha A; Ram, Nilam; Bell, Martha Ann; Fox, Nathan A; Pérez-Edgar, Koraly

2018-05-01

This study examined how timing (i.e., relative maturity) and rate (i.e., how quickly infants attain proficiency) of A-not-B performance were related to changes in brain activity from age 6 to 12 months. A-not-B performance and resting EEG (electroencephalography) were measured monthly from age 6 to 12 months in 28 infants and were modeled using logistic and linear growth curve models. Infants with faster performance rates reached performance milestones earlier. Infants with faster rates of increase in A-not-B performance had lower occipital power at 6 months and greater linear increases in occipital power. The results underscore the importance of considering nonlinear change processes for studying infants' cognitive development as well as how these changes are related to trajectories of EEG power. © 2018 The Authors. Child Development © 2018 Society for Research in Child Development, Inc.

Dynamic modeling and characteristics analysis of a modal-independent linear ultrasonic motor.

PubMed

Li, Xiang; Yao, Zhiyuan; Zhou, Shengli; Lv, Qibao; Liu, Zhen

2016-12-01

In this paper, an integrated model is developed to analyze the fundamental characteristics of a modal-independent linear ultrasonic motor with double piezoelectric vibrators. The energy method is used to model the dynamics of the two piezoelectric vibrators. The interface forces are coupled into the dynamic equations of the two vibrators and the moving platform, forming a whole machine model of the motor. The behavior of the force transmission of the motor is analyzed via the resulting model to understand the drive mechanism. In particular, the relative contact length is proposed to describe the intermittent contact characteristic between the stator and the mover, and its role in evaluating motor performance is discussed. The relations between the output speed and various inputs to the motor and the start-stop transients of the motor are analyzed by numerical simulations, which are validated by experiments. Furthermore, the dead-zone behavior is predicted and clarified analytically using the proposed model, which is also observed in experiments. These results are useful for designing servo control scheme for the motor. Copyright © 2016 Elsevier B.V. All rights reserved.

Quantifying the sensitivity of feedstock properties and process conditions on hydrochar yield, carbon content, and energy content.

PubMed

Li, Liang; Wang, Yiying; Xu, Jiting; Flora, Joseph R V; Hoque, Shamia; Berge, Nicole D

2018-08-01

Hydrothermal carbonization (HTC) is a wet, low temperature thermal conversion process that continues to gain attention for the generation of hydrochar. The importance of specific process conditions and feedstock properties on hydrochar characteristics is not well understood. To evaluate this, linear and non-linear models were developed to describe hydrochar characteristics based on data collected from HTC-related literature. A Sobol analysis was subsequently conducted to identify parameters that most influence hydrochar characteristics. Results from this analysis indicate that for each investigated hydrochar property, the model fit and predictive capability associated with the random forest models is superior to both the linear and regression tree models. Based on results from the Sobol analysis, the feedstock properties and process conditions most influential on hydrochar yield, carbon content, and energy content were identified. In addition, a variational process parameter sensitivity analysis was conducted to determine how feedstock property importance changes with process conditions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model.

PubMed

Saini, Harsh; Raicar, Gaurav; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

2015-12-07

Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cell kill by megavoltage protons with high LET.

PubMed

Kuperman, Vadim Y

2016-07-21

The aim of the current study is to develop a radiobiological model which describes the effect of linear energy transfer (LET) on cell survival and relative biological effectiveness (RBE) of megavoltage protons. By assuming the existence of critical sites within a cell, analytical expression for cell survival S as a function of LET is derived. The obtained results indicate that in cases where dose per fraction is small, [Formula: see text] is a linear-quadratic (LQ) function of dose while both alpha and beta radio-sensitivities are non-linearly dependent on LET. In particular, in the current model alpha increases with increasing LET while beta decreases. Conversely, in the case of large dose per fraction, the LQ dependence of [Formula: see text] on dose is invalid. The proposed radiobiological model predicts cell survival probability and RBE which, in general, deviate from the results obtained by using conventional LQ formalism. The differences between the LQ model and that described in the current study are reflected in the calculated RBE of protons.

Use of simulation tools to illustrate the effect of data management practices for low and negative plate counts on the estimated parameters of microbial reduction models.

PubMed

Garcés-Vega, Francisco; Marks, Bradley P

2014-08-01

In the last 20 years, the use of microbial reduction models has expanded significantly, including inactivation (linear and nonlinear), survival, and transfer models. However, a major constraint for model development is the impossibility to directly quantify the number of viable microorganisms below the limit of detection (LOD) for a given study. Different approaches have been used to manage this challenge, including ignoring negative plate counts, using statistical estimations, or applying data transformations. Our objective was to illustrate and quantify the effect of negative plate count data management approaches on parameter estimation for microbial reduction models. Because it is impossible to obtain accurate plate counts below the LOD, we performed simulated experiments to generate synthetic data for both log-linear and Weibull-type microbial reductions. We then applied five different, previously reported data management practices and fit log-linear and Weibull models to the resulting data. The results indicated a significant effect (α = 0.05) of the data management practices on the estimated model parameters and performance indicators. For example, when the negative plate counts were replaced by the LOD for log-linear data sets, the slope of the subsequent log-linear model was, on average, 22% smaller than for the original data, the resulting model underpredicted lethality by up to 2.0 log, and the Weibull model was erroneously selected as the most likely correct model for those data. The results demonstrate that it is important to explicitly report LODs and related data management protocols, which can significantly affect model results, interpretation, and utility. Ultimately, we recommend using only the positive plate counts to estimate model parameters for microbial reduction curves and avoiding any data value substitutions or transformations when managing negative plate counts to yield the most accurate model parameters.

Chaotic examination

NASA Astrophysics Data System (ADS)

Bildirici, Melike; Sonustun, Fulya Ozaksoy; Sonustun, Bahri

2018-01-01

In the regards of chaos theory, new concepts such as complexity, determinism, quantum mechanics, relativity, multiple equilibrium, complexity, (continuously) instability, nonlinearity, heterogeneous agents, irregularity were widely questioned in economics. It is noticed that linear models are insufficient for analyzing unpredictable, irregular and noncyclical oscillations of economies, and for predicting bubbles, financial crisis, business cycles in financial markets. Therefore, economists gave great consequence to use appropriate tools for modelling non-linear dynamical structures and chaotic behaviors of the economies especially in macro and the financial economy. In this paper, we aim to model the chaotic structure of exchange rates (USD-TL and EUR-TL). To determine non-linear patterns of the selected time series, daily returns of the exchange rates were tested by BDS during the period from January 01, 2002 to May 11, 2017 which covers after the era of the 2001 financial crisis. After specifying the non-linear structure of the selected time series, it was aimed to examine the chaotic characteristic for the selected time period by Lyapunov Exponents. The findings verify the existence of the chaotic structure of the exchange rate returns in the analyzed time period.

RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis.

PubMed

Javed, Faizan; Chan, Gregory S H; Savkin, Andrey V; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-01-01

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).

Approximate reduction of linear population models governed by stochastic differential equations: application to multiregional models.

PubMed

Sanz, Luis; Alonso, Juan Antonio

2017-12-01

In this work we develop approximate aggregation techniques in the context of slow-fast linear population models governed by stochastic differential equations and apply the results to the treatment of populations with spatial heterogeneity. Approximate aggregation techniques allow one to transform a complex system involving many coupled variables and in which there are processes with different time scales, by a simpler reduced model with a fewer number of 'global' variables, in such a way that the dynamics of the former can be approximated by that of the latter. In our model we contemplate a linear fast deterministic process together with a linear slow process in which the parameters are affected by additive noise, and give conditions for the solutions corresponding to positive initial conditions to remain positive for all times. By letting the fast process reach equilibrium we build a reduced system with a lesser number of variables, and provide results relating the asymptotic behaviour of the first- and second-order moments of the population vector for the original and the reduced system. The general technique is illustrated by analysing a multiregional stochastic system in which dispersal is deterministic and the rate growth of the populations in each patch is affected by additive noise.

Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR)

PubMed Central

Wagner, Wolfgang; Pathe, Carsten; Doubkova, Marcela; Sabel, Daniel; Bartsch, Annett; Hasenauer, Stefan; Blöschl, Günter; Scipal, Klaus; Martínez-Fernández, José; Löw, Alexander

2008-01-01

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments. PMID:27879759

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Novel associative-memory-based self-learning neurocontrol model

NASA Astrophysics Data System (ADS)

Chen, Ke

1992-09-01

Intelligent control is an important field of AI application, which is closely related to machine learning, and the neurocontrol is a kind of intelligent control that controls actions of a physical system or a plant. Linear associative memory model is a good analytic tool for artificial neural networks. In this paper, we present a novel self-learning neurocontrol on the basis of the linear associative memory model to support intelligent control. Using our self-learning neurocontrol model, the learning process is viewed as an extension of one of J. Piaget's developmental stages. After a particular linear associative model developed by us is presented, a brief introduction to J. Piaget's cognitive theory is described as the basis of our self-learning style control. It follows that the neurocontrol model is presented, which usually includes two learning stages, viz. primary learning and high-level learning. As a demonstration of our neurocontrol model, an example is also presented with simulation techniques, called that `bird' catches an aim. The tentative experimental results show that the learning and controlling performance of this approach is surprisingly good. In conclusion, future research is pointed out to improve our self-learning neurocontrol model and explore other areas of application.

Effects from Unsaturated Zone Flow during Oscillatory Hydraulic Testing

NASA Astrophysics Data System (ADS)

Lim, D.; Zhou, Y.; Cardiff, M. A.; Barrash, W.

2014-12-01

In analyzing pumping tests on unconfined aquifers, the impact of the unsaturated zone is often neglected. Instead, desaturation at the water table is often treated as a free-surface boundary, which is simple and allows for relatively fast computation. Richards' equation models, which account for unsaturated flow, can be compared with saturated flow models to validate the use of Darcy's Law. In this presentation, we examine the appropriateness of using fast linear steady-periodic models based on linearized water table conditions in order to simulate oscillatory pumping tests in phreatic aquifers. We compare oscillatory pumping test models including: 1) a 2-D radially-symmetric phreatic aquifer model with a partially penetrating well, simulated using both Darcy's Law and Richards' Equation in COMSOL; and 2) a linear phase-domain numerical model developed in MATLAB. Both COMSOL and MATLAB models are calibrated to match oscillatory pumping test data collected in the summer of 2013 at the Boise Hydrogeophysical Research Site (BHRS), and we examine the effect of model type on the associated parameter estimates. The results of this research will aid unconfined aquifer characterization efforts and help to constrain the impact of the simplifying physical assumptions often employed during test analysis.

Modeling the relationships between quality and biochemical composition of fatty liver in mule ducks.

PubMed

Theron, L; Cullere, M; Bouillier-Oudot, M; Manse, H; Dalle Zotte, A; Molette, C; Fernandez, X; Vitezica, Z G

2012-09-01

The fatty liver of mule ducks (i.e., French "foie gras") is the most valuable product in duck production systems. Its quality is measured by the technological yield, which is the opposite of the fat loss during cooking. The purpose of this study was to determine whether biochemical measures of fatty liver could be used to accurately predict the technological yield (TY). Ninety-one male mule ducks were bred, overfed, and slaughtered under commercial conditions. Fatty liver weight (FLW) and biochemical variables, such as DM, lipid (LIP), and protein content (PROT), were collected. To evaluate evidence for nonlinear fat loss during cooking, we compared regression models describing linear and nonlinear relations between biochemical measures and TY. We detected significantly greater (P = 0.02) linear relation between DM and TY. Our results indicate that LIP and PROT follow a different pattern (linear) than DM and showed that LIP and PROT are nonexclusive contributing factors to TY. Other components, such as carbohydrates, other than those measured in this study, could contribute to DM. Stepwise regression for TY was performed. The traditional model with FLW was tested. The results showed that the weight of the liver is of limited value in the determination of fat loss during cooking (R(2) = 0.14). The most accurate TY prediction equation included DM (in linear and quadratic terms), FLW, and PROT (R(2) = 0.43). Biochemical measures in the fatty liver were more accurate predictors of TY than FLW. The model is useful in commercial conditions because DM, PROT, and FLW are noninvasive measures.

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;

Low dose radiation risks for women surviving the a-bombs in Japan: generalized additive model.

PubMed

Dropkin, Greg

2016-11-24

Analyses of cancer mortality and incidence in Japanese A-bomb survivors have been used to estimate radiation risks, which are generally higher for women. Relative Risk (RR) is usually modelled as a linear function of dose. Extrapolation from data including high doses predicts small risks at low doses. Generalized Additive Models (GAMs) are flexible methods for modelling non-linear behaviour. GAMs are applied to cancer incidence in female low dose subcohorts, using anonymous public data for the 1958 - 1998 Life Span Study, to test for linearity, explore interactions, adjust for the skewed dose distribution, examine significance below 100 mGy, and estimate risks at 10 mGy. For all solid cancer incidence, RR estimated from 0 - 100 mGy and 0 - 20 mGy subcohorts is significantly raised. The response tapers above 150 mGy. At low doses, RR increases with age-at-exposure and decreases with time-since-exposure, the preferred covariate. Using the empirical cumulative distribution of dose improves model fit, and capacity to detect non-linear responses. RR is elevated over wide ranges of covariate values. Results are stable under simulation, or when removing exceptional data cells, or adjusting neutron RBE. Estimates of Excess RR at 10 mGy using the cumulative dose distribution are 10 - 45 times higher than extrapolations from a linear model fitted to the full cohort. Below 100 mGy, quasipoisson models find significant effects for all solid, squamous, uterus, corpus, and thyroid cancers, and for respiratory cancers when age-at-exposure > 35 yrs. Results for the thyroid are compatible with studies of children treated for tinea capitis, and Chernobyl survivors. Results for the uterus are compatible with studies of UK nuclear workers and the Techa River cohort. Non-linear models find large, significant cancer risks for Japanese women exposed to low dose radiation from the atomic bombings. The risks should be reflected in protection standards.

Do Student Perceptions of Diversity Emphasis Relate to Perceived Learning of Psychology?

ERIC Educational Resources Information Center

Elicker, Joelle D.; Snell, Andrea F.; O'Malley, Alison L.

2010-01-01

We examined the extent to which students' perceived inclusion of diversity issues in the Introduction to Psychology course related to perceptions of learning. Based on the responses of 625 students, multilevel linear modeling analyses revealed that student perceptions of diversity emphasis in the class were positively related to how well students…

Differential Relations of Constructivist and Didactic Instruction to Students' Cognition, Motivation, and Achievement

ERIC Educational Resources Information Center

Nie, Youyan; Lau, Shun

2010-01-01

This study examined how constructivist and didactic instruction was related to students' cognitive, motivational, and achievement outcomes in English classrooms, using a sample of 3000 Grade 9 students from 108 classrooms in 39 secondary schools in Singapore. Results of hierarchical linear modeling showed differential cross-level relations. After…

A Nth-order linear algorithm for extracting diffuse correlation spectroscopy blood flow indices in heterogeneous tissues.

PubMed

Shang, Yu; Yu, Guoqiang

2014-09-29

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a N th-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD B ). The purpose of this study is to extend the capability of the N th-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different types of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD B in the brain layer with a step decrement of 10% while maintaining αD B values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order ( N  ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The N th-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.

Order-constrained linear optimization.

PubMed

Tidwell, Joe W; Dougherty, Michael R; Chrabaszcz, Jeffrey S; Thomas, Rick P

2017-11-01

Despite the fact that data and theories in the social, behavioural, and health sciences are often represented on an ordinal scale, there has been relatively little emphasis on modelling ordinal properties. The most common analytic framework used in psychological science is the general linear model, whose variants include ANOVA, MANOVA, and ordinary linear regression. While these methods are designed to provide the best fit to the metric properties of the data, they are not designed to maximally model ordinal properties. In this paper, we develop an order-constrained linear least-squares (OCLO) optimization algorithm that maximizes the linear least-squares fit to the data conditional on maximizing the ordinal fit based on Kendall's τ. The algorithm builds on the maximum rank correlation estimator (Han, 1987, Journal of Econometrics, 35, 303) and the general monotone model (Dougherty & Thomas, 2012, Psychological Review, 119, 321). Analyses of simulated data indicate that when modelling data that adhere to the assumptions of ordinary least squares, OCLO shows minimal bias, little increase in variance, and almost no loss in out-of-sample predictive accuracy. In contrast, under conditions in which data include a small number of extreme scores (fat-tailed distributions), OCLO shows less bias and variance, and substantially better out-of-sample predictive accuracy, even when the outliers are removed. We show that the advantages of OCLO over ordinary least squares in predicting new observations hold across a variety of scenarios in which researchers must decide to retain or eliminate extreme scores when fitting data. © 2017 The British Psychological Society.

Reanalysis of cancer mortality in Japanese A-bomb survivors exposed to low doses of radiation: bootstrap and simulation methods

PubMed Central

2009-01-01

Background The International Commission on Radiological Protection (ICRP) recommended annual occupational dose limit is 20 mSv. Cancer mortality in Japanese A-bomb survivors exposed to less than 20 mSv external radiation in 1945 was analysed previously, using a latency model with non-linear dose response. Questions were raised regarding statistical inference with this model. Methods Cancers with over 100 deaths in the 0 - 20 mSv subcohort of the 1950-1990 Life Span Study are analysed with Poisson regression models incorporating latency, allowing linear and non-linear dose response. Bootstrap percentile and Bias-corrected accelerated (BCa) methods and simulation of the Likelihood Ratio Test lead to Confidence Intervals for Excess Relative Risk (ERR) and tests against the linear model. Results The linear model shows significant large, positive values of ERR for liver and urinary cancers at latencies from 37 - 43 years. Dose response below 20 mSv is strongly non-linear at the optimal latencies for the stomach (11.89 years), liver (36.9), lung (13.6), leukaemia (23.66), and pancreas (11.86) and across broad latency ranges. Confidence Intervals for ERR are comparable using Bootstrap and Likelihood Ratio Test methods and BCa 95% Confidence Intervals are strictly positive across latency ranges for all 5 cancers. Similar risk estimates for 10 mSv (lagged dose) are obtained from the 0 - 20 mSv and 5 - 500 mSv data for the stomach, liver, lung and leukaemia. Dose response for the latter 3 cancers is significantly non-linear in the 5 - 500 mSv range. Conclusion Liver and urinary cancer mortality risk is significantly raised using a latency model with linear dose response. A non-linear model is strongly superior for the stomach, liver, lung, pancreas and leukaemia. Bootstrap and Likelihood-based confidence intervals are broadly comparable and ERR is strictly positive by bootstrap methods for all 5 cancers. Except for the pancreas, similar estimates of latency and risk from 10 mSv are obtained from the 0 - 20 mSv and 5 - 500 mSv subcohorts. Large and significant cancer risks for Japanese survivors exposed to less than 20 mSv external radiation from the atomic bombs in 1945 cast doubt on the ICRP recommended annual occupational dose limit. PMID:20003238

The Debye light scattering equation's scaling relation reveals the purity of synthetic dendrimers

NASA Astrophysics Data System (ADS)

Tseng, Hui-Yu; Chen, Hsiao-Ping; Tang, Yi-Hsuan; Chen, Hui-Ting; Kao, Chai-Lin; Wang, Shau-Chun

2016-03-01

Spherical dendrimer structures cannot be structurally modeled using conventional polymer models of random coil or rod-like configurations during the calibration of the static light scattering (LS) detectors used to determine the molecular weight (M.W.) of a dendrimer or directly assess the purity of a synthetic compound. In this paper, we used the Debye equation-based scaling relation, which predicts that the static LS intensity per unit concentration is linearly proportional to the M.W. of a synthetic dendrimer in a dilute solution, as a tool to examine the purity of high-generational compounds and to monitor the progress of dendrimer preparations. Without using expensive equipment, such as nuclear magnetic resonance or mass spectrometry, this method only required an affordable flow injection set-up with an LS detector. Solutions of the purified dendrimers, including the poly(amidoamine) (PAMAM) dendrimer and its fourth to seventh generation pyridine derivatives with size range of 5-9 nm, were used to establish the scaling relation with high linearity. The use of artificially impure mixtures of six or seven generations revealed significant deviations from linearity. The raw synthesized products of the pyridine-modified PAMAM dendrimer, which included incompletely reacted dendrimers, were also examined to gauge the reaction progress. As a reaction toward a particular generational derivative of the PAMAM dendrimers proceeded over time, deviations from the linear scaling relation decreased. The difference between the polydispersity index of the incompletely converted products and that of the pure compounds was only about 0.01. The use of the Debye equation-based scaling relation, therefore, is much more useful than the polydispersity index for monitoring conversion processes toward an indicated functionality number in a given preparation.

Black-hole kicks from numerical-relativity surrogate models

NASA Astrophysics Data System (ADS)

Gerosa, Davide; Hébert, François; Stein, Leo C.

2018-05-01

Binary black holes radiate linear momentum in gravitational waves as they merge. Recoils imparted to the black-hole remnant can reach thousands of km /s , thus ejecting black holes from their host galaxies. We exploit recent advances in gravitational waveform modeling to quickly and reliably extract recoils imparted to generic, precessing, black-hole binaries. Our procedure uses a numerical-relativity surrogate model to obtain the gravitational waveform given a set of binary parameters; then, from this waveform we directly integrate the gravitational-wave linear momentum flux. This entirely bypasses the need for fitting formulas which are typically used to model black-hole recoils in astrophysical contexts. We provide a thorough exploration of the black-hole kick phenomenology in the parameter space, summarizing and extending previous numerical results on the topic. Our extraction procedure is made publicly available as a module for the Python programming language named surrkick. Kick evaluations take ˜0.1 s on a standard off-the-shelf machine, thus making our code ideal to be ported to large-scale astrophysical studies.

Relation of the external mechanical stress to the properties of piezoelectric materials for energy harvesting

NASA Astrophysics Data System (ADS)

Jeong, Soon-Jong; Kim, Min-Soo; Lee, Dae-Su; Song, Jae-Sung; Cho, Kyung-Ho

2013-12-01

We investigated the piezoelectric properties and the generation of voltage and power under the mechanical compressive loads for three types of piezoelectric ceramics 0.2Pb(Mg1/3Nb2/3)O3-0.8Pb(Zr0.475Ti0.525)O3 (soft-PZT), 0.1Pb(Mg1/3Sb2/3)O3- 0.9Pb(Zr0.475Ti0.525)O3 (hard-PZT) and [0.675Pb(Mg1/3Nb2/3)O3-0.35PbTiO3]+5 wt% BaTiO3 (textured-PMNT). The piezoelectric d 33 coefficients of all specimens increased with increasing compressive load. The generated voltage and power showed a linear relation and square relation to the applied stress, respectively. These results were larger than those calculated using the simple piezoelectric equation due to the non-linear characteristics of the ceramics, so they were evaluated with a simple model based on a non-linear relation.

Anisotropic piezoelectric twist actuation of helicopter rotor blades: Aeroelastic analysis and design optimization

NASA Astrophysics Data System (ADS)

Wilkie, William Keats

1997-12-01

An aeroelastic model suitable for control law and preliminary structural design of composite helicopter rotor blades incorporating embedded anisotropic piezoelectric actuator laminae is developed. The aeroelasticity model consists of a linear, nonuniform beam representation of the blade structure, including linear piezoelectric actuation terms, coupled with a nonlinear, finite-state unsteady aerodynamics model. A Galerkin procedure and numerical integration in the time domain are used to obtain a soluti An aeroelastic model suitable for control law and preliminary structural design of composite helicopter rotor blades incorporating embedded anisotropic piezoelectric actuator laminae is developed. The aeroelasticity model consists of a linear, nonuniform beam representation of the blade structure, including linear piezoelectric actuation terms, coupled with a nonlinear, finite-state unsteady aerodynamics model. A Galerkin procedure and numerical integration in the time domain are used to obtain amited additional piezoelectric material mass, it is shown that blade twist actuation approaches which exploit in-plane piezoelectric free-stain anisotropies are capable of producing amplitudes of oscillatory blade twisting sufficient for rotor vibration reduction applications. The second study examines the effectiveness of using embedded piezoelectric actuator laminae to alleviate vibratory loads due to retreating blade stall. A 10 to 15 percent improvement in dynamic stall limited forward flight speed, and a 5 percent improvement in stall limited rotor thrust were numerically demonstrated for the active twist rotor blade relative to a conventional blade design. The active twist blades are also demonstrated to be more susceptible than the conventional blades to dynamic stall induced vibratory loads when not operating with twist actuation. This is the result of designing the active twist blades with low torsional stiffness in order to maximize piezoelectric twist authority. Determining the optimum tradeoff between blade torsional stiffness and piezoelectric twist actuation authority is the subject of the third study. For this investigation, a linearized hovering-flight eigenvalue analysis is developed. Linear optimal control theory is then utilized to develop an optimum active twist blade design in terms of reducing structural energy and control effort cost. The forward flight vibratory loads characteristics of the torsional stiffness optimized active twist blade are then examined using the nonlinear, forward flight aeroelastic analysis. The optimized active twist rotor blade is shown to have improved passive and active vibratory loads characteristics relative to the baseline active twist blades.

Computational Modelling and Optimal Control of Ebola Virus Disease with non-Linear Incidence Rate

NASA Astrophysics Data System (ADS)

Takaidza, I.; Makinde, O. D.; Okosun, O. K.

2017-03-01

The 2014 Ebola outbreak in West Africa has exposed the need to connect modellers and those with relevant data as pivotal to better understanding of how the disease spreads and quantifying the effects of possible interventions. In this paper, we model and analyse the Ebola virus disease with non-linear incidence rate. The epidemic model created is used to describe how the Ebola virus could potentially evolve in a population. We perform an uncertainty analysis of the basic reproductive number R 0 to quantify its sensitivity to other disease-related parameters. We also analyse the sensitivity of the final epidemic size to the time control interventions (education, vaccination, quarantine and safe handling) and provide the cost effective combination of the interventions.

Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem.

PubMed

Lawson, Daniel J; Holtrop, Grietje; Flint, Harry

2011-07-01

Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Linearization improves the repeatability of quantitative dynamic contrast-enhanced MRI.

PubMed

Jones, Kyle M; Pagel, Mark D; Cárdenas-Rodríguez, Julio

2018-04-01

The purpose of this study was to compare the repeatabilities of the linear and nonlinear Tofts and reference region models (RRM) for dynamic contrast-enhanced MRI (DCE-MRI). Simulated and experimental DCE-MRI data from 12 rats with a flank tumor of C6 glioma acquired over three consecutive days were analyzed using four quantitative and semi-quantitative DCE-MRI metrics. The quantitative methods used were: 1) linear Tofts model (LTM), 2) non-linear Tofts model (NTM), 3) linear RRM (LRRM), and 4) non-linear RRM (NRRM). The following semi-quantitative metrics were used: 1) maximum enhancement ratio (MER), 2) time to peak (TTP), 3) initial area under the curve (iauc64), and 4) slope. LTM and NTM were used to estimate K trans , while LRRM and NRRM were used to estimate K trans relative to muscle (R Ktrans ). Repeatability was assessed by calculating the within-subject coefficient of variation (wSCV) and the percent intra-subject variation (iSV) determined with the Gage R&R analysis. The iSV for R Ktrans using LRRM was two-fold lower compared to NRRM at all simulated and experimental conditions. A similar trend was observed for the Tofts model, where LTM was at least 50% more repeatable than the NTM under all experimental and simulated conditions. The semi-quantitative metrics iauc64 and MER were as equally repeatable as K trans and R Ktrans estimated by LTM and LRRM respectively. The iSV for iauc64 and MER were significantly lower than the iSV for slope and TTP. In simulations and experimental results, linearization improves the repeatability of quantitative DCE-MRI by at least 30%, making it as repeatable as semi-quantitative metrics. Copyright © 2017 Elsevier Inc. All rights reserved.

Simplifying and upscaling water resources systems models that combine natural and engineered components

NASA Astrophysics Data System (ADS)

McIntyre, N.; Keir, G.

2014-12-01

Water supply systems typically encompass components of both natural systems (e.g. catchment runoff, aquifer interception) and engineered systems (e.g. process equipment, water storages and transfers). Many physical processes of varying spatial and temporal scales are contained within these hybrid systems models. The need to aggregate and simplify system components has been recognised for reasons of parsimony and comprehensibility; and the use of probabilistic methods for modelling water-related risks also prompts the need to seek computationally efficient up-scaled conceptualisations. How to manage the up-scaling errors in such hybrid systems models has not been well-explored, compared to research in the hydrological process domain. Particular challenges include the non-linearity introduced by decision thresholds and non-linear relations between water use, water quality, and discharge strategies. Using a case study of a mining region, we explore the nature of up-scaling errors in water use, water quality and discharge, and we illustrate an approach to identification of a scale-adjusted model including an error model. Ways forward for efficient modelling of such complex, hybrid systems are discussed, including interactions with human, energy and carbon systems models.

Linear and non-linear bias: predictions versus measurements

NASA Astrophysics Data System (ADS)

Hoffmann, K.; Bel, J.; Gaztañaga, E.

2017-02-01

We study the linear and non-linear bias parameters which determine the mapping between the distributions of galaxies and the full matter density fields, comparing different measurements and predictions. Associating galaxies with dark matter haloes in the Marenostrum Institut de Ciències de l'Espai (MICE) Grand Challenge N-body simulation, we directly measure the bias parameters by comparing the smoothed density fluctuations of haloes and matter in the same region at different positions as a function of smoothing scale. Alternatively, we measure the bias parameters by matching the probability distributions of halo and matter density fluctuations, which can be applied to observations. These direct bias measurements are compared to corresponding measurements from two-point and different third-order correlations, as well as predictions from the peak-background model, which we presented in previous papers using the same data. We find an overall variation of the linear bias measurements and predictions of ˜5 per cent with respect to results from two-point correlations for different halo samples with masses between ˜1012and1015 h-1 M⊙ at the redshifts z = 0.0 and 0.5. Variations between the second- and third-order bias parameters from the different methods show larger variations, but with consistent trends in mass and redshift. The various bias measurements reveal a tight relation between the linear and the quadratic bias parameters, which is consistent with results from the literature based on simulations with different cosmologies. Such a universal relation might improve constraints on cosmological models, derived from second-order clustering statistics at small scales or higher order clustering statistics.

New Approach To Hour-By-Hour Weather Forecast

NASA Astrophysics Data System (ADS)

Liao, Q. Q.; Wang, B.

2017-12-01

Fine hourly forecast in single station weather forecast is required in many human production and life application situations. Most previous MOS (Model Output Statistics) which used a linear regression model are hard to solve nonlinear natures of the weather prediction and forecast accuracy has not been sufficient at high temporal resolution. This study is to predict the future meteorological elements including temperature, precipitation, relative humidity and wind speed in a local region over a relatively short period of time at hourly level. By means of hour-to-hour NWP (Numeral Weather Prediction)meteorological field from Forcastio (https://darksky.net/dev/docs/forecast) and real-time instrumental observation including 29 stations in Yunnan and 3 stations in Tianjin of China from June to October 2016, predictions are made of the 24-hour hour-by-hour ahead. This study presents an ensemble approach to combine the information of instrumental observation itself and NWP. Use autoregressive-moving-average (ARMA) model to predict future values of the observation time series. Put newest NWP products into the equations derived from the multiple linear regression MOS technique. Handle residual series of MOS outputs with autoregressive (AR) model for the linear property presented in time series. Due to the complexity of non-linear property of atmospheric flow, support vector machine (SVM) is also introduced . Therefore basic data quality control and cross validation makes it able to optimize the model function parameters , and do 24 hours ahead residual reduction with AR/SVM model. Results show that AR model technique is better than corresponding multi-variant MOS regression method especially at the early 4 hours when the predictor is temperature. MOS-AR combined model which is comparable to MOS-SVM model outperform than MOS. Both of their root mean square error and correlation coefficients for 2 m temperature are reduced to 1.6 degree Celsius and 0.91 respectively. The forecast accuracy of 24- hour forecast deviation no more than 2 degree Celsius is 78.75 % for MOS-AR model and 81.23 % for AR model.

