Functional state modelling approach validation for yeast and bacteria cultivations
Roeva, Olympia; Pencheva, Tania
2014-01-01
In this paper, the functional state modelling approach is validated for modelling of the cultivation of two different microorganisms: yeast (Saccharomyces cerevisiae) and bacteria (Escherichia coli). Based on the available experimental data for these fed-batch cultivation processes, three different functional states are distinguished, namely primary product synthesis state, mixed oxidative state and secondary product synthesis state. Parameter identification procedures for different local models are performed using genetic algorithms. The simulation results show high degree of adequacy of the models describing these functional states for both S. cerevisiae and E. coli cultivations. Thus, the local models are validated for the cultivation of both microorganisms. This fact is a strong structure model verification of the functional state modelling theory not only for a set of yeast cultivations, but also for bacteria cultivation. As such, the obtained results demonstrate the efficiency and efficacy of the functional state modelling approach. PMID:26740778
Renton, Michael; Hanan, Jim; Burrage, Kevin
2005-06-01
Functional-structural plant models that include detailed mechanistic representation of underlying physiological processes can be expensive to construct and the resulting models can also be extremely complicated. On the other hand, purely empirical models are not able to simulate plant adaptability and response to different conditions. In this paper, we present an intermediate approach to modelling plant function that can simulate plant response without requiring detailed knowledge of underlying physiology. Plant function is modelled using a 'canonical' modelling approach, which uses compartment models with flux functions of a standard mathematical form, while plant structure is modelled using L-systems. Two modelling examples are used to demonstrate that canonical modelling can be used in conjunction with L-systems to create functional-structural plant models where function is represented either in an accurate and descriptive way, or in a more mechanistic and explanatory way. We conclude that canonical modelling provides a useful, flexible and relatively simple approach to modelling plant function at an intermediate level of abstraction. PMID:15869646
The Thirring-Wess model revisited: a functional integral approach
Belvedere, L.V. . E-mail: armflavio@if.uff.br
2005-06-01
We consider the Wess-Zumino-Witten theory to obtain the functional integral bosonization of the Thirring-Wess model with an arbitrary regularization parameter. Proceeding a systematic of decomposing the Bose field algebra into gauge-invariant- and gauge-non-invariant field subalgebras, we obtain the local decoupled quantum action. The generalized operator solutions for the equations of motion are reconstructed from the functional integral formalism. The isomorphism between the QED {sub 2} (QCD {sub 2}) with broken gauge symmetry by a regularization prescription and the Abelian (non-Abelian) Thirring-Wess model with a fixed bare mass for the meson field is established.
A Model-Based Approach to Constructing Music Similarity Functions
NASA Astrophysics Data System (ADS)
West, Kris; Lamere, Paul
2006-12-01
Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machine-learning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated.
Efremov, A. V.; Teryaev, O. V.; Schweitzer, P.; Zavada, P.
2009-07-01
Transverse parton momentum dependent distribution functions (TMDs) of the nucleon are studied in a covariant model, which describes the intrinsic motion of partons in terms of a covariant momentum distribution. The consistency of the approach is demonstrated, and model relations among TMDs are studied. As a by-product it is shown how the approach allows to formulate the nonrelativistic limit.
Approaches to Modelling the Dynamical Activity of Brain Function Based on the Electroencephalogram
NASA Astrophysics Data System (ADS)
Liley, David T. J.; Frascoli, Federico
The brain is arguably the quintessential complex system as indicated by the patterns of behaviour it produces. Despite many decades of concentrated research efforts, we remain largely ignorant regarding the essential processes that regulate and define its function. While advances in functional neuroimaging have provided welcome windows into the coarse organisation of the neuronal networks that underlie a range of cognitive functions, they have largely ignored the fact that behaviour, and by inference brain function, unfolds dynamically. Modelling the brain's dynamics is therefore a critical step towards understanding the underlying mechanisms of its functioning. To date, models have concentrated on describing the sequential organisation of either abstract mental states (functionalism, hard AI) or the objectively measurable manifestations of the brain's ongoing activity (rCBF, EEG, MEG). While the former types of modelling approach may seem to better characterise brain function, they do so at the expense of not making a definite connection with the actual physical brain. Of the latter, only models of the EEG (or MEG) offer a temporal resolution well matched to the anticipated temporal scales of brain (mental processes) function. This chapter will outline the most pertinent of these modelling approaches, and illustrate, using the electrocortical model of Liley et al, how the detailed application of the methods of nonlinear dynamics and bifurcation theory is central to exploring and characterising their various dynamical features. The rich repertoire of dynamics revealed by such dynamical systems approaches arguably represents a critical step towards an understanding of the complexity of brain function.
Berhane, Kiros; Molitor, Nuoo-Ting
2008-01-01
Flexible multilevel models are proposed to allow for cluster-specific smooth estimation of growth curves in a mixed-effects modeling format that includes subject-specific random effects on the growth parameters. Attention is then focused on models that examine between-cluster comparisons of the effects of an ecologic covariate of interest (e.g. air pollution) on nonlinear functionals of growth curves (e.g. maximum rate of growth). A Gibbs sampling approach is used to get posterior mean estimates of nonlinear functionals along with their uncertainty estimates. A second-stage ecologic random-effects model is used to examine the association between a covariate of interest (e.g. air pollution) and the nonlinear functionals. A unified estimation procedure is presented along with its computational and theoretical details. The models are motivated by, and illustrated with, lung function and air pollution data from the Southern California Children's Health Study. PMID:18349036
Quasiclassical approach to partition functions of ions in a chemical plasma model
Shpatakovskaya, G. V.
2008-03-15
The partition functions of ions that are used in a chemical plasma model are estimated by the Thomas-Fermi free ion model without reference to empirical data. Different form factors limiting the number of the excitation levels taken into account are considered, namely, those corresponding to the average atomic radius criterion, the temperature criterion, and the Planck-Brillouin-Larkin approximation. Expressions are presented for the average excitation energy and for the temperature and volume derivatives of the partition function. A comparison with the results of the empirical approach is made for the aluminum and iron plasmas.
Morris, Jeffrey S.
2012-01-01
In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry (Cromwell) and 2D gel electrophoresis (Pinnacle) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods
Morris, Jeffrey S
2012-01-01
In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry (Cromwell) and 2D gel electrophoresis (Pinnacle) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods
NASA Astrophysics Data System (ADS)
Reich, P. B.; Butler, E. E.
2015-12-01
This project will advance global land models by shifting from the current plant functional type approach to one that better utilizes what is known about the importance and variability of plant traits, within a framework of simultaneously improving fundamental physiological relations that are at the core of model carbon cycling algorithms. Existing models represent the global distribution of vegetation types using the Plant Functional Typeconcept. Plant Functional Types are classes of plant species with similar evolutionary and life history withpresumably similar responses to environmental conditions like CO2, water and nutrient availability. Fixedproperties for each Plant Functional Type are specified through a collection of physiological parameters, or traits.These traits, mostly physiological in nature (e.g., leaf nitrogen and longevity) are used in model algorithms to estimate ecosystem properties and/or drive calculated process rates. In most models, 5 to 15 functional types represent terrestrial vegetation; in essence, they assume there are a total of only 5 to 15 different kinds of plants on the entire globe. This assumption of constant plant traits captured within the functional type concept has serious limitations, as a single set of traits does not reflect trait variation observed within and between species and communities. While this simplification was necessary decades past, substantial improvement is now possible. Rather than assigning a small number of constant parameter values to all grid cells in a model, procedures will be developed that predict a frequency distribution of values for any given grid cell. Thus, the mean and variance, and how these change with time, will inform and improve model performance. The trait-based approach will improve land modeling by (1) incorporating patterns and heterogeneity of traits into model parameterization, thus evolving away from a framework that considers large areas of vegetation to have near identical trait
Modeling and Simulation Approaches for Cardiovascular Function and Their Role in Safety Assessment
Collins, TA; Bergenholm, L; Abdulla, T; Yates, JWT; Evans, N; Chappell, MJ; Mettetal, JT
2015-01-01
Systems pharmacology modeling and pharmacokinetic-pharmacodynamic (PK/PD) analysis of drug-induced effects on cardiovascular (CV) function plays a crucial role in understanding the safety risk of new drugs. The aim of this review is to outline the current modeling and simulation (M&S) approaches to describe and translate drug-induced CV effects, with an emphasis on how this impacts drug safety assessment. Current limitations are highlighted and recommendations are made for future effort in this vital area of drug research. PMID:26225237
A new approach to wall modeling in LES of incompressible flow via function enrichment
NASA Astrophysics Data System (ADS)
Krank, Benjamin; Wall, Wolfgang A.
2016-07-01
A novel approach to wall modeling for the incompressible Navier-Stokes equations including flows of moderate and large Reynolds numbers is presented. The basic idea is that a problem-tailored function space allows prediction of turbulent boundary layer gradients with very coarse meshes. The proposed function space consists of a standard polynomial function space plus an enrichment, which is constructed using Spalding's law-of-the-wall. The enrichment function is not enforced but "allowed" in a consistent way and the overall methodology is much more general and also enables other enrichment functions. The proposed method is closely related to detached-eddy simulation as near-wall turbulence is modeled statistically and large eddies are resolved in the bulk flow. Interpreted in terms of a three-scale separation within the variational multiscale method, the standard scale resolves large eddies and the enrichment scale represents boundary layer turbulence in an averaged sense. The potential of the scheme is shown applying it to turbulent channel flow of friction Reynolds numbers from Reτ = 590 and up to 5,000, flow over periodic constrictions at the Reynolds numbers ReH = 10 , 595 and 19,000 as well as backward-facing step flow at Reh = 5 , 000, all with extremely coarse meshes. Excellent agreement with experimental and DNS data is observed with the first grid point located at up to y1+ = 500 and especially under adverse pressure gradients as well as in separated flows.
A signal subspace approach for modeling the hemodynamic response function in fMRI.
Hossein-Zadeh, Gholam-Ali; Ardekani, Babak A; Soltanian-Zadeh, Hamid
2003-10-01
Many fMRI analysis methods use a model for the hemodynamic response function (HRF). Common models of the HRF, such as the Gaussian or Gamma functions, have parameters that are usually selected a priori by the data analyst. A new method is presented that characterizes the HRF over a wide range of parameters via three basis signals derived using principal component analysis (PCA). Covering the HRF variability, these three basis signals together with the stimulation pattern define signal subspaces which are applicable to both linear and nonlinear modeling and identification of the HRF and for various activation detection strategies. Analysis of simulated fMRI data using the proposed signal subspace showed increased detection sensitivity compared to the case of using a previously proposed trigonometric subspace. The methodology was also applied to activation detection in both event-related and block design experimental fMRI data using both linear and nonlinear modeling of the HRF. The activated regions were consistent with previous studies, indicating the ability of the proposed approach in detecting brain activation without a priori assumptions about the shape parameters of the HRF. The utility of the proposed basis functions in identifying the HRF is demonstrated by estimating the HRF in different activated regions. PMID:14599533
A function space approach to state and model error estimation for elliptic systems
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1983-01-01
An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal esimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed. While this paper focuses on theoretical developments, applications arising in the area of large structure static shape determination are contained in a closely related paper (Rodriguez and Scheid, 1982).
A function space approach to state and model error estimation for elliptic systems
NASA Technical Reports Server (NTRS)
Rodriguez, G.
1983-01-01
An approach is advanced for the concurrent estimation of the state and of the model errors of a system described by elliptic equations. The estimates are obtained by a deterministic least-squares approach that seeks to minimize a quadratic functional of the model errors, or equivalently, to find the vector of smallest norm subject to linear constraints in a suitably defined function space. The minimum norm solution can be obtained by solving either a Fredholm integral equation of the second kind for the case with continuously distributed data or a related matrix equation for the problem with discretely located measurements. Solution of either one of these equations is obtained in a batch-processing mode in which all of the data is processed simultaneously or, in certain restricted geometries, in a spatially scanning mode in which the data is processed recursively. After the methods for computation of the optimal estimates are developed, an analysis of the second-order statistics of the estimates and of the corresponding estimation error is conducted. Based on this analysis, explicit expressions for the mean-square estimation error associated with both the state and model error estimates are then developed.
NASA Astrophysics Data System (ADS)
Stradi, Daniele; Martinez, Umberto; Blom, Anders; Brandbyge, Mads; Stokbro, Kurt
2016-04-01
Metal-semiconductor contacts are a pillar of modern semiconductor technology. Historically, their microscopic understanding has been hampered by the inability of traditional analytical and numerical methods to fully capture the complex physics governing their operating principles. Here we introduce an atomistic approach based on density functional theory and nonequilibrium Green's function, which includes all the relevant ingredients required to model realistic metal-semiconductor interfaces and allows for a direct comparison between theory and experiments via I -Vbias curve simulations. We apply this method to characterize an Ag/Si interface relevant for photovoltaic applications and study the rectifying-to-Ohmic transition as a function of the semiconductor doping. We also demonstrate that the standard "activation energy" method for the analysis of I -Vbias data might be inaccurate for nonideal interfaces as it neglects electron tunneling, and that finite-size atomistic models have problems in describing these interfaces in the presence of doping due to a poor representation of space-charge effects. Conversely, the present method deals effectively with both issues, thus representing a valid alternative to conventional procedures for the accurate characterization of metal-semiconductor interfaces.
Optogenetic approaches to evaluate striatal function in animal models of Parkinson disease
Parker, Krystal L.; Kim, Youngcho; Alberico, Stephanie L.; Emmons, Eric B.; Narayanan, Nandakumar S.
2016-01-01
Optogenetics refers to the ability to control cells that have been genetically modified to express light-sensitive ion channels. The introduction of optogenetic approaches has facilitated the dissection of neural circuits. Optogenetics allows for the precise stimulation and inhibition of specific sets of neurons and their projections with fine temporal specificity. These techniques are ideally suited to investigating neural circuitry underlying motor and cognitive dysfunction in animal models of human disease. Here, we focus on how optogenetics has been used over the last decade to probe striatal circuits that are involved in Parkinson disease, a neurodegenerative condition involving motor and cognitive abnormalities resulting from degeneration of midbrain dopaminergic neurons. The precise mechanisms underlying the striatal contribution to both cognitive and motor dysfunction in Parkinson disease are unknown. Although optogenetic approaches are somewhat removed from clinical use, insight from these studies can help identify novel therapeutic targets and may inspire new treatments for Parkinson disease. Elucidating how neuronal and behavioral functions are influenced and potentially rescued by optogenetic manipulation in animal models could prove to be translatable to humans. These insights can be used to guide future brain-stimulation approaches for motor and cognitive abnormalities in Parkinson disease and other neuropsychiatric diseases. PMID:27069384
NASA Astrophysics Data System (ADS)
Zenzerovic, I.; Kropp, W.; Pieringer, A.
2016-08-01
Curve squeal is a strong tonal sound that may arise when a railway vehicle negotiates a tight curve. In contrast to frequency-domain models, time-domain models are able to capture the nonlinear and transient nature of curve squeal. However, these models are computationally expensive due to requirements for fine spatial and time discretization. In this paper, a computationally efficient engineering model for curve squeal in the time-domain is proposed. It is based on a steady-state point-contact model for the tangential wheel/rail contact and a Green's functions approach for wheel and rail dynamics. The squeal model also includes a simple model of sound radiation from the railway wheel from the literature. A validation of the tangential point-contact model against Kalker's transient variational contact model reveals that the point-contact model performs well within the squeal model up to at least 5 kHz. The proposed squeal model is applied to investigate the influence of lateral creepage, friction and wheel/rail contact position on squeal occurrence and amplitude. The study indicates a significant influence of the wheel/rail contact position on squeal frequencies and amplitudes. Friction and lateral creepage show an influence on squeal occurrence and amplitudes, but this is only secondary to the influence of the contact position.
An overview of the recent approaches for terroir functional modelling, footprinting and zoning
NASA Astrophysics Data System (ADS)
Vaudour, E.; Costantini, E.; Jones, G. V.; Mocali, S.
2014-11-01
Notions of terroir and their conceptualization through agri-environmental sciences have become popular in many parts of world. Originally developed for wine, terroir now encompasses many other crops including fruits, vegetables, cheese, olive oil, coffee, cacao and other crops, linking the uniqueness and quality of both beverages and foods to the environment where they are produced, giving the consumer a sense of place. Climate, geology, geomorphology, and soil are the main environmental factors which compose the terroir effect at different scales. Often considered immutable at the cultural scale, the natural components of terroir are actually a set of processes, which together create a delicate equilibrium and regulation of its effect on products in both space and time. Due to both a greater need to better understand regional to site variations in crop production and the growth in spatial analytic technologies, the study of terroir has shifted from a largely descriptive regional science to a more applied, technical research field. Furthermore, the explosion of spatial data availability and sensing technologies has made the within-field scale of study more valuable to the individual grower. The result has been greater adoption but also issues associated with both the spatial and temporal scales required for practical applications, as well as the relevant approaches for data synthesis. Moreover, as soil microbial communities are known to be of vital importance for terrestrial processes by driving the major soil geochemical cycles and supporting healthy plant growth, an intensive investigation of the microbial organization and their function is also required. Our objective is to present an overview of existing data and modelling approaches for terroir functional modelling, footprinting and zoning at local and regional scales. This review will focus on three main areas of recent terroir research: (1) quantifying the influences of terroir components on plant growth
A conditional Granger causality model approach for group analysis in functional MRI
Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun
2011-01-01
Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve
An overview of the recent approaches to terroir functional modelling, footprinting and zoning
NASA Astrophysics Data System (ADS)
Vaudour, E.; Costantini, E.; Jones, G. V.; Mocali, S.
2015-03-01
Notions of terroir and their conceptualization through agro-environmental sciences have become popular in many parts of world. Originally developed for wine, terroir now encompasses many other crops including fruits, vegetables, cheese, olive oil, coffee, cacao and other crops, linking the uniqueness and quality of both beverages and foods to the environment where they are produced, giving the consumer a sense of place. Climate, geology, geomorphology and soil are the main environmental factors which make up the terroir effect on different scales. Often considered immutable culturally, the natural components of terroir are actually a set of processes, which together create a delicate equilibrium and regulation of its effect on products in both space and time. Due to both a greater need to better understand regional-to-site variations in crop production and the growth in spatial analytic technologies, the study of terroir has shifted from a largely descriptive regional science to a more applied, technical research field. Furthermore, the explosion of spatial data availability and sensing technologies has made the within-field scale of study more valuable to the individual grower. The result has been greater adoption of these technologies but also issues associated with both the spatial and temporal scales required for practical applications, as well as the relevant approaches for data synthesis. Moreover, as soil microbial communities are known to be of vital importance for terrestrial processes by driving the major soil geochemical cycles and supporting healthy plant growth, an intensive investigation of the microbial organization and their function is also required. Our objective is to present an overview of existing data and modelling approaches for terroir functional modelling, footprinting and zoning on local and regional scales. This review will focus on two main areas of recent terroir research: (1) using new tools to unravel the biogeochemical cycles of both
An overview of the recent approaches for terroir functional modelling, footprinting and zoning
NASA Astrophysics Data System (ADS)
Costantini, Edoardo; Emmanuelle, Vaudour; Jones, Gregory; Mocali, Stefano
2014-05-01
Notions of terroir and their conceptualization through agri-environmental sciences have become popular in many parts of world. Originally developed for wine, terroir is now investigated for fruits, vegetables, cheese, olive oil, coffee, cacao and other crops, linking the uniqueness and quality of both beverages and foods to the environment where they are produced, giving the consumer a sense of place. Climate, geology, geomorphology, and soil are the main environmental factors which compose the terroir effect at different scales. Often considered immutable at the cultural scale, the natural components of terroir are actually a set of processes, which together create a delicate equilibrium and regulation of its effect on products in both space and time. Due to both a greater need to better understand regional to site variations in crop production and the growth in spatial analytic technologies, the study of terroir has shifted from a largely descriptive regional science to a more applied, technical research field. Furthermore, the explosion of spatial data availability and elaboration technologies have made the scale of study more valuable to the individual grower, resulting in greater adoption and application. Moreover, as soil microbial communities are known to be of vital importance for terrestrial processes by driving the major soil geochemical cycles and supporting healthy plant growth, an intensive investigation of the microbial organization and their function is also required. Our objective is to present an overview of existing data and modeling approaches for terroir functional modeling, footprinting and zoning at local and regional scales. This review will focus on four main areas of recent terroir research: 1) quantifying the influences of terroir components on plant growth, fruit composition and quality, mostly examining climate-soil-water relationships; 2) the metagenomic approach as new tool to unravel the biogeochemical cycles of both macro- and
Modeling solvation effects in real-space and real-time within density functional approaches
Delgado, Alain; Corni, Stefano; Pittalis, Stefano; Rozzi, Carlo Andrea
2015-10-14
The Polarizable Continuum Model (PCM) can be used in conjunction with Density Functional Theory (DFT) and its time-dependent extension (TDDFT) to simulate the electronic and optical properties of molecules and nanoparticles immersed in a dielectric environment, typically liquid solvents. In this contribution, we develop a methodology to account for solvation effects in real-space (and real-time) (TD)DFT calculations. The boundary elements method is used to calculate the solvent reaction potential in terms of the apparent charges that spread over the van der Waals solute surface. In a real-space representation, this potential may exhibit a Coulomb singularity at grid points that are close to the cavity surface. We propose a simple approach to regularize such singularity by using a set of spherical Gaussian functions to distribute the apparent charges. We have implemented the proposed method in the OCTOPUS code and present results for the solvation free energies and solvatochromic shifts for a representative set of organic molecules in water.
NASA Astrophysics Data System (ADS)
Beragoui, Manel; Aguir, Chadlia; Khalfaoui, Mohamed; Enciso, Eduardo; Torralvo, Maria José; Duclaux, Laurent; Reinert, Laurence; Vayer, Marylène; Ben Lamine, Abdelmottaleb
2015-03-01
The present work involves the study of bovine serum albumin adsorption onto five functionalized polystyrene lattices. The adsorption measurements have been carried out using a quartz crystal microbalance. Poly(styrene-co-itaconic acid) was found to be an effective adsorbent for bovine serum albumin molecule adsorption. The experimental isotherm data were analyzed using theoretical models based on a statistical physics approach, namely monolayer, double layer with two successive energy levels, finite multilayer, and modified Brunauer-Emmet-Teller. The equilibrium data were then analyzed using five different non-linear error analysis methods and it was found that the finite multilayer model best describes the protein adsorption data. Surface characteristics, i.e., surface charge density and number density of surface carboxyl groups, were used to investigate their effect on the adsorption capacity. The combination of the results obtained from the number of adsorbed layers, the number of adsorbed molecules per site, and the thickness of the adsorbed bovine serum albumin layer allows us to predict that the adsorption of this protein molecule can also be distinguished by monolayer or multilayer adsorption with end-on, side-on, and overlap conformations. The magnitudes of the calculated adsorption energy indicate that bovine serum albumin molecules are physisorbed onto the adsorbent lattices.
Efremov, A. V.; Teryaev, O. V.; Schweitzer, P.; Zavada, P.
2011-03-01
We derive relations between transverse momentum dependent distribution functions and the usual parton distribution functions in the 3D covariant parton model, which follow from Lorentz invariance and the assumption of a rotationally symmetric distribution of parton momenta in the nucleon rest frame. Using the known parton distribution functions f{sub 1}{sup a}(x) and g{sub 1}{sup a}(x) as input we predict the x- and p{sub T}-dependence of all twist-2 T-even transverse momentum dependent distribution functions.
ERIC Educational Resources Information Center
Herndon, Mary Anne
1978-01-01
In a model of the functioning of short term memory, the encoding of information for subsequent storage in long term memory is simulated. In the encoding process, semantically equivalent paragraphs are detected for recombination into a macro information unit. (HOD)
NASA Astrophysics Data System (ADS)
Naber, R. R.; Bahai, H.; Jones, B. E.
2006-05-01
The ability to model acoustic emission (AE) plays an important role in advancing the reliability of AE source characterisation. In this paper, an efficient numerical approach is proposed for modelling AE waves in isotropic solids. The approach is based on evaluating the reciprocal band-limited Green's functions using the finite element (FE) method. In the first section, known analytical solutions of the Green's function for an elastic isotropic infinite plate subjected to point monopole surface loading are used to validate the approach. Then, a study investigating the effects of the spatial resolution of the FE model on the accuracy of the numerical solutions is presented. Furthermore, comparisons between numerical calculations and experimental measurements are presented for a glass plate subjected to two known AE sources (pencil lead break and ball impact). Finally, the reciprocal relation between the source and the receiver is confirmed using numerical simulations of a plane stress model of an elastic isotropic plate.
Integrative approaches for modeling regulation and function of the respiratory system
Ben-Tal, Alona
2013-01-01
Mathematical models have been central to understanding the interaction between neural control and breathing. Models of the entire respiratory system – which comprises the lungs and the neural circuitry that controls their ventilation - have been derived using simplifying assumptions to compartmentalise each component of the system and to define the interactions between components. These full system models often rely – through necessity - on empirically derived relationships or parameters, in addition to physiological values. In parallel with the development of whole respiratory system models are mathematical models that focus on furthering a detailed understanding of the neural control network, or of the several functions that contribute to gas exchange within the lung. These models are biophysically based, and rely on physiological parameters. They include single-unit models for a breathing lung or neural circuit, through to spatially-distributed models of ventilation and perfusion, or multi-circuit models for neural control. The challenge is to bring together these more recent advances in models of neural control with models of lung function, into a full simulation for the respiratory system that builds upon the more detailed models but remains computationally tractable. This requires first understanding the mathematical models that have been developed for the respiratory system at different levels, and which could be used to study how physiological levels of O2 and CO2 in the blood are maintained. PMID:24591490
A.V. Efremov, P. Schweitzer, O.V. Teryaev, P. Zavada
2011-03-01
We derive relations between transverse momentum dependent distribution functions (TMDs) and the usual parton distribution functions (PDFs) in the 3D covariant parton model, which follow from Lorentz invariance and the assumption of a rotationally symmetric distribution of parton momenta in the nucleon rest frame. Using the known PDFs f_1(x) and g_1(x) as input we predict the x- and pT-dependence of all twist-2 T-even TMDs.
A new approach for determining fully empirical altimeter wind speed model functions
NASA Astrophysics Data System (ADS)
Freilich, Michael H.; Challenor, Peter G.
1994-12-01
A statistical technique is developed for determining fully empirical model functions relating altimeter radar backscatter (σ0) measurements to near-surface neutral stability wind speed. By assuming that σ0 varies monotonically and uniquely with wind speed, the method requires knowledge only of the separate, rather than joint, distribution functions of σ0 and wind speed. Analytic simplifications result from using a Weibull distribution to approximate the global ocean wind speed distribution; several different wind data sets are used to demonstrate the validity of the Weibull approximation. The technique has been applied to 1 year of Geosat data. Validation of the new and historical model functions using an independent buoy data set demonstrates that the present model function not only has small overall bias and RMS errors, but yields smaller systematic error trends with wind speed and pseudowave age than previously published models. The present analysis suggests that generally accurate altimeter model functions can be derived without the use of collocated measurements, nor is additional significant wave height information measured by the altimeter necessary.
NASA Astrophysics Data System (ADS)
Choudhury, Pallabee; Chopra, Sumer; Roy, Ketan Singha; Sharma, Jyoti
2016-04-01
In this study, ground motions are estimated for scenario earthquakes of Mw 6.0, 6.5 and 7.0 at 17 sites in Gujarat region using Empirical Green's function technique. The Dholavira earthquake of June 19, 2012 (Mw 5.1) which occurred in the Kachchh region of Gujarat is considered as an element earthquake. We estimated the focal mechanism and source parameters of the element earthquake using standard methodologies. The moment tensor inversion technique is used to determine the fault plane solution (strike = 8°, dip = 51°, and rake = - 7°). The seismic moment and the stress drop are 5.6 × 1016 Nm and 120 bars respectively. The validity of the approach was tested for a smaller earthquake. A few possible directivity scenarios were also tested to find out the effect of directivity on the level of ground motions. Our study reveals that source complexities and site effects play a very important role in deciding the level of ground motions at a site which are difficult to model by GMPEs. Our results shed new light on the expected accelerations in the region and suggest that the Kachchh region can expect maximum acceleration of around 500 cm/s2 at few sites near source and around 200 cm/s2 at most of the sites located within 50 km from the epicentre for a Mw 7.0 earthquake. The estimated ground accelerations can be used by the administrators and planners for providing a guiding framework to undertake mitigation investments and activities in the region.
