Processing Motion: Using Code to Teach Newtonian Physics

NASA Astrophysics Data System (ADS)

Massey, M. Ryan

Prior to instruction, students often possess a common-sense view of motion, which is inconsistent with Newtonian physics. Effective physics lessons therefore involve conceptual change. To provide a theoretical explanation for concepts and how they change, the triangulation model brings together key attributes of prototypes, exemplars, theories, Bayesian learning, ontological categories, and the causal model theory. The triangulation model provides a theoretical rationale for why coding is a viable method for physics instruction. As an experiment, thirty-two adolescent students participated in summer coding academies to learn how to design Newtonian simulations. Conceptual and attitudinal data was collected using the Force Concept Inventory and the Colorado Learning Attitudes about Science Survey. Results suggest that coding is an effective means for teaching Newtonian physics.

Grey signal processing and data reconstruction in the non-diffracting beam triangulation measurement system

NASA Astrophysics Data System (ADS)

Meng, Hao; Wang, Zhongyu; Fu, Jihua

2008-12-01

The non-diffracting beam triangulation measurement system possesses the advantages of longer measurement range, higher theoretical measurement accuracy and higher resolution over the traditional laser triangulation measurement system. Unfortunately the measurement accuracy of the system is greatly degraded due to the speckle noise, the CCD photoelectric noise and the background light noise in practical applications. Hence, some effective signal processing methods must be applied to improve the measurement accuracy. In this paper a novel effective method for removing the noises in the non-diffracting beam triangulation measurement system is proposed. In the method the grey system theory is used to process and reconstruct the measurement signal. Through implementing the grey dynamic filtering based on the dynamic GM(1,1), the noises can be effectively removed from the primary measurement data and the measurement accuracy of the system can be improved as a result.

Kinetic and dynamic Delaunay tetrahedralizations in three dimensions

NASA Astrophysics Data System (ADS)

Schaller, Gernot; Meyer-Hermann, Michael

2004-09-01

We describe algorithms to implement fully dynamic and kinetic three-dimensional unconstrained Delaunay triangulations, where the time evolution of the triangulation is not only governed by moving vertices but also by a changing number of vertices. We use three-dimensional simplex flip algorithms, a stochastic visibility walk algorithm for point location and in addition, we propose a new simple method of deleting vertices from an existing three-dimensional Delaunay triangulation while maintaining the Delaunay property. As an example, we analyse the performance in various cases of practical relevance. The dual Dirichlet tessellation can be used to solve differential equations on an irregular grid, to define partitions in cell tissue simulations, for collision detection etc.

Parallel Processing and Scientific Applications

DTIC Science & Technology

1992-11-30

Lattice QCD Calculations on the Connection Machine), SIAM News 24, 1 (May 1991) 5. C. F. Baillie and D. A. Johnston, Crumpling Dynamically Triangulated...hypercubic lattice ; in the second, the surface is randomly triangulated once at the beginning of the simulation; and in the third the random...Sharpe, QCD with Dynamical Wilson Fermions 1I, Phys. Rev. D44, 3272 (1991), 8. R. Gupta and C. F. Baillie, Critical Behavior of the 2D XY Model, Phys

Focus on quantum Einstein gravity Focus on quantum Einstein gravity

NASA Astrophysics Data System (ADS)

Ambjorn, Jan; Reuter, Martin; Saueressig, Frank

2012-09-01

The gravitational asymptotic safety program summarizes the attempts to construct a consistent and predictive quantum theory of gravity within Wilson's generalized framework of renormalization. Its key ingredient is a non-Gaussian fixed point of the renormalization group flow which controls the behavior of the theory at trans-Planckian energies and renders gravity safe from unphysical divergences. Provided that the fixed point comes with a finite number of ultraviolet-attractive (relevant) directions, this construction gives rise to a consistent quantum field theory which is as predictive as an ordinary, perturbatively renormalizable one. This opens up the exciting possibility of establishing quantum Einstein gravity as a fundamental theory of gravity, without introducing supersymmetry or extra dimensions, and solely based on quantization techniques that are known to work well for the other fundamental forces of nature. While the idea of gravity being asymptotically safe was proposed by Steven Weinberg more than 30 years ago [1], the technical tools for investigating this scenario only emerged during the last decade. Here a key role is played by the exact functional renormalization group equation for gravity, which allows the construction of non-perturbative approximate solutions for the RG-flow of the gravitational couplings. Most remarkably, all solutions constructed to date exhibit a suitable non-Gaussian fixed point, lending strong support to the asymptotic safety conjecture. Moreover, the functional renormalization group also provides indications that the central idea of a non-Gaussian fixed point providing a safe ultraviolet completion also carries over to more realistic scenarios where gravity is coupled to a suitable matter sector like the standard model. These theoretical successes also triggered a wealth of studies focusing on the consequences of asymptotic safety in a wide range of phenomenological applications covering the physics of black holes, early time cosmology and the big bang, as well as TeV-scale gravity models testable at the Large Hadron Collider. On different grounds, Monte-Carlo studies of the gravitational partition function based on the discrete causal dynamical triangulations approach provide an a priori independent avenue towards unveiling the non-perturbative features of gravity. As a highlight, detailed simulations established that the phase diagram underlying causal dynamical triangulations contains a phase where the triangulations naturally give rise to four-dimensional, macroscopic universes. Moreover, there are indications for a second-order phase transition that naturally forms the discrete analog of the non-Gaussian fixed point seen in the continuum computations. Thus there is a good chance that the discrete and continuum computations will converge to the same fundamental physics. This focus issue collects a series of papers that outline the current frontiers of the gravitational asymptotic safety program. We hope that readers get an impression of the depth and variety of this research area as well as our excitement about the new and ongoing developments. References [1] Weinberg S 1979 General Relativity, an Einstein Centenary Survey ed S W Hawking and W Israel (Cambridge: Cambridge University Press)

Radio triangulation - mapping the 3D position of the solar radio emission

NASA Astrophysics Data System (ADS)

Magdalenic, Jasmina

2016-04-01

Understanding the relative position of the sources of the radio emission and the associated solar eruptive phenomena (CME and the associated shock wave) has always been a challenge. While ground-based radio interferometer observations provide us with the 2D position information for the radio emission originating from the low corona (up to 2.5 Ro), this is not the case for the radio emission originating at larger heights. The radio triangulation measurements (also referred to as direction-finding or goniopolarimetric measurements) from two or more widely separated spacecraft can provide information on the 3D positions of the sources of the radio emission. This type of interplanetary radio observations are currently performed by STEREO WAVES and WIND WAVES instruments, providing a unique possibility for up to three simultaneous radio triangulations (using up to three different pairs of spacecraft). The recent results of the radio triangulation studies bring new insight into the causal relationship of the solar radio emission and CMEs. In this presentation I will discuss some of the most intriguing results on the source positions of: a) type III radio bursts indicating propagation of the fast electrons accelerated along the open field lines, b) type II radio bursts indicating interaction of the CME-driven shocks and other coronal structures e.g. streamers and c) type IV-like radio bursts possibly associated with CME-CME interaction.

The Movement of Conflict in Organizations: The Joint Dynamics of Splitting and Triangulation.

ERIC Educational Resources Information Center

Smith, Kenwyn K.

1989-01-01

Based on a long-term public school system study, the paper examines the sociopsychological process through which conflicts move around in organizations and are transported from one place to another. The conceptualization draws on "triangulation" from social psychology and family therapy and "splitting," developed in…

Improved laser-based triangulation sensor with enhanced range and resolution through adaptive optics-based active beam control.

PubMed

Reza, Syed Azer; Khwaja, Tariq Shamim; Mazhar, Mohsin Ali; Niazi, Haris Khan; Nawab, Rahma

2017-07-20

Various existing target ranging techniques are limited in terms of the dynamic range of operation and measurement resolution. These limitations arise as a result of a particular measurement methodology, the finite processing capability of the hardware components deployed within the sensor module, and the medium through which the target is viewed. Generally, improving the sensor range adversely affects its resolution and vice versa. Often, a distance sensor is designed for an optimal range/resolution setting depending on its intended application. Optical triangulation is broadly classified as a spatial-signal-processing-based ranging technique and measures target distance from the location of the reflected spot on a position sensitive detector (PSD). In most triangulation sensors that use lasers as a light source, beam divergence-which severely affects sensor measurement range-is often ignored in calculations. In this paper, we first discuss in detail the limitations to ranging imposed by beam divergence, which, in effect, sets the sensor dynamic range. Next, we show how the resolution of laser-based triangulation sensors is limited by the interpixel pitch of a finite-sized PSD. In this paper, through the use of tunable focus lenses (TFLs), we propose a novel design of a triangulation-based optical rangefinder that improves both the sensor resolution and its dynamic range through adaptive electronic control of beam propagation parameters. We present the theory and operation of the proposed sensor and clearly demonstrate a range and resolution improvement with the use of TFLs. Experimental results in support of our claims are shown to be in strong agreement with theory.

Lattice gas simulations of dynamical geometry in two dimensions.

PubMed

Klales, Anna; Cianci, Donato; Needell, Zachary; Meyer, David A; Love, Peter J

2010-10-01

We present a hydrodynamic lattice gas model for two-dimensional flows on curved surfaces with dynamical geometry. This model is an extension to two dimensions of the dynamical geometry lattice gas model previously studied in one dimension. We expand upon a variation of the two-dimensional flat space Frisch-Hasslacher-Pomeau (FHP) model created by Frisch [Phys. Rev. Lett. 56, 1505 (1986)] and independently by Wolfram, and modified by Boghosian [Philos. Trans. R. Soc. London, Ser. A 360, 333 (2002)]. We define a hydrodynamic lattice gas model on an arbitrary triangulation whose flat space limit is the FHP model. Rules that change the geometry are constructed using the Pachner moves, which alter the triangulation but not the topology. We present results on the growth of the number of triangles as a function of time. Simulations show that the number of triangles grows with time as t(1/3), in agreement with a mean-field prediction. We also present preliminary results on the distribution of curvature for a typical triangulation in these simulations.

Towards the map of quantum gravity

NASA Astrophysics Data System (ADS)

Mielczarek, Jakub; Trześniewski, Tomasz

2018-06-01

In this paper we point out some possible links between different approaches to quantum gravity and theories of the Planck scale physics. In particular, connections between loop quantum gravity, causal dynamical triangulations, Hořava-Lifshitz gravity, asymptotic safety scenario, Quantum Graphity, deformations of relativistic symmetries and nonlinear phase space models are discussed. The main focus is on quantum deformations of the Hypersurface Deformations Algebra and Poincaré algebra, nonlinear structure of phase space, the running dimension of spacetime and nontrivial phase diagram of quantum gravity. We present an attempt to arrange the observed relations in the form of a graph, highlighting different aspects of quantum gravity. The analysis is performed in the spirit of a mind map, which represents the architectural approach to the studied theory, being a natural way to describe the properties of a complex system. We hope that the constructed graphs (maps) will turn out to be helpful in uncovering the global picture of quantum gravity as a particular complex system and serve as a useful guide for the researchers.

Analysis of the relationship between lung cancer drug response level and atom connectivity dynamics based on trimmed Delaunay triangulation

NASA Astrophysics Data System (ADS)

Zou, Bin; Wang, Debby D.; Ma, Lichun; Chen, Lijiang; Yan, Hong

2016-05-01

Epidermal growth factor receptor (EGFR) mutation is a pathogenic factor of non-small cell lung cancer (NSCLC). Tyrosine kinase inhibitors (TKIs), such as gefitinib, are widely used in NSCLC treatment. In this work, we investigated the relationship between the number of EGFR residues connected with gefitinib and the response level for each EGFR mutation type. Three-dimensional trimmed Delaunay triangulation was applied to construct connections between EGFR residues and gefitinib atoms. Through molecular dynamics (MD) simulations, we discovered that when the number of EGFR residues connected with gefitinib increases, the response level of the corresponding EGFR mutation tends to descend.

PREFACE: Conceptual and Technical Challenges for Quantum Gravity 2014 - Parallel session: Noncommutative Geometry and Quantum Gravity

NASA Astrophysics Data System (ADS)

Martinetti, P.; Wallet, J.-C.; Amelino-Camelia, G.

2015-08-01

The conference Conceptual and Technical Challenges for Quantum Gravity at Sapienza University of Rome, from 8 to 12 September 2014, has provided a beautiful opportunity for an encounter between different approaches and different perspectives on the quantum-gravity problem. It contributed to a higher level of shared knowledge among the quantum-gravity communities pursuing each specific research program. There were plenary talks on many different approaches, including in particular string theory, loop quantum gravity, spacetime noncommutativity, causal dynamical triangulations, asymptotic safety and causal sets. Contributions from the perspective of philosophy of science were also welcomed. In addition several parallel sessions were organized. The present volume collects contributions from the Noncommutative Geometry and Quantum Gravity parallel session4, with additional invited contributions from specialists in the field. Noncommutative geometry in its many incarnations appears at the crossroad of many researches in theoretical and mathematical physics: • from models of quantum space-time (with or without breaking of Lorentz symmetry) to loop gravity and string theory, • from early considerations on UV-divergencies in quantum field theory to recent models of gauge theories on noncommutative spacetime, • from Connes description of the standard model of elementary particles to recent Pati-Salam like extensions. This volume provides an overview of these various topics, interesting for the specialist as well as accessible to the newcomer. 4partially funded by CNRS PEPS /PTI ''Metric aspect of noncommutative geometry: from Monge to Higgs''

Bootstrapping quarks and gluons

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

Chew, G.F.

1979-04-01

Dual topological unitarization (DTU) - the approach to S-matrix causality and unitarity through combinatorial topology - is reviewed. Amplitudes associated with triangulated spheres are shown to constitute the core of particle physics. Each sphere is covered by triangulated disc faces corresponding to hadrons. The leading current candidate for the hadron-face triangulation pattern employs 3-triangle basic subdiscs whose orientations correspond to baryon number and topological color. Additional peripheral triangles lie along the hadron-face perimeter. Certain combinations of peripheral triangles with a basic-disc triangle can be identified as quarks, the flavor of a quark corresponding to the orientation of its edges thatmore » lie on the hadron-face perimeter. Both baryon number and flavor are additively conserved. Quark helicity, which can be associated with triangle-interior orientation, is not uniformly conserved and interacts with particle momentum, whereas flavor does not. Three different colors attach to the 3 quarks associated with a single basic subdisc, but there is no additive physical conservation law associated with color. There is interplay between color and quark helicity. In hadron faces with more than one basic subdisc, there may occur pairs of adjacent flavorless but colored triangles with net helicity +-1 that are identifiable as gluons. Broken symmetry is an automatic feature of the bootstrap. T, C and P symmetries, as well as up-down flavor symmetry, persist on all orientable surfaces.« less

Summing Feynman graphs by Monte Carlo: Planar ϕ3-theory and dynamically triangulated random surfaces

NASA Astrophysics Data System (ADS)

Boulatov, D. V.; Kazakov, V. A.

1988-12-01

New combinatorial identities are suggested relating the ratio of (n - 1)th and nth orders of (planar) perturbation expansion for any quantity to some average over the ensemble of all planar graphs of the nth order. These identities are used for Monte Carlo calculation of critical exponents γstr (string susceptibility) in planar ϕ3-theory and in the dynamically triangulated random surface (DTRS) model near the convergence circle for various dimensions. In the solvable case D = 1 the exact critical properties of the theory are reproduced numerically. After August 3, 1988 the address will be: Cybernetics Council, Academy of Science, ul. Vavilova 40, 117333 Moscow, USSR.

Granger causality revisited

PubMed Central

Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

2014-01-01

This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks

NASA Astrophysics Data System (ADS)

Tucker, Gregory E.; Lancaster, Stephen T.; Gasparini, Nicole M.; Bras, Rafael L.; Rybarczyk, Scott M.

2001-10-01

We describe a new set of data structures and algorithms for dynamic terrain modeling using a triangulated irregular network (TINs). The framework provides an efficient method for storing, accessing, and updating a Delaunay triangulation and its associated Voronoi diagram. The basic data structure consists of three interconnected data objects: triangles, nodes, and directed edges. Encapsulating each of these geometric elements within a data object makes it possible to essentially decouple the TIN representation from the modeling applications that make use of it. Both the triangulation and its corresponding Voronoi diagram can be rapidly retrieved or updated, making these methods well suited to adaptive remeshing schemes. We develop a set of algorithms for defining drainage networks and identifying closed depressions (e.g., lakes) for hydrologic and geomorphic modeling applications. We also outline simple numerical algorithms for solving network routing and 2D transport equations within the TIN framework. The methods are illustrated with two example applications, a landscape evolution model and a distributed rainfall-runoff model.

Application Possibility of Smartphone as Payload for Photogrammetric Uav System

NASA Astrophysics Data System (ADS)

Yun, M. H.; Kim, J.; Seo, D.; Lee, J.; Choi, C.

2012-07-01

Smartphone can not only be operated under 3G network environment anytime and anyplace but also cost less than the existing photogrammetric UAV since it provides high-resolution image, 3D location and attitude data on a real-time basis from a variety of built-in sensors. This study is aimed to assess the possibility of smartphone as a payload for photogrammetric UAV system. Prior to such assessment, a smartphone-based photogrammetric UAV system application was developed, through which real-time image, location and attitude data was obtained using smartphone under both static and dynamic conditions. Subsequently the accuracy assessment on the location and attitude data obtained and sent by this system was conducted. The smartphone images were converted into ortho-images through image triangulation. The image triangulation was conducted in accordance with presence or absence of consideration of the interior orientation (IO) parameters determined by camera calibration. In case IO parameters were taken into account in the static experiment, the results from triangulation for any smartphone type were within 1.5 pixel (RMSE), which was improved at least by 35% compared to when IO parameters were not taken into account. On the contrary, the improvement effect of considering IO parameters on accuracy in triangulation for smartphone images in dynamic experiment was not significant compared to the static experiment. It was due to the significant impact of vibration and sudden attitude change of UAV on the actuator for automatic focus control within the camera built in smartphone under the dynamic condition. This cause appears to have a negative impact on the image-based DEM generation. Considering these study findings, it is suggested that smartphone is very feasible as a payload for UAV system. It is also expected that smartphone may be loaded onto existing UAV playing direct or indirect roles significantly.

Causality networks from multivariate time series and application to epilepsy.