Extraction of diffuse correlation spectroscopy flow index by integration of Nth-order linear model with Monte Carlo simulation

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

Shang, Yu; Lin, Yu; Yu, Guoqiang, E-mail: guoqiang.yu@uky.edu

2014-05-12

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αD{sub B}) in tissues with small volume and large curvature. We proposed an algorithm integrating Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of αD{sub B}. The volume and geometry of the measured tissue were incorporated in the Monte Carlo simulation, which overcome the semi-infinite restrictions. The algorithm was tested using computer simulations on four tissue models with varied volumes/geometries and applied on an in vivo strokemore » model of mouse. Computer simulations shows that the high-order (N ≥ 5) linear algorithm was more accurate in extracting αD{sub B} (errors < ±2%) from the noise-free DCS data than the semi-infinite solution (errors: −5.3% to −18.0%) for different tissue models. Although adding random noises to DCS data resulted in αD{sub B} variations, the mean values of errors in extracting αD{sub B} were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αD{sub B} using both linear algorithm and semi-infinite solution were fairly small (errors < ±2.0%) and did not rely on the tissue volume/geometry. The experimental results from the in vivo stroke mice agreed with those in simulations, demonstrating the robustness of the linear algorithm. DCS with the high-order linear algorithm shows the potential for the inter-subject comparison and longitudinal monitoring of absolute BFI in a variety of tissues/organs with different volumes/geometries.« less

Study on a linear relationship between limited pressure difference and coil current of on/off valve and its influential factors.

PubMed

Zhang, Junzhi; Lv, Chen; Yue, Xiaowei; Li, Yutong; Yuan, Ye

2014-01-01

On/off solenoid valves with PWM control are widely used in all types of vehicle electro-hydraulic control systems respecting to their desirable properties of reliable, low cost and fast acting. However, it can hardly achieve a linear hydraulic modulation by using on/off valves mainly due to the nonlinear behaviors of valve dynamics and fluid, which affects the control accuracy significantly. In this paper, a linear relationship between limited pressure difference and coil current of an on/off valve in its critical closed state is proposed and illustrated, which has a great potential to be applied to improve hydraulic control performance. The hydraulic braking system of case study is modeled. The linear correspondence between limited pressure difference and coil current of the inlet valve is simulated and further verified experimentally. Based on validated simulation models, the impacts of key parameters are researched. The limited pressure difference affected by environmental temperatures is experimentally studied, and the amended linear relation is given according to the test data. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Linear diffusion model dating of cinder cones in Central Anatolia, Turkey

NASA Astrophysics Data System (ADS)

O'Sadnick, L. G.; Reid, M. R.; Cline, M. L.; Cosca, M. A.; Kuscu, G.

2013-12-01

The progressive decrease in slope angle, cone height and cone height/width ratio over time provides the basis for geomorphic dating of cinder cones using linear diffusion models. Previous research using diffusion models to date cinder cones has focused on the cone height/width ratio as the basis for dating cones of unknown age [1,2]. Here we apply linear diffusion models to dating cinder cones. A suite of 16 cinder cones from the Hasandağ volcano area of the Neogene-Quaternary Central Anatolian Volcanic Zone, for which samples are available, were selected for morphologic dating analysis. New 40Ar/39Ar dates for five of these cones range from 62 × 4 to 517 × 9 ka. Linear diffusion models were used to model the erosional degradation of each cone. Diffusion coefficients (κ) for the 5 cinder cones with known ages were constrained by comparing various modeled slope profiles to the current slope profile. The resulting κ is 7.5×0.5 m2kyr-1. Using this κ value, eruption ages were modeled for the remaining 11 cinder cones and range from 53×3 to 455×30 ka. These ages are within the range of ages previously reported for cinder cones in the Hasandağ region. The linear diffusion model-derived ages are being compared to additional new 40Ar/39Ar dates in order to further assess the applicability of morphological dating to constrain the ages of cinder cones. The relatively well-constrained κ value we obtained by applying the linear diffusion model to cinder cones that range in age by nearly 500 ka suggests that this model can be used to date cinder cones. This κ value is higher than the well-established value of κ =3.9 for a cinder cone in a similar climate [3]. Therefore our work confirms the importance of determining appropriate κ values from nearby cones with known ages. References 1. C.A. Wood, J. Volcanol. Geotherm. Res. 8, 137 (1980) 2. D.M. Wood, M.F. Sheridan, J. Volcanol. Geotherm. Res. 83, 241 (1998) 3. J.D. Pelletier, M.L. Cline, Geology 35, 1067 (2007)

LINEAR - DERIVATION AND DEFINITION OF A LINEAR AIRCRAFT MODEL

NASA Technical Reports Server (NTRS)

Duke, E. L.

1994-01-01

The Derivation and Definition of a Linear Model program, LINEAR, provides the user with a powerful and flexible tool for the linearization of aircraft aerodynamic models. LINEAR was developed to provide a standard, documented, and verified tool to derive linear models for aircraft stability analysis and control law design. Linear system models define the aircraft system in the neighborhood of an analysis point and are determined by the linearization of the nonlinear equations defining vehicle dynamics and sensors. 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. LINEAR is capable of extracting both linearized engine effects, such as net thrust, torque, and gyroscopic effects and including these effects in the linear system model. The point at which this linear model is defined is determined either by completely specifying the state and control variables, or by specifying an analysis point on a trajectory and directing the program to determine the control variables and the remaining state variables. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to provide easy selection of state, control, and observation variables to be used in a particular model. Thus, the order of the system model is completely under user control. Further, the program provides the flexibility of allowing alternate formulations of both the state and observation equations. Data describing the aircraft and the test case is input to the program through a terminal or formatted data files. All data can be modified interactively from case to case. The aerodynamic model can be defined in two ways: a set of nondimensional stability and control derivatives for the flight point of interest, or a full non-linear aerodynamic model as used in simulations. LINEAR is written in FORTRAN and has been implemented on a DEC VAX computer operating under VMS with a virtual memory requirement of approximately 296K of 8 bit bytes. Both an interactive and batch version are included. LINEAR was developed in 1988.

Recurrence relations in one-dimensional Ising models.

PubMed

da Conceição, C M Silva; Maia, R N P

2017-09-01

The exact finite-size partition function for the nonhomogeneous one-dimensional (1D) Ising model is found through an approach using algebra operators. Specifically, in this paper we show that the partition function can be computed through a trace from a linear second-order recurrence relation with nonconstant coefficients in matrix form. A relation between the finite-size partition function and the generalized Lucas polynomials is found for the simple homogeneous model, thus establishing a recursive formula for the partition function. This is an important property and it might indicate the possible existence of recurrence relations in higher-dimensional Ising models. Moreover, assuming quenched disorder for the interactions within the model, the quenched averaged magnetic susceptibility displays a nontrivial behavior due to changes in the ferromagnetic concentration probability.

Special Holonomy and Two-Dimensional Supersymmetric Sigma-Models

NASA Astrophysics Data System (ADS)

Stojevic, Vid

2006-11-01

Two-dimensional sigma-models describing superstrings propagating on manifolds of special holonomy are characterized by symmetries related to covariantly constant forms that these manifolds hold, which are generally non-linear and close in a field dependent sense. The thesis explores various aspects of the special holonomy symmetries.

The Quantum Arnold Transformation for the damped harmonic oscillator: from the Caldirola-Kanai model toward the Bateman model

NASA Astrophysics Data System (ADS)

López-Ruiz, F. F.; Guerrero, J.; Aldaya, V.; Cossío, F.

2012-08-01

Using a quantum version of the Arnold transformation of classical mechanics, all quantum dynamical systems whose classical equations of motion are non-homogeneous linear second-order ordinary differential equations (LSODE), including systems with friction linear in velocity such as the damped harmonic oscillator, can be related to the quantum free-particle dynamical system. This implies that symmetries and simple computations in the free particle can be exported to the LSODE-system. The quantum Arnold transformation is given explicitly for the damped harmonic oscillator, and an algebraic connection between the Caldirola-Kanai model for the damped harmonic oscillator and the Bateman system will be sketched out.

Impact of a New Law to Reduce the Legal Blood Alcohol Concentration Limit - A Poisson Regression Analysis and Descriptive Approach.

PubMed

Nistal-Nuño, Beatriz

2017-03-31

In Chile, a new law introduced in March 2012 lowered the blood alcohol concentration (BAC) limit for impaired drivers from 0.1% to 0.08% and the BAC limit for driving under the influence of alcohol from 0.05% to 0.03%, but its effectiveness remains uncertain. The goal of this investigation was to evaluate the effects of this enactment on road traffic injuries and fatalities in Chile. A retrospective cohort study. Data were analyzed using a descriptive and a Generalized Linear Models approach, type of Poisson regression, to analyze deaths and injuries in a series of additive Log-Linear Models accounting for the effects of law implementation, month influence, a linear time trend and population exposure. A review of national databases in Chile was conducted from 2003 to 2014 to evaluate the monthly rates of traffic fatalities and injuries associated to alcohol and in total. It was observed a decrease by 28.1 percent in the monthly rate of traffic fatalities related to alcohol as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 20.9 percent (P<0.001).There was a reduction in the monthly rate of traffic injuries related to alcohol by 10.5 percent as compared to before the law (P<0.001). Adding a linear time trend as a predictor, the decrease was by 24.8 percent (P<0.001). Positive results followed from this new 'zero-tolerance' law implemented in 2012 in Chile. Chile experienced a significant reduction in alcohol-related traffic fatalities and injuries, being a successful public health intervention.

Linear regression metamodeling as a tool to summarize and present simulation model results.

PubMed

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Symmetric linear systems - An application of algebraic systems theory

NASA Technical Reports Server (NTRS)

Hazewinkel, M.; Martin, C.

1983-01-01

Dynamical systems which contain several identical subsystems occur in a variety of applications ranging from command and control systems and discretization of partial differential equations, to the stability augmentation of pairs of helicopters lifting a large mass. Linear models for such systems display certain obvious symmetries. In this paper, we discuss how these symmetries can be incorporated into a mathematical model that utilizes the modern theory of algebraic systems. Such systems are inherently related to the representation theory of algebras over fields. We will show that any control scheme which respects the dynamical structure either implicitly or explicitly uses the underlying algebra.

An hourly PM10 diagnosis model for the Bilbao metropolitan area using a linear regression methodology.

PubMed

González-Aparicio, I; Hidalgo, J; Baklanov, A; Padró, A; Santa-Coloma, O

2013-07-01

There is extensive evidence of the negative impacts on health linked to the rise of the regional background of particulate matter (PM) 10 levels. These levels are often increased over urban areas becoming one of the main air pollution concerns. This is the case on the Bilbao metropolitan area, Spain. This study describes a data-driven model to diagnose PM10 levels in Bilbao at hourly intervals. The model is built with a training period of 7-year historical data covering different urban environments (inland, city centre and coastal sites). The explanatory variables are quantitative-log [NO2], temperature, short-wave incoming radiation, wind speed and direction, specific humidity, hour and vehicle intensity-and qualitative-working days/weekends, season (winter/summer), the hour (from 00 to 23 UTC) and precipitation/no precipitation. Three different linear regression models are compared: simple linear regression; linear regression with interaction terms (INT); and linear regression with interaction terms following the Sawa's Bayesian Information Criteria (INT-BIC). Each type of model is calculated selecting two different periods: the training (it consists of 6 years) and the testing dataset (it consists of 1 year). The results of each type of model show that the INT-BIC-based model (R(2) = 0.42) is the best. Results were R of 0.65, 0.63 and 0.60 for the city centre, inland and coastal sites, respectively, a level of confidence similar to the state-of-the art methodology. The related error calculated for longer time intervals (monthly or seasonal means) diminished significantly (R of 0.75-0.80 for monthly means and R of 0.80 to 0.98 at seasonally means) with respect to shorter periods.

Communication: Symmetrical quasi-classical analysis of linear optical spectroscopy

NASA Astrophysics Data System (ADS)

Provazza, Justin; Coker, David F.

2018-05-01

The symmetrical quasi-classical approach for propagation of a many degree of freedom density matrix is explored in the context of computing linear spectra. Calculations on a simple two state model for which exact results are available suggest that the approach gives a qualitative description of peak positions, relative amplitudes, and line broadening. Short time details in the computed dipole autocorrelation function result in exaggerated tails in the spectrum.

Molecular Dynamics Simulation Studies of Fracture in Two Dimensions

DTIC Science & Technology

1980-05-01

reversibility of trajectories. The microscopic elastic constants, dispersion relation and phonon spectrum of the system were determined by lattice dynamics. These... linear elasticity theory of a two-dimensional crack embedded in an infinite medium. System con- sists of 436 particles arranged in a tri- angular lattice ...satisfying these demands. In evaluating the mechanical energy of his model, Griffith used a result from linear elasticity theory, namely that for any body

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.

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

PubMed Central

Bornschein, Jörg; Henniges, Marc; Lücke, Jörg

2013-01-01

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex. PMID:23754938

Finite element modeling of temperature load effects on the vibration of local modes in multi-cable structures

NASA Astrophysics Data System (ADS)

Treyssède, Fabien

2018-01-01

Understanding thermal effects on the vibration of local (cable-dominant) modes in multi-cable structures is a complicated task. The main difficulty lies in the modification by temperature change of cable tensions, which are then undetermined. This paper applies a finite element procedure to investigate the effects of thermal loads on the linear dynamics of prestressed self-weighted multi-cable structures. Provided that boundary conditions are carefully handled, the discretization of cables with nonlinear curved beam elements can properly represent the thermoelastic behavior of cables as well as their linearized dynamics. A three-step procedure that aims to replace applied pretension forces with displacement continuity conditions is used. Despite an increase in the computational cost related to beam rotational degrees of freedom, such an approach has several advantages. Nonlinear beam finite elements are usually available in commercial codes. The overall method follows a thermoelastic geometrically non-linear analysis and hereby includes the main sources of non-linearities in multi-cable structures. The effects of cable bending stiffness, which can be significant, are also naturally accounted for. The accuracy of the numerical approach is assessed thanks to an analytical model for the vibration of a single inclined cable under temperature change. Then, the effects of thermal loads are investigated for two cable bridges, highlighting how natural frequencies can be affected by temperature. Although counterintuitive, a reverse relative change of natural frequency may occur for certain local modes. This phenomenon can be explained by two distinct mechanisms, one related to the physics intrinsic to cables and the other related to the thermal deflection of the superstructure. Numerical results show that cables cannot be isolated from the rest of the structure and the importance of modeling the whole structure for a quantitative analysis of temperature effects on the dynamics of cable bridges.

Atypical transitions in material response during constant strain rate, hot deformation of austenitic steel

NASA Astrophysics Data System (ADS)

Borah, Utpal; Aashranth, B.; Samantaray, Dipti; Kumar, Santosh; Davinci, M. Arvinth; Albert, Shaju K.; Bhaduri, A. K.

2017-10-01

Work hardening, dynamic recovery and dynamic recrystallization (DRX) occurring during hot working of austenitic steel have been extensively studied. Various empirical models describe the nature and effects of these phenomena in a typical framework. However, the typical model is sometimes violated following atypical transitions in deformation mechanisms of the material. To ascertain the nature of these atypical transitions, researchers have intentionally introduced discontinuities in the deformation process, such as interrupting the deformation as in multi-step rolling and abruptly changing the rate of deformation. In this work, we demonstrate that atypical transitions are possible even in conventional single-step, constant strain rate deformation of austenitic steel. Towards this aim, isothermal, constant true strain rate deformation of austenitic steel has been carried out in a temperature range of 1173-1473 K and strain rate range of 0.01-100 s-1. The microstructural response corresponding to each deformation condition is thoroughly investigated. The conventional power-law variation of deformation grain size (D) with peak stress (σp) during DRX is taken as a typical model and experimental data is tested against it. It is shown that σp-D relations exhibit an atypical two-slope linear behaviour rather than a continuous power law relation. Similarly, the reduction in σp with temperature (T) is found to consist of two discrete linear segments. In practical terms, the two linear segments denote two distinct microstructural responses to deformation. As a consequence of this distinction, the typical model breaks down and is unable to completely relate microstructural evolution to flow behaviour. The present work highlights the microstructural mechanisms responsible for this atypical behavior and suggests strategies to incorporate the two-slope behaviour in the DRX model.

Wave-induced hydraulic forces on submerged aquatic plants in shallow lakes.

PubMed

Schutten, J; Dainty, J; Davy, A J

2004-03-01

Hydraulic pulling forces arising from wave action are likely to limit the presence of freshwater macrophytes in shallow lakes, particularly those with soft sediments. The aim of this study was to develop and test experimentally simple models, based on linear wave theory for deep water, to predict such forces on individual shoots. Models were derived theoretically from the action of the vertical component of the orbital velocity of the waves on shoot size. Alternative shoot-size descriptors (plan-form area or dry mass) and alternative distributions of the shoot material along its length (cylinder or inverted cone) were examined. Models were tested experimentally in a flume that generated sinusoidal waves which lasted 1 s and were up to 0.2 m high. Hydraulic pulling forces were measured on plastic replicas of Elodea sp. and on six species of real plants with varying morphology (Ceratophyllum demersum, Chara intermedia, Elodea canadensis, Myriophyllum spicatum, Potamogeton natans and Potamogeton obtusifolius). Measurements on the plastic replicas confirmed predicted relationships between force and wave phase, wave height and plant submergence depth. Predicted and measured forces were linearly related over all combinations of wave height and submergence depth. Measured forces on real plants were linearly related to theoretically derived predictors of the hydraulic forces (integrals of the products of the vertical orbital velocity raised to the power 1.5 and shoot size). The general applicability of the simplified wave equations used was confirmed. Overall, dry mass and plan-form area performed similarly well as shoot-size descriptors, as did the conical or cylindrical models of shoot distribution. The utility of the modelling approach in predicting hydraulic pulling forces from relatively simple plant and environmental measurements was validated over a wide range of forces, plant sizes and species.

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

NASA Technical Reports Server (NTRS)

Trejo, Leonard J.; Shensa, Mark J.; Remington, Roger W. (Technical Monitor)

1998-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

NASA Technical Reports Server (NTRS)

Trejo, L. J.; Shensa, M. J.

1999-01-01

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

The non-linear association between low-level lead exposure and maternal stress among pregnant women.

PubMed

Li, Shufang; Xu, Jian; Liu, Zhiwei; Yan, Chong-Huai

2017-03-01

Neuro-developmental impairments in the developing fetus due to exposure to low-level lead have been well documented. However, few studies have investigated the relation between maternal stress levels and low-level lead exposure among pregnant women. To investigate the relation between maternal blood lead and stress levels during index pregnancy. 1931 pregnant women (gestational week 28-36) were investigated using stratified-cluster-sampling in Shanghai in 2010. Maternal life event stress and emotional stress were assessed using "Life-Event-Stress-Scale-for-Pregnant-Women" (LESPW) and "Symptom-Checklist-90-Revised" (SCL-90-R), respectively. Maternal whole blood lead levels were determined, and other data on covariates were obtained from maternal interviews and medical records. Two piecewise linear regression models were applied to assess the relations between blood lead and stress levels using a data-driven approach according to spline smoothing fitting of the data. Maternal blood lead levels ranged from 0.80 to 14.84μg/dL, and the geometric mean was 3.97μg/dL. The P-values for the two piecewise linear models against the single linear regression models were 0.010, 0.003 and 0.017 for models predicting GSI, depression and anxiety symptom scores, respectively. When blood lead levels were below 2.57μg/dL, each unit increase in log10 transformed blood lead levels (μg/dL) was associated with about 18% increase in maternal GSI, depression and anxiety symptom scores (P GSI =0.013, P depression =0.002, P anxiety =0.019, respectively). However, no significant relation was found when blood lead levels were above 2.57μg/dL (all P-values>0.05). Our findings suggested a nonlinear relationship between blood lead and emotional stress levels among pregnant women. Emotional stress increased along with blood lead levels, and appeared to be plateaued when blood lead levels reached 2.57μg/dL. Copyright © 2016 Elsevier B.V. All rights reserved.

Developmental models for estimating ecological responses to environmental variability: structural, parametric, and experimental issues.

PubMed

Moore, Julia L; Remais, Justin V

2014-03-01

Developmental models that account for the metabolic effect of temperature variability on poikilotherms, such as degree-day models, have been widely used to study organism emergence, range and development, particularly in agricultural and vector-borne disease contexts. Though simple and easy to use, structural and parametric issues can influence the outputs of such models, often substantially. Because the underlying assumptions and limitations of these models have rarely been considered, this paper reviews the structural, parametric, and experimental issues that arise when using degree-day models, including the implications of particular structural or parametric choices, as well as assumptions that underlie commonly used models. Linear and non-linear developmental functions are compared, as are common methods used to incorporate temperature thresholds and calculate daily degree-days. Substantial differences in predicted emergence time arose when using linear versus non-linear developmental functions to model the emergence time in a model organism. The optimal method for calculating degree-days depends upon where key temperature threshold parameters fall relative to the daily minimum and maximum temperatures, as well as the shape of the daily temperature curve. No method is shown to be universally superior, though one commonly used method, the daily average method, consistently provides accurate results. The sensitivity of model projections to these methodological issues highlights the need to make structural and parametric selections based on a careful consideration of the specific biological response of the organism under study, and the specific temperature conditions of the geographic regions of interest. When degree-day model limitations are considered and model assumptions met, the models can be a powerful tool for studying temperature-dependent development.

Breakfast intake among adults with type 2 diabetes: is bigger better?

PubMed Central

Jarvandi, Soghra; Schootman, Mario; Racette, Susan B.

2015-01-01

Objective To assess the association between breakfast energy and total daily energy intake among individuals with type 2 diabetes. Design Cross-sectional study. Daily energy intake was computed from a 24-h dietary recall. Multiple regression models were used to estimate the association between daily energy intake (dependent variable) and quartiles of energy intake at breakfast (independent variable) expressed as either absolute or relative (% of total daily energy intake) terms. Orthogonal polynomial contrasts were used to test for linear and quadratic trends. Models were controlled for sex, age, race/ethnicity, body mass index, physical activity and smoking. In addition, we used separate multiple regression models to test the effect of quartiles of absolute and relative breakfast energy on intake at lunch, dinner, and snacks. Setting The 1999–2004 National Health and Nutrition Examination Survey (NHANES). Subjects Participants aged ≥ 30 years with self-reported history of diabetes (N = 1,146). Results Daily energy intake increased as absolute breakfast energy intake increased (linear trend, P < 0.0001; quadratic trend, P = 0.02), but decreased as relative breakfast energy intake increased (linear trend, P < 0.0001). In addition, while higher quartiles of absolute breakfast intake had no associations with energy intake at subsequent meals, higher quartiles of relative breakfast intake were associated with lower energy intake during all subsequent meals and snacks (P < 0.05). Conclusions Consuming a breakfast that provided less energy or comprised a greater proportion of daily energy intake was associated with lower total daily energy intake in adults with type 2 diabetes. PMID:25529061

Optimization of autoregressive, exogenous inputs-based typhoon inundation forecasting models using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Ouyang, Huei-Tau

2017-07-01

Three types of model for forecasting inundation levels during typhoons were optimized: the linear autoregressive model with exogenous inputs (LARX), the nonlinear autoregressive model with exogenous inputs with wavelet function (NLARX-W) and the nonlinear autoregressive model with exogenous inputs with sigmoid function (NLARX-S). The forecast performance was evaluated by three indices: coefficient of efficiency, error in peak water level and relative time shift. Historical typhoon data were used to establish water-level forecasting models that satisfy all three objectives. A multi-objective genetic algorithm was employed to search for the Pareto-optimal model set that satisfies all three objectives and select the ideal models for the three indices. Findings showed that the optimized nonlinear models (NLARX-W and NLARX-S) outperformed the linear model (LARX). Among the nonlinear models, the optimized NLARX-W model achieved a more balanced performance on the three indices than the NLARX-S models and is recommended for inundation forecasting during typhoons.

Development of a Dynamic, End-to-End Free Piston Stirling Convertor Model

NASA Technical Reports Server (NTRS)

Regan, Timothy F.; Gerber, Scott S.; Roth, Mary Ellen

2004-01-01

A dynamic model for a free-piston Stirling convertor is being developed at the NASA Glenn Research Center. The model is an end-to-end system model that includes the cycle thermodynamics, the dynamics, and electrical aspects of the system. The subsystems of interest are the heat source, the springs, the moving masses, the linear alternator, the controller, and the end-user load. The envisioned use of the model will be in evaluating how changes in a subsystem could affect the operation of the convertor. The model under development will speed the evaluation of improvements to a subsystem and aid in determining areas in which most significant improvements may be found. One of the first uses of the end-toend model will be in the development of controller architectures. Another related area is in evaluating changes to details in the linear alternator.

Development of a Dynamic, End-to-End Free Piston Stirling Convertor Model

NASA Astrophysics Data System (ADS)

Regan, Timothy F.; Gerber, Scott S.; Roth, Mary Ellen

2003-01-01

A dynamic model for a free-piston Stirling convertor is being developed at the NASA Glenn Research Center. The model is an end-to-end system model that includes the cycle thermodynamics, the dynamics, and electrical aspects of the system. The subsystems of interest are the heat source, the springs, the moving masses, the linear alternator, the controller and the end-user load. The envisioned use of the model will be in evaluating how changes in a subsystem could affect the operation of the convertor. The model under development will speed the evaluation of improvements to a subsystem and aid in determining areas in which most significant improvements may be found. One of the first uses of the end-to-end model will be in the development of controller architectures. Another related area is in evaluating changes to details in the linear alternator.

[Analysis on the trend of long-term change of blood pressure in hypertensive patients treated with benazepril].

PubMed

Lu, Jun; Li, Li-Ming; He, Ping-Ping; Cao, Wei-Hua; Zhan, Si-Yan; Hu, Yong-Hua

2004-06-01

To introduce the application of mixed linear model in the analysis of secular trend of blood pressure under antihypertensive treatment. A community-based postmarketing surveillance of benazepril was conducted in 1831 essential hypertensive patients (age range from 35 to 88 years) in Shanghai. Data of blood pressure was analyzed every 3 months with mixed linear model to describe the secular trend of blood pressure and changes of age-specific and gender-specific. The changing trends of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were found to fit the curvilinear models. A piecewise model was fit for pulse pressure (PP), i.e., curvilinear model in the first 9 months and linear model after 9 months of taking medication. Both blood pressure and its velocity gradually slowed down. There were significant variation for the curve parameters of intercept, slope, and acceleration. Blood pressure in patients with higher initial levels was persistently declining in the 3-year-treatment. However blood pressures of patients with relatively low initial levels remained low when dropped down to some degree. Elderly patients showed high SBP but low DBP, so as with higher PP. The velocity and sizes of blood pressure reductions increased with the initial level of blood pressure. Mixed linear model is flexible and robust when applied to the analysis of longitudinal data but with missing values and can also make the maximum use of available information.

Kelvin-Voigt model of wave propagation in fragmented geomaterials with impact damping

NASA Astrophysics Data System (ADS)

Khudyakov, Maxim; Pasternak, Elena; Dyskin, Arcady

2017-04-01

When a wave propagates through real materials, energy dissipation occurs. The effect of loss of energy in homogeneous materials can be accounted for by using simple viscous models. However, a reliable model representing the effect in fragmented geomaterials has not been established yet. The main reason for that is a mechanism how vibrations are transmitted between the elements (fragments) in these materials. It is hypothesised that the fragments strike against each other, in the process of oscillation, and the impacts lead to the energy loss. We assume that the energy loss is well represented by the restitution coefficient. The principal element of this concept is the interaction of two adjacent blocks. We model it by a simple linear oscillator (a mass on an elastic spring) with an additional condition: each time the system travels through the neutral point, where the displacement is equal to zero, the velocity reduces by multiplying itself by the restitution coefficient, which characterises an impact of the fragments. This additional condition renders the system non-linear. We show that the behaviour of such a model averaged over times much larger than the system period can approximately be represented by a conventional linear oscillator with linear damping characterised by a damping coefficient expressible through the restitution coefficient. Based on this the wave propagation at times considerably greater than the resonance period of oscillations of the neighbouring blocks can be modelled using the Kelvin-Voigt model. The wave velocities and the dispersion relations are obtained.

Constructivist Approach to Teacher Education: An Integrative Model for Reflective Teaching

ERIC Educational Resources Information Center

Vijaya Kumari, S. N.

2014-01-01

The theory of constructivism states that learning is non-linear, recursive, continuous, complex and relational--Despite the difficulty of deducing constructivist pedagogy from constructivist theories, there are models and common elements to consider in planning new program. Reflective activities are a common feature of all the programs of…

Adaptive Control Of Remote Manipulator

NASA Technical Reports Server (NTRS)

Seraji, Homayoun

1989-01-01

Robotic control system causes remote manipulator to follow closely reference trajectory in Cartesian reference frame in work space, without resort to computationally intensive mathematical model of robot dynamics and without knowledge of robot and load parameters. System, derived from linear multivariable theory, uses relatively simple feedforward and feedback controllers with model-reference adaptive control.

Dynamics of Permanent-Magnet Biased Active Magnetic Bearings

NASA Technical Reports Server (NTRS)

Fukata, Satoru; Yutani, Kazuyuki

1996-01-01

Active magnetic radial bearings are constructed with a combination of permanent magnets to provide bias forces and electromagnets to generate control forces for the reduction of cost and the operating energy consumption. Ring-shaped permanent magnets with axial magnetization are attached to a shaft and share their magnet stators with the electromagnets. The magnet cores are made of solid iron for simplicity. A simplified magnetic circuit of the combined magnet system is analyzed with linear circuit theory by approximating the characteristics of permanent magnets with a linear relation. A linearized dynamical model of the control force is presented with the first-order approximation of the effects of eddy currents. Frequency responses of the rotor motion to disturbance inputs and the motion for impulsive forces are tested in the non-rotating state. The frequency responses are compared with numerical results. The decay of rotor speed due to magnetic braking is examined. The experimental results and the presented linearized model are similar to those of the all-electromagnetic design.

Influence of wave modelling on the prediction of fatigue for offshore wind turbines

NASA Astrophysics Data System (ADS)

Veldkamp, H. F.; van der Tempel, J.

2005-01-01

Currently it is standard practice to use Airy linear wave theory combined with Morison's formula for the calculation of fatigue loads for offshore wind turbines. However, offshore wind turbines are typically placed in relatively shallow water depths of 5-25 m where linear wave theory has limited accuracy and where ideally waves generated with the Navier-Stokes approach should be used. This article examines the differences in fatigue for some representative offshore wind turbines that are found if first-order, second-order and fully non-linear waves are used. The offshore wind turbines near Blyth are located in an area where non-linear wave effects are common. Measurements of these waves from the OWTES project are used to compare the different wave models with the real world in spectral form. Some attention is paid to whether the shape of a higher-order wave height spectrum (modified JONSWAP) corresponds to reality for other places in the North Sea, and which values for the drag and inertia coefficients should be used. Copyright

Holographic dark energy in higher derivative gravity with time varying model parameter c2

NASA Astrophysics Data System (ADS)

Borah, B.; Ansari, M.

2015-01-01

Purpose of this paper is to study holographic dark energy in higher derivative gravity assuming the model parameter c2 as a slowly time varying function. Since dark energy emerges as combined effect of linear as well as non-linear terms of curvature, therefore it is important to see holographic dark energy at higher derivative gravity, where action contains both linear as well as non-linear terms of Ricci curvature R. We consider non-interacting scenario of the holographic dark energy with dark matter in spatially flat universe and obtain evolution of the equation of state parameter. Also, we determine deceleration parameter as well as the evolution of dark energy density to explain expansion of the universe. Further, we investigate validity of generalized second law of thermodynamics in this scenario. Finally, we find out a cosmological application of our work by evaluating a relation for the equation of state of holographic dark energy for low red-shifts containing c2 correction.

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

Shang, Yu; Yu, Guoqiang, E-mail: guoqiang.yu@uky.edu

Conventional semi-infinite analytical solutions of correlation diffusion equation may lead to errors when calculating blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements in tissues with irregular geometries. Very recently, we created an algorithm integrating a Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in homogenous tissues with arbitrary geometries for extraction of BFI (i.e., αD{sub B}). The purpose of this study is to extend the capability of the Nth-order linear algorithm for extracting BFI in heterogeneous tissues with arbitrary geometries. The previous linear algorithm was modified to extract BFIs in different typesmore » of tissues simultaneously through utilizing DCS data at multiple source-detector separations. We compared the proposed linear algorithm with the semi-infinite homogenous solution in a computer model of adult head with heterogeneous tissue layers of scalp, skull, cerebrospinal fluid, and brain. To test the capability of the linear algorithm for extracting relative changes of cerebral blood flow (rCBF) in deep brain, we assigned ten levels of αD{sub B} in the brain layer with a step decrement of 10% while maintaining αD{sub B} values constant in other layers. Simulation results demonstrate the accuracy (errors < 3%) of high-order (N ≥ 5) linear algorithm in extracting BFIs in different tissue layers and rCBF in deep brain. By contrast, the semi-infinite homogenous solution resulted in substantial errors in rCBF (34.5% ≤ errors ≤ 60.2%) and BFIs in different layers. The Nth-order linear model simplifies data analysis, thus allowing for online data processing and displaying. Future study will test this linear algorithm in heterogeneous tissues with different levels of blood flow variations and noises.« less

Nonlinear identification of the total baroreflex arc: chronic hypertension model.