A Hybrid Approach to Structure and Function Modeling of G Protein-Coupled Receptors.
Latek, Dorota; Bajda, Marek; Filipek, Sławomir
2016-04-25
The recent GPCR Dock 2013 assessment of serotonin receptor 5-HT1B and 5-HT2B, and smoothened receptor SMO targets, exposed the strengths and weaknesses of the currently used computational approaches. The test cases of 5-HT1B and 5-HT2B demonstrated that both the receptor structure and the ligand binding mode can be predicted with the atomic-detail accuracy, as long as the target-template sequence similarity is relatively high. On the other hand, the observation of a low target-template sequence similarity, e.g., between SMO from the frizzled GPCR family and members of the rhodopsin family, hampers the GPCR structure prediction and ligand docking. Indeed, in GPCR Dock 2013, accurate prediction of the SMO target was still beyond the capabilities of most research groups. Another bottleneck in the current GPCR research, as demonstrated by the 5-HT2B target, is the reliable prediction of global conformational changes induced by activation of GPCRs. In this work, we report details of our protocol used during GPCR Dock 2013. Our structure prediction and ligand docking protocol was especially successful in the case of 5-HT1B and 5-HT2B-ergotamine complexes for which we provide one of the most accurate predictions. In addition to a description of the GPCR Dock 2013 results, we propose a novel hybrid computational methodology to improve GPCR structure and function prediction. This computational methodology employs two separate rankings for filtering GPCR models. The first ranking is ligand-based while the second is based on the scoring scheme of the recently published BCL method. In this work, we prove that the use of knowledge-based potentials implemented in BCL is an efficient way to cope with major bottlenecks in the GPCR structure prediction. Thereby, we also demonstrate that the knowledge-based potentials for membrane proteins were significantly improved, because of the recent surge in available experimental structures. PMID:26978043
Liu, Yang; Magnus, Brooke E; Thissen, David
2016-06-01
Differential item functioning (DIF), referring to between-group variation in item characteristics above and beyond the group-level disparity in the latent variable of interest, has long been regarded as an important item-level diagnostic. The presence of DIF impairs the fit of the single-group item response model being used, and calls for either model modification or item deletion in practice, depending on the mode of analysis. Methods for testing DIF with continuous covariates, rather than categorical grouping variables, have been developed; however, they are restrictive in parametric forms, and thus are not sufficiently flexible to describe complex interaction among latent variables and covariates. In the current study, we formulate the probability of endorsing each test item as a general bivariate function of a unidimensional latent trait and a single covariate, which is then approximated by a two-dimensional smoothing spline. The accuracy and precision of the proposed procedure is evaluated via Monte Carlo simulations. If anchor items are available, we proposed an extended model that simultaneously estimates item characteristic functions (ICFs) for anchor items, ICFs conditional on the covariate for non-anchor items, and the latent variable density conditional on the covariate-all using regression splines. A permutation DIF test is developed, and its performance is compared to the conventional parametric approach in a simulation study. We also illustrate the proposed semiparametric DIF testing procedure with an empirical example. PMID:26155757
Optimization of global model composed of radial basis functions using the term-ranking approach
Cai, Peng; Tao, Chao Liu, Xiao-Jun
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
NASA Astrophysics Data System (ADS)
Bodegom, P. V.
2015-12-01
In recent years a number of approaches have been developed to provide alternatives to the use of plant functional types (PFTs) with constant vegetation characteristics for simulating vegetation responses to climate changes. In this presentation, an overview of those approaches and their challenges is given. Some new approaches aim at removing PFTs altogether by determining the combination of vegetation characteristics that would fit local conditions best. Others describe the variation in traits within PFTs as a function of environmental drivers, based on community assembly principles. In the first approach, after an equilibrium has been established, vegetation composition and its functional attributes can change by allowing the emergence of a new type that is more fit. In the latter case, changes in vegetation attributes in space and time as assumed to be the result intraspecific variation, genetic adaptation and species turnover, without quantifying their respective importance. Hence, it is assumed that -by whatever mechanism- the community as a whole responds without major time lags to changes in environmental drivers. Recently, we showed that intraspecific variation is highly species- and trait-specific and that none of the current hypotheses on drivers of this variation seems to hold. Also genetic adaptation varies considerably among species and it is uncertain whether it will be fast enough to cope with climate change. Species turnover within a community is especially fast in herbaceous communities, but much slower in forest communities. Hence, it seems that assumptions made may not hold for forested ecosystems, but solutions to deal with this do not yet exist. Even despite the fact that responsiveness of vegetation to environmental change may be overestimated, we showed that -upon implementation of trait-environment relationships- major changes in global vegetation distribution are projected, to similar extents as to those without such responsiveness.
Operator function modeling: An approach to cognitive task analysis in supervisory control systems
NASA Technical Reports Server (NTRS)
Mitchell, Christine M.
1987-01-01
In a study of models of operators in complex, automated space systems, an operator function model (OFM) methodology was extended to represent cognitive as well as manual operator activities. Development continued on a software tool called OFMdraw, which facilitates construction of an OFM by permitting construction of a heterarchic network of nodes and arcs. Emphasis was placed on development of OFMspert, an expert system designed both to model human operation and to assist real human operators. The system uses a blackboard method of problem solving to make an on-line representation of operator intentions, called ACTIN (actions interpreter).
Stress and Resilience in Functional Somatic Syndromes – A Structural Equation Modeling Approach
Fischer, Susanne; Lemmer, Gunnar; Gollwitzer, Mario; Nater, Urs M.
2014-01-01
Background Stress has been suggested to play a role in the development and perpetuation of functional somatic syndromes. The mechanisms of how this might occur are not clear. Purpose We propose a multi-dimensional stress model which posits that childhood trauma increases adult stress reactivity (i.e., an individual's tendency to respond strongly to stressors) and reduces resilience (e.g., the belief in one's competence). This in turn facilitates the manifestation of functional somatic syndromes via chronic stress. We tested this model cross-sectionally and prospectively. Methods Young adults participated in a web survey at two time points. Structural equation modeling was used to test our model. The final sample consisted of 3′054 participants, and 429 of these participated in the follow-up survey. Results Our proposed model fit the data in the cross-sectional (χ2(21) = 48.808, p<.001, CFI = .995, TLI = .992, RMSEA = .021, 90% CI [.013.029]) and prospective analyses (χ2(21) = 32.675, p<.05, CFI = .982, TLI = .969, RMSEA = .036, 90% CI [.001.059]). Discussion Our findings have several clinical implications, suggesting a role for stress management training in the prevention and treatment of functional somatic syndromes. PMID:25396736
Vitkin, Edward; Shlomi, Tomer
2012-01-01
Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files. PMID:23194418
NASA Astrophysics Data System (ADS)
Benedetti, Dario; Lahoche, Vincent
2016-05-01
We develop the functional renormalization group formalism for a tensorial group field theory with closure constraint, in the case of a just renormalizable model over U{(1)}\\otimes 6, with quartic interactions. The method allows us to obtain a closed but non-autonomous system of differential equations which describe the renormalization group flow of the couplings beyond perturbation theory. The explicit dependence of the beta functions on the running scale is due to the existence of an external scale in the model, the radius of {S}1≃ U(1). We study the occurrence of fixed points and their critical properties in two different approximate regimes, corresponding to the deep UV and deep IR. Besides confirming the asymptotic freedom of the model, we find also a non-trivial fixed point, with one relevant direction. Our results are qualitatively similar to those found previously for a rank-3 model without closure constraint, and it is thus tempting to speculate that the presence of a Wilson-Fisher-like fixed point is a general feature of asymptotically free tensorial group field theories.
de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo
2014-01-01
Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The ‘communities’ of questionnaire items that emerge from our community detection method form possible ‘functional constructs’ inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such ‘functional constructs’ suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling. PMID:25036766
de Vries, Natalie Jane; Carlson, Jamie; Moscato, Pablo
2014-01-01
Online consumer behavior in general and online customer engagement with brands in particular, has become a major focus of research activity fuelled by the exponential increase of interactive functions of the internet and social media platforms and applications. Current research in this area is mostly hypothesis-driven and much debate about the concept of Customer Engagement and its related constructs remains existent in the literature. In this paper, we aim to propose a novel methodology for reverse engineering a consumer behavior model for online customer engagement, based on a computational and data-driven perspective. This methodology could be generalized and prove useful for future research in the fields of consumer behaviors using questionnaire data or studies investigating other types of human behaviors. The method we propose contains five main stages; symbolic regression analysis, graph building, community detection, evaluation of results and finally, investigation of directed cycles and common feedback loops. The 'communities' of questionnaire items that emerge from our community detection method form possible 'functional constructs' inferred from data rather than assumed from literature and theory. Our results show consistent partitioning of questionnaire items into such 'functional constructs' suggesting the method proposed here could be adopted as a new data-driven way of human behavior modeling. PMID:25036766
NASA Astrophysics Data System (ADS)
Freire, Hermann; Corrêa, Eberth
2012-02-01
We apply a functional implementation of the field-theoretical renormalization group (RG) method up to two loops to the single-impurity Anderson model. To achieve this, we follow a RG strategy similar to that proposed by Vojta et al. (in Phys. Rev. Lett. 85:4940, 2000), which consists of defining a soft ultraviolet regulator in the space of Matsubara frequencies for the renormalized Green's function. Then we proceed to derive analytically and solve numerically integro-differential flow equations for the effective couplings and the quasiparticle weight of the present model, which fully treat the interplay of particle-particle and particle-hole parquet diagrams and the effect of the two-loop self-energy feedback into them. We show that our results correctly reproduce accurate numerical renormalization group data for weak to slightly moderate interactions. These results are in excellent agreement with other functional Wilsonian RG works available in the literature. Since the field-theoretical RG method turns out to be easier to implement at higher loops than the Wilsonian approach, higher-order calculations within the present approach could improve further the results for this model at stronger couplings. We argue that the present RG scheme could thus offer a possible alternative to other functional RG methods to describe electronic correlations within this model.
Tomellini, Massimo; Fanfoni, Massimo
2014-11-01
The statistical methods exploiting the "Correlation-Functions" or the "Differential-Critical-Region" are both suitable for describing phase transformation kinetics ruled by nucleation and growth. We present a critical analysis of these two approaches, with particular emphasis to transformations ruled by diffusional growth which cannot be described by the Kolmogorov-Johnson-Mehl-Avrami (KJMA) theory. In order to bridge the gap between these two methods, the conditional probability functions entering the "Differential-Critical-Region" approach are determined in terms of correlation functions. The formulation of these probabilities by means of cluster expansion is also derived, which improves the accuracy of the computation. The model is applied to 2D and 3D parabolic growths occurring at constant value of either actual or phantom-included nucleation rates. Computer simulations have been employed for corroborating the theoretical modeling. The contribution to the kinetics of phantom overgrowth is estimated and it is found to be of a few percent in the case of constant value of the actual nucleation rate. It is shown that for a parabolic growth law both approaches do not provide a closed-form solution of the kinetics. In this respect, the two methods are equivalent and the longstanding overgrowth phenomenon, which limits the KJMA theory, does not admit an exact analytical solution. PMID:25493802
Salazar, Ramon B. E-mail: hilatikh@purdue.edu; Appenzeller, Joerg; Ilatikhameneh, Hesameddin E-mail: hilatikh@purdue.edu; Rahman, Rajib; Klimeck, Gerhard
2015-10-28
A new compact modeling approach is presented which describes the full current-voltage (I-V) characteristic of high-performance (aggressively scaled-down) tunneling field-effect-transistors (TFETs) based on homojunction direct-bandgap semiconductors. The model is based on an analytic description of two key features, which capture the main physical phenomena related to TFETs: (1) the potential profile from source to channel and (2) the elliptic curvature of the complex bands in the bandgap region. It is proposed to use 1D Poisson's equations in the source and the channel to describe the potential profile in homojunction TFETs. This allows to quantify the impact of source/drain doping on device performance, an aspect usually ignored in TFET modeling but highly relevant in ultra-scaled devices. The compact model is validated by comparison with state-of-the-art quantum transport simulations using a 3D full band atomistic approach based on non-equilibrium Green's functions. It is shown that the model reproduces with good accuracy the data obtained from the simulations in all regions of operation: the on/off states and the n/p branches of conduction. This approach allows calculation of energy-dependent band-to-band tunneling currents in TFETs, a feature that allows gaining deep insights into the underlying device physics. The simplicity and accuracy of the approach provide a powerful tool to explore in a quantitatively manner how a wide variety of parameters (material-, size-, and/or geometry-dependent) impact the TFET performance under any bias conditions. The proposed model presents thus a practical complement to computationally expensive simulations such as the 3D NEGF approach.
NASA Astrophysics Data System (ADS)
Salazar, Ramon B.; Ilatikhameneh, Hesameddin; Rahman, Rajib; Klimeck, Gerhard; Appenzeller, Joerg
2015-10-01
A new compact modeling approach is presented which describes the full current-voltage (I-V) characteristic of high-performance (aggressively scaled-down) tunneling field-effect-transistors (TFETs) based on homojunction direct-bandgap semiconductors. The model is based on an analytic description of two key features, which capture the main physical phenomena related to TFETs: (1) the potential profile from source to channel and (2) the elliptic curvature of the complex bands in the bandgap region. It is proposed to use 1D Poisson's equations in the source and the channel to describe the potential profile in homojunction TFETs. This allows to quantify the impact of source/drain doping on device performance, an aspect usually ignored in TFET modeling but highly relevant in ultra-scaled devices. The compact model is validated by comparison with state-of-the-art quantum transport simulations using a 3D full band atomistic approach based on non-equilibrium Green's functions. It is shown that the model reproduces with good accuracy the data obtained from the simulations in all regions of operation: the on/off states and the n/p branches of conduction. This approach allows calculation of energy-dependent band-to-band tunneling currents in TFETs, a feature that allows gaining deep insights into the underlying device physics. The simplicity and accuracy of the approach provide a powerful tool to explore in a quantitatively manner how a wide variety of parameters (material-, size-, and/or geometry-dependent) impact the TFET performance under any bias conditions. The proposed model presents thus a practical complement to computationally expensive simulations such as the 3D NEGF approach.
Perveen, Nazia; Barot, Sébastien; Alvarez, Gaël; Klumpp, Katja; Martin, Raphael; Rapaport, Alain; Herfurth, Damien; Louault, Frédérique; Fontaine, Sébastien
2014-04-01
Integration of the priming effect (PE) in ecosystem models is crucial to better predict the consequences of global change on ecosystem carbon (C) dynamics and its feedbacks on climate. Over the last decade, many attempts have been made to model PE in soil. However, PE has not yet been incorporated into any ecosystem models. Here, we build plant/soil models to explore how PE and microbial diversity influence soil/plant interactions and ecosystem C and nitrogen (N) dynamics in response to global change (elevated CO2 and atmospheric N depositions). Our results show that plant persistence, soil organic matter (SOM) accumulation, and low N leaching in undisturbed ecosystems relies on a fine adjustment of microbial N mineralization to plant N uptake. This adjustment can be modeled in the SYMPHONY model by considering the destruction of SOM through PE, and the interactions between two microbial functional groups: SOM decomposers and SOM builders. After estimation of parameters, SYMPHONY provided realistic predictions on forage production, soil C storage and N leaching for a permanent grassland. Consistent with recent observations, SYMPHONY predicted a CO2 -induced modification of soil microbial communities leading to an intensification of SOM mineralization and a decrease in the soil C stock. SYMPHONY also indicated that atmospheric N deposition may promote SOM accumulation via changes in the structure and metabolic activities of microbial communities. Collectively, these results suggest that the PE and functional role of microbial diversity may be incorporated in ecosystem models with a few additional parameters, improving accuracy of predictions. PMID:24339186
2-D Modeling of Nanoscale MOSFETs: Non-Equilibrium Green's Function Approach
NASA Technical Reports Server (NTRS)
Svizhenko, Alexei; Anantram, M. P.; Govindan, T. R.; Biegel, Bryan
2001-01-01
We have developed physical approximations and computer code capable of realistically simulating 2-D nanoscale transistors, using the non-equilibrium Green's function (NEGF) method. This is the most accurate full quantum model yet applied to 2-D device simulation. Open boundary conditions and oxide tunneling are treated on an equal footing. Electrons in the ellipsoids of the conduction band are treated within the anisotropic effective mass approximation. Electron-electron interaction is treated within Hartree approximation by solving NEGF and Poisson equations self-consistently. For the calculations presented here, parallelization is performed by distributing the solution of NEGF equations to various processors, energy wise. We present simulation of the "benchmark" MIT 25nm and 90nm MOSFETs and compare our results to those from the drift-diffusion simulator and the quantum-corrected results available. In the 25nm MOSFET, the channel length is less than ten times the electron wavelength, and the electron scattering time is comparable to its transit time. Our main results are: (1) Simulated drain subthreshold current characteristics are shown, where the potential profiles are calculated self-consistently by the corresponding simulation methods. The current predicted by our quantum simulation has smaller subthreshold slope of the Vg dependence which results in higher threshold voltage. (2) When gate oxide thickness is less than 2 nm, gate oxide leakage is a primary factor which determines off-current of a MOSFET (3) Using our 2-D NEGF simulator, we found several ways to drastically decrease oxide leakage current without compromising drive current. (4) Quantum mechanically calculated electron density is much smaller than the background doping density in the poly silicon gate region near oxide interface. This creates an additional effective gate voltage. Different ways to. include this effect approximately will be discussed.
Tabacchi, G; Hutter, J; Mundy, C
2005-04-07
A combined linear response--frozen electron density model has been implemented in a molecular dynamics scheme derived from an extended Lagrangian formalism. This approach is based on a partition of the electronic charge distribution into a frozen region described by Kim-Gordon theory, and a response contribution determined by the instaneous ionic configuration of the system. The method is free from empirical pair-potentials and the parameterization protocol involves only calculations on properly chosen subsystems. They apply this method to a series of alkali halides in different physical phases and are able to reproduce experimental structural and thermodynamic properties with an accuracy comparable to Kohn-Sham density functional calculations.
Functional Generalized Additive Models.
McLean, Mathew W; Hooker, Giles; Staicu, Ana-Maria; Scheipl, Fabian; Ruppert, David
2014-01-01
We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number of principal components as in Müller and Yao (2008), our model incorporates the functional predictor directly and thus our model can be viewed as the natural functional extension of generalized additive models. We estimate F(·,·) using tensor-product B-splines with roughness penalties. A pointwise quantile transformation of the functional predictor is also considered to ensure each tensor-product B-spline has observed data on its support. The methods are evaluated using simulated data and their predictive performance is compared with other competing scalar-on-function regression alternatives. We illustrate the usefulness of our approach through an application to brain tractography, where X(t) is a signal from diffusion tensor imaging at position, t, along a tract in the brain. In one example, the response is disease-status (case or control) and in a second example, it is the score on a cognitive test. R code for performing the simulations and fitting the FGAM can be found in supplemental materials available online. PMID:24729671
NASA Astrophysics Data System (ADS)
Echavarria, E.; Tomiyama, T.; van Bussel, G. J. W.
2007-07-01
The objective of this on-going research is to develop a design methodology to increase the availability for offshore wind farms, by means of an intelligent maintenance system capable of responding to faults by reconfiguring the system or subsystems, without increasing service visits, complexity, or costs. The idea is to make use of the existing functional redundancies within the system and sub-systems to keep the wind turbine operational, even at a reduced capacity if necessary. Re-configuration is intended to be a built-in capability to be used as a repair strategy, based on these existing functionalities provided by the components. The possible solutions can range from using information from adjacent wind turbines, such as wind speed and direction, to setting up different operational modes, for instance re-wiring, re-connecting, changing parameters or control strategy. The methodology described in this paper is based on qualitative physics and consists of a fault diagnosis system based on a model-based reasoner (MBR), and on a functional redundancy designer (FRD). Both design tools make use of a function-behaviour-state (FBS) model. A design methodology based on the re-configuration concept to achieve self-maintained wind turbines is an interesting and promising approach to reduce stoppage rate, failure events, maintenance visits, and to maintain energy output possibly at reduced rate until the next scheduled maintenance.
NASA Astrophysics Data System (ADS)
Grewe, V.; Frömming, C.; Matthes, S.; Brinkop, S.; Ponater, M.; Dietmüller, S.; Jöckel, P.; Garny, H.; Tsati, E.; Dahlmann, K.; Søvde, O. A.; Fuglestvedt, J.; Berntsen, T. K.; Shine, K. P.; Irvine, E. A.; Champougny, T.; Hullah, P.
2014-01-01
In addition to CO2, the climate impact of aviation is strongly influenced by non-CO2 emissions, such as nitrogen oxides, influencing ozone and methane, and water vapour, which can lead to the formation of persistent contrails in ice-supersaturated regions. Because these non-CO2 emission effects are characterised by a short lifetime, their climate impact largely depends on emission location and time; that is to say, emissions in certain locations (or times) can lead to a greater climate impact (even on the global average) than the same emission in other locations (or times). Avoiding these climate-sensitive regions might thus be beneficial to climate. Here, we describe a modelling chain for investigating this climate impact mitigation option. This modelling chain forms a multi-step modelling approach, starting with the simulation of the fate of emissions released at a certain location and time (time-region grid points). This is performed with the chemistry-climate model EMAC, extended via the two submodels AIRTRAC (V1.0) and CONTRAIL (V1.0), which describe the contribution of emissions to the composition of the atmosphere and to contrail formation, respectively. The impact of emissions from the large number of time-region grid points is efficiently calculated by applying a Lagrangian scheme. EMAC also includes the calculation of radiative impacts, which are, in a second step, the input to climate metric formulas describing the global climate impact of the emission at each time-region grid point. The result of the modelling chain comprises a four-dimensional data set in space and time, which we call climate cost functions and which describes the global climate impact of an emission at each grid point and each point in time. In a third step, these climate cost functions are used in an air traffic simulator (SAAM) coupled to an emission tool (AEM) to optimise aircraft trajectories for the North Atlantic region. Here, we describe the details of this new modelling
NASA Astrophysics Data System (ADS)
Grewe, V.; Frömming, C.; Matthes, S.; Brinkop, S.; Ponater, M.; Dietmüller, S.; Jöckel, P.; Garny, H.; Tsati, E.; Søvde, O. A.; Fuglestvedt, J.; Berntsen, T. K.; Shine, K. P.; Irvine, E. A.; Champougny, T.; Hullah, P.
2013-08-01
In addition to CO2, the climate impact of aviation is strongly influenced by non-CO2 emissions, such as nitrogen oxides, influencing ozone and methane, and water vapour, forming contrails. Because these non-CO2 emission effects are characterised by a short lifetime, their climate impact largely depends on emission location and time, i.e. emissions in certain locations (or times) can lead to a greater climate impact (even on the global average) than the same emission in other locations (or times). Avoiding these climate sensitive regions might thus be beneficial to climate. Here, we describe a modelling chain for investigating this climate impact mitigation option. It forms a multi-step modelling approach, starting with the simulation of the fate of emissions released at a certain location and time (time-region). This is performed with the chemistry-climate model EMAC, extended by the two submodels AIRTRAC 1.0 and CONTRAIL 1.0, which describe the contribution of emissions to the composition of the atmosphere and the contrail formation. Numerous time-regions are efficiently calculated by applying a Lagrangian scheme. EMAC also includes the calculation of radiative impacts, which are, in a second step, the input to climate metric formulas describing the climate impact of the time-region emission. The result of the modelling chain comprises a four dimensional dataset in space and time, which we call climate cost functions, and which describe at each grid point and each point in time, the climate impact of an emission. In a third step, these climate cost functions are used in a traffic simulator (SAAM), coupled to an emission tool (AEM) to optimise aircraft trajectories for the North Atlantic region. Here, we describe the details of this new modelling approach and show some example results. A number of sensitivity analyses are performed to motivate the settings of individual parameters. A stepwise sanity check of the results of the modelling chain is undertaken to
Turan, Başak; Selçuki, Cenk
2014-09-01
Amino acids are constituents of proteins and enzymes which take part almost in all metabolic reactions. Glutamic acid, with an ability to form a negatively charged side chain, plays a major role in intra and intermolecular interactions of proteins, peptides, and enzymes. An exhaustive conformational analysis has been performed for all eight possible forms at B3LYP/cc-pVTZ level. All possible neutral, zwitterionic, protonated, and deprotonated forms of glutamic acid structures have been investigated in solution by using polarizable continuum model mimicking water as the solvent. Nine families based on the dihedral angles have been classified for eight glutamic acid forms. The electrostatic effects included in the solvent model usually stabilize the charged forms more. However, the stability of the zwitterionic form has been underestimated due to the lack of hydrogen bonding between the solute and solvent; therefore, it is observed that compact neutral glutamic acid structures are more stable in solution than they are in vacuum. Our calculations have shown that among all eight possible forms, some are not stable in solution and are immediately converted to other more stable forms. Comparison of isoelectronic glutamic acid forms indicated that one of the structures among possible zwitterionic and anionic forms may dominate over the other possible forms. Additional investigations using explicit solvent models are necessary to determine the stability of charged forms of glutamic acid in solution as our results clearly indicate that hydrogen bonding and its type have a major role in the structure and energy of conformers. PMID:25135067
Modelling approaches for angiogenesis.
Taraboletti, G; Giavazzi, R
2004-04-01
The development of a functional vasculature within a tumour is a requisite for its growth and progression. This fact has led to the design of therapies directed toward the tumour vasculature, aiming either to prevent the formation of new vessels (anti-angiogenic) or to damage existing vessels (vascular targeting). The development of agents with different mechanisms of action requires powerful preclinical models for the analysis and optimization of these therapies. This review concerns 'classical' assays of angiogenesis in vitro and in vivo, recent approaches to target identification (analysis of gene and protein expression), and the study of morphological and functional changes in the vasculature in vivo (imaging techniques). It mainly describes assays designed for anti-angiogenic compounds, indicating, where possible, their application to the study of vascular-targeting agents. PMID:15120043
2015-01-01
Escherichia coli thymidylate synthase (TS) is an enzyme that is indispensable to DNA synthesis and cell division, as it provides the only de novo source of dTMP by catalyzing the reductive methylation of dUMP, thus making it a key target for chemotherapeutic agents. High resolution X-ray crystallographic structures are available for TS and, owing to its relatively small size, successful experimental mutagenesis studies have been conducted on the enzyme. In this study, an in silico mutagenesis technique is used to investigate the effects of single amino acid substitutions in TS on enzymatic activity, one that employs the TS protein structure as well as a knowledge-based, four-body statistical potential. For every single residue TS variant, this approach yields both a global structural perturbation score and a set of local environmental perturbation scores that characterize the mutated position as well as all structurally neighboring residues. Global scores for the TS variants are capable of uniquely characterizing groups of residue positions in the enzyme according to their physicochemical, functional, or structural properties. Additionally, these global scores elucidate a statistically significant structure–function relationship among a collection of 372 single residue TS variants whose activity levels have been experimentally determined. Predictive models of TS variant activity are subsequently trained on this dataset of experimental mutants, whose respective feature vectors encode information regarding the mutated position as well as its six nearest residue neighbors in the TS structure, including their environmental perturbation scores. PMID:25648456
[Partial lease squares approach to functional analysis].
Preda, C
2006-01-01
We extend the partial least squares (PLS) approach to functional data represented in our models by sample paths of stochastic process with continuous time. Due to the infinite dimension, when functional data are used as a predictor for linear regression and classification models, the estimation problem is an ill-posed one. In this context, PLS offers a simple and efficient alternative to the methods based on the principal components of the stochastic process. We compare the results given by the PLS approach and other linear models using several datasets from economy, industry and medical fields. PMID:17124795
NASA Astrophysics Data System (ADS)
Maitra, Subrata; Banerjee, Debamalya
2010-10-01
Present article is based on application of the product quality and improvement of design related with the nature of failure of machineries and plant operational problems of an industrial blower fan Company. The project aims at developing the product on the basis of standardized production parameters for selling its products in the market. Special attention is also being paid to the blower fans which have been ordered directly by the customer on the basis of installed capacity of air to be provided by the fan. Application of quality function deployment is primarily a customer oriented approach. Proposed model of QFD integrated with AHP to select and rank the decision criterions on the commercial and technical factors and the measurement of the decision parameters for selection of best product in the compettitive environment. The present AHP-QFD model justifies the selection of a blower fan with the help of the group of experts' opinion by pairwise comparison of the customer's and ergonomy based technical design requirements. The steps invoved in implementation of the QFD—AHP and selection of weighted criterion may be helpful for all similar purpose industries maintaining cost and utility for competitive product.