PubMed

Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2015-08-01

Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

Econometric testing on linear and nonlinear dynamic relation between stock prices and macroeconomy in China

NASA Astrophysics Data System (ADS)

Borjigin, Sumuya; Yang, Yating; Yang, Xiaoguang; Sun, Leilei

2018-03-01

Many researchers have realized that there is a strong correlation between stock prices and macroeconomy. In order to make this relationship clear, a lot of studies have been done. However, the causal relationship between stock prices and macroeconomy has still not been well explained. A key point is that, most of the existing research adopts linear and stable models to investigate the correlation of stock prices and macroeconomy, while the real causality of that may be nonlinear and dynamic. To fill this research gap, we investigate the nonlinear and dynamic causal relationships between stock prices and macroeconomy. Based on the case of China's stock prices and acroeconomy measures from January 1992 to March 2017, we compare the linear Granger causality test models with nonlinear ones. Results demonstrate that the nonlinear dynamic Granger causality is much stronger than linear Granger causality. From the perspective of nonlinear dynamic Granger causality, China's stock prices can be viewed as "national economic barometer". On the one hand, this study will encourage researchers to take nonlinearity and dynamics into account when they investigate the correlation of stock prices and macroeconomy; on the other hand, our research can guide regulators and investors to make better decisions.

A social impact assessment of the floodwater spreading project on the Gareh-Bygone plain in Iran: A causal comparative approach

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

Ahmadvand, Mostafa; Karami, Ezatollah

2009-02-15

The purpose of this study was to explore the social impacts of the floodwater spreading project (FWSP) on the Gareh-Bygone plain, Iran. The study was in the form of a causal comparative design, and a triangulation technique was used to collect data including the use of survey data, archival data, and a participatory rural appraisal (PRA). The causal comparative method requires a comparison of villages with and without the FWSP. Therefore, a survey was conducted using stratified random sampling to select 202 households in villages with and without FWSP in the plain. Significant differences were found between the respondents inmore » villages with and without FWSP with regard to social impact criteria. In spite of the project had negative impact on perceived wellbeing, social capital, social structure development; it had positive impact on quality of life, rural and agricultural economic conditions, and conservation of community resources. However, no significant difference was found between women and men regarding the SIA of FWSP in Gareh-Bygone plain. Analysis of the archival data and PRA techniques supported the survey results and demonstrated that the project improved environmental criteria and deteriorated social dimensions.« less

Spectral Analysis for Weighted Iterated Triangulations of Graphs

NASA Astrophysics Data System (ADS)

Chen, Yufei; Dai, Meifeng; Wang, Xiaoqian; Sun, Yu; Su, Weiyi

Much information about the structural properties and dynamical aspects of a network is measured by the eigenvalues of its normalized Laplacian matrix. In this paper, we aim to present a first study on the spectra of the normalized Laplacian of weighted iterated triangulations of graphs. We analytically obtain all the eigenvalues, as well as their multiplicities from two successive generations. As an example of application of these results, we then derive closed-form expressions for their multiplicative Kirchhoff index, Kemeny’s constant and number of weighted spanning trees.

Internal and Interpersonal: The Family Transmission of Father-Daughter Incest.

ERIC Educational Resources Information Center

Greenspun, Wendy S.

1994-01-01

Utilizes psychoanalytic and family systems theories to describe dynamics in families with father-daughter incest. The pattern in incest is explained via the concept of projective identification; experiences of victimization are played out in the marriage. The victimized daughter is later triangulated into this marital dynamic, setting the stage…

Causality, mediation and time: a dynamic viewpoint

PubMed Central

Aalen, Odd O; Røysland, Kjetil; Gran, Jon Michael; Ledergerber, Bruno

2012-01-01

Summary. Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations ‘at a glance’. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented. PMID:23193356

Unveiling causal activity of complex networks

NASA Astrophysics Data System (ADS)

Williams-García, Rashid V.; Beggs, John M.; Ortiz, Gerardo

2017-07-01

We introduce a novel tool for analyzing complex network dynamics, allowing for cascades of causally-related events, which we call causal webs (c-webs), to be separated from other non-causally-related events. This tool shows that traditionally-conceived avalanches may contain mixtures of spatially-distinct but temporally-overlapping cascades of events, and dynamical disorder or noise. In contrast, c-webs separate these components, unveiling previously hidden features of the network and dynamics. We apply our method to mouse cortical data with resulting statistics which demonstrate for the first time that neuronal avalanches are not merely composed of causally-related events. The original version of this article was uploaded to the arXiv on March 17th, 2016 [1].

An improved triangulation laser rangefinder using a custom CMOS HDR linear image sensor

NASA Astrophysics Data System (ADS)

Liscombe, Michael

3-D triangulation laser rangefinders are used in many modern applications, from terrain mapping to biometric identification. Although a wide variety of designs have been proposed, laser speckle noise still provides a fundamental limitation on range accuracy. These works propose a new triangulation laser rangefinder designed specifically to mitigate the effects of laser speckle noise. The proposed rangefinder uses a precision linear translator to laterally reposition the imaging system (e.g., image sensor and imaging lens). For a given spatial location of the laser spot, capturing N spatially uncorrelated laser spot profiles is shown to improve range accuracy by a factor of N . This technique has many advantages over past speckle-reduction technologies, such as a fixed system cost and form factor, and the ability to virtually eliminate laser speckle noise. These advantages are made possible through spatial diversity and come at the cost of increased acquisition time. The rangefinder makes use of the ICFYKWG1 linear image sensor, a custom CMOS sensor developed at the Vision Sensor Laboratory (York University). Tests are performed on the image sensor's innovative high dynamic range technology to determine its effects on range accuracy. As expected, experimental results have shown that the sensor provides a trade-off between dynamic range and range accuracy.

Who Is the Dynamic Duo? How Infants Learn about the Identity of Objects in a Causal Chain

ERIC Educational Resources Information Center

Rakison, David H.; Smith, Gabriel Tobin; Ali, Areej

2016-01-01

Four experiments investigated infants' and adults' knowledge of the identity of objects in a causal sequence of events. In Experiments 1 and 2, 18- and 22-month-olds in the visual habituation procedure were shown a 3-step causal chain event in which the relation between an object's part (dynamic or static) and its causal role was either consistent…

The Model Analyst’s Toolkit: Scientific Model Development, Analysis, and Validation

DTIC Science & Technology

2013-11-20

Granger causality F-test validation 3.1.2. Dynamic time warping for uneven temporal relationships Many causal relationships are imperfectly...mapping for dynamic feedback models Granger causality and DTW can identify causal relationships and consider complex temporal factors. However, many ...variant of the tf-idf algorithm (Manning, Raghavan, Schutze et al., 2008), typically used in search engines, to “score” features. The (-log tf) in

Dynamics of a barium release in the magnetospheric tail

NASA Technical Reports Server (NTRS)

Mende, S. B.; Swenson, G. R.; Geller, S. P.; Doolittle, J. H.; Haerendel, G.

1989-01-01

The late time behavior of the May 13, 1985 magnetotail barium cloud is examined. The bulk dynamics of the cloud are studied based on triangulated data and data from Fabry-Perot Doppler velocity measurements. The changes in cloud morphology in relation to the in situ measurements made by the Ion Release Module satellite are discussed.

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks

PubMed Central

Courellis, Hristos; Mullen, Tim; Poizner, Howard; Cauwenberghs, Gert; Iversen, John R.

2017-01-01

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a “reach/saccade to spatial target” cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI. PMID:28566997

Dynamics of Quantum Causal Structures

NASA Astrophysics Data System (ADS)

Castro-Ruiz, Esteban; Giacomini, Flaminia; Brukner, Časlav

2018-01-01

It was recently suggested that causal structures are both dynamical, because of general relativity, and indefinite, because of quantum theory. The process matrix formalism furnishes a framework for quantum mechanics on indefinite causal structures, where the order between operations of local laboratories is not definite (e.g., one cannot say whether operation in laboratory A occurs before or after operation in laboratory B ). Here, we develop a framework for "dynamics of causal structures," i.e., for transformations of process matrices into process matrices. We show that, under continuous and reversible transformations, the causal order between operations is always preserved. However, the causal order between a subset of operations can be changed under continuous yet nonreversible transformations. An explicit example is that of the quantum switch, where a party in the past affects the causal order of operations of future parties, leading to a transition from a channel from A to B , via superposition of causal orders, to a channel from B to A . We generalize our framework to construct a hierarchy of quantum maps based on transformations of process matrices and transformations thereof.

Coherent Structure Detection using Persistent Homology and other Topological Tools

NASA Astrophysics Data System (ADS)

Smith, Spencer; Roberts, Eric; Sindi, Suzanne; Mitchell, Kevin

2017-11-01

For non-autonomous, aperiodic fluid flows, coherent structures help organize the dynamics, much as invariant manifolds and periodic orbits do for autonomous or periodic systems. The prevalence of such flows in nature and industry has motivated many successful techniques for defining and detecting coherent structures. However, often these approaches require very fine trajectory data to reconstruct velocity fields and compute Cauchy-Green-tensor-related quantities. We use topological techniques to help detect coherent trajectory sets in relatively sparse 2D advection problems. More specifically, we have developed a homotopy-based algorithm, the ensemble-based topological entropy calculation (E-tec), which assigns to each edge in an initial triangulation of advected points a topologically forced lower bound on its future stretching rate. The triangulation and its weighted edges allow us to analyze flows using persistent homology. This topological data analysis tool detects clusters and loops in the triangulation that are robust in the presence of noise and in this case correspond to coherent trajectory sets.

Optimal causal inference: estimating stored information and approximating causal architecture.

PubMed

Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.

Public health triangulation: approach and application to synthesizing data to understand national and local HIV epidemics.

PubMed

Rutherford, George W; McFarland, William; Spindler, Hilary; White, Karen; Patel, Sadhna V; Aberle-Grasse, John; Sabin, Keith; Smith, Nathan; Taché, Stephanie; Calleja-Garcia, Jesus M; Stoneburner, Rand L

2010-07-29

Public health triangulation is a process for reviewing, synthesising and interpreting secondary data from multiple sources that bear on the same question to make public health decisions. It can be used to understand the dynamics of HIV transmission and to measure the impact of public health programs. While traditional intervention research and meta-analysis would be ideal sources of information for public health decision making, they are infrequently available, and often decisions can be based only on surveillance and survey data. The process involves examination of a wide variety of data sources and both biological, behavioral and program data and seeks input from stakeholders to formulate meaningful public health questions. Finally and most importantly, it uses the results to inform public health decision-making. There are 12 discrete steps in the triangulation process, which included identification and assessment of key questions, identification of data sources, refining questions, gathering data and reports, assessing the quality of those data and reports, formulating hypotheses to explain trends in the data, corroborating or refining working hypotheses, drawing conclusions, communicating results and recommendations and taking public health action. Triangulation can be limited by the quality of the original data, the potentials for ecological fallacy and "data dredging" and reproducibility of results. Nonetheless, we believe that public health triangulation allows for the interpretation of data sets that cannot be analyzed using meta-analysis and can be a helpful adjunct to surveillance, to formal public health intervention research and to monitoring and evaluation, which in turn lead to improved national strategic planning and resource allocation.

Quantification of causal couplings via dynamical effects: A unifying perspective

NASA Astrophysics Data System (ADS)

Smirnov, Dmitry A.

2014-12-01

Quantitative characterization of causal couplings from time series is crucial in studies of complex systems of different origin. Various statistical tools for that exist and new ones are still being developed with a tendency to creating a single, universal, model-free quantifier of coupling strength. However, a clear and generally applicable way of interpreting such universal characteristics is lacking. This work suggests a general conceptual framework for causal coupling quantification, which is based on state space models and extends the concepts of virtual interventions and dynamical causal effects. Namely, two basic kinds of interventions (state space and parametric) and effects (orbital or transient and stationary or limit) are introduced, giving four families of coupling characteristics. The framework provides a unifying view of apparently different well-established measures and allows us to introduce new characteristics, always with a definite "intervention-effect" interpretation. It is shown that diverse characteristics cannot be reduced to any single coupling strength quantifier and their interpretation is inevitably model based. The proposed set of dynamical causal effect measures quantifies different aspects of "how the coupling manifests itself in the dynamics," reformulating the very question about the "causal coupling strength."

Material Phase Causality or a Dynamics-Statistical Interpretation of Quantum Mechanics

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

Koprinkov, I. G.

2010-11-25

The internal phase dynamics of a quantum system interacting with an electromagnetic field is revealed in details. Theoretical and experimental evidences of a causal relation of the phase of the wave function to the dynamics of the quantum system are presented sistematically for the first time. A dynamics-statistical interpretation of the quantum mechanics is introduced.

The use of triangulation in qualitative research.

PubMed

Carter, Nancy; Bryant-Lukosius, Denise; DiCenso, Alba; Blythe, Jennifer; Neville, Alan J

2014-09-01

Triangulation refers to the use of multiple methods or data sources in qualitative research to develop a comprehensive understanding of phenomena (Patton, 1999). Triangulation also has been viewed as a qualitative research strategy to test validity through the convergence of information from different sources. Denzin (1978) and Patton (1999) identified four types of triangulation: (a) method triangulation, (b) investigator triangulation, (c) theory triangulation, and (d) data source triangulation. The current article will present the four types of triangulation followed by a discussion of the use of focus groups (FGs) and in-depth individual (IDI) interviews as an example of data source triangulation in qualitative inquiry.

Causality analysis in business performance measurement system using system dynamics methodology

NASA Astrophysics Data System (ADS)

Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

2014-07-01

One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets

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

Lu, Fengbin, E-mail: fblu@amss.ac.cn

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relationsmore » evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.« less

Time-varying coefficient vector autoregressions model based on dynamic correlation with an application to crude oil and stock markets.

PubMed

Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze

2017-01-01

This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Causal Discovery of Dynamic Systems

ERIC Educational Resources Information Center

Voortman, Mark

2010-01-01

Recently, several philosophical and computational approaches to causality have used an interventionist framework to clarify the concept of causality [Spirtes et al., 2000, Pearl, 2000, Woodward, 2005]. The characteristic feature of the interventionist approach is that causal models are potentially useful in predicting the effects of manipulations.…

Quadtree of TIN: a new algorithm of dynamic LOD

NASA Astrophysics Data System (ADS)

Zhang, Junfeng; Fei, Lifan; Chen, Zhen

2009-10-01

Currently, Real-time visualization of large-scale digital elevation model mainly employs the regular structure of GRID based on quadtree and triangle simplification methods based on irregular triangulated network (TIN). TIN is a refined means to express the terrain surface in the computer science, compared with GRID. However, the data structure of TIN model is complex, and is difficult to realize view-dependence representation of level of detail (LOD) quickly. GRID is a simple method to realize the LOD of terrain, but contains more triangle count. A new algorithm, which takes full advantage of the two methods' merit, is presented in this paper. This algorithm combines TIN with quadtree structure to realize the view-dependence LOD controlling over the irregular sampling point sets, and holds the details through the distance of viewpoint and the geometric error of terrain. Experiments indicate that this approach can generate an efficient quadtree triangulation hierarchy over any irregular sampling point sets and achieve dynamic and visual multi-resolution performance of large-scale terrain at real-time.

Optoelectronic scanning system upgrade by energy center localization methods

NASA Astrophysics Data System (ADS)

Flores-Fuentes, W.; Sergiyenko, O.; Rodriguez-Quiñonez, J. C.; Rivas-López, M.; Hernández-Balbuena, D.; Básaca-Preciado, L. C.; Lindner, L.; González-Navarro, F. F.

2016-11-01

A problem of upgrading an optoelectronic scanning system with digital post-processing of the signal based on adequate methods of energy center localization is considered. An improved dynamic triangulation analysis technique is proposed by an example of industrial infrastructure damage detection. A modification of our previously published method aimed at searching for the energy center of an optoelectronic signal is described. Application of the artificial intelligence algorithm of compensation for the error of determining the angular coordinate in calculating the spatial coordinate through dynamic triangulation is demonstrated. Five energy center localization methods are developed and tested to select the best method. After implementation of these methods, digital compensation for the measurement error, and statistical data analysis, a non-parametric behavior of the data is identified. The Wilcoxon signed rank test is applied to improve the result further. For optical scanning systems, it is necessary to detect a light emitter mounted on the infrastructure being investigated to calculate its spatial coordinate by the energy center localization method.

Positioning (Mis)Aligned: The (Un)Making of Intercultural Asynchronous Computer-Mediated Communication

ERIC Educational Resources Information Center

Wu, Zhiwei

2018-01-01

Framed from positioning theory and dynamic systems theory, the paper reports on a naturalistic study involving four Chinese participants and their American peers in an intercultural asynchronous computer-mediated communication (ACMC) activity. Based on the moment-by-moment analysis and triangulation of forum posts, reflective essays, and…

Detecting dynamic causal inference in nonlinear two-phase fracture flow

NASA Astrophysics Data System (ADS)

Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

Causal tapestries for psychology and physics.

PubMed

Sulis, William H

2012-04-01

Archetypal dynamics is a formal approach to the modeling of information flow in complex systems used to study emergence. It is grounded in the Fundamental Triad of realisation (system), interpretation (archetype) and representation (formal model). Tapestries play a fundamental role in the framework of archetypal dynamics as a formal representational system. They represent information flow by means of multi layered, recursive, interlinked graphical structures that express both geometry (form or sign) and logic (semantics). This paper presents a detailed mathematical description of a specific tapestry model, the causal tapestry, selected for use in describing behaving systems such as appear in psychology and physics from the standpoint of Process Theory. Causal tapestries express an explicit Lorentz invariant transient now generated by means of a reality game. Observables are represented by tapestry informons while subjective or hidden components (for example intellectual and emotional processes) are incorporated into the reality game that determines the tapestry dynamics. As a specific example, we formulate a random graphical dynamical system using causal tapestries.

Dynamics of safety performance and culture: a group model building approach.

PubMed

Goh, Yang Miang; Love, Peter E D; Stagbouer, Greg; Annesley, Chris

2012-09-01

The management of occupational health and safety (OHS) including safety culture interventions is comprised of complex problems that are often hard to scope and define. Due to the dynamic nature and complexity of OHS management, the concept of system dynamics (SD) is used to analyze accident prevention. In this paper, a system dynamics group model building (GMB) approach is used to create a causal loop diagram of the underlying factors influencing the OHS performance of a major drilling and mining contractor in Australia. While the organization has invested considerable resources into OHS their disabling injury frequency rate (DIFR) has not been decreasing. With this in mind, rich individualistic knowledge about the dynamics influencing the DIFR was acquired from experienced employees with operations, health and safety and training background using a GMB workshop. Findings derived from the workshop were used to develop a series of causal loop diagrams that includes a wide range of dynamics that can assist in better understanding the causal influences OHS performance. The causal loop diagram provides a tool for organizations to hypothesize the dynamics influencing effectiveness of OHS management, particularly the impact on DIFR. In addition the paper demonstrates that the SD GMB approach has significant potential in understanding and improving OHS management. Copyright © 2011 Elsevier Ltd. All rights reserved.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

Implications of causality for quantum biology - I: topology change

NASA Astrophysics Data System (ADS)

Scofield, D. F.; Collins, T. C.