PubMed

Moslehpour, Mohsen; Kawada, Toru; Sunagawa, Kenji; Sugimachi, Masaru; Mukkamala, Ramakrishna

2016-05-01

The total baroreflex arc is the open-loop system relating carotid sinus pressure (CSP) to arterial pressure (AP). Its linear dynamic functioning has been shown to be preserved in spontaneously hypertensive rats (SHR). However, the system is known to exhibit nonlinear dynamic behaviors. The aim of this study was to establish nonlinear dynamic models of the total arc (and its subsystems) in hypertensive rats and to compare these models with previously published models for normotensive rats. Hypertensive rats were studied under anesthesia. The vagal and aortic depressor nerves were sectioned. The carotid sinus regions were isolated and attached to a servo-controlled piston pump. AP and sympathetic nerve activity were measured while CSP was controlled via the pump using Gaussian white noise stimulation. Second-order, nonlinear dynamics models were developed by application of nonparametric system identification to a portion of the measurements. The models of the total arc predicted AP 21-43% better (P < 0.005) than conventional linear dynamic models in response to a new portion of the CSP measurement. The linear and nonlinear terms of these validated models were compared with the corresponding terms of an analogous model for normotensive rats. The nonlinear gains for the hypertensive rats were significantly larger than those for the normotensive rats [-0.38 ± 0.04 (unitless) vs. -0.22 ± 0.03, P < 0.01], whereas the linear gains were similar. Hence, nonlinear dynamic functioning of the sympathetically mediated total arc may enhance baroreflex buffering of AP increases more in SHR than normotensive rats. Copyright © 2016 the American Physiological Society.

Aggression and Adaptive Functioning: The Bright Side to Bad Behavior.

ERIC Educational Resources Information Center

Hawley, Patricia H.; Vaughn, Brian E.

2003-01-01

Asserts that effective children and adolescents can engage in socially undesirable behavior to attain personal goals at relatively little personal or interpersonal cost, implying that relations between adjustment and aggression may not be optimally described by standard linear models. Suggests that if researchers recognize that some aggression…

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

PubMed

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

Inelastic compaction, dilation and hysteresis of sandstones under hydrostatic conditions

NASA Astrophysics Data System (ADS)

Shalev, Eyal; Lyakhovsky, Vladimir; Ougier-Simonin, Audrey; Hamiel, Yariv; Zhu, Wenlu

2014-05-01

Sandstones display non-linear and inelastic behaviour such as hysteresis when subjected to cyclic loading. We present three hydrostatic compaction experiments with multiple loading-unloading cycles on Berea and Darley Dale sandstones and explain their hysteretic behaviour using non-linear inelastic compaction and dilation. Each experiment included eight to nine loading-unloading cycles with increasing maximum pressure in each subsequent cycle. Different pressure-volumetric strain relations during loading and unloading were observed. During the first cycles, under relatively low pressures, not all of the volumetric strain is recovered at the end of each cycle whereas at the last cycles, under relatively high pressures, the strain is recovered and the pressure-volumetric strain hysteresis loops are closed. The observed pressure-volumetric strain relations are non-linear and the effective bulk modulus of the sandstones changes between cycles. Observations are modelled with two inelastic deformation processes: irreversible compaction caused by changes in grain packing and recoverable compaction associated with grain contact adhesion, frictional sliding on grains or frictional sliding on cracks. The irreversible compaction is suggested to reflect rearrangement of grains into a more compact mode as the maximum pressure increases. Our model describes the `inelastic compaction envelope' in which sandstone sample will follow during hydrostatic loading. Irreversible compaction occurs when pressure is greater than a threshold value defined by the `inelastic compaction envelope'.

Projecting temperature-related years of life lost under different climate change scenarios in one temperate megacity, China.

PubMed

Li, Yixue; Li, Guoxing; Zeng, Qiang; Liang, Fengchao; Pan, Xiaochuan

2018-02-01

Temperature has been associated with population health, but few studies have projected the future temperature-related years of life lost attributable to climate change. To project future temperature-related disease burden in Tianjin, we selected years of life lost (YLL) as the dependent variable to explore YLL attributable to climate change. A generalized linear model (GLM) and distributed lag non-linear model were combined to assess the non-linear and delayed effects of temperature on the YLL of non-accidental mortality. Then, we calculated the YLL changes attributable to future climate scenarios in 2055 and 2090. The relationships of daily mean temperature with the YLL of non-accident mortality were basically U-shaped. Both the daily mean temperature increase on high-temperature days and its drop on low-temperature days caused an increase of YLL and non-accidental deaths. The temperature-related YLL will worsen if future climate change exceeds 2 °C. In addition, the adverse effects of extreme temperature on YLL occurred more quickly than that of the overall temperature. The impact of low temperature was greater than that of high temperature. Men were vulnerable to high temperature compared with women. This analysis highlights that the government should formulate environmental policies to reach the Paris Agreement goal. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimation of suspended-sediment rating curves and mean suspended-sediment loads

USGS Publications Warehouse

Crawford, Charles G.

1991-01-01

A simulation study was done to evaluate: (1) the accuracy and precision of parameter estimates for the bias-corrected, transformed-linear and non-linear models obtained by the method of least squares; (2) the accuracy of mean suspended-sediment loads calculated by the flow-duration, rating-curve method using model parameters obtained by the alternative methods. Parameter estimates obtained by least squares for the bias-corrected, transformed-linear model were considerably more precise than those obtained for the non-linear or weighted non-linear model. The accuracy of parameter estimates obtained for the biascorrected, transformed-linear and weighted non-linear model was similar and was much greater than the accuracy obtained by non-linear least squares. The improved parameter estimates obtained by the biascorrected, transformed-linear or weighted non-linear model yield estimates of mean suspended-sediment load calculated by the flow-duration, rating-curve method that are more accurate and precise than those obtained for the non-linear model.

Temporal diagnostic analysis of the SWAT model to detect dominant periods of poor model performance

NASA Astrophysics Data System (ADS)

Guse, Björn; Reusser, Dominik E.; Fohrer, Nicola

2013-04-01

Hydrological models generally include thresholds and non-linearities, such as snow-rain-temperature thresholds, non-linear reservoirs, infiltration thresholds and the like. When relating observed variables to modelling results, formal methods often calculate performance metrics over long periods, reporting model performance with only few numbers. Such approaches are not well suited to compare dominating processes between reality and model and to better understand when thresholds and non-linearities are driving model results. We present a combination of two temporally resolved model diagnostic tools to answer when a model is performing (not so) well and what the dominant processes are during these periods. We look at the temporal dynamics of parameter sensitivities and model performance to answer this question. For this, the eco-hydrological SWAT model is applied in the Treene lowland catchment in Northern Germany. As a first step, temporal dynamics of parameter sensitivities are analyzed using the Fourier Amplitude Sensitivity test (FAST). The sensitivities of the eight model parameters investigated show strong temporal variations. High sensitivities were detected for two groundwater (GW_DELAY, ALPHA_BF) and one evaporation parameters (ESCO) most of the time. The periods of high parameter sensitivity can be related to different phases of the hydrograph with dominances of the groundwater parameters in the recession phases and of ESCO in baseflow and resaturation periods. Surface runoff parameters show high parameter sensitivities in phases of a precipitation event in combination with high soil water contents. The dominant parameters give indication for the controlling processes during a given period for the hydrological catchment. The second step included the temporal analysis of model performance. For each time step, model performance was characterized with a "finger print" consisting of a large set of performance measures. These finger prints were clustered into four reoccurring patterns of typical model performance, which can be related to different phases of the hydrograph. Overall, the baseflow cluster has the lowest performance. By combining the periods with poor model performance with the dominant model components during these phases, the groundwater module was detected as the model part with the highest potential for model improvements. The detection of dominant processes in periods of poor model performance enhances the understanding of the SWAT model. Based on this, concepts how to improve the SWAT model structure for the application in German lowland catchment are derived.

Measures of GCM Performance as Functions of Model Parameters Affecting Clouds and Radiation

NASA Astrophysics Data System (ADS)

Jackson, C.; Mu, Q.; Sen, M.; Stoffa, P.

2002-05-01

This abstract is one of three related presentations at this meeting dealing with several issues surrounding optimal parameter and uncertainty estimation of model predictions of climate. Uncertainty in model predictions of climate depends in part on the uncertainty produced by model approximations or parameterizations of unresolved physics. Evaluating these uncertainties is computationally expensive because one needs to evaluate how arbitrary choices for any given combination of model parameters affects model performance. Because the computational effort grows exponentially with the number of parameters being investigated, it is important to choose parameters carefully. Evaluating whether a parameter is worth investigating depends on two considerations: 1) does reasonable choices of parameter values produce a large range in model response relative to observational uncertainty? and 2) does the model response depend non-linearly on various combinations of model parameters? We have decided to narrow our attention to selecting parameters that affect clouds and radiation, as it is likely that these parameters will dominate uncertainties in model predictions of future climate. We present preliminary results of ~20 to 30 AMIPII style climate model integrations using NCAR's CCM3.10 that show model performance as functions of individual parameters controlling 1) critical relative humidity for cloud formation (RHMIN), and 2) boundary layer critical Richardson number (RICR). We also explore various definitions of model performance that include some or all observational data sources (surface air temperature and pressure, meridional and zonal winds, clouds, long and short-wave cloud forcings, etc...) and evaluate in a few select cases whether the model's response depends non-linearly on the parameter values we have selected.

A Large Signal Model for CMUT Arrays with Arbitrary Membrane Geometries Operating in Non-Collapsed Mode

PubMed Central

Satir, Sarp; Zahorian, Jaime; Degertekin, F. Levent

2014-01-01

A large signal, transient model has been developed to predict the output characteristics of a CMUT array operated in the non-collapse mode. The model is based on separation of the nonlinear electrostatic voltage-to-force relation and the linear acoustic array response. For linear acoustic radiation and crosstalk effects, the boundary element method is used. The stiffness matrix in the vibroacoustics calculations is obtained using static finite element analysis of a single membrane which can have arbitrary geometry and boundary conditions. A lumped modeling approach is used to reduce the order of the system for modeling the transient nonlinear electrostatic actuation. To accurately capture the dynamics of the non-uniform electrostatic force distribution over the CMUT electrode during large deflections, the membrane electrode is divided into patches shaped to match higher order membrane modes, each introducing a variable to the system model. This reduced order nonlinear lumped model is solved in the time domain using Simulink. The model has two linear blocks to calculate the displacement profile of the electrode patches and the output pressure for a given force distribution over the array, respectively. The force to array displacement block uses the linear acoustic model, and the Rayleigh integral is evaluated to calculate the pressure at any field point. Using the model, the transient transmitted pressure can be simulated for different large signal drive signal configurations. The acoustic model is verified by comparison to harmonic FEA in vacuum and fluid for high and low aspect ratio membranes as well as mass-loaded membranes. The overall Simulink model is verified by comparison to transient 3D FEA and experimental results for different large drive signals; and an example for a phased array simulation is given. PMID:24158297

A formulation of rotor-airframe coupling for design analysis of vibrations of helicopter airframes

NASA Technical Reports Server (NTRS)

Kvaternik, R. G.; Walton, W. C., Jr.

1982-01-01

A linear formulation of rotor airframe coupling intended for vibration analysis in airframe structural design is presented. The airframe is represented by a finite element analysis model; the rotor is represented by a general set of linear differential equations with periodic coefficients; and the connections between the rotor and airframe are specified through general linear equations of constraint. Coupling equations are applied to the rotor and airframe equations to produce one set of linear differential equations governing vibrations of the combined rotor airframe system. These equations are solved by the harmonic balance method for the system steady state vibrations. A feature of the solution process is the representation of the airframe in terms of forced responses calculated at the rotor harmonics of interest. A method based on matrix partitioning is worked out for quick recalculations of vibrations in design studies when only relatively few airframe members are varied. All relations are presented in forms suitable for direct computer implementation.

New non-linear model of groundwater recharge: Inclusion of memory, heterogeneity and visco-elasticity

NASA Astrophysics Data System (ADS)

Spannenberg, Jescica; Atangana, Abdon; Vermeulen, P. D.

2017-09-01

Fractional differentiation has adequate use for investigating real world scenarios related to geological formations associated with elasticity, heterogeneity, viscoelasticity, and the memory effect. Since groundwater systems exist in these geological formations, modelling groundwater recharge as a real world scenario is a challenging task to do because existing recharge estimation methods are governed by linear equations which make use of constant field parameters. This is inadequate because in reality these parameters are a function of both space and time. This study therefore concentrates on modifying the recharge equation governing the EARTH model, by application of the Eton approach. Accordingly, this paper presents a modified equation which is non-linear, and accounts for parameters in a way that it is a function of both space and time. To be more specific, herein, recharge and drainage resistance which are parameters within the equation, became a function of both space and time. Additionally, the study entailed solving the non-linear equation using an iterative method as well as numerical solutions by means of the Crank-Nicolson scheme. The numerical solutions were used alongside the Riemann-Liouville, Caputo-Fabrizio, and Atangana-Baleanu derivatives, so that account was taken for elasticity, heterogeneity, viscoelasticity, and the memory effect. In essence, this paper presents a more adequate model for recharge estimation.

Extensions of the Ferry shear wave model for active linear and nonlinear microrheology

PubMed Central

Mitran, Sorin M.; Forest, M. Gregory; Yao, Lingxing; Lindley, Brandon; Hill, David B.

2009-01-01

The classical oscillatory shear wave model of Ferry et al. [J. Polym. Sci. 2:593-611, (1947)] is extended for active linear and nonlinear microrheology. In the Ferry protocol, oscillation and attenuation lengths of the shear wave measured from strobe photographs determine storage and loss moduli at each frequency of plate oscillation. The microliter volumes typical in biology require modifications of experimental method and theory. Microbead tracking replaces strobe photographs. Reflection from the top boundary yields counterpropagating modes which are modeled here for linear and nonlinear viscoelastic constitutive laws. Furthermore, bulk imposed strain is easily controlled, and we explore the onset of normal stress generation and shear thinning using nonlinear viscoelastic models. For this paper, we present the theory, exact linear and nonlinear solutions where possible, and simulation tools more generally. We then illustrate errors in inverse characterization by application of the Ferry formulas, due to both suppression of wave reflection and nonlinearity, even if there were no experimental error. This shear wave method presents an active and nonlinear analog of the two-point microrheology of Crocker et al. [Phys. Rev. Lett. 85: 888 - 891 (2000)]. Nonlocal (spatially extended) deformations and stresses are propagated through a small volume sample, on wavelengths long relative to bead size. The setup is ideal for exploration of nonlinear threshold behavior. PMID:20011614

User's manual for LINEAR, a FORTRAN program to derive linear aircraft models

NASA Technical Reports Server (NTRS)

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

1987-01-01

This report documents a FORTRAN program that provides a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear system model using nonlinear equations of motion and a user-supplied nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both 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.

Latent log-linear models for handwritten digit classification.

PubMed

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

Age related neuromuscular changes in sEMG of m. Tibialis Anterior using higher order statistics (Gaussianity & linearity test).

PubMed

Siddiqi, Ariba; Arjunan, Sridhar P; Kumar, Dinesh K

2016-08-01

Age-associated changes in the surface electromyogram (sEMG) of Tibialis Anterior (TA) muscle can be attributable to neuromuscular alterations that precede strength loss. We have used our sEMG model of the Tibialis Anterior to interpret the age-related changes and compared with the experimental sEMG. Eighteen young (20-30 years) and 18 older (60-85 years) performed isometric dorsiflexion at 6 different percentage levels of maximum voluntary contractions (MVC), and their sEMG from the TA muscle was recorded. Six different age-related changes in the neuromuscular system were simulated using the sEMG model at the same MVCs as the experiment. The maximal power of the spectrum, Gaussianity and Linearity Test Statistics were computed from the simulated and experimental sEMG. A correlation analysis at α=0.05 was performed between the simulated and experimental age-related change in the sEMG features. The results show the loss in motor units was distinguished by the Gaussianity and Linearity test statistics; while the maximal power of the PSD distinguished between the muscular factors. The simulated condition of 40% loss of motor units with halved the number of fast fibers best correlated with the age-related change observed in the experimental sEMG higher order statistical features. The simulated aging condition found by this study corresponds with the moderate motor unit remodelling and negligible strength loss reported in literature for the cohorts aged 60-70 years.

The morphing of geographical features by Fourier transformation.

PubMed

Li, Jingzhong; Liu, Pengcheng; Yu, Wenhao; Cheng, Xiaoqiang

2018-01-01

This paper presents a morphing model of vector geographical data based on Fourier transformation. This model involves three main steps. They are conversion from vector data to Fourier series, generation of intermediate function by combination of the two Fourier series concerning a large scale and a small scale, and reverse conversion from combination function to vector data. By mirror processing, the model can also be used for morphing of linear features. Experimental results show that this method is sensitive to scale variations and it can be used for vector map features' continuous scale transformation. The efficiency of this model is linearly related to the point number of shape boundary and the interceptive value n of Fourier expansion. The effect of morphing by Fourier transformation is plausible and the efficiency of the algorithm is acceptable.

Earth survey applications division: Research leading to the effective use of space technology in applications relating to the Earth's surface and interior

NASA Technical Reports Server (NTRS)

Carpenter, L. (Editor)

1980-01-01

Accomplishments and future plans are described for the following areas: (1) geology - geobotanical indicators and geopotential data; (2) modeling magnetic fields; (3) modeling the structure, composition, and evolution of the Earth's crust; (4) global and regional motions of the Earth's crust and earthquake occurrence; (5) modeling geopotential from satellite tracking data; (6) modeling the Earth's gravity field; (7) global Earth dynamics; (8) sea surface topography, ocean dynamics; and geophysical interpretation; (9) land cover and land use; (10) physical and remote sensing attributes important in detecting, measuring, and monitoring agricultural crops; (11) prelaunch studies using LANDSAT D; (12) the multispectral linear array; (13) the aircraft linear array pushbroom radiometer; and (14) the spaceborne laser ranging system.

Statistical quality assessment criteria for a linear mixing model with elliptical t-distribution errors

NASA Astrophysics Data System (ADS)

Manolakis, Dimitris G.

2004-10-01

The linear mixing model is widely used in hyperspectral imaging applications to model the reflectance spectra of mixed pixels in the SWIR atmospheric window or the radiance spectra of plume gases in the LWIR atmospheric window. In both cases it is important to detect the presence of materials or gases and then estimate their amount, if they are present. The detection and estimation algorithms available for these tasks are related but they are not identical. The objective of this paper is to theoretically investigate how the heavy tails observed in hyperspectral background data affect the quality of abundance estimates and how the F-test, used for endmember selection, is robust to the presence of heavy tails when the model fits the data.

Conceptualization of the Sexual Response Models in Men: Are there Differences Between Sexually Functional and Dysfunctional Men?

PubMed

Connaughton, Catherine; McCabe, Marita; Karantzas, Gery

2016-03-01

Research to validate models of sexual response empirically in men with and without sexual dysfunction (MSD), as currently defined, is limited. To explore the extent to which the traditional linear or the Basson circular model best represents male sexual response for men with MSD and sexually functional men. In total, 573 men completed an online questionnaire to assess sexual function and aspects of the models of sexual response. In total, 42.2% of men (242) were sexually functional, and 57.8% (331) had at least one MSD. Models were built and tested using bootstrapping and structural equation modeling. Fit of models for men with and without MSD. The linear model and the initial circular model were a poor fit for men with and without MSD. A modified version of the circular model demonstrated adequate fit for the two groups and showed important interactions between psychological factors and sexual response for men with and without MSD. Male sexual response was not represented by the linear model for men with or without MSD, excluding possible healthy responsive desire. The circular model provided a better fit for the two groups of men but demonstrated that the relations between psychological factors and phases of sexual response were different for men with and without MSD as currently defined. Copyright © 2016 International Society for Sexual Medicine. Published by Elsevier Inc. All rights reserved.

Numerical investigation of frequency spectrum in the Hasegawa-Wakatani model

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

Kim, Juhyung; Terry, P. W.

2013-10-15

The wavenumber-frequency spectrum of the two-dimensional Hasegawa-Wakatani model is investigated in the hydrodynamic, intermediate, and adiabatic regimes. A nonlinear frequency and a line width related to energy transfer properties provide a measure of the average frequency and spectral broadening, respectively. In the adiabatic regime, narrow spectra, typical of wave turbulence, are observed with a nonlinear frequency shift in the electron drift direction. In the hydrodynamic regime, broad spectra with almost zero nonlinear frequencies are observed. Nonlinear frequency shifts are shown to be related to nonlinear energy transfer by vorticity advection through the high frequency region of the spectrum. In themore » intermediate regime, the nonlinear frequency shift for density fluctuations is observed to be weaker than that of electrostatic potential fluctuations. The weaker frequency shift of the density fluctuations is due to nonlinear density advection, which favors energy transfer in the low frequency range. Both the nonlinear frequency and the spectral width increase with poloidal wavenumber k{sub y}. In addition, in the adiabatic regime where the nonlinear interactions manifest themselves in the nonlinear frequency shift, the cross-phase between the density and potential fluctuations is observed to match a linear relation, but only if the linear response of the linearly stable eigenmode branch is included. Implications of these numerical observations are discussed.« less

Emotionally numb: Desensitization to community violence exposure among urban youth.

PubMed

Kennedy, Traci M; Ceballo, Rosario

2016-05-01

Community violence exposure (CVE) is associated with numerous psychosocial outcomes among youth. Although linear, cumulative effects models have typically been used to describe these relations, emerging evidence suggests the presence of curvilinear associations that may represent a pattern of emotional desensitization among youth exposed to chronic community violence. This study uses longitudinal data to investigate relations between CVE and both internalizing and externalizing symptoms among 3,480 youth ages 3 to 12 at baseline and 9 to 18 at outcome. Results support desensitization models, as evidenced by longitudinal quadratic associations between Wave 2 CVE and Wave 3 anxiety/depressive symptoms, alongside cross-sectional linear associations between Wave 3 CVE and Wave 3 aggression. Neither age nor gender moderated the associations between CVE and well-being. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Glovebox Integrated Microgravity Isolation Technology (g-LIMIT): A Linearized State-Space Model

NASA Technical Reports Server (NTRS)

Hampton, R. David; Calhoun, Philip C.; Whorton, Mark S.

2001-01-01

Vibration acceleration levels on large space platforms exceed the requirements of many space experiments. The Glovebox Integrated Microgravity Isolation Technology (g-LIMIT) is being built by the NASA Marshall Space Flight Center to attenuate these disturbances to acceptable levels. G-LIMIT uses Lorentz (voice-coil) magnetic actuators to levitate and isolate payloads at the individual experiment/sub-experiment (versus rack) level. Payload acceleration, relative position, and relative orientation measurements are fed to a state-space controller. The controller, in turn, determines the actuator Currents needed for effective experiment isolation. This paper presents the development of an algebraic, state-space model of g-LIMIT, in a form suitable for optimal controller design. The equations are first derived using Newton's Second Law directly, then simplified to a linear form for the purpose of controller design.

Nonlocal theory of curved rods. 2-D, high order, Timoshenko's and Euler-Bernoulli models

NASA Astrophysics Data System (ADS)

Zozulya, V. V.

2017-09-01

New models for plane curved rods based on linear nonlocal theory of elasticity have been developed. The 2-D theory is developed from general 2-D equations of linear nonlocal elasticity using a special curvilinear system of coordinates related to the middle line of the rod along with special hypothesis based on assumptions that take into account the fact that the rod is thin. High order theory is based on the expansion of the equations of the theory of elasticity into Fourier series in terms of Legendre polynomials. First, stress and strain tensors, vectors of displacements and body forces have been expanded into Fourier series in terms of Legendre polynomials with respect to a thickness coordinate. Thereby, all equations of elasticity including nonlocal constitutive relations have been transformed to the corresponding equations for Fourier coefficients. Then, in the same way as in the theory of local elasticity, a system of differential equations in terms of displacements for Fourier coefficients has been obtained. First and second order approximations have been considered in detail. Timoshenko's and Euler-Bernoulli theories are based on the classical hypothesis and the 2-D equations of linear nonlocal theory of elasticity which are considered in a special curvilinear system of coordinates related to the middle line of the rod. The obtained equations can be used to calculate stress-strain and to model thin walled structures in micro- and nanoscales when taking into account size dependent and nonlocal effects.

Collective Interaction in a Linear Array of Supersonic Rectangular Jets: A Linear Spatial Instability Study

NASA Technical Reports Server (NTRS)

Miles, Jeffrey Hilton

1999-01-01

A linear spatial instability model for multiple spatially periodic supersonic rectangular jets is solved using Floquet-Bloch theory. It is assumed that in the region of interest a coherent wave can propagate. For the case studied large spatial growth rates are found. This work is motivated by an increase in mixing found in experimental measurements of spatially periodic supersonic rectangular jets with phase-locked screech and edge tone feedback locked subsonic jets. The results obtained in this paper suggests that phase-locked screech or edge tones may produce correlated spatially periodic jet flow downstream of the nozzles which creates a large span wise multi-nozzle region where a coherent wave can propagate. The large spatial growth rates for eddies obtained by model calculation herein are related to the increased mixing since eddies are the primary mechanism that transfer energy from the mean flow to the large turbulent structures. Calculations of spacial growth rates will be presented for a set of relative Mach numbers and spacings for which experimental measurements have been made. Calculations of spatial growth rates are presented for relative Mach numbers from 1.25 to 1.75 with ratios of nozzle spacing to nozzle width ratios from s/w(sub N) = 4 to s/w(sub N) = 13.7. The model may be of significant scientific and engineering value in the quest to understand and construct supersonic mixer-ejector nozzles which provide increased mixing and reduced noise.

Flow behaviour and constitutive modelling of a ferritic stainless steel at elevated temperatures

NASA Astrophysics Data System (ADS)

Zhao, Jingwei; Jiang, Zhengyi; Zu, Guoqing; Du, Wei; Zhang, Xin; Jiang, Laizhu

2016-05-01

The flow behaviour of a ferritic stainless steel (FSS) was investigated by a Gleeble 3500 thermal-mechanical test simulator over the temperature range of 900-1100 °C and strain rate range of 1-50 s-1. Empirical and phenomenological constitutive models were established, and a comparative study was made on the predictability of them. The results indicate that the flow stress decreases with increasing the temperature and decreasing the strain rate. High strain rate may cause a drop in flow stress after a peak value due to the adiabatic heating. The Zener-Hollomon parameter depends linearly on the flow stress, and decreases with raising the temperature and reducing the strain rate. Significant deviations occur in the prediction of flow stress by the Johnson-Cook (JC) model, indicating that the JC model cannot accurately track the flow behaviour of the FSS during hot deformation. Both the multiple-linear and the Arrhenius-type models can track the flow behaviour very well under the whole hot working conditions, and have much higher accuracy in predicting the flow behaviour than that of the JC model. The multiple-linear model is recommended in the current work due to its simpler structure and less time needed for solving the equations relative to the Arrhenius-type model.

A 1-D model of the nonlinear dynamics of the human lumbar intervertebral disc

NASA Astrophysics Data System (ADS)

Marini, Giacomo; Huber, Gerd; Püschel, Klaus; Ferguson, Stephen J.

2017-01-01

Lumped parameter models of the spine have been developed to investigate its response to whole body vibration. However, these models assume the behaviour of the intervertebral disc to be linear-elastic. Recently, the authors have reported on the nonlinear dynamic behaviour of the human lumbar intervertebral disc. This response was shown to be dependent on the applied preload and amplitude of the stimuli. However, the mechanical properties of a standard linear elastic model are not dependent on the current deformation state of the system. The aim of this study was therefore to develop a model that is able to describe the axial, nonlinear quasi-static response and to predict the nonlinear dynamic characteristics of the disc. The ability to adapt the model to an individual disc's response was a specific focus of the study, with model validation performed against prior experimental data. The influence of the numerical parameters used in the simulations was investigated. The developed model exhibited an axial quasi-static and dynamic response, which agreed well with the corresponding experiments. However, the model needs further improvement to capture additional peculiar characteristics of the system dynamics, such as the change of mean point of oscillation exhibited by the specimens when oscillating in the region of nonlinear resonance. Reference time steps were identified for specific integration scheme. The study has demonstrated that taking into account the nonlinear-elastic behaviour typical of the intervertebral disc results in a predicted system oscillation much closer to the physiological response than that provided by linear-elastic models. For dynamic analysis, the use of standard linear-elastic models should be avoided, or restricted to study cases where the amplitude of the stimuli is relatively small.

Robust Linear Models for Cis-eQTL Analysis.

PubMed

Rantalainen, Mattias; Lindgren, Cecilia M; Holmes, Christopher C

2015-01-01

Expression Quantitative Trait Loci (eQTL) analysis enables characterisation of functional genetic variation influencing expression levels of individual genes. In outbread populations, including humans, eQTLs are commonly analysed using the conventional linear model, adjusting for relevant covariates, assuming an allelic dosage model and a Gaussian error term. However, gene expression data generally have noise that induces heavy-tailed errors relative to the Gaussian distribution and often include atypical observations, or outliers. Such departures from modelling assumptions can lead to an increased rate of type II errors (false negatives), and to some extent also type I errors (false positives). Careful model checking can reduce the risk of type-I errors but often not type II errors, since it is generally too time-consuming to carefully check all models with a non-significant effect in large-scale and genome-wide studies. Here we propose the application of a robust linear model for eQTL analysis to reduce adverse effects of deviations from the assumption of Gaussian residuals. We present results from a simulation study as well as results from the analysis of real eQTL data sets. Our findings suggest that in many situations robust models have the potential to provide more reliable eQTL results compared to conventional linear models, particularly in respect to reducing type II errors due to non-Gaussian noise. Post-genomic data, such as that generated in genome-wide eQTL studies, are often noisy and frequently contain atypical observations. Robust statistical models have the potential to provide more reliable results and increased statistical power under non-Gaussian conditions. The results presented here suggest that robust models should be considered routinely alongside other commonly used methodologies for eQTL analysis.

High-resolution observations of low-luminosity gigahertz-peaked spectrum and compact steep-spectrum sources

NASA Astrophysics Data System (ADS)

Collier, J. D.; Tingay, S. J.; Callingham, J. R.; Norris, R. P.; Filipović, M. D.; Galvin, T. J.; Huynh, M. T.; Intema, H. T.; Marvil, J.; O'Brien, A. N.; Roper, Q.; Sirothia, S.; Tothill, N. F. H.; Bell, M. E.; For, B.-Q.; Gaensler, B. M.; Hancock, P. J.; Hindson, L.; Hurley-Walker, N.; Johnston-Hollitt, M.; Kapińska, A. D.; Lenc, E.; Morgan, J.; Procopio, P.; Staveley-Smith, L.; Wayth, R. B.; Wu, C.; Zheng, Q.; Heywood, I.; Popping, A.

2018-06-01

We present very long baseline interferometry observations of a faint and low-luminosity (L1.4 GHz < 1027 W Hz-1) gigahertz-peaked spectrum (GPS) and compact steep-spectrum (CSS) sample. We select eight sources from deep radio observations that have radio spectra characteristic of a GPS or CSS source and an angular size of θ ≲ 2 arcsec, and detect six of them with the Australian Long Baseline Array. We determine their linear sizes, and model their radio spectra using synchrotron self-absorption (SSA) and free-free absorption (FFA) models. We derive statistical model ages, based on a fitted scaling relation, and spectral ages, based on the radio spectrum, which are generally consistent with the hypothesis that GPS and CSS sources are young and evolving. We resolve the morphology of one CSS source with a radio luminosity of 10^{25} W Hz^{-1}, and find what appear to be two hotspots spanning 1.7 kpc. We find that our sources follow the turnover-linear size relation, and that both homogeneous SSA and an inhomogeneous FFA model can account for the spectra with observable turnovers. All but one of the FFA models do not require a spectral break to account for the radio spectrum, while all but one of the alternative SSA and power-law models do require a spectral break to account for the radio spectrum. We conclude that our low-luminosity sample is similar to brighter samples in terms of their spectral shape, turnover frequencies, linear sizes, and ages, but cannot test for a difference in morphology.

Simplified large African carnivore density estimators from track indices.