Various modeling approaches have been developed for metal binding on humic substances. However, most of these models are still curve-fitting exercises-- the resulting set of parameters such as affinity constants (or the distribution of them) is found to depend on pH, ionic stren...
Tang, Jau
1996-02-01
As an alternative to better physical explanations of the mechanisms of quantum interference and the origins of uncertainty broadening, a linear hopping model is proposed with ``color-varying`` dynamics to reflect fast exchange between time-reversed states. Intricate relations between this model, particle-wave dualism, and relativity are discussed. The wave function is shown to possess dual characteristics of a stable, localized ``soliton-like`` de Broglie wavelet and a delocalized, interfering Schroedinger carrier wave function.
NASA Astrophysics Data System (ADS)
Pavlick, R.; Drewry, D. T.; Bohn, K.; Reu, B.; Kleidon, A.
2013-06-01
Terrestrial biosphere models typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical plant functional types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM) as a new approach to terrestrial biosphere modelling with a richer representation of functional diversity than traditional modelling approaches based on a small number of fixed PFTs. JeDi-DGVM simulates the performance of a large number of randomly generated plant growth strategies, each defined by a set of 15 trait parameters which characterize various aspects of plant functioning including carbon allocation, ecophysiology and phenology. Each trait parameter is involved in one or more functional trade-offs. These trade-offs ultimately determine whether a strategy is able to survive under the climatic conditions in a given model grid cell and its performance relative to the other strategies. The biogeochemical fluxes and land surface properties of the individual strategies are aggregated to the grid-cell scale using a mass-based weighting scheme. We evaluate the simulated global biogeochemical patterns against a variety of field and satellite-based observations following a protocol established by the Carbon-Land Model Intercomparison Project. The land surface fluxes and vegetation structural properties are reasonably well simulated by JeDi-DGVM, and compare favourably with other state-of-the-art global vegetation models. We also evaluate the simulated patterns of functional diversity and the sensitivity of the JeDi-DGVM modelling approach to the number of sampled strategies. Altogether, the results demonstrate the parsimonious and flexible
An evolutionary approach to Function
2010-01-01
Background Understanding the distinction between function and role is vexing and difficult. While it appears to be useful, in practice this distinction is hard to apply, particularly within biology. Results I take an evolutionary approach, considering a series of examples, to develop and generate definitions for these concepts. I test them in practice against the Ontology for Biomedical Investigations (OBI). Finally, I give an axiomatisation and discuss methods for applying these definitions in practice. Conclusions The definitions in this paper are applicable, formalizing current practice. As such, they make a significant contribution to the use of these concepts within biomedical ontologies. PMID:20626924
Hadjipantelis, P. Z.; Aston, J. A. D.; Müller, H. G.; Evans, J. P.
2015-01-01
Mandarin Chinese is characterized by being a tonal language; the pitch (or F 0) of its utterances carries considerable linguistic information. However, speech samples from different individuals are subject to changes in amplitude and phase, which must be accounted for in any analysis that attempts to provide a linguistically meaningful description of the language. A joint model for amplitude, phase, and duration is presented, which combines elements from functional data analysis, compositional data analysis, and linear mixed effects models. By decomposing functions via a functional principal component analysis, and connecting registration functions to compositional data analysis, a joint multivariate mixed effect model can be formulated, which gives insights into the relationship between the different modes of variation as well as their dependence on linguistic and nonlinguistic covariates. The model is applied to the COSPRO-1 dataset, a comprehensive database of spoken Taiwanese Mandarin, containing approximately 50,000 phonetically diverse sample F 0 contours (syllables), and reveals that phonetic information is jointly carried by both amplitude and phase variation. Supplementary materials for this article are available online. PMID:26692591
ERIC Educational Resources Information Center
Lloyd, Rebecca
2015-01-01
Background: Physical Education (PE) programmes are expanding to include alternative activities yet what is missing is a conceptual model that facilitates how the learning process may be understood and assessed beyond the dominant sport-technique paradigm. Purpose: The purpose of this article was to feature the emergence of a Function-to-Flow (F2F)…
Pe'er, Guy; Henle, Klaus; Dislich, Claudia; Frank, Karin
2011-01-01
Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting ‘functional connectivity’, namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully
NASA Astrophysics Data System (ADS)
Pavlick, R.; Drewry, D. T.; Bohn, K.; Reu, B.; Kleidon, A.
2012-04-01
Dynamic Global Vegetation Models (DGVMs) typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical Plant Functional Types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity DGVM (JeDi-DGVM) as a new approach to global vegetation modelling with a richer representation of functional diversity than traditional modelling approaches based on a small number of fixed PFTs. JeDi-DGVM simulates the performance of a large number of randomly-generated plant growth strategies (PGSs), each defined by a set of 15 trait parameters which characterize various aspects of plant functioning including carbon allocation, ecophysiology and phenology. Each trait parameter is involved in one or more functional trade-offs. These trade-offs ultimately determine whether a PGS is able to survive under the climatic conditions in a given model grid cell and its performance relative to the other PGSs. The biogeochemical fluxes and land-surface properties of the individual PGSs are aggregated to the grid cell scale using a mass-based weighting scheme. Simulated global biogeochemical and biogeographical patterns are evaluated against a variety of field and satellite-based observations following a protocol established by the Carbon-Land Model Intercomparison Project. The land surface fluxes and vegetation structural properties are reasonably well simulated by JeDi-DGVM, and compare favorably with other state-of-the-art terrestrial biosphere models. This is despite the parameters describing the ecophysiological functioning and allometry of JeDi-DGVM plants evolving as a function of vegetation survival in a given climate, as opposed to typical approaches that fix land surface
Shakouri, Payman; Ordys, Andrzej; Askari, Mohamad R
2012-09-01
In the design of adaptive cruise control (ACC) system two separate control loops - an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position - are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. Control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements. PMID:22704362
Modeling approaches for active systems
NASA Astrophysics Data System (ADS)
Herold, Sven; Atzrodt, Heiko; Mayer, Dirk; Thomaier, Martin
2006-03-01
To solve a wide range of vibration problems with the active structures technology, different simulation approaches for several models are needed. The selection of an appropriate modeling strategy is depending, amongst others, on the frequency range, the modal density and the control target. An active system consists of several components: the mechanical structure, at least one sensor and actuator, signal conditioning electronics and the controller. For each individual part of the active system the simulation approaches can be different. To integrate the several modeling approaches into an active system simulation and to ensure a highly efficient and accurate calculation, all sub models must harmonize. For this purpose, structural models considered in this article are modal state-space formulations for the lower frequency range and transfer function based models for the higher frequency range. The modal state-space formulations are derived from finite element models and/or experimental modal analyses. Consequently, the structure models which are based on transfer functions are directly derived from measurements. The transfer functions are identified with the Steiglitz-McBride iteration method. To convert them from the z-domain to the s-domain a least squares solution is implemented. An analytical approach is used to derive models of active interfaces. These models are transferred into impedance formulations. To couple mechanical and electrical sub-systems with the active materials, the concept of impedance modeling was successfully tested. The impedance models are enhanced by adapting them to adequate measurements. The controller design strongly depends on the frequency range and the number of modes to be controlled. To control systems with a small number of modes, techniques such as active damping or independent modal space control may be used, whereas in the case of systems with a large number of modes or with modes that are not well separated, other control
NASA Astrophysics Data System (ADS)
Fakhri, H.; Dehghani, A.; Mojaveri, B.
Using second-order differential operators as a realization of the su(1,1) Lie algebra by the associated Laguerre functions, it is shown that the quantum states of the Calogero-Sutherland, half-oscillator and radial part of a 3D harmonic oscillator constitute the unitary representations for the same algebra. This su(1,1) Lie algebra symmetry leads to derivation of the Barut-Girardello and Klauder-Perelomov coherent states for those models. The explicit compact forms of these coherent states are calculated. Also, to realize the resolution of the identity, their corresponding positive definite measures on the complex plane are obtained in terms of the known functions.
Introducing linear functions: an alternative statistical approach
NASA Astrophysics Data System (ADS)
Nolan, Caroline; Herbert, Sandra
2015-12-01
The introduction of linear functions is the turning point where many students decide if mathematics is useful or not. This means the role of parameters and variables in linear functions could be considered to be `threshold concepts'. There is recognition that linear functions can be taught in context through the exploration of linear modelling examples, but this has its limitations. Currently, statistical data is easily attainable, and graphics or computer algebra system (CAS) calculators are common in many classrooms. The use of this technology provides ease of access to different representations of linear functions as well as the ability to fit a least-squares line for real-life data. This means these calculators could support a possible alternative approach to the introduction of linear functions. This study compares the results of an end-of-topic test for two classes of Australian middle secondary students at a regional school to determine if such an alternative approach is feasible. In this study, test questions were grouped by concept and subjected to concept by concept analysis of the means of test results of the two classes. This analysis revealed that the students following the alternative approach demonstrated greater competence with non-standard questions.
Estimating Function Approaches for Spatial Point Processes
NASA Astrophysics Data System (ADS)
Deng, Chong
Spatial point pattern data consist of locations of events that are often of interest in biological and ecological studies. Such data are commonly viewed as a realization from a stochastic process called spatial point process. To fit a parametric spatial point process model to such data, likelihood-based methods have been widely studied. However, while maximum likelihood estimation is often too computationally intensive for Cox and cluster processes, pairwise likelihood methods such as composite likelihood, Palm likelihood usually suffer from the loss of information due to the ignorance of correlation among pairs. For many types of correlated data other than spatial point processes, when likelihood-based approaches are not desirable, estimating functions have been widely used for model fitting. In this dissertation, we explore the estimating function approaches for fitting spatial point process models. These approaches, which are based on the asymptotic optimal estimating function theories, can be used to incorporate the correlation among data and yield more efficient estimators. We conducted a series of studies to demonstrate that these estmating function approaches are good alternatives to balance the trade-off between computation complexity and estimating efficiency. First, we propose a new estimating procedure that improves the efficiency of pairwise composite likelihood method in estimating clustering parameters. Our approach combines estimating functions derived from pairwise composite likeli-hood estimation and estimating functions that account for correlations among the pairwise contributions. Our method can be used to fit a variety of parametric spatial point process models and can yield more efficient estimators for the clustering parameters than pairwise composite likelihood estimation. We demonstrate its efficacy through a simulation study and an application to the longleaf pine data. Second, we further explore the quasi-likelihood approach on fitting
NASA Astrophysics Data System (ADS)
Martín-Ruiz, A.; Cambiaso, M.; Urrutia, L. F.
2016-02-01
The Green's function method is used to analyze the boundary effects produced by a Chern-Simons extension to electrodynamics. We consider the electromagnetic field coupled to a θ term that is piecewise constant in different regions of space, separated by a common interface Σ , the θ boundary, model which we will refer to as θ electrodynamics. This model provides a correct low-energy effective action for describing topological insulators. Features arising due to the presence of the boundary, such as magnetoelectric effects, are already known in Chern-Simons extended electrodynamics, and solutions for some experimental setups have been found with a specific configuration of sources. In this work we construct the static Green's function in θ electrodynamics for different geometrical configurations of the θ boundary, namely, planar, spherical and cylindrical θ -interfaces. Also, we adapt the standard Green's theorem to include the effects of the θ boundary. These are the most important results of our work, since they allow one to obtain the corresponding static electric and magnetic fields for arbitrary sources and arbitrary boundary conditions in the given geometries. Also, the method provides a well-defined starting point for either analytical or numerical approximations in the cases where the exact analytical calculations are not possible. Explicit solutions for simple cases in each of the aforementioned geometries for θ boundaries are provided. On the one hand, the adapted Green's theorem is illustrated by studying the problem of a pointlike electric charge interacting with a planar topological insulator with prescribed boundary conditions. On the other hand, we calculate the electric and magnetic static fields produced by the following sources: (i) a pointlike electric charge near a spherical θ boundary, (ii) an infinitely straight current-carrying wire near a cylindrical θ boundary and (iii) an infinitely straight uniformly charged wire near a
Arooj, Mahreen; Thangapandian, Sundarapandian; John, Shalini; Hwang, Swan; Park, Jong Keun; Lee, Keun Woo
2011-01-01
Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating “Hypo1”, it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC50) data thus successfully validating “Hypo1” by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors. PMID:22272131
NASA Astrophysics Data System (ADS)
Hibbard, Bill
2012-05-01
Orseau and Ring, as well as Dewey, have recently described problems, including self-delusion, with the behavior of agents using various definitions of utility functions. An agent's utility function is defined in terms of the agent's history of interactions with its environment. This paper argues, via two examples, that the behavior problems can be avoided by formulating the utility function in two steps: 1) inferring a model of the environment from interactions, and 2) computing utility as a function of the environment model. Basing a utility function on a model that the agent must learn implies that the utility function must initially be expressed in terms of specifications to be matched to structures in the learned model. These specifications constitute prior assumptions about the environment so this approach will not work with arbitrary environments. But the approach should work for agents designed by humans to act in the physical world. The paper also addresses the issue of self-modifying agents and shows that if provided with the possibility to modify their utility functions agents will not choose to do so, under some usual assumptions.
Kavitha, Rengarajan; Karunagaran, Subramanian; Chandrabose, Subramaniam Subhash; Lee, Keun Woo; Meganathan, Chandrasekaran
2015-12-01
Fructose catabolism starts with phosphorylation of d-fructose to fructose 1-phosphate, which is performed by ketohexokinase (KHK). Fructose metabolism may be the key to understand the long-term consumption of fructose in human's obesity, diabetes and metabolic states in western populations. The inhibition of KHK has medicinally potential roles in fructose metabolism and the metabolic syndrome. To identify the essential chemical features for KHK inhibition, a three-dimensional (3D) chemical-feature-based QSAR pharmacophore model was developed for the first time by using Discovery Studio v2.5 (DS). The best pharmacophore hypothesis (Hypo1) consisting two hydrogen bond donor, two hydrophobic features and has exhibited high correlation co-efficient (0.97), cost difference (76.1) and low RMS (0.66) value. The robustness and predictability of Hypo1 was validated by fisher's randomization method, test set, and the decoy set. Subsequently, chemical databases like NCI, Chembridge and Maybridge were screened for validated Hypo1. The screened compounds were further analyzed by applying drug-like filters such as Lipinski's rule of five, ADME properties, and molecular docking studies. Further, the highest occupied molecular orbital, lowest unoccupied molecular orbital and energy gap values were calculated for the hits compounds using density functional theory. Finally, 3 hit compounds were selected based on their good molecular interactions with key amino acids in the KHK active site, GOLD fitness score, and lowest energy gaps. PMID:26521124
Menouar, Salah; Maamache, Mustapha; Choi, Jeong Ryeol
2010-08-15
The quantum states of time-dependent coupled oscillator model for charged particles subjected to variable magnetic field are investigated using the invariant operator methods. To do this, we have taken advantage of an alternative method, so-called unitary transformation approach, available in the framework of quantum mechanics, as well as a generalized canonical transformation method in the classical regime. The transformed quantum Hamiltonian is obtained using suitable unitary operators and is represented in terms of two independent harmonic oscillators which have the same frequencies as that of the classically transformed one. Starting from the wave functions in the transformed system, we have derived the full wave functions in the original system with the help of the unitary operators. One can easily take a complete description of how the charged particle behaves under the given Hamiltonian by taking advantage of these analytical wave functions.
Transfer Function Identification Using Orthogonal Fourier Transform Modeling Functions
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.
2013-01-01
A method for transfer function identification, including both model structure determination and parameter estimation, was developed and demonstrated. The approach uses orthogonal modeling functions generated from frequency domain data obtained by Fourier transformation of time series data. The method was applied to simulation data to identify continuous-time transfer function models and unsteady aerodynamic models. Model fit error, estimated model parameters, and the associated uncertainties were used to show the effectiveness of the method for identifying accurate transfer function models from noisy data.
Meson wave function from holographic approaches
Vega, Alfredo; Schmidt, Ivan; Branz, Tanja; Gutsche, Thomas; Lyubovitskij, Valery E.
2010-08-04
We discuss the light-front wave function for the valence quark state of mesons using the AdS/CFT correspondence. We consider two kinds of wave functions obtained in different holographic Soft-Wall approaches.
Suhendi, Endi; Syariati, Rifki; Noor, Fatimah A.; Khairurrijal; Kurniasih, Neny
2014-03-24
We modeled a tunneling current in a p-n junction based on armchair graphene nanoribbons (AGNRs) by using an Airy function approach (AFA) and a transfer matrix method (TMM). We used β-type AGNRs, in which its band gap energy and electron effective mass depends on its width as given by the extended Huckel theory. It was shown that the tunneling currents evaluated by employing the AFA are the same as those obtained under the TMM. Moreover, the calculated tunneling current was proportional to the voltage bias and inversely with temperature.
Approaches for modeling magnetic nanoparticle dynamics
Reeves, Daniel B; Weaver, John B
2014-01-01
Magnetic nanoparticles are useful biological probes as well as therapeutic agents. There have been several approaches used to model nanoparticle magnetization dynamics for both Brownian as well as Néel rotation. The magnetizations are often of interest and can be compared with experimental results. Here we summarize these approaches including the Stoner-Wohlfarth approach, and stochastic approaches including thermal fluctuations. Non-equilibrium related temperature effects can be described by a distribution function approach (Fokker-Planck equation) or a stochastic differential equation (Langevin equation). Approximate models in several regimes can be derived from these general approaches to simplify implementation. PMID:25271360
Pineda, Jaime A.; Friedrich, Elisabeth V. C.; LaMarca, Kristen
2014-01-01
Autism Spectrum Disorder (ASD) is an increasingly prevalent condition with core deficits in the social domain. Understanding its neuroetiology is critical to providing insights into the relationship between neuroanatomy, physiology and social behaviors, including imitation learning, language, empathy, theory of mind, and even self-awareness. Equally important is the need to find ways to arrest its increasing prevalence and to ameliorate its symptoms. In this review, we highlight neurofeedback studies as viable treatment options for high-functioning as well as low-functioning children with ASD. Lower-functioning groups have the greatest need for diagnosis and treatment, the greatest barrier to communication, and may experience the greatest benefit if a treatment can improve function or prevent progression of the disorder at an early stage. Therefore, we focus on neurofeedback interventions combined with other kinds of behavioral conditioning to induce neuroplastic changes that can address the full spectrum of the autism phenotype. PMID:25147521
Röling, Wilfred F. M.; van Bodegom, Peter M.
2014-01-01
Molecular ecology approaches are rapidly advancing our insights into the microorganisms involved in the degradation of marine oil spills and their metabolic potentials. Yet, many questions remain open: how do oil-degrading microbial communities assemble in terms of functional diversity, species abundances and organization and what are the drivers? How do the functional properties of microorganisms scale to processes at the ecosystem level? How does mass flow among species, and which factors and species control and regulate fluxes, stability and other ecosystem functions? Can generic rules on oil-degradation be derived, and what drivers underlie these rules? How can we engineer oil-degrading microbial communities such that toxic polycyclic aromatic hydrocarbons are degraded faster? These types of questions apply to the field of microbial ecology in general. We outline how recent advances in single-species systems biology might be extended to help answer these questions. We argue that bottom-up mechanistic modeling allows deciphering the respective roles and interactions among microorganisms. In particular constraint-based, metagenome-derived community-scale flux balance analysis appears suited for this goal as it allows calculating degradation-related fluxes based on physiological constraints and growth strategies, without needing detailed kinetic information. We subsequently discuss what is required to make these approaches successful, and identify a need to better understand microbial physiology in order to advance microbial ecology. We advocate the development of databases containing microbial physiological data. Answering the posed questions is far from trivial. Oil-degrading communities are, however, an attractive setting to start testing systems biology-derived models and hypotheses as they are relatively simple in diversity and key activities, with several key players being isolated and a high availability of experimental data and approaches. PMID:24723922
Isojunno, Saana; Miller, Patrick J O
2016-01-01
The biological consequences of behavioral responses to anthropogenic noise depend on context. We explore the links between individual motivation, condition, and external constraints in a concept model and illustrate the use of motivational-behavioral states as a means to quantify the biologically relevant effects of tagging. Behavioral states were estimated from multiple streams of data in a hidden Markov model and used to test the change in foraging effort and the change in energetic success or cost given the effort. The presence of a tag boat elicited a short-term reduction in time spent in foraging states but not for proxies for success or cost within foraging states. PMID:26610996
NASA Astrophysics Data System (ADS)
Lee, Ji-Hwan; Tak, Youngjoo; Lee, Taehun; Soon, Aloysius
Ceria (CeO2-x) is widely studied as a choice electrolyte material for intermediate-temperature (~ 800 K) solid oxide fuel cells. At this temperature, maintaining its chemical stability and thermal-mechanical integrity of this oxide are of utmost importance. To understand their thermal-elastic properties, we firstly test the influence of various approximations to the density-functional theory (DFT) xc functionals on specific thermal-elastic properties of both CeO2 and Ce2O3. Namely, we consider the local-density approximation (LDA), the generalized gradient approximation (GGA-PBE) with and without additional Hubbard U as applied to the 4 f electron of Ce, as well as the recently popularized hybrid functional due to Heyd-Scuseria-Ernzehof (HSE06). Next, we then couple this to a volume-dependent Debye-Grüneisen model to determine the thermodynamic quantities of ceria at arbitrary temperatures. We find an explicit description of the strong correlation (e.g. via the DFT + U and hybrid functional approach) is necessary to have a good agreement with experimental values, in contrast to the mean-field treatment in standard xc approximations (such as LDA or GGA-PBE). We acknowledge support from Samsung Research Funding Center of Samsung Electronics (SRFC-MA1501-03).
NASA Astrophysics Data System (ADS)
Schafroth, S.; Rodríguez-Núñez, J. J.
1999-08-01
We evaluate the one-particle and double-occupied Green functions for the Hubbard model at half-filling using the moment approach of Nolting [Z. Phys. 255, 25 (1972); Grund Kurs: Theoretische Physik. 7 Viel-Teilchen-Theorie (Verlag Zimmermann-Neufang, Ulmen, 1992)]. Our starting point is a self-energy, Σ(k-->,ω), which has a single pole, Ω(k-->), with spectral weight, α(k-->), and quasiparticle lifetime, γ(k-->) [J. J. Rodríguez-Núñez and S. Schafroth, J. Phys. Condens. Matter 10, L391 (1998); J. J. Rodríguez-Núñez, S. Schafroth, and H. Beck, Physica B (to be published); (unpublished)]. In our approach, Σ(k-->,ω) becomes the central feature of the many-body problem and due to three unknown k--> parameters we have to satisfy only the first three sum rules instead of four as in the canonical formulation of Nolting [Z. Phys. 255, 25 (1972); Grund Kurs: Theoretische Physik. 7 Viel-Teilchen-Theorie (Verlag Zimmermann-Neufang, Ulmen, 1992)]. This self-energy choice forces our system to be a non-Fermi liquid for any value of the interaction, since it does not vanish at zero frequency. The one-particle Green function, G(k-->,ω), shows the fingerprint of a strongly correlated system, i.e., a double peak structure in the one-particle spectral density, A(k-->,ω), vs ω for intermediate values of the interaction. Close to the Mott insulator-transition, A(k-->,ω) becomes a wide single peak, signaling the absence of quasiparticles. Similar behavior is observed for the real and imaginary parts of the self-energy, Σ(k-->,ω). The double-occupied Green function, G2(q-->,ω), has been obtained from G(k-->,ω) by means of the equation of motion. The relation between G2(q-->,ω) and the self-energy, Σ(k-->,ω), is formally established and numerical results for the spectral function of G2(k-->,ω), χ(2)(k-->,ω)≡-(1/π)δ-->0+Im[G2(k-->,ω)], are given. Our approach represents the simplest way to include (1) lifetime effects in the moment approach of Nolting, as
Li, Xin; Carravetta, Vincenzo; Li, Cui; Monti, Susanna; Rinkevicius, Zilvinas; Ågren, Hans
2016-07-12
Motivated by the growing importance of organometallic nanostructured materials and nanoparticles as microscopic devices for diagnostic and sensing applications, and by the recent considerable development in the simulation of such materials, we here choose a prototype system - para-nitroaniline (pNA) on gold nanoparticles - to demonstrate effective strategies for designing metal nanoparticles with organic conjugates from fundamental principles. We investigated the motion, adsorption mode, and physical chemistry properties of gold-pNA particles, increasing in size, through classical molecular dynamics (MD) simulations in connection with quantum chemistry (QC) calculations. We apply the quantum mechanics-capacitance molecular mechanics method [Z. Rinkevicius et al. J. Chem. Theory Comput. 2014, 10, 989] for calculations of the properties of the conjugate nanoparticles, where time dependent density functional theory is used for the QM part and a capacitance-polarizability parametrization of the MM part, where induced dipoles and charges by metallic charge transfer are considered. Dispersion and short-range repulsion forces are included as well. The scheme is applied to one- and two-photon absorption of gold-pNA clusters increasing in size toward the nanometer scale. Charge imaging of the surface introduces red-shifts both because of altered excitation energy dependence and variation of the relative intensity of the inherent states making up for the total band profile. For the smaller nanoparticles the difference in the crystal facets are important for the spectral outcome which is also influenced by the surrounding MM environment. PMID:27224666
I. M. Robertson; A. Beaudoin; J. Lambros
2004-01-05
OAK-135 Development and validation of constitutive models for polycrystalline materials subjected to high strain rate loading over a range of temperatures are needed to predict the response of engineering materials to in-service type conditions (foreign object damage, high-strain rate forging, high-speed sheet forming, deformation behavior during forming, response to extreme conditions, etc.). To account accurately for the complex effects that can occur during extreme and variable loading conditions, requires significant and detailed computational and modeling efforts. These efforts must be closely coupled with precise and targeted experimental measurements that not only verify the predictions of the models, but also provide input about the fundamental processes responsible for the macroscopic response. Achieving this coupling between modeling and experimentation is the guiding principle of this program. Specifically, this program seeks to bridge the length scale between discrete dislocation interactions with grain boundaries and continuum models for polycrystalline plasticity. Achieving this goal requires incorporating these complex dislocation-interface interactions into the well-defined behavior of single crystals. Despite the widespread study of metal plasticity, this aspect is not well understood for simple loading conditions, let alone extreme ones. Our experimental approach includes determining the high-strain rate response as a function of strain and temperature with post-mortem characterization of the microstructure, quasi-static testing of pre-deformed material, and direct observation of the dislocation behavior during reloading by using the in situ transmission electron microscope deformation technique. These experiments will provide the basis for development and validation of physically-based constitutive models, which will include dislocation-grain boundary interactions for polycrystalline systems. One aspect of the program will involve the dire ct
Holland, Alissa K; Carmona, Joseph E; Harrison, David W
2012-01-01
Regulatory control of emotions and expressive fluency (verbal or design) have historically been associated with the frontal lobes. Moreover, research has demonstrated the importance of cerebral laterality with a prominent role of the right frontal regions in the regulation of negative affect (anger, hostility) and in the fluent production of designs rather than verbal fluency. In the present research, participants identified with high and with low levels of hostility were evaluated on a design fluency test twice in one experimental session. Before the second administration of the fluency test, each participant underwent physiological (cold pressor) stress. It was hypothesized that diminished right frontal capacity in high-hostile men would be evident through lowered performance on this cognitive stressor. Convergent validity of the capacity model was supported wherein high-hostile men evidenced reduced delta magnitude over the right frontal region after exposure to the physiological stressor but failed to maintain consistent levels of right cerebral activation across conditions. The results suggest an inability for high-hostile men to maintain stable levels of cerebral activation after exposure to physiological and cognitive stress. Moreover, low-hostiles showed enhanced cognitive performance on the design task with lower levels of arousal (heightened delta magnitude). In contrast, reduced arousal yielded increased executive deficits in high-hostiles as evidenced through increased perseverative errors on the design fluency task. PMID:22091622
Borodovsky, M.