2018-06-01

A framework for describing the causal, topology changing, evolution of interacting biomolecules is developed. The quantum dynamical manifold equations (QDMEs) derived from this framework can be related to the causality restrictions implied by a finite speed of light and to Planck's constant to set a transition frequency scale. The QDMEs imply conserved stress-energy, angular-momentum and Noether currents. The functional whose extremisation leads to this result provides a causal, time-dependent, non-equilibrium generalisation of the Hohenberg-Kohn theorem. The system of dynamical equations derived from this functional and the currents J derived from the QDMEs are shown to be causal and consistent with the first and second laws of thermodynamics. This has the potential of allowing living systems to be quantum mechanically distinguished from non-living ones.

A Triangulation Methodology in Research on Social Cultures.

ERIC Educational Resources Information Center

Owens, Robert G.; And Others

The purpose of this research was to develop, test, and demonstrate a systematic methodology of triangulation. Triangulation is a technique used to establish credibility of data gathered in qualitative ways. Triangulated conclusions are more stable than any of the individual vantage points from which they were triangulated. Using a previous study…

How to Be Causal: Time, Spacetime and Spectra

ERIC Educational Resources Information Center

Kinsler, Paul

2011-01-01

I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…

The Influence of Institutional Culture on Presidential Selection. ASHE 1987 Annual Meeting Paper.

ERIC Educational Resources Information Center

Kolman, Eileen M.; And Others

The influence of institutional culture on the selection of college presidents was investigated at three Catholic colleges sponsored by women's religious communities. The concepts of institutional saga and culture were used to describe the dynamics at work in presidential selection. The constant comparative method and triangulation (i.e., using…

Dynamic Granger-Geweke causality modeling with application to interictal spike propagation

PubMed Central

Lin, Fa-Hsuan; Hara, Keiko; Solo, Victor; Vangel, Mark; Belliveau, John W.; Stufflebeam, Steven M.; Hamalainen, Matti S.

2010-01-01

A persistent problem in developing plausible neurophysiological models of perception, cognition, and action is the difficulty of characterizing the interactions between different neural systems. Previous studies have approached this problem by estimating causal influences across brain areas activated during cognitive processing using Structural Equation Modeling and, more recently, with Granger-Geweke causality. While SEM is complicated by the need for a priori directional connectivity information, the temporal resolution of dynamic Granger-Geweke estimates is limited because the underlying autoregressive (AR) models assume stationarity over the period of analysis. We have developed a novel optimal method for obtaining data-driven directional causality estimates with high temporal resolution in both time and frequency domains. This is achieved by simultaneously optimizing the length of the analysis window and the chosen AR model order using the SURE criterion. Dynamic Granger-Geweke causality in time and frequency domains is subsequently calculated within a moving analysis window. We tested our algorithm by calculating the Granger-Geweke causality of epileptic spike propagation from the right frontal lobe to the left frontal lobe. The results quantitatively suggested the epileptic activity at the left frontal lobe was propagated from the right frontal lobe, in agreement with the clinical diagnosis. Our novel computational tool can be used to help elucidate complex directional interactions in the human brain. PMID:19378280

A Dynamic Causal Modeling Analysis of the Effective Connectivities Underlying Top-Down Letter Processing

ERIC Educational Resources Information Center

Liu, Jiangang; Li, Jun; Rieth, Cory A.; Huber, David E.; Tian, Jie; Lee, Kang

2011-01-01

The present study employed dynamic causal modeling to investigate the effective functional connectivity between regions of the neural network involved in top-down letter processing. We used an illusory letter detection paradigm in which participants detected letters while viewing pure noise images. When participants detected letters, the response…

Synchrony dynamics underlying effective connectivity reconstruction of neuronal circuits

NASA Astrophysics Data System (ADS)

Yu, Haitao; Guo, Xinmeng; Qin, Qing; Deng, Yun; Wang, Jiang; Liu, Jing; Cao, Yibin

2017-04-01

Reconstruction of effective connectivity between neurons is essential for neural systems with function-related significance, characterizing directionally causal influences among neurons. In this work, causal interactions between neurons in spinal dorsal root ganglion, activated by manual acupuncture at Zusanli acupoint of experimental rats, are estimated using Granger causality (GC) method. Different patterns of effective connectivity are obtained for different frequencies and types of acupuncture. Combined with synchrony analysis between neurons, we show a dependence of effective connection on the synchronization dynamics. Based on the experimental findings, a neuronal circuit model with synaptic connections is constructed. The variation of neuronal effective connectivity with respect to its structural connectivity and synchronization dynamics is further explored. Simulation results show that reciprocally causal interactions with statistically significant are formed between well-synchronized neurons. The effective connectivity may be not necessarily equivalent to synaptic connections, but rather depend on the synchrony relationship. Furthermore, transitions of effective interaction between neurons are observed following the synchronization transitions induced by conduction delay and synaptic conductance. These findings are helpful to further investigate the dynamical mechanisms underlying the reconstruction of effective connectivity of neuronal population.

Granger Causality Testing with Intensive Longitudinal Data.

PubMed

Molenaar, Peter C M

2018-06-01

The availability of intensive longitudinal data obtained by means of ambulatory assessment opens up new prospects for prevention research in that it allows the derivation of subject-specific dynamic networks of interacting variables by means of vector autoregressive (VAR) modeling. The dynamic networks thus obtained can be subjected to Granger causality testing in order to identify causal relations among the observed time-dependent variables. VARs have two equivalent representations: standard and structural. Results obtained with Granger causality testing depend upon which representation is chosen, yet no criteria exist on which this important choice can be based. A new equivalent representation is introduced called hybrid VARs with which the best representation can be chosen in a data-driven way. Partial directed coherence, a frequency-domain statistic for Granger causality testing, is shown to perform optimally when based on hybrid VARs. An application to real data is provided.

Economic growth, combustible renewables and waste consumption, and CO₂ emissions in North Africa.

PubMed

Ben Jebli, Mehdi; Ben Youssef, Slim

2015-10-01

This paper uses panel cointegration techniques and Granger causality tests to examine the dynamic causal link between per capita real gross domestic product (GDP), combustible renewables and waste (CRW) consumption, and CO2 emissions for a panel of five North African countries during the period 1971-2008. Granger causality test results suggest short- and long-run unidirectional causalities running from CO2 emissions and CRW consumption to real GDP and a short-run unidirectional causality running from CRW to CO2 emissions. The results from panel long-run fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimates show that CO2 emissions and CRW consumption have a positive and statistically significant impact on GDP. Our policy recommendations are that these countries should use more CRW because this increases their output, reduces their energy dependency on fossil energy, and may decrease their CO2 emissions.

Forces and Motion: How Young Children Understand Causal Events

ERIC Educational Resources Information Center

Goksun, Tilbe; George, Nathan R.; Hirsh-Pasek, Kathy; Golinkoff, Roberta M.

2013-01-01

How do children evaluate complex causal events? This study investigates preschoolers' representation of "force dynamics" in causal scenes, asking whether (a) children understand how single and dual forces impact an object's movement and (b) this understanding varies across cause types (Cause, Enable, Prevent). Three-and-a half- to…

Causal analysis of self-sustaining processes in the logarithmic layer of wall-bounded turbulence

NASA Astrophysics Data System (ADS)

Bae, H. J.; Encinar, M. P.; Lozano-Durán, A.

2018-04-01

Despite the large amount of information provided by direct numerical simulations of turbulent flows, their underlying dynamics remain elusive even in the most simple and canonical configurations. Most common approaches to investigate the turbulence phenomena do not provide a clear causal inference between events, which is essential to determine the dynamics of self-sustaining processes. In the present work, we examine the causal interactions between streaks, rolls and mean shear in the logarithmic layer of a minimal turbulent channel flow. Causality between structures is assessed in a non-intrusive manner by transfer entropy, i.e., how much the uncertainty of one structure is reduced by knowing the past states of the others. We choose to represent streaks by the first Fourier modes of the streamwise velocity, while rolls are defined by the wall-normal and spanwise velocity modes. The results show that the process is mainly unidirectional rather than cyclic, and that the log-layer motions are sustained by extracting energy from the mean shear which controls the dynamics and time-scales. The well-known lift-up effect is also identified, but shown to be of secondary importance in the causal network between shear, streaks and rolls.

Causal analysis of self-sustaining processes in the log-layer of wall-bounded turbulence

NASA Astrophysics Data System (ADS)

Lozano-Duran, Adrian; Bae, Hyunji Jane

2017-11-01

Despite the large amount of information provided by direct numerical simulations of turbulent flows, the underlying dynamics remain elusive even in the most simple and canonical configurations. Most standard methods used to investigate turbulence do not provide a clear causal inference between events, which is necessary to determine this dynamics, particularly in self-sustaning processes. In the present work, we examine the causal interactions between streaks and rolls in the logarithmic layer of minimal turbulent channel flow. Causality between structures is assessed in a non-intrusive manner by transfer entropy, i.e., how much the uncertainty of one structure is reduced by knowing the past states of the others. Streaks are represented by the first Fourier modes of the streamwise velocity, while rolls are defined by the wall-normal and spanwise velocities. The results show that the process is mainly unidirectional rather than cyclic, and that the log-layer motions are sustained by extracting energy from the mean shear, which controls the dynamics and time-scales. The well-known lift-up effect is shown to be not a key ingredient in the causal network between shear, streaks and rolls. Funded by ERC Coturb Madrid Summer Program.

Study on Practical Technologies of Aerial Triangulation for Real Scene 3d Moeling with Oblique Photography

NASA Astrophysics Data System (ADS)

Cai, Z.; Liu, W.; Luo, G.; Xiang, Z.

2018-04-01

The key technologies in the real scene 3D modeling of oblique photography mainly include the data acquisition of oblique photography, layout and surveying of photo control points, oblique camera calibration, aerial triangulation, dense matching of multi-angle image, building of triangulation irregular network (TIN) and TIN simplification and automatic texture mapping, among which aerial triangulation is the core and the results of aerial triangulation directly affect the later model effect and the corresponding data accuracy. Starting from this point of view, this paper aims to study the practical technologies of aerial triangulation for real scene 3D modeling with oblique photography and finally proposes a technical method of aerial triangulation with oblique photography which can be put into practice.

Effect of science teaching on the young child's concept of piagetian physical causality: Animism and dynamism

NASA Astrophysics Data System (ADS)

Wolfinger, Donna M.

The purpose of this research was to determine whether the young child's understanding of physical causality is affected by school science instruction. Sixty-four subjects, four and one-half through seven years of age, received 300 min of instruction designed to affect the subject's conception of causality as reflected in animism and dynamism. Instruction took place for 30 min per day on ten successive school days. Pretesting was done to allow a stratified random sample to be based on vocabulary level and developmental stage as well as on age and gender. Post-testing consisted of testing of developmental level and level within the causal relations of animism and dynamism. Significant differences (1.05 level) were found between the experimental and control groups for animism. Within the experimental group, males differed significantly (1.001 level) from females. The elimination of animism appeared to have occurred. For dynamism, significant differences (0.05 level) were found only between concrete operational subjects in the experimental and control groups, indicating a concrete level of operations was necessary if dynamism was to be affected. However, a review of interview protocols indicated that subjects classified as nonanimistic had learned to apply a definition rather than to think in a nonanimistic manner.

Methodological triangulation in a study of social support for siblings of children with cancer.

PubMed

Murray, J S

1999-10-01

Triangulation is an approach to research that is becoming increasingly popular among nurse researchers. Five types of triangulation are used in nursing research: data, methodological, theoretical, researcher, and analytical triangulation. Methodological triangulation is an attempt to improve validity by combining various techniques in one study. In this article, an example of quantitative and qualitative triangulation is discussed to illustrate the procedures used and the results achieved. The secondary data used as an example are from a previous study that was conducted by the researcher and investigated nursing interventions used by pediatric oncology nurses to provide social support to siblings of children with cancer. Results show that methodological triangulation was beneficial in this study for three reasons. First, the careful comparison of quantitative and qualitative data added support for the social support variables under investigation. Second, the comparison showed more in-depth dimensions about pediatric oncology nurses providing social support to siblings of children with cancer. Finally, the use of methodological triangulation provided insight into revisions for the quantitative instrument.

Adolescent Triangulation into Parental Conflicts: Longitudinal Implications for Appraisals and Adolescent-Parent Relations

ERIC Educational Resources Information Center

Fosco, Gregory M.; Grych, John H.

2010-01-01

Although triangulation into parental conflict is a risk factor for child and adolescent maladjustment, little is known about how triangulation affects adolescents' functioning or the factors that lead children to be drawn into parental disagreements. This prospective study examined the relations between triangulation, appraisals of conflict, and…

Triangulation of the neurocomputational architecture underpinning reading aloud

PubMed Central

Hoffman, Paul; Lambon Ralph, Matthew A.; Woollams, Anna M.

2015-01-01

The goal of cognitive neuroscience is to integrate cognitive models with knowledge about underlying neural machinery. This significant challenge was explored in relation to word reading, where sophisticated computational-cognitive models exist but have made limited contact with neural data. Using distortion-corrected functional MRI and dynamic causal modeling, we investigated the interactions between brain regions dedicated to orthographic, semantic, and phonological processing while participants read words aloud. We found that the lateral anterior temporal lobe exhibited increased activation when participants read words with irregular spellings. This area is implicated in semantic processing but has not previously been considered part of the reading network. We also found meaningful individual differences in the activation of this region: Activity was predicted by an independent measure of the degree to which participants use semantic knowledge to read. These characteristics are predicted by the connectionist Triangle Model of reading and indicate a key role for semantic knowledge in reading aloud. Premotor regions associated with phonological processing displayed the reverse characteristics. Changes in the functional connectivity of the reading network during irregular word reading also were consistent with semantic recruitment. These data support the view that reading aloud is underpinned by the joint operation of two neural pathways. They reveal that (i) the ATL is an important element of the ventral semantic pathway and (ii) the division of labor between the two routes varies according to both the properties of the words being read and individual differences in the degree to which participants rely on each route. PMID:26124121

Effects of Student-Generated Diagrams versus Student-Generated Summaries on Conceptual Understanding of Causal and Dynamic Knowledge in Plate Tectonics.

ERIC Educational Resources Information Center

Gobert, Janice D.; Clement, John J.

1999-01-01

Grade five students' (n=58) conceptual understanding of plate tectonics was measured by analysis of student-generated summaries and diagrams, and by posttest assessment of both the spatial/static and causal/dynamic aspects of the domain. The diagram group outperformed the summary and text-only groups on the posttest measures. Discusses the effects…

A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates.

PubMed

Chen, Yanhua; Mantegna, Rosario N; Pantelous, Athanasios A; Zuev, Konstantin M

2018-01-01

In this study, we assess the dynamic evolution of short-term correlation, long-term cointegration and Error Correction Model (hereafter referred to as ECM)-based long-term Granger causality between each pair of US, UK, and Eurozone stock markets from 1980 to 2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. The results obtained show that the dynamic correlation, cointegration and ECM-based long-run Granger causality vary significantly over the whole sample period. The degree of dynamic correlation and cointegration between pairs of stock markets rises in periods of high volatility and uncertainty, especially under the influence of economic, financial and political shocks. Meanwhile, we observe the weaker and decreasing correlation and cointegration among the three developed stock markets during the recovery periods. Interestingly, the most persistent and significant cointegration among the three developed stock markets exists during the 2007-09 global financial crisis. Finally, the exchange rate fluctuations, also influence the dynamic integration and causality between all pairs of stock indices, with that influence increasing under the local currency terms. Our results suggest that the potential for diversifying risk by investing in the US, UK and Eurozone stock markets is limited during the periods of economic, financial and political shocks.

Application of dynamic uncertain causality graph in spacecraft fault diagnosis: Logic cycle

NASA Astrophysics Data System (ADS)

Yao, Quanying; Zhang, Qin; Liu, Peng; Yang, Ping; Zhu, Ma; Wang, Xiaochen

2017-04-01

Intelligent diagnosis system are applied to fault diagnosis in spacecraft. Dynamic Uncertain Causality Graph (DUCG) is a new probability graphic model with many advantages. In the knowledge expression of spacecraft fault diagnosis, feedback among variables is frequently encountered, which may cause directed cyclic graphs (DCGs). Probabilistic graphical models (PGMs) such as bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning, but BN does not allow DCGs. In this paper, DUGG is applied to fault diagnosis in spacecraft: introducing the inference algorithm for the DUCG to deal with feedback. Now, DUCG has been tested in 16 typical faults with 100% diagnosis accuracy.

Causal Cognition, Force Dynamics and Early Hunting Technologies

PubMed Central

Gärdenfors, Peter; Lombard, Marlize

2018-01-01

With this contribution we analyze ancient hunting technologies as one way to explore the development of causal cognition in the hominin lineage. Building on earlier work, we separate seven grades of causal thinking. By looking at variations in force dynamics as a central element in causal cognition, we analyze the thinking required for different hunting technologies such as stabbing spears, throwing spears, launching atlatl darts, shooting arrows with a bow, and the use of poisoned arrows. Our interpretation demonstrates that there is an interplay between the extension of human body through technology and expanding our cognitive abilities to reason about causes. It adds content and dimension to the trend of including embodied cognition in evolutionary studies and in the interpretation of the archeological record. Our method could explain variation in technology sets between archaic and modern human groups. PMID:29483885

Granger causality network reconstruction of conductance-based integrate-and-fire neuronal systems.

PubMed

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (I&F) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based I&F neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings.

Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems

PubMed Central

Zhou, Douglas; Xiao, Yanyang; Zhang, Yaoyu; Xu, Zhiqin; Cai, David

2014-01-01

Reconstruction of anatomical connectivity from measured dynamical activities of coupled neurons is one of the fundamental issues in the understanding of structure-function relationship of neuronal circuitry. Many approaches have been developed to address this issue based on either electrical or metabolic data observed in experiment. The Granger causality (GC) analysis remains one of the major approaches to explore the dynamical causal connectivity among individual neurons or neuronal populations. However, it is yet to be clarified how such causal connectivity, i.e., the GC connectivity, can be mapped to the underlying anatomical connectivity in neuronal networks. We perform the GC analysis on the conductance-based integrate-and-fire (IF) neuronal networks to obtain their causal connectivity. Through numerical experiments, we find that the underlying synaptic connectivity amongst individual neurons or subnetworks, can be successfully reconstructed by the GC connectivity constructed from voltage time series. Furthermore, this reconstruction is insensitive to dynamical regimes and can be achieved without perturbing systems and prior knowledge of neuronal model parameters. Surprisingly, the synaptic connectivity can even be reconstructed by merely knowing the raster of systems, i.e., spike timing of neurons. Using spike-triggered correlation techniques, we establish a direct mapping between the causal connectivity and the synaptic connectivity for the conductance-based IF neuronal networks, and show the GC is quadratically related to the coupling strength. The theoretical approach we develop here may provide a framework for examining the validity of the GC analysis in other settings. PMID:24586285

Assessing Command and Control System Vulnerabilities in Underdeveloped, Degraded and Denied Operational Environments

DTIC Science & Technology

2013-06-01

simulation of complex systems (Sterman 2000, Meadows 2008): a) Causal Loop Diagrams. A Causal Loop Diagram ( CLD ) is used to represent the feedback...structure of the dynamic system. CLDs consist of variables in the system being connected by arrows to show their causal influences and relationships. It is...distribution of orders will be included in the model. 6.4.2 Causal Loop Diagrams The CLD , as seen in Figure 5, is derived from the WDA constructs for the

Unimodular lattice triangulations as small-world and scale-free random graphs

NASA Astrophysics Data System (ADS)

Krüger, B.; Schmidt, E. M.; Mecke, K.