PubMed

Winterbach, Christiaan W; Ferreira, Sam M; Funston, Paul J; Somers, Michael J

2016-01-01

The range, population size and trend of large carnivores are important parameters to assess their status globally and to plan conservation strategies. One can use linear models to assess population size and trends of large carnivores from track-based surveys on suitable substrates. The conventional approach of a linear model with intercept may not intercept at zero, but may fit the data better than linear model through the origin. We assess whether a linear regression through the origin is more appropriate than a linear regression with intercept to model large African carnivore densities and track indices. We did simple linear regression with intercept analysis and simple linear regression through the origin and used the confidence interval for ß in the linear model y  =  αx  + ß, Standard Error of Estimate, Mean Squares Residual and Akaike Information Criteria to evaluate the models. The Lion on Clay and Low Density on Sand models with intercept were not significant ( P  > 0.05). The other four models with intercept and the six models thorough origin were all significant ( P  < 0.05). The models using linear regression with intercept all included zero in the confidence interval for ß and the null hypothesis that ß = 0 could not be rejected. All models showed that the linear model through the origin provided a better fit than the linear model with intercept, as indicated by the Standard Error of Estimate and Mean Square Residuals. Akaike Information Criteria showed that linear models through the origin were better and that none of the linear models with intercept had substantial support. Our results showed that linear regression through the origin is justified over the more typical linear regression with intercept for all models we tested. A general model can be used to estimate large carnivore densities from track densities across species and study areas. The formula observed track density = 3.26 × carnivore density can be used to estimate densities of large African carnivores using track counts on sandy substrates in areas where carnivore densities are 0.27 carnivores/100 km 2 or higher. To improve the current models, we need independent data to validate the models and data to test for non-linear relationship between track indices and true density at low densities.

General Model of Photon-Pair Detection with an Image Sensor

NASA Astrophysics Data System (ADS)

Defienne, Hugo; Reichert, Matthew; Fleischer, Jason W.

2018-05-01

We develop an analytic model that relates intensity correlation measurements performed by an image sensor to the properties of photon pairs illuminating it. Experiments using an effective single-photon counting camera, a linear electron-multiplying charge-coupled device camera, and a standard CCD camera confirm the model. The results open the field of quantum optical sensing using conventional detectors.

Rhombic micro-displacement amplifier for piezoelectric actuator and its linear and hybrid model

NASA Astrophysics Data System (ADS)

Chen, Jinglong; Zhang, Chunlin; Xu, Minglong; Zi, Yanyang; Zhang, Xinong

2015-01-01

This paper proposes rhombic micro-displacement amplifier (RMDA) for piezoelectric actuator (PA). First, the geometric amplification relations are analyzed and linear model is built to analyze the mechanical and electrical properties of this amplifier. Next, the accurate modeling method of amplifier is studied for important application of precise servo control. The classical Preisach model (CPM) is generally implemented using a numerical technique based on the first-order reversal curves (FORCs). The accuracy of CPM mainly depends on the number of FORCs. However, it is generally difficult to achieve enough number of FORCs in practice. So, Support Vector Machine (SVM) is employed in the work to circumvent the deficiency of the CPM. Then the hybrid model, which is based on discrete CPM and SVM is developed to account for hysteresis and dynamic effects. Finally, experimental validation is carried out. The analyzed result shows that this amplifier with the hybrid model is suitable for control application.

The semiotics of control and modeling relations in complex systems.

PubMed

Joslyn, C

2001-01-01

We provide a conceptual analysis of ideas and principles from the systems theory discourse which underlie Pattee's semantic or semiotic closure, which is itself foundational for a school of theoretical biology derived from systems theory and cybernetics, and is now being related to biological semiotics and explicated in the relational biological school of Rashevsky and Rosen. Atomic control systems and models are described as the canonical forms of semiotic organization, sharing measurement relations, but differing topologically in that control systems are circularly and models linearly related to their environments. Computation in control systems is introduced, motivating hierarchical decomposition, hybrid modeling and control systems, and anticipatory or model-based control. The semiotic relations in complex control systems are described in terms of relational constraints, and rules and laws are distinguished as contingent and necessary functional entailments, respectively. Finally, selection as a meta-level of constraint is introduced as the necessary condition for semantic relations in control systems and models.

Linear decentralized systems with special structure. [for twin lift helicopters

NASA Technical Reports Server (NTRS)

Martin, C. F.

1982-01-01

Certain fundamental structures associated with linear systems having internal symmetries are outlined. It is shown that the theory of finite-dimensional algebras and their representations are closely related to such systems. It is also demonstrated that certain problems in the decentralized control of symmetric systems are equivalent to long-standing problems of linear systems theory. Even though the structure imposed arose in considering the problems of twin-lift helicopters, any large system composed of several identical intercoupled control systems can be modeled by a linear system that satisfies the constraints imposed. Internal symmetry can be exploited to yield new system-theoretic invariants and a better understanding of the way in which the underlying structure affects overall system performance.

A generalized fuzzy linear programming approach for environmental management problem under uncertainty.

PubMed

Fan, Yurui; Huang, Guohe; Veawab, Amornvadee

2012-01-01

In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO2) control planning model to identify effective SO2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP and then identify desired policies for SO2-emission control under uncertainty.

A recursive Bayesian updating model of haptic stiffness perception.

PubMed

Wu, Bing; Klatzky, Roberta L

2018-06-01

Stiffness of many materials follows Hooke's Law, but the mechanism underlying the haptic perception of stiffness is not as simple as it seems in the physical definition. The present experiments support a model by which stiffness perception is adaptively updated during dynamic interaction. Participants actively explored virtual springs and estimated their stiffness relative to a reference. The stimuli were simulations of linear springs or nonlinear springs created by modulating a linear counterpart with low-amplitude, half-cycle (Experiment 1) or full-cycle (Experiment 2) sinusoidal force. Experiment 1 showed that subjective stiffness increased (decreased) as a linear spring was positively (negatively) modulated by a half-sinewave force. In Experiment 2, an opposite pattern was observed for full-sinewave modulations. Modeling showed that the results were best described by an adaptive process that sequentially and recursively updated an estimate of stiffness using the force and displacement information sampled over trajectory and time. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Do Stress Trajectories Predict Mortality in Older Men? Longitudinal Findings from the VA Normative Aging Study

PubMed Central

Aldwin, Carolyn M.; Molitor, Nuoo-Ting; Avron, Spiro; Levenson, Michael R.; Molitor, John; Igarashi, Heidi

2011-01-01

We examined long-term patterns of stressful life events (SLE) and their impact on mortality contrasting two theoretical models: allostatic load (linear relationship) and hormesis (inverted U relationship) in 1443 NAS men (aged 41–87 in 1985; M = 60.30, SD = 7.3) with at least two reports of SLEs over 18 years (total observations = 7,634). Using a zero-inflated Poisson growth mixture model, we identified four patterns of SLE trajectories, three showing linear decreases over time with low, medium, and high intercepts, respectively, and one an inverted U, peaking at age 70. Repeating the analysis omitting two health-related SLEs yielded only the first three linear patterns. Compared to the low-stress group, both the moderate and the high-stress groups showed excess mortality, controlling for demographics and health behavior habits, HRs = 1.42 and 1.37, ps <.01 and <.05. The relationship between stress trajectories and mortality was complex and not easily explained by either theoretical model. PMID:21961066

Independence of the effective dielectric constant of an electrolytic solution on the ionic distribution in the linear Poisson-Nernst-Planck model.

PubMed

Alexe-Ionescu, A L; Barbero, G; Lelidis, I

2014-08-28

We consider the influence of the spatial dependence of the ions distribution on the effective dielectric constant of an electrolytic solution. We show that in the linear version of the Poisson-Nernst-Planck model, the effective dielectric constant of the solution has to be considered independent of any ionic distribution induced by the external field. This result follows from the fact that, in the linear approximation of the Poisson-Nernst-Planck model, the redistribution of the ions in the solvent due to the external field gives rise to a variation of the dielectric constant that is of the first order in the effective potential, and therefore it has to be neglected in the Poisson's equation that relates the actual electric potential across the electrolytic cell to the bulk density of ions. The analysis is performed in the case where the electrodes are perfectly blocking and the adsorption at the electrodes is negligible, and in the absence of any ion dissociation-recombination effect.

Comparison of heaving buoy and oscillating flap wave energy converters

NASA Astrophysics Data System (ADS)

Abu Bakar, Mohd Aftar; Green, David A.; Metcalfe, Andrew V.; Najafian, G.

2013-04-01

Waves offer an attractive source of renewable energy, with relatively low environmental impact, for communities reasonably close to the sea. Two types of simple wave energy converters (WEC), the heaving buoy WEC and the oscillating flap WEC, are studied. Both WECs are considered as simple energy converters because they can be modelled, to a first approximation, as single degree of freedom linear dynamic systems. In this study, we estimate the response of both WECs to typical wave inputs; wave height for the buoy and corresponding wave surge for the flap, using spectral methods. A nonlinear model of the oscillating flap WEC that includes the drag force, modelled by the Morison equation is also considered. The response to a surge input is estimated by discrete time simulation (DTS), using central difference approximations to derivatives. This is compared with the response of the linear model obtained by DTS and also validated using the spectral method. Bendat's nonlinear system identification (BNLSI) technique was used to analyze the nonlinear dynamic system since the spectral analysis was only suitable for linear dynamic system. The effects of including the nonlinear term are quantified.

Optimal design of focused experiments and surveys

NASA Astrophysics Data System (ADS)

Curtis, Andrew

1999-10-01

Experiments and surveys are often performed to obtain data that constrain some previously underconstrained model. Often, constraints are most desired in a particular subspace of model space. Experiment design optimization requires that the quality of any particular design can be both quantified and then maximized. This study shows how the quality can be defined such that it depends on the amount of information that is focused in the particular subspace of interest. In addition, algorithms are presented which allow one particular focused quality measure (from the class of focused measures) to be evaluated efficiently. A subclass of focused quality measures is also related to the standard variance and resolution measures from linearized inverse theory. The theory presented here requires that the relationship between model parameters and data can be linearized around a reference model without significant loss of information. Physical and financial constraints define the space of possible experiment designs. Cross-well tomographic examples are presented, plus a strategy for survey design to maximize information about linear combinations of parameters such as bulk modulus, κ =λ+ 2μ/3.

Testing the adaptation to poverty-related stress model: predicting psychopathology symptoms in families facing economic hardship.

PubMed

Wadsworth, Martha E; Raviv, Tali; Santiago, Catherine Decarlo; Etter, Erica M

2011-01-01

This study tested the Adaptation to Poverty-related Stress Model and its proposed relations between poverty-related stress, effortful and involuntary stress responses, and symptoms of psychopathology in an ethnically diverse sample of low-income children and their parents. Prospective Hierarchical Linear Modeling analyses conducted with 98 families (300 family members: 136 adults, 82 adolescents and preadolescents, 82 school-age children) revealed that, consistent with the model, primary and secondary control coping were protective against poverty-related stress primarily for internalizing symptoms. Conversely, disengagement coping exacerbated externalizing symptoms over time. In addition, involuntary engagement stress responses exacerbated the effects of poverty-related stress for internalizing symptoms, whereas involuntary disengagement responses exacerbated externalizing symptoms. Age and gender effects were found in most models, reflecting more symptoms of both types for parents than children and higher levels of internalizing symptoms for girls.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Characterization of Non-Linearized Spacecraft Relative Motion using Nonlinear Normal Modes

DTIC Science & Technology

2016-04-20

10 5.1 Results for Four Models with Different Nonlinearities ..................................................11 5.2 Effects of...Force Research Laboratory or the U.S. Government. 1.0 SUMMARY In this report, the effects of incorporating nonlinearities in sequential relative orbit...exactly. Huxel and Bishop [1] discussed the effects of using both inertial range measurements from tracking stations and relative range measurements

Recursive linearization of multibody dynamics equations of motion

NASA Technical Reports Server (NTRS)

Lin, Tsung-Chieh; Yae, K. Harold

1989-01-01

The equations of motion of a multibody system are nonlinear in nature, and thus pose a difficult problem in linear control design. One approach is to have a first-order approximation through the numerical perturbations at a given configuration, and to design a control law based on the linearized model. Here, a linearized model is generated analytically by following the footsteps of the recursive derivation of the equations of motion. The equations of motion are first written in a Newton-Euler form, which is systematic and easy to construct; then, they are transformed into a relative coordinate representation, which is more efficient in computation. A new computational method for linearization is obtained by applying a series of first-order analytical approximations to the recursive kinematic relationships. The method has proved to be computationally more efficient because of its recursive nature. It has also turned out to be more accurate because of the fact that analytical perturbation circumvents numerical differentiation and other associated numerical operations that may accumulate computational error, thus requiring only analytical operations of matrices and vectors. The power of the proposed linearization algorithm is demonstrated, in comparison to a numerical perturbation method, with a two-link manipulator and a seven degrees of freedom robotic manipulator. Its application to control design is also demonstrated.

Kinetic Features in the Ion Flux Spectrum

NASA Astrophysics Data System (ADS)

Vafin, S.; Riazantseva, M.; Yoon, P. H.

2017-11-01

An interesting feature of solar wind fluctuations is the occasional presence of a well-pronounced peak near the spectral knee. These peaks are well investigated in the context of magnetic field fluctuations in the magnetosheath and they are typically related to kinetic plasma instabilities. Recently, similar peaks were observed in the spectrum of ion flux fluctuations of the solar wind and magnetosheath. In this paper, we propose a simple analytical model to describe such peaks in the ion flux spectrum based on the linear theory of plasma fluctuations. We compare our predictions with a sample observation in the solar wind. For the given observation, the peak requires ˜10 minutes to grow up to the observed level that agrees with the quasi-linear relaxation time. Moreover, our model well reproduces the form of the measured peak in the ion flux spectrum. The observed lifetime of the peak is about 50 minutes, which is relatively close to the nonlinear Landau damping time of 30-40 minutes. Overall, our model proposes a plausible scenario explaining the observation.

Non-resonant dynamic stark control of vibrational motion with optimized laser pulses

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

Thomas, Esben F.; Henriksen, Niels E.

2016-06-28

The term dynamic Stark control (DSC) has been used to describe methods of quantum control related to the dynamic Stark effect, i.e., a time-dependent distortion of energy levels. Here, we employ analytical models that present clear and concise interpretations of the principles behind DSC. Within a linearly forced harmonic oscillator model of vibrational excitation, we show how the vibrational amplitude is related to the pulse envelope, and independent of the carrier frequency of the laser pulse, in the DSC regime. Furthermore, we shed light on the DSC regarding the construction of optimal pulse envelopes — from a time-domain as wellmore » as a frequency-domain perspective. Finally, in a numerical study beyond the linearly forced harmonic oscillator model, we show that a pulse envelope can be constructed such that a vibrational excitation into a specific excited vibrational eigenstate is accomplished. The pulse envelope is constructed such that high intensities are avoided in order to eliminate the process of ionization.« less

Responses to rotating linear acceleration vectors considered in relation to a model of the otolith organs. [human oculomotor response to transverse acceleration stress

NASA Technical Reports Server (NTRS)

Benson, A. J.; Barnes, G. R.

1973-01-01

Human subjects were exposed to a linear acceleration vector that rotated in the transverse plane of the skull without angular counterrotation. Lateral eye movements showed a sinusoidal change in slow phase velocity and an asymmetry or bias in the same direction as vector rotation. A model is developed that attributes the oculomotor response to otolithic mechanisms. It is suggested that the bias component is the manifestation of torsion of the statoconial plaque relative to the base of the utricular macula and that the sinusoidal component represents the translational oscillation of the statoconia. The model subsumes a hypothetical neural mechanism which allows x- and y-axis accelerations to be resolved. Derivation of equations of motion for the statoconial plaque in torsion and translation, which take into account forces acting in shear and normal to the macula, yield estimates of bias and sinusoidal components that are in qualitative agreement with the diverse experimental findings.

Observation Impacts for Longer Forecast Lead-Times

NASA Astrophysics Data System (ADS)

Mahajan, R.; Gelaro, R.; Todling, R.

2013-12-01

Observation impact on forecasts evaluated using adjoint-based techniques (e.g. Langland and Baker, 2004) are limited by the validity of the assumptions underlying the forecasting model adjoint. Most applications of this approach have focused on deriving observation impacts on short-range forecasts (e.g. 24-hour) in part to stay well within linearization assumptions. The most widely used measure of observation impact relies on the availability of the analysis for verifying the forecasts. As pointed out by Gelaro et al. (2007), and more recently by Todling (2013), this introduces undesirable correlations in the measure that are likely to affect the resulting assessment of the observing system. Stappers and Barkmeijer (2012) introduced a technique that, in principle, allows extending the validity of tangent linear and corresponding adjoint models to longer lead-times, thereby reducing the correlations in the measures used for observation impact assessments. The methodology provides the means to better represent linearized models by making use of Gaussian quadrature relations to handle various underlying non-linear model trajectories. The formulation is exact for particular bi-linear dynamics; it corresponds to an approximation for general-type nonlinearities and must be tested for large atmospheric models. The present work investigates the approach of Stappers and Barkmeijer (2012)in the context of NASA's Goddard Earth Observing System Version 5 (GEOS-5) atmospheric data assimilation system (ADAS). The goal is to calculate observation impacts in the GEOS-5 ADAS for forecast lead-times of at least 48 hours in order to reduce the potential for undesirable correlations that occur at shorter forecast lead times. References [1]Langland, R. H., and N. L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56A, 189-201. [2] Gelaro, R., Y. Zhu, and R. M. Errico, 2007: Examination of various-order adjoint-based approximations of observation impact. Meteoroloische Zeitschrift, 16, 685-692. [3]Stappers, R. J. J., and J. Barkmeijer, 2012: Optimal linearization trajectories for tangent linear models. Q. J. R. Meteorol. Soc., 138, 170-184. [4] Todling, R. 2013: Comparing two approaches for assessing observation impact. Mon. Wea. Rev., 141, 1484-1505.

Imprints of dark energy on cosmic structure formation - I. Realistic quintessence models and the non-linear matter power spectrum

NASA Astrophysics Data System (ADS)

Alimi, J.-M.; Füzfa, A.; Boucher, V.; Rasera, Y.; Courtin, J.; Corasaniti, P.-S.

2010-01-01

Quintessence has been proposed to account for dark energy (DE) in the Universe. This component causes a typical modification of the background cosmic expansion, which, in addition to its clustering properties, can leave a potentially distinctive signature on large-scale structures. Many previous studies have investigated this topic, particularly in relation to the non-linear regime of structure formation. However, no careful pre-selection of viable quintessence models with high precision cosmological data was performed. Here we show that this has led to a misinterpretation (and underestimation) of the imprint of quintessence on the distribution of large-scale structures. To this purpose, we perform a likelihood analysis of the combined Supernova Ia UNION data set and Wilkinson Microwave Anisotropy Probe 5-yr data to identify realistic quintessence models. These are specified by different model parameter values, but still statistically indistinguishable from the vanilla Λ cold dark matter (ΛCDM). Differences are especially manifest in the predicted amplitude and shape of the linear matter power spectrum though these remain within the uncertainties of the Sloan Digital Sky Survey data. We use these models as a benchmark for studying the clustering properties of dark matter haloes by performing a series of high-resolution N-body simulations. In this first paper, we specifically focus on the non-linear matter power spectrum. We find that realistic quintessence models allow for relevant differences of the dark matter distribution with respect to the ΛCDM scenario well into the non-linear regime, with deviations of up to 40 per cent in the non-linear power spectrum. Such differences are shown to depend on the nature of DE, as well as the scale and epoch considered. At small scales (k ~ 1-5hMpc-1, depending on the redshift), the structure formation process is about 20 per cent more efficient than in ΛCDM. We show that these imprints are a specific record of the cosmic structure formation history in DE cosmologies and therefore cannot be accounted for in standard fitting functions of the non-linear matter power spectrum.

Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia-A generalised linear model with break-point analysis.

PubMed

Alkhaldy, Ibrahim

2017-04-01

The aim of this study was to examine the role of environmental factors in the temporal distribution of dengue fever in Jeddah, Saudi Arabia. The relationship between dengue fever cases and climatic factors such as relative humidity and temperature was investigated during 2006-2009 to determine whether there is any relationship between dengue fever cases and climatic parameters in Jeddah City, Saudi Arabia. A generalised linear model (GLM) with a break-point was used to determine how different levels of temperature and relative humidity affected the distribution of the number of cases of dengue fever. Break-point analysis was performed to modelled the effect before and after a break-point (change point) in the explanatory parameters under various scenarios. Akaike information criterion (AIC) and cross validation (CV) were used to assess the performance of the models. The results showed that maximum temperature and mean relative humidity are most probably the better predictors of the number of dengue fever cases in Jeddah. In this study three scenarios were modelled: no time lag, 1-week lag and 2-weeks lag. Among these scenarios, the 1-week lag model using mean relative humidity as an explanatory variable showed better performance. This study showed a clear relationship between the meteorological variables and the number of dengue fever cases in Jeddah. The results also demonstrated that meteorological variables can be successfully used to estimate the number of dengue fever cases for a given period of time. Break-point analysis provides further insight into the association between meteorological parameters and dengue fever cases by dividing the meteorological parameters into certain break-points. Copyright © 2016 Elsevier B.V. All rights reserved.

Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat

PubMed Central

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-01-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models. PMID:23275882

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

PubMed

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Active galactic nuclei as cosmological probes.

NASA Astrophysics Data System (ADS)

Lusso, Elisabeta; Risaliti, Guido

2018-01-01

I will present the latest results on our analysis of the non-linear X-ray to UV relation in a sample of optically selected quasars from the Sloan Digital Sky Survey, cross-matched with the most recent XMM-Newton and Chandra catalogues. I will show that this correlation is not only very tight, but can be potentially even tighter by including a further dependence on the emission line full-width half maximum. This result imply that the non-linear X-ray to optical-ultraviolet luminosity relation is the manifestation of an ubiquitous physical mechanism, whose details are still unknown, that regulates the energy transfer from the accretion disc to the X-ray emitting corona in quasars. I will discuss what the perspectives of AGN in the context of observational cosmology are. I will introduce a novel technique to test the cosmological model using quasars as “standard candles” by employing the non-linear X-ray to UV relation as an absolute distance indicator.

Light propagation and the distance-redshift relation in a realistic inhomogeneous universe

NASA Technical Reports Server (NTRS)

Futamase, Toshifumi; Sasaki, Misao

1989-01-01

The propagation of light rays in a clumpy universe constructed by cosmological version of the post-Newtonian approximation was investigated. It is shown that linear approximation to the propagation equations is valid in the region where zeta is approximately less than 1 even if the density contrast is much larger than unity. Based on a gerneral order-of-magnitude statistical consideration, it is argued that the linear approximation is still valid where zeta is approximately greater than 1. A general formula for the distance-redshift relation in a clumpy universe is given. An explicit expression is derived for a simplified situation in which the effect of the gravitational potential of inhomogeneities dominates. In the light of the derived relation, the validity of the Dyer-Roeder distance is discussed. Also, statistical properties of light rays are investigated for a simple model of an inhomogeneous universe. The result of this example supports the validity of the linear approximation.

Assessment of Health-Cost Externalities of Air Pollution at the National Level using the EVA Model System

NASA Astrophysics Data System (ADS)

Brandt, Jørgen; Silver, Jeremy David; Heile Christensen, Jesper; Skou Andersen, Mikael; Geels, Camilla; Gross, Allan; Buus Hansen, Ayoe; Mantzius Hansen, Kaj; Brandt Hedegaard, Gitte; Ambelas Skjøth, Carsten

2010-05-01

Air pollution has significant negative impacts on human health and well-being, which entail substantial economic consequences. We have developed an integrated model system, EVA (External Valuation of Air pollution), to assess health-related economic externalities of air pollution resulting from specific emission sources/sectors. The EVA system was initially developed to assess externalities from power production, but in this study it is extended to evaluate costs at the national level. The EVA system integrates a regional-scale atmospheric chemistry transport model (DEHM), address-level population data, exposure-response functions and monetary values applicable for Danish/European conditions. Traditionally, systems that assess economic costs of health impacts from air pollution assume linear approximations in the source-receptor relationships. However, atmospheric chemistry is non-linear and therefore the uncertainty involved in the linear assumption can be large. The EVA system has been developed to take into account the non-linear processes by using a comprehensive, state-of-the-art chemical transport model when calculating how specific changes to emissions affect air pollution levels and the subsequent impacts on human health and cost. Furthermore, we present a new "tagging" method, developed to examine how specific emission sources influence air pollution levels without assuming linearity of the non-linear behaviour of atmospheric chemistry. This method is more precise than the traditional approach based on taking the difference between two concentration fields. Using the EVA system, we have estimated the total external costs from the main emission sectors in Denmark, representing the ten major SNAP codes. Finally, we assess the impacts and external costs of emissions from international ship traffic around Denmark, since there is a high volume of ship traffic in the region.

Classroom Age Composition and Developmental Change in 70 Urban Preschool Classrooms

ERIC Educational Resources Information Center

Moller, Arlen C.; Forbes-Jones, Emma; Hightower, A. Dirk

2008-01-01

A multilevel modeling approach was used to investigate the influence of age composition in 70 urban preschool classrooms. A series of hierarchical linear models demonstrated that greater variance in classroom age composition was negatively related to development on the Child Observation Record (COR) Cognitive, Motor, and Social subscales. This was…

Longitudinal Models of Reading Achievement of Students with Learning Disabilities and without Disabilities

ERIC Educational Resources Information Center

Sullivan, Amanda L.; Kohli, Nidhi; Farnsworth, Elyse M.; Sadeh, Shanna; Jones, Leila

2017-01-01

Objective: Accurate estimation of developmental trajectories can inform instruction and intervention. We compared the fit of linear, quadratic, and piecewise mixed-effects models of reading development among students with learning disabilities relative to their typically developing peers. Method: We drew an analytic sample of 1,990 students from…

Classrooms as Complex Adaptive Systems: A Relational Model

ERIC Educational Resources Information Center

Burns, Anne; Knox, John S.

2011-01-01

In this article, we describe and model the language classroom as a complex adaptive system (see Logan & Schumann, 2005). We argue that linear, categorical descriptions of classroom processes and interactions do not sufficiently explain the complex nature of classrooms, and cannot account for how classroom change occurs (or does not occur), over…

Teaching Aerobic Cell Respiration Using the 5Es

ERIC Educational Resources Information Center

Patro, Edward T.

2008-01-01

The 5E teaching model provides a five step method for teaching science. While the sequence of the model is strictly linear, it does provide opportunities for the teacher to "revisit" prior learning before moving on. The 5E method is described as it relates to the teaching of aerobic cell respiration.

Fortran Programs for Weapon Systems Analysis

DTIC Science & Technology

1990-06-01

interested in ballistics and related work. The programs include skeletal combat models , a set of discrete-event timing routines, mathematical and...32 4.3 LinEqs: Solve Linear Equations Like a Textbook ........................................................................... 34...military applications as it is of computer science. This crisis occurs in all fields, including the modeling of logistics, mobility, ballistics, and combat

Alternative Method to Simulate a Sub-idle Engine Operation in Order to Synthesize Its Control System

NASA Astrophysics Data System (ADS)

Sukhovii, Sergii I.; Sirenko, Feliks F.; Yepifanov, Sergiy V.; Loboda, Igor

2016-09-01

The steady-state and transient engine performances in control systems are usually evaluated by applying thermodynamic engine models. Most models operate between the idle and maximum power points, only recently, they sometimes address a sub-idle operating range. The lack of information about the component maps at the sub-idle modes presents a challenging problem. A common method to cope with the problem is to extrapolate the component performances to the sub-idle range. Precise extrapolation is also a challenge. As a rule, many scientists concern only particular aspects of the problem such as the lighting combustion chamber or the turbine operation under the turned-off conditions of the combustion chamber. However, there are no reports about a model that considers all of these aspects and simulates the engine starting. The proposed paper addresses a new method to simulate the starting. The method substitutes the non-linear thermodynamic model with a linear dynamic model, which is supplemented with a simplified static model. The latter model is the set of direct relations between parameters that are used in the control algorithms instead of commonly used component performances. Specifically, this model consists of simplified relations between the gas path parameters and the corrected rotational speed.

Effects of Nonlinear Inhomogeneity on the Cosmic Expansion with Numerical Relativity.

PubMed

Bentivegna, Eloisa; Bruni, Marco

2016-06-24

We construct a three-dimensional, fully relativistic numerical model of a universe filled with an inhomogeneous pressureless fluid, starting from initial data that represent a perturbation of the Einstein-de Sitter model. We then measure the departure of the average expansion rate with respect to this homogeneous and isotropic reference model, comparing local quantities to the predictions of linear perturbation theory. We find that collapsing perturbations reach the turnaround point much earlier than expected from the reference spherical top-hat collapse model and that the local deviation of the expansion rate from the homogeneous one can be as high as 28% at an underdensity, for an initial density contrast of 10^{-2}. We then study, for the first time, the exact behavior of the backreaction term Q_{D}. We find that, for small values of the initial perturbations, this term exhibits a 1/a scaling, and that it is negative with a linearly growing absolute value for larger perturbation amplitudes, thereby contributing to an overall deceleration of the expansion. Its magnitude, on the other hand, remains very small even for relatively large perturbations.

Scilab software as an alternative low-cost computing in solving the linear equations problem

NASA Astrophysics Data System (ADS)

Agus, Fahrul; Haviluddin

2017-02-01

Numerical computation packages are widely used both in teaching and research. These packages consist of license (proprietary) and open source software (non-proprietary). One of the reasons to use the package is a complexity of mathematics function (i.e., linear problems). Also, number of variables in a linear or non-linear function has been increased. The aim of this paper was to reflect on key aspects related to the method, didactics and creative praxis in the teaching of linear equations in higher education. If implemented, it could be contribute to a better learning in mathematics area (i.e., solving simultaneous linear equations) that essential for future engineers. The focus of this study was to introduce an additional numerical computation package of Scilab as an alternative low-cost computing programming. In this paper, Scilab software was proposed some activities that related to the mathematical models. In this experiment, four numerical methods such as Gaussian Elimination, Gauss-Jordan, Inverse Matrix, and Lower-Upper Decomposition (LU) have been implemented. The results of this study showed that a routine or procedure in numerical methods have been created and explored by using Scilab procedures. Then, the routine of numerical method that could be as a teaching material course has exploited.

hi-class: Horndeski in the Cosmic Linear Anisotropy Solving System

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

Zumalacárregui, Miguel; Bellini, Emilio; Sawicki, Ignacy

We present the public version of hi-class (www.hiclass-code.net), an extension of the Boltzmann code CLASS to a broad ensemble of modifications to general relativity. In particular, hi-class can calculate predictions for models based on Horndeski's theory, which is the most general scalar-tensor theory described by second-order equations of motion and encompasses any perfect-fluid dark energy, quintessence, Brans-Dicke, f ( R ) and covariant Galileon models. hi-class has been thoroughly tested and can be readily used to understand the impact of alternative theories of gravity on linear structure formation as well as for cosmological parameter extraction.

Computing Linear Mathematical Models Of Aircraft

NASA Technical Reports Server (NTRS)

Duke, Eugene L.; Antoniewicz, Robert F.; Krambeer, Keith D.