2013-04-11
Algorithmic methods for gene prediction have been developed and successfully applied to many different prokaryotic genome sequences. As the set of genes in a particular genome is not homogeneous with respect to DNA sequence composition features, the GeneMark.hmm program utilizes two Markov models representing distinct classes of protein coding genes denoted "typical" and "atypical". Atypical genes are those whose DNA features deviate significantly from those classified as typical and they represent approximately 10% of any given genome. In addition to the inherent interest of more accurately predicting genes, the atypical status of these genes may also reflect their separate evolutionary ancestry from other genes in that genome. We hypothesize that atypical genes are largely comprised of those genes that have been relatively recently acquired through lateral gene transfer (LGT). If so, what fraction of atypical genes are such bona fide LGTs? We have made atypical gene predictions for all fully completed prokaryotic genomes; we have been able to compare these results to other "surrogate" methods of LGT prediction.
Sun, Haitao; Ryno, Sean; Zhong, Cheng; Ravva, Mahesh Kumar; Sun, Zhenrong; Körzdörfer, Thomas; Brédas, Jean-Luc
2016-06-14
We propose a new methodology for the first-principles description of the electronic properties relevant for charge transport in organic molecular crystals. This methodology, which is based on the combination of a nonempirical, optimally tuned range-separated hybrid functional with the polarizable continuum model, is applied to a series of eight representative molecular semiconductor crystals. We show that it provides ionization energies, electron affinities, and transport gaps in very good agreement with experimental values, as well as with the results of many-body perturbation theory within the GW approximation at a fraction of the computational costs. Hence, this approach represents an easily applicable and computationally efficient tool to estimate the gas-to-crystal phase shifts of the frontier-orbital quasiparticle energies in organic electronic materials. PMID:27183355
Muccioli, Luca; D'Avino, Gabriele; Berardi, Roberto; Orlandi, Silvia; Pizzirusso, Antonio; Ricci, Matteo; Roscioni, Otello Maria; Zannoni, Claudio
2014-01-01
The molecular organization of functional organic materials is one of the research areas where the combination of theoretical modeling and experimental determinations is most fruitful. Here we present a brief summary of the simulation approaches used to investigate the inner structure of organic materials with semiconducting behavior, paying special attention to applications in organic photovoltaics and clarifying the often obscure jargon hindering the access of newcomers to the literature of the field. Special attention is paid to the choice of the computational "engine" (Monte Carlo or Molecular Dynamics) used to generate equilibrium configurations of the molecular system under investigation and, more importantly, to the choice of the chemical details in describing the molecular interactions. Recent literature dealing with the simulation of organic semiconductors is critically reviewed in order of increasing complexity of the system studied, from low molecular weight molecules to semiflexible polymers, including the challenging problem of determining the morphology of heterojunctions between two different materials. PMID:24322782
NASA Astrophysics Data System (ADS)
Lee, Taehun; Soon, Aloysius
2012-02-01
For high-temperature applications, the chemical stability, as well as the mechanical integrity of the oxide material used is of utmost importance. Solving these problems demands a thorough and fundamental understanding of their thermal-elastic properties. In this work, we report density-functional theory (DFT) calculations to investigate the influence of the xc functional on specific thermal-elastic properties of some common oxides CeO2, Cu2O, and MgO. Namely, we consider the local-density approximation (LDA), the generalized gradient approximation due to Perdew, Burke, and Ernzerhof (GGA-PBE), as well as a recently popularized hybrid functional due to Heyd-Scuseria-Ernzehof (HSE06). In addition, we will also report DFT+U results where we introduce a Hubbard U term to the Cu 3d and the Ce 4f states. Upon obtaining the DFT total energies, we then couple this to a volume-dependent Debye-Gr"uneisen model [1] to determine the thermodynamic quantities of these oxides at arbitrary pressures and temperatures. We find an explicit description of the strong correlation (e.g. via the DFT+U approach and using HSE06) is necessary to have a good agreement with experimental values. [1] A. Otero-de-la-Roza, D. Abbasi-P'erez et al. Com. Phys. Com. 182 (2011) 2232
Functional Risk Modeling for Lunar Surface Systems
NASA Technical Reports Server (NTRS)
Thomson, Fraser; Mathias, Donovan; Go, Susie; Nejad, Hamed
2010-01-01
We introduce an approach to risk modeling that we call functional modeling , which we have developed to estimate the capabilities of a lunar base. The functional model tracks the availability of functions provided by systems, in addition to the operational state of those systems constituent strings. By tracking functions, we are able to identify cases where identical functions are provided by elements (rovers, habitats, etc.) that are connected together on the lunar surface. We credit functional diversity in those cases, and in doing so compute more realistic estimates of operational mode availabilities. The functional modeling approach yields more realistic estimates of the availability of the various operational modes provided to astronauts by the ensemble of surface elements included in a lunar base architecture. By tracking functional availability the effects of diverse backup, which often exists when two or more independent elements are connected together, is properly accounted for.
An Inverse Approach for Elucidating Dendritic Function
Torben-Nielsen, Benjamin; Stiefel, Klaus M.
2010-01-01
We outline an inverse approach for investigating dendritic function–structure relationships by optimizing dendritic trees for a priori chosen computational functions. The inverse approach can be applied in two different ways. First, we can use it as a “hypothesis generator” in which we optimize dendrites for a function of general interest. The optimization yields an artificial dendrite that is subsequently compared to real neurons. This comparison potentially allows us to propose hypotheses about the function of real neurons. In this way, we investigated dendrites that optimally perform input-order detection. Second, we can use it as a “function confirmation” by optimizing dendrites for functions hypothesized to be performed by classes of neurons. If the optimized, artificial, dendrites resemble the dendrites of real neurons the artificial dendrites corroborate the hypothesized function of the real neuron. Moreover, properties of the artificial dendrites can lead to predictions about yet unmeasured properties. In this way, we investigated wide-field motion integration performed by the VS cells of the fly visual system. In outlining the inverse approach and two applications, we also elaborate on the nature of dendritic function. We furthermore discuss the role of optimality in assigning functions to dendrites and point out interesting future directions. PMID:21258425
Detection of Differential Item Functioning Using the Lasso Approach
ERIC Educational Resources Information Center
Magis, David; Tuerlinckx, Francis; De Boeck, Paul
2015-01-01
This article proposes a novel approach to detect differential item functioning (DIF) among dichotomously scored items. Unlike standard DIF methods that perform an item-by-item analysis, we propose the "LR lasso DIF method": logistic regression (LR) model is formulated for all item responses. The model contains item-specific intercepts,…
Shankar Subramaniam
2009-04-01
This final project report summarizes progress made towards the objectives described in the proposal entitled “Developing New Mathematical Models for Multiphase Flows Based on a Fundamental Probability Density Function Approach”. Substantial progress has been made in theory, modeling and numerical simulation of turbulent multiphase flows. The consistent mathematical framework based on probability density functions is described. New models are proposed for turbulent particle-laden flows and sprays.
I. Robertson; A. Beaudoin; J. Lambros
2005-01-31
Development and validation of constitutive models for polycrystalline materials subjected to high strain rate loading over a range of temperatures are needed to predict the response of engineering materials to in-service type conditions (foreign object damage, high-strain rate forging, high-speed sheet forming, deformation behavior during forming, response to extreme conditions, etc.). To account accurately for the complex effects that can occur during extreme and variable loading conditions, requires significant and detailed computational and modeling efforts. These efforts must be closely coupled with precise and targeted experimental measurements that not only verify the predictions of the models, but also provide input about the fundamental processes responsible for the macroscopic response. Achieving this coupling between modeling and experimentation is the guiding principle of this program. Specifically, this program seeks to bridge the length scale between discrete dislocation interactions with grain boundaries and continuum models for polycrystalline plasticity. Achieving this goal requires incorporating these complex dislocation-interface interactions into the well-defined behavior of single crystals. Despite the widespread study of metal plasticity, this aspect is not well understood for simple loading conditions, let alone extreme ones. Our experimental approach includes determining the high-strain rate response as a function of strain and temperature with post-mortem characterization of the microstructure, quasi-static testing of pre-deformed material, and direct observation of the dislocation behavior during reloading by using the in situ transmission electron microscope deformation technique. These experiments will provide the basis for development and validation of physically-based constitutive models, which will include dislocation-grain boundary interactions for polycrystalline systems. One aspect of the program will involve the direct observation
Quadratic function approaching method for magnetotelluric soundingdata inversion
Liangjun, Yan; Wenbao, Hu; Zhang, Keni
2004-04-05
The quadratic function approaching method (QFAM) is introduced for magnetotelluric sounding (MT) data inversion. The method takes the advantage of that quadratic function has single extreme value, which avoids leading to an inversion solution for local minimum and ensures the solution for global minimization of an objective function. The method does not need calculation of sensitivity matrix and not require a strict initial earth model. Examples for synthetic data and field measurement data indicate that the proposed inversion method is effective.
New approaches to probing Minkowski functionals
NASA Astrophysics Data System (ADS)
Munshi, D.; Smidt, J.; Cooray, A.; Renzi, A.; Heavens, A.; Coles, P.
2013-10-01
We generalize the concept of the ordinary skew-spectrum to probe the effect of non-Gaussianity on the morphology of cosmic microwave background (CMB) maps in several domains: in real space (where they are commonly known as cumulant-correlators), and in harmonic and needlet bases. The essential aim is to retain more information than normally contained in these statistics, in order to assist in determining the source of any measured non-Gaussianity, in the same spirit as Munshi & Heavens skew-spectra were used to identify foreground contaminants to the CMB bispectrum in Planck data. Using a perturbative series to construct the Minkowski functionals (MFs), we provide a pseudo-C_ℓ based approach in both harmonic and needlet representations to estimate these spectra in the presence of a mask and inhomogeneous noise. Assuming homogeneous noise, we present approximate expressions for error covariance for the purpose of joint estimation of these spectra. We present specific results for four different models of primordial non-Gaussianity local, equilateral, orthogonal and enfolded models, as well as non-Gaussianity caused by unsubtracted point sources. Closed form results of next-order corrections to MFs too are obtained in terms of a quadruplet of kurt-spectra. We also use the method of modal decomposition of the bispectrum and trispectrum to reconstruct the MFs as an alternative method of reconstruction of morphological properties of CMB maps. Finally, we introduce the odd-parity skew-spectra to probe the odd-parity bispectrum and its impact on the morphology of the CMB sky. Although developed for the CMB, the generic results obtained here can be useful in other areas of cosmology.
Functional renormalization group approach to noncollinear magnets
NASA Astrophysics Data System (ADS)
Delamotte, B.; Dudka, M.; Mouhanna, D.; Yabunaka, S.
2016-02-01
A functional renormalization group approach to d -dimensional, N -component, noncollinear magnets is performed using various truncations of the effective action relevant to study their long distance behavior. With help of these truncations we study the existence of a stable fixed point for dimensions between d =2.8 and d =4 for various values of N focusing on the critical value Nc(d ) that, for a given dimension d , separates a first-order region for N
Modelling of graphene functionalization.
Pykal, Martin; Jurečka, Petr; Karlický, František; Otyepka, Michal
2016-03-01
Graphene has attracted great interest because of its remarkable properties and numerous potential applications. A comprehensive understanding of its structural and dynamic properties and those of its derivatives will be required to enable the design and optimization of sophisticated new nanodevices. While it is challenging to perform experimental studies on nanoscale systems at the atomistic level, this is the 'native' scale of computational chemistry. Consequently, computational methods are increasingly being used to complement experimental research in many areas of chemistry and nanotechnology. However, it is difficult for non-experts to get to grips with the plethora of computational tools that are available and their areas of application. This perspective briefly describes the available theoretical methods and models for simulating graphene functionalization based on quantum and classical mechanics. The benefits and drawbacks of the individual methods are discussed, and we provide numerous examples showing how computational methods have provided new insights into the physical and chemical features of complex systems including graphene and graphene derivatives. We believe that this overview will help non-expert readers to understand this field and its great potential. PMID:26323438
Modeling of functional brain imaging data
NASA Astrophysics Data System (ADS)
Horwitz, Barry
1999-03-01
The richness and complexity of data sets obtained from functional neuroimaging studies of human cognitive behavior, using techniques such as positron emission tomography and functional magnetic resonance imaging, have until recently not been exploited by computational neural modeling methods. In this article, following a brief introduction to functional neuroimaging methodology, two neural modeling approaches for use with functional brain imaging data are described. One, which uses structural equation modeling, examines the effective functional connections between various brain regions during specific cognitive tasks. The second employs large-scale neural modeling to relate functional neuroimaging signals in multiple, interconnected brain regions to the underlying neurobiological time-varying activities in each region. These two modeling procedures are illustrated using a visual processing paradigm.
Approaches toward functional fluid supported lipid bilayers
NASA Astrophysics Data System (ADS)
Weng, Kevin Chun-I.
Planar supported lipid bilayers (PSLBs) have attracted immense interest for their properties as model cell membranes and for potential applications in biosensors and lab-on-a-chip devices. Our study covers three aspects of the construction, characterization, and application of functional PSLBs. First, a combination of micro-fabrication, the Langmuir-Blodgett (LB) technique, and fusion of extruded small unilamellar vesicle (E-SUVs) in sequence was used to create polymer-cushioned PSLBs in a microarray format. Random lipo-glycocopolymer mixed with L-alpha-phosphatidylcholine (egg PC) was compressed at the air-water interface and transferred onto the photoresist-patterned substrate by the LB technique to achieve spatially directed deposition. Construction of planar bilayers in an aqueous environment was subsequently completed by vesicle fusion. Epifluorescence microscopy, fluorescence recovery after photobleaching (FRAP), and electrophoresis-relaxation were employed to examine the resulting patterns as well as to verify the two-dimensional mobility of the supported membrane systems. This approach could possibly provide a useful route to create functional arrays of polymer-supported lipid bilayers. Second, we report the formation of fluid planar biomembranes on hydrophilic silica aerogels and xerogels. When the aerogel/xerogel was pre-hydrated and then allowed to incubate in egg PC E-SUV solution, lipid bilayers were formed due to the favorable interaction of vesicles with the hydroxyl-abundant silica surface. FRAP was used to determine the lateral diffusivity of membranes on aerogels. Quartz crystal microbalance with dissipation monitoring (QCM-D) was used to monitor the kinetics of the irreversible adsorption and fusion of vesicles into bilayers on xerogel thin films. Finally, we compared the formation of PSLBs with and without incorporation of monosialoganglioside GM1 (GM1) as the antigen for in situ antibody binding. Quantifiable differences were observed in the
Modeling Protein Domain Function
ERIC Educational Resources Information Center
Baker, William P.; Jones, Carleton "Buck"; Hull, Elizabeth
2007-01-01
This simple but effective laboratory exercise helps students understand the concept of protein domain function. They use foam beads, Styrofoam craft balls, and pipe cleaners to explore how domains within protein active sites interact to form a functional protein. The activity allows students to gain content mastery and an understanding of the…
ERIC Educational Resources Information Center
Metzger, Jesse A.
2010-01-01
The aims of this research were to 1) examine the qualities for which applicants are selected for entrance into clinical psychology Ph.D. programs, and 2) investigate the prevalence and impact of the mentor-model approach to admissions on multiple domains of programs and the field at large. Fifty Directors of Clinical Training (DCTs) provided data…
Mixed Languages: A Functional-Communicative Approach.
ERIC Educational Resources Information Center
Matras, Yaron
2000-01-01
Argues that the compartmentalism of structures observed in mixed languages is the result of the cumulative effect of different contact mechanisms. These mechanisms are defined in terms of the cognitive and communicative motivations that lead speakers to model certain functions of language on an alternative linguistic system: lexical…
Pharmacological approaches to restore mitochondrial function
Andreux, Pénélope A.; Houtkooper, Riekelt H.; Auwerx, Johan
2014-01-01
Mitochondrial dysfunction is not only a hallmark of rare inherited mitochondrial disorders, but is also implicated in age-related diseases, including those that affect the metabolic and nervous system, such as type 2 diabetes and Parkinson’s disease. Numerous pathways maintain and/or restore proper mitochondrial function, including mitochondrial biogenesis, mitochondrial dynamics, mitophagy, and the mitochondrial unfolded protein response. New and powerful phenotypic assays in cell-based models, as well as multicellular organisms, have been developed to explore these different aspects of mitochondrial function. Modulating mitochondrial function has therefore emerged as an attractive therapeutic strategy for a range of diseases, which has spurred active drug discovery efforts in this area. PMID:23666487
Computational Models for Neuromuscular Function
Valero-Cuevas, Francisco J.; Hoffmann, Heiko; Kurse, Manish U.; Kutch, Jason J.; Theodorou, Evangelos A.
2011-01-01
Computational models of the neuromuscular system hold the potential to allow us to reach a deeper understanding of neuromuscular function and clinical rehabilitation by complementing experimentation. By serving as a means to distill and explore specific hypotheses, computational models emerge from prior experimental data and motivate future experimental work. Here we review computational tools used to understand neuromuscular function including musculoskeletal modeling, machine learning, control theory, and statistical model analysis. We conclude that these tools, when used in combination, have the potential to further our understanding of neuromuscular function by serving as a rigorous means to test scientific hypotheses in ways that complement and leverage experimental data. PMID:21687779
Estimating variability in functional images using a synthetic resampling approach
Maitra, R.; O`Sullivan, F.
1996-12-31
Functional imaging of biologic parameters like in vivo tissue metabolism is made possible by Positron Emission Tomography (PET). Many techniques, such as mixture analysis, have been suggested for extracting such images from dynamic sequences of reconstructed PET scans. Methods for assessing the variability in these functional images are of scientific interest. The nonlinearity of the methods used in the mixture analysis approach makes analytic formulae for estimating variability intractable. The usual resampling approach is infeasible because of the prohibitive computational effort in simulating a number of sinogram. datasets, applying image reconstruction, and generating parametric images for each replication. Here we introduce an approach that approximates the distribution of the reconstructed PET images by a Gaussian random field and generates synthetic realizations in the imaging domain. This eliminates the reconstruction steps in generating each simulated functional image and is therefore practical. Results of experiments done to evaluate the approach on a model one-dimensional problem are very encouraging. Post-processing of the estimated variances is seen to improve the accuracy of the estimation method. Mixture analysis is used to estimate functional images; however, the suggested approach is general enough to extend to other parametric imaging methods.
Computational modeling approaches in gonadotropin signaling.
Ayoub, Mohammed Akli; Yvinec, Romain; Crépieux, Pascale; Poupon, Anne
2016-07-01
Follicle-stimulating hormone and LH play essential roles in animal reproduction. They exert their function through binding to their cognate receptors, which belong to the large family of G protein-coupled receptors. This recognition at the plasma membrane triggers a plethora of cellular events, whose processing and integration ultimately lead to an adapted biological response. Understanding the nature and the kinetics of these events is essential for innovative approaches in drug discovery. The study and manipulation of such complex systems requires the use of computational modeling approaches combined with robust in vitro functional assays for calibration and validation. Modeling brings a detailed understanding of the system and can also be used to understand why existing drugs do not work as well as expected, and how to design more efficient ones. PMID:27165991
Synchronization-based approach for detecting functional activation of brain
NASA Astrophysics Data System (ADS)
Hong, Lei; Cai, Shi-Min; Zhang, Jie; Zhuo, Zhao; Fu, Zhong-Qian; Zhou, Pei-Ling
2012-09-01
In this paper, we investigate a synchronization-based, data-driven clustering approach for the analysis of functional magnetic resonance imaging (fMRI) data, and specifically for detecting functional activation from fMRI data. We first define a new measure of similarity between all pairs of data points (i.e., time series of voxels) integrating both complete phase synchronization and amplitude correlation. These pairwise similarities are taken as the coupling between a set of Kuramoto oscillators, which in turn evolve according to a nearest-neighbor rule. As the network evolves, similar data points naturally synchronize with each other, and distinct clusters will emerge. The clustering behavior of the interaction network of the coupled oscillators, therefore, mirrors the clustering property of the original multiple time series. The clustered regions whose cross-correlation coefficients are much greater than other regions are considered as the functionally activated brain regions. The analysis of fMRI data in auditory and visual areas shows that the recognized brain functional activations are in complete correspondence with those from the general linear model of statistical parametric mapping, but with a significantly lower time complexity. We further compare our results with those from traditional K-means approach, and find that our new clustering approach can distinguish between different response patterns more accurately and efficiently than the K-means approach, and therefore more suitable in detecting functional activation from event-related experimental fMRI data.
Response Surface Modeling Using Multivariate Orthogonal Functions
NASA Technical Reports Server (NTRS)
Morelli, Eugene A.; DeLoach, Richard
2001-01-01
A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.
Nonaka, Junko
2012-01-01
The objective of this study was to develop a probabilistic model to predict the end of lag time (λ) during the growth of Bacillus cereus vegetative cells as a function of temperature, pH, and salt concentration using logistic regression. The developed λ model was subsequently combined with a logistic differential equation to simulate bacterial numbers over time. To develop a novel model for λ, we determined whether bacterial growth had begun, i.e., whether λ had ended, at each time point during the growth kinetics. The growth of B. cereus was evaluated by optical density (OD) measurements in culture media for various pHs (5.5 ∼ 7.0) and salt concentrations (0.5 ∼ 2.0%) at static temperatures (10 ∼ 20°C). The probability of the end of λ was modeled using dichotomous judgments obtained at each OD measurement point concerning whether a significant increase had been observed. The probability of the end of λ was described as a function of time, temperature, pH, and salt concentration and showed a high goodness of fit. The λ model was validated with independent data sets of B. cereus growth in culture media and foods, indicating acceptable performance. Furthermore, the λ model, in combination with a logistic differential equation, enabled a simulation of the population of B. cereus in various foods over time at static and/or fluctuating temperatures with high accuracy. Thus, this newly developed modeling procedure enables the description of λ using observable environmental parameters without any conceptual assumptions and the simulation of bacterial numbers over time with the use of a logistic differential equation. PMID:22729541
Koseki, Shige; Nonaka, Junko
2012-09-01
The objective of this study was to develop a probabilistic model to predict the end of lag time (λ) during the growth of Bacillus cereus vegetative cells as a function of temperature, pH, and salt concentration using logistic regression. The developed λ model was subsequently combined with a logistic differential equation to simulate bacterial numbers over time. To develop a novel model for λ, we determined whether bacterial growth had begun, i.e., whether λ had ended, at each time point during the growth kinetics. The growth of B. cereus was evaluated by optical density (OD) measurements in culture media for various pHs (5.5 ∼ 7.0) and salt concentrations (0.5 ∼ 2.0%) at static temperatures (10 ∼ 20°C). The probability of the end of λ was modeled using dichotomous judgments obtained at each OD measurement point concerning whether a significant increase had been observed. The probability of the end of λ was described as a function of time, temperature, pH, and salt concentration and showed a high goodness of fit. The λ model was validated with independent data sets of B. cereus growth in culture media and foods, indicating acceptable performance. Furthermore, the λ model, in combination with a logistic differential equation, enabled a simulation of the population of B. cereus in various foods over time at static and/or fluctuating temperatures with high accuracy. Thus, this newly developed modeling procedure enables the description of λ using observable environmental parameters without any conceptual assumptions and the simulation of bacterial numbers over time with the use of a logistic differential equation. PMID:22729541
Green functions of graphene: An analytic approach
NASA Astrophysics Data System (ADS)
Lawlor, James A.; Ferreira, Mauro S.
2015-04-01
In this article we derive the lattice Green Functions (GFs) of graphene using a Tight Binding Hamiltonian incorporating both first and second nearest neighbour hoppings and allowing for a non-orthogonal electron wavefunction overlap. It is shown how the resulting GFs can be simplified from a double to a single integral form to aid computation, and that when considering off-diagonal GFs in the high symmetry directions of the lattice this single integral can be approximated very accurately by an algebraic expression. By comparing our results to the conventional first nearest neighbour model commonly found in the literature, it is apparent that the extended model leads to a sizeable change in the electronic structure away from the linear regime. As such, this article serves as a blueprint for researchers who wish to examine quantities where these considerations are important.
Unified approach to partition functions of RNA secondary structures.
Bundschuh, Ralf
2014-11-01
RNA secondary structure formation is a field of considerable biological interest as well as a model system for understanding generic properties of heteropolymer folding. This system is particularly attractive because the partition function and thus all thermodynamic properties of RNA secondary structure ensembles can be calculated numerically in polynomial time for arbitrary sequences and homopolymer models admit analytical solutions. Such solutions for many different aspects of the combinatorics of RNA secondary structure formation share the property that the final solution depends on differences of statistical weights rather than on the weights alone. Here, we present a unified approach to a large class of problems in the field of RNA secondary structure formation. We prove a generic theorem for the calculation of RNA folding partition functions. Then, we show that this approach can be applied to the study of the molten-native transition, denaturation of RNA molecules, as well as to studies of the glass phase of random RNA sequences. PMID:24177391
Distribution function approach to redshift space distortions
Seljak, Uroš; McDonald, Patrick E-mail: pvmcdonald@lbl.gov
2011-11-01
We develop a phase space distribution function approach to redshift space distortions (RSD), in which the redshift space density can be written as a sum over velocity moments of the distribution function. These moments are density weighted and have well defined physical interpretation: their lowest orders are density, momentum density, and stress energy density. The series expansion is convergent if kμu/aH < 1, where k is the wavevector, H the Hubble parameter, u the typical gravitational velocity and μ = cos θ, with θ being the angle between the Fourier mode and the line of sight. We perform an expansion of these velocity moments into helicity modes, which are eigenmodes under rotation around the axis of Fourier mode direction, generalizing the scalar, vector, tensor decomposition of perturbations to an arbitrary order. We show that only equal helicity moments correlate and derive the angular dependence of the individual contributions to the redshift space power spectrum. We show that the dominant term of μ{sup 2} dependence on large scales is the cross-correlation between the density and scalar part of momentum density, which can be related to the time derivative of the matter power spectrum. Additional terms contributing to μ{sup 2} and dominating on small scales are the vector part of momentum density-momentum density correlations, the energy density-density correlations, and the scalar part of anisotropic stress density-density correlations. The second term is what is usually associated with the small scale Fingers-of-God damping and always suppresses power, but the first term comes with the opposite sign and always adds power. Similarly, we identify 7 terms contributing to μ{sup 4} dependence. Some of the advantages of the distribution function approach are that the series expansion converges on large scales and remains valid in multi-stream situations. We finish with a brief discussion of implications for RSD in galaxies relative to dark matter
HEDR modeling approach: Revision 1
Shipler, D.B.; Napier, B.A.
1994-05-01
This report is a revision of the previous Hanford Environmental Dose Reconstruction (HEDR) Project modeling approach report. This revised report describes the methods used in performing scoping studies and estimating final radiation doses to real and representative individuals who lived in the vicinity of the Hanford Site. The scoping studies and dose estimates pertain to various environmental pathways during various periods of time. The original report discussed the concepts under consideration in 1991. The methods for estimating dose have been refined as understanding of existing data, the scope of pathways, and the magnitudes of dose estimates were evaluated through scoping studies.
Calculus of Functions and Their Inverses: A Unified Approach
ERIC Educational Resources Information Center
Krishnan, Srilal N.
2006-01-01
In this pedagogical article, I explore a unified approach in obtaining the derivatives of functions and their inverses by adopting a guided self-discovery approach. I begin by finding the derivative of the exponential functions and the derivative of their inverses, the logarithmic functions. I extend this approach to generate formulae for the…
The NJL Model for Quark Fragmentation Functions
T. Ito, W. Bentz, I. Cloet, A W Thomas, K. Yazaki
2009-10-01
A description of fragmentation functions which satisfy the momentum and isospin sum rules is presented in an effective quark theory. Concentrating on the pion fragmentation function, we first explain the reason why the elementary (lowest order) fragmentation process q → qπ is completely inadequate to describe the empirical data, although the “crossed” process π → qq describes the quark distribution functions in the pion reasonably well. Then, taking into account cascade-like processes in a modified jet-model approach, we show that the momentum and isospin sum rules can be satisfied naturally without introducing any ad-hoc parameters. We present numerical results for the Nambu-Jona-Lasinio model in the invariant mass regularization scheme, and compare the results with the empirical parametrizations. We argue that this NJL-jet model provides a very useful framework to calculate the fragmentation functions in an effective chiral quark theory.