2015-02-01

Real-world networks, e.g., the social relations or world-wide-web graphs, exhibit both small-world and scale-free behaviour. We interpret lattice triangulations as planar graphs by identifying triangulation vertices with graph nodes and one-dimensional simplices with edges. Since these triangulations are ergodic with respect to a certain Pachner flip, applying different Monte Carlo simulations enables us to calculate average properties of random triangulations, as well as canonical ensemble averages, using an energy functional that is approximately the variance of the degree distribution. All considered triangulations have clustering coefficients comparable with real-world graphs; for the canonical ensemble there are inverse temperatures with small shortest path length independent of system size. Tuning the inverse temperature to a quasi-critical value leads to an indication of scale-free behaviour for degrees k≥slant 5. Using triangulations as a random graph model can improve the understanding of real-world networks, especially if the actual distance of the embedded nodes becomes important.

Application of 3D triangulations of airborne laser scanning data to estimate boreal forest leaf area index

NASA Astrophysics Data System (ADS)

Majasalmi, Titta; Korhonen, Lauri; Korpela, Ilkka; Vauhkonen, Jari

2017-07-01

We propose 3D triangulations of airborne Laser Scanning (ALS) point clouds as a new approach to derive 3D canopy structures and to estimate forest canopy effective LAI (LAIe). Computational geometry and topological connectivity were employed to filter the triangulations to yield a quasi-optimal relationship with the field measured LAIe. The optimal filtering parameters were predicted based on ALS height metrics, emulating the production of maps of LAIe and canopy volume for large areas. The LAIe from triangulations was validated with field measured LAIe and compared with a reference LAIe calculated from ALS data using logarithmic model based on Beer's law. Canopy transmittance was estimated using All Echo Cover Index (ACI), and the mean projection of unit foliage area (β) was obtained using no-intercept regression with field measured LAIe. We investigated the influence species and season on the triangulated LAIe and demonstrated the relationship between triangulated LAIe and canopy volume. Our data is from 115 forest plots located at the southern boreal forest area in Finland and for each plot three different ALS datasets were available to apply the triangulations. The triangulation approach was found applicable for both leaf-on and leaf-off datasets after initial calibration. Results showed the Root Mean Square Errors (RMSEs) between LAIe from triangulations and field measured values agreed the most using the highest pulse density data (RMSE = 0.63, the coefficient of determination (R2) = 0.53). Yet, the LAIe calculated using ACI-index agreed better with the field measured LAIe (RMSE = 0.53 and R2 = 0.70). The best models to predict the optimal alpha value contained the ACI-index, which indicates that within-crown transmittance is accounted by the triangulation approach. The cover indices may be recommended for retrieving LAIe only, but for applications which require more sophisticated information on canopy shape and volume, such as radiative transfer models, the triangulation approach may be preferred.

A dynamic analysis of S&P 500, FTSE 100 and EURO STOXX 50 indices under different exchange rates

PubMed Central

Chen, Yanhua; Mantegna, Rosario N.; Zuev, Konstantin M.

2018-01-01

In this study, we assess the dynamic evolution of short-term correlation, long-term cointegration and Error Correction Model (hereafter referred to as ECM)-based long-term Granger causality between each pair of US, UK, and Eurozone stock markets from 1980 to 2015 using the rolling-window technique. A comparative analysis of pairwise dynamic integration and causality of stock markets, measured in common and domestic currency terms, is conducted to evaluate comprehensively how exchange rate fluctuations affect the time-varying integration among the S&P 500, FTSE 100 and EURO STOXX 50 indices. The results obtained show that the dynamic correlation, cointegration and ECM-based long-run Granger causality vary significantly over the whole sample period. The degree of dynamic correlation and cointegration between pairs of stock markets rises in periods of high volatility and uncertainty, especially under the influence of economic, financial and political shocks. Meanwhile, we observe the weaker and decreasing correlation and cointegration among the three developed stock markets during the recovery periods. Interestingly, the most persistent and significant cointegration among the three developed stock markets exists during the 2007–09 global financial crisis. Finally, the exchange rate fluctuations, also influence the dynamic integration and causality between all pairs of stock indices, with that influence increasing under the local currency terms. Our results suggest that the potential for diversifying risk by investing in the US, UK and Eurozone stock markets is limited during the periods of economic, financial and political shocks. PMID:29529092

Delaunay Refinement Mesh Generation

DTIC Science & Technology

1997-05-18

edge is locally Delaunay; thus, by Lemma 3, every edge is Delaunay. Theorem 5 Let V be a set of three or more vertices in the plane that are not all...this document. Delaunay triangulations are valuable in part because they have the following optimality properties. Theorem 6 Among all triangulations of...no locally Delaunay edges. By Theorem 5, a triangulation with no locally Delaunay edges is the Delaunay triangulation. The property of max-min

Aristotle and Information Theory: A Comparison of the Influence of Causal Assumptions on Two Theories of Communication. (Janua Linguarum Series Maior 35.)

ERIC Educational Resources Information Center

Rosenfield, Lawrence William

This study sought to discover what critical apparatus would be most appropriate for observers of verbal discourse who choose to accept Aristotelian or "information theory" causal accounts of dynamic process. The major conclusions were: (1) Both causal systems employ a static grid to express relationships; but while the Aristotelian relations are…

CauseMap: fast inference of causality from complex time series.

PubMed

Maher, M Cyrus; Hernandez, Ryan D

2015-01-01

Background. Establishing health-related causal relationships is a central pursuit in biomedical research. Yet, the interdependent non-linearity of biological systems renders causal dynamics laborious and at times impractical to disentangle. This pursuit is further impeded by the dearth of time series that are sufficiently long to observe and understand recurrent patterns of flux. However, as data generation costs plummet and technologies like wearable devices democratize data collection, we anticipate a coming surge in the availability of biomedically-relevant time series data. Given the life-saving potential of these burgeoning resources, it is critical to invest in the development of open source software tools that are capable of drawing meaningful insight from vast amounts of time series data. Results. Here we present CauseMap, the first open source implementation of convergent cross mapping (CCM), a method for establishing causality from long time series data (≳25 observations). Compared to existing time series methods, CCM has the advantage of being model-free and robust to unmeasured confounding that could otherwise induce spurious associations. CCM builds on Takens' Theorem, a well-established result from dynamical systems theory that requires only mild assumptions. This theorem allows us to reconstruct high dimensional system dynamics using a time series of only a single variable. These reconstructions can be thought of as shadows of the true causal system. If reconstructed shadows can predict points from opposing time series, we can infer that the corresponding variables are providing views of the same causal system, and so are causally related. Unlike traditional metrics, this test can establish the directionality of causation, even in the presence of feedback loops. Furthermore, since CCM can extract causal relationships from times series of, e.g., a single individual, it may be a valuable tool to personalized medicine. We implement CCM in Julia, a high-performance programming language designed for facile technical computing. Our software package, CauseMap, is platform-independent and freely available as an official Julia package. Conclusions. CauseMap is an efficient implementation of a state-of-the-art algorithm for detecting causality from time series data. We believe this tool will be a valuable resource for biomedical research and personalized medicine.

An Efficient Two-Tier Causal Protocol for Mobile Distributed Systems

PubMed Central

Dominguez, Eduardo Lopez; Pomares Hernandez, Saul E.; Gomez, Gustavo Rodriguez; Medina, Maria Auxilio

2013-01-01

Causal ordering is a useful tool for mobile distributed systems (MDS) to reduce the non-determinism induced by three main aspects: host mobility, asynchronous execution, and unpredictable communication delays. Several causal protocols for MDS exist. Most of them, in order to reduce the overhead and the computational cost over wireless channels and mobile hosts (MH), ensure causal ordering at and according to the causal view of the Base Stations. Nevertheless, these protocols introduce certain disadvantage, such as unnecessary inhibition at the delivery of messages. In this paper, we present an efficient causal protocol for groupware that satisfies the MDS's constraints, avoiding unnecessary inhibitions and ensuring the causal delivery based on the view of the MHs. One interesting aspect of our protocol is that it dynamically adapts the causal information attached to each message based on the number of messages with immediate dependency relation, and this is not directly proportional to the number of MHs. PMID:23585828

Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder.

PubMed

Wittenborn, A K; Rahmandad, H; Rick, J; Hosseinichimeh, N

2016-02-01

Depression is a complex public health problem with considerable variation in treatment response. The systemic complexity of depression, or the feedback processes among diverse drivers of the disorder, contribute to the persistence of depression. This paper extends prior attempts to understand the complex causal feedback mechanisms that underlie depression by presenting the first broad boundary causal loop diagram of depression dynamics. We applied qualitative system dynamics methods to map the broad feedback mechanisms of depression. We used a structured approach to identify candidate causal mechanisms of depression in the literature. We assessed the strength of empirical support for each mechanism and prioritized those with support from validation studies. Through an iterative process, we synthesized the empirical literature and created a conceptual model of major depressive disorder. The literature review and synthesis resulted in the development of the first causal loop diagram of reinforcing feedback processes of depression. It proposes candidate drivers of illness, or inertial factors, and their temporal functioning, as well as the interactions among drivers of depression. The final causal loop diagram defines 13 key reinforcing feedback loops that involve nine candidate drivers of depression. Future research is needed to expand upon this initial model of depression dynamics. Quantitative extensions may result in a better understanding of the systemic syndrome of depression and contribute to personalized methods of evaluation, prevention and intervention.

Depression as a systemic syndrome: mapping the feedback loops of major depressive disorder

PubMed Central

Wittenborn, A. K.; Rahmandad, H.; Rick, J.; Hosseinichimeh, N.

2016-01-01

Background Depression is a complex public health problem with considerable variation in treatment response. The systemic complexity of depression, or the feedback processes among diverse drivers of the disorder, contribute to the persistence of depression. This paper extends prior attempts to understand the complex causal feedback mechanisms that underlie depression by presenting the first broad boundary causal loop diagram of depression dynamics. Method We applied qualitative system dynamics methods to map the broad feedback mechanisms of depression. We used a structured approach to identify candidate causal mechanisms of depression in the literature. We assessed the strength of empirical support for each mechanism and prioritized those with support from validation studies. Through an iterative process, we synthesized the empirical literature and created a conceptual model of major depressive disorder. Results The literature review and synthesis resulted in the development of the first causal loop diagram of reinforcing feedback processes of depression. It proposes candidate drivers of illness, or inertial factors, and their temporal functioning, as well as the interactions among drivers of depression. The final causal loop diagram defines 13 key reinforcing feedback loops that involve nine candidate drivers of depression. Conclusions Future research is needed to expand upon this initial model of depression dynamics. Quantitative extensions may result in a better understanding of the systemic syndrome of depression and contribute to personalized methods of evaluation, prevention and intervention. PMID:26621339

Tools for Detecting Causality in Space Systems

NASA Astrophysics Data System (ADS)

Johnson, J.; Wing, S.

2017-12-01

Complex systems such as the solar and magnetospheric envivonment often exhibit patterns of behavior that suggest underlying organizing principles. Causality is a key organizing principle that is particularly difficult to establish in strongly coupled nonlinear systems, but essential for understanding and modeling the behavior of systems. While traditional methods of time-series analysis can identify linear correlations, they do not adequately quantify the distinction between causal and coincidental dependence. We discuss tools for detecting causality including: granger causality, transfer entropy, conditional redundancy, and convergent cross maps. The tools are illustrated by applications to magnetospheric and solar physics including radiation belt, Dst (a magnetospheric state variable), substorm, and solar cycle dynamics.

How causal analysis can reveal autonomy in models of biological systems

NASA Astrophysics Data System (ADS)

Marshall, William; Kim, Hyunju; Walker, Sara I.; Tononi, Giulio; Albantakis, Larissa

2017-11-01

Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (Schizosaccharomyces pombe) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem. This article is part of the themed issue 'Reconceptualizing the origins of life'.

The relationships between stressful life events during childhood and differentiation of self and intergenerational triangulation in adulthood.

PubMed

Peleg, Ora

2014-12-01

This study examined the relationships between stressful life events in childhood and differentiation of self and intergenerational triangulation in adulthood. The sample included 217 students (173 females and 44 males) from a college in northern Israel. Participants completed the Hebrew versions of Life Events Checklist (LEC), Differentiation of Self Inventory-Revised (DSI-R) and intergenerational triangulation (INTRI). The main findings were that levels of stressful life events during childhood and adolescence among both genders were positively correlated with the levels of fusion with others and intergenerational triangulation. The levels of positive life events were negatively related to levels of emotional reactivity, emotional cut-off and intergenerational triangulation. Levels of stressful life events in females were positively correlated with emotional reactivity. Intergenerational triangulation was correlated with emotional reactivity, emotional cut-off, fusion with others and I-position. Findings suggest that families that experience higher levels of stressful life events may be at risk for higher levels of intergenerational triangulation and lower levels of differentiation of self. © 2014 International Union of Psychological Science.

Encoding dependence in Bayesian causal networks

USDA-ARS?s Scientific Manuscript database

Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...

Temporal Expression Profiling Identifies Pathways Mediating Effect of Causal Variant on Phenotype

PubMed Central

Gupta, Saumya; Radhakrishnan, Aparna; Raharja-Liu, Pandu; Lin, Gen; Steinmetz, Lars M.; Gagneur, Julien; Sinha, Himanshu

2015-01-01

Even with identification of multiple causal genetic variants for common human diseases, understanding the molecular processes mediating the causal variants’ effect on the disease remains a challenge. This understanding is crucial for the development of therapeutic strategies to prevent and treat disease. While static profiling of gene expression is primarily used to get insights into the biological bases of diseases, it makes differentiating the causative from the correlative effects difficult, as the dynamics of the underlying biological processes are not monitored. Using yeast as a model, we studied genome-wide gene expression dynamics in the presence of a causal variant as the sole genetic determinant, and performed allele-specific functional validation to delineate the causal effects of the genetic variant on the phenotype. Here, we characterized the precise genetic effects of a functional MKT1 allelic variant in sporulation efficiency variation. A mathematical model describing meiotic landmark events and conditional activation of MKT1 expression during sporulation specified an early meiotic role of this variant. By analyzing the early meiotic genome-wide transcriptional response, we demonstrate an MKT1-dependent role of novel modulators, namely, RTG1/3, regulators of mitochondrial retrograde signaling, and DAL82, regulator of nitrogen starvation, in additively effecting sporulation efficiency. In the presence of functional MKT1 allele, better respiration during early sporulation was observed, which was dependent on the mitochondrial retrograde regulator, RTG3. Furthermore, our approach showed that MKT1 contributes to sporulation independent of Puf3, an RNA-binding protein that steady-state transcription profiling studies have suggested to mediate MKT1-pleiotropic effects during mitotic growth. These results uncover interesting regulatory links between meiosis and mitochondrial retrograde signaling. In this study, we highlight the advantage of analyzing allele-specific transcriptional dynamics of mediating genes. Applications in higher eukaryotes can be valuable for inferring causal molecular pathways underlying complex dynamic processes, such as development, physiology and disease progression. PMID:26039065

Comparing the Cognitive Process of Circular Causality in Two Patients with Strokes through Qualitative Analysis.

PubMed

Derakhshanrad, Seyed Alireza; Piven, Emily; Ghoochani, Bahareh Zeynalzadeh

2017-10-01

Walter J. Freeman pioneered the neurodynamic model of brain activity when he described the brain dynamics for cognitive information transfer as the process of circular causality at intention, meaning, and perception (IMP) levels. This view contributed substantially to establishment of the Intention, Meaning, and Perception Model of Neuro-occupation in occupational therapy. As described by the model, IMP levels are three components of the brain dynamics system, with nonlinear connections that enable cognitive function to be processed in a circular causality fashion, known as Cognitive Process of Circular Causality (CPCC). Although considerable research has been devoted to study the brain dynamics by sophisticated computerized imaging techniques, less attention has been paid to study it through investigating the adaptation process of thoughts and behaviors. To explore how CPCC manifested thinking and behavioral patterns, a qualitative case study was conducted on two matched female participants with strokes, who were of comparable ages, affected sides, and other characteristics, except for their resilience and motivational behaviors. CPCC was compared by matrix analysis between two participants, using content analysis with pre-determined categories. Different patterns of thinking and behavior may have happened, due to disparate regulation of CPCC between two participants.

Evaluating the Dynamics of Agent-Environment Interaction

DTIC Science & Technology

2001-05-01

a color sensor in the gripper, a radio transmitter/receiver for communication and data gathering, and an ultrasound /radio triangulation system for...Cooperative Mobile Robot Control’, Autonomous Robots 4(4), 387{403. Vaughan, R. T., Sty, K., Sukhatme, G. S. & Mataric, M. J. (2000), Whistling in the Dark...sensor in the gripper, a radio transmitter/receiver for communication and data gathering, and an ultrasound /radio triangu- lation system for

Information flow and causality as rigorous notions ab initio

NASA Astrophysics Data System (ADS)

Liang, X. San

2016-11-01

Information flow or information transfer the widely applicable general physics notion can be rigorously derived from first principles, rather than axiomatically proposed as an ansatz. Its logical association with causality is firmly rooted in the dynamical system that lies beneath. The principle of nil causality that reads, an event is not causal to another if the evolution of the latter is independent of the former, which transfer entropy analysis and Granger causality test fail to verify in many situations, turns out to be a proven theorem here. Established in this study are the information flows among the components of time-discrete mappings and time-continuous dynamical systems, both deterministic and stochastic. They have been obtained explicitly in closed form, and put to applications with the benchmark systems such as the Kaplan-Yorke map, Rössler system, baker transformation, Hénon map, and stochastic potential flow. Besides unraveling the causal relations as expected from the respective systems, some of the applications show that the information flow structure underlying a complex trajectory pattern could be tractable. For linear systems, the resulting remarkably concise formula asserts analytically that causation implies correlation, while correlation does not imply causation, providing a mathematical basis for the long-standing philosophical debate over causation versus correlation.

Simulations of four-dimensional simplicial quantum gravity as dynamical triangulation

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

Agishtein, M.E.; Migdal, A.A.