1991-01-01

Derivation and Definition of Linear Aircraft Model (LINEAR) computer program provides user with powerful, and flexible, standard, documented, and verified software tool for linearization of mathematical models of aerodynamics of aircraft. Intended for use in software tool to drive linear analysis of stability and design of control laws for aircraft. Capable of both extracting such linearized engine effects as net thrust, torque, and gyroscopic effects, and including these effects in linear model of system. Designed to provide easy selection of state, control, and observation variables used in particular model. Also provides flexibility of allowing alternate formulations of both state and observation equations. Written in FORTRAN.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

UCODE_2005 and six other computer codes for universal sensitivity analysis, calibration, and uncertainty evaluation constructed using the JUPITER API

USGS Publications Warehouse

Poeter, Eileen E.; Hill, Mary C.; Banta, Edward R.; Mehl, Steffen; Christensen, Steen

2006-01-01

This report documents the computer codes UCODE_2005 and six post-processors. Together the codes can be used with existing process models to perform sensitivity analysis, data needs assessment, calibration, prediction, and uncertainty analysis. Any process model or set of models can be used; the only requirements are that models have numerical (ASCII or text only) input and output files, that the numbers in these files have sufficient significant digits, that all required models can be run from a single batch file or script, and that simulated values are continuous functions of the parameter values. Process models can include pre-processors and post-processors as well as one or more models related to the processes of interest (physical, chemical, and so on), making UCODE_2005 extremely powerful. An estimated parameter can be a quantity that appears in the input files of the process model(s), or a quantity used in an equation that produces a value that appears in the input files. In the latter situation, the equation is user-defined. UCODE_2005 can compare observations and simulated equivalents. The simulated equivalents can be any simulated value written in the process-model output files or can be calculated from simulated values with user-defined equations. The quantities can be model results, or dependent variables. For example, for ground-water models they can be heads, flows, concentrations, and so on. Prior, or direct, information on estimated parameters also can be considered. Statistics are calculated to quantify the comparison of observations and simulated equivalents, including a weighted least-squares objective function. In addition, data-exchange files are produced that facilitate graphical analysis. UCODE_2005 can be used fruitfully in model calibration through its sensitivity analysis capabilities and its ability to estimate parameter values that result in the best possible fit to the observations. Parameters are estimated using nonlinear regression: a weighted least-squares objective function is minimized with respect to the parameter values using a modified Gauss-Newton method or a double-dogleg technique. Sensitivities needed for the method can be read from files produced by process models that can calculate sensitivities, such as MODFLOW-2000, or can be calculated by UCODE_2005 using a more general, but less accurate, forward- or central-difference perturbation technique. Problems resulting from inaccurate sensitivities and solutions related to the perturbation techniques are discussed in the report. Statistics are calculated and printed for use in (1) diagnosing inadequate data and identifying parameters that probably cannot be estimated; (2) evaluating estimated parameter values; and (3) evaluating how well the model represents the simulated processes. Results from UCODE_2005 and codes RESIDUAL_ANALYSIS and RESIDUAL_ANALYSIS_ADV can be used to evaluate how accurately the model represents the processes it simulates. Results from LINEAR_UNCERTAINTY can be used to quantify the uncertainty of model simulated values if the model is sufficiently linear. Results from MODEL_LINEARITY and MODEL_LINEARITY_ADV can be used to evaluate model linearity and, thereby, the accuracy of the LINEAR_UNCERTAINTY results. UCODE_2005 can also be used to calculate nonlinear confidence and predictions intervals, which quantify the uncertainty of model simulated values when the model is not linear. CORFAC_PLUS can be used to produce factors that allow intervals to account for model intrinsic nonlinearity and small-scale variations in system characteristics that are not explicitly accounted for in the model or the observation weighting. The six post-processing programs are independent of UCODE_2005 and can use the results of other programs that produce the required data-exchange files. UCODE_2005 and the other six codes are intended for use on any computer operating system. The programs con

Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

PubMed

Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

2017-05-01

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

Just Another Club? The Distinctiveness of the Relation between Religious Service Attendance and Adolescent Psychosocial Adjustment

ERIC Educational Resources Information Center

Good, Marie; Willoughby, Teena; Fritjers, Jan

2009-01-01

This study used hierarchical linear modeling to compare longitudinal patterns of adolescent religious service attendance and club attendance, and to contrast the longitudinal relations between adolescent adjustment and religious service versus club attendance. Participants included 1050 students (47% girls) encompassing a school district in…

Constitutive modelling of creep in a long fiber random glass mat thermoplastic composite

NASA Astrophysics Data System (ADS)

Dasappa, Prasad

The primary objective of this proposed research is to characterize and model the creep behaviour of Glass Mat Thermoplastic (GMT) composites under thermo-mechanical loads. In addition, tensile testing has been performed to study the variability in mechanical properties. The thermo-physical properties of the polypropylene matrix including crystallinity level, transitions and the variation of the stiffness with temperature have also been determined. In this work, the creep of a long fibre GMT composite has been investigated for a relatively wide range of stresses from 5 to 80 MPa and temperatures from 25 to 90°C. The higher limit for stress is approximately 90% of the nominal tensile strength of the material. A Design of Experiments (ANOVA) statistical method was applied to determine the effects of stress and temperature in the random mat material which is known for wild experimental scatter. Two sets of creep tests were conducted. First, preliminary short-term creep tests consisting of 30 minutes creep followed by recovery were carried out over a wide range of stresses and temperatures. These tests were carried out to determine the linear viscoelastic region of the material. From these tests, the material was found to be linear viscoelastic up-to 20 MPa at room temperature and considerable non-linearities were observed with both stress and temperature. Using Time-Temperature superposition (TTS) a long term master curve for creep compliance for up-to 185 years at room temperature has been obtained. Further, viscoplastic strains were developed in these tests indicating the need for a non-linear viscoelastic viscoplastic constitutive model. The second set of creep tests was performed to develop a general non-linear viscoelastic viscoplastic constitutive model. Long term creep-recovery tests consisting of 1 day creep followed by recovery has been conducted over the stress range between 20 and 70 MPa at four temperatures: 25°C, 40°C, 60°C and 80°C. Findley's model, which is the reduced form of the Schapery non-linear viscoelastic model, was found to be sufficient to model the viscoelastic behaviour. The viscoplastic strains were modeled using the Zapas and Crissman viscoplastic model. A parameter estimation method which isolates the viscoelastic component from the viscoplastic part of the non-linear model has been developed. The non-linear parameters in the Findley's non-linear viscoelastic model have been found to be dependent on both stress and temperature and have been modeled as a product of functions of stress and temperature. The viscoplastic behaviour for temperatures up to 40°C was similar indicating similar damage mechanisms. Moreover, the development of viscoplastic strains at 20 and 30 MPa were similar over all the entire temperature range considered implying similar damage mechanisms. It is further recommended that the material should not be used at temperature greater than 60°C at stresses over 50 MPa. To further study the viscoplastic behaviour of continuous fibre glass mat thermoplastic composite at room temperature, multiple creep-recovery experiments of increasing durations between 1 and 24 hours have been conducted on a single specimen. The purpose of these tests was to experimentally and numerically decouple the viscoplastic strains from total creep response. This enabled the characterization of the evolution of viscoplastic strains as a function of time, stress and loading cycles and also to co-relate the development of viscoplastic strains with progression of failure mechanisms such as interfacial debonding and matrix cracking which were captured in-situ. A viscoplastic model developed from partial data analysis, as proposed by Nordin, had excellent agreement with experimental results for all stresses and times considered. Furthermore, the viscoplastic strain development is accelerated with increasing number of cycles at higher stress levels. These tests further validate the technique proposed for numerical separation of viscoplastic strains employed in obtaining the non-linear viscoelastic viscoplastic model parameters. These tests also indicate that the viscoelastic strains during creep are affected by the previous viscoplastic strain history. (Abstract shortened by UMI.)

A Steeper than Linear Disk Mass-Stellar Mass Scaling Relation

NASA Astrophysics Data System (ADS)

Pascucci, Ilaria; SLICK, EOS

2017-01-01

The disk mass is among the most important input parameter of planet formation models as it determines the number and masses of the planets that can form. I will present an ALMA 887 micron survey of the disk population around objects from 2 to 0.03Msun in the nearby 2 Myr-old Chamaeleon I star-forming region. Assuming isothermal and optically thin emission, we convert the 887 micron flux densities into dust disk masses (Mdust) and show that the Mdust-Mstar scaling relation is steeper than linear. By re-analyzing all millimeter data available for nearby regions in a self-consistent way, we find that the 1-3 Myr-old regions of Taurus, Lupus, and Chamaeleon I share the same Mdust-Mstar relation, while the 10 Myr-old Upper Sco association has an even steeper relation. Theoretical models of grain growth, drift, and fragmentation reproduce this trend and suggest that disks are in the fragmentation-limited regime. In this regime millimeter grains will be located closer in around lower-mass stars, a prediction that can be tested with deeper and higher spatial resolution ALMA observations.

Uncertainty of relative sensitivity factors in glow discharge mass spectrometry

NASA Astrophysics Data System (ADS)

Meija, Juris; Methven, Brad; Sturgeon, Ralph E.

2017-10-01

The concept of the relative sensitivity factors required for the correction of the measured ion beam ratios in pin-cell glow discharge mass spectrometry is examined in detail. We propose a data-driven model for predicting the relative response factors, which relies on a non-linear least squares adjustment and analyte/matrix interchangeability phenomena. The model provides a self-consistent set of response factors for any analyte/matrix combination of any element that appears as either an analyte or matrix in at least one known response factor.

Annual variation in Internet keyword searches: Linking dieting interest to obesity and negative health outcomes.

PubMed

Markey, Patrick M; Markey, Charlotte N

2013-07-01

This study investigated the annual variation in Internet searches regarding dieting. Time-series analysis was first used to examine the annual trends of Google keyword searches during the past 7 years for topics related to dieting within the United States. The results indicated that keyword searches for dieting fit a consistent 12-month linear model, peaking in January (following New Year's Eve) and then linearly decreasing until surging again the following January. Additional state-level analyses revealed that the size of the December-January dieting-related keyword surge was predictive of both obesity and mortality rates due to diabetes, heart disease, and stroke.

Ergonomics study on mobile phones for thumb physiology discomfort

NASA Astrophysics Data System (ADS)

Bendero, J. M. S.; Doon, M. E. R.; Quiogue, K. C. A.; Soneja, L. C.; Ong, N. R.; Sauli, Z.; Vairavan, R.

2017-09-01

The study was conducted on Filipino undergraduate college students and aimed to find out about the significant factors associated with mobile phone usage and its effect on thumb pain.A correlation-prediction analysisand Multiple Linear Regression was adopted and used as the main tool in determining the significant factors and coming up with predictive models on thumb related pain. With the use of the software Statistical Package for the Social Sciences or SPSS in conducting linear regression, 2 significant factors on thumb-related pain (percentage of time using portrait as screen orientation when text messaging, amount of time playing games using one hand in a day) were found.

Gain, noise, and contrast sensitivity of linear visual neurons

NASA Technical Reports Server (NTRS)

Watson, Andrew B.

1990-01-01

Contrast sensitivity is a measure of the ability of an observer to detect contrast signals of particular spatial and temporal frequencies. A formal definition of contrast sensitivity that can be applied to individual linear visual neurons is derived. A neuron is modeled by a contrast transfer function and its modulus, contrast gain, and by a noise power spectrum. The distributions of neural responses to signal and blank presentations are derived, and from these, a definition of contrast sensitivity is obtained. This formal definition may be used to relate the sensitivities of various populations of neurons, and to relate the sensitivities of neurons to that of the behaving animal.

Dynamic modelling and simulation of linear Fresnel solar field model based on molten salt heat transfer fluid

NASA Astrophysics Data System (ADS)

Hakkarainen, Elina; Tähtinen, Matti

2016-05-01

Demonstrations of direct steam generation (DSG) in linear Fresnel collectors (LFC) have given promising results related to higher steam parameters compared to the current state-of-the-art parabolic trough collector (PTC) technology using oil as heat transfer fluid (HTF). However, DSG technology lacks feasible solution for long-term thermal energy storage (TES) system. This option is important for CSP technology in order to offer dispatchable power. Recently, molten salts have been proposed to be used as HTF and directly as storage medium in both line-focusing solar fields, offering storage capacity of several hours. This direct molten salt (DMS) storage concept has already gained operational experience in solar tower power plant, and it is under demonstration phase both in the case of LFC and PTC systems. Dynamic simulation programs offer a valuable effort for design and optimization of solar power plants. In this work, APROS dynamic simulation program is used to model a DMS linear Fresnel solar field with two-tank TES system, and example simulation results are presented in order to verify the functionality of the model and capability of APROS for CSP modelling and simulation.

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.

Modeling the dose effects of soybean oil in salad dressing on carotenoid and fat-soluble vitamin bioavailability in salad vegetables.

PubMed

White, Wendy S; Zhou, Yang; Crane, Agatha; Dixon, Philip; Quadt, Frits; Flendrig, Leonard M

2017-10-01

Background: Previously, we showed that vegetable oil is necessary for carotenoid absorption from salad vegetables. Research is needed to better define the dose effect and its interindividual variation for carotenoids and fat-soluble vitamins. Objective: The objective was to model the dose-response relation between the amount of soybean oil in salad dressing and the absorption of 1 ) carotenoids, phylloquinone, and tocopherols in salad vegetables and 2 ) retinyl palmitate formed from the provitamin A carotenoids. Design: Women ( n = 12) each consumed 5 vegetable salads with salad dressings containing 0, 2, 4, 8, or 32 g soybean oil. Blood was collected at selected time points. The outcome variables were the chylomicron carotenoid and fat-soluble vitamin area under the curve (AUC) and maximum content in the plasma chylomicron fraction ( C max ). The individual-specific and group-average dose-response relations were investigated by fitting linear mixed-effects random coefficient models. Results: Across the entire 0-32-g range, soybean oil was linearly related to the chylomicron AUC and C max values for α-carotene, lycopene, phylloquinone, and retinyl palmitate. Across 0-8 g of soybean oil, there was a linear increase in the chylomicron AUC and C max values for β-carotene. Across a more limited 0-4-g range of soybean oil, there were minor linear increases in the chylomicron AUC for lutein and α- and total tocopherol. Absorption of all carotenoids and fat-soluble vitamins was highest with 32 g oil ( P < 0.002). For 32 g oil, the interindividual rank order of the chylomicron AUCs was consistent across the carotenoids and fat-soluble vitamins ( P < 0.0001). Conclusions: Within the linear range, the average absorption of carotenoids and fat-soluble vitamins could be largely predicted by the soybean oil effect. However, the effect varied widely, and some individuals showed a negligible response. There was a global soybean oil effect such that those who absorbed more of one carotenoid and fat-soluble vitamin also tended to absorb more of the others. This trial was registered at clinicaltrials.gov as NCT02867488. © 2017 American Society for Nutrition.

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.

Analyzing Multilevel Factors Underlying Adolescent Smoking Behaviors: The Roles of Friendship Network, Family Relations, and School Environment.

PubMed

Kim, Harris H-S; Chun, JongSerl

2018-06-01

This study investigates the extent to which friendship network, family relations, and school context are related to adolescent cigarette smoking. Friendship network is measured in terms of delinquent peers; family relations in terms of parental supervision; and school environment in terms of objective (eg, antismoking policy) and subjective (eg, school attachment) characteristics. Findings are based on the secondary analysis of the health behavior in school-aged children, 2009-2010. Two-level hierarchical generalized linear models are estimated using hierarchical linear modeling 7. At the student level, ties to delinquent friends is significantly related to higher odds of smoking, while greater parental supervision is associated with lower odds. At the school level, antismoking policy and curriculum independently lower smoking behavior. Better within-class peer relations, greater school attachment, and higher academic performance are also negatively related to smoking. Last, the positive association between delinquent friends and smoking is weaker in schools with a formally enacted antismoking policy. However, this association is stronger in schools with better peer relations. Adolescent smoking behavior is embedded in a broader ecological setting. This research reveals that a proper understanding of it requires comprehensive analysis that incorporates factors measured at individual (student) and contextual (school) levels. © 2018, American School Health Association.

Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China.

PubMed

Gou, Faxiang; Liu, Xinfeng; He, Jian; Liu, Dongpeng; Cheng, Yao; Liu, Haixia; Yang, Xiaoting; Wei, Kongfu; Zheng, Yunhe; Jiang, Xiaojuan; Meng, Lei; Hu, Wenbiao

2018-01-08

To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission. Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways. GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2.8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 °C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 °C of Jiuquan to 16 °C in Tianshui; and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui. Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan; Relative humidity's effect on HFMD showed a non-linear interacting relationship with average temperature.

Development and validation of a general purpose linearization program for rigid aircraft models

NASA Technical Reports Server (NTRS)

Duke, E. L.; Antoniewicz, R. F.

1985-01-01

A FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models is discussed. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. 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. Also, included in the report is a comparison of linear and nonlinear models for a high performance aircraft.

Analysis of single-degree-of-freedom piezoelectric energy harvester with stopper by incremental harmonic balance method

NASA Astrophysics Data System (ADS)

Zhao, Dan; Wang, Xiaoman; Cheng, Yuan; Liu, Shaogang; Wu, Yanhong; Chai, Liqin; Liu, Yang; Cheng, Qianju

2018-05-01

Piecewise-linear structure can effectively broaden the working frequency band of the piezoelectric energy harvester, and improvement of its research can promote the practical process of energy collection device to meet the requirements for powering microelectronic components. In this paper, the incremental harmonic balance (IHB) method is introduced for the complicated and difficult analysis process of the piezoelectric energy harvester to solve these problems. After obtaining the nonlinear dynamic equation of the single-degree-of-freedom piecewise-linear energy harvester by mathematical modeling and the equation is solved based on the IHB method, the theoretical amplitude-frequency curve of open-circuit voltage is achieved. Under 0.2 g harmonic excitation, a piecewise-linear energy harvester is experimentally tested by unidirectional frequency-increasing scanning. The results demonstrate that the theoretical and experimental amplitudes have the same trend, and the width of the working band with high voltage output are 4.9 Hz and 4.7 Hz, respectively, and the relative error is 4.08%. The open-output peak voltage are 21.53 V and 18.25 V, respectively, and the relative error is 15.23%. Since the theoretical value is consistent with the experimental results, the theoretical model and the incremental harmonic balance method used in this paper are suitable for solving single-degree-of-freedom piecewise-linear piezoelectric energy harvester and can be applied to further parameter optimized design.

STICK-SLIP-SEPARATION Analysis and Non-Linear Stiffness and Damping Characterization of Friction Contacts Having Variable Normal Load

NASA Astrophysics Data System (ADS)

Yang, B. D.; Chu, M. L.; Menq, C. H.

1998-03-01

Mechanical systems in which moving components are mutually constrained through contacts often lead to complex contact kinematics involving tangential and normal relative motions. A friction contact model is proposed to characterize this type of contact kinematics that imposes both friction non-linearity and intermittent separation non-linearity on the system. The stick-slip friction phenomenon is analyzed by establishing analytical criteria that predict the transition between stick, slip, and separation of the interface. The established analytical transition criteria are particularly important to the proposed friction contact model for the transition conditions of the contact kinematics are complicated by the effect of normal load variation and possible interface separation. With these transition criteria, the induced friction force on the contact plane and the variable normal load perpendicular to the contact plane, can be predicted for any given cyclic relative motions at the contact interface and hysteresis loops can be produced so as to characterize the equivalent damping and stiffness of the friction contact. These-non-linear damping and stiffness methods along with the harmonic balance method are then used to predict the resonant response of a frictionally constrained two-degree-of-freedom oscillator. The predicted results are compared with those of the time integration method and the damping effect, the resonant frequency shift, and the jump phenomenon are examined.

Effect of Intensity-Modulated Pelvic Radiotherapy on Second Cancer Risk in the Postoperative Treatment of Endometrial and Cervical Cancer

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

Zwahlen, Daniel R.; Department of Radiation Oncology, University Hospital Zurich, Zurich; Ruben, Jeremy D.

2009-06-01

Purpose: To estimate and compare intensity-modulated radiotherapy (IMRT) with three-dimensional conformal radiotherapy (3DCRT) in terms of second cancer risk (SCR) for postoperative treatment of endometrial and cervical cancer. Methods and Materials: To estimate SCR, the organ equivalent dose concept with a linear-exponential, a plateau, and a linear dose-response model was applied to dose distributions, calculated in a planning computed tomography scan of a 68-year-old woman. Three plans were computed: four-field 18-MV 3DCRT and nine-field IMRT with 6- and 18-MV photons. SCR was estimated as a function of target dose (50.4 Gy/28 fractions) in organs of interest according to the Internationalmore » Commission on Radiological Protection Results: Cumulative SCR relative to 3DCRT was +6% (3% for a plateau model, -4% for a linear model) for 6-MV IMRT and +26% (25%, 4%) for the 18-MV IMRT plan. For an organ within the primary beam, SCR was +12% (0%, -12%) for 6-MV and +5% (-2%, -7%) for 18-MV IMRT. 18-MV IMRT increased SCR 6-7 times for organs away from the primary beam relative to 3DCRT and 6-MV IMRT. Skin SCR increased by 22-37% for 6-MV and 50-69% for 18-MV IMRT inasmuch as a larger volume of skin was exposed. Conclusion: Cancer risk after IMRT for cervical and endometrial cancer is dependent on treatment energy. 6-MV pelvic IMRT represents a safe alternative with respect to SCR relative to 3DCRT, independently of the dose-response model. 18-MV IMRT produces second neutrons that modestly increase the SCR.« less

Predicting Endurance Time in a Repetitive Lift and Carry Task Using Linear Mixed Models

PubMed Central

Ham, Daniel J.; Best, Stuart A.; Carstairs, Greg L.; Savage, Robert J.; Straney, Lahn; Caldwell, Joanne N.

2016-01-01

Objectives Repetitive manual handling tasks account for a substantial portion of work-related injuries. However, few studies report endurance time in repetitive manual handling tasks. Consequently, there is little guidance to inform expected work time for repetitive manual handling tasks. We aimed to investigate endurance time and oxygen consumption of a repetitive lift and carry task using linear mixed models. Methods Fourteen male soldiers (age 22.4 ± 4.5 yrs, height 1.78 ± 0.04 m, body mass 76.3 ± 10.1 kg) conducted four assessment sessions that consisted of one maximal box lifting session and three lift and carry sessions. The relationships between carry mass (range 17.5–37.5 kg) and the duration of carry, and carry mass and oxygen consumption, were assessed using linear mixed models with random effects to account for between-subject variation. Results Results demonstrated that endurance time was inversely associated with carry mass (R2 = 0.24), with significant individual-level variation (R2 = 0.85). Normalising carry mass to performance in a maximal box lifting test improved the prediction of endurance time (R2 = 0.40). Oxygen consumption presented relative to total mass (body mass, external load and carried mass) was not significantly related to lift and carry mass (β1 = 0.16, SE = 0.10, 95%CI: -0.04, 0.36, p = 0.12), indicating that there was no change in oxygen consumption relative to total mass with increasing lift and carry mass. Conclusion Practically, these data can be used to guide work-rest schedules and provide insight into methods assessing the physical capacity of workers conducting repetitive manual handling tasks. PMID:27379902

Dynamic linear models using the Kalman filter for early detection and early warning of malaria outbreaks

NASA Astrophysics Data System (ADS)

Merkord, C. L.; Liu, Y.; DeVos, M.; Wimberly, M. C.

2015-12-01

Malaria early detection and early warning systems are important tools for public health decision makers in regions where malaria transmission is seasonal and varies from year to year with fluctuations in rainfall and temperature. Here we present a new data-driven dynamic linear model based on the Kalman filter with time-varying coefficients that are used to identify malaria outbreaks as they occur (early detection) and predict the location and timing of future outbreaks (early warning). We fit linear models of malaria incidence with trend and Fourier form seasonal components using three years of weekly malaria case data from 30 districts in the Amhara Region of Ethiopia. We identified past outbreaks by comparing the modeled prediction envelopes with observed case data. Preliminary results demonstrated the potential for improved accuracy and timeliness over commonly-used methods in which thresholds are based on simpler summary statistics of historical data. Other benefits of the dynamic linear modeling approach include robustness to missing data and the ability to fit models with relatively few years of training data. To predict future outbreaks, we started with the early detection model for each district and added a regression component based on satellite-derived environmental predictor variables including precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and land surface temperature (LST) and spectral indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). We included lagged environmental predictors in the regression component of the model, with lags chosen based on cross-correlation of the one-step-ahead forecast errors from the first model. Our results suggest that predictions of future malaria outbreaks can be improved by incorporating lagged environmental predictors.

Linear and nonlinear winter atmospheric responses to extreme phases of low frequency Pacific sea surface temperature variability

NASA Astrophysics Data System (ADS)

Cao, Dandan; Wu, Qigang; Hu, Aixue; Yao, Yonghong; Liu, Shizuo; Schroeder, Steven R.; Yang, Fucheng

2018-02-01

This study examines Northern Hemisphere winter (DJFM) atmospheric responses to opposite strong phases of interdecadal (low frequency, LF) Pacific sea surface temperature (SST) forcing, which resembles El Niño-Southern Oscillation (ENSO) on a longer time scale, in observations and GFDL and CAM4 model simulations. Over the Pacific-North America (PNA) sector, linear observed responses of 500-hPa height (Z500) anomalies resemble the PNA teleconnection pattern, but show a PNA-like nonlinear response because of a westward Z500 shift in the negative (LF-) relative to the positive LF (LF+) phase. Significant extratropical linear responses include a North Atlantic Oscillation (NAO)-like Z500 anomaly, a dipole-like Z500 anomaly over northern Eurasia associated with warming over mid-high latitude Eurasia, and a Southern Annular anomaly pattern associated with warming in southern land areas. Significant nonlinear Z500 responses also include a NAO-like anomaly pattern. Models forced by LF+ and LF- SST anomalies reproduce many aspects of observed linear and nonlinear responses over the Pacific-North America sector, and linear responses over southern land, but not in the North Atlantic-European sector and Eurasia. Both models simulate PNA-like linear responses in the North Pacific-North America region similar to observed, but show larger PNA-like LF+ responses, resulting in a PNA nonlinear response. The nonlinear PNA responses result from both nonlinear western tropical Pacific rainfall changes and extratropical transient eddy feedbacks. With LF tropical Pacific forcing only (LFTP+ and LFTP-, climatological SST elsewhere), CAM4 simulates a significant NAO response to LFTP-, including a linear negative and nonlinear positive NAO response.

The determination of third order linear models from a seventh order nonlinear jet engine model

NASA Technical Reports Server (NTRS)

Lalonde, Rick J.; Hartley, Tom T.; De Abreu-Garcia, J. Alex

1989-01-01

Results are presented that demonstrate how good reduced-order models can be obtained directly by recursive parameter identification using input/output (I/O) data of high-order nonlinear systems. Three different methods of obtaining a third-order linear model from a seventh-order nonlinear turbojet engine model are compared. The first method is to obtain a linear model from the original model and then reduce the linear model by standard reduction techniques such as residualization and balancing. The second method is to identify directly a third-order linear model by recursive least-squares parameter estimation using I/O data of the original model. The third method is to obtain a reduced-order model from the original model and then linearize the reduced model. Frequency responses are used as the performance measure to evaluate the reduced models. The reduced-order models along with their Bode plots are presented for comparison purposes.

The Physical Origin of Long Gas Depletion Times in Galaxies

NASA Astrophysics Data System (ADS)

Semenov, Vadim A.; Kravtsov, Andrey V.; Gnedin, Nickolay Y.

2017-08-01

We present a model that explains why galaxies form stars on a timescale significantly longer than the timescales of processes governing the evolution of interstellar gas. We show that gas evolves from a non-star-forming to a star-forming state on a relatively short timescale, and thus the rate of this evolution does not limit the star formation rate (SFR). Instead, the SFR is limited because only a small fraction of star-forming gas is converted into stars before star-forming regions are dispersed by feedback and dynamical processes. Thus, gas cycles into and out of a star-forming state multiple times, which results in a long timescale on which galaxies convert gas into stars. Our model does not rely on the assumption of equilibrium and can be used to interpret trends of depletion times with the properties of observed galaxies and the parameters of star formation and feedback recipes in simulations. In particular, the model explains how feedback self-regulates the SFR in simulations and makes it insensitive to the local star formation efficiency. We illustrate our model using the results of an isolated L *-sized galaxy simulation that reproduces the observed Kennicutt-Schmidt relation for both molecular and atomic gas. Interestingly, the relation for molecular gas is almost linear on kiloparsec scales, although a nonlinear relation is adopted in simulation cells. We discuss how a linear relation emerges from non-self-similar scaling of the gas density PDF with the average gas surface density.

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

Semenov, Vadim A.; Kravtsov, Andrey V.; Gnedin, Nickolay Y., E-mail: semenov@uchicago.edu

We present a model that explains why galaxies form stars on a timescale significantly longer than the timescales of processes governing the evolution of interstellar gas. We show that gas evolves from a non-star-forming to a star-forming state on a relatively short timescale, and thus the rate of this evolution does not limit the star formation rate (SFR). Instead, the SFR is limited because only a small fraction of star-forming gas is converted into stars before star-forming regions are dispersed by feedback and dynamical processes. Thus, gas cycles into and out of a star-forming state multiple times, which results inmore » a long timescale on which galaxies convert gas into stars. Our model does not rely on the assumption of equilibrium and can be used to interpret trends of depletion times with the properties of observed galaxies and the parameters of star formation and feedback recipes in simulations. In particular, the model explains how feedback self-regulates the SFR in simulations and makes it insensitive to the local star formation efficiency. We illustrate our model using the results of an isolated L {sub *}-sized galaxy simulation that reproduces the observed Kennicutt–Schmidt relation for both molecular and atomic gas. Interestingly, the relation for molecular gas is almost linear on kiloparsec scales, although a nonlinear relation is adopted in simulation cells. We discuss how a linear relation emerges from non-self-similar scaling of the gas density PDF with the average gas surface density.« less

Development of a Linear Stirling Model with Varying Heat Inputs

NASA Technical Reports Server (NTRS)

Regan, Timothy F.; Lewandowski, Edward J.

2007-01-01

The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC s non-linear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.

Revealing the nonadiabatic nature of dark energy perturbations from galaxy clustering data

NASA Astrophysics Data System (ADS)

Velten, Hermano; Fazolo, Raquel

2017-10-01

We study structure formation using relativistic cosmological linear perturbation theory in the presence of intrinsic and relative (with respect to matter) nonadiabatic dark energy perturbations. For different dark energy models we assess the impact of nonadiabaticity on the matter growth promoting a comparison with growth rate data. The dark energy models studied lead to peculiar signatures of the (non)adiabatic nature of dark energy perturbations in the evolution of the f σ8(z ) observable. We show that nonadiabatic dark energy models become close to be degenerated with respect to the Λ CDM model at first order in linear perturbations. This would avoid the identification of the nonadiabatic nature of dark energy using current available data. Therefore, such evidence indicates that new probes are necessary to reveal the nonadiabatic features in the dark energy sector.

The morphing of geographical features by Fourier transformation

PubMed Central

Liu, Pengcheng; Yu, Wenhao; Cheng, Xiaoqiang

2018-01-01

This paper presents a morphing model of vector geographical data based on Fourier transformation. This model involves three main steps. They are conversion from vector data to Fourier series, generation of intermediate function by combination of the two Fourier series concerning a large scale and a small scale, and reverse conversion from combination function to vector data. By mirror processing, the model can also be used for morphing of linear features. Experimental results show that this method is sensitive to scale variations and it can be used for vector map features’ continuous scale transformation. The efficiency of this model is linearly related to the point number of shape boundary and the interceptive value n of Fourier expansion. The effect of morphing by Fourier transformation is plausible and the efficiency of the algorithm is acceptable. PMID:29351344

Analytical flow duration curves for summer streamflow in Switzerland

NASA Astrophysics Data System (ADS)

Santos, Ana Clara; Portela, Maria Manuela; Rinaldo, Andrea; Schaefli, Bettina

2018-04-01

This paper proposes a systematic assessment of the performance of an analytical modeling framework for streamflow probability distributions for a set of 25 Swiss catchments. These catchments show a wide range of hydroclimatic regimes, including namely snow-influenced streamflows. The model parameters are calculated from a spatially averaged gridded daily precipitation data set and from observed daily discharge time series, both in a forward estimation mode (direct parameter calculation from observed data) and in an inverse estimation mode (maximum likelihood estimation). The performance of the linear and the nonlinear model versions is assessed in terms of reproducing observed flow duration curves and their natural variability. Overall, the nonlinear model version outperforms the linear model for all regimes, but the linear model shows a notable performance increase with catchment elevation. More importantly, the obtained results demonstrate that the analytical model performs well for summer discharge for all analyzed streamflow regimes, ranging from rainfall-driven regimes with summer low flow to snow and glacier regimes with summer high flow. These results suggest that the model's encoding of discharge-generating events based on stochastic soil moisture dynamics is more flexible than previously thought. As shown in this paper, the presence of snowmelt or ice melt is accommodated by a relative increase in the discharge-generating frequency, a key parameter of the model. Explicit quantification of this frequency increase as a function of mean catchment meteorological conditions is left for future research.

New insights into soil temperature time series modeling: linear or nonlinear?