Piehler, Timothy F; Bloomquist, Michael L; August, Gerald J; Gewirtz, Abigail H; Lee, Susanne S; Lee, Wendy S C
2014-01-01
A culturally diverse sample of formerly homeless youth (ages 6-12) and their families (n = 223) participated in a cluster randomized controlled trial of the Early Risers conduct problems prevention program in a supportive housing setting. Parents provided 4 annual behaviorally-based ratings of executive functioning (EF) and conduct problems, including at baseline, over 2 years of intervention programming, and at a 1-year follow-up assessment. Using intent-to-treat analyses, a multilevel latent growth model revealed that the intervention group demonstrated reduced growth in conduct problems over the 4 assessment points. In order to examine mediation, a multilevel parallel process latent growth model was used to simultaneously model growth in EF and growth in conduct problems along with intervention status as a covariate. A significant mediational process emerged, with participation in the intervention promoting growth in EF, which predicted negative growth in conduct problems. The model was consistent with changes in EF fully mediating intervention-related changes in youth conduct problems over the course of the study. These findings highlight the critical role that EF plays in behavioral change and lends further support to its importance as a target in preventive interventions with populations at risk for conduct problems. PMID:24141709
Piehler, Timothy F.; Bloomquist, Michael L.; August, Gerald J.; Gewirtz, Abigail H.; Lee, Susanne S.; Lee, Wendy S. C.
2013-01-01
A culturally diverse sample of formerly homeless youth (ages 6 – 12) and their families (n=223) participated in a cluster randomized controlled trial of the Early Risers conduct problems prevention program in a supportive housing setting. Parents provided 4 annual behaviorally-based ratings of executive functioning (EF) and conduct problems, including at baseline, over 2 years of intervention programming, and at a 1-year follow-up assessment. Using intent-to-treat analyses, a multilevel latent growth model revealed that the intervention group demonstrated reduced growth in conduct problems over the 4 assessment points. In order to examine mediation, a multilevel parallel process latent growth model was used to simultaneously model growth in EF and growth in conduct problems along with intervention status as a covariate. A significant mediational process emerged, with participation in the intervention promoting growth in EF, which predicted negative growth in conduct problems. The model was consistent with changes in EF fully mediating intervention-related changes in youth conduct problems over the course of the study. These findings highlight the critical role that EF plays in behavioral change and lends further support to its importance as a target in preventive interventions with populations at risk for conduct problems. PMID:24141709
The Linearized Kinetic Equation -- A Functional Analytic Approach
NASA Astrophysics Data System (ADS)
Brinkmann, Ralf Peter
2009-10-01
Kinetic models of plasma phenomena are difficult to address for two reasons. They i) are given as systems of nonlinear coupled integro-differential equations, and ii) involve generally six-dimensional distribution functions f(r,v,t). In situations which can be addressed in a linear regime, the first difficulty disappears, but the second one still poses considerable practical problems. This contribution presents an abstract approach to linearized kinetic theory which employs the methods of functional analysis. A kinetic electron equation with elastic electron-neutral interaction is studied in the electrostatic approximation. Under certain boundary conditions, a nonlinear functional, the kinetic free energy, exists which has the properties of a Lyapunov functional. In the linear regime, the functional becomes a quadratic form which motivates the definition of a bilinear scalar product, turning the space of all distribution functions into a Hilbert space. The linearized kinetic equation can then be described in terms of dynamical operators with well-defined properties. Abstract solutions can be constructed which have mathematically plausible properties. As an example, the formalism is applied to the example of the multipole resonance probe (MRP). Under the assumption of a Maxwellian background distribution, the kinetic model of that diagnostics device is compared to a previously investigated fluid model.
Modeling Approaches in Planetary Seismology
NASA Technical Reports Server (NTRS)
Weber, Renee; Knapmeyer, Martin; Panning, Mark; Schmerr, Nick
2014-01-01
Of the many geophysical means that can be used to probe a planet's interior, seismology remains the most direct. Given that the seismic data gathered on the Moon over 40 years ago revolutionized our understanding of the Moon and are still being used today to produce new insight into the state of the lunar interior, it is no wonder that many future missions, both real and conceptual, plan to take seismometers to other planets. To best facilitate the return of high-quality data from these instruments, as well as to further our understanding of the dynamic processes that modify a planet's interior, various modeling approaches are used to quantify parameters such as the amount and distribution of seismicity, tidal deformation, and seismic structure on and of the terrestrial planets. In addition, recent advances in wavefield modeling have permitted a renewed look at seismic energy transmission and the effects of attenuation and scattering, as well as the presence and effect of a core, on recorded seismograms. In this chapter, we will review these approaches.
Nonperturbative approach to the attractive Hubbard model
Allen, S.; Tremblay, A.-M. S.
2001-08-15
A nonperturbative approach to the single-band attractive Hubbard model is presented in the general context of functional-derivative approaches to many-body theories. As in previous work on the repulsive model, the first step is based on a local-field-type ansatz, on enforcement of the Pauli principle and a number of crucial sumrules. The Mermin-Wagner theorem in two dimensions is automatically satisfied. At this level, two-particle self-consistency has been achieved. In the second step of the approximation, an improved expression for the self-energy is obtained by using the results of the first step in an exact expression for the self-energy, where the high- and low-frequency behaviors appear separately. The result is a cooperon-like formula. The required vertex corrections are included in this self-energy expression, as required by the absence of a Migdal theorem for this problem. Other approaches to the attractive Hubbard model are critically compared. Physical consequences of the present approach and agreement with Monte Carlo simulations are demonstrated in the accompanying paper (following this one).
NASA Astrophysics Data System (ADS)
Choubey, Sanjay K.; Mariadasse, Richard; Rajendran, Santhosh; Jeyaraman, Jeyakanthan
2016-12-01
Overexpression of HDAC1, a member of Class I histone deacetylase is reported to be implicated in breast cancer. Epigenetic alteration in carcinogenesis has been the thrust of research for few decades. Increased deacetylation leads to accelerated cell proliferation, cell migration, angiogenesis and invasion. HDAC1 is pronounced as the potential drug target towards the treatment of breast cancer. In this study, the biochemical potential of 6-aminonicotinamide derivatives was rationalized. Five point pharmacophore model with one hydrogen-bond acceptor (A3), two hydrogen-bond donors (D5, D6), one ring (R12) and one hydrophobic group (H8) was developed using 6-aminonicotinamide derivatives. The pharmacophore hypothesis yielded a 3D-QSAR model with correlation-coefficient (r2 = 0.977, q2 = 0.801) and it was externally validated with (r2pred = 0.929, r2cv = 0.850 and r2m = 0.856) which reveals the statistical significance of the model having high predictive power. The model was then employed as 3D search query for virtual screening against compound libraries (Zinc, Maybridge, Enamine, Asinex, Toslab, LifeChem and Specs) in order to identify novel scaffolds which can be experimentally validated to design future drug molecule. Density Functional Theory (DFT) at B3LYP/6-31G* level was employed to explore the electronic features of the ligands involved in charge transfer reaction during receptor ligand interaction. Binding free energy (ΔGbind) calculation was done using MM/GBSA which defines the affinity of ligands towards the receptor.
Chu, Congying; Fan, Lingzhong; Eickhoff, Claudia R; Liu, Yong; Yang, Yong; Eickhoff, Simon B; Jiang, Tianzi
2015-08-15
Recent progress in functional neuroimaging has prompted studies of brain activation during various cognitive tasks. Coordinate-based meta-analysis has been utilized to discover the brain regions that are consistently activated across experiments. However, within-experiment co-activation relationships, which can reflect the underlying functional relationships between different brain regions, have not been widely studied. In particular, voxel-wise co-activation, which may be able to provide a detailed configuration of the co-activation network, still needs to be modeled. To estimate the voxel-wise co-activation pattern and deduce the co-activation network, a Co-activation Probability Estimation (CoPE) method was proposed to model within-experiment activations for the purpose of defining the co-activations. A permutation test was adopted as a significance test. Moreover, the co-activations were automatically separated into local and long-range ones, based on distance. The two types of co-activations describe distinct features: the first reflects convergent activations; the second represents co-activations between different brain regions. The validation of CoPE was based on five simulation tests and one real dataset derived from studies of working memory. Both the simulated and the real data demonstrated that CoPE was not only able to find local convergence but also significant long-range co-activation. In particular, CoPE was able to identify a 'core' co-activation network in the working memory dataset. As a data-driven method, the CoPE method can be used to mine underlying co-activation relationships across experiments in future studies. PMID:26037052
Modelling approaches for evaluating multiscale tendon mechanics.
Fang, Fei; Lake, Spencer P
2016-02-01
Tendon exhibits anisotropic, inhomogeneous and viscoelastic mechanical properties that are determined by its complicated hierarchical structure and varying amounts/organization of different tissue constituents. Although extensive research has been conducted to use modelling approaches to interpret tendon structure-function relationships in combination with experimental data, many issues remain unclear (i.e. the role of minor components such as decorin, aggrecan and elastin), and the integration of mechanical analysis across different length scales has not been well applied to explore stress or strain transfer from macro- to microscale. This review outlines mathematical and computational models that have been used to understand tendon mechanics at different scales of the hierarchical organization. Model representations at the molecular, fibril and tissue levels are discussed, including formulations that follow phenomenological and microstructural approaches (which include evaluations of crimp, helical structure and the interaction between collagen fibrils and proteoglycans). Multiscale modelling approaches incorporating tendon features are suggested to be an advantageous methodology to understand further the physiological mechanical response of tendon and corresponding adaptation of properties owing to unique in vivo loading environments. PMID:26855747
Systematic approach for modeling tetrachloroethene biodegradation
Bagley, D.M.
1998-11-01
The anaerobic biodegradation of tetrachloroethene (PCE) is a reasonably well understood process. Specific organisms capable of using PCE as an electron acceptor for growth require the addition of an electron donor to remove PCE from contaminated ground waters. However, competition from other anaerobic microorganisms for added electron donor will influence the rate and completeness of PCE degradation. The approach developed here allows for the explicit modeling of PCE and byproduct biodegradation as a function of electron donor and byproduct concentrations, and the microbiological ecology of the system. The approach is general and can be easily modified for ready use with in situ ground-water models or ex situ reactor models. Simulations conducted with models developed from this approach show the sensitivity of PCE biodegradation to input parameter values, in particular initial biomass concentrations. Additionally, the dechlorination rate will be strongly influenced by the microbial ecology of the system. Finally, comparison with experimental acclimation results indicates that existing kinetic constants may not be generally applicable. Better techniques for measuring the biomass of specific organisms groups in mixed systems are required.
Component Modeling Approach Software Tool
Energy Science and Technology Software Center (ESTSC)
2010-08-23
The Component Modeling Approach Software Tool (CMAST) establishes a set of performance libraries of approved components (frames, glass, and spacer) which can be accessed for configuring fenestration products for a project, and btaining a U-factor, Solar Heat Gain Coefficient (SHGC), and Visible Transmittance (VT) rating for those products, which can then be reflected in a CMA Label Certificate for code compliance. CMAST is web-based as well as client-based. The completed CMA program and software toolmore » will be useful in several ways for a vast array of stakeholders in the industry: Generating performance ratings for bidding projects Ascertaining credible and accurate performance data Obtaining third party certification of overall product performance for code compliance« less
Introducing Linear Functions: An Alternative Statistical Approach
ERIC Educational Resources Information Center
Nolan, Caroline; Herbert, Sandra
2015-01-01
The introduction of linear functions is the turning point where many students decide if mathematics is useful or not. This means the role of parameters and variables in linear functions could be considered to be "threshold concepts". There is recognition that linear functions can be taught in context through the exploration of linear…
Systems approaches to microbial communities and their functioning.
Röling, Wilfred F M; Ferrer, Manuel; Golyshin, Peter N
2010-08-01
Recent advances in molecular microbial ecology and systems biology enhance insight into microbial community structure and functioning. They provide conceptual and technical bases for the translation of species-data and community-data into a model framework accounting for the functioning of and interactions between metabolic networks of species in multispecies environments. Function-directed and single cell-directed approaches supplement and improve metagenomics-derived community information. The topology of the metabolic network, reconstructed from a species' genome sequence, provides insight into its metabolic environments and interactions with other microorganisms. Progress in the theoretical and experimental analysis of flux through metabolic networks paves the way for their application at the community level, contributing to understanding of material flows between and within species and their resilience toward perturbations. PMID:20637597
Mixture models for distance sampling detection functions.
Miller, David L; Thomas, Len
2015-01-01
We present a new class of models for the detection function in distance sampling surveys of wildlife populations, based on finite mixtures of simple parametric key functions such as the half-normal. The models share many of the features of the widely-used "key function plus series adjustment" (K+A) formulation: they are flexible, produce plausible shapes with a small number of parameters, allow incorporation of covariates in addition to distance and can be fitted using maximum likelihood. One important advantage over the K+A approach is that the mixtures are automatically monotonic non-increasing and non-negative, so constrained optimization is not required to ensure distance sampling assumptions are honoured. We compare the mixture formulation to the K+A approach using simulations to evaluate its applicability in a wide set of challenging situations. We also re-analyze four previously problematic real-world case studies. We find mixtures outperform K+A methods in many cases, particularly spiked line transect data (i.e., where detectability drops rapidly at small distances) and larger sample sizes. We recommend that current standard model selection methods for distance sampling detection functions are extended to include mixture models in the candidate set. PMID:25793744
dos Santos, Sandra C.; Teixeira, Miguel C.; Dias, Paulo J.; Sá-Correia, Isabel
2014-01-01
Multidrug/Multixenobiotic resistance (MDR/MXR) is a widespread phenomenon with clinical, agricultural and biotechnological implications, where MDR/MXR transporters that are presumably able to catalyze the efflux of multiple cytotoxic compounds play a key role in the acquisition of resistance. However, although these proteins have been traditionally considered drug exporters, the physiological function of MDR/MXR transporters and the exact mechanism of their involvement in resistance to cytotoxic compounds are still open to debate. In fact, the wide range of structurally and functionally unrelated substrates that these transporters are presumably able to export has puzzled researchers for years. The discussion has now shifted toward the possibility of at least some MDR/MXR transporters exerting their effect as the result of a natural physiological role in the cell, rather than through the direct export of cytotoxic compounds, while the hypothesis that MDR/MXR transporters may have evolved in nature for other purposes than conferring chemoprotection has been gaining momentum in recent years. This review focuses on the drug transporters of the Major Facilitator Superfamily (MFS; drug:H+ antiporters) in the model yeast Saccharomyces cerevisiae. New insights into the natural roles of these transporters are described and discussed, focusing on the knowledge obtained or suggested by post-genomic research. The new information reviewed here provides clues into the unexpectedly complex roles of these transporters, including a proposed indirect regulation of the stress response machinery and control of membrane potential and/or internal pH, with a special emphasis on a genome-wide view of the regulation and evolution of MDR/MXR-MFS transporters. PMID:24847282
Augmented approach to desirability function based on MM estimator
NASA Astrophysics Data System (ADS)
Midi, Habshah; Mustafa, Mohd Shafie; Fitrianto, Anuar
2013-04-01
The desirability function approach is commonly used in industry to tackle multiple response optimization problems. The shortcoming of this approach is that the variability in each predicted response is ignored. It is now evident that the actual response may fall outside the acceptable region even though the predicted response at the optimal solution has a high overall desirability score. An augmented approach to the desirability function (AADF) is put forward to rectify this problem. Nevertheless the AADF is easily affected by outliers since the AADF is constructed based on the Ordinary Least Squares (OLS) estimate which is not resistant to outliers. As an alternative, we propose a robust MM-estimator to estimate the parameters of the Response Surface Model (RSM) and incorporated the estimated parameters in the augmented approach framework. A numerical example is presented to assess the performance of the AADF-MM based method. The numerical results signify that the AADF-MM based is more efficient than the AADF-OLS based method.
Chemogenetic approach to model hypofrontality.
Peña, Ike Dela; Shi, Wei-Xing
2016-08-01
Clinical evidence suggests that the prefrontal cortex (PFC) is hypofunctional in disorders including schizophrenia, drug addiction, and attention-deficit/hyperactivity disorder (ADHD). In schizophrenia, hypofrontality has been further suggested to cause both the negative and cognitive symptoms, and overactivity of dopamine neurons that project to subcortical areas. The latter may contribute to the development of positive symptoms of the disorder. Nevertheless, what causes hypofrontality and how it alters dopamine transmission in subcortical structures remain unclear due, in part, to the difficulty in modeling hypofrontality using previous techniques (e.g. PFC lesioning, focal cooling, repeated treatment with psychotomimetic drugs). We propose that the use of designer receptors exclusively activated by designer drugs (DREADDs) chemogenetic technique will allow precise interrogations of PFC functions. Combined with electrophysiological recordings, we can investigate the effects of PFC hypofunction on activity of dopamine neurons. Importantly, from a drug target discovery perspective, the use of DREADDs will enable us to examine whether chemogenetically enhancing PFC activity will reverse the behavioral abnormalities associated with PFC hypofunction and dopamine neuron overactivity, and also explore druggable targets for the treatment of schizophrenia and other disorders associated with abnormalities via modulation of the G-protein coupled receptor signaling pathway. In conclusion, the use of the DREADDs technique has several advantages over other previously employed strategies to simulate PFC hypofunction not only in terms of disease modeling but also from the viewpoint of drug target discovery. PMID:27372868
Functional Error Models to Accelerate Nested Sampling
NASA Astrophysics Data System (ADS)
Josset, L.; Elsheikh, A. H.; Demyanov, V.; Lunati, I.
2014-12-01
The main challenge in groundwater problems is the reliance on large numbers of unknown parameters with wide rage of associated uncertainties. To translate this uncertainty to quantities of interest (for instance the concentration of pollutant in a drinking well), a large number of forward flow simulations is required. To make the problem computationally tractable, Josset et al. (2013, 2014) introduced the concept of functional error models. It consists in two elements: a proxy model that is cheaper to evaluate than the full physics flow solver and an error model to account for the missing physics. The coupling of the proxy model and the error models provides reliable predictions that approximate the full physics model's responses. The error model is tailored to the problem at hand by building it for the question of interest. It follows a typical approach in machine learning where both the full physics and proxy models are evaluated for a training set (subset of realizations) and the set of responses is used to construct the error model using functional data analysis. Once the error model is devised, a prediction of the full physics response for a new geostatistical realization can be obtained by computing the proxy response and applying the error model. We propose the use of functional error models in a Bayesian inference context by combining it to the Nested Sampling (Skilling 2006; El Sheikh et al. 2013, 2014). Nested Sampling offers a mean to compute the Bayesian Evidence by transforming the multidimensional integral into a 1D integral. The algorithm is simple: starting with an active set of samples, at each iteration, the sample with the lowest likelihood is kept aside and replaced by a sample of higher likelihood. The main challenge is to find this sample of higher likelihood. We suggest a new approach: first the active set is sampled, both proxy and full physics models are run and the functional error model is build. Then, at each iteration of the Nested
A Functional Analytic Approach to Group Psychotherapy
ERIC Educational Resources Information Center
Vandenberghe, Luc
2009-01-01
This article provides a particular view on the use of Functional Analytical Psychotherapy (FAP) in a group therapy format. This view is based on the author's experiences as a supervisor of Functional Analytical Psychotherapy Groups, including groups for women with depression and groups for chronic pain patients. The contexts in which this approach…
Work Functions for Models of Scandate Surfaces
NASA Technical Reports Server (NTRS)
Mueller, Wolfgang
1997-01-01
The electronic structure, surface dipole properties, and work functions of scandate surfaces have been investigated using the fully relativistic scattered-wave cluster approach. Three different types of model surfaces are considered: (1) a monolayer of Ba-Sc-O on W(100), (2) Ba or BaO adsorbed on Sc2O3 + W, and (3) BaO on SC2O3 + WO3. Changes in the work function due to Ba or BaO adsorption on the different surfaces are calculated by employing the depolarization model of interacting surface dipoles. The largest work function change and the lowest work function of 1.54 eV are obtained for Ba adsorbed on the Sc-O monolayer on W(100). The adsorption of Ba on Sc2O3 + W does not lead to a low work function, but the adsorption of BaO results in a work function of about 1.6-1.9 eV. BaO adsorbed on Sc2O3 + WO3, or scandium tungstates, may also lead to low work functions.
Career Exploration Program: A Composite Systematic Functional Objective Model.
ERIC Educational Resources Information Center
Mohamed, Othman
The composite systematic functional objective career exploration program model integrates various career development theoretical approaches. These approaches emphasize self-concept, life values, personality, the environment, and academic achievement and training as separate functions in explaining career development. Current social development in…
Translation: Towards a Critical-Functional Approach
ERIC Educational Resources Information Center
Sadeghi, Sima; Ketabi, Saeed
2010-01-01
The controversy over the place of translation in the teaching of English as a Foreign Language (EFL) is a thriving field of inquiry. Many older language teaching methodologies such as the Direct Method, the Audio-lingual Method, and Natural and Communicative Approaches, tended to either neglect the role of translation, or prohibit it entirely as a…
Functional Approaches to Written Text: Classroom Applications.
ERIC Educational Resources Information Center
Miller, Tom, Ed.
Noting that little in language can be understood without taking into consideration the wider picture of communicative purpose, content, context, and audience, this book address practical uses of various approaches to discourse analysis. Several assumptions run through the chapters: knowledge is socially constructed; the manner in which language…
Linearized Functional Minimization for Inverse Modeling
Wohlberg, Brendt; Tartakovsky, Daniel M.; Dentz, Marco
2012-06-21
Heterogeneous aquifers typically consist of multiple lithofacies, whose spatial arrangement significantly affects flow and transport. The estimation of these lithofacies is complicated by the scarcity of data and by the lack of a clear correlation between identifiable geologic indicators and attributes. We introduce a new inverse-modeling approach to estimate both the spatial extent of hydrofacies and their properties from sparse measurements of hydraulic conductivity and hydraulic head. Our approach is to minimize a functional defined on the vectors of values of hydraulic conductivity and hydraulic head fields defined on regular grids at a user-determined resolution. This functional is constructed to (i) enforce the relationship between conductivity and heads provided by the groundwater flow equation, (ii) penalize deviations of the reconstructed fields from measurements where they are available, and (iii) penalize reconstructed fields that are not piece-wise smooth. We develop an iterative solver for this functional that exploits a local linearization of the mapping from conductivity to head. This approach provides a computationally efficient algorithm that rapidly converges to a solution. A series of numerical experiments demonstrates the robustness of our approach.
Transfer function modeling of damping mechanisms in distributed parameter models
NASA Technical Reports Server (NTRS)
Slater, J. C.; Inman, D. J.
1994-01-01
This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.
Kim, Sunghee; Kim, Ki Chul; Lee, Seung Woo; Jang, Seung Soon
2016-07-27
Understanding the thermodynamic stability and redox properties of oxygen functional groups on graphene is critical to systematically design stable graphene-based positive electrode materials with high potential for lithium-ion battery applications. In this work, we study the thermodynamic and redox properties of graphene functionalized with carbonyl and hydroxyl groups, and the evolution of these properties with the number, types and distribution of functional groups by employing the density functional theory method. It is found that the redox potential of the functionalized graphene is sensitive to the types, number, and distribution of oxygen functional groups. First, the carbonyl group induces higher redox potential than the hydroxyl group. Second, more carbonyl groups would result in higher redox potential. Lastly, the locally concentrated distribution of the carbonyl group is more beneficial to have higher redox potential compared to the uniformly dispersed distribution. In contrast, the distribution of the hydroxyl group does not affect the redox potential significantly. Thermodynamic investigation demonstrates that the incorporation of carbonyl groups at the edge of graphene is a promising strategy for designing thermodynamically stable positive electrode materials with high redox potentials. PMID:27412373
Statistical approaches and software for clustering islet cell functional heterogeneity
Wills, Quin F.; Boothe, Tobias; Asadi, Ali; Ao, Ziliang; Warnock, Garth L.; Kieffer, Timothy J.
2016-01-01
ABSTRACT Worldwide efforts are underway to replace or repair lost or dysfunctional pancreatic β-cells to cure diabetes. However, it is unclear what the final product of these efforts should be, as β-cells are thought to be heterogeneous. To enable the analysis of β-cell heterogeneity in an unbiased and quantitative way, we developed model-free and model-based statistical clustering approaches, and created new software called TraceCluster. Using an example data set, we illustrate the utility of these approaches by clustering dynamic intracellular Ca2+ responses to high glucose in ∼300 simultaneously imaged single islet cells. Using feature extraction from the Ca2+ traces on this reference data set, we identified 2 distinct populations of cells with β-like responses to glucose. To the best of our knowledge, this report represents the first unbiased cluster-based analysis of human β-cell functional heterogeneity of simultaneous recordings. We hope that the approaches and tools described here will be helpful for those studying heterogeneity in primary islet cells, as well as excitable cells derived from embryonic stem cells or induced pluripotent cells. PMID:26909740
Evaluating face trustworthiness: a model based approach
Baron, Sean G.; Oosterhof, Nikolaas N.
2008-01-01
Judgments of trustworthiness from faces determine basic approach/avoidance responses and approximate the valence evaluation of faces that runs across multiple person judgments. Here, based on trustworthiness judgments and using a computer model for face representation, we built a model for representing face trustworthiness (study 1). Using this model, we generated novel faces with an increased range of trustworthiness and used these faces as stimuli in a functional Magnetic Resonance Imaging study (study 2). Although participants did not engage in explicit evaluation of the faces, the amygdala response changed as a function of face trustworthiness. An area in the right amygdala showed a negative linear response—as the untrustworthiness of faces increased so did the amygdala response. Areas in the left and right putamen, the latter area extended into the anterior insula, showed a similar negative linear response. The response in the left amygdala was quadratic—strongest for faces on both extremes of the trustworthiness dimension. The medial prefrontal cortex and precuneus also showed a quadratic response, but their response was strongest to faces in the middle range of the trustworthiness dimension. PMID:19015102
Recent molecular approaches to understanding astrocyte function in vivo
Davila, David; Thibault, Karine; Fiacco, Todd A.; Agulhon, Cendra
2013-01-01
Astrocytes are a predominant glial cell type in the nervous systems, and are becoming recognized as important mediators of normal brain function as well as neurodevelopmental, neurological, and neurodegenerative brain diseases. Although numerous potential mechanisms have been proposed to explain the role of astrocytes in the normal and diseased brain, research into the physiological relevance of these mechanisms in vivo is just beginning. In this review, we will summarize recent developments in innovative and powerful molecular approaches, including knockout mouse models, transgenic mouse models, and astrocyte-targeted gene transfer/expression, which have led to advances in understanding astrocyte biology in vivo that were heretofore inaccessible to experimentation. We will examine the recently improved understanding of the roles of astrocytes – with an emphasis on astrocyte signaling – in the context of both the healthy and diseased brain, discuss areas where the role of astrocytes remains debated, and suggest new research directions. PMID:24399932
Quantum thermodynamics: a nonequilibrium Green's function approach.
Esposito, Massimiliano; Ochoa, Maicol A; Galperin, Michael
2015-02-27
We establish the foundations of a nonequilibrium theory of quantum thermodynamics for noninteracting open quantum systems strongly coupled to their reservoirs within the framework of the nonequilibrium Green's functions. The energy of the system and its coupling to the reservoirs are controlled by a slow external time-dependent force treated to first order beyond the quasistatic limit. We derive the four basic laws of thermodynamics and characterize reversible transformations. Stochastic thermodynamics is recovered in the weak coupling limit. PMID:25768745
ONION: Functional Approach for Integration of Lipidomics and Transcriptomics Data
Piwowar, Monika; Jurkowski, Wiktor
2015-01-01
To date, the massive quantity of data generated by high-throughput techniques has not yet met bioinformatics treatment required to make full use of it. This is partially due to a mismatch in experimental and analytical study design but primarily due to a lack of adequate analytical approaches. When integrating multiple data types e.g. transcriptomics and metabolomics, multidimensional statistical methods are currently the techniques of choice. Typical statistical approaches, such as canonical correlation analysis (CCA), that are applied to find associations between metabolites and genes are failing due to small numbers of observations (e.g. conditions, diet etc.) in comparison to data size (number of genes, metabolites). Modifications designed to cope with this issue are not ideal due to the need to add simulated data resulting in a lack of p-value computation or by pruning of variables hence losing potentially valid information. Instead, our approach makes use of verified or putative molecular interactions or functional association to guide analysis. The workflow includes dividing of data sets to reach the expected data structure, statistical analysis within groups and interpretation of results. By applying pathway and network analysis, data obtained by various platforms are grouped with moderate stringency to avoid functional bias. As a consequence CCA and other multivariate models can be applied to calculate robust statistics and provide easy to interpret associations between metabolites and genes to leverage understanding of metabolic response. Effective integration of lipidomics and transcriptomics is demonstrated on publically available murine nutrigenomics data sets. We are able to demonstrate that our approach improves detection of genes related to lipid metabolism, in comparison to applying statistics alone. This is measured by increased percentage of explained variance (95% vs. 75–80%) and by identifying new metabolite-gene associations related to lipid
Functional genomics approach to hypoxia signaling.