1992-04-20

In this paper, Four-Dimensional Simplicial Quantum Gravity is simulated using the dynamical triangulation approach. The authors studied simplicial manifolds of spherical topology and found the critical line for the cosmological constant as a function of the gravitational one, separating the phases of opened and closed Universe. When the bare cosmological constant approaches this line from above, the four-volume grows: the authors reached about 5 {times} 10{sup 4} simplexes, which proved to be sufficient for the statistical limit of infinite volume. However, for the genuine continuum theory of gravity, the parameters of the lattice model should be further adjusted to reachmore » the second order phase transition point, where the correlation length grows to infinity. The authors varied the gravitational constant, and they found the first order phase transition, similar to the one found in three-dimensional model, except in 4D the fluctuations are rather large at the transition point, so that this is close to the second order phase transition. The average curvature in cutoff units is large and positive in one phase (gravity), and small negative in another (antigravity). The authors studied the fractal geometry of both phases, using the heavy particle propagator to define the geodesic map, as well as with the old approach using the shortest lattice paths.« less

The relations between inadequate parent-child boundaries and borderline personality disorder in adolescence.

PubMed

Vanwoerden, Salome; Kalpakci, Allison; Sharp, Carla

2017-11-01

Borderline Personality Disorder (BPD) is a severe mental illness that onsets in adolescence. Research has demonstrated the central role of parent-child relationships for the development and maintenance of BPD although more research is necessary to clarify the specific dynamics that relate to BPD during adolescence. Based on preliminary research establishing the importance of parent-child boundaries for adolescent BPD, this study sought to evaluate the relations between different forms of inadequate boundaries and BPD in adolescence using a multi-method approach. To that end, 301 adolescents (65.1% female; ages 12-17) inpatients were recruited; parents and adolescents completed questionnaire- and interview-based measures of BPD features in adolescent children and a questionnaire-based measure of parent-child boundaries. Relations were found between parental guilt induction and psychological control with children's BPD features above and beyond relations with psychiatric severity and gender. Relations between parent reports of triangulation (when children are recruited to mediate parental marital conflict) and children's BPD were contingent on the level of children's perceptions of triangulation. Findings confirm previous research suggesting the relevance of inadequate parent-child boundaries to children's BPD features and have important implications for understanding the dynamics in families with adolescents with BPD, representing a relevant treatment target. Copyright © 2017 Elsevier B.V. All rights reserved.

An exploration of family dynamics and attachment strategies in a family with ADHD/conduct problems.

PubMed

Dallos, Rudi; Smart, Cordet

2011-10-01

This article reports the preliminary findings of a study of attachment patterns and relationship themes using the TAAI (Transition to Adulthood Attachment Interview), AAI (Adult Attachment Interview) and family interviews (based on the first of 15 families). Research data is presented on a young man aged 16 with a diagnosis of ADHD and his family. Individual interviews, attachment interviews, and family interviews were conducted in order to explore the link between family dynamics, ADHD and attachment strategies. In contrast to findings from existing research indicating pre-occupied patterns for young people diagnosed with ADHD, the young man displayed a complex 'disoriented' attachment pattern which primarily featured a dismissive strategy. However, this was combined with pre-occupied patterns triggered by intrusions from unresolved traumas and memories of his parents' continuing unresolved conflicts. His sense of confusion and lack of a coherent strategy appeared to be closely related to his position of being triangulated into his parents' conflicts. Trans-generational processes were also influential, in that the parents' corrective intentions at more positive parenting were impeded by their own lack of experience of positive attachments in their own childhoods. The study emphasizes the need to consider the relationship between attachment patterns and problems within wider systemic process in the family, in particular triangulation and corrective scripts.

Methodological triangulation: an approach to understanding data.

PubMed

Bekhet, Abir K; Zauszniewski, Jaclene A

2012-01-01

To describe the use of methodological triangulation in a study of how people who had moved to retirement communities were adjusting. Methodological triangulation involves using more than one kind of method to study a phenomenon. It has been found to be beneficial in providing confirmation of findings, more comprehensive data, increased validity and enhanced understanding of studied phenomena. While many researchers have used this well-established technique, there are few published examples of its use. The authors used methodological triangulation in their study of people who had moved to retirement communities in Ohio, US. A blended qualitative and quantitative approach was used. The collected qualitative data complemented and clarified the quantitative findings by helping to identify common themes. Qualitative data also helped in understanding interventions for promoting 'pulling' factors and for overcoming 'pushing' factors of participants. The authors used focused research questions to reflect the research's purpose and four evaluative criteria--'truth value', 'applicability', 'consistency' and 'neutrality'--to ensure rigour. This paper provides an example of how methodological triangulation can be used in nursing research. It identifies challenges associated with methodological triangulation, recommends strategies for overcoming them, provides a rationale for using triangulation and explains how to maintain rigour. Methodological triangulation can be used to enhance the analysis and the interpretation of findings. As data are drawn from multiple sources, it broadens the researcher's insight into the different issues underlying the phenomena being studied.

Causal capture effects in chimpanzees (Pan troglodytes).

PubMed

Matsuno, Toyomi; Tomonaga, Masaki

2017-01-01

Extracting a cause-and-effect structure from the physical world is an important demand for animals living in dynamically changing environments. Human perceptual and cognitive mechanisms are known to be sensitive and tuned to detect and interpret such causal structures. In contrast to rigorous investigations of human causal perception, the phylogenetic roots of this perception are not well understood. In the present study, we aimed to investigate the susceptibility of nonhuman animals to mechanical causality by testing whether chimpanzees perceived an illusion called causal capture (Scholl & Nakayama, 2002). Causal capture is a phenomenon in which a type of bistable visual motion of objects is perceived as causal collision due to a bias from a co-occurring causal event. In our experiments, we assessed the susceptibility of perception of a bistable stream/bounce motion event to a co-occurring causal event in chimpanzees. The results show that, similar to in humans, causal "bounce" percepts were significantly increased in chimpanzees with the addition of a task-irrelevant causal bounce event that was synchronously presented. These outcomes suggest that the perceptual mechanisms behind the visual interpretation of causal structures in the environment are evolutionarily shared between human and nonhuman animals. Copyright © 2016 Elsevier B.V. All rights reserved.

Causal relations among events and states in dynamic geographical phenomena

NASA Astrophysics Data System (ADS)

Huang, Zhaoqiang; Feng, Xuezhi; Xuan, Wenling; Chen, Xiuwan

2007-06-01

There is only a static state of the real world to be recorded in conventional geographical information systems. However, there is not only static information but also dynamic information in geographical phenomena. So that how to record the dynamic information and reveal the relations among dynamic information is an important issue in a spatio-temporal information system. From an ontological perspective, we can initially divide the spatio-temporal entities in the world into continuants and occurrents. Continuant entities endure through some extended (although possibly very short) interval of time (e.g., houses, roads, cities, and real-estate). Occurrent entities happen and are then gone (e.g., a house repair job, road construction project, urban expansion, real-estate transition). From an information system perspective, continuants and occurrents that have a unique identity in the system are referred to as objects and events, respectively. And the change is represented implicitly by static snapshots in current spatial temporal information systems. In the previous models, the objects can be considered as the fundamental components of the system, and the change is modeled by considering time-varying attributes of these objects. In the spatio-temporal database, the temporal information that is either interval or instant is involved and the underlying data structures and indexes for temporal are considerable investigated. However, there is the absence of explicit ways of considering events, which affect the attributes of objects or the state. So the research issue of this paper focuses on how to model events in conceptual models of dynamic geographical phenomena and how to represent the causal relations among events and the objects or states. Firstly, the paper reviews the conceptual modeling in a temporal GIS by researchers. Secondly, this paper discusses the spatio-temporal entities: objects and events. Thirdly, this paper investigates the causal relations amongst events and states. The qualitative spatiotemporal change is an important issue in the dynamic geographic-scale phenomena. In real estate transition, the events and states are needed to be represented explicitly. In our modeling the evolution of a dynamic system, it can not avoid fetching in the view of causality. The object's transition is represented by the state of object. Event causes the state of objects changing and causes other events happen. Events connect with objects closely. The basic causal relations are the state-event and event-state relationships. Lastly, the paper concludes with the overview about the causal relations amongst events and states. And this future work is pointed.

An overview of the stereo correlation and triangulation formulations used in DICe.

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

Turner, Daniel Z.

This document provides a detailed overview of the stereo correlation algorithm and triangulation formulation used in the Digital Image Correlation Engine (DICe) to triangulate three dimensional motion in space given the image coordinates and camera calibration parameters.

Retrieving hydrological connectivity from empirical causality in karst systems

NASA Astrophysics Data System (ADS)

Delforge, Damien; Vanclooster, Marnik; Van Camp, Michel; Poulain, Amaël; Watlet, Arnaud; Hallet, Vincent; Kaufmann, Olivier; Francis, Olivier

2017-04-01

Because of their complexity, karst systems exhibit nonlinear dynamics. Moreover, if one attempts to model a karst, the hidden behavior complicates the choice of the most suitable model. Therefore, both intense investigation methods and nonlinear data analysis are needed to reveal the underlying hydrological connectivity as a prior for a consistent physically based modelling approach. Convergent Cross Mapping (CCM), a recent method, promises to identify causal relationships between time series belonging to the same dynamical systems. The method is based on phase space reconstruction and is suitable for nonlinear dynamics. As an empirical causation detection method, it could be used to highlight the hidden complexity of a karst system by revealing its inner hydrological and dynamical connectivity. Hence, if one can link causal relationships to physical processes, the method should show great potential to support physically based model structure selection. We present the results of numerical experiments using karst model blocks combined in different structures to generate time series from actual rainfall series. CCM is applied between the time series to investigate if the empirical causation detection is consistent with the hydrological connectivity suggested by the karst model.

Fertility and Female Employment: Problems of Causal Direction.

ERIC Educational Resources Information Center

Cramer, James C.

1980-01-01

Considers multicollinearity in nonrecursive models, misspecification of models, discrepancies between attitudes and behavior, and differences between static and dynamic models as explanations for contradictory information on the causal relationship between fertility and female employment. Finds that initially fertility affects employment but that,…

Feasibility of employing a smartphone as the payload in a photogrammetric UAV system

NASA Astrophysics Data System (ADS)

Kim, Jinsoo; Lee, Seongkyu; Ahn, Hoyong; Seo, Dongju; Park, Soyoung; Choi, Chuluong

2013-05-01

Smartphones can be operated in a 3G network environment at any time or location, and they also cost less than existing photogrammetric UAV systems, providing high-resolution images and 3D location and attitude data from a variety of built-in sensors. This study aims to assess the feasibility of using a smartphone as the payload for a photogrammetric UAV system. To carry out the assessment, a smartphone-based photogrammetric UAV system was developed and utilized to obtain image, location, and attitude data under both static and dynamic conditions. The accuracy of the location and attitude data obtained and sent by this system was then evaluated. The smartphone images were converted into ortho-images via image triangulation, which was carried out both with and without consideration of the interior orientation (IO) parameters determined by camera calibration. In the static experiment, when the IO parameters were taken into account, the triangulation results were less than 1.28 pixels (RMSE) for all smartphone types, an improvement of at least 47% compared with the case when IO parameters were not taken into account. In the dynamic experiment, on the other hand, the accuracy of smartphone image triangulation was not significantly improved by considering IO parameters. This was because the electronic rolling shutter within the complementary metal-oxide semiconductor (CMOS) sensor built into the smartphone and the actuator for the voice coil motor (VCM)-type auto-focusing affected by the vibration and the speed of the UAV, which is likely to have a negative effect on image-based digital elevation model (DEM) generation. However, considering that these results were obtained using a single smartphone, this suggests that a smartphone is not only feasible as the payload for a photogrammetric UAV system but it may also play a useful role when installed in existing UAV systems.

Network cosmology.

PubMed

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

Network Cosmology

PubMed Central

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

EMG-Torque Dynamics Change With Contraction Bandwidth.

PubMed

Golkar, Mahsa A; Jalaleddini, Kian; Kearney, Robert E

2018-04-01

An accurate model for ElectroMyoGram (EMG)-torque dynamics has many uses. One of its applications which has gained high attention among researchers is its use, in estimating the muscle contraction level for the efficient control of prosthesis. In this paper, the dynamic relationship between the surface EMG and torque during isometric contractions at the human ankle was studied using system identification techniques. Subjects voluntarily modulated their ankle torque in dorsiflexion direction, by activating their tibialis anterior muscle, while tracking a pseudo-random binary sequence in a torque matching task. The effects of contraction bandwidth, described by torque spectrum, on EMG-torque dynamics were evaluated by varying the visual command switching time. Nonparametric impulse response functions (IRF) were estimated between the processed surface EMG and torque. It was demonstrated that: 1) at low contraction bandwidths, the identified IRFs had unphysiological anticipatory (i.e., non-causal) components, whose amplitude decreased as the contraction bandwidth increased. We hypothesized that this non-causal behavior arose, because the EMG input contained a component due to feedback from the output torque, i.e., it was recorded from within a closed-loop. Vision was not the feedback source since the non-causal behavior persisted when visual feedback was removed. Repeating the identification using a nonparametric closed-loop identification algorithm yielded causal IRFs at all bandwidths, supporting this hypothesis. 2) EMG-torque dynamics became faster and the bandwidth of system increased as contraction modulation rate increased. Thus, accurate prediction of torque from EMG signals must take into account the contraction bandwidth sensitivity of this system.

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases.

PubMed

Zhang, Qin; Yao, Quanying

2018-05-01

The dynamic uncertain causality graph (DUCG) is a newly presented framework for uncertain causality representation and probabilistic reasoning. It has been successfully applied to online fault diagnoses of large, complex industrial systems, and decease diagnoses. This paper extends the DUCG to model more complex cases than what could be previously modeled, e.g., the case in which statistical data are in different groups with or without overlap, and some domain knowledge and actions (new variables with uncertain causalities) are introduced. In other words, this paper proposes to use -mode, -mode, and -mode of the DUCG to model such complex cases and then transform them into either the standard -mode or the standard -mode. In the former situation, if no directed cyclic graph is involved, the transformed result is simply a Bayesian network (BN), and existing inference methods for BNs can be applied. In the latter situation, an inference method based on the DUCG is proposed. Examples are provided to illustrate the methodology.

Bulk viscosity and relaxation time of causal dissipative relativistic fluid dynamics

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

Huang Xuguang; Rischke, Dirk H.; Institut fuer Theoretische Physik, J.W. Goethe-Universitaet, D-60438 Frankfurt am Main

2011-02-15

The microscopic formulas of the bulk viscosity {zeta} and the corresponding relaxation time {tau}{sub {Pi}} in causal dissipative relativistic fluid dynamics are derived by using the projection operator method. In applying these formulas to the pionic fluid, we find that the renormalizable energy-momentum tensor should be employed to obtain consistent results. In the leading-order approximation in the chiral perturbation theory, the relaxation time is enhanced near the QCD phase transition, and {tau}{sub {Pi}} and {zeta} are related as {tau}{sub {Pi}={zeta}}/[{beta}{l_brace}(1/3-c{sub s}{sup 2})({epsilon}+P)-2({epsilon}-3P)/9{r_brace}], where {epsilon}, P, and c{sub s} are the energy density, pressure, and velocity of sound, respectively. The predictedmore » {zeta} and {tau}{sub {Pi}} should satisfy the so-called causality condition. We compare our result with the results of the kinetic calculation by Israel and Stewart and the string theory, and confirm that all three approaches are consistent with the causality condition.« less

Causal learning is collaborative: Examining explanation and exploration in social contexts.

PubMed

Legare, Cristine H; Sobel, David M; Callanan, Maureen

2017-10-01

Causal learning in childhood is a dynamic and collaborative process of explanation and exploration within complex physical and social environments. Understanding how children learn causal knowledge requires examining how they update beliefs about the world given novel information and studying the processes by which children learn in collaboration with caregivers, educators, and peers. The objective of this article is to review evidence for how children learn causal knowledge by explaining and exploring in collaboration with others. We review three examples of causal learning in social contexts, which elucidate how interaction with others influences causal learning. First, we consider children's explanation-seeking behaviors in the form of "why" questions. Second, we examine parents' elaboration of meaning about causal relations. Finally, we consider parents' interactive styles with children during free play, which constrains how children explore. We propose that the best way to understand children's causal learning in social context is to combine results from laboratory and natural interactive informal learning environments.

EPRL/FK asymptotics and the flatness problem

NASA Astrophysics Data System (ADS)

Oliveira, José Ricardo

2018-05-01

Spin foam models are an approach to quantum gravity based on the concept of sum over states, which aims to describe quantum spacetime dynamics in a way that its parent framework, loop quantum gravity, has not as of yet succeeded. Since these models’ relation to classical Einstein gravity is not explicit, an important test of their viabilitiy is the study of asymptotics—the classical theory should be obtained in a limit where quantum effects are negligible, taken to be the limit of large triangle areas in a triangulated manifold with boundary. In this paper we will briefly introduce the EPRL/FK spin foam model and known results about its asymptotics, proceeding then to describe a practical computation of spin foam and semiclassical geometric data for a simple triangulation with only one interior triangle. The results are used to comment on the ‘flatness problem’—a hypothesis raised by Bonzom (2009 Phys. Rev. D 80 064028) suggesting that EPRL/FK’s classical limit only describes flat geometries in vacuum.

Triangulation 2.0

ERIC Educational Resources Information Center

Denzin, Norman K.

2012-01-01

The author's thesis is simple and direct. Those in the mixed methods qualitative inquiry community need a new story line, one that does not confuse pragmatism for triangulation, and triangulation for mixed methods research (MMR). A different third way is required, one that inspires generative politics and dialogic democracy and helps shape…

Understanding social forces involved in diabetes outcomes: a systems science approach to quality-of-life research.

PubMed

Lounsbury, David W; Hirsch, Gary B; Vega, Chawntel; Schwartz, Carolyn E

2014-04-01

The field of quality-of-life (QOL) research would benefit from learning about and integrating systems science approaches that model how social forces interact dynamically with health and affect the course of chronic illnesses. Our purpose is to describe the systems science mindset and to illustrate the utility of a system dynamics approach to promoting QOL research in chronic disease, using diabetes as an example. We build a series of causal loop diagrams incrementally, introducing new variables and their dynamic relationships at each stage. These causal loop diagrams demonstrate how a common set of relationships among these variables can generate different disease and QOL trajectories for people with diabetes and also lead to a consideration of non-clinical (psychosocial and behavioral) factors that can have implications for program design and policy formulation. The policy implications of the causal loop diagrams are discussed, and empirical next steps to validate the diagrams and quantify the relationships are described.

Causal learning and inference as a rational process: the new synthesis.