NASA Astrophysics Data System (ADS)

Bonakdari, Hossein; Moeeni, Hamid; Ebtehaj, Isa; Zeynoddin, Mohammad; Mahoammadian, Abdolmajid; Gharabaghi, Bahram

2018-03-01

Soil temperature (ST) is an important dynamic parameter, whose prediction is a major research topic in various fields including agriculture because ST has a critical role in hydrological processes at the soil surface. In this study, a new linear methodology is proposed based on stochastic methods for modeling daily soil temperature (DST). With this approach, the ST series components are determined to carry out modeling and spectral analysis. The results of this process are compared with two linear methods based on seasonal standardization and seasonal differencing in terms of four DST series. The series used in this study were measured at two stations, Champaign and Springfield, at depths of 10 and 20 cm. The results indicate that in all ST series reviewed, the periodic term is the most robust among all components. According to a comparison of the three methods applied to analyze the various series components, it appears that spectral analysis combined with stochastic methods outperformed the seasonal standardization and seasonal differencing methods. In addition to comparing the proposed methodology with linear methods, the ST modeling results were compared with the two nonlinear methods in two forms: considering hydrological variables (HV) as input variables and DST modeling as a time series. In a previous study at the mentioned sites, Kim and Singh Theor Appl Climatol 118:465-479, (2014) applied the popular Multilayer Perceptron (MLP) neural network and Adaptive Neuro-Fuzzy Inference System (ANFIS) nonlinear methods and considered HV as input variables. The comparison results signify that the relative error projected in estimating DST by the proposed methodology was about 6%, while this value with MLP and ANFIS was over 15%. Moreover, MLP and ANFIS models were employed for DST time series modeling. Due to these models' relatively inferior performance to the proposed methodology, two hybrid models were implemented: the weights and membership function of MLP and ANFIS (respectively) were optimized with the particle swarm optimization (PSO) algorithm in conjunction with the wavelet transform and nonlinear methods (Wavelet-MLP & Wavelet-ANFIS). A comparison of the proposed methodology with individual and hybrid nonlinear models in predicting DST time series indicates the lowest Akaike Information Criterion (AIC) index value, which considers model simplicity and accuracy simultaneously at different depths and stations. The methodology presented in this study can thus serve as an excellent alternative to complex nonlinear methods that are normally employed to examine DST.

Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase

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

Sadat Hayatshahi, Sayyed Hamed; Abdolmaleki, Parviz; Safarian, Shahrokh

2005-12-16

Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, themore » previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.« less

Non-linear wave phenomena in Josephson elements for superconducting electronics

NASA Astrophysics Data System (ADS)

Christiansen, P. L.; Parmentier, R. D.; Skovgaard, O.

1985-07-01

The long and intermediate length Josephson tunnel junction oscillator with overlap geometry of linear and circular configuration, is investigated by computational solution of the perturbed sine-Gordon equation model and by experimental measurements. The model predicts the experimental results very well. Line oscillators as well as ring oscillators are treated. For long junctions soliton perturbation methods are developed and turn out to be efficient prediction tools, also providing physical understanding of the dynamics of the oscillator. For intermediate length junctions expansions in terms of linear cavity modes reduce computational costs. The narrow linewidth of the electromagnetic radiation (typically 1 kHz of a line at 10 GHz) is demonstrated experimentally. Corresponding computer simulations requiring a relative accuracy of less than 10 to the -7th power are performed on supercomputer CRAY-1-S. The broadening of linewidth due to external microradiation and internal thermal noise is determined.

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

A linear geospatial streamflow modeling system for data sparse environments

USGS Publications Warehouse

Asante, Kwabena O.; Arlan, Guleid A.; Pervez, Md Shahriar; Rowland, James

2008-01-01

In many river basins around the world, inaccessibility of flow data is a major obstacle to water resource studies and operational monitoring. This paper describes a geospatial streamflow modeling system which is parameterized with global terrain, soils and land cover data and run operationally with satellite‐derived precipitation and evapotranspiration datasets. Simple linear methods transfer water through the subsurface, overland and river flow phases, and the resulting flows are expressed in terms of standard deviations from mean annual flow. In sample applications, the modeling system was used to simulate flow variations in the Congo, Niger, Nile, Zambezi, Orange and Lake Chad basins between 1998 and 2005, and the resulting flows were compared with mean monthly values from the open‐access Global River Discharge Database. While the uncalibrated model cannot predict the absolute magnitude of flow, it can quantify flow anomalies in terms of relative departures from mean flow. Most of the severe flood events identified in the flow anomalies were independently verified by the Dartmouth Flood Observatory (DFO) and the Emergency Disaster Database (EM‐DAT). Despite its limitations, the modeling system is valuable for rapid characterization of the relative magnitude of flood hazards and seasonal flow changes in data sparse settings.

A study of temperature-related non-linearity at the metal-silicon interface

NASA Astrophysics Data System (ADS)

Gammon, P. M.; Donchev, E.; Pérez-Tomás, A.; Shah, V. A.; Pang, J. S.; Petrov, P. K.; Jennings, M. R.; Fisher, C. A.; Mawby, P. A.; Leadley, D. R.; McN. Alford, N.

2012-12-01

In this paper, we investigate the temperature dependencies of metal-semiconductor interfaces in an effort to better reproduce the current-voltage-temperature (IVT) characteristics of any Schottky diode, regardless of homogeneity. Four silicon Schottky diodes were fabricated for this work, each displaying different degrees of inhomogeneity; a relatively homogeneous NiV/Si diode, a Ti/Si and Cr/Si diode with double bumps at only the lowest temperatures, and a Nb/Si diode displaying extensive non-linearity. The 77-300 K IVT responses are modelled using a semi-automated implementation of Tung's electron transport model, and each of the diodes are well reproduced. However, in achieving this, it is revealed that each of the three key fitting parameters within the model display a significant temperature dependency. In analysing these dependencies, we reveal how a rise in thermal energy "activates" exponentially more interfacial patches, the activation rate being dependent on the carrier concentration at the patch saddle point (the patch's maximum barrier height), which in turn is linked to the relative homogeneity of each diode. Finally, in a review of Tung's model, problems in the divergence of the current paths at low temperature are explained to be inherent due to the simplification of an interface that will contain competing defects and inhomogeneities.

A model for the relative biological effectiveness of protons: the tissue specific parameter α/β of photons is a predictor for the sensitivity to LET changes.

PubMed

Wedenberg, Minna; Lind, Bengt K; Hårdemark, Björn

2013-04-01

The biological effects of particles are often expressed in relation to that of photons through the concept of relative biological effectiveness, RBE. In proton radiotherapy, a constant RBE of 1.1 is usually assumed. However, there is experimental evidence that RBE depends on various factors. The aim of this study is to develop a model to predict the RBE based on linear energy transfer (LET), dose, and the tissue specific parameter α/β of the linear-quadratic model for the reference radiation. Moreover, the model should capture the basic features of the RBE using a minimum of assumptions, each supported by experimental data. The α and β parameters for protons were studied with respect to their dependence on LET. An RBE model was proposed where the dependence of LET is affected by the (α/β)phot ratio of photons. Published cell survival data with a range of well-defined LETs and cell types were selected for model evaluation rendering a total of 10 cell lines and 24 RBE values. A statistically significant relation was found between α for protons and LET. Moreover, the strength of that relation varied significantly with (α/β)phot. In contrast, no significant relation between β and LET was found. On the whole, the resulting RBE model provided a significantly improved fit (p-value < 0.01) to the experimental data compared to the standard constant RBE. By accounting for the α/β ratio of photons, clearer trends between RBE and LET of protons were found, and our results suggest that late responding tissues are more sensitive to LET changes than early responding tissues and most tumors. An advantage with the proposed RBE model in optimization and evaluation of treatment plans is that it only requires dose, LET, and (α/β)phot as input parameters. Hence, no proton specific biological parameters are needed.

A Three-Phase Microgrid Restoration Model Considering Unbalanced Operation of Distributed Generation

DOE PAGES

Wang, Zeyu; Wang, Jianhui; Chen, Chen

2016-12-07

Recent severe outages highlight the urgency of improving grid resiliency in the U.S. Microgrid formation schemes are proposed to restore critical loads after outages occur. Most distribution networks have unbalanced configurations that are not represented in sufficient detail by single-phase models. This study provides a microgrid formation plan that adopts a three-phase network model to represent unbalanced distribution networks. The problem formulation has a quadratic objective function with mixed-integer linear constraints. The three-phase network model enables us to examine the three-phase power outputs of distributed generators (DGs), preventing unbalanced operation that might trip DGs. Because the DG unbalanced operation constraintmore » is non-convex, an iterative process is presented that checks whether the unbalanced operation limits for DGs are satisfied after each iteration of optimization. We also develop a relatively conservative linear approximation on the unbalanced operation constraint to handle larger networks. Compared with the iterative solution process, the conservative linear approximation is able to accelerate the solution process at the cost of sacrificing optimality to a limited extent. Simulation in the IEEE 34 node and IEEE 123 test feeders indicate that the proposed method yields more practical microgrid formations results. In addition, this paper explores the coordinated operation of DGs and energy storage (ES) installations. The unbalanced three-phase outputs of ESs combined with the relatively balanced outputs of DGs could supply unbalanced loads. In conclusion, the case study also validates the DG-ES coordination.« less

A Three-Phase Microgrid Restoration Model Considering Unbalanced Operation of Distributed Generation

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

Wang, Zeyu; Wang, Jianhui; Chen, Chen

Recent severe outages highlight the urgency of improving grid resiliency in the U.S. Microgrid formation schemes are proposed to restore critical loads after outages occur. Most distribution networks have unbalanced configurations that are not represented in sufficient detail by single-phase models. This study provides a microgrid formation plan that adopts a three-phase network model to represent unbalanced distribution networks. The problem formulation has a quadratic objective function with mixed-integer linear constraints. The three-phase network model enables us to examine the three-phase power outputs of distributed generators (DGs), preventing unbalanced operation that might trip DGs. Because the DG unbalanced operation constraintmore » is non-convex, an iterative process is presented that checks whether the unbalanced operation limits for DGs are satisfied after each iteration of optimization. We also develop a relatively conservative linear approximation on the unbalanced operation constraint to handle larger networks. Compared with the iterative solution process, the conservative linear approximation is able to accelerate the solution process at the cost of sacrificing optimality to a limited extent. Simulation in the IEEE 34 node and IEEE 123 test feeders indicate that the proposed method yields more practical microgrid formations results. In addition, this paper explores the coordinated operation of DGs and energy storage (ES) installations. The unbalanced three-phase outputs of ESs combined with the relatively balanced outputs of DGs could supply unbalanced loads. In conclusion, the case study also validates the DG-ES coordination.« less

Asymptotic Linear Spectral Statistics for Spiked Hermitian Random Matrices

NASA Astrophysics Data System (ADS)

Passemier, Damien; McKay, Matthew R.; Chen, Yang

2015-07-01

Using the Coulomb Fluid method, this paper derives central limit theorems (CLTs) for linear spectral statistics of three "spiked" Hermitian random matrix ensembles. These include Johnstone's spiked model (i.e., central Wishart with spiked correlation), non-central Wishart with rank-one non-centrality, and a related class of non-central matrices. For a generic linear statistic, we derive simple and explicit CLT expressions as the matrix dimensions grow large. For all three ensembles under consideration, we find that the primary effect of the spike is to introduce an correction term to the asymptotic mean of the linear spectral statistic, which we characterize with simple formulas. The utility of our proposed framework is demonstrated through application to three different linear statistics problems: the classical likelihood ratio test for a population covariance, the capacity analysis of multi-antenna wireless communication systems with a line-of-sight transmission path, and a classical multiple sample significance testing problem.

Origin of nonsaturating linear magnetoresistivity

NASA Astrophysics Data System (ADS)

Kisslinger, Ferdinand; Ott, Christian; Weber, Heiko B.

2017-01-01

The observation of nonsaturating classical linear magnetoresistivity has been an enigmatic phenomenon in solid-state physics. We present a study of a two-dimensional ohmic conductor, including local Hall effect and a self-consistent consideration of the environment. An equivalent-circuit scheme delivers a simple and convincing argument why the magnetoresistivity is linear in strong magnetic field, provided that current and biasing electric field are misaligned by a nonlocal mechanism. A finite-element model of a two-dimensional conductor is suited to display the situations that create such deviating currents. Besides edge effects next to electrodes, charge carrier density fluctuations are efficiently generating this effect. However, mobility fluctuations that have frequently been related to linear magnetoresistivity are barely relevant. Despite its rare observation, linear magnetoresitivity is rather the rule than the exception in a regime of low charge carrier densities, misaligned current pathways and strong magnetic field.

Nonuniform sampling theorems for random signals in the linear canonical transform domain

NASA Astrophysics Data System (ADS)

Shuiqing, Xu; Congmei, Jiang; Yi, Chai; Youqiang, Hu; Lei, Huang

2018-06-01

Nonuniform sampling can be encountered in various practical processes because of random events or poor timebase. The analysis and applications of the nonuniform sampling for deterministic signals related to the linear canonical transform (LCT) have been well considered and researched, but up to now no papers have been published regarding the various nonuniform sampling theorems for random signals related to the LCT. The aim of this article is to explore the nonuniform sampling and reconstruction of random signals associated with the LCT. First, some special nonuniform sampling models are briefly introduced. Second, based on these models, some reconstruction theorems for random signals from various nonuniform samples associated with the LCT have been derived. Finally, the simulation results are made to prove the accuracy of the sampling theorems. In addition, the latent real practices of the nonuniform sampling for random signals have been also discussed.

Variation of depth to the brittle-ductile transition due to cooling of a midcrustal intrusion.

USGS Publications Warehouse

Gettings, M.E.

1988-01-01

The depth to the brittle-ductile transition in the crust is often defined by the intersection of a shear resistance relation in the brittle upper crust that increases linearly with depth and a power law relation for ductile flow in the lower crust that depends strongly on T. Transient variation of this depth caused by a magmatic intrusion at a depth near the regional transition can be modelled by a heat conduction model for a rectangular parallelepiped superimposed on a linear geothermal gradient. When parameters appropriate for the southeastern US are used, a moderate-sized intrusion is found to decrease the transition depth by as much as 7 km; significant variations last approx 10 m.y. Since the base of the seismogenic zone is identified with the brittle-ductile transition, these results imply that intrusions of late Tertiary age or younger could be important sources of clustered seismicity. -A.W.H.

Archimedes' law explains penetration of solids into granular media.

PubMed

Kang, Wenting; Feng, Yajie; Liu, Caishan; Blumenfeld, Raphael

2018-03-16

Understanding the response of granular matter to intrusion of solid objects is key to modelling many aspects of behaviour of granular matter, including plastic flow. Here we report a general model for such a quasistatic process. Using a range of experiments, we first show that the relation between the penetration depth and the force resisting it, transiently nonlinear and then linear, is scalable to a universal form. We show that the gradient of the steady-state part, K ϕ , depends only on the medium's internal friction angle, ϕ, and that it is nonlinear in μ = tan ϕ, in contrast to an existing conjecture. We further show that the intrusion of any convex solid shape satisfies a modified Archimedes' law and use this to: relate the zero-depth intercept of the linear part to K ϕ and the intruder's cross-section; explain the curve's nonlinear part in terms of the stagnant zone's development.

Application of Hierarchical Linear Models/Linear Mixed-Effects Models in School Effectiveness Research

ERIC Educational Resources Information Center

Ker, H. W.

2014-01-01

Multilevel data are very common in educational research. Hierarchical linear models/linear mixed-effects models (HLMs/LMEs) are often utilized to analyze multilevel data nowadays. This paper discusses the problems of utilizing ordinary regressions for modeling multilevel educational data, compare the data analytic results from three regression…

The Overgeneralization of Linear Models among University Students' Mathematical Productions: A Long-Term Study

ERIC Educational Resources Information Center

Esteley, Cristina B.; Villarreal, Monica E.; Alagia, Humberto R.

2010-01-01

Over the past several years, we have been exploring and researching a phenomenon that occurs among undergraduate students that we called extension of linear models to non-linear contexts or overgeneralization of linear models. This phenomenon appears when some students use linear representations in situations that are non-linear. In a first phase,…

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

Dhawan, Suhail; Goobar, Ariel; Mörtsell, Edvard

Recent re-calibration of the Type Ia supernova (SNe Ia) magnitude-redshift relation combined with cosmic microwave background (CMB) and baryon acoustic oscillation (BAO) data have provided excellent constraints on the standard cosmological model. Here, we examine particular classes of alternative cosmologies, motivated by various physical mechanisms, e.g. scalar fields, modified gravity and phase transitions to test their consistency with observations of SNe Ia and the ratio of the angular diameter distances from the CMB and BAO. Using a model selection criterion for a relative comparison of the models (the Bayes Factor), we find moderate to strong evidence that the data prefermore » flat ΛCDM over models invoking a thawing behaviour of the quintessence scalar field. However, some exotic models like the growing neutrino mass cosmology and vacuum metamorphosis still present acceptable evidence values. The bimetric gravity model with only the linear interaction term as well as a simplified Galileon model can be ruled out by the combination of SNe Ia and CMB/BAO datasets whereas the model with linear and quadratic interaction terms has a comparable evidence value to standard ΛCDM. Thawing models are found to have significantly poorer evidence compared to flat ΛCDM cosmology under the assumption that the CMB compressed likelihood provides an adequate description for these non-standard cosmologies. We also present estimates for constraints from future data and find that geometric probes from oncoming surveys can put severe limits on non-standard cosmological models.« less

Price Strategies between a Dominant Retailer and Manufacturers

NASA Astrophysics Data System (ADS)

Cho, Hsun Jung; Mak, Hou Kit

2009-08-01

Supply chain-related game theoretical applications have been discussed for decades. This research accounts for the emergence of a dominant retailer, and the retailer Stackelberg pricing models of distribution channels. Research in the channel pricing game may use different definitions of pricing decision variables. In this research, we pay attentions to the retailer Stackelberg pricing game, and discuss the effects when choosing different decision variables. According the literature it was shown that the strategies between channel members depend critically on the form of the demand function. Two different demand forms—linear and non-linear—will be considered in our numerical example respectively. Our major finding is the outcomes are not relative to manufacturers' pricing decisions but to the retailer's pricing decision and choosing percentage margin as retailer's decision variable is the best strategy for the retailer but worst for manufacturers. The numerical results show that it is consistence between linear and non-linear demand form.

On the Inefficiency of Equilibria in Linear Bottleneck Congestion Games

NASA Astrophysics Data System (ADS)

de Keijzer, Bart; Schäfer, Guido; Telelis, Orestis A.

We study the inefficiency of equilibrium outcomes in bottleneck congestion games. These games model situations in which strategic players compete for a limited number of facilities. Each player allocates his weight to a (feasible) subset of the facilities with the goal to minimize the maximum (weight-dependent) latency that he experiences on any of these facilities. We derive upper and (asymptotically) matching lower bounds on the (strong) price of anarchy of linear bottleneck congestion games for a natural load balancing social cost objective (i.e., minimize the maximum latency of a facility). We restrict our studies to linear latency functions. Linear bottleneck congestion games still constitute a rich class of games and generalize, for example, load balancing games with identical or uniformly related machines with or without restricted assignments.

Effects of temperature on mortality in Hong Kong: a time series analysis

NASA Astrophysics Data System (ADS)

Yi, Wen; Chan, Albert P. C.

2015-07-01

Although interest in assessing the impacts of hot temperature and mortality in Hong Kong has increased, less evidence on the effect of cold temperature on mortality is available. We examined both the effects of heat and cold temperatures on daily mortality in Hong Kong for the last decade (2002-2011). A quasi-Poisson model combined with a distributed lag non-linear model was used to assess the non-linear and delayed effects of temperatures on cause-specific and age-specific mortality. Non-linear effects of temperature on mortality were identified. The relative risk of non-accidental mortality associated with cold temperature (11.1 °C, 1st percentile of temperature) relative to 19.4 °C (25th percentile of temperature) was 1.17 (95 % confidence interval (CI): 1.04, 1.29) for lags 0-13. The relative risk of non-accidental mortality associated with high temperature (31.5 °C, 99th percentile of temperature) relative to 27.8 °C (75th percentile of temperature) was 1.09 (95 % CI: 1.03, 1.17) for lags 0-3. In Hong Kong, extreme cold and hot temperatures increased the risk of mortality. The effect of cold lasted longer and greater than that of heat. People older than 75 years were the most vulnerable group to cold temperature, while people aged 65-74 were the most vulnerable group to hot temperature. Our findings may have implications for developing intervention strategies for extreme cold and hot temperatures.

Effects of temperature on mortality in Hong Kong: a time series analysis.

PubMed

Yi, Wen; Chan, Albert P C

2015-07-01

Although interest in assessing the impacts of hot temperature and mortality in Hong Kong has increased, less evidence on the effect of cold temperature on mortality is available. We examined both the effects of heat and cold temperatures on daily mortality in Hong Kong for the last decade (2002-2011). A quasi-Poisson model combined with a distributed lag non-linear model was used to assess the non-linear and delayed effects of temperatures on cause-specific and age-specific mortality. Non-linear effects of temperature on mortality were identified. The relative risk of non-accidental mortality associated with cold temperature (11.1 °C, 1st percentile of temperature) relative to 19.4 °C (25th percentile of temperature) was 1.17 (95% confidence interval (CI): 1.04, 1.29) for lags 0-13. The relative risk of non-accidental mortality associated with high temperature (31.5 °C, 99th percentile of temperature) relative to 27.8 °C (75th percentile of temperature) was 1.09 (95% CI: 1.03, 1.17) for lags 0-3. In Hong Kong, extreme cold and hot temperatures increased the risk of mortality. The effect of cold lasted longer and greater than that of heat. People older than 75 years were the most vulnerable group to cold temperature, while people aged 65-74 were the most vulnerable group to hot temperature. Our findings may have implications for developing intervention strategies for extreme cold and hot temperatures.

Susceptibility to Heat-Related Fluid and Electrolyte Imbalance Emergency Department Visits in Atlanta, Georgia, USA.

PubMed

Heidari, Leila; Winquist, Andrea; Klein, Mitchel; O'Lenick, Cassandra; Grundstein, Andrew; Ebelt Sarnat, Stefanie

2016-10-02

Identification of populations susceptible to heat effects is critical for targeted prevention and more accurate risk assessment. Fluid and electrolyte imbalance (FEI) may provide an objective indicator of heat morbidity. Data on daily ambient temperature and FEI emergency department (ED) visits were collected in Atlanta, Georgia, USA during 1993-2012. Associations of warm-season same-day temperatures and FEI ED visits were estimated using Poisson generalized linear models. Analyses explored associations between FEI ED visits and various temperature metrics (maximum, minimum, average, and diurnal change in ambient temperature, apparent temperature, and heat index) modeled using linear, quadratic, and cubic terms to allow for non-linear associations. Effect modification by potential determinants of heat susceptibility (sex; race; comorbid congestive heart failure, kidney disease, and diabetes; and neighborhood poverty and education levels) was assessed via stratification. Higher warm-season ambient temperature was significantly associated with FEI ED visits, regardless of temperature metric used. Stratified analyses suggested heat-related risks for all populations, but particularly for males. This work highlights the utility of FEI as an indicator of heat morbidity, the health threat posed by warm-season temperatures, and the importance of considering susceptible populations in heat-health research.

Measurement of the viscoelastic compliance of the eustachian tube using a modified forced-response test.

PubMed

Ghadiali, Samir N; Federspiel, William J; Swarts, J Douglas; Doyle, William J

2002-01-01

Eustachian tube compliance (ETC) was suggested to be an important determinate of function. Previous attempts to quantify ETC used summary measures that are not clearly related to the physical properties of the system. Here, we present a new method for measuring ETC that conforms more closely to the engineering definition of compliance. The forced response test was modified to include oscillations in applied flow after the forced tubal opening. Pressure and flow were recorded during the standard and modified test in 12 anesthetized cynomolgus monkeys. The resulting pressure-flow, hysteresis loops were compared with those predicted by a simple fluid-structure model of the Eustachian tube with linear-elastic or viscoelastic properties. The tubal compliance index (TCI) and a viscoelastic compliance (C(v)) were calculated from these data for each monkey. The behavior of a viscoelastic, but not a linear elastic model accurately reproduced the experimental data for the monkey. The TCI and C(v) were linearly related, but the shared variance in these measures was only 63%. This new method for measuring ETC captures all information contained in the traditional TCI, but also provides information regarding the contribution of wall viscosity to Eustachian tube mechanics.

Susceptibility to Heat-Related Fluid and Electrolyte Imbalance Emergency Department Visits in Atlanta, Georgia, USA

PubMed Central

Heidari, Leila; Winquist, Andrea; Klein, Mitchel; O’Lenick, Cassandra; Grundstein, Andrew; Ebelt Sarnat, Stefanie

2016-01-01

Identification of populations susceptible to heat effects is critical for targeted prevention and more accurate risk assessment. Fluid and electrolyte imbalance (FEI) may provide an objective indicator of heat morbidity. Data on daily ambient temperature and FEI emergency department (ED) visits were collected in Atlanta, Georgia, USA during 1993–2012. Associations of warm-season same-day temperatures and FEI ED visits were estimated using Poisson generalized linear models. Analyses explored associations between FEI ED visits and various temperature metrics (maximum, minimum, average, and diurnal change in ambient temperature, apparent temperature, and heat index) modeled using linear, quadratic, and cubic terms to allow for non-linear associations. Effect modification by potential determinants of heat susceptibility (sex; race; comorbid congestive heart failure, kidney disease, and diabetes; and neighborhood poverty and education levels) was assessed via stratification. Higher warm-season ambient temperature was significantly associated with FEI ED visits, regardless of temperature metric used. Stratified analyses suggested heat-related risks for all populations, but particularly for males. This work highlights the utility of FEI as an indicator of heat morbidity, the health threat posed by warm-season temperatures, and the importance of considering susceptible populations in heat-health research. PMID:27706089

Effects of frequency- and direction-dependent elastic materials on linearly elastic MRE image reconstructions

NASA Astrophysics Data System (ADS)

Perreard, I. M.; Pattison, A. J.; Doyley, M.; McGarry, M. D. J.; Barani, Z.; Van Houten, E. E.; Weaver, J. B.; Paulsen, K. D.

2010-11-01

The mechanical model commonly used in magnetic resonance elastography (MRE) is linear elasticity. However, soft tissue may exhibit frequency- and direction-dependent (FDD) shear moduli in response to an induced excitation causing a purely linear elastic model to provide an inaccurate image reconstruction of its mechanical properties. The goal of this study was to characterize the effects of reconstructing FDD data using a linear elastic inversion (LEI) algorithm. Linear and FDD phantoms were manufactured and LEI images were obtained from time-harmonic MRE acquisitions with variations in frequency and driving signal amplitude. LEI responses to artificially imposed uniform phase shifts in the displacement data from both purely linear elastic and FDD phantoms were also evaluated. Of the variety of FDD phantoms considered, LEI appeared to tolerate viscoelastic data-model mismatch better than deviations caused by poroelastic and anisotropic mechanical properties in terms of visual image contrast. However, the estimated shear modulus values were substantially incorrect relative to independent mechanical measurements even in the successful viscoelastic cases and the variations in mean values with changes in experimental conditions associated with uniform phase shifts, driving signal frequency and amplitude were unpredictable. Overall, use of LEI to reconstruct data acquired in phantoms with FDD material properties provided biased results under the best conditions and significant artifacts in the worst cases. These findings suggest that the success with which LEI is applied to MRE data in tissue will depend on the underlying mechanical characteristics of the tissues and/or organs systems of clinical interest.

Effects of frequency- and direction-dependent elastic materials on linearly elastic MRE image reconstructions.

PubMed

Perreard, I M; Pattison, A J; Doyley, M; McGarry, M D J; Barani, Z; Van Houten, E E; Weaver, J B; Paulsen, K D

2010-11-21

The mechanical model commonly used in magnetic resonance elastography (MRE) is linear elasticity. However, soft tissue may exhibit frequency- and direction-dependent (FDD) shear moduli in response to an induced excitation causing a purely linear elastic model to provide an inaccurate image reconstruction of its mechanical properties. The goal of this study was to characterize the effects of reconstructing FDD data using a linear elastic inversion (LEI) algorithm. Linear and FDD phantoms were manufactured and LEI images were obtained from time-harmonic MRE acquisitions with variations in frequency and driving signal amplitude. LEI responses to artificially imposed uniform phase shifts in the displacement data from both purely linear elastic and FDD phantoms were also evaluated. Of the variety of FDD phantoms considered, LEI appeared to tolerate viscoelastic data-model mismatch better than deviations caused by poroelastic and anisotropic mechanical properties in terms of visual image contrast. However, the estimated shear modulus values were substantially incorrect relative to independent mechanical measurements even in the successful viscoelastic cases and the variations in mean values with changes in experimental conditions associated with uniform phase shifts, driving signal frequency and amplitude were unpredictable. Overall, use of LEI to reconstruct data acquired in phantoms with FDD material properties provided biased results under the best conditions and significant artifacts in the worst cases. These findings suggest that the success with which LEI is applied to MRE data in tissue will depend on the underlying mechanical characteristics of the tissues and/or organs systems of clinical interest.

Evaluation of force-velocity and power-velocity relationship of arm muscles.

PubMed

Sreckovic, Sreten; Cuk, Ivan; Djuric, Sasa; Nedeljkovic, Aleksandar; Mirkov, Dragan; Jaric, Slobodan

2015-08-01

A number of recent studies have revealed an approximately linear force-velocity (F-V) and, consequently, a parabolic power-velocity (P-V) relationship of multi-joint tasks. However, the measurement characteristics of their parameters have been neglected, particularly those regarding arm muscles, which could be a problem for using the linear F-V model in both research and routine testing. Therefore, the aims of the present study were to evaluate the strength, shape, reliability, and concurrent validity of the F-V relationship of arm muscles. Twelve healthy participants performed maximum bench press throws against loads ranging from 20 to 70 % of their maximum strength, and linear regression model was applied on the obtained range of F and V data. One-repetition maximum bench press and medicine ball throw tests were also conducted. The observed individual F-V relationships were exceptionally strong (r = 0.96-0.99; all P < 0.05) and fairly linear, although it remains unresolved whether a polynomial fit could provide even stronger relationships. The reliability of parameters obtained from the linear F-V regressions proved to be mainly high (ICC > 0.80), while their concurrent validity regarding directly measured F, P, and V ranged from high (for maximum F) to medium-to-low (for maximum P and V). The findings add to the evidence that the linear F-V and, consequently, parabolic P-V models could be used to study the mechanical properties of muscular systems, as well as to design a relatively simple, reliable, and ecologically valid routine test of the muscle ability of force, power, and velocity production.

Flow Rates Measurement and Uncertainty Analysis in Multiple-Zone Water-Injection Wells from Fluid Temperature Profiles

PubMed Central

Reges, José E. O.; Salazar, A. O.; Maitelli, Carla W. S. P.; Carvalho, Lucas G.; Britto, Ursula J. B.

2016-01-01

This work is a contribution to the development of flow sensors in the oil and gas industry. It presents a methodology to measure the flow rates into multiple-zone water-injection wells from fluid temperature profiles and estimate the measurement uncertainty. First, a method to iteratively calculate the zonal flow rates using the Ramey (exponential) model was described. Next, this model was linearized to perform an uncertainty analysis. Then, a computer program to calculate the injected flow rates from experimental temperature profiles was developed. In the experimental part, a fluid temperature profile from a dual-zone water-injection well located in the Northeast Brazilian region was collected. Thus, calculated and measured flow rates were compared. The results proved that linearization error is negligible for practical purposes and the relative uncertainty increases as the flow rate decreases. The calculated values from both the Ramey and linear models were very close to the measured flow rates, presenting a difference of only 4.58 m³/d and 2.38 m³/d, respectively. Finally, the measurement uncertainties from the Ramey and linear models were equal to 1.22% and 1.40% (for injection zone 1); 10.47% and 9.88% (for injection zone 2). Therefore, the methodology was successfully validated and all objectives of this work were achieved. PMID:27420068

Vascular mechanics of the coronary artery

NASA Technical Reports Server (NTRS)

Veress, A. I.; Vince, D. G.; Anderson, P. M.; Cornhill, J. F.; Herderick, E. E.; Klingensmith, J. D.; Kuban, B. D.; Greenberg, N. L.; Thomas, J. D.

2000-01-01

This paper describes our research into the vascular mechanics of the coronary artery and plaque. The three sections describe the determination of arterial mechanical properties using intravascular ultrasound (IVUS), a constitutive relation for the arterial wall, and finite element method (FEM) models of the arterial wall and atheroma. METHODS: Inflation testing of porcine left anterior descending coronary arteries was conducted. The changes in the vessel geometry were monitored using IVUS, and intracoronary pressure was recorded using a pressure transducer. The creep and quasistatic stress/strain responses were determined. A Standard Linear Solid (SLS) was modified to reproduce the non-linear elastic behavior of the arterial wall. This Standard Non-linear Solid (SNS) was implemented into an axisymetric thick-walled cylinder numerical model. Finite element analysis models were created for five age groups and four levels of stenosis using the Pathobiological Determinants of Atherosclerosis Youth (PDAY) database. RESULTS: The arteries exhibited non-linear elastic behavior. The total tissue creep strain was epsilon creep = 0.082 +/- 0.018 mm/mm. The numerical model could reproduce both the non-linearity of the porcine data and time dependent behavior of the arterial wall found in the literature with a correlation coefficient of 0.985. Increasing age had a strong positive correlation with the shoulder stress level, (r = 0.95). The 30% stenosis had the highest shoulder stress due to the combination of a fully formed lipid pool and a thin cap. CONCLUSIONS: Studying the solid mechanics of the arterial wall and the atheroma provide important insights into the mechanisms involved in plaque rupture.