Seta, Karen A; Millhorn, David E
2004-02-01
Mammalian cells require a constant supply of oxygen to maintain energy balance, and sustained hypoxia can result in cell death. It is therefore not surprising that sophisticated adaptive mechanisms have evolved that enhance cell survival during hypoxia. During the past few years, there have been a growing number of reports on hypoxia-induced transcription of specific genes. In this review, we describe a unique experimental approach that utilizes focused cDNA libraries coupled to microarray analyses to identify hypoxia-responsive signal transduction pathways and genes that confer the hypoxia-tolerant phenotype. We have used the subtractive suppression hybridization (SSH) method to create a cDNA library enriched in hypoxia-regulated genes in oxygen-sensing pheochromocytoma cells and have used this library to create microarrays that allow us to examine hundreds of genes at a time. This library contains over 300 genes and expressed sequence tags upregulated by hypoxia, including tyrosine hydroxylase, vascular endothelial growth factor, and junB. Hypoxic regulation of these and other genes in the library has been confirmed by microarray, Northern blot, and real-time PCR analyses. Coupling focused SSH libraries with microarray analyses allows one to specifically study genes relevant to a phenotype of interest while reducing much of the biological noise associated with these types of studies. When used in conjunction with high-throughput, dye-based assays for cell survival and apoptosis, this approach offers a rapid method for discovering validated therapeutic targets for the treatment of cardiovascular disease, stroke, and tumors. PMID:14715686
A moving approach for the Vector Hysteron Model
NASA Astrophysics Data System (ADS)
Cardelli, E.; Faba, A.; Laudani, A.; Quondam Antonio, S.; Riganti Fulginei, F.; Salvini, A.
2016-04-01
A moving approach for the VHM (Vector Hysteron Model) is here described, to reconstruct both scalar and rotational magnetization of electrical steels with weak anisotropy, such as the non oriented grain Silicon steel. The hysterons distribution is postulated to be function of the magnetization state of the material, in order to overcome the practical limitation of the congruency property of the standard VHM approach. By using this formulation and a suitable accommodation procedure, the results obtained indicate that the model is accurate, in particular in reproducing the experimental behavior approaching to the saturation region, allowing a real improvement respect to the previous approach.
Functional models of power electronic components for system studies
NASA Technical Reports Server (NTRS)
Tam, Kwa-Sur; Yang, Lifeng; Dravid, Narayan
1991-01-01
A novel approach to model power electronic circuits has been developed to facilitate simulation studies of system-level issues. The underlying concept for this approach is to develop an equivalent circuit, the functional model, that performs the same functions as the actual circuit but whose operation can be simulated by using larger time step size and the reduction in model complexity, the computation time required by a functional model is significantly shorter than that required by alternative approaches. The authors present this novel modeling approach and discuss the functional models of two major power electronic components, the DC/DC converter unit and the load converter, that are being considered by NASA for use in the Space Station Freedom electric power system. The validity of these models is established by comparing the simulation results with available experimental data and other simulation results obtained by using a more established modeling approach. The usefulness of this approach is demonstrated by incorporating these models into a power system model and simulating the system responses and interactions between components under various conditions.
ERIC Educational Resources Information Center
And Others; deLannoy, Peter
1996-01-01
Describes an integrated approach to teaching a biochemistry laboratory focusing on the relationship between the three-dimensional structure of a macromolecule and its function. RNA is chosen as the model system. Discusses curriculum and student assessment. (AIM)
Interactively Open Autonomy Unifies Two Approaches to Function
NASA Astrophysics Data System (ADS)
Collier, John
2004-08-01
Functionality is essential to any form of anticipation beyond simple directedness at an end. In the literature on function in biology, there are two distinct approaches. One, the etiological view, places the origin of function in selection, while the other, the organizational view, individuates function by organizational role. Both approaches have well-known advantages and disadvantages. I propose a reconciliation of the two approaches, based in an interactivist approach to the individuation and stability of organisms. The approach was suggested by Kant in the Critique of Judgment, but since it requires, on his account, the identification a new form of causation, it has not been accessible by analytical techniques. I proceed by construction of the required concept to fit certain design requirements. This construction builds on concepts introduced in my previous four talks to these meetings.
Sturmian function approach and {bar N}N bound states
Yan, Y.; Tegen, R.; Gutsche, T.; Faessler, A.
1997-09-01
A suitable numerical approach based on Sturmian functions is employed to solve the {bar N}N bound state problem for local and nonlocal potentials. The approach accounts for both the strong short-range nuclear potential and the long-range Coulomb force and provides directly the wave function of protonium and {bar N}N deep bound states with complex eigenvalues E=E{sub R}{minus}i({Gamma}/2). The spectrum of {bar N}N bound states has two parts, the atomic states bound by several keV, and the deep bound states which are bound by several hundred MeV. The observed very small hyperfine splitting of the 1s level and the 1s and 2p decay widths are reasonably well reproduced by both the Paris and Bonn potentials (supplemented with a microscopically derived quark annihilation potential), although there are differences in magnitude and level ordering. We present further arguments for the identification of the {sup 13}PF{sub 2} deep bound state with the exotic tensor meson f{sub 2}(1520). Both investigated models can accommodate the f{sub 2}(1520) but differ greatly in the total number of levels and in their ordering. The model based on the Paris potential predicts the {sup 13}P{sub 0} level slightly below 1.1 GeV while the model based on the Bonn potential puts this state below 0.8 GeV. It remains to be seen if this state can be identified with a scalar partner of the f{sub 2}(1520). {copyright} {ital 1997} {ital The American Physical Society}
A Semantic Modeling Approach to Metadata.
ERIC Educational Resources Information Center
Brasethvik, Terje
1998-01-01
Explores problems in information sharing; discusses the concept of metadata; illustrates its use on the World Wide Web, as well as other related approaches; and presents an approach to information sharing that uses a semantic modeling language (referent model language) as the basis for expressing semantics of information and designing metadata…
Model compilation: An approach to automated model derivation
NASA Technical Reports Server (NTRS)
Keller, Richard M.; Baudin, Catherine; Iwasaki, Yumi; Nayak, Pandurang; Tanaka, Kazuo
1990-01-01
An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration.
A new approach to turbulence modeling
NASA Technical Reports Server (NTRS)
Perot, B.; Moin, P.
1996-01-01
A new approach to Reynolds averaged turbulence modeling is proposed which has a computational cost comparable to two equation models but a predictive capability approaching that of Reynolds stress transport models. This approach isolates the crucial information contained within the Reynolds stress tensor, and solves transport equations only for a set of 'reduced' variables. In this work, Direct Numerical Simulation (DNS) data is used to analyze the nature of these newly proposed turbulence quantities and the source terms which appear in their respective transport equations. The physical relevance of these quantities is discussed and some initial modeling results for turbulent channel flow are presented.
A Unified Approach to Modeling Multidisciplinary Interactions
NASA Technical Reports Server (NTRS)
Samareh, Jamshid A.; Bhatia, Kumar G.
2000-01-01
There are a number of existing methods to transfer information among various disciplines. For a multidisciplinary application with n disciplines, the traditional methods may be required to model (n(exp 2) - n) interactions. This paper presents a unified three-dimensional approach that reduces the number of interactions from (n(exp 2) - n) to 2n by using a computer-aided design model. The proposed modeling approach unifies the interactions among various disciplines. The approach is independent of specific discipline implementation, and a number of existing methods can be reformulated in the context of the proposed unified approach. This paper provides an overview of the proposed unified approach and reformulations for two existing methods. The unified approach is specially tailored for application environments where the geometry is created and managed through a computer-aided design system. Results are presented for a blended-wing body and a high-speed civil transport.
Defining and Applying a Functionality Approach to Intellectual Disability
ERIC Educational Resources Information Center
Luckasson, R.; Schalock, R. L.
2013-01-01
Background: The current functional models of disability do not adequately incorporate significant changes of the last three decades in our understanding of human functioning, and how the human functioning construct can be applied to clinical functions, professional practices and outcomes evaluation. Methods: The authors synthesise current…
Questionnaire of executive function for dancers: an ecological approach.
Wong, Alina; Rodríguez, Mabel; Quevedo, Liliana; Fernández de Cossío, Lourdes; Borges, Ariel; Reyes, Alicia; Corral, Roberto; Blanco, Florentino; Alvarez, Miguel
2012-09-01
There is a current debate about the ecological validity of executive function (EF) tests. Consistent with the verisimilitude approach, this research proposes the ballet executive scale (BES), a self-rating questionnaire that assimilates idiosyncratic executive behaviors of classical dance community. The BES was administrated to 149 adolescents, students of the Cuban Ballet School. Results present a Cronbach's alpha coefficient of .80 and a split-half Spearman-Brown coefficient r (SB) = .81. An exploratory factor analysis describes a bifactorial pattern of EF dimensions, with a self-regulation component, which explains more than 40% of variance, and a Developmental component, which accounts for more than 20% of variance. The questionnaire's total scores fit linear regression models with two external criteria of academic records, confirming concurrent validity. These findings support the hypothesis that the internalization of specific contextual cultural meanings has a mediating influence in the development of EF. PMID:21266371
Multicomponent Equilibrium Models for Testing Geothermometry Approaches
Cooper, D. Craig; Palmer, Carl D.; Smith, Robert W.; McLing, Travis L.
2013-02-01
Geothermometry is an important tool for estimating deep reservoir temperature from the geochemical composition of shallower and cooler waters. The underlying assumption of geothermometry is that the waters collected from shallow wells and seeps maintain a chemical signature that reflects equilibrium in the deeper reservoir. Many of the geothermometers used in practice are based on correlation between water temperatures and composition or using thermodynamic calculations based a subset (typically silica, cations or cation ratios) of the dissolved constituents. An alternative approach is to use complete water compositions and equilibrium geochemical modeling to calculate the degree of disequilibrium (saturation index) for large number of potential reservoir minerals as a function of temperature. We have constructed several “forward” geochemical models using The Geochemist’s Workbench to simulate the change in chemical composition of reservoir fluids as they migrate toward the surface. These models explicitly account for the formation (mass and composition) of a steam phase and equilibrium partitioning of volatile components (e.g., CO2, H2S, and H2) into the steam as a result of pressure decreases associated with upward fluid migration from depth. We use the synthetic data generated from these simulations to determine the advantages and limitations of various geothermometry and optimization approaches for estimating the likely conditions (e.g., temperature, pCO2) to which the water was exposed in the deep subsurface. We demonstrate the magnitude of errors that can result from boiling, loss of volatiles, and analytical error from sampling and instrumental analysis. The estimated reservoir temperatures for these scenarios are also compared to conventional geothermometers. These results can help improve estimation of geothermal resource temperature during exploration and early development.
A general approach to association using cluster partition functions
NASA Astrophysics Data System (ADS)
Hendriks, E. M.; Walsh, J.; van Bergen, A. R. D.
1997-06-01
A systematic and fundamental approach to associating mixtures is presented. It is shown how the thermodynamic functions may be computed starting from a partition function based on the cluster concept such as occurs in chemical theory. The theory provides a basis for and an extension of the existing chemical theory of (continuous) association. It is applicable to arbitrary association schemes. Analysis of separate cases is not necessary. The assumptions that were made to allow the development were chosen such as to make the principle of reactivity valid. It is this same principle that links various theories: the chemical theory of continuous association, the lattice fluid hydrogen bonding model, and first-order perturbation theory. The equivalence between these theories in appropriate limits is shown in a general and rigorous way. The theory is believed to provide a practical framework for engineering modeling work. Binary interaction parameters can be incorporated. The association scheme is accounted for by a set of generic equations, which should facilitate robust implementation in computer programs.
Nonrelativistic approaches derived from point-coupling relativistic models
Lourenco, O.; Dutra, M.; Delfino, A.; Sa Martins, J. S.
2010-03-15
We construct nonrelativistic versions of relativistic nonlinear hadronic point-coupling models, based on new normalized spinor wave functions after small component reduction. These expansions give us energy density functionals that can be compared to their relativistic counterparts. We show that the agreement between the nonrelativistic limit approach and the Skyrme parametrizations becomes strongly dependent on the incompressibility of each model. We also show that the particular case A=B=0 (Walecka model) leads to the same energy density functional of the Skyrme parametrizations SV and ZR2, while the truncation scheme, up to order {rho}{sup 3}, leads to parametrizations for which {sigma}=1.
Modelling functional effects of muscle geometry.
van der Linden, B J; Koopman, H F; Grootenboer, H J; Huijing, P A
1998-04-01
Muscle architecture is an important aspect of muscle functioning. Hence, geometry and material properties of muscle have great influence on the force-length characteristics of muscle. We compared experimental results for the gastrocnemius medialis muscle (GM) of the rat to model results of simple geometric models such as a planimetric model and three-dimensional versions of this model. The capabilities of such models to adequately calculate muscle geometry and force-length characteristics were investigated. The planimetric model with elastic aponeurosis predicted GM muscle geometry well: maximal differences are 6, 1, 4 and 6% for fiber length, aponeurosis length, fiber angle and aponeurosis angle respectively. A slanted cylinder model with circular fiber cross-section did not predict muscle geometry as well as the planimetric model, whereas the geometry results of a second slanted cylinder model were identical to the planimetric model. It is concluded that the planimetric model is capable of adequately calculating the muscle geometry over the muscle length range studied. However, for modelling of force-length characteristics more complex models are needed, as none of the models yielded results sufficiently close to experimental data. Modelled force-length characteristics showed an overestimation of muscle optimum length by 2 mm with respect to experimental data, and the force at the ascending limb of the length force curve was underestimated. The models presented neglect important aspects such as non-linear geometry of muscle, certain passive material properties and mechanical interactions of fibers. These aspects may be responsible for short-comings in the modelling. It is argued that, considering the inability to adequately model muscle length-force characteristics for an isolated maximally activated (in situ) muscle, it is to be expected that prediction will fail for muscle properties in conditions of complex movement with many interacting factors. Therefore
Inverse Modeling Via Linearized Functional Minimization
NASA Astrophysics Data System (ADS)
Barajas-Solano, D. A.; Wohlberg, B.; Vesselinov, V. V.; Tartakovsky, D. M.
2014-12-01
We present a novel parameter estimation methodology for transient models of geophysical systems with uncertain, spatially distributed, heterogeneous and piece-wise continuous parameters.The methodology employs a bayesian approach to propose an inverse modeling problem for the spatial configuration of the model parameters.The likelihood of the configuration is formulated using sparse measurements of both model parameters and transient states.We propose using total variation regularization (TV) as the prior reflecting the heterogeneous, piece-wise continuity assumption on the parameter distribution.The maximum a posteriori (MAP) estimator of the parameter configuration is then computed by minimizing the negative bayesian log-posterior using a linearized functional minimization approach. The computation of the MAP estimator is a large-dimensional nonlinear minimization problem with two sources of nonlinearity: (1) the TV operator, and (2) the nonlinear relation between states and parameters provided by the model's governing equations.We propose a a hybrid linearized functional minimization (LFM) algorithm in two stages to efficiently treat both sources of nonlinearity.The relation between states and parameters is linearized, resulting in a linear minimization sub-problem equipped with the TV operator; this sub-problem is then minimized using the Alternating Direction Method of Multipliers (ADMM). The methodology is illustrated with a transient saturated groundwater flow application in a synthetic domain, stimulated by external point-wise loadings representing aquifer pumping, together with an array of discrete measurements of hydraulic conductivity and transient measurements of hydraulic head.We show that our inversion strategy is able to recover the overall large-scale features of the parameter configuration, and that the reconstruction is improved by the addition of transient information of the state variable.
Matrix model approach to cosmology
NASA Astrophysics Data System (ADS)
Chaney, A.; Lu, Lei; Stern, A.
2016-03-01
We perform a systematic search for rotationally invariant cosmological solutions to toy matrix models. These models correspond to the bosonic sector of Lorentzian Ishibashi, Kawai, Kitazawa and Tsuchiya (IKKT)-type matrix models in dimensions d less than ten, specifically d =3 and d =5 . After taking a continuum (or commutative) limit they yield d -1 dimensional Poisson manifolds. The manifolds have a Lorentzian induced metric which can be associated with closed, open, or static space-times. For d =3 , we obtain recursion relations from which it is possible to generate rotationally invariant matrix solutions which yield open universes in the continuum limit. Specific examples of matrix solutions have also been found which are associated with closed and static two-dimensional space-times in the continuum limit. The solutions provide for a resolution of cosmological singularities, at least within the context of the toy matrix models. The commutative limit reveals other desirable features, such as a solution describing a smooth transition from an initial inflation to a noninflationary era. Many of the d =3 solutions have analogues in higher dimensions. The case of d =5 , in particular, has the potential for yielding realistic four-dimensional cosmologies in the continuum limit. We find four-dimensional de Sitter d S4 or anti-de Sitter AdS4 solutions when a totally antisymmetric term is included in the matrix action. A nontrivial Poisson structure is attached to these manifolds which represents the lowest order effect of noncommutativity. For the case of AdS4 , we find one particular limit where the lowest order noncommutativity vanishes at the boundary, but not in the interior.
Combining Formal and Functional Approaches to Topic Structure
ERIC Educational Resources Information Center
Zellers, Margaret; Post, Brechtje
2012-01-01
Fragmentation between formal and functional approaches to prosodic variation is an ongoing problem in linguistic research. In particular, the frameworks of the Phonetics of Talk-in-Interaction (PTI) and Empirical Phonology (EP) take very different theoretical and methodological approaches to this kind of variation. We argue that it is fruitful to…
Roth, Jason L.; Capel, Paul D.
2012-01-01
Crop agriculture occupies 13 percent of the conterminous United States. Agricultural management practices, such as crop and tillage types, affect the hydrologic flow paths through the landscape. Some agricultural practices, such as drainage and irrigation, create entirely new hydrologic flow paths upon the landscapes where they are implemented. These hydrologic changes can affect the magnitude and partitioning of water budgets and sediment erosion. Given the wide degree of variability amongst agricultural settings, changes in the magnitudes of hydrologic flow paths and sediment erosion induced by agricultural management practices commonly are difficult to characterize, quantify, and compare using only field observations. The Water Erosion Prediction Project (WEPP) model was used to simulate two landscape characteristics (slope and soil texture) and three agricultural management practices (land cover/crop type, tillage type, and selected agricultural land management practices) to evaluate their effects on the water budgets of and sediment yield from agricultural lands. An array of sixty-eight 60-year simulations were run, each representing a distinct natural or agricultural scenario with various slopes, soil textures, crop or land cover types, tillage types, and select agricultural management practices on an isolated 16.2-hectare field. Simulations were made to represent two common agricultural climate regimes: arid with sprinkler irrigation and humid. These climate regimes were constructed with actual climate and irrigation data. The results of these simulations demonstrate the magnitudes of potential changes in water budgets and sediment yields from lands as a result of landscape characteristics and agricultural practices adopted on them. These simulations showed that variations in landscape characteristics, such as slope and soil type, had appreciable effects on water budgets and sediment yields. As slopes increased, sediment yields increased in both the arid and
HABITAT MODELING APPROACHES FOR RESTORATION SITE SELECTION
Numerous modeling approaches have been used to develop predictive models of species-environment and species-habitat relationships. These models have been used in conservation biology and habitat or species management, but their application to restoration efforts has been minimal...
An Instructional Approach to Modeling in Microevolution.
ERIC Educational Resources Information Center
Thompson, Steven R.
1988-01-01
Describes an approach to teaching population genetics and evolution and some of the ways models can be used to enhance understanding of the processes being studied. Discusses the instructional plan, and the use of models including utility programs and analysis with models. Provided are a basic program and sample program outputs. (CW)
Functional renormalization group - a new approach to frustrated quantum magnetism
NASA Astrophysics Data System (ADS)
Reuther, Johannes
The experimental and theoretical investigation of quantum spin systems has become one of the central disciplines of contemporary condensed matter physics. From an experimental viewpoint, the field has been significantly fueled by the recent synthesis of novel strongly correlated materials with exotic magnetic or quantum paramagnetic ground states. From a theoretical perspective, however, the numerical treatment of realistic models for quantum magnetism in two and three spatial dimensions still constitutes a serious challenge. This particularly applies to frustrated systems, which complicate the employment of established methods. This talk intends to propagate the pseudofermion functional renormalization group (PFFRG) as a novel approach to determine large size ground state correlations of a wide class of spin Hamiltonians. Using a diagrammatic pseudofermion representation for quantum spin models, the PFFRG performs systematic summations in all two-particle fermionic interaction channels, capturing the correct balance between classical magnetic ordering and quantum fluctuations. Numerical results for various frustrated spin models on different 2D and 3D lattices are reviewed, and benchmarked against other methods if available.
Function Model for Community Health Service Information
NASA Astrophysics Data System (ADS)
Yang, Peng; Pan, Feng; Liu, Danhong; Xu, Yongyong
In order to construct a function model of community health service (CHS) information for development of CHS information management system, Integration Definition for Function Modeling (IDEF0), an IEEE standard which is extended from Structured Analysis and Design(SADT) and now is a widely used function modeling method, was used to classifying its information from top to bottom. The contents of every level of the model were described and coded. Then function model for CHS information, which includes 4 super-classes, 15 classes and 28 sub-classed of business function, 43 business processes and 168 business activities, was established. This model can facilitate information management system development and workflow refinement.
Social learning in Models and Cases - an Interdisciplinary Approach
NASA Astrophysics Data System (ADS)
Buhl, Johannes; De Cian, Enrica; Carrara, Samuel; Monetti, Silvia; Berg, Holger
2016-04-01
Our paper follows an interdisciplinary understanding of social learning. We contribute to the literature on social learning in transition research by bridging case-oriented research and modelling-oriented transition research. We start by describing selected theories on social learning in innovation, diffusion and transition research. We present theoretical understandings of social learning in techno-economic and agent-based modelling. Then we elaborate on empirical research on social learning in transition case studies. We identify and synthetize key dimensions of social learning in transition case studies. In the following we bridge between more formal and generalising modelling approaches towards social learning processes and more descriptive, individualising case study approaches by interpreting the case study analysis into a visual guide on functional forms of social learning typically identified in the cases. We then try to exemplarily vary functional forms of social learning in integrated assessment models. We conclude by drawing the lessons learned from the interdisciplinary approach - methodologically and empirically.
Uniqueness of place: uniqueness of models. The FLEX modelling approach
NASA Astrophysics Data System (ADS)
Fenicia, F.; Savenije, H. H. G.; Wrede, S.; Schoups, G.; Pfister, L.
2009-04-01
The current practice in hydrological modelling is to make use of model structures that are fixed and a-priori defined. However, for a model to reflect uniqueness of place while maintaining parsimony, it is necessary to be flexible in its architecture. We have developed a new approach for the development and testing of hydrological models, named the FLEX approach. This approach allows the formulation of alternative model structures that vary in configuration and complexity, and uses an objective method for testing and comparing model performance. We have tested this approach on three headwater catchments in Luxembourg with marked differences in hydrological response, where we have generated 15 alternative model structures. Each of the three catchments is best represented by a different model architecture. Our results clearly show that uniqueness of place necessarily leads to uniqueness of models.
Challenges in structural approaches to cell modeling.
Im, Wonpil; Liang, Jie; Olson, Arthur; Zhou, Huan-Xiang; Vajda, Sandor; Vakser, Ilya A
2016-07-31
Computational modeling is essential for structural characterization of biomolecular mechanisms across the broad spectrum of scales. Adequate understanding of biomolecular mechanisms inherently involves our ability to model them. Structural modeling of individual biomolecules and their interactions has been rapidly progressing. However, in terms of the broader picture, the focus is shifting toward larger systems, up to the level of a cell. Such modeling involves a more dynamic and realistic representation of the interactomes in vivo, in a crowded cellular environment, as well as membranes and membrane proteins, and other cellular components. Structural modeling of a cell complements computational approaches to cellular mechanisms based on differential equations, graph models, and other techniques to model biological networks, imaging data, etc. Structural modeling along with other computational and experimental approaches will provide a fundamental understanding of life at the molecular level and lead to important applications to biology and medicine. A cross section of diverse approaches presented in this review illustrates the developing shift from the structural modeling of individual molecules to that of cell biology. Studies in several related areas are covered: biological networks; automated construction of three-dimensional cell models using experimental data; modeling of protein complexes; prediction of non-specific and transient protein interactions; thermodynamic and kinetic effects of crowding; cellular membrane modeling; and modeling of chromosomes. The review presents an expert opinion on the current state-of-the-art in these various aspects of structural modeling in cellular biology, and the prospects of future developments in this emerging field. PMID:27255863
Improving Treatment Integrity through a Functional Approach to Intervention Support
ERIC Educational Resources Information Center
Liaupsin, Carl J.
2015-01-01
A functional approach to intervention planning has been shown to be effective in reducing problem behaviors and promoting appropriate behaviors in children and youth with behavior disorders. When function-based intervention plans are not successful, it is often due to issues of treatment integrity in which teachers omit or do not sufficiently…
Challenges and opportunities for integrating lake ecosystem modelling approaches
Mooij, Wolf M.; Trolle, Dennis; Jeppesen, Erik; Arhonditsis, George; Belolipetsky, Pavel V.; Chitamwebwa, Deonatus B.R.; Degermendzhy, Andrey G.; DeAngelis, Donald L.; Domis, Lisette N. De Senerpont; Downing, Andrea S.; Elliott, J. Alex; Ruberto, Carlos Ruberto, Jr.; Gaedke, Ursula; Genova, Svetlana N.; Gulati, Ramesh D.; Hakanson, Lars; Hamilton, David P.; Hipsey, Matthew R.; Hoen, Jochem 't; Hulsmann, Stephan; Los, F. Hans; Makler-Pick, Vardit; Petzoldt, Thomas; Prokopkin, Igor G.; Rinke, Karsten; Schep, Sebastiaan A.; Tominaga, Koji; Van Dam, Anne A.; Van Nes, Egbert H.; Wells, Scott A.; Janse, Jan H.
2010-01-01
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative
NASA Astrophysics Data System (ADS)
Cecchet, F.; Lis, D.; Caudano, Y.; Mani, A. A.; Peremans, A.; Champagne, B.; Guthmuller, J.
2012-03-01
The knowledge of the first hyperpolarizability tensor elements of molecular groups is crucial for a quantitative interpretation of the sum frequency generation (SFG) activity of thin organic films at interfaces. Here, the SFG response of the terminal methyl group of a dodecanethiol (DDT) monolayer has been interpreted on the basis of calculations performed at the density functional theory (DFT) level of approximation. In particular, DFT calculations have been carried out on three classes of models for the aliphatic chains. The first class of models consists of aliphatic chains, containing from 3 to 12 carbon atoms, in which only one methyl group can freely vibrate, while the rest of the chain is frozen by a strong overweight of its C and H atoms. This enables us to localize the probed vibrational modes on the methyl group. In the second class, only one methyl group is frozen, while the entire remaining chain is allowed to vibrate. This enables us to analyse the influence of the aliphatic chain on the methyl stretching vibrations. Finally, the dodecanethiol (DDT) molecule is considered, for which the effects of two dielectrics, i.e. n-hexane and n-dodecane, are investigated. Moreover, DDT calculations are also carried out by using different exchange-correlation (XC) functionals in order to assess the DFT approximations. Using the DFT IR vectors and Raman tensors, the SFG spectrum of DDT has been simulated and the orientation of the methyl group has then been deduced and compared with that obtained using an analytical approach based on a bond additivity model. This analysis shows that when using DFT molecular properties, the predicted orientation of the terminal methyl group tends to converge as a function of the alkyl chain length and that the effects of the chain as well as of the dielectric environment are small. Instead, a more significant difference is observed when comparing the DFT-based results with those obtained from the analytical approach, thus indicating
A Hierarchical Systems Approach to Model Validation
NASA Astrophysics Data System (ADS)
Easterbrook, S. M.