PubMed

Holyoak, Keith J; Cheng, Patricia W

2011-01-01

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Onomatopoeia characters extraction from comic images using constrained Delaunay triangulation

NASA Astrophysics Data System (ADS)

Liu, Xiangping; Shoji, Kenji; Mori, Hiroshi; Toyama, Fubito

2014-02-01

A method for extracting onomatopoeia characters from comic images was developed based on stroke width feature of characters, since they nearly have a constant stroke width in a number of cases. An image was segmented with a constrained Delaunay triangulation. Connected component grouping was performed based on the triangles generated by the constrained Delaunay triangulation. Stroke width calculation of the connected components was conducted based on the altitude of the triangles generated with the constrained Delaunay triangulation. The experimental results proved the effectiveness of the proposed method.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

NASA Astrophysics Data System (ADS)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Effective connectivity: Influence, causality and biophysical modeling

PubMed Central

Valdes-Sosa, Pedro A.; Roebroeck, Alard; Daunizeau, Jean; Friston, Karl

2011-01-01

This is the final paper in a Comments and Controversies series dedicated to “The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution”. We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments. PMID:21477655

Participatory System Dynamics Modeling: Increasing Stakeholder Engagement and Precision to Improve Implementation Planning in Systems.

PubMed

Zimmerman, Lindsey; Lounsbury, David W; Rosen, Craig S; Kimerling, Rachel; Trafton, Jodie A; Lindley, Steven E

2016-11-01

Implementation planning typically incorporates stakeholder input. Quality improvement efforts provide data-based feedback regarding progress. Participatory system dynamics modeling (PSD) triangulates stakeholder expertise, data and simulation of implementation plans prior to attempting change. Frontline staff in one VA outpatient mental health system used PSD to examine policy and procedural "mechanisms" they believe underlie local capacity to implement evidence-based psychotherapies (EBPs) for PTSD and depression. We piloted the PSD process, simulating implementation plans to improve EBP reach. Findings indicate PSD is a feasible, useful strategy for building stakeholder consensus, and may save time and effort as compared to trial-and-error EBP implementation planning.

Triangulation, Respondent Validation, and Democratic Participation in Mixed Methods Research

ERIC Educational Resources Information Center

Torrance, Harry

2012-01-01

Over the past 10 years or so the "Field" of "Mixed Methods Research" (MMR) has increasingly been exerting itself as something separate, novel, and significant, with some advocates claiming paradigmatic status. Triangulation is an important component of mixed methods designs. Triangulation has its origins in attempts to validate research findings…

Comparative analysis of hierarchical triangulated irregular networks to represent 3D elevation in terrain databases

NASA Astrophysics Data System (ADS)

Abdelguerfi, Mahdi; Wynne, Chris; Cooper, Edgar; Ladner, Roy V.; Shaw, Kevin B.

1997-08-01

Three-dimensional terrain representation plays an important role in a number of terrain database applications. Hierarchical triangulated irregular networks (TINs) provide a variable-resolution terrain representation that is based on a nested triangulation of the terrain. This paper compares and analyzes existing hierarchical triangulation techniques. The comparative analysis takes into account how aesthetically appealing and accurate the resulting terrain representation is. Parameters, such as adjacency, slivers, and streaks, are used to provide a measure on how aesthetically appealing the terrain representation is. Slivers occur when the triangulation produces thin and slivery triangles. Streaks appear when there are too many triangulations done at a given vertex. Simple mathematical expressions are derived for these parameters, thereby providing a fairer and a more easily duplicated comparison. In addition to meeting the adjacency requirement, an aesthetically pleasant hierarchical TINs generation algorithm is expected to reduce both slivers and streaks while maintaining accuracy. A comparative analysis of a number of existing approaches shows that a variant of a method originally proposed by Scarlatos exhibits better overall performance.

Profiles of Cognitive Appraisals and Triangulation into Interparental Conflict: Implications for Adolescent Adjustment

PubMed Central

Fosco, Gregory M.; Bray, Bethany C.

2016-01-01

Youth appraisals and triangulation into conflicts are key mechanisms by which interparental conflict places youth at risk for psychological maladjustment. Although evidence suggests that there are multiple mechanisms at work (e.g., Fosco & Feinberg, 2015; Grych, Harold, & Miles, 2003), this body of work has relied on variable-centered analyses that are limited to the unique contributions of each process to the variance in outcomes. In reality, it is possible that different combinations of these risk mechanisms may account for multifinality in risk outcomes. Using latent profile analysis (LPA) we examined profiles of threat appraisals, self-blaming attributions, and triangulation in relation to internalizing and externalizing problems in a sample of 285, ethnically diverse high school students. The current analyses revealed five distinct profiles of appraisals and triangulation, including an overall low-risk group and a global high-risk group, in which all three processes were below average or above average, respectively. Additional profiles included combinations of threat and blame, threat and triangulation, and blame and triangulation. Links between these profiles and emotional distress, problem behavior, and academic outcomes are discussed. PMID:26963695

Socioeconomic inequality in health in the British household panel: Tests of the social causation, health selection and the indirect selection hypothesis using dynamic fixed effects panel models.

PubMed

Foverskov, Else; Holm, Anders

2016-02-01

Despite social inequality in health being well documented, it is still debated which causal mechanism best explains the negative association between socioeconomic position (SEP) and health. This paper is concerned with testing the explanatory power of three widely proposed causal explanations for social inequality in health in adulthood: the social causation hypothesis (SEP determines health), the health selection hypothesis (health determines SEP) and the indirect selection hypothesis (no causal relationship). We employ dynamic data of respondents aged 30 to 60 from the last nine waves of the British Household Panel Survey. Household income and location on the Cambridge Scale is included as measures of different dimensions of SEP and health is measured as a latent factor score. The causal hypotheses are tested using a time-based Granger approach by estimating dynamic fixed effects panel regression models following the method suggested by Anderson and Hsiao. We propose using this method to estimate the associations over time since it allows one to control for all unobserved time-invariant factors and hence lower the chances of biased estimates due to unobserved heterogeneity. The results showed no proof of the social causation hypothesis over a one to five year period and limited support for the health selection hypothesis was seen only for men in relation to HH income. These findings were robust in multiple sensitivity analysis. We conclude that the indirect selection hypothesis may be the most important in explaining social inequality in health in adulthood, indicating that the well-known cross-sectional correlations between health and SEP in adulthood seem not to be driven by a causal relationship, but instead by dynamics and influences in place before the respondents turn 30 years old that affect both their health and SEP onwards. The conclusion is limited in that we do not consider the effect of specific diseases and causal relationships in adulthood may be present over a longer timespan than 5 years. Copyright © 2015 Elsevier Ltd. All rights reserved.

Large N Limits in Tensor Models: Towards More Universality Classes of Colored Triangulations in Dimension d≥2

NASA Astrophysics Data System (ADS)

Bonzom, Valentin

2016-07-01

We review an approach which aims at studying discrete (pseudo-)manifolds in dimension d≥ 2 and called random tensor models. More specifically, we insist on generalizing the two-dimensional notion of p-angulations to higher dimensions. To do so, we consider families of triangulations built out of simplices with colored faces. Those simplices can be glued to form new building blocks, called bubbles which are pseudo-manifolds with boundaries. Bubbles can in turn be glued together to form triangulations. The main challenge is to classify the triangulations built from a given set of bubbles with respect to their numbers of bubbles and simplices of codimension two. While the colored triangulations which maximize the number of simplices of codimension two at fixed number of simplices are series-parallel objects called melonic triangulations, this is not always true anymore when restricting attention to colored triangulations built from specific bubbles. This opens up the possibility of new universality classes of colored triangulations. We present three existing strategies to find those universality classes. The first two strategies consist in building new bubbles from old ones for which the problem can be solved. The third strategy is a bijection between those colored triangulations and stuffed, edge-colored maps, which are some sort of hypermaps whose hyperedges are replaced with edge-colored maps. We then show that the present approach can lead to enumeration results and identification of universality classes, by working out the example of quartic tensor models. They feature a tree-like phase, a planar phase similar to two-dimensional quantum gravity and a phase transition between them which is interpreted as a proliferation of baby universes. While this work is written in the context of random tensors, it is almost exclusively of combinatorial nature and we hope it is accessible to interested readers who are not familiar with random matrices, tensors and quantum field theory.

The relationship between energy consumption and economic growth in Malaysia: ARDL bound test approach

NASA Astrophysics Data System (ADS)

Razali, Radzuan; Khan, Habib; Shafie, Afza; Hassan, Abdul Rahman

2016-11-01

The objective of this paper is to examine the short-run and long-run dynamic causal relationship between energy consumption and income per capita both in bivariate and multivariate framework over the period 1971-2014 in the case of Malaysia [1]. The study applies ARDL Bound test procedure for the long run co-integration and Granger causality test for investigation of causal link between the variables. The ARDL bound test confirms the existence of long run co-integration relationship between the variables. The causality test show a feed-back hypothesis between income per capita and energy consumption over the period in the case of Malaysia.

Inferring causal molecular networks: empirical assessment through a community-based effort.

PubMed

Hill, Steven M; Heiser, Laura M; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K; Carlin, Daniel E; Zhang, Yang; Sokolov, Artem; Paull, Evan O; Wong, Chris K; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V; Favorov, Alexander V; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W; Long, Byron L; Noren, David P; Bisberg, Alexander J; Mills, Gordon B; Gray, Joe W; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A; Fertig, Elana J; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M; Spellman, Paul T; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-04-01

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Does Causality Matter More Now? Increase in the Proportion of Causal Language in English Texts.

PubMed

Iliev, Rumen; Axelrod, Robert

2016-05-01

The vast majority of the work on culture and cognition has focused on cross-cultural comparisons, largely ignoring the dynamic aspects of culture. In this article, we provide a diachronic analysis of causal cognition over time. We hypothesized that the increased role of education, science, and technology in Western societies should be accompanied by greater attention to causal connections. To test this hypothesis, we compared word frequencies in English texts from different time periods and found an increase in the use of causal language of about 40% over the past two centuries. The observed increase was not attributable to general language effects or to changing semantics of causal words. We also found that there was a consistent difference between the 19th and the 20th centuries, and that the increase happened mainly in the 20th century. © The Author(s) 2016.

Optimal quantum networks and one-shot entropies

NASA Astrophysics Data System (ADS)

Chiribella, Giulio; Ebler, Daniel

2016-09-01

We develop a semidefinite programming method for the optimization of quantum networks, including both causal networks and networks with indefinite causal structure. Our method applies to a broad class of performance measures, defined operationally in terms of interative tests set up by a verifier. We show that the optimal performance is equal to a max relative entropy, which quantifies the informativeness of the test. Building on this result, we extend the notion of conditional min-entropy from quantum states to quantum causal networks. The optimization method is illustrated in a number of applications, including the inversion, charge conjugation, and controlization of an unknown unitary dynamics. In the non-causal setting, we show a proof-of-principle application to the maximization of the winning probability in a non-causal quantum game.

Automatic Generation of CFD-Ready Surface Triangulations from CAD Geometry

NASA Technical Reports Server (NTRS)

Aftosmis, M. J.; Delanaye, M.; Haimes, R.; Nixon, David (Technical Monitor)

1998-01-01

This paper presents an approach for the generation of closed manifold surface triangulations from CAD geometry. CAD parts and assemblies are used in their native format, without translation, and a part's native geometry engine is accessed through a modeler-independent application programming interface (API). In seeking a robust and fully automated procedure, the algorithm is based on a new physical space manifold triangulation technique which was developed to avoid robustness issues associated with poorly conditioned mappings. In addition, this approach avoids the usual ambiguities associated with floating-point predicate evaluation on constructed coordinate geometry in a mapped space, The technique is incremental, so that each new site improves the triangulation by some well defined quality measure. Sites are inserted using a variety of priority queues to ensure that new insertions will address the worst triangles first, As a result of this strategy, the algorithm will return its 'best' mesh for a given (prespecified) number of sites. Alternatively, the algorithm may be allowed to terminate naturally after achieving a prespecified measure of mesh quality. The resulting triangulations are 'CFD-ready' in that: (1) Edges match the underlying part model to within a specified tolerance. (2) Triangles on disjoint surfaces in close proximity have matching length-scales. (3) The algorithm produces a triangulation such that no angle is less than a given angle bound, alpha, or greater than Pi - 2alpha This result also sets bounds on the maximum vertex degree, triangle aspect-ratio and maximum stretching rate for the triangulation. In addition to tile output triangulations for a variety of CAD parts, tile discussion presents related theoretical results which assert the existence of such all angle bound, and demonstrate that maximum bounds of between 25 deg and 30 deg may be achieved in practice.

What Can Causal Networks Tell Us about Metabolic Pathways?

PubMed Central

Blair, Rachael Hageman; Kliebenstein, Daniel J.; Churchill, Gary A.

2012-01-01

Graphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question: “What can causal networks tell us about metabolic pathways?”. Using data from an Arabidopsis BaySha population and simulated data from dynamic models of pathway motifs, we assess our ability to reconstruct metabolic pathways using graphical models. Our results highlight the necessity of non-genetic residual biological variation for reliable inference. Recovery of the ordering within a pathway is possible, but should not be expected. Causal inference is sensitive to subtle patterns in the correlation structure that may be driven by a variety of factors, which may not emphasize the substrate-product relationship. We illustrate the effects of metabolic pathway architecture, epistasis and stochastic variation on correlation structure and graphical model-derived networks. We conclude that graphical models should be interpreted cautiously, especially if the implied causal relationships are to be used in the design of intervention strategies. PMID:22496633

Weak form of Stokes-Dirac structures and geometric discretization of port-Hamiltonian systems

NASA Astrophysics Data System (ADS)

Kotyczka, Paul; Maschke, Bernhard; Lefèvre, Laurent

2018-05-01

We present the mixed Galerkin discretization of distributed parameter port-Hamiltonian systems. On the prototypical example of hyperbolic systems of two conservation laws in arbitrary spatial dimension, we derive the main contributions: (i) A weak formulation of the underlying geometric (Stokes-Dirac) structure with a segmented boundary according to the causality of the boundary ports. (ii) The geometric approximation of the Stokes-Dirac structure by a finite-dimensional Dirac structure is realized using a mixed Galerkin approach and power-preserving linear maps, which define minimal discrete power variables. (iii) With a consistent approximation of the Hamiltonian, we obtain finite-dimensional port-Hamiltonian state space models. By the degrees of freedom in the power-preserving maps, the resulting family of structure-preserving schemes allows for trade-offs between centered approximations and upwinding. We illustrate the method on the example of Whitney finite elements on a 2D simplicial triangulation and compare the eigenvalue approximation in 1D with a related approach.

The Application of a Multiphase Triangulation Approach to Mixed Methods: The Research of an Aspiring School Principal Development Program

ERIC Educational Resources Information Center

Youngs, Howard; Piggot-Irvine, Eileen

2012-01-01

Mixed methods research has emerged as a credible alternative to unitary research approaches. The authors show how a combination of a triangulation convergence model with a triangulation multilevel model was used to research an aspiring school principal development pilot program. The multilevel model is used to show the national and regional levels…

An advancing front Delaunay triangulation algorithm designed for robustness

NASA Technical Reports Server (NTRS)

Mavriplis, D. J.

1992-01-01

A new algorithm is described for generating an unstructured mesh about an arbitrary two-dimensional configuration. Mesh points are generated automatically by the algorithm in a manner which ensures a smooth variation of elements, and the resulting triangulation constitutes the Delaunay triangulation of these points. The algorithm combines the mathematical elegance and efficiency of Delaunay triangulation algorithms with the desirable point placement features, boundary integrity, and robustness traditionally associated with advancing-front-type mesh generation strategies. The method offers increased robustness over previous algorithms in that it cannot fail regardless of the initial boundary point distribution and the prescribed cell size distribution throughout the flow-field.

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

PubMed

Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

From structure to function, via dynamics

NASA Astrophysics Data System (ADS)

Stetter, O.; Soriano, J.; Geisel, T.; Battaglia, D.

2013-01-01

Neurons in the brain are wired into a synaptic network that spans multiple scales, from local circuits within cortical columns to fiber tracts interconnecting distant areas. However, brain function require the dynamic control of inter-circuit interactions on time-scales faster than synaptic changes. In particular, strength and direction of causal influences between neural populations (described by the so-called directed functional connectivity) must be reconfigurable even when the underlying structural connectivity is fixed. Such directed functional influences can be quantified resorting to causal analysis of time-series based on tools like Granger Causality or Transfer Entropy. The ability to quickly reorganize inter-areal interactions is a chief requirement for performance in a changing natural environment. But how can manifold functional networks stem "on demand" from an essentially fixed structure? We explore the hypothesis that the self-organization of neuronal synchronous activity underlies the control of brain functional connectivity. Based on simulated and real recordings of critical neuronal cultures in vitro, as well as on mean-field and spiking network models of interacting brain areas, we have found that "function follows dynamics", rather than structure. Different dynamic states of a same structural network, characterized by different synchronization properties, are indeed associated to different functional digraphs (functional multiplicity). We also highlight the crucial role of dynamics in establishing a structure-to-function link, by showing that whenever different structural topologies lead to similar dynamical states, than the associated functional connectivities are also very similar (structural degeneracy).

Representing Causation

ERIC Educational Resources Information Center

Wolff, Phillip

2007-01-01

The dynamics model, which is based on L. Talmy's (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from…

When Russians Learn English: How the Semantics of Causation May Change

ERIC Educational Resources Information Center

Wolff, Phillip; Ventura, Tatyana

2009-01-01

We examined how the semantics of causal expressions in Russian and English might differ and how these differences might lead to changes in the way second language learners understand causal expressions in their first language. According to the dynamics model of causation (Wolff, 2007), expressions of causation based on CAUSE verbs (make, force)…

The influence of causal knowledge on the willingness to change attitude towards climate change: results from an empirical study

NASA Astrophysics Data System (ADS)

Tasquier, Giulia; Pongiglione, Francesca

2017-09-01

Climate change is one of the significant global challenges currently facing humanity. Even though its seriousness seems to be common knowledge among the public, the reaction of individuals to it has been slow and uncertain. Many studies assert that simply knowing about climate change is not enough to generate people's behavioural response. They claim, indeed, that in some cases scientific literacy can even obstruct behavioural response instead. However, recent surveys show a rather poor understanding of climate dynamics and argue that lack of knowledge about causal relationships within climate dynamics can hinder behavioural response, since the individual is not able to understand his/her role as causal agent and therefore doesn't know how to take proper action. This study starts from the hypothesis that scientific knowledge focused on clarifying climate dynamics can make people understand not only dynamics themselves, but also their interactive relationship with the environment. Teaching materials on climate change based on such considerations were designed and implemented in a course for secondary-school students with the aim of investigating whether this kind of knowledge had an influence on students' willingness to adopt pro-environmental behaviours. Questionnaires were delivered for testing the effect of the teaching experience on knowledge and behaviour.