Non-linear, connectivity and threshold-dominated runoff-generation controls DOC and heavy metal export in a small peat catchment

NASA Astrophysics Data System (ADS)

Birkel, Christian; Broder, Tanja; Biester, Harald

2017-04-01

Peat soils act as important carbon sinks, but they also release large amounts of dissolved organic carbon (DOC) to the aquatic system. The DOC export is strongly tied to the export of soluble heavy metals. The accumulation of potentially toxic substances due to anthropogenic activities, and their natural export from peat soils to the aquatic system is an important health and environmental issue. However, limited knowledge exists as to how much of these substances are mobilized, how they are mobilized in terms of flow pathways and under which hydrometeorological conditions. In this study, we report from a combined experimental and modelling effort to provide greater process understanding from a small, lead (Pb) and arsenic (As) contaminated upland peat catchment in northwestern Germany. We developed a minimally parameterized, but process-based, coupled hydrology-biogeochemistry model applied to simulate detailed hydrometric and biogeochemical data. The model was based on an initial data mining analysis, in combination with regression relationships of discharge, DOC and element export. We assessed the internal model DOC-processing based on stream-DOC hysteresis patterns and 3-hourly time step groundwater level and soil DOC data (not used for calibration as an independent model test) for two consecutive summer periods in 2013 and 2014. We found that Pb and As mobilization can be efficiently predicted from DOC transport alone, but Pb showed a significant non-linear relationship with DOC, while As was linearly related to DOC. The relatively parsimonious model (nine calibrated parameters in total) showed the importance of non-linear and rapid near-surface runoff-generation mechanisms that caused around 60% of simulated DOC load. The total load was high even though these pathways were only activated during storm events on average 30% of the monitoring time - as also shown by the experimental data. Overall, the drier period 2013 resulted in increased nonlinearity, but exported less DOC (115 kg C ha-1 yr-1 ± 11 kg C ha-1 yr-1) compared to the equivalent but wetter period in 2014 (189 kg C ha-1 yr-1 ± 38 kg C ha-1 yr-1). The exceedance of a critical water table threshold (-10 cm) triggered a rapid near-surface runoff response with associated higher DOC transport connecting all available DOC pools, and with subsequent dilution. We conclude that the combination of detailed experimental work with relatively simple, coupled hydrology-biogeochemistry models allowed not only the model to be internally constrained, but also provided important insight into how DOC and tightly coupled heavy metals are mobilized.

Modeling Reader and Text Interactions during Narrative Comprehension: A Test of the Lexical Quality Hypothesis

ERIC Educational Resources Information Center

Hamilton, Stephen T.; Freed, Erin M.; Long, Debra L.

2013-01-01

The goal of this study was to examine predictions derived from the Lexical Quality Hypothesis regarding relations among word decoding, working-memory capacity, and the ability to integrate new concepts into a developing discourse representation. Hierarchical Linear Modeling was used to quantify the effects of three text properties (length,…

A parametric study of the drift-tearing mode using an extended-magnetohydrodynamic model

DOE PAGES

King, Jacob R.; Kruger, S. E.

2014-10-24

The linear, collisional, constant-ψ drift-tearing mode is analyzed for different regimes of the plasma-β, ion-skin-depth parameter space with an unreduced, extended-magnetohydrodynamic model. Here, new dispersion relations are found at moderate plasma β and previous drift-tearing results are classified as applicable at small plasma β.

Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data

USGS Publications Warehouse

Junttila, Virpi; Kauranne, Tuomo; Finley, Andrew O.; Bradford, John B.

2015-01-01

Modern operational forest inventory often uses remotely sensed data that cover the whole inventory area to produce spatially explicit estimates of forest properties through statistical models. The data obtained by airborne light detection and ranging (LiDAR) correlate well with many forest inventory variables, such as the tree height, the timber volume, and the biomass. To construct an accurate model over thousands of hectares, LiDAR data must be supplemented with several hundred field sample measurements of forest inventory variables. This can be costly and time consuming. Different LiDAR-data-based and spatial-data-based sampling designs can reduce the number of field sample plots needed. However, problems arising from the features of the LiDAR data, such as a large number of predictors compared with the sample size (overfitting) or a strong correlation among predictors (multicollinearity), may decrease the accuracy and precision of the estimates and predictions. To overcome these problems, a Bayesian linear model with the singular value decomposition of predictors, combined with regularization, is proposed. The model performance in predicting different forest inventory variables is verified in ten inventory areas from two continents, where the number of field sample plots is reduced using different sampling designs. The results show that, with an appropriate field plot selection strategy and the proposed linear model, the total relative error of the predicted forest inventory variables is only 5%–15% larger using 50 field sample plots than the error of a linear model estimated with several hundred field sample plots when we sum up the error due to both the model noise variance and the model’s lack of fit.

Assessing the relative potency of (S)- and (R)-warfarin with a new PK-PD model, in relation to VKORC1 genotypes.

PubMed

Ferrari, Myriam; Pengo, Vittorio; Barolo, Massimiliano; Bezzo, Fabrizio; Padrini, Roberto

2017-06-01

The purpose of this study is to develop a new pharmacokinetic-pharmacodynamic (PK-PD) model to characterise the contribution of (S)- and (R)-warfarin to the anticoagulant effect on patients in treatment with rac-warfarin. Fifty-seven patients starting warfarin (W) therapy were studied, from the first dose and during chronic treatment at INR stabilization. Plasma concentrations of (S)- and (R)-W and INRs were measured 12, 36 and 60 h after the first dose and at steady state 12-14 h after dosing. Patients were also genotyped for the G>A VKORC1 polymorphism. The PK-PD model assumed a linear relationship between W enantiomer concentration and INR and included a scaling factor k to account for a different potency of (R)-W. Two parallel compartment chains with different transit times (MTT 1 and MTT 2 ) were used to model the delay in the W effect. PD parameters were estimated with the maximum likelihood approach. The model satisfactorily described the mean time-course of INR, both after the initial dose and during long-term treatment. (R)-W contributed to the rac-W anticoagulant effect with a potency of about 27% that of (S)-W. This effect was independent of VKORC1 genotype. As expected, the slope of the PK/PD linear correlation increased stepwise from GG to GA and from GA to AA VKORC1 genotype (0.71, 0.90 and 1.49, respectively). Our PK-PD linear model can quantify the partial pharmacodynamic activity of (R)-W in patients contemporaneously exposed to therapeutic (S)-W plasma levels. This concept may be useful in improving the performance of future algorithms aiming at identifying the most appropriate W maintenance dose.

Relative Biological Effectiveness of HZE Particles for Chromosomal Exchanges and Other Surrogate Cancer Risk Endpoints.

PubMed

Cacao, Eliedonna; Hada, Megumi; Saganti, Premkumar B; George, Kerry A; Cucinotta, Francis A

2016-01-01

The biological effects of high charge and energy (HZE) particle exposures are of interest in space radiation protection of astronauts and cosmonauts, and estimating secondary cancer risks for patients undergoing Hadron therapy for primary cancers. The large number of particles types and energies that makeup primary or secondary radiation in HZE particle exposures precludes tumor induction studies in animal models for all but a few particle types and energies, thus leading to the use of surrogate endpoints to investigate the details of the radiation quality dependence of relative biological effectiveness (RBE) factors. In this report we make detailed RBE predictions of the charge number and energy dependence of RBE's using a parametric track structure model to represent experimental results for the low dose response for chromosomal exchanges in normal human lymphocyte and fibroblast cells with comparison to published data for neoplastic transformation and gene mutation. RBE's are evaluated against acute doses of γ-rays for doses near 1 Gy. Models that assume linear or non-targeted effects at low dose are considered. Modest values of RBE (<10) are found for simple exchanges using a linear dose response model, however in the non-targeted effects model for fibroblast cells large RBE values (>10) are predicted at low doses <0.1 Gy. The radiation quality dependence of RBE's against the effects of acute doses γ-rays found for neoplastic transformation and gene mutation studies are similar to those found for simple exchanges if a linear response is assumed at low HZE particle doses. Comparisons of the resulting model parameters to those used in the NASA radiation quality factor function are discussed.

Relative Biological Effectiveness of HZE Particles for Chromosomal Exchanges and Other Surrogate Cancer Risk Endpoints

DOE PAGES

Cacao, Eliedonna; Hada, Megumi; Saganti, Premkumar B.; ...

2016-04-25

The biological effects of high charge and energy (HZE) particle exposures are of interest in space radiation protection of astronauts and cosmonauts, and estimating secondary cancer risks for patients undergoing Hadron therapy for primary cancers. The large number of particles types and energies that makeup primary or secondary radiation in HZE particle exposures precludes tumor induction studies in animal models for all but a few particle types and energies, thus leading to the use of surrogate endpoints to investigate the details of the radiation quality dependence of relative biological effectiveness (RBE) factors. In this report we make detailed RBE predictionsmore » of the charge number and energy dependence of RBE’s using a parametric track structure model to represent experimental results for the low dose response for chromosomal exchanges in normal human lymphocyte and fibroblast cells with comparison to published data for neoplastic transformation and gene mutation. RBE’s are evaluated against acute doses of γ-rays for doses near 1 Gy. Models that assume linear or non-targeted effects at low dose are considered. Modest values of RBE (<10) are found for simple exchanges using a linear dose response model, however in the non-targeted effects model for fibroblast cells large RBE values (>10) are predicted at low doses <0.1 Gy. The radiation quality dependence of RBE’s against the effects of acute doses γ-rays found for neoplastic transformation and gene mutation studies are similar to those found for simple exchanges if a linear response is assumed at low HZE particle doses. Finally, we discuss comparisons of the resulting model parameters to those used in the NASA radiation quality factor function.« less

Relative Biological Effectiveness of HZE Particles for Chromosomal Exchanges and Other Surrogate Cancer Risk Endpoints

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

Cacao, Eliedonna; Hada, Megumi; Saganti, Premkumar B.

The biological effects of high charge and energy (HZE) particle exposures are of interest in space radiation protection of astronauts and cosmonauts, and estimating secondary cancer risks for patients undergoing Hadron therapy for primary cancers. The large number of particles types and energies that makeup primary or secondary radiation in HZE particle exposures precludes tumor induction studies in animal models for all but a few particle types and energies, thus leading to the use of surrogate endpoints to investigate the details of the radiation quality dependence of relative biological effectiveness (RBE) factors. In this report we make detailed RBE predictionsmore » of the charge number and energy dependence of RBE’s using a parametric track structure model to represent experimental results for the low dose response for chromosomal exchanges in normal human lymphocyte and fibroblast cells with comparison to published data for neoplastic transformation and gene mutation. RBE’s are evaluated against acute doses of γ-rays for doses near 1 Gy. Models that assume linear or non-targeted effects at low dose are considered. Modest values of RBE (<10) are found for simple exchanges using a linear dose response model, however in the non-targeted effects model for fibroblast cells large RBE values (>10) are predicted at low doses <0.1 Gy. The radiation quality dependence of RBE’s against the effects of acute doses γ-rays found for neoplastic transformation and gene mutation studies are similar to those found for simple exchanges if a linear response is assumed at low HZE particle doses. Finally, we discuss comparisons of the resulting model parameters to those used in the NASA radiation quality factor function.« less

Comparison of stream invertebrate response models for bioassessment metric

USGS Publications Warehouse

Waite, Ian R.; Kennen, Jonathan G.; May, Jason T.; Brown, Larry R.; Cuffney, Thomas F.; Jones, Kimberly A.; Orlando, James L.

2012-01-01

We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R2 from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R2 values than RICHTOL for the two regions tested. Modeled O/E R2 values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.

Mapping the Dark Matter with 6dFGS

NASA Astrophysics Data System (ADS)

Mould, Jeremy R.; Magoulas, C.; Springob, C.; Colless, M.; Jones, H.; Lucey, J.; Erdogdu, P.; Campbell, L.

2012-05-01

Fundamental plane distances from the 6dF Galaxy Redshift Survey are fitted to a model of the density field within 200/h Mpc. Likelihood is maximized for a single value of the local galaxy density, as expected in linear theory for the relation between overdensity and peculiar velocity. The dipole of the inferred southern hemisphere early type galaxy peculiar velocities is calculated within 150/h Mpc, before and after correction for the individual galaxy velocities predicted by the model. The former agrees with that obtained by other peculiar velocity studies (e.g. SFI++). The latter is only of order 150 km/sec and consistent with the expectations of the standard cosmological model and recent forecasts of the cosmic mach number, which show linearly declining bulk flow with increasing scale.

Charge-based MOSFET model based on the Hermite interpolation polynomial

NASA Astrophysics Data System (ADS)

Colalongo, Luigi; Richelli, Anna; Kovacs, Zsolt

2017-04-01

An accurate charge-based compact MOSFET model is developed using the third order Hermite interpolation polynomial to approximate the relation between surface potential and inversion charge in the channel. This new formulation of the drain current retains the same simplicity of the most advanced charge-based compact MOSFET models such as BSIM, ACM and EKV, but it is developed without requiring the crude linearization of the inversion charge. Hence, the asymmetry and the non-linearity in the channel are accurately accounted for. Nevertheless, the expression of the drain current can be worked out to be analytically equivalent to BSIM, ACM and EKV. Furthermore, thanks to this new mathematical approach the slope factor is rigorously defined in all regions of operation and no empirical assumption is required.

Numerical modelling of instantaneous plate tectonics

NASA Technical Reports Server (NTRS)

Minster, J. B.; Haines, E.; Jordan, T. H.; Molnar, P.

1974-01-01

Assuming lithospheric plates to be rigid, 68 spreading rates, 62 fracture zones trends, and 106 earthquake slip vectors are systematically inverted to obtain a self-consistent model of instantaneous relative motions for eleven major plates. The inverse problem is linearized and solved iteratively by a maximum-likelihood procedure. Because the uncertainties in the data are small, Gaussian statistics are shown to be adequate. The use of a linear theory permits (1) the calculation of the uncertainties in the various angular velocity vectors caused by uncertainties in the data, and (2) quantitative examination of the distribution of information within the data set. The existence of a self-consistent model satisfying all the data is strong justification of the rigid plate assumption. Slow movement between North and South America is shown to be resolvable.

Linear complementarity formulation for 3D frictional sliding problems

USGS Publications Warehouse

Kaven, Joern; Hickman, Stephen H.; Davatzes, Nicholas C.; Mutlu, Ovunc

2012-01-01

Frictional sliding on quasi-statically deforming faults and fractures can be modeled efficiently using a linear complementarity formulation. We review the formulation in two dimensions and expand the formulation to three-dimensional problems including problems of orthotropic friction. This formulation accurately reproduces analytical solutions to static Coulomb friction sliding problems. The formulation accounts for opening displacements that can occur near regions of non-planarity even under large confining pressures. Such problems are difficult to solve owing to the coupling of relative displacements and tractions; thus, many geomechanical problems tend to neglect these effects. Simple test cases highlight the importance of including friction and allowing for opening when solving quasi-static fault mechanics models. These results also underscore the importance of considering the effects of non-planarity in modeling processes associated with crustal faulting.

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…

Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models.

PubMed

Yock, Adam D; Rao, Arvind; Dong, Lei; Beadle, Beth M; Garden, Adam S; Kudchadker, Rajat J; Court, Laurence E

2014-05-01

The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: -11.6%-23.8%) and 14.6% (range: -7.3%-27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: -6.8%-40.3%) and 13.1% (range: -1.5%-52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: -11.1%-20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method.

PubMed

Liang, Gaozhen; Dong, Chunwang; Hu, Bin; Zhu, Hongkai; Yuan, Haibo; Jiang, Yongwen; Hao, Guoshuang

2018-05-18

Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L * ) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

Analytical potential-density pairs for bars

NASA Astrophysics Data System (ADS)

Vogt, D.; Letelier, P. S.

2010-11-01

An identity that relates multipolar solutions of the Einstein equations to Newtonian potentials of bars with linear densities proportional to Legendre polynomials is used to construct analytical potential-density pairs of infinitesimally thin bars with a given linear density profile. By means of a suitable transformation, softened bars that are free of singularities are also obtained. As an application we study the equilibrium points and stability for the motion of test particles in the gravitational field for three models of rotating bars.

Development of a Multiple Linear Regression Model to Forecast Facility Electrical Consumption at an Air Force Base.

DTIC Science & Technology

1981-09-01

corresponds to the same square footage that consumed the electrical energy. 3. The basic assumptions of multiple linear regres- sion, as enumerated in...7. Data related to the sample of bases is assumed to be representative of bases in the population. Limitations Basic limitations on this research were... Ratemaking --Overview. Rand Report R-5894, Santa Monica CA, May 1977. Chatterjee, Samprit, and Bertram Price. Regression Analysis by Example. New York: John

An algebraic method for constructing stable and consistent autoregressive filters

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

Harlim, John, E-mail: jharlim@psu.edu; Department of Meteorology, the Pennsylvania State University, University Park, PA 16802; Hong, Hoon, E-mail: hong@ncsu.edu

2015-02-15

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides amore » discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.« less

Generation of High Frequency Response in a Dynamically Loaded, Nonlinear Soil Column

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

Spears, Robert Edward; Coleman, Justin Leigh

2015-08-01

Detailed guidance on linear seismic analysis of soil columns is provided in “Seismic Analysis of Safety-Related Nuclear Structures and Commentary (ASCE 4, 1998),” which is currently under revision. A new Appendix in ASCE 4-2014 (draft) is being added to provide guidance for nonlinear time domain analysis which includes evaluation of soil columns. When performing linear analysis, a given soil column is typically evaluated with a linear, viscous damped constitutive model. When submitted to a sine wave motion, this constitutive model produces a smooth hysteresis loop. For nonlinear analysis, the soil column can be modelled with an appropriate nonlinear hysteretic soilmore » model. For the model in this paper, the stiffness and energy absorption result from a defined post yielding shear stress versus shear strain curve. This curve is input with tabular data points. When submitted to a sine wave motion, this constitutive model produces a hysteresis loop that looks similar in shape to the input tabular data points on the sides with discontinuous, pointed ends. This paper compares linear and nonlinear soil column results. The results show that the nonlinear analysis produces additional high frequency response. The paper provides additional study to establish what portion of the high frequency response is due to numerical noise associated with the tabular input curve and what portion is accurately caused by the pointed ends of the hysteresis loop. Finally, the paper shows how the results are changed when a significant structural mass is added to the top of the soil column.« less

Threshold and Beyond: Modeling The Intensity Dependence of Auditory Responses

PubMed Central

2007-01-01

In many studies of auditory-evoked responses to low-intensity sounds, the response amplitude appears to increase roughly linearly with the sound level in decibels (dB), corresponding to a logarithmic intensity dependence. But the auditory system is assumed to be linear in the low-intensity limit. The goal of this study was to resolve the seeming contradiction. Based on assumptions about the rate-intensity functions of single auditory-nerve fibers and the pattern of cochlear excitation caused by a tone, a model for the gross response of the population of auditory nerve fibers was developed. In accordance with signal detection theory, the model denies the existence of a threshold. This implies that regarding the detection of a significant stimulus-related effect, a reduction in sound intensity can always be compensated for by increasing the measurement time, at least in theory. The model suggests that the gross response is proportional to intensity when the latter is low (range I), and a linear function of sound level at higher intensities (range III). For intensities in between, it is concluded that noisy experimental data may provide seemingly irrefutable evidence of a linear dependence on sound pressure (range II). In view of the small response amplitudes that are to be expected for intensity range I, direct observation of the predicted proportionality with intensity will generally be a challenging task for an experimenter. Although the model was developed for the auditory nerve, the basic conclusions are probably valid for higher levels of the auditory system, too, and might help to improve models for loudness at threshold. PMID:18008105

Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images

NASA Astrophysics Data System (ADS)

Fernández-Manso, O.; Fernández-Manso, A.; Quintano, C.

2014-09-01

Aboveground biomass (AGB) estimation from optical satellite data is usually based on regression models of original or synthetic bands. To overcome the poor relation between AGB and spectral bands due to mixed-pixels when a medium spatial resolution sensor is considered, we propose to base the AGB estimation on fraction images from Linear Spectral Mixture Analysis (LSMA). Our study area is a managed Mediterranean pine woodland (Pinus pinaster Ait.) in central Spain. A total of 1033 circular field plots were used to estimate AGB from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) optical data. We applied Pearson correlation statistics and stepwise multiple regression to identify suitable predictors from the set of variables of original bands, fraction imagery, Normalized Difference Vegetation Index and Tasselled Cap components. Four linear models and one nonlinear model were tested. A linear combination of ASTER band 2 (red, 0.630-0.690 μm), band 8 (short wave infrared 5, 2.295-2.365 μm) and green vegetation fraction (from LSMA) was the best AGB predictor (Radj2=0.632, the root-mean-squared error of estimated AGB was 13.3 Mg ha-1 (or 37.7%), resulting from cross-validation), rather than other combinations of the above cited independent variables. Results indicated that using ASTER fraction images in regression models improves the AGB estimation in Mediterranean pine forests. The spatial distribution of the estimated AGB, based on a multiple linear regression model, may be used as baseline information for forest managers in future studies, such as quantifying the regional carbon budget, fuel accumulation or monitoring of management practices.

Circuit models and three-dimensional electromagnetic simulations of a 1-MA linear transformer driver stage

NASA Astrophysics Data System (ADS)

Rose, D. V.; Miller, C. L.; Welch, D. R.; Clark, R. E.; Madrid, E. A.; Mostrom, C. B.; Stygar, W. A.; Lechien, K. R.; Mazarakis, M. A.; Langston, W. L.; Porter, J. L.; Woodworth, J. R.

2010-09-01

A 3D fully electromagnetic (EM) model of the principal pulsed-power components of a high-current linear transformer driver (LTD) has been developed. LTD systems are a relatively new modular and compact pulsed-power technology based on high-energy density capacitors and low-inductance switches located within a linear-induction cavity. We model 1-MA, 100-kV, 100-ns rise-time LTD cavities [A. A. Kim , Phys. Rev. ST Accel. Beams 12, 050402 (2009)PRABFM1098-440210.1103/PhysRevSTAB.12.050402] which can be used to drive z-pinch and material dynamics experiments. The model simulates the generation and propagation of electromagnetic power from individual capacitors and triggered gas switches to a radially symmetric output line. Multiple cavities, combined to provide voltage addition, drive a water-filled coaxial transmission line. A 3D fully EM model of a single 1-MA 100-kV LTD cavity driving a simple resistive load is presented and compared to electrical measurements. A new model of the current loss through the ferromagnetic cores is developed for use both in circuit representations of an LTD cavity and in the 3D EM simulations. Good agreement between the measured core current, a simple circuit model, and the 3D simulation model is obtained. A 3D EM model of an idealized ten-cavity LTD accelerator is also developed. The model results demonstrate efficient voltage addition when driving a matched impedance load, in good agreement with an idealized circuit model.

Frequency Response of Synthetic Vocal Fold Models with Linear and Nonlinear Material Properties

PubMed Central

Shaw, Stephanie M.; Thomson, Scott L.; Dromey, Christopher; Smith, Simeon

2014-01-01

Purpose The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency during anterior-posterior stretching. Method Three materially linear and three materially nonlinear models were created and stretched up to 10 mm in 1 mm increments. Phonation onset pressure (Pon) and fundamental frequency (F0) at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1 mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Results Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Conclusions Nonlinear synthetic models appear to more accurately represent the human vocal folds than linear models, especially with respect to F0 response. PMID:22271874

Frequency response of synthetic vocal fold models with linear and nonlinear material properties.

PubMed

Shaw, Stephanie M; Thomson, Scott L; Dromey, Christopher; Smith, Simeon

2012-10-01

The purpose of this study was to create synthetic vocal fold models with nonlinear stress-strain properties and to investigate the effect of linear versus nonlinear material properties on fundamental frequency (F0) during anterior-posterior stretching. Three materially linear and 3 materially nonlinear models were created and stretched up to 10 mm in 1-mm increments. Phonation onset pressure (Pon) and F0 at Pon were recorded for each length. Measurements were repeated as the models were relaxed in 1-mm increments back to their resting lengths, and tensile tests were conducted to determine the stress-strain responses of linear versus nonlinear models. Nonlinear models demonstrated a more substantial frequency response than did linear models and a more predictable pattern of F0 increase with respect to increasing length (although range was inconsistent across models). Pon generally increased with increasing vocal fold length for nonlinear models, whereas for linear models, Pon decreased with increasing length. Nonlinear synthetic models appear to more accurately represent the human vocal folds than do linear models, especially with respect to F0 response.

Step-response of a torsional device with multiple discontinuous non-linearities: Formulation of a vibratory experiment

NASA Astrophysics Data System (ADS)

Krak, Michael D.; Dreyer, Jason T.; Singh, Rajendra

2016-03-01

A vehicle clutch damper is intentionally designed to contain multiple discontinuous non-linearities, such as multi-staged springs, clearances, pre-loads, and multi-staged friction elements. The main purpose of this practical torsional device is to transmit a wide range of torque while isolating torsional vibration between an engine and transmission. Improved understanding of the dynamic behavior of the device could be facilitated by laboratory measurement, and thus a refined vibratory experiment is proposed. The experiment is conceptually described as a single degree of freedom non-linear torsional system that is excited by an external step torque. The single torsional inertia (consisting of a shaft and torsion arm) is coupled to ground through parallel production clutch dampers, which are characterized by quasi-static measurements provided by the manufacturer. Other experimental objectives address physical dimensions, system actuation, flexural modes, instrumentation, and signal processing issues. Typical measurements show that the step response of the device is characterized by three distinct non-linear regimes (double-sided impact, single-sided impact, and no-impact). Each regime is directly related to the non-linear features of the device and can be described by peak angular acceleration values. Predictions of a simplified single degree of freedom non-linear model verify that the experiment performs well and as designed. Accordingly, the benchmark measurements could be utilized to validate non-linear models and simulation codes, as well as characterize dynamic parameters of the device including its dissipative properties.

Extending Linear Models to Non-Linear Contexts: An In-Depth Study about Two University Students' Mathematical Productions

ERIC Educational Resources Information Center

Esteley, Cristina; Villarreal, Monica; Alagia, Humberto

2004-01-01

This research report presents a study of the work of agronomy majors in which an extension of linear models to non-linear contexts can be observed. By linear models we mean the model y=a.x+b, some particular representations of direct proportionality and the diagram for the rule of three. Its presence and persistence in different types of problems…

Short-term effects of meteorological factors on hand, foot and mouth disease among children in Shenzhen, China: Non-linearity, threshold and interaction.

PubMed

Zhang, Zhen; Xie, Xu; Chen, Xiliang; Li, Yuan; Lu, Yan; Mei, Shujiang; Liao, Yuxue; Lin, Hualiang

2016-01-01

Various meteorological factors have been associated with hand, foot and mouth disease (HFMD) among children; however, fewer studies have examined the non-linearity and interaction among the meteorological factors. A generalized additive model with a log link allowing Poisson auto-regression and over-dispersion was applied to investigate the short-term effects daily meteorological factors on children HFMD with adjustment of potential confounding factors. We found positive effects of mean temperature and wind speed, the excess relative risk (ERR) was 2.75% (95% CI: 1.98%, 3.53%) for one degree increase in daily mean temperature on lag day 6, and 3.93% (95% CI: 2.16% to 5.73%) for 1m/s increase in wind speed on lag day 3. We found a non-linear effect of relative humidity with thresholds with the low threshold at 45% and high threshold at 85%, within which there was positive effect, the ERR was 1.06% (95% CI: 0.85% to 1.27%) for 1 percent increase in relative humidity on lag day 5. No significant effect was observed for rainfall and sunshine duration. For the interactive effects, we found a weak additive interaction between mean temperature and relative humidity, and slightly antagonistic interaction between mean temperature and wind speed, and between relative humidity and wind speed in the additive models, but the interactions were not statistically significant. This study suggests that mean temperature, relative humidity and wind speed might be risk factors of children HFMD in Shenzhen, and the interaction analysis indicates that these meteorological factors might have played their roles individually. Copyright © 2015 Elsevier B.V. All rights reserved.

Mathematical model for the contribution of individual organs to non-zero y-intercepts in single and multi-compartment linear models of whole-body energy expenditure.

PubMed

Kaiyala, Karl J

2014-01-01

Mathematical models for the dependence of energy expenditure (EE) on body mass and composition are essential tools in metabolic phenotyping. EE scales over broad ranges of body mass as a non-linear allometric function. When considered within restricted ranges of body mass, however, allometric EE curves exhibit 'local linearity.' Indeed, modern EE analysis makes extensive use of linear models. Such models typically involve one or two body mass compartments (e.g., fat free mass and fat mass). Importantly, linear EE models typically involve a non-zero (usually positive) y-intercept term of uncertain origin, a recurring theme in discussions of EE analysis and a source of confounding in traditional ratio-based EE normalization. Emerging linear model approaches quantify whole-body resting EE (REE) in terms of individual organ masses (e.g., liver, kidneys, heart, brain). Proponents of individual organ REE modeling hypothesize that multi-organ linear models may eliminate non-zero y-intercepts. This could have advantages in adjusting REE for body mass and composition. Studies reveal that individual organ REE is an allometric function of total body mass. I exploit first-order Taylor linearization of individual organ REEs to model the manner in which individual organs contribute to whole-body REE and to the non-zero y-intercept in linear REE models. The model predicts that REE analysis at the individual organ-tissue level will not eliminate intercept terms. I demonstrate that the parameters of a linear EE equation can be transformed into the parameters of the underlying 'latent' allometric equation. This permits estimates of the allometric scaling of EE in a diverse variety of physiological states that are not represented in the allometric EE literature but are well represented by published linear EE analyses.

Development of a Linear Stirling System Model with Varying Heat Inputs

NASA Technical Reports Server (NTRS)

Regan, Timothy F.; Lewandowski, Edward J.

2007-01-01

The linear model of the Stirling system developed by NASA Glenn Research Center (GRC) has been extended to include a user-specified heat input. Previously developed linear models were limited to the Stirling convertor and electrical load. They represented the thermodynamic cycle with pressure factors that remained constant. The numerical values of the pressure factors were generated by linearizing GRC's nonlinear System Dynamic Model (SDM) of the convertor at a chosen operating point. The pressure factors were fixed for that operating point, thus, the model lost accuracy if a transition to a different operating point were simulated. Although the previous linear model was used in developing controllers that manipulated current, voltage, and piston position, it could not be used in the development of control algorithms that regulated hot-end temperature. This basic model was extended to include the thermal dynamics associated with a hot-end temperature that varies over time in response to external changes as well as to changes in the Stirling cycle. The linear model described herein includes not only dynamics of the piston, displacer, gas, and electrical circuit, but also the transient effects of the heater head thermal inertia. The linear version algebraically couples two separate linear dynamic models, one model of the Stirling convertor and one model of the thermal system, through the pressure factors. The thermal system model includes heat flow of heat transfer fluid, insulation loss, and temperature drops from the heat source to the Stirling convertor expansion space. The linear model was compared to a nonlinear model, and performance was very similar. The resulting linear model can be implemented in a variety of computing environments, and is suitable for analysis with classical and state space controls analysis techniques.

Accounting for time- and space-varying changes in the gravity field to improve the network adjustment of relative-gravity data

USGS Publications Warehouse

Kennedy, Jeffrey R.; Ferre, Ty P.A.

2015-01-01

The relative gravimeter is the primary terrestrial instrument for measuring spatially and temporally varying gravitational fields. The background noise of the instrument—that is, non-linear drift and random tares—typically requires some form of least-squares network adjustment to integrate data collected during a campaign that may take several days to weeks. Here, we present an approach to remove the change in the observed relative-gravity differences caused by hydrologic or other transient processes during a single campaign, so that the adjusted gravity values can be referenced to a single epoch. The conceptual approach is an example of coupled hydrogeophysical inversion, by which a hydrologic model is used to inform and constrain the geophysical forward model. The hydrologic model simulates the spatial variation of the rate of change of gravity as either a linear function of distance from an infiltration source, or using a 3-D numerical groundwater model. The linear function can be included in and solved for as part of the network adjustment. Alternatively, the groundwater model is used to predict the change of gravity at each station through time, from which the accumulated gravity change is calculated and removed from the data prior to the network adjustment. Data from a field experiment conducted at an artificial-recharge facility are used to verify our approach. Maximum gravity change due to hydrology (observed using a superconducting gravimeter) during the relative-gravity field campaigns was up to 2.6 μGal d−1, each campaign was between 4 and 6 d and one month elapsed between campaigns. The maximum absolute difference in the estimated gravity change between two campaigns, two months apart, using the standard network adjustment method and the new approach, was 5.5 μGal. The maximum gravity change between the same two campaigns was 148 μGal, and spatial variation in gravity change revealed zones of preferential infiltration and areas of relatively high groundwater storage. The accommodation for spatially varying gravity change would be most important for long-duration campaigns, campaigns with very rapid changes in gravity and (or) campaigns where especially precise observed relative-gravity differences are used in the network adjustment.