2011-12-01
Existing approaches to the question of how climate models should be evaluated tend to rely on either philosophical arguments about the status of models as scientific tools, or on empirical arguments about how well runs from a given model match observational data. These have led to quantitative approaches expressed in terms of model bias or forecast skill, and ensemble approaches where models are assessed according to the extent to which the ensemble brackets the observational data. Unfortunately, such approaches focus the evaluation on models per se (or more specifically, on the simulation runs they produce) as though the models can be isolated from their context. Such approach may overlook a number of important aspects of the use of climate models: - the process by which models are selected and configured for a given scientific question. - the process by which model outputs are selected, aggregated and interpreted by a community of expertise in climatology. - the software fidelity of the models (i.e. whether the running code is actually doing what the modellers think it's doing). - the (often convoluted) history that begat a given model, along with the modelling choices long embedded in the code. - variability in the scientific maturity of different model components within a coupled system. These omissions mean that quantitative approaches cannot assess whether a model produces the right results for the wrong reasons, or conversely, the wrong results for the right reasons (where, say the observational data is problematic, or the model is configured to be unlike the earth system for a specific reason). Hence, we argue that it is a mistake to think that validation is a post-hoc process to be applied to an individual "finished" model, to ensure it meets some criteria for fidelity to the real world. We are therefore developing a framework for model validation that extends current approaches down into the detailed codebase and the processes by which the code is built
An approach to solving large reliability models
NASA Technical Reports Server (NTRS)
Boyd, Mark A.; Veeraraghavan, Malathi; Dugan, Joanne Bechta; Trivedi, Kishor S.
1988-01-01
This paper describes a unified approach to the problem of solving large realistic reliability models. The methodology integrates behavioral decomposition, state trunction, and efficient sparse matrix-based numerical methods. The use of fault trees, together with ancillary information regarding dependencies to automatically generate the underlying Markov model state space is proposed. The effectiveness of this approach is illustrated by modeling a state-of-the-art flight control system and a multiprocessor system. Nonexponential distributions for times to failure of components are assumed in the latter example. The modeling tool used for most of this analysis is HARP (the Hybrid Automated Reliability Predictor).
Dynamic geometry, brain function modeling, and consciousness.
Roy, Sisir; Llinás, Rodolfo
2008-01-01
Pellionisz and Llinás proposed, years ago, a geometric interpretation towards understanding brain function. This interpretation assumes that the relation between the brain and the external world is determined by the ability of the central nervous system (CNS) to construct an internal model of the external world using an interactive geometrical relationship between sensory and motor expression. This approach opened new vistas not only in brain research but also in understanding the foundations of geometry itself. The approach named tensor network theory is sufficiently rich to allow specific computational modeling and addressed the issue of prediction, based on Taylor series expansion properties of the system, at the neuronal level, as a basic property of brain function. It was actually proposed that the evolutionary realm is the backbone for the development of an internal functional space that, while being purely representational, can interact successfully with the totally different world of the so-called "external reality". Now if the internal space or functional space is endowed with stochastic metric tensor properties, then there will be a dynamic correspondence between events in the external world and their specification in the internal space. We shall call this dynamic geometry since the minimal time resolution of the brain (10-15 ms), associated with 40 Hz oscillations of neurons and their network dynamics, is considered to be responsible for recognizing external events and generating the concept of simultaneity. The stochastic metric tensor in dynamic geometry can be written as five-dimensional space-time where the fifth dimension is a probability space as well as a metric space. This extra dimension is considered an imbedded degree of freedom. It is worth noticing that the above-mentioned 40 Hz oscillation is present both in awake and dream states where the central difference is the inability of phase resetting in the latter. This framework of dynamic
Shell Model in a First Principles Approach
Navratil, P; Nogga, A; Lloyd, R; Vary, J P; Ormand, W E; Barrett, B R
2004-01-08
We develop and apply an ab-initio approach to nuclear structure. Starting with the NN interaction, that fits two-body scattering and bound state data, and adding a theoretical NNN potential, we evaluate nuclear properties in a no-core approach. For presently feasible no-core model spaces, we evaluate an effective Hamiltonian in a cluster approach which is guaranteed to provide exact answers for sufficiently large model spaces and/or sufficiently large clusters. A number of recent applications are surveyed including an initial application to exotic multiquark systems.
Accurate definition of brain regions position through the functional landmark approach.
Thirion, Bertrand; Varoquaux, Gaël; Poline, Jean-Baptiste
2010-01-01
In many application of functional Magnetic Resonance Imaging (fMRI), including clinical or pharmacological studies, the definition of the location of the functional activity between subjects is crucial. While current acquisition and normalization procedures improve the accuracy of the functional signal localization, it is also important to ensure that functional foci detection yields accurate results, and reflects between-subject variability. Here we introduce a fast functional landmark detection procedure, that explicitly models the spatial variability of activation foci in the observed population. We compare this detection approach to standard statistical maps peak extraction procedures: we show that it yields more accurate results on simulations, and more reproducible results on a large cohort of subjects. These results demonstrate that explicit functional landmark modeling approaches are more effective than standard statistical mapping for brain functional focus detection. PMID:20879321
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal
Fernando, Chrisantha; Szathmáry, Eörs; Husbands, Phil
2012-01-01
We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse, and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price’s covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity, and variability) as the most powerful mechanism for search in a sparsely occupied search space. Examples are given of cases where parallel competitive search with information transfer among the units is more efficient than search without information transfer between units. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise. PMID:22557963
Is protein classification necessary? Towards alternative approaches to function annotation
Petrey, Donald; Honig, Barry
2009-01-01
The current non-redundant protein sequence database contains over seven million entries and the number of individual functional domains is significantly larger than this value. The vast quantity of data associated with these proteins poses enormous challenges to any attempt at function annotation. Classification of proteins into sequence and structural groups has been widely used as an approach to simplifying the problem. In this article we question such strategies. We describe how the multi-functionality and structural diversity of even closely related proteins confounds efforts to assign function based on overall sequence or structural similarity. Rather, we suggest that strategies that avoid classification may offer a more robust approach to protein function annotation. PMID:19269161
Heterogeneous Factor Analysis Models: A Bayesian Approach.
ERIC Educational Resources Information Center
Ansari, Asim; Jedidi, Kamel; Dube, Laurette
2002-01-01
Developed Markov Chain Monte Carlo procedures to perform Bayesian inference, model checking, and model comparison in heterogeneous factor analysis. Tested the approach with synthetic data and data from a consumption emotion study involving 54 consumers. Results show that traditional psychometric methods cannot fully capture the heterogeneity in…
Facet Modelling: An Approach to Flexible and Integrated Conceptual Modelling.
ERIC Educational Resources Information Center
Opdahl, Andreas L.; Sindre, Guttorm
1997-01-01
Identifies weaknesses of conceptual modelling languages for the problem domain of information systems (IS) development. Outlines an approach called facet modelling of real-world problem domains to deal with the complexity of contemporary analysis problems. Shows how facet models can be defined and visualized; discusses facet modelling in relation…
Robust, Adaptive Functional Regression in Functional Mixed Model Framework
Zhu, Hongxiao; Brown, Philip J.; Morris, Jeffrey S.
2012-01-01
Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient
Filtered density function approach for reactive transport in groundwater
NASA Astrophysics Data System (ADS)
Suciu, Nicolae; Schüler, Lennart; Attinger, Sabine; Knabner, Peter
2016-04-01
Spatial filtering may be used in coarse-grained simulations (CGS) of reactive transport in groundwater, similar to the large eddy simulations (LES) in turbulence. The filtered density function (FDF), stochastically equivalent to a probability density function (PDF), provides a statistical description of the sub-grid, unresolved, variability of the concentration field. Besides closing the chemical source terms in the transport equation for the mean concentration, like in LES-FDF methods, the CGS-FDF approach aims at quantifying the uncertainty over the whole hierarchy of heterogeneity scales exhibited by natural porous media. Practically, that means estimating concentration PDFs on coarse grids, at affordable computational costs. To cope with the high dimensionality of the problem in case of multi-component reactive transport and to reduce the numerical diffusion, FDF equations are solved by particle methods. But, while trajectories of computational particles are modeled as stochastic processes indexed by time, the concentration's heterogeneity is modeled as a random field, with multi-dimensional, spatio-temporal sets of indices. To overcome this conceptual inconsistency, we consider FDFs/PDFs of random species concentrations weighted by conserved scalars and we show that their evolution equations can be formulated as Fokker-Planck equations describing stochastically equivalent processes in concentration-position spaces. Numerical solutions can then be approximated by the density in the concentration-position space of an ensemble of computational particles governed by the associated Itô equations. Instead of sequential particle methods we use a global random walk (GRW) algorithm, which is stable, free of numerical diffusion, and practically insensitive to the increase of the number of particles. We illustrate the general FDF approach and the GRW numerical solution for a reduced complexity problem consisting of the transport of a single scalar in groundwater
Models of Protocellular Structure, Function and Evolution
NASA Technical Reports Server (NTRS)
New, Michael H.; Pohorille, Andrew; Szostak, Jack W.; Keefe, Tony; Lanyi, Janos K.; DeVincenzi, Donald L. (Technical Monitor)
2001-01-01
In the absence of any record of protocells, the most direct way to test our understanding, of the origin of cellular life is to construct laboratory models that capture important features of protocellular systems. Such efforts are currently underway in a collaborative project between NASA-Ames, Harvard Medical School and University of California. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures. The centerpiece of this project is a method for the in vitro evolution of protein enzymes toward arbitrary catalytic targets. A similar approach has already been developed for nucleic acids in which a small number of functional molecules are selected from a large, random population of candidates. The selected molecules are next vastly multiplied using the polymerase chain reaction.
Stormwater infiltration trenches: a conceptual modelling approach.
Freni, Gabriele; Mannina, Giorgio; Viviani, Gaspare
2009-01-01
In recent years, limitations linked to traditional urban drainage schemes have been pointed out and new approaches are developing introducing more natural methods for retaining and/or disposing of stormwater. These mitigation measures are generally called Best Management Practices or Sustainable Urban Drainage System and they include practices such as infiltration and storage tanks in order to reduce the peak flow and retain part of the polluting components. The introduction of such practices in urban drainage systems entails an upgrade of existing modelling frameworks in order to evaluate their efficiency in mitigating the impact of urban drainage systems on receiving water bodies. While storage tank modelling approaches are quite well documented in literature, some gaps are still present about infiltration facilities mainly dependent on the complexity of the involved physical processes. In this study, a simplified conceptual modelling approach for the simulation of the infiltration trenches is presented. The model enables to assess the performance of infiltration trenches. The main goal is to develop a model that can be employed for the assessment of the mitigation efficiency of infiltration trenches in an integrated urban drainage context. Particular care was given to the simulation of infiltration structures considering the performance reduction due to clogging phenomena. The proposed model has been compared with other simplified modelling approaches and with a physically based model adopted as benchmark. The model performed better compared to other approaches considering both unclogged facilities and the effect of clogging. On the basis of a long-term simulation of six years of rain data, the performance and the effectiveness of an infiltration trench measure are assessed. The study confirmed the important role played by the clogging phenomenon on such infiltration structures. PMID:19587416
New approach to folding with the Coulomb wave function
Blokhintsev, L. D.; Savin, D. A.; Kadyrov, A. S.; Mukhamedzhanov, A. M.
2015-05-15
Due to the long-range character of the Coulomb interaction theoretical description of low-energy nuclear reactions with charged particles still remains a formidable task. One way of dealing with the problem in an integral-equation approach is to employ a screened Coulomb potential. A general approach without screening requires folding of kernels of the integral equations with the Coulomb wave. A new method of folding a function with the Coulomb partial waves is presented. The partial-wave Coulomb function both in the configuration and momentum representations is written in the form of separable series. Each term of the series is represented as a product of a factor depending only on the Coulomb parameter and a function depending on the spatial variable in the configuration space and the momentum variable if the momentum representation is used. Using a trial function, the method is demonstrated to be efficient and reliable.
A multiscale modeling approach to adhesive contact
NASA Astrophysics Data System (ADS)
Fan, KangQi; Wang, WeiDong; Zhu, YingMin; Zhang, XiuYan
2011-09-01
In order to model the adhesive contact across different length scales, a multiscale approach is developed and used to study the adhesive contact behaviors between a rigid cylinder and an elastic face-centered cubic (FCC) substrate. The approach combines an atomistic treatment of the interfacial region with an elastic mechanics method description of the continuum region. The two regions are connected by a coupling region where nodes of the continuum region are refined to atoms of the atomistic region. Moreover, the elastic constants of FCC crystals are obtained directly from the Lennard-Jones potential to describe the elastic response characteristics of the continuum region, which ensures the consistency of material proprieties between atomistic and continuum regions. The multiscale approach is examined by comparing it with the pure MD simulation, and the results indicate that the multiscale modeling approach agrees well with the MD method in studying the adhesive contact behaviors.
Graphical Approach to Model Reduction for Nonlinear Biochemical Networks
Holland, David O.; Krainak, Nicholas C.; Saucerman, Jeffrey J.
2011-01-01
Model reduction is a central challenge to the development and analysis of multiscale physiology models. Advances in model reduction are needed not only for computational feasibility but also for obtaining conceptual insights from complex systems. Here, we introduce an intuitive graphical approach to model reduction based on phase plane analysis. Timescale separation is identified by the degree of hysteresis observed in phase-loops, which guides a “concentration-clamp” procedure for estimating explicit algebraic relationships between species equilibrating on fast timescales. The primary advantages of this approach over Jacobian-based timescale decomposition are that: 1) it incorporates nonlinear system dynamics, and 2) it can be easily visualized, even directly from experimental data. We tested this graphical model reduction approach using a 25-variable model of cardiac β1-adrenergic signaling, obtaining 6- and 4-variable reduced models that retain good predictive capabilities even in response to new perturbations. These 6 signaling species appear to be optimal “kinetic biomarkers” of the overall β1-adrenergic pathway. The 6-variable reduced model is well suited for integration into multiscale models of heart function, and more generally, this graphical model reduction approach is readily applicable to a variety of other complex biological systems. PMID:21901136
Thilaga, M; Vijayalakshmi, R; Nadarajan, R; Nandagopal, D
2016-06-01
The complex nature of neuronal interactions of the human brain has posed many challenges to the research community. To explore the underlying mechanisms of neuronal activity of cohesive brain regions during different cognitive activities, many innovative mathematical and computational models are required. This paper presents a novel Common Functional Pattern Mining approach to demonstrate the similar patterns of interactions due to common behavior of certain brain regions. The electrode sites of EEG-based functional brain network are modeled as a set of transactions and node-based complex network measures as itemsets. These itemsets are transformed into a graph data structure called Functional Pattern Graph. By mining this Functional Pattern Graph, the common functional patterns due to specific brain functioning can be identified. The empirical analyses show the efficiency of the proposed approach in identifying the extent to which the electrode sites (transactions) are similar during various cognitive load states. PMID:27401999
Towards new approaches in phenological modelling
NASA Astrophysics Data System (ADS)
Chmielewski, Frank-M.; Götz, Klaus-P.; Rawel, Harshard M.; Homann, Thomas
2014-05-01
Modelling of phenological stages is based on temperature sums for many decades, describing both the chilling and the forcing requirement of woody plants until the beginning of leafing or flowering. Parts of this approach go back to Reaumur (1735), who originally proposed the concept of growing degree-days. Now, there is a growing body of opinion that asks for new methods in phenological modelling and more in-depth studies on dormancy release of woody plants. This requirement is easily understandable if we consider the wide application of phenological models, which can even affect the results of climate models. To this day, in phenological models still a number of parameters need to be optimised on observations, although some basic physiological knowledge of the chilling and forcing requirement of plants is already considered in these approaches (semi-mechanistic models). Limiting, for a fundamental improvement of these models, is the lack of knowledge about the course of dormancy in woody plants, which cannot be directly observed and which is also insufficiently described in the literature. Modern metabolomic methods provide a solution for this problem and allow both, the validation of currently used phenological models as well as the development of mechanistic approaches. In order to develop this kind of models, changes of metabolites (concentration, temporal course) must be set in relation to the variability of environmental (steering) parameters (weather, day length, etc.). This necessarily requires multi-year (3-5 yr.) and high-resolution (weekly probes between autumn and spring) data. The feasibility of this approach has already been tested in a 3-year pilot-study on sweet cherries. Our suggested methodology is not only limited to the flowering of fruit trees, it can be also applied to tree species of the natural vegetation, where even greater deficits in phenological modelling exist.
Bovell, Adonis Miguel; Warncke, Kurt
2013-02-26
Ethanolamine ammonia-lyase (EAL) is a 5'-deoxyadenosylcobalamin-dependent bacterial enzyme that catalyzes the deamination of the short-chain vicinal amino alcohols, aminoethanol and (S)- and (R)-2-aminopropanol. The coding sequence for EAL is located within the 17-gene eut operon, which encodes the broad spectrum of proteins that comprise the ethanolamine utilization (eut) metabolosome suborganelle structure. A high-resolution structure of the ∼500 kDa EAL [(EutB-EutC)₂]₃ oligomer from Escherichia coli has been determined by X-ray crystallography, but high-resolution spectroscopic determinations of reactant intermediate-state structures and detailed kinetic and thermodynamic studies of EAL have been conducted for the Salmonella typhimurium enzyme. Therefore, a statistically robust homology model for the S. typhimurium EAL is constructed from the E. coli structure. The model structure is used to describe the hierarchy of EutB and EutC subunit interactions that construct the native EAL oligomer and, specifically, to address the long-standing challenge of reconstitution of the functional oligomer from isolated, purified subunits. Model prediction that the (EutB₂)₃ oligomer assembly will occur from isolated EutB, and that this hexameric structure will template the formation of the complete, native [(EutB-EutC)₂]₃ oligomer, is verified by biochemical methods. Prediction that cysteine residues on the exposed subunit-subunit contact surfaces of isolated EutB and EutC will interfere with assembly by cystine formation is verified by activating effects of disulfide reducing agents. Angstrom-scale congruence of the reconstituted and native EAL in the active site region is shown by electron paramagnetic resonance spectroscopy. Overall, the hierarchy of subunit interactions and microscopic features of the contact surfaces, which are revealed by the homology model, guide and provide a rationale for a refined genetic and biochemical approach to reconstitution of the
Bootstrapped models for intrinsic random functions
Campbell, K.
1987-01-01
The use of intrinsic random function stochastic models as a basis for estimation in geostatistical work requires the identification of the generalized covariance function of the underlying process, and the fact that this function has to be estimated from the data introduces an additional source of error into predictions based on the model. This paper develops the sample reuse procedure called the ''bootstrap'' in the context of intrinsic random functions to obtain realistic estimates of these errors. Simulation results support the conclusion that bootstrap distributions of functionals of the process, as well as of their ''kriging variance,'' provide a reasonable picture of the variability introduced by imperfect estimation of the generalized covariance function.
Bootstrapped models for intrinsic random functions
Campbell, K.
1988-08-01
Use of intrinsic random function stochastic models as a basis for estimation in geostatistical work requires the identification of the generalized covariance function of the underlying process. The fact that this function has to be estimated from data introduces an additional source of error into predictions based on the model. This paper develops the sample reuse procedure called the bootstrap in the context of intrinsic random functions to obtain realistic estimates of these errors. Simulation results support the conclusion that bootstrap distributions of functionals of the process, as well as their kriging variance, provide a reasonable picture of variability introduced by imperfect estimation of the generalized covariance function.
A functional approach to emotion in autonomous systems.
Sanz, Ricardo; Hernández, Carlos; Gómez, Jaime; Hernando, Adolfo
2010-01-01
The construction of fully effective systems seems to pass through the proper exploitation of goal-centric self-evaluative capabilities that let the system teleologically self-manage. Emotions seem to provide this kind of functionality to biological systems and hence the interest in emotion for function sustainment in artificial systems performing in changing and uncertain environments; far beyond the media hullabaloo of displaying human-like emotion-laden faces in robots. This chapter provides a brief analysis of the scientific theories of emotion and presents an engineering approach for developing technology for robust autonomy by implementing functionality inspired in that of biological emotions. PMID:20020352
Questionnaire of Executive Function for Dancers: An Ecological Approach
ERIC Educational Resources Information Center
Wong, Alina; Rodriguez, Mabel; Quevedo, Liliana; de Cossio, Lourdes Fernandez; Borges, Ariel; Reyes, Alicia; Corral, Roberto; Blanco, Florentino; Alvarez, Miguel
2012-01-01
There is a current debate about the ecological validity of executive function (EF) tests. Consistent with the verisimilitude approach, this research proposes the Ballet Executive Scale (BES), a self-rating questionnaire that assimilates idiosyncratic executive behaviors of classical dance community. The BES was administrated to 149 adolescents,…
From Equation to Inequality Using a Function-Based Approach
ERIC Educational Resources Information Center
Verikios, Petros; Farmaki, Vassiliki
2010-01-01
This article presents features of a qualitative research study concerning the teaching and learning of school algebra using a function-based approach in a grade 8 class, of 23 students, in 26 lessons, in a state school of Athens, in the school year 2003-2004. In this article, we are interested in the inequality concept and our aim is to…
Kelly, Mollie L; Chernoff, Jonathan
2012-04-01
p21-activated kinases are a family of highly conserved protein serine/threonine kinases that are increasingly recognized as playing essential roles in a variety of key signaling processes. Genetic analyses in mice, using constitutive or regulated gene disruption, have provided important new insights into PAK function. In this paper, we review the genetic analysis of all six PAK genes in mice. These data address the singular and redundant functions of the various PAK genes and suggest therapeutic possibilities for small molecule PAK inhibitors or activators. PMID:23162740
A New Mixed Model Based on the Velocity Structure Function
NASA Astrophysics Data System (ADS)
Brun, Christophe; Friedrich, Rainer; Da Silva, Carlos B.; Métais, Olivier
We propose a new mixed model for Large Eddy-Simulation based on the 3D spatial velocity increment. This approach blends the non-linear properties of the Increment model (Brun & Friedrich (2001)) with the eddy viscosity characteristics of the Structure Function model (Métais & Lesieur (1992)). The behaviour of this subgrid scale model is studied both via a priori tests of a plane jet at ReH=3000 and Large Eddy-Simulation of a round jet at ReD=25000. This approach allows to describe both forward and backward energy transfer encountered in transitional shear flows.
Approaches for functional analysis of flagellar proteins in African trypanosomes
Oberholzer, Michael; Lopez, Miguel A.; Ralston, Katherine S.; Hill, Kent L.
2013-01-01
The eukaryotic flagellum is a highly conserved organelle serving motility, sensory and transport functions. Although genetic, genomic and proteomic studies have led to the identification of hundreds of flagellar and putative flagellar proteins, precisely how these proteins function individually and collectively to drive flagellum motility and other functions remains to be determined. In this chapter we provide an overview of tools and approaches available for studying flagellum protein function in the protozoan parasite Trypanosoma brucei. We begin by outlining techniques for in vitro cultivation of both T. brucei lifecycle stages, as well as transfection protocols for the delivery of DNA constructs. We then describe specific assays used to assess flagellum function including flagellum preparation and quantitative motility assays. We conclude the chapter with a description of molecular genetic approaches for manipulating gene function. In summary, the availability of potent molecular tools, as well as the health and economic relevance of T. brucei as a pathogen, combine to make the parasite an attractive and integral experimental system for the functional analysis of flagellar proteins. PMID:20409810
Nuclear collective excitations: A relativistic density functional approach
NASA Astrophysics Data System (ADS)
Piekarewicz, J.
2015-08-01
Density functional theory provides the most promising, and likely unique, microscopic framework to describe nuclear systems ranging from finite nuclei to neutron stars. Properly optimized energy density functionals define a new paradigm in nuclear theory where predictive capability is possible and uncertainty quantification is demanded. Moreover, density functional theory offers a consistent approach to the linear response of the nuclear ground state. In this paper, we review the fundamental role played by nuclear collective modes in uncovering novel excitations and in guiding the optimization of the density functional. Indeed, without collective excitations the determination of the density functional remains incomplete. Without collective excitations, the equation of state of neutron-rich matter continues to be poorly constrained. We conclude with a discussion of some of the remaining challenges in this field and propose a path forward to address these challenges.
Models of Protocellular Structure, Function and Evolution
NASA Technical Reports Server (NTRS)
New, Michael H.; Pohorille, Andrew; Szostak, Jack W.; Keefe, Tony; Lanyi, Janos K.
2001-01-01
In the absence of any record of protocells, the most direct way to test our understanding of the origin of cellular life is to construct laboratory models that capture important features of protocellular systems. Such efforts are currently underway in a collaborative project between NASA-Ames, Harvard Medical School and University of California. They are accompanied by computational studies aimed at explaining self-organization of simple molecules into ordered structures. The centerpiece of this project is a method for the in vitro evolution of protein enzymes toward arbitrary catalytic targets. A similar approach has already been developed for nucleic acids in which a small number of functional molecules are selected from a large, random population of candidates. The selected molecules are next vastly multiplied using the polymerase chain reaction. A mutagenic approach, in which the sequences of selected molecules are randomly altered, can yield further improvements in performance or alterations of specificities. Unfortunately, the catalytic potential of nucleic acids is rather limited. Proteins are more catalytically capable but cannot be directly amplified. In the new technique, this problem is circumvented by covalently linking each protein of the initial, diverse, pool to the RNA sequence that codes for it. Then, selection is performed on the proteins, but the nucleic acids are replicated. Additional information is contained in the original extended abstract.
Järvinen, Anna; Ng, Rowena; Bellugi, Ursula
2015-11-01
Williams syndrome (WS) is a neurogenetic disorder that is saliently characterized by a unique social phenotype, most notably associated with a dramatically increased affinity and approachability toward unfamiliar people. Despite a recent proliferation of studies into the social profile of WS, the underpinnings of the pro-social predisposition are poorly understood. To this end, the present study was aimed at elucidating approach behavior of individuals with WS contrasted with typical development (TD) by employing a multidimensional design combining measures of autonomic arousal, social functioning, and two levels of approach evaluations. Given previous evidence suggesting that approach behaviors of individuals with WS are driven by a desire for social closeness, approachability tendencies were probed across two levels of social interaction: talking versus befriending. The main results indicated that while overall level of approachability did not differ between groups, an important qualitative between-group difference emerged across the two social interaction contexts: whereas individuals with WS demonstrated a similar willingness to approach strangers across both experimental conditions, TD individuals were significantly more willing to talk to than to befriend strangers. In WS, high approachability to positive faces across both social interaction levels was further associated with more normal social functioning. A novel finding linked autonomic responses with willingness to befriend negative faces in the WS group: elevated autonomic responsivity was associated with increased affiliation to negative face stimuli, which may represent an autonomic correlate of approach behavior in WS. Implications for underlying organization of the social brain are discussed. PMID:26459097
Modeling superhelical DNA: recent analytical and dynamic approaches.
Schlick, T
1995-04-01
During the past year, a variety of diverse and complementary approaches have been presented for modeling superhelical DNA, offering new physical and biological insights into fundamental functional processes of DNA. Analytical approaches have probed deeper into the effects of entropy and thermal fluctuations on DNA structure and on various topological constraints induced by DNA-binding proteins. In tandem, new kinetic approaches--by molecular, Langevin and Brownian dynamics, as well as extensions of elastic-rod theory--have begun to offer dynamic information associated with supercoiling. Such dynamic approaches, along with other equilibrium studies, are refining the basic elastic-rod and polymer framework and incorporating more realistic treatments of salt and sequence-specific features. These collective advances in modeling large DNA molecules, in concert with technological innovations, are pointing to an exciting interplay between theory and experiment on the horizon. PMID:7648328
Towards a Multiscale Approach to Cybersecurity Modeling
Hogan, Emilie A.; Hui, Peter SY; Choudhury, Sutanay; Halappanavar, Mahantesh; Oler, Kiri J.; Joslyn, Cliff A.