Inferring causal molecular networks: empirical assessment through a community-based effort

PubMed Central

Hill, Steven M.; Heiser, Laura M.; Cokelaer, Thomas; Unger, Michael; Nesser, Nicole K.; Carlin, Daniel E.; Zhang, Yang; Sokolov, Artem; Paull, Evan O.; Wong, Chris K.; Graim, Kiley; Bivol, Adrian; Wang, Haizhou; Zhu, Fan; Afsari, Bahman; Danilova, Ludmila V.; Favorov, Alexander V.; Lee, Wai Shing; Taylor, Dane; Hu, Chenyue W.; Long, Byron L.; Noren, David P.; Bisberg, Alexander J.; Mills, Gordon B.; Gray, Joe W.; Kellen, Michael; Norman, Thea; Friend, Stephen; Qutub, Amina A.; Fertig, Elana J.; Guan, Yuanfang; Song, Mingzhou; Stuart, Joshua M.; Spellman, Paul T.; Koeppl, Heinz; Stolovitzky, Gustavo; Saez-Rodriguez, Julio; Mukherjee, Sach

2016-01-01

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks. PMID:26901648

Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information

NASA Astrophysics Data System (ADS)

Li, Songting; Xiao, Yanyang; Zhou, Douglas; Cai, David

2018-05-01

The Granger causality (GC) analysis has been extensively applied to infer causal interactions in dynamical systems arising from economy and finance, physics, bioinformatics, neuroscience, social science, and many other fields. In the presence of potential nonlinearity in these systems, the validity of the GC analysis in general is questionable. To illustrate this, here we first construct minimal nonlinear systems and show that the GC analysis fails to infer causal relations in these systems—it gives rise to all types of incorrect causal directions. In contrast, we show that the time-delayed mutual information (TDMI) analysis is able to successfully identify the direction of interactions underlying these nonlinear systems. We then apply both methods to neuroscience data collected from experiments and demonstrate that the TDMI analysis but not the GC analysis can identify the direction of interactions among neuronal signals. Our work exemplifies inference hazards in the GC analysis in nonlinear systems and suggests that the TDMI analysis can be an appropriate tool in such a case.

Parental causal attributions and emotions in daily learning situations with the child.

PubMed

Enlund, Emmi; Aunola, Kaisa; Tolvanen, Asko; Nurmi, Jari-Erik

2015-08-01

This study investigated the dynamics between the causal attributions parents reported daily for their children's success in learning situations and parental positive emotions. The sample consisted of 159 mothers and 147 fathers of 162 first graders (83 girls, 79 boys; aged from 6 to 7 years, M = 7.5 years, SD = 3.6 months). Parents filled in a structured diary questionnaire concerning their causal attributions and emotions over 7 successive days in the fall semester and again over 7 successive days in the spring semester. Multilevel analyses showed that both parental causal attributions and positive emotions varied more within parents (between days over the week) than between parents. Furthermore, mothers' positive emotions on a certain day predicted their causal attributions on that same day rather than vice versa. The higher the level of positive emotions parents reported in a specific day, the more they used effort and ability as causal attributions for their offspring's success on that same day. (c) 2015 APA, all rights reserved).

Health and Wealth of Elderly Couples: Causality Tests Using Dynamic Panel Data Models*

PubMed Central

Michaud, Pierre-Carl; van Soest, Arthur

2010-01-01

A positive relationship between socio-economic status (SES) and health, the “health-wealth gradient”, is repeatedly found in many industrialized countries. This study analyzes competing explanations for this gradient: causal effects from health to wealth (health causation) and causal effects from wealth to health (wealth or social causation). Using six biennial waves of couples aged 51–61 in 1992 from the U.S. Health and Retirement Study, we test for causality in panel data models incorporating unobserved heterogeneity and a lag structure supported by specification tests. In contrast to tests relying on models with only first order lags or without unobserved heterogeneity, these tests provide no evidence of causal wealth health effects. On the other hand, we find strong evidence of causal effects from both spouses’ health on household wealth. We also find an effect of the husband’s health on the wife’s mental health, but no other effects from one spouse’s health to health of the other spouse. PMID:18513809

Detection on vehicle vibration induced by the engine shaking based on the laser triangulation

NASA Astrophysics Data System (ADS)

Chen, Wenxue; Yang, Biwu; Ni, Zhibin; Hu, Xinhan; Han, Tieqiang; Hu, Yaocheng; Zhang, Wu; Wang, Yunfeng

2017-10-01

The magnitude of engine shaking is chosen to evaluate the vehicle performance. The engine shaking is evaluated by the vehicle vibration. Based on the laser triangulation, the vehicle vibration is measured by detecting the distance variation between the bodywork and road surface. The results represent the magnitude of engine shaking. The principle and configuration of the laser triangulation is also introduced in this paper.

Yet another method for triangulation and contouring for automated cartography

NASA Technical Reports Server (NTRS)

De Floriani, L.; Falcidieno, B.; Nasy, G.; Pienovi, C.

1982-01-01

An algorithm is presented for hierarchical subdivision of a set of three-dimensional surface observations. The data structure used for obtaining the desired triangulation is also singularly appropriate for extracting contours. Some examples are presented, and the results obtained are compared with those given by Delaunay triangulation. The data points selected by the algorithm provide a better approximation to the desired surface than do randomly selected points.

On triangulations of the plane by pencils of conics. II

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

Lazareva, V B; Shelekhov, A M

2013-06-30

The present work continues our previous paper in which all possible triangulations of the plane using three pencils of circles were listed. In the present article we find all projectively distinct triangulations of the plane by pencils of conics that are obtained by projecting regular three-webs, cut out on a nondegenerate cubic surface by three pencils of planes, whose axes lie on this surface. Bibliography: 6 titles.

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

PubMed Central

Young, Gerald

2015-01-01

Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved) that inform approaches to nosology, or classification, such as in the DSM-5 [Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; (1)]. However, network approaches to symptom interaction [i.e., symptoms are formative of the construct; e.g., (2), for posttraumatic stress disorder (PTSD)] are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth non-linear dynamical systems theory (NLDST). The article applies the concept of emergent circular causality (3) to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning) and universal (e.g., causal) processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments. PMID:26635639

Accuracy analysis for triangulation and tracking based on time-multiplexed structured light.

PubMed

Wagner, Benjamin; Stüber, Patrick; Wissel, Tobias; Bruder, Ralf; Schweikard, Achim; Ernst, Floris

2014-08-01

The authors' research group is currently developing a new optical head tracking system for intracranial radiosurgery. This tracking system utilizes infrared laser light to measure features of the soft tissue on the patient's forehead. These features are intended to offer highly accurate registration with respect to the rigid skull structure by means of compensating for the soft tissue. In this context, the system also has to be able to quickly generate accurate reconstructions of the skin surface. For this purpose, the authors have developed a laser scanning device which uses time-multiplexed structured light to triangulate surface points. The accuracy of the authors' laser scanning device is analyzed and compared for different triangulation methods. These methods are given by the Linear-Eigen method and a nonlinear least squares method. Since Microsoft's Kinect camera represents an alternative for fast surface reconstruction, the authors' results are also compared to the triangulation accuracy of the Kinect device. Moreover, the authors' laser scanning device was used for tracking of a rigid object to determine how this process is influenced by the remaining triangulation errors. For this experiment, the scanning device was mounted to the end-effector of a robot to be able to calculate a ground truth for the tracking. The analysis of the triangulation accuracy of the authors' laser scanning device revealed a root mean square (RMS) error of 0.16 mm. In comparison, the analysis of the triangulation accuracy of the Kinect device revealed a RMS error of 0.89 mm. It turned out that the remaining triangulation errors only cause small inaccuracies for the tracking of a rigid object. Here, the tracking accuracy was given by a RMS translational error of 0.33 mm and a RMS rotational error of 0.12°. This paper shows that time-multiplexed structured light can be used to generate highly accurate reconstructions of surfaces. Furthermore, the reconstructed point sets can be used for high-accuracy tracking of objects, meeting the strict requirements of intracranial radiosurgery.

Three essays on price dynamics and causations among energy markets and macroeconomic information

NASA Astrophysics Data System (ADS)

Hong, Sung Wook

This dissertation examines three important issues in energy markets: price dynamics, information flow, and structural change. We discuss each issue in detail, building empirical time series models, analyzing the results, and interpreting the findings. First, we examine the contemporaneous interdependencies and information flows among crude oil, natural gas, and electricity prices in the United States (US) through the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model, Directed Acyclic Graph (DAG) for contemporaneous causal structures and Bernanke factorization for price dynamic processes. Test results show that the DAG from residuals of out-of-sample-forecast is consistent with the DAG from residuals of within-sample-fit. The result supports innovation accounting analysis based on DAGs using residuals of out-of-sample-forecast. Second, we look at the effects of the federal fund rate and/or WTI crude oil price shock on US macroeconomic and financial indicators by using a Factor Augmented Vector Autoregression (FAVAR) model and a graphical model without any deductive assumption. The results show that, in contemporaneous time, the federal fund rate shock is exogenous as the identifying assumption in the Vector Autoregression (VAR) framework of the monetary shock transmission mechanism, whereas the WTI crude oil price return is not exogenous. Third, we examine price dynamics and contemporaneous causality among the price returns of WTI crude oil, gasoline, corn, and the S&P 500. We look for structural break points and then build an econometric model to find the consistent sub-periods having stable parameters in a given VAR framework and to explain recent movements and interdependency among returns. We found strong evidence of two structural breaks and contemporaneous causal relationships among the residuals, but also significant differences between contemporaneous causal structures for each sub-period.

Causal Latent Markov Model for the Comparison of Multiple Treatments in Observational Longitudinal Studies

ERIC Educational Resources Information Center

Bartolucci, Francesco; Pennoni, Fulvia; Vittadini, Giorgio

2016-01-01

We extend to the longitudinal setting a latent class approach that was recently introduced by Lanza, Coffman, and Xu to estimate the causal effect of a treatment. The proposed approach enables an evaluation of multiple treatment effects on subpopulations of individuals from a dynamic perspective, as it relies on a latent Markov (LM) model that is…

Impact of environmental inputs on reverse-engineering approach to network structures.

PubMed

Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng

2009-12-04

Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

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

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized inmore » the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.« less

A Detailed Evaluation of a Laser Triangulation Ranging System for Mobile Robots

DTIC Science & Technology

1983-08-01

System Accuracy Factors ..................10 2.1.2 Detector "Cone of Vision" Problem ..................... 10 2. 1.3 Laser Triangulation Justification... product of these advances. Since 1968, when the effort began under a NASA grant, the project has undergone many changes both in the design goals and in...MD Vision System Accuracy Factors The accuracy of the data obtained by triangulation system depends on essentially three independent factors . They

Thickness and topographic inspection of RPG contact lenses by optical triangulation

NASA Astrophysics Data System (ADS)

Costa, Manuel F. M.

2001-06-01

Optical triangulation as a non-destructive test method extensively proved its usefulness on the dimensional and topographic inspection of a large range of objects and surfaces. In this communication the issue of microtopographic and thickness inspection of hard contact lenses (RPG) is addressed. The use of optical triangulation is discussed based on the results of the application of our MICROTOP.03.MFC microtopographer to this kind of tasks will be presented.

STAMP-Based HRA Considering Causality Within a Sociotechnical System: A Case of Minuteman III Missile Accident.

PubMed

Rong, Hao; Tian, Jin

2015-05-01

The study contributes to human reliability analysis (HRA) by proposing a method that focuses more on human error causality within a sociotechnical system, illustrating its rationality and feasibility by using a case of the Minuteman (MM) III missile accident. Due to the complexity and dynamics within a sociotechnical system, previous analyses of accidents involving human and organizational factors clearly demonstrated that the methods using a sequential accident model are inadequate to analyze human error within a sociotechnical system. System-theoretic accident model and processes (STAMP) was used to develop a universal framework of human error causal analysis. To elaborate the causal relationships and demonstrate the dynamics of human error, system dynamics (SD) modeling was conducted based on the framework. A total of 41 contributing factors, categorized into four types of human error, were identified through the STAMP-based analysis. All factors are related to a broad view of sociotechnical systems, and more comprehensive than the causation presented in the accident investigation report issued officially. Recommendations regarding both technical and managerial improvement for a lower risk of the accident are proposed. The interests of an interdisciplinary approach provide complementary support between system safety and human factors. The integrated method based on STAMP and SD model contributes to HRA effectively. The proposed method will be beneficial to HRA, risk assessment, and control of the MM III operating process, as well as other sociotechnical systems. © 2014, Human Factors and Ergonomics Society.

Characterizing time series: when Granger causality triggers complex networks

NASA Astrophysics Data System (ADS)

Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Bond graph modelling of multibody dynamics and its symbolic scheme

NASA Astrophysics Data System (ADS)

Kawase, Takehiko; Yoshimura, Hiroaki

A bond graph method of modeling multibody dynamics is demonstrated. Specifically, a symbolic generation scheme which fully utilizes the bond graph information is presented. It is also demonstrated that structural understanding and representation in bond graph theory is quite powerful for the modeling of such large scale systems, and that the nonenergic multiport of junction structure, which is a multiport expression of the system structure, plays an important role, as first suggested by Paynter. The principal part of the proposed symbolic scheme, that is, the elimination of excess variables, is done through tearing and interconnection in the sense of Kron using newly defined causal and causal coefficient arrays.

Dynamic linkages among the gold market, US dollar and crude oil market

NASA Astrophysics Data System (ADS)

Mo, Bin; Nie, He; Jiang, Yonghong

2018-02-01

This paper aims to examine the dynamic linkages among the gold market, US dollar and crude oil market. The analysis also delves more deeply into the effect of the global financial crisis on the short-term relationship. We use fractional cointegration to analyze the long-term memory feature of these volatility processes to investigate whether they are tied through a common long-term equilibrium. The DCC-MGARCH model is employed to investigate the time-varying long-term linkages among these markets. The Krystou-Labys non-linear asymmetric Granger causality method is used to examine the effect of the financial crisis. We find that (i) there is clearly a long-term dependence among these markets; (ii) the dynamic gold-oil relationship is always positive and the oil-dollar relationship is always negative; and (iii) after the crisis, we can observe evidence of a positive non-linear causal relationship from gold to US dollar and US dollar to crude oil, and a negative non-linear causal relationship from US dollar to gold. Investors who want to construct their optimal portfolios and policymakers who aim to make effective macroeconomic policies should take these findings into account.

On the Use of CAD-Native Predicates and Geometry in Surface Meshing

NASA Technical Reports Server (NTRS)

Aftosmis, M. J.

1999-01-01

Several paradigms for accessing CAD geometry during surface meshing for CFD are discussed. File translation, inconsistent geometry engines and non-native point construction are all identified as sources of non-robustness. The paper argues in favor of accessing CAD parts and assemblies in their native format, without translation, and for the use of CAD-native predicates and constructors in surface mesh generation. The discussion also emphasizes the importance of examining the computational requirements for exact evaluation of triangulation predicates during surface meshing. The native approach is demonstrated through an algorithm for the generation of closed manifold surface triangulations from CAD geometry. CAD parts and assemblies are used in their native format, and a part's native geometry engine is accessed through a modeler-independent application programming interface (API). In seeking a robust and fully automated procedure, the algorithm is based on a new physical space manifold triangulation technique specially developed to avoid robustness issues associated with poorly conditioned mappings. In addition, this approach avoids the usual ambiguities associated with floating-point predicate evaluation on constructed coordinate geometry in a mapped space. The technique is incremental, so that each new site improves the triangulation by some well defined quality measure. The algorithm terminates after achieving a prespecified measure of mesh quality and produces a triangulation such that no angle is less than a given angle bound, a or greater than pi - 2alpha. This result also sets bounds on the maximum vertex degree, triangle aspect-ratio and maximum stretching rate for the triangulation. In addition to the output triangulations for a variety of CAD parts, the discussion presents related theoretical results which assert the existence of such an angle bound, and demonstrate that maximum bounds of between 25 deg and 30 deg may be achieved in practice.

Family processes that shape the impact of interparental conflict on adolescents.

PubMed

Grych, John H; Raynor, Sarah R; Fosco, Gregory M

2004-01-01

This study draws on the family systems concepts of triangulation and wholism to investigate how interparental conflict may affect adolescents' psychological adjustment. An ethnically and socioeconomically diverse sample (N = 388) of 14- to 18-year-olds completed measures of interparental conflict, family relationships, internalizing problems, and externalizing problems. We found that triangulation into parental disagreements mediated the association between parental conflict and both internalizing and externalizing problems. Adolescents exposed to more frequent, intense, and poorly resolved conflict were more likely to feel triangulated, but this association was moderated by the nature of the alliances they had with their parents. Specifically, at low levels of interparental conflict, adolescents who had substantially stronger alliances with one parent than the other reported greater triangulation than those with more balanced alliances. At high levels of conflict, these groups reported similar degrees of triangulation. We also found that supportive parent-child relationships reduced adolescents' appraisals of threat and self-blame for interparental conflict, while more empathic relationships with siblings increased these appraisals. Finally, close relationships with fathers acted as a protective factor that reduced symptoms of maladjustment.

Three-dimensional unstructured grid generation via incremental insertion and local optimization

NASA Technical Reports Server (NTRS)

Barth, Timothy J.; Wiltberger, N. Lyn; Gandhi, Amar S.

1992-01-01

Algorithms for the generation of 3D unstructured surface and volume grids are discussed. These algorithms are based on incremental insertion and local optimization. The present algorithms are very general and permit local grid optimization based on various measures of grid quality. This is very important; unlike the 2D Delaunay triangulation, the 3D Delaunay triangulation appears not to have a lexicographic characterization of angularity. (The Delaunay triangulation is known to minimize that maximum containment sphere, but unfortunately this is not true lexicographically). Consequently, Delaunay triangulations in three-space can result in poorly shaped tetrahedral elements. Using the present algorithms, 3D meshes can be constructed which optimize a certain angle measure, albeit locally. We also discuss the combinatorial aspects of the algorithm as well as implementational details.

Causality Analysis of fMRI Data Based on the Directed Information Theory Framework.

PubMed

Wang, Zhe; Alahmadi, Ahmed; Zhu, David C; Li, Tongtong

2016-05-01

This paper aims to conduct fMRI-based causality analysis in brain connectivity by exploiting the directed information (DI) theory framework. Unlike the well-known Granger causality (GC) analysis, which relies on the linear prediction technique, the DI theory framework does not have any modeling constraints on the sequences to be evaluated and ensures estimation convergence. Moreover, it can be used to generate the GC graphs. In this paper, first, we introduce the core concepts in the DI framework. Second, we present how to conduct causality analysis using DI measures between two time series. We provide the detailed procedure on how to calculate the DI for two finite-time series. The two major steps involved here are optimal bin size selection for data digitization and probability estimation. Finally, we demonstrate the applicability of DI-based causality analysis using both the simulated data and experimental fMRI data, and compare the results with that of the GC analysis. Our analysis indicates that GC analysis is effective in detecting linear or nearly linear causal relationship, but may have difficulty in capturing nonlinear causal relationships. On the other hand, DI-based causality analysis is more effective in capturing both linear and nonlinear causal relationships. Moreover, it is observed that brain connectivity among different regions generally involves dynamic two-way information transmissions between them. Our results show that when bidirectional information flow is present, DI is more effective than GC to quantify the overall causal relationship.