Pseudo-second order models for the adsorption of safranin onto activated carbon: comparison of linear and non-linear regression methods.

PubMed

Kumar, K Vasanth

2007-04-02

Kinetic experiments were carried out for the sorption of safranin onto activated carbon particles. The kinetic data were fitted to pseudo-second order model of Ho, Sobkowsk and Czerwinski, Blanchard et al. and Ritchie by linear and non-linear regression methods. Non-linear method was found to be a better way of obtaining the parameters involved in the second order rate kinetic expressions. Both linear and non-linear regression showed that the Sobkowsk and Czerwinski and Ritchie's pseudo-second order models were the same. Non-linear regression analysis showed that both Blanchard et al. and Ho have similar ideas on the pseudo-second order model but with different assumptions. The best fit of experimental data in Ho's pseudo-second order expression by linear and non-linear regression method showed that Ho pseudo-second order model was a better kinetic expression when compared to other pseudo-second order kinetic expressions.

Non-Linear Dynamics of Saturn's Rings

NASA Astrophysics Data System (ADS)

Esposito, L. W.

2016-12-01

Non-linear processes can explain why Saturn's rings are so active and dynamic. Ring systems differ from simple linear systems in two significant ways: 1. They are systems of granular material: where particle-to-particle collisions dominate; thus a kinetic, not a fluid description needed. Stresses are strikingly inhomogeneous and fluctuations are large compared to equilibrium. 2. They are strongly forced by resonances: which drive a non-linear response, that push the system across thresholds that lead to persistent states. Some of this non-linearity is captured in a simple Predator-Prey Model: Periodic forcing from the moon causes streamline crowding; This damps the relative velocity. About a quarter phase later, the aggregates stir the system to higher relative velocity and the limit cycle repeats each orbit, with relative velocity ranging from nearly zero to a multiple of the orbit average. Summary of Halo Results: A predator-prey model for ring dynamics produces transient structures like `straw' that can explain the halo morphology and spectroscopy: Cyclic velocity changes cause perturbed regions to reach higher collision speeds at some orbital phases, which preferentially removes small regolith particles; surrounding particles diffuse back too slowly to erase the effect: this gives the halo morphology; this requires energetic collisions (v ≈ 10m/sec, with throw distances about 200km, implying objects of scale R ≈ 20km).Transform to Duffing Eqn : With the coordinate transformation, z = M2/3, the Predator-Prey equations can be combined to form a single second-order differential equation with harmonic resonance forcing.Ring dynamics and history implications: Moon-triggered clumping explains both small and large particles at resonances. We calculate the stationary size distribution using a cell-to-cell mapping procedure that converts the phase-plane trajectories to a Markov chain. Approximating it as an asymmetric random walk with reflecting boundaries determines the power law index, using results of numerical simulations in the tidal environment. Aggregates can explain many dynamic aspects of the rings and can renew rings by shielding and recycling the material within them, depending on how long the mass is sequestered. We can ask: Are Saturn's rings a chaotic non-linear driven system?

[Scale Relativity Theory in living beings morphogenesis: fratal, determinism and chance].

PubMed

Chaline, J

2012-10-01

The Scale Relativity Theory has many biological applications from linear to non-linear and, from classical mechanics to quantum mechanics. Self-similar laws have been used as model for the description of a huge number of biological systems. Theses laws may explain the origin of basal life structures. Log-periodic behaviors of acceleration or deceleration can be applied to branching macroevolution, to the time sequences of major evolutionary leaps. The existence of such a law does not mean that the role of chance in evolution is reduced, but instead that randomness and contingency may occur within a framework which may itself be structured in a partly statistical way. The scale relativity theory can open new perspectives in evolution. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Linearized aerodynamic and control law models of the X-29A airplane and comparison with flight data

NASA Technical Reports Server (NTRS)

Bosworth, John T.

1992-01-01

Flight control system design and analysis for aircraft rely on mathematical models of the vehicle dynamics. In addition to a six degree of freedom nonlinear simulation, the X-29A flight controls group developed a set of programs that calculate linear perturbation models throughout the X-29A flight envelope. The models include the aerodynamics as well as flight control system dynamics and were used for stability, controllability, and handling qualities analysis. These linear models were compared to flight test results to help provide a safe flight envelope expansion. A description is given of the linear models at three flight conditions and two flight control system modes. The models are presented with a level of detail that would allow the reader to reproduce the linear results if desired. Comparison between the response of the linear model and flight measured responses are presented to demonstrate the strengths and weaknesses of the linear models' ability to predict flight dynamics.

A model for the microwave emissivity of the ocean's surface as a function of wind speed

NASA Technical Reports Server (NTRS)

Wilheit, T. T.

1979-01-01

A quanitative model is presented which describes the ocean surface as a ensemble of flat facets with a normal distribution of slopes. The variance of the slope distribution is linearly related to frequency up to 35 GHz and constant at higher frequencies. These facets are partially covered with an absorbing nonpolarized foam layer. Experimental evidence is presented for this model.

Fast Algorithms for Mining Co-evolving Time Series

DTIC Science & Technology

2011-09-01

Keogh et al., 2001, 2004] and (b) forecasting, like an autoregressive integrated moving average model ( ARIMA ) and related meth- ods [Box et al., 1994...computing hardware? We develop models to mine time series with missing values, to extract compact representation from time sequences, to segment the...sequences, and to do forecasting. For large scale data, we propose algorithms for learning time series models , in particular, including Linear Dynamical

A Maximin Model for Test Design with Practical Constraints. Project Psychometric Aspects of Item Banking No. 25. Research Report 87-10.

ERIC Educational Resources Information Center

van der Linden, Wim J.; Boekkooi-Timminga, Ellen

A "maximin" model for item response theory based test design is proposed. In this model only the relative shape of the target test information function is specified. It serves as a constraint subject to which a linear programming algorithm maximizes the information in the test. In the practice of test construction there may be several…

Analysis and synthesis of distributed-lumped-active networks by digital computer

NASA Technical Reports Server (NTRS)

1973-01-01

The use of digital computational techniques in the analysis and synthesis of DLA (distributed lumped active) networks is considered. This class of networks consists of three distinct types of elements, namely, distributed elements (modeled by partial differential equations), lumped elements (modeled by algebraic relations and ordinary differential equations), and active elements (modeled by algebraic relations). Such a characterization is applicable to a broad class of circuits, especially including those usually referred to as linear integrated circuits, since the fabrication techniques for such circuits readily produce elements which may be modeled as distributed, as well as the more conventional lumped and active ones.

A simple white noise analysis of neuronal light responses.

PubMed

Chichilnisky, E J

2001-05-01

A white noise technique is presented for estimating the response properties of spiking visual system neurons. The technique is simple, robust, efficient and well suited to simultaneous recordings from multiple neurons. It provides a complete and easily interpretable model of light responses even for neurons that display a common form of response nonlinearity that precludes classical linear systems analysis. A theoretical justification of the technique is presented that relies only on elementary linear algebra and statistics. Implementation is described with examples. The technique and the underlying model of neural responses are validated using recordings from retinal ganglion cells, and in principle are applicable to other neurons. Advantages and disadvantages of the technique relative to classical approaches are discussed.

Rheology modification with ring polymers

NASA Astrophysics Data System (ADS)

Vlassopoulos, Dimitris

It is now established that experimental unconcatenated ring polymers can be purified effectively by means of fractionation at the critical condition. For molecular weights well above the entanglement threshold, purified rings relax stress via power-law (with an exponent of about -0.4), sharply departing from their linear counterparts. Experimental results are in harmony with modeling predictions and simulations. Here, we present results from recent interdisciplinary efforts and discuss two challenges: (i) the nonlinear shear rheology of purified ring melts is also very different from that of unlinked chains. Whereas the latter exhibit features that can be explained, to a first approach, in the framework in the tube model, the former behave akin to unentangled chains with finite extensibility and exhibit much small deformation at steady state. (ii) blends of rings and linear polymers exhibit unique features in different regimes: The addition of minute amounts of linear chains drastically affects ring dynamics. This relates to ring purity and the ability of unlinked linear chains to thread rings. With the help of simulations, it is possible to rationalize the observed surprisingly slow viscoelastic relaxation, which is attributed to ring-linear and ring-ring penetrations. On the other hand, adding small amounts of rings to linear polymers of different molecular weights influences their linear and nonlinear rheology in an unprecedented way. The blend viscosity exceeds that of the slower component (linear) in this non-interacting mixture, and its dependencies on composition and molecular weight ratio are examined, whereas the role of molecular architecture is also addressed. Consequently, closing the ends of a linear chain can serve as a powerful means for molecular manipulation of its rheology. This presentation reflects collaborative efforts with S. Costanzo, Z-C. Yan, R. Pasquino, M. Kaliva, S. Kamble, Y. Jeong, P. Lutz, J. Allgaier, T. Chang, D. Talikis, V. Mavrantzas and M. Rubinstein.

Mechanism-based pharmacokinetic-pharmacodynamic modeling of the antinociceptive effect of buprenorphine in healthy volunteers.

PubMed

Yassen, Ashraf; Olofsen, Erik; Romberg, Raymonda; Sarton, Elise; Danhof, Meindert; Dahan, Albert

2006-06-01

The objective of this investigation was to characterize the pharmacokinetic-pharmacodynamic relation of buprenorphine's antinociceptive effect in healthy volunteers. Data on the time course of the antinociceptive effect after intravenous administration of 0.05-0.6 mg/70 kg buprenorphine in healthy volunteers was analyzed in conjunction with plasma concentrations by nonlinear mixed-effects analysis. A three-compartment pharmacokinetic model best described the concentration time course. Four structurally different pharmacokinetic-pharmacodynamic models were evaluated for their appropriateness to describe the time course of buprenorphine's antinociceptive effect: (1) E(max) model with an effect compartment model, (2) "power" model with an effect compartment model, (3) receptor association-dissociation model with a linear transduction function, and (4) combined biophase equilibration/receptor association-dissociation model with a linear transduction function. The latter pharmacokinetic-pharmacodynamic model described the time course of effect best and was used to explain time dependencies in buprenorphine's pharmacodynamics. The model converged, yielding precise estimation of the parameters characterizing hysteresis and the relation between relative receptor occupancy and antinociceptive effect. The rate constant describing biophase equilibration (k(eo)) was 0.00447 min(-1) (95% confidence interval, 0.00299-0.00595 min(-1)). The receptor dissociation rate constant (k(off)) was 0.0785 min(-1) (95% confidence interval, 0.0352-0.122 min(-1)), and k(on) was 0.0631 ml . ng(-1) . min(-1) (95% confidence interval, 0.0390-0.0872 ml . ng(-1) . min(-1)). This is consistent with observations in rats, suggesting that the rate-limiting step in the onset and offset of the antinociceptive effect is biophase distribution rather than slow receptor association-dissociation. In the dose range studied, no saturation of receptor occupancy occurred explaining the lack of a ceiling effect for antinociception.

Changes of Linearity in MF2 Index with R12 and Solar Activity Maximum

NASA Astrophysics Data System (ADS)

Villanueva, L.

2013-05-01

Critical frequency of F2 layer is related to the solar activity, and the sunspot number has been the standard index for ionospheric prediction programs. This layer, being considered the most important in HF radio communications due to its highest electron density, determines the maximum frequency coming back from ground base transmitter signals, and shows irregular variation in time and space. Nowadays the spatial variation, better understood due to the availability of TEC measurements, let Space Weather Centers have observations almost in real time. However, it is still the most difficult layer to predict in time. Short time variations are improved in IRI model, but long term predictions are only related to the well-known CCIR and URSI coefficients and Solar activity R12 predictions, (or ionospheric indexes in regional models). The concept of the "saturation" of the ionosphere is based on data observations around 3 solar cycles before 1970, (NBS, 1968). There is a linear relationship among MUF (0Km) and R12, for smooth Sunspot numbers R12 less than 100, but constant for higher R12, so, no rise of MUF is expected for R12 higher than 100. This recommendation has been used in most of the known Ionospheric prediction programs for HF Radio communication. In this work, observations of smoothed ionospheric index MF2 related to R12 are presented to find common features of the linear relationship, which is found to persist in different ranges of R12 depending on the specific maximum level of each solar cycle. In the analysis of individual solar cycles, the lapse of linearity is less than 100 for a low solar cycle and higher than 100 for a high solar cycle. To improve ionospheric predictions we can establish levels for solar cycle maximum sunspot numbers R12 around low 100, medium 150 and high 200 and specify the ranges of linearity of MUF(0Km) related to R12 which is not only 100 as assumed for all the solar cycles. For lower levels of solar cycle, discussions of present observations are presented.

Wave‐induced Hydraulic Forces on Submerged Aquatic Plants in Shallow Lakes

PubMed Central

SCHUTTEN, J.; DAINTY, J.; DAVY, A. J.

2004-01-01

• Background and Aims Hydraulic pulling forces arising from wave action are likely to limit the presence of freshwater macrophytes in shallow lakes, particularly those with soft sediments. The aim of this study was to develop and test experimentally simple models, based on linear wave theory for deep water, to predict such forces on individual shoots. • Methods Models were derived theoretically from the action of the vertical component of the orbital velocity of the waves on shoot size. Alternative shoot‐size descriptors (plan‐form area or dry mass) and alternative distributions of the shoot material along its length (cylinder or inverted cone) were examined. Models were tested experimentally in a flume that generated sinusoidal waves which lasted 1 s and were up to 0·2 m high. Hydraulic pulling forces were measured on plastic replicas of Elodea sp. and on six species of real plants with varying morphology (Ceratophyllum demersum, Chara intermedia, Elodea canadensis, Myriophyllum spicatum, Potamogeton natans and Potamogeton obtusifolius). • Key Results Measurements on the plastic replicas confirmed predicted relationships between force and wave phase, wave height and plant submergence depth. Predicted and measured forces were linearly related over all combinations of wave height and submergence depth. Measured forces on real plants were linearly related to theoretically derived predictors of the hydraulic forces (integrals of the products of the vertical orbital velocity raised to the power 1·5 and shoot size). • Conclusions The general applicability of the simplified wave equations used was confirmed. Overall, dry mass and plan‐form area performed similarly well as shoot‐size descriptors, as did the conical or cylindrical models of shoot distribution. The utility of the modelling approach in predicting hydraulic pulling forces from relatively simple plant and environmental measurements was validated over a wide range of forces, plant sizes and species. PMID:14988098

On some stochastic formulations and related statistical moments of pharmacokinetic models.

PubMed

Matis, J H; Wehrly, T E; Metzler, C M

1983-02-01

This paper presents the deterministic and stochastic model for a linear compartment system with constant coefficients, and it develops expressions for the mean residence times (MRT) and the variances of the residence times (VRT) for the stochastic model. The expressions are relatively simple computationally, involving primarily matrix inversion, and they are elegant mathematically, in avoiding eigenvalue analysis and the complex domain. The MRT and VRT provide a set of new meaningful response measures for pharmacokinetic analysis and they give added insight into the system kinetics. The new analysis is illustrated with an example involving the cholesterol turnover in rats.

Continuous functional magnetic resonance imaging reveals dynamic nonlinearities of "dose-response" curves for finger opposition.

PubMed

Berns, G S; Song, A W; Mao, H

1999-07-15

Linear experimental designs have dominated the field of functional neuroimaging, but although successful at mapping regions of relative brain activation, the technique assumes that both cognition and brain activation are linear processes. To test these assumptions, we performed a continuous functional magnetic resonance imaging (MRI) experiment of finger opposition. Subjects performed a visually paced bimanual finger-tapping task. The frequency of finger tapping was continuously varied between 1 and 5 Hz, without any rest blocks. After continuous acquisition of fMRI images, the task-related brain regions were identified with independent components analysis (ICA). When the time courses of the task-related components were plotted against tapping frequency, nonlinear "dose- response" curves were obtained for most subjects. Nonlinearities appeared in both the static and dynamic sense, with hysteresis being prominent in several subjects. The ICA decomposition also demonstrated the spatial dynamics with different components active at different times. These results suggest that the brain response to tapping frequency does not scale linearly, and that it is history-dependent even after accounting for the hemodynamic response function. This implies that finger tapping, as measured with fMRI, is a nonstationary process. When analyzed with a conventional general linear model, a strong correlation to tapping frequency was identified, but the spatiotemporal dynamics were not apparent.

Linear and nonlinear thermodynamics of a kinetic heat engine with fast transformations

NASA Astrophysics Data System (ADS)

Cerino, Luca; Puglisi, Andrea; Vulpiani, Angelo

2016-04-01

We investigate a kinetic heat engine model composed of particles enclosed in a box where one side acts as a thermostat and the opposite side is a piston exerting a given pressure. Pressure and temperature are varied in a cyclical protocol of period τ : their relative excursions, δ and ɛ , respectively, constitute the thermodynamic forces dragging the system out of equilibrium. The analysis of the entropy production of the system allows us to define the conjugated fluxes, which are proportional to the extracted work and the consumed heat. In the limit of small δ and ɛ the fluxes are linear in the forces through a τ -dependent Onsager matrix whose off-diagonal elements satisfy a reciprocal relation. The dynamics of the piston can be approximated, through a coarse-graining procedure, by a Klein-Kramers equation which—in the linear regime—yields analytic expressions for the Onsager coefficients and the entropy production. A study of the efficiency at maximum power shows that the Curzon-Ahlborn formula is always an upper limit which is approached at increasing values of the thermodynamic forces, i.e., outside of the linear regime. In all our analysis the adiabatic limit τ →∞ and the the small-force limit δ ,ɛ →0 are not directly related.

Displacement analysis of diagnostic ultrasound backscatter: A methodology for characterizing, modeling, and monitoring high intensity focused ultrasound therapy

PubMed Central

Speyer, Gavriel; Kaczkowski, Peter J.; Brayman, Andrew A.; Crum, Lawrence A.

2010-01-01

Accurate monitoring of high intensity focused ultrasound (HIFU) therapy is critical for widespread clinical use. Pulse-echo diagnostic ultrasound (DU) is known to exhibit temperature sensitivity through relative changes in time-of-flight between two sets of radio frequency (RF) backscatter measurements, one acquired before and one after therapy. These relative displacements, combined with knowledge of the exposure protocol, material properties, heat transfer, and measurement noise statistics, provide a natural framework for estimating the administered heating, and thereby therapy. The proposed method, termed displacement analysis, identifies the relative displacements using linearly independent displacement patterns, or modes, each induced by a particular time-varying heating applied during the exposure interval. These heating modes are themselves linearly independent. This relationship implies that a linear combination of displacement modes aligning the DU measurements is the response to an identical linear combination of heating modes, providing the heating estimate. Furthermore, the accuracy of coefficient estimates in this approximation is determined a priori, characterizing heating, thermal dose, and temperature estimates for any given protocol. Predicted performance is validated using simulations and experiments in alginate gel phantoms. Evidence for a spatially distributed interaction between temperature and time-of-flight changes is presented. PMID:20649206

From Spiking Neuron Models to Linear-Nonlinear Models

PubMed Central

Ostojic, Srdjan; Brunel, Nicolas

2011-01-01

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates. PMID:21283777

From spiking neuron models to linear-nonlinear models.

PubMed

Ostojic, Srdjan; Brunel, Nicolas

2011-01-20

Neurons transform time-varying inputs into action potentials emitted stochastically at a time dependent rate. The mapping from current input to output firing rate is often represented with the help of phenomenological models such as the linear-nonlinear (LN) cascade, in which the output firing rate is estimated by applying to the input successively a linear temporal filter and a static non-linear transformation. These simplified models leave out the biophysical details of action potential generation. It is not a priori clear to which extent the input-output mapping of biophysically more realistic, spiking neuron models can be reduced to a simple linear-nonlinear cascade. Here we investigate this question for the leaky integrate-and-fire (LIF), exponential integrate-and-fire (EIF) and conductance-based Wang-Buzsáki models in presence of background synaptic activity. We exploit available analytic results for these models to determine the corresponding linear filter and static non-linearity in a parameter-free form. We show that the obtained functions are identical to the linear filter and static non-linearity determined using standard reverse correlation analysis. We then quantitatively compare the output of the corresponding linear-nonlinear cascade with numerical simulations of spiking neurons, systematically varying the parameters of input signal and background noise. We find that the LN cascade provides accurate estimates of the firing rates of spiking neurons in most of parameter space. For the EIF and Wang-Buzsáki models, we show that the LN cascade can be reduced to a firing rate model, the timescale of which we determine analytically. Finally we introduce an adaptive timescale rate model in which the timescale of the linear filter depends on the instantaneous firing rate. This model leads to highly accurate estimates of instantaneous firing rates.

Second-order kinetic model for the sorption of cadmium onto tree fern: a comparison of linear and non-linear methods.

PubMed

Ho, Yuh-Shan

2006-01-01

A comparison was made of the linear least-squares method and a trial-and-error non-linear method of the widely used pseudo-second-order kinetic model for the sorption of cadmium onto ground-up tree fern. Four pseudo-second-order kinetic linear equations are discussed. Kinetic parameters obtained from the four kinetic linear equations using the linear method differed but they were the same when using the non-linear method. A type 1 pseudo-second-order linear kinetic model has the highest coefficient of determination. Results show that the non-linear method may be a better way to obtain the desired parameters.

LTI system order reduction approach based on asymptotical equivalence and the Co-operation of biology-related algorithms

NASA Astrophysics Data System (ADS)

Ryzhikov, I. S.; Semenkin, E. S.; Akhmedova, Sh A.

2017-02-01

A novel order reduction method for linear time invariant systems is described. The method is based on reducing the initial problem to an optimization one, using the proposed model representation, and solving the problem with an efficient optimization algorithm. The proposed method of determining the model allows all the parameters of the model with lower order to be identified and by definition, provides the model with the required steady-state. As a powerful optimization tool, the meta-heuristic Co-Operation of Biology-Related Algorithms was used. Experimental results proved that the proposed approach outperforms other approaches and that the reduced order model achieves a high level of accuracy.

Global Surrogates for the Upshift of the Critical Threshold in the Gradient for ITG Driven Turbulence

NASA Astrophysics Data System (ADS)

Michoski, Craig; Janhunen, Salomon; Faghihi, Danial; Carey, Varis; Moser, Robert

2017-10-01

The suppression of micro-turbulence and ultimately the inhibition of large-scale instabilities observed in tokamak plasmas is partially characterized by the onset of a global stationary state. This stationary attractor corresponds experimentally to a state of ``marginal stability'' in the plasma. The critical threshold that characterizes the onset in the nonlinear regime is observed both experimentally and numerically to exhibit an upshift relative to the linear theory. That is, the onset in the stationary state is up-shifted from those predicted by the linear theory as a function of the ion temperature gradient R0 /LT . Because the transition to this state with enhanced transport and therefore reduced confinement times is inaccessible to the linear theory, strategies for developing nonlinear reduced physics models to predict the upshift have been ongoing. As a complement to these effort, the principle aim of this work is to establish low-fidelity surrogate models that can be used to predict instability driven loss of confinement using training data from high-fidelity models. DE-SC0008454 and DE-AC02-09CH11466.

Minimum mean squared error (MSE) adjustment and the optimal Tykhonov-Phillips regularization parameter via reproducing best invariant quadratic uniformly unbiased estimates (repro-BIQUUE)

NASA Astrophysics Data System (ADS)

Schaffrin, Burkhard

2008-02-01

In a linear Gauss-Markov model, the parameter estimates from BLUUE (Best Linear Uniformly Unbiased Estimate) are not robust against possible outliers in the observations. Moreover, by giving up the unbiasedness constraint, the mean squared error (MSE) risk may be further reduced, in particular when the problem is ill-posed. In this paper, the α-weighted S-homBLE (Best homogeneously Linear Estimate) is derived via formulas originally used for variance component estimation on the basis of the repro-BIQUUE (reproducing Best Invariant Quadratic Uniformly Unbiased Estimate) principle in a model with stochastic prior information. In the present model, however, such prior information is not included, which allows the comparison of the stochastic approach (α-weighted S-homBLE) with the well-established algebraic approach of Tykhonov-Phillips regularization, also known as R-HAPS (Hybrid APproximation Solution), whenever the inverse of the “substitute matrix” S exists and is chosen as the R matrix that defines the relative impact of the regularizing term on the final result.

Method validation using weighted linear regression models for quantification of UV filters in water samples.

PubMed

da Silva, Claudia Pereira; Emídio, Elissandro Soares; de Marchi, Mary Rosa Rodrigues

2015-01-01

This paper describes the validation of a method consisting of solid-phase extraction followed by gas chromatography-tandem mass spectrometry for the analysis of the ultraviolet (UV) filters benzophenone-3, ethylhexyl salicylate, ethylhexyl methoxycinnamate and octocrylene. The method validation criteria included evaluation of selectivity, analytical curve, trueness, precision, limits of detection and limits of quantification. The non-weighted linear regression model has traditionally been used for calibration, but it is not necessarily the optimal model in all cases. Because the assumption of homoscedasticity was not met for the analytical data in this work, a weighted least squares linear regression was used for the calibration method. The evaluated analytical parameters were satisfactory for the analytes and showed recoveries at four fortification levels between 62% and 107%, with relative standard deviations less than 14%. The detection limits ranged from 7.6 to 24.1 ng L(-1). The proposed method was used to determine the amount of UV filters in water samples from water treatment plants in Araraquara and Jau in São Paulo, Brazil. Copyright © 2014 Elsevier B.V. All rights reserved.

Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI.

PubMed

Wang, S; Martinez-Lage, M; Sakai, Y; Chawla, S; Kim, S G; Alonso-Basanta, M; Lustig, R A; Brem, S; Mohan, S; Wolf, R L; Desai, A; Poptani, H

2016-01-01

Early assessment of treatment response is critical in patients with glioblastomas. A combination of DTI and DSC perfusion imaging parameters was evaluated to distinguish glioblastomas with true progression from mixed response and pseudoprogression. Forty-one patients with glioblastomas exhibiting enhancing lesions within 6 months after completion of chemoradiation therapy were retrospectively studied. All patients underwent surgery after MR imaging and were histologically classified as having true progression (>75% tumor), mixed response (25%-75% tumor), or pseudoprogression (<25% tumor). Mean diffusivity, fractional anisotropy, linear anisotropy coefficient, planar anisotropy coefficient, spheric anisotropy coefficient, and maximum relative cerebral blood volume values were measured from the enhancing tissue. A multivariate logistic regression analysis was used to determine the best model for classification of true progression from mixed response or pseudoprogression. Significantly elevated maximum relative cerebral blood volume, fractional anisotropy, linear anisotropy coefficient, and planar anisotropy coefficient and decreased spheric anisotropy coefficient were observed in true progression compared with pseudoprogression (P < .05). There were also significant differences in maximum relative cerebral blood volume, fractional anisotropy, planar anisotropy coefficient, and spheric anisotropy coefficient measurements between mixed response and true progression groups. The best model to distinguish true progression from non-true progression (pseudoprogression and mixed) consisted of fractional anisotropy, linear anisotropy coefficient, and maximum relative cerebral blood volume, resulting in an area under the curve of 0.905. This model also differentiated true progression from mixed response with an area under the curve of 0.901. A combination of fractional anisotropy and maximum relative cerebral blood volume differentiated pseudoprogression from nonpseudoprogression (true progression and mixed) with an area under the curve of 0.807. DTI and DSC perfusion imaging can improve accuracy in assessing treatment response and may aid in individualized treatment of patients with glioblastomas. © 2016 by American Journal of Neuroradiology.

Synaptic control of the shape of the motoneuron pool input-output function

PubMed Central

Heckman, Charles J.

2017-01-01

Although motoneurons have often been considered to be fairly linear transducers of synaptic input, recent evidence suggests that strong persistent inward currents (PICs) in motoneurons allow neuromodulatory and inhibitory synaptic inputs to induce large nonlinearities in the relation between the level of excitatory input and motor output. To try to estimate the possible extent of this nonlinearity, we developed a pool of model motoneurons designed to replicate the characteristics of motoneuron input-output properties measured in medial gastrocnemius motoneurons in the decerebrate cat with voltage-clamp and current-clamp techniques. We drove the model pool with a range of synaptic inputs consisting of various mixtures of excitation, inhibition, and neuromodulation. We then looked at the relation between excitatory drive and total pool output. Our results revealed that the PICs not only enhance gain but also induce a strong nonlinearity in the relation between the average firing rate of the motoneuron pool and the level of excitatory input. The relation between the total simulated force output and input was somewhat more linear because of higher force outputs in later-recruited units. We also found that the nonlinearity can be increased by increasing neuromodulatory input and/or balanced inhibitory input and minimized by a reciprocal, push-pull pattern of inhibition. We consider the possibility that a flexible input-output function may allow motor output to be tuned to match the widely varying demands of the normal motor repertoire. NEW & NOTEWORTHY Motoneuron activity is generally considered to reflect the level of excitatory drive. However, the activation of voltage-dependent intrinsic conductances can distort the relation between excitatory drive and the total output of a pool of motoneurons. Using a pool of realistic motoneuron models, we show that pool output can be a highly nonlinear function of synaptic input but linearity can be achieved through adjusting the time course of excitatory and inhibitory synaptic inputs. PMID:28053245

Aerodynamic parameter estimation via Fourier modulating function techniques

NASA Technical Reports Server (NTRS)

Pearson, A. E.

1995-01-01

Parameter estimation algorithms are developed in the frequency domain for systems modeled by input/output ordinary differential equations. The approach is based on Shinbrot's method of moment functionals utilizing Fourier based modulating functions. Assuming white measurement noises for linear multivariable system models, an adaptive weighted least squares algorithm is developed which approximates a maximum likelihood estimate and cannot be biased by unknown initial or boundary conditions in the data owing to a special property attending Shinbrot-type modulating functions. Application is made to perturbation equation modeling of the longitudinal and lateral dynamics of a high performance aircraft using flight-test data. Comparative studies are included which demonstrate potential advantages of the algorithm relative to some well established techniques for parameter identification. Deterministic least squares extensions of the approach are made to the frequency transfer function identification problem for linear systems and to the parameter identification problem for a class of nonlinear-time-varying differential system models.

Short-wavelength buckling and shear failures for compression-loaded composite laminates. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Shuart, M. J.

1985-01-01

The short-wavelength buckling (or the microbuckling) and the interlaminar and inplane shear failures of multi-directional composite laminates loaded in uniaxial compression are investigated. A laminate model is presented that idealizes each lamina. The fibers in the lamina are modeled as a plate, and the matrix in the lamina is modeled as an elastic foundation. The out-of-plane w displacement for each plate is expressed as a trigonometric series in the half-wavelength of the mode shape for laminate short-wavelength buckling. Nonlinear strain-displacement relations are used. The model is applied to symmetric laminates having linear material behavior. The laminates are loaded in uniform end shortening and are simply supported. A linear analysis is used to determine the laminate stress, strain, and mode shape when short-wavelength buckling occurs. The equations for the laminate compressive stress at short-wavelength buckling are dominated by matrix contributions.

ITOUGH2(UNIX). Inverse Modeling for TOUGH2 Family of Multiphase Flow Simulators

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

Finsterle, S.

1999-03-01

ITOUGH2 provides inverse modeling capabilities for the TOUGH2 family of numerical simulators for non-isothermal multiphase flows in fractured-porous media. The ITOUGH2 can be used for estimating parameters by automatic modeling calibration, for sensitivity analyses, and for uncertainity propagation analyses (linear and Monte Carlo simulations). Any input parameter to the TOUGH2 simulator can be estimated based on any type of observation for which a corresponding TOUGH2 output is calculated. ITOUGH2 solves a non-linear least-squares problem using direct or gradient-based minimization algorithms. A detailed residual and error analysis is performed, which includes the evaluation of model identification criteria. ITOUGH2 can also bemore » run in forward mode, solving subsurface flow problems related to nuclear waste isolation, oil, gas, and geothermal resevoir engineering, and vadose zone hydrology.« less

Approximating high-dimensional dynamics by barycentric coordinates with linear programming

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

Hirata, Yoshito, E-mail: yoshito@sat.t.u-tokyo.ac.jp; Aihara, Kazuyuki; Suzuki, Hideyuki

The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics ofmore » the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.« less