2013-11-12
We propose a multiscale approach to modeling cyber networks, with the goal of capturing a view of the network and overall situational awareness with respect to a few key properties--- connectivity, distance, and centrality--- for a system under an active attack. We focus on theoretical and algorithmic foundations of multiscale graphs, coming from an algorithmic perspective, with the goal of modeling cyber system defense as a specific use case scenario. We first define a notion of \\emph{multiscale} graphs, in contrast with their well-studied single-scale counterparts. We develop multiscale analogs of paths and distance metrics. As a simple, motivating example of a common metric, we present a multiscale analog of the all-pairs shortest-path problem, along with a multiscale analog of a well-known algorithm which solves it. From a cyber defense perspective, this metric might be used to model the distance from an attacker's position in the network to a sensitive machine. In addition, we investigate probabilistic models of connectivity. These models exploit the hierarchy to quantify the likelihood that sensitive targets might be reachable from compromised nodes. We believe that our novel multiscale approach to modeling cyber-physical systems will advance several aspects of cyber defense, specifically allowing for a more efficient and agile approach to defending these systems.
A Conceptual Modeling Approach for OLAP Personalization
NASA Astrophysics Data System (ADS)
Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan
Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.
Post-16 Biology--Some Model Approaches?
ERIC Educational Resources Information Center
Lock, Roger
1997-01-01
Outlines alternative approaches to the teaching of difficult concepts in A-level biology which may help student learning by making abstract ideas more concrete and accessible. Examples include models, posters, and poems for illustrating meiosis, mitosis, genetic mutations, and protein synthesis. (DDR)
A Functional Developmental Approach to Autism Spectrum Disorders.
ERIC Educational Resources Information Center
Greenspan, Stanley I.; Wieder, Serena
1999-01-01
This article describes a dynamic, developmental model to be used to guide assessment and intervention in children with autism. The Developmental, Individual-Difference, Relationship-Based model conceptualizes the child's functional emotional developmental capacities, individual differences in sensory processing and modulation, motor planning and…
Tensor renormalization group approach to classical dimer models
NASA Astrophysics Data System (ADS)
Roychowdhury, Krishanu; Huang, Ching-Yu
2015-05-01
We analyze classical dimer models on a square and a triangular lattice using a tensor network representation of the dimers. The correlation functions are numerically calculated using the recently developed "tensor renormalization group" (TRG) technique. The partition function for the dimer problem can be calculated exactly by the Pfaffian method, which is used here as a platform for comparing the numerical results. The TRG approach turns out to be a powerful tool for describing gapped systems with exponentially decaying correlations very efficiently due to its fast convergence. This is the case for the dimer model on the triangular lattice. However, the convergence becomes very slow and unstable in the case of the square lattice where the model has algebraically decaying correlations. We highlight these aspects with numerical simulations and critically appraise the robustness of the TRG approach by contrasting the results for small and large system sizes against the exact calculations. Furthermore, we benchmark our TRG results with the classical Monte Carlo method.
Semitransparent one-dimensional potential: a Green's function approach
NASA Astrophysics Data System (ADS)
Maldonado-Villamizar, F. H.
2015-06-01
We study the unstable harmonic oscillator and the unstable linear potential in the presence of the point potential, which is the superposition of the Dirac δ (x) and its derivative {{δ }\\prime }(x). Using the physical boundary conditions for the Green's function we derive for both systems the resonance poles and the resonance wave functions. The matching conditions for the resonance wave functions coincide with those obtained by the self-adjoint extensions of the point potentials and also by the modelling of the {{δ }\\prime }(x) function. We find that, with our definitions, the pure b{{δ }\\prime }(x) barrier is semi-transparent independent of the value of b.
Limitations of Discrete Stereology: Steps Toward a More Functional Approach
NASA Astrophysics Data System (ADS)
Proussevitch, A. A.; Sahagian, D. L.; Jutzeler, M.
2012-12-01
Stereology is a statistical and mathematical means to obtain 3D information (such as size, shape, and spatial orientation statistical distributions) from observed 2D cross-section cuts through a volume containing many embedded objects. Examples are SEM imagery of voids in a volcanic rock or tephra, objects in an X-ray tomographic slice, a thin section, a polished section of granite, a planar outcrop of welded volcanic pyroclasts, or sizing of igneous, sedimentary and metamorphic formations from maps. There are three possible approaches to addressing the stereology formulation: 1. Rough approximation using binned data conversion, i.e. discrete stereology. (BAD) 2. Semi-functional data deconvolution, i.e. hybrid of discrete and functional stereology. (BETTER) 3. Solution with 2D-3D functional transformation, i.e. functional stereology (the next step) (BEST). Discrete Stereology: Historically, stereology has been limited to observations of object sizes grouped into discrete bins, or what we now call "discrete" stereology. This approach suffers from severe limitations when applied to natural materials. The most serious of which are exponential error propagation and bias introduced by small numbers of objects in the extremities of the size distribution, and compounded non-spherical shapes and preferred spatial orientations. These limitations do not allow for accurate size distributions of pyroclastic materials, vesicles, and crystals, except for impractically large sample populations. Semi-Functional Stereology: In order to improve the method, a simple first step already taken is "semi-functional" stereology. It combines both discrete object sizing and pre-defined functions of 2D and 3D distributions. Discrete binned observational data is represented by a histogram from which a best fit function for 2D distribution is assigned. This function is then discretized and a 3D distribution is derived from that as in discrete stereology. This approach eliminates some problems
Building Water Models: A Different Approach
2015-01-01
Simplified classical water models are currently an indispensable component in practical atomistic simulations. Yet, despite several decades of intense research, these models are still far from perfect. Presented here is an alternative approach to constructing widely used point charge water models. In contrast to the conventional approach, we do not impose any geometry constraints on the model other than the symmetry. Instead, we optimize the distribution of point charges to best describe the “electrostatics” of the water molecule. The resulting “optimal” 3-charge, 4-point rigid water model (OPC) reproduces a comprehensive set of bulk properties significantly more accurately than commonly used rigid models: average error relative to experiment is 0.76%. Close agreement with experiment holds over a wide range of temperatures. The improvements in the proposed model extend beyond bulk properties: compared to common rigid models, predicted hydration free energies of small molecules using OPC are uniformly closer to experiment, with root-mean-square error <1 kcal/mol. PMID:25400877
An Evolutionary Computation Approach to Examine Functional Brain Plasticity.
Roy, Arnab; Campbell, Colin; Bernier, Rachel A; Hillary, Frank G
2016-01-01
One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength
An Evolutionary Computation Approach to Examine Functional Brain Plasticity
Roy, Arnab; Campbell, Colin; Bernier, Rachel A.; Hillary, Frank G.
2016-01-01
One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength
An object-oriented approach to energy-economic modeling
Wise, M.A.; Fox, J.A.; Sands, R.D.
1993-12-01
In this paper, the authors discuss the experiences in creating an object-oriented economic model of the U.S. energy and agriculture markets. After a discussion of some central concepts, they provide an overview of the model, focusing on the methodology of designing an object-oriented class hierarchy specification based on standard microeconomic production functions. The evolution of the model from the class definition stage to programming it in C++, a standard object-oriented programming language, will be detailed. The authors then discuss the main differences between writing the object-oriented program versus a procedure-oriented program of the same model. Finally, they conclude with a discussion of the advantages and limitations of the object-oriented approach based on the experience in building energy-economic models with procedure-oriented approaches and languages.
Quasielastic scattering with the relativistic Green’s function approach
Meucci, Andrea; Giusti, Carlotta
2015-05-15
A relativistic model for quasielastic (QE) lepton-nucleus scattering is presented. The effects of final-state interactions (FSI) between the ejected nucleon and the residual nucleus are described in the relativistic Green’s function (RGF) model where FSI are consistently described with exclusive scattering using a complex optical potential. The results of the model are compared with experimental results of electron and neutrino scattering.
Elements of a function analytic approach to probability.
Ghanem, Roger Georges; Red-Horse, John Robert
2008-02-01
We first provide a detailed motivation for using probability theory as a mathematical context in which to analyze engineering and scientific systems that possess uncertainties. We then present introductory notes on the function analytic approach to probabilistic analysis, emphasizing the connections to various classical deterministic mathematical analysis elements. Lastly, we describe how to use the approach as a means to augment deterministic analysis methods in a particular Hilbert space context, and thus enable a rigorous framework for commingling deterministic and probabilistic analysis tools in an application setting.
Accuracy of functional surfaces on comparatively modeled protein structures
Zhao, Jieling; Dundas, Joe; Kachalo, Sema; Ouyang, Zheng; Liang, Jie
2012-01-01
Identification and characterization of protein functional surfaces are important for predicting protein function, understanding enzyme mechanism, and docking small compounds to proteins. As the rapid speed of accumulation of protein sequence information far exceeds that of structures, constructing accurate models of protein functional surfaces and identify their key elements become increasingly important. A promising approach is to build comparative models from sequences using known structural templates such as those obtained from structural genome projects. Here we assess how well this approach works in modeling binding surfaces. By systematically building three-dimensional comparative models of proteins using Modeller, we determine how well functional surfaces can be accurately reproduced. We use an alpha shape based pocket algorithm to compute all pockets on the modeled structures, and conduct a large-scale computation of similarity measurements (pocket RMSD and fraction of functional atoms captured) for 26,590 modeled enzyme protein structures. Overall, we find that when the sequence fragment of the binding surfaces has more than 45% identity to that of the tempalte protein, the modeled surfaces have on average an RMSD of 0.5 Å, and contain 48% or more of the binding surface atoms, with nearly all of the important atoms in the signatures of binding pockets captured. PMID:21541664
A hybrid modeling approach for option pricing
NASA Astrophysics Data System (ADS)
Hajizadeh, Ehsan; Seifi, Abbas
2011-11-01
The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.
Generating functional approach to Bose-Einstein correlations
Suzuki, N.; Biyajima, M.; Andreev, I.V.
1997-11-01
Bose-Einstein correlations are considered in the presence of M independent chaotic sources and a coherent source. Our approach is an extension of the formulation in the quantum optics given by Glauber and Lachs. The generating functional (GF) of Bose-Einstein correlation (BEC) functions is derived, and higher order BEC functions are obtained from the GF. A diagrammatic representation for cumulants is made. The number M is explicitly contained in our formulation, which is different from that given by Cramer {ital et al.} The possibility of estimating the number M from the analysis of BEC functions and cumulants is pointed out. Moreover, source size dependence of multiplicity distributions is shown in a simplified case. {copyright} {ital 1997} {ital The American Physical Society}
New approaches to enhance active steering system functionalities: preliminary results
NASA Astrophysics Data System (ADS)
Serarslan, Benan
2014-09-01
An important development of the steering systems in general is active steering systems like active front steering and steer-by-wire systems. In this paper the current functional possibilities in application of active steering systems are explored. A new approach and additional functionalities are presented that can be implemented to the active steering systems without additional hardware such as new sensors and electronic control units. Commercial active steering systems are controlling the steering angle depending on the driving situation only. This paper introduce methods for enhancing active steering system functionalities depending not only on the driving situation but also vehicle parameters like vehicle mass, tyre and road condition. In this regard, adaptation of the steering ratio as a function of above mentioned vehicle parameters is presented with examples. With some selected vehicle parameter changes, the reduction of the undesired influences on vehicle dynamics of these parameter changes has been demonstrated theoretically with simulations and with real-time driving measurements.
Computational approaches for rational design of proteins with novel functionalities
Tiwari, Manish Kumar; Singh, Ranjitha; Singh, Raushan Kumar; Kim, In-Won; Lee, Jung-Kul
2012-01-01
Proteins are the most multifaceted macromolecules in living systems and have various important functions, including structural, catalytic, sensory, and regulatory functions. Rational design of enzymes is a great challenge to our understanding of protein structure and physical chemistry and has numerous potential applications. Protein design algorithms have been applied to design or engineer proteins that fold, fold faster, catalyze, catalyze faster, signal, and adopt preferred conformational states. The field of de novo protein design, although only a few decades old, is beginning to produce exciting results. Developments in this field are already having a significant impact on biotechnology and chemical biology. The application of powerful computational methods for functional protein designing has recently succeeded at engineering target activities. Here, we review recently reported de novo functional proteins that were developed using various protein design approaches, including rational design, computational optimization, and selection from combinatorial libraries, highlighting recent advances and successes. PMID:24688643
A subgrid based approach for morphodynamic modelling
NASA Astrophysics Data System (ADS)
Volp, N. D.; van Prooijen, B. C.; Pietrzak, J. D.; Stelling, G. S.
2016-07-01
To improve the accuracy and the efficiency of morphodynamic simulations, we present a subgrid based approach for a morphodynamic model. This approach is well suited for areas characterized by sub-critical flow, like in estuaries, coastal areas and in low land rivers. This new method uses a different grid resolution to compute the hydrodynamics and the morphodynamics. The hydrodynamic computations are carried out with a subgrid based, two-dimensional, depth-averaged model. This model uses a coarse computational grid in combination with a subgrid. The subgrid contains high resolution bathymetry and roughness information to compute volumes, friction and advection. The morphodynamic computations are carried out entirely on a high resolution grid, the bed grid. It is key to find a link between the information defined on the different grids in order to guaranty the feedback between the hydrodynamics and the morphodynamics. This link is made by using a new physics-based interpolation method. The method interpolates water levels and velocities from the coarse grid to the high resolution bed grid. The morphodynamic solution improves significantly when using the subgrid based method compared to a full coarse grid approach. The Exner equation is discretised with an upwind method based on the direction of the bed celerity. This ensures a stable solution for the Exner equation. By means of three examples, it is shown that the subgrid based approach offers a significant improvement at a minimal computational cost.
A Bayesian Shrinkage Approach for AMMI Models.
da Silva, Carlos Pereira; de Oliveira, Luciano Antonio; Nuvunga, Joel Jorge; Pamplona, Andrezza Kéllen Alves; Balestre, Marcio
2015-01-01
Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI) model, are widely applicable to genotype-by-environment interaction (GEI) studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05) in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct posterior
A Bayesian Shrinkage Approach for AMMI Models
de Oliveira, Luciano Antonio; Nuvunga, Joel Jorge; Pamplona, Andrezza Kéllen Alves
2015-01-01
Linear-bilinear models, especially the additive main effects and multiplicative interaction (AMMI) model, are widely applicable to genotype-by-environment interaction (GEI) studies in plant breeding programs. These models allow a parsimonious modeling of GE interactions, retaining a small number of principal components in the analysis. However, one aspect of the AMMI model that is still debated is the selection criteria for determining the number of multiplicative terms required to describe the GE interaction pattern. Shrinkage estimators have been proposed as selection criteria for the GE interaction components. In this study, a Bayesian approach was combined with the AMMI model with shrinkage estimators for the principal components. A total of 55 maize genotypes were evaluated in nine different environments using a complete blocks design with three replicates. The results show that the traditional Bayesian AMMI model produces low shrinkage of singular values but avoids the usual pitfalls in determining the credible intervals in the biplot. On the other hand, Bayesian shrinkage AMMI models have difficulty with the credible interval for model parameters, but produce stronger shrinkage of the principal components, converging to GE matrices that have more shrinkage than those obtained using mixed models. This characteristic allowed more parsimonious models to be chosen, and resulted in models being selected that were similar to those obtained by the Cornelius F-test (α = 0.05) in traditional AMMI models and cross validation based on leave-one-out. This characteristic allowed more parsimonious models to be chosen and more GEI pattern retained on the first two components. The resulting model chosen by posterior distribution of singular value was also similar to those produced by the cross-validation approach in traditional AMMI models. Our method enables the estimation of credible interval for AMMI biplot plus the choice of AMMI model based on direct posterior
Green-function approach for scattering quantum walks
Andrade, F. M.; Luz, M. G. E. da
2011-10-15
In this work a Green-function approach for scattering quantum walks is developed. The exact formula has the form of a sum over paths and always can be cast into a closed analytic expression for arbitrary topologies and position-dependent quantum amplitudes. By introducing the step and path operators, it is shown how to extract any information about the system from the Green function. The method's relevant features are demonstrated by discussing in detail an example, a general diamond-shaped graph.
Bayesian non-parametrics and the probabilistic approach to modelling
Ghahramani, Zoubin
2013-01-01
Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman’s coalescent, Dirichlet diffusion trees and Wishart processes. PMID:23277609
Fuzzy set approach to quality function deployment: An investigation
NASA Technical Reports Server (NTRS)
Masud, Abu S. M.
1992-01-01
The final report of the 1992 NASA/ASEE Summer Faculty Fellowship at the Space Exploration Initiative Office (SEIO) in Langley Research Center is presented. Quality Function Deployment (QFD) is a process, focused on facilitating the integration of the customer's voice in the design and development of a product or service. Various input, in the form of judgements and evaluations, are required during the QFD analyses. All the input variables in these analyses are treated as numeric variables. The purpose of the research was to investigate how QFD analyses can be performed when some or all of the input variables are treated as linguistic variables with values expressed as fuzzy numbers. The reason for this consideration is that human judgement, perception, and cognition are often ambiguous and are better represented as fuzzy numbers. Two approaches for using fuzzy sets in QFD have been proposed. In both cases, all the input variables are considered as linguistic variables with values indicated as linguistic expressions. These expressions are then converted to fuzzy numbers. The difference between the two approaches is due to how the QFD computations are performed with these fuzzy numbers. In Approach 1, the fuzzy numbers are first converted to their equivalent crisp scores and then the QFD computations are performed using these crisp scores. As a result, the output of this approach are crisp numbers, similar to those in traditional QFD. In Approach 2, all the QFD computations are performed with the fuzzy numbers and the output are fuzzy numbers also. Both the approaches have been explained with the help of illustrative examples of QFD application. Approach 2 has also been applied in a QFD application exercise in SEIO, involving a 'mini moon rover' design. The mini moon rover is a proposed tele-operated vehicle that will traverse and perform various tasks, including autonomous operations, on the moon surface. The output of the moon rover application exercise is a
Neurocomputing approaches to modelling of drying process dynamics
Kaminski, W.; Strumillo, P.; Tomczak, E.
1998-07-01
The application of artificial neural networks to mathematical modeling of drying kinetics, degradation kinetics and smoothing of experimental data is discussed in the paper. A theoretical foundation of drying process description by means of artificial neural networks is presented. Two network types are proposed for drying process modelling, namely the multilayer perceptron network and the radial basis functions network. These were validated experimentally for fresh green peals and diced potatoes which represent diverse food products. Network training procedures based on experimental data are explained. Additionally, the proposed neural network modelling approach is tested on drying experiments of silica gel saturated with ascorbic acid solution.
Quantum cluster approach to the spinful Haldane-Hubbard model
NASA Astrophysics Data System (ADS)
Wu, Jingxiang; Faye, Jean Paul Latyr; Sénéchal, David; Maciejko, Joseph
2016-02-01
We study the spinful fermionic Haldane-Hubbard model at half-filling using a combination of quantum cluster methods: cluster perturbation theory, the variational cluster approximation, and cluster dynamical mean-field theory. We explore possible zero-temperature phases of the model as a function of onsite repulsive interaction strength and next-nearest-neighbor hopping amplitude and phase. Our approach allows us to access the regime of intermediate interaction strength, where charge fluctuations are significant and effective spin model descriptions may not be justified. Our approach also improves upon mean-field solutions of the Haldane-Hubbard model by retaining local quantum fluctuations and treating them nonperturbatively. We find a correlated topological Chern insulator for weak interactions and a topologically trivial Néel antiferromagnetic insulator for strong interactions. For intermediate interactions, we find that topologically nontrivial Néel antiferromagnetic insulating phases and/or a topologically nontrivial nonmagnetic insulating phase may be stabilized.
A Multi-Level Model of Moral Functioning Revisited
ERIC Educational Resources Information Center
Reed, Don Collins
2009-01-01
The model of moral functioning scaffolded in the 2008 "JME" Special Issue is here revisited in response to three papers criticising that volume. As guest editor of that Special Issue I have formulated the main body of this response, concerning the dynamic systems approach to moral development, the problem of moral relativism and the role of…
Systems Engineering Interfaces: A Model Based Approach
NASA Technical Reports Server (NTRS)
Fosse, Elyse; Delp, Christopher
2013-01-01
Currently: Ops Rev developed and maintains a framework that includes interface-specific language, patterns, and Viewpoints. Ops Rev implements the framework to design MOS 2.0 and its 5 Mission Services. Implementation de-couples interfaces and instances of interaction Future: A Mission MOSE implements the approach and uses the model based artifacts for reviews. The framework extends further into the ground data layers and provides a unified methodology.
Algebraic operator approach to gas kinetic models
NASA Astrophysics Data System (ADS)
Il'ichov, L. V.
1997-02-01
Some general properties of the linear Boltzmann kinetic equation are used to present it in the form ∂ tϕ = - Â†Âϕ with the operators ÂandÂ† possessing some nontrivial algebraic properties. When applied to the Keilson-Storer kinetic model, this method gives an example of quantum ( q-deformed) Lie algebra. This approach provides also a natural generalization of the “kangaroo model”.
Exact Approach to Inflationary Universe Models
NASA Astrophysics Data System (ADS)
del Campo, Sergio
In this chapter we introduce a study of inflationary universe models that are characterized by a single scalar inflation field . The study of these models is based on two dynamical equations: one corresponding to the Klein-Gordon equation for the inflaton field and the other to a generalized Friedmann equation. After describing the kinematics and dynamics of the models under the Hamilton-Jacobi scheme, we determine in some detail scalar density perturbations and relic gravitational waves. We also introduce the study of inflation under the hierarchy of the slow-roll parameters together with the flow equations. We apply this approach to the modified Friedmann equation that we call the Friedmann-Chern-Simons equation, characterized by F(H) = H^2- α H4, and the brane-world inflationary models expressed by the modified Friedmann equation.
Muñoz-Martínez, Amanda M; Coletti, Juan Pablo
2015-01-01
Abstract Functional Analytic Psychotherapy (FAP) is a therapeutic approach developed in
Gauge-invariant Green function dynamics: A unified approach
Swiecicki, Sylvia D. Sipe, J.E.
2013-11-15
We present a gauge-invariant description of Green function dynamics introduced by means of a generalized Peirels phase involving an arbitrary differentiable path in space–time. Two other approaches to formulating a gauge-invariant description of systems, the Green function treatment of Levanda and Fleurov [M. Levanda, V. Fleurov, J. Phys.: Condens. Matter 6 (1994) 7889] and the usual multipolar expansion for an atom, are shown to arise as special cases of our formalism. We argue that the consideration of paths in the generalized Peirels phase that do not lead to introduction of an effective gauge-invariant Hamiltonian with polarization and magnetization fields may prove useful for the treatment of the response of materials with short electron correlation lengths. -- Highlights: •Peirels phase for an arbitrary path in space–time established. •Gauge-invariant Green functions and the Power–Zienau–Wooley transformation connected. •Limitations on possible polarization and magnetization fields established.
Controlled Chemistry Approach to the Oxo-Functionalization of Graphene.
Eigler, Siegfried
2016-05-17
Graphene is the best-studied 2D material available. However, its production is still challenging and the quality depends on the preparation procedure. Now, more than a decade after the outstanding experiments conducted on graphene, the most successful wet-chemical approach to graphene and functionalized graphene is based on the oxidation of graphite. Graphene oxide has been known for more than a century; however, the structure bears variable large amounts of lattice defects that render the development of a controlled chemistry impossible. The controlled oxo-functionalization of graphene avoids the formation of defects within the σ-framework of carbon atoms, making the synthesis of specific molecular architectures possible. The scope of this review is to introduce the field of oxo-functionalizing graphene. In particular, the differences between GO and oxo-functionalized graphene are described in detail. Moreover analytical methods that allow determining lattice defects and functional groups are introduced followed by summarizing the current state of controlled oxo-functionalization of graphene. PMID:26990805
Development of a structured approach for decomposition of complex systems on a functional basis
NASA Astrophysics Data System (ADS)
Yildirim, Unal; Felician Campean, I.
2014-07-01
The purpose of this paper is to present the System State Flow Diagram (SSFD) as a structured and coherent methodology to decompose a complex system on a solution- independent functional basis. The paper starts by reviewing common function modelling frameworks in literature and discusses practical requirements of the SSFD in the context of the current literature and current approaches in industry. The proposed methodology is illustrated through the analysis of a case study: design analysis of a generic Bread Toasting System (BTS).
Approaches to modelling hydrology and ecosystem interactions
NASA Astrophysics Data System (ADS)
Silberstein, Richard P.
2014-05-01
As the pressures of industry, agriculture and mining on groundwater resources increase there is a burgeoning un-met need to be able to capture these multiple, direct and indirect stresses in a formal framework that will enable better assessment of impact scenarios. While there are many catchment hydrological models and there are some models that represent ecological states and change (e.g. FLAMES, Liedloff and Cook, 2007), these have not been linked in any deterministic or substantive way. Without such coupled eco-hydrological models quantitative assessments of impacts from water use intensification on water dependent ecosystems under changing climate are difficult, if not impossible. The concept would include facility for direct and indirect water related stresses that may develop around mining and well operations, climate stresses, such as rainfall and temperature, biological stresses, such as diseases and invasive species, and competition such as encroachment from other competing land uses. Indirect water impacts could be, for example, a change in groundwater conditions has an impact on stream flow regime, and hence aquatic ecosystems. This paper reviews previous work examining models combining ecology and hydrology with a view to developing a conceptual framework linking a biophysically defensable model that combines ecosystem function with hydrology. The objective is to develop a model capable of representing the cumulative impact of multiple stresses on water resources and associated ecosystem function.
NARX prediction of some rare chaotic flows: Recurrent fuzzy functions approach
NASA Astrophysics Data System (ADS)
Goudarzi, Sobhan; Jafari, Sajad; Moradi, Mohammad Hassan; Sprott, J. C.
2016-02-01
The nonlinear and dynamic accommodating capability of time domain models makes them a useful representation of chaotic time series for analysis, modeling and prediction. This paper is devoted to the modeling and prediction of chaotic time series with hidden attractors using a nonlinear autoregressive model with exogenous inputs (NARX) based on a novel recurrent fuzzy functions (RFFs) approach. Case studies of recently introduced chaotic systems with hidden attractors plus classical chaotic systems demonstrate that the proposed modeling methodology exhibits better prediction performance from different viewpoints (short term and long term) compared to some other existing methods.
Modeling of human artery tissue with probabilistic approach.
Xiong, Linfei; Chui, Chee-Kong; Fu, Yabo; Teo, Chee-Leong; Li, Yao
2015-04-01
Accurate modeling of biological soft tissue properties is vital for realistic medical simulation. Mechanical response of biological soft tissue always exhibits a strong variability due to the complex microstructure and different loading conditions. The inhomogeneity in human artery tissue is modeled with a computational probabilistic approach by assuming that the instantaneous stress at a specific strain varies according to normal distribution. Material parameters of the artery tissue which are modeled with a combined logarithmic and polynomial energy equation are represented by a statistical function with normal distribution. Mean and standard deviation of the material parameters are determined using genetic algorithm (GA) and inverse mean-value first-order second-moment (IMVFOSM) method, respectively. This nondeterministic approach was verified using computer simulation based on the Monte-Carlo (MC) method. Cumulative distribution function (CDF) of the MC simulation corresponds well with that of the experimental stress-strain data and the probabilistic approach is further validated using data from other studies. By taking into account the inhomogeneous mechanical properties of human biological tissue, the proposed method is suitable for realistic virtual simulation as well as an accurate computational approach for medical device validation. PMID:25748681
Finite Element Model Calibration Approach for Ares I-X
NASA Technical Reports Server (NTRS)
Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Lazor, Daniel R.; Gaspar, James L.; Parks, Russel A.; Bartolotta, Paul A.
2010-01-01
Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of nonconventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pre-test predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.
Finite Element Model Calibration Approach for Area I-X
NASA Technical Reports Server (NTRS)
Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Gaspar, James L.; Lazor, Daniel R.; Parks, Russell A.; Bartolotta, Paul A.
2010-01-01
Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of non-conventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pretest predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.