Effect of DEM resolution on rainfall-triggered landslide modeling within a triangulated network-based model. A case study in the Luquillo Forest, Puerto Rico

NASA Astrophysics Data System (ADS)

Arnone, E.; Dialynas, Y. G.; Noto, L. V.; Bras, R. L.

2013-12-01

Catchment slope distribution is one of the topographic characteristics that significantly control rainfall-triggered landslide modeling, in both direct and indirect ways. Slope directly determines the soil volume associated with instability. Indirectly slope also affects the subsurface lateral redistribution of soil moisture across the basin, which in turn determines the water pore pressure conditions that impact slope stability. In this study, we investigate the influence of DEM resolution on slope stability and the slope stability analysis by using a distributed eco-hydrological and landslide model, the tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator - VEGetation Generator for Interactive Evolution). The model implements a triangulated irregular network to describe the topography, and it is capable of evaluating vegetation dynamics and predicting shallow landslides triggered by rainfall. The impact of DEM resolution on the landslide prediction was studied using five TINs derived from five grid DEMs at different resolutions, i.e. 10, 20, 30, 50 and 70 m respectively. The analysis was carried out on the Mameyes Basin, located in the Luquillo Experimental Forest in Puerto Rico, where previous landslide analyses have been carried out. Results showed that the use of the irregular mesh reduced the loss of accuracy in the derived slope distribution when coarser resolutions were used. The impact of the different resolutions on soil moisture patterns was important only when the lateral redistribution was considerable, depending on hydrological properties and rainfall forcing. In some cases, the use of different DEM resolutions did not significantly affect tRIBS-VEGGIE landslide output, in terms of landslide locations, and values of slope and soil moisture at failure.

Systemic risk and causality dynamics of the world international shipping market

NASA Astrophysics Data System (ADS)

Zhang, Xin; Podobnik, Boris; Kenett, Dror Y.; Eugene Stanley, H.

2014-12-01

Various studies have reported that many economic systems have been exhibiting an increase in the correlation between different market sectors, a factor that exacerbates the level of systemic risk. We measure this systemic risk of three major world shipping markets, (i) the new ship market, (ii) the second-hand ship market, and (iii) the freight market, as well as the shipping stock market. Based on correlation networks during three time periods, that prior to the financial crisis, during the crisis, and after the crisis, minimal spanning trees (MSTs) and hierarchical trees (HTs) both exhibit complex dynamics, i.e., different market sectors tend to be more closely linked during financial crisis. Brownian distance correlation and Granger causality test both can be used to explore the directional interconnectedness of market sectors, while Brownian distance correlation captures more dependent relationships, which are not observed in the Granger causality test. These two measures can also identify and quantify market regression periods, implying that they contain predictive power for the current crisis.

Discovering Coherent Structures Using Local Causal States

NASA Astrophysics Data System (ADS)

Rupe, Adam; Crutchfield, James P.; Kashinath, Karthik; Prabhat, Mr.

2017-11-01

Coherent structures were introduced in the study of fluid dynamics and were initially defined as regions characterized by high levels of coherent vorticity, i.e. regions where instantaneously space and phase correlated vorticity are high. In a more general spatiotemporal setting, coherent structures can be seen as localized broken symmetries which persist in time. Building off the computational mechanics framework, which integrates tools from computation and information theory to capture pattern and structure in nonlinear dynamical systems, we introduce a theory of coherent structures, in the more general sense. Central to computational mechanics is the causal equivalence relation, and a local spatiotemporal generalization of it is used to construct the local causal states, which are utilized to uncover a system's spatiotemporal symmetries. Coherent structures are then identified as persistent, localized deviations from these symmetries. We illustrate how novel patterns and structures can be discovered in cellular automata and outline the path from them to laminar, transitional and turbulent flows. Funded by Intel through the Big Data Center at LBNL and the IPCC at UC Davis.

Implementing quantum Ricci curvature

NASA Astrophysics Data System (ADS)

Klitgaard, N.; Loll, R.

2018-05-01

Quantum Ricci curvature has been introduced recently as a new, geometric observable characterizing the curvature properties of metric spaces, without the need for a smooth structure. Besides coordinate invariance, its key features are scalability, computability, and robustness. We demonstrate that these properties continue to hold in the context of nonperturbative quantum gravity, by evaluating the quantum Ricci curvature numerically in two-dimensional Euclidean quantum gravity, defined in terms of dynamical triangulations. Despite the well-known, highly nonclassical properties of the underlying quantum geometry, its Ricci curvature can be matched well to that of a five-dimensional round sphere.

On the Use of CAD-Native Predicates and Geometry in Surface Meshing

NASA Technical Reports Server (NTRS)

Aftosmis, M. J.

1999-01-01

Several paradigms for accessing computer-aided design (CAD) geometry during surface meshing for computational fluid dynamics are discussed. File translation, inconsistent geometry engines, and nonnative point construction are all identified as sources of nonrobustness. The paper argues in favor of accessing CAD parts and assemblies in their native format, without translation, and for the use of CAD-native predicates and constructors in surface mesh generation. The discussion also emphasizes the importance of examining the computational requirements for exact evaluation of triangulation predicates during surface meshing.

Formalizing the Role of Agent-Based Modeling in Causal Inference and Epidemiology

PubMed Central

Marshall, Brandon D. L.; Galea, Sandro

2015-01-01

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry. PMID:25480821

Time, frequency, and time-varying Granger-causality measures in neuroscience.

PubMed

Cekic, Sezen; Grandjean, Didier; Renaud, Olivier

2018-05-20

This article proposes a systematic methodological review and an objective criticism of existing methods enabling the derivation of time, frequency, and time-varying Granger-causality statistics in neuroscience. The capacity to describe the causal links between signals recorded at different brain locations during a neuroscience experiment is indeed of primary interest for neuroscientists, who often have very precise prior hypotheses about the relationships between recorded brain signals. The increasing interest and the huge number of publications related to this topic calls for this systematic review, which describes the very complex methodological aspects underlying the derivation of these statistics. In this article, we first present a general framework that allows us to review and compare Granger-causality statistics in the time domain, and the link with transfer entropy. Then, the spectral and the time-varying extensions are exposed and discussed together with their estimation and distributional properties. Although not the focus of this article, partial and conditional Granger causality, dynamical causal modelling, directed transfer function, directed coherence, partial directed coherence, and their variant are also mentioned. Copyright © 2018 John Wiley & Sons, Ltd.

Causality discovery technology

NASA Astrophysics Data System (ADS)

Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.

2012-11-01

Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.

Comparison of causality analysis on simultaneously measured fMRI and NIRS signals during motor tasks.

PubMed

Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Galka, Andreas; Granert, Oliver; Wolff, Stephan; Deuschl, Guenther; Raethjen, Jan; Heute, Ulrich; Muthuraman, Muthuraman

2013-01-01

Brain activity can be measured using different modalities. Since most of the modalities tend to complement each other, it seems promising to measure them simultaneously. In to be presented research, the data recorded from Functional Magnetic Resonance Imaging (fMRI) and Near Infrared Spectroscopy (NIRS), simultaneously, are subjected to causality analysis using time-resolved partial directed coherence (tPDC). Time-resolved partial directed coherence uses the principle of state space modelling to estimate Multivariate Autoregressive (MVAR) coefficients. This method is useful to visualize both frequency and time dynamics of causality between the time series. Afterwards, causality results from different modalities are compared by estimating the Spearman correlation. In to be presented study, we used directionality vectors to analyze correlation, rather than actual signal vectors. Results show that causality analysis of the fMRI correlates more closely to causality results of oxy-NIRS as compared to deoxy-NIRS in case of a finger sequencing task. However, in case of simple finger tapping, no clear difference between oxy-fMRI and deoxy-fMRI correlation is identified.

Dynamics of charged viscous dissipative cylindrical collapse with full causal approach

NASA Astrophysics Data System (ADS)

Shah, S. M.; Abbas, G.

2017-11-01

The aim of this paper is to investigate the dynamical aspects of a charged viscous cylindrical source by using the Misner approach. To this end, we have considered the more general charged dissipative fluid enclosed by the cylindrical symmetric spacetime. The dissipative nature of the source is due to the presence of dissipative variables in the stress-energy tensor. The dynamical equations resulting from such charged cylindrical dissipative source have been coupled with the causal transport equations for heat flux, shear and bulk viscosity, in the context of the Israel-Steward theory. In this case, we have the considered Israel-Steward transportation equations without excluding the thermodynamics viscous/heat coupling coefficients. The results are compared with the previous works in which such coefficients were excluded and viscosity variables do not satisfy the casual transportation equations.

The dynamic relationship between structural change and CO2 emissions in Malaysia: a cointegrating approach.

PubMed

Ali, Wajahat; Abdullah, Azrai; Azam, Muhammad

2017-05-01

The current study investigates the dynamic relationship between structural changes, real GDP per capita, energy consumption, trade openness, population density, and carbon dioxide (CO 2 ) emissions within the EKC framework over a period 1971-2013. The study used the autoregressive distributed lagged (ARDL) approach to investigate the long-run relationship between the selected variables. The study also employed the dynamic ordinary least squared (DOLS) technique to obtain the robust long-run estimates. Moreover, the causal relationship between the variables is explored using the VECM Granger causality test. Empirical results reveal a negative relationship between structural change and CO 2 emissions in the long run. The results indicate a positive relationship between energy consumption, trade openness, and CO 2 emissions. The study applied the turning point formula of Itkonen (2012) rather than the conventional formula of the turning point. The empirical estimates of the study do not support the presence of the EKC relationship between income and CO 2 emissions. The Granger causality test indicates the presence of long-run bidirectional causality between energy consumption, structural change, and CO 2 emissions in the long run. Economic growth, openness to trade, and population density unidirectionally cause CO 2 emissions. These results suggest that the government should focus more on information-based services rather than energy-intensive manufacturing activities. The feedback relationship between energy consumption and CO 2 emissions suggests that there is an ominous need to refurbish the energy-related policy reforms to ensure the installations of some energy-efficient modern technologies.

Spatiotemporal causal modeling for the management of Dengue Fever

NASA Astrophysics Data System (ADS)

Yu, Hwa-Lung; Huang, Tailin; Lee, Chieh-Han

2015-04-01

Increasing climatic extremes have caused growing concerns about the health effects and disease outbreaks. The association between climate variation and the occurrence of epidemic diseases play an important role on a country's public health systems. Part of the impacts are direct casualties associated with the increasing frequency and intensity of typhoons, the proliferation of disease vectors and the short-term increase of clinic visits on gastro-intestinal discomforts, diarrhea, dermatosis, or psychological trauma. Other impacts come indirectly from the influence of disasters on the ecological and socio-economic systems, including the changes of air/water quality, living environment and employment condition. Previous risk assessment studies on dengue fever focus mostly on climatic and non-climatic factors and their association with vectors' reproducing pattern. The public-health implication may appear simple. Considering the seasonal changes and regional differences, however, the causality of the impacts is full of uncertainties. Without further investigation, the underlying dengue fever risk dynamics may not be assessed accurately. The objective of this study is to develop an epistemic framework for assessing dynamic dengue fever risk across space and time. The proposed framework integrates cross-departmental data, including public-health databases, precipitation data over time and various socio-economic data. We explore public-health issues induced by typhoon through literature review and spatiotemporal analytic techniques on public health databases. From those data, we identify relevant variables and possible causal relationships, and their spatiotemporal patterns derived from our proposed spatiotemporal techniques. Eventually, we create a spatiotemporal causal network and a framework for modeling dynamic dengue fever risk.

Retrocausation in quantum mechanics and the effects of minds on the creation of physical reality

NASA Astrophysics Data System (ADS)

Stapp, Henry P.

2017-05-01

The classical physical theories that prevailed in science from the time of Isaac Newton until the dawn of the twentieth century were empirically based on human experience and made predictions about our mental experiences, yet excluded from the dynamics all mental properties. But how can one rationally get mental things out if no mental elements are put in? The key step in the creation of quantum mechanics during 1925 by Heisenberg and his colleagues was to recognize and emphasize the essential dynamical role of mental properties in the creation of our mental empirical findings. This basic feature of quantum mechanics was cast into rigorous mathematical form by John von Neumann, and was made a central feature of contemporary relativistic quantum field theory by the work of Tomonaga and Schwinger. That theory is causally strictly forward in time. But it is explained here how it can nevertheless accommodate the seeming backward-in-time causal effects reported by D.J. Bem, and many others, by means of a slight biasing of the famous Born Rule. The purpose of this communication is to explain how those reported retrocausal findings can be explained by a strictly forward-in-time and nearly orthodox causal dynamics that, however, permits the Born Rule to be slightly biased under certain conditions. A feasible experiment is proposed that, if it gives the outcomes predicted by the proposed theory, will provide evidence in favor of this causally forward-in-time and nearly orthodox explanation of the reported retrocausal effects.

Bias Reduction and Filter Convergence for Long Range Stereo

NASA Technical Reports Server (NTRS)

Sibley, Gabe; Matthies, Larry; Sukhatme, Gaurav

2005-01-01

We are concerned here with improving long range stereo by filtering image sequences. Traditionally, measurement errors from stereo camera systems have been approximated as 3-D Gaussians, where the mean is derived by triangulation and the covariance by linearized error propagation. However, there are two problems that arise when filtering such 3-D measurements. First, stereo triangulation suffers from a range dependent statistical bias; when filtering this leads to over-estimating the true range. Second, filtering 3-D measurements derived via linearized error propagation leads to apparent filter divergence; the estimator is biased to under-estimate range. To address the first issue, we examine the statistical behavior of stereo triangulation and show how to remove the bias by series expansion. The solution to the second problem is to filter with image coordinates as measurements instead of triangulated 3-D coordinates.

Triangulation and the importance of establishing valid methods for food safety culture evaluation.

PubMed

Jespersen, Lone; Wallace, Carol A

2017-10-01

The research evaluates maturity of food safety culture in five multi-national food companies using method triangulation, specifically self-assessment scale, performance documents, and semi-structured interviews. Weaknesses associated with each individual method are known but there are few studies in food safety where a method triangulation approach is used for both data collection and data analysis. Significantly, this research shows that individual results taken in isolation can lead to wrong conclusions, resulting in potentially failing tactics and wasted investments. However, by applying method triangulation and reviewing results from a range of culture measurement tools it is possible to better direct investments and interventions. The findings add to the food safety culture paradigm beyond a single evaluation of food safety culture using generic culture surveys. Copyright © 2017. Published by Elsevier Ltd.

Understanding and Managing Causality of Change in Socio-Technical Systems 3

DTIC Science & Technology

2012-01-06

influence, and (4) management and control. The questions are listed below. Dynamics and Context  What can be learned from patterns of causal...has proven to be an insufficient method to determine existence of behavioral and performance patterns . Cognitive work analysis, on the other hand...to provide a point of comparison, including Victorian bushfires, Queensland and Victorian floods, and the mine collapse in Chile. Privacy, Threats

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Phase Transitions in Living Neural Networks

NASA Astrophysics Data System (ADS)

Williams-Garcia, Rashid Vladimir

Our nervous systems are composed of intricate webs of interconnected neurons interacting in complex ways. These complex interactions result in a wide range of collective behaviors with implications for features of brain function, e.g., information processing. Under certain conditions, such interactions can drive neural network dynamics towards critical phase transitions, where power-law scaling is conjectured to allow optimal behavior. Recent experimental evidence is consistent with this idea and it seems plausible that healthy neural networks would tend towards optimality. This hypothesis, however, is based on two problematic assumptions, which I describe and for which I present alternatives in this thesis. First, critical transitions may vanish due to the influence of an environment, e.g., a sensory stimulus, and so living neural networks may be incapable of achieving "critical" optimality. I develop a framework known as quasicriticality, in which a relative optimality can be achieved depending on the strength of the environmental influence. Second, the power-law scaling supporting this hypothesis is based on statistical analysis of cascades of activity known as neuronal avalanches, which conflate causal and non-causal activity, thus confounding important dynamical information. In this thesis, I present a new method to unveil causal links, known as causal webs, between neuronal activations, thus allowing for experimental tests of the quasicriticality hypothesis and other practical applications.

The stochastic system approach for estimating dynamic treatments effect.

PubMed

Commenges, Daniel; Gégout-Petit, Anne

2015-10-01

The problem of assessing the effect of a treatment on a marker in observational studies raises the difficulty that attribution of the treatment may depend on the observed marker values. As an example, we focus on the analysis of the effect of a HAART on CD4 counts, where attribution of the treatment may depend on the observed marker values. This problem has been treated using marginal structural models relying on the counterfactual/potential response formalism. Another approach to causality is based on dynamical models, and causal influence has been formalized in the framework of the Doob-Meyer decomposition of stochastic processes. Causal inference however needs assumptions that we detail in this paper and we call this approach to causality the "stochastic system" approach. First we treat this problem in discrete time, then in continuous time. This approach allows incorporating biological knowledge naturally. When working in continuous time, the mechanistic approach involves distinguishing the model for the system and the model for the observations. Indeed, biological systems live in continuous time, and mechanisms can be expressed in the form of a system of differential equations, while observations are taken at discrete times. Inference in mechanistic models is challenging, particularly from a numerical point of view, but these models can yield much richer and reliable results.

Granger causal time-dependent source connectivity in the somatosensory network

NASA Astrophysics Data System (ADS)

Gao, Lin; Sommerlade, Linda; Coffman, Brian; Zhang, Tongsheng; Stephen, Julia M.; Li, Dichen; Wang, Jue; Grebogi, Celso; Schelter, Bjoern

2015-05-01

Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects. A new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling, is employed to estimate fast changing information flow among the sources. Source analysis confirmed that somatosensory evoked MEG was mainly generated from the contralateral primary somatosensory cortex (SI) and bilateral secondary somatosensory cortices (SII). Transient Granger causality shows a serial processing of somatosensory information, 1) from contralateral SI to contralateral SII, 2) from contralateral SI to ipsilateral SII, 3) from contralateral SII to contralateral SI, and 4) from contralateral SII to ipsilateral SII. These results are consistent with established anatomical connectivity between somatosensory regions and previous source modeling results, thereby providing empirical validation of the time-varying connectivity analysis. We argue that the suggested approach provides novel information regarding transient cortical dynamic connectivity, which previous approaches could not assess.

The Effective Connectivity Between the Two Primary Motor Areas in the Brain during Bilateral Tapping of Hand Fingers

NASA Astrophysics Data System (ADS)

Yusoff, A. N.; Hamid, K. A.

Dynamic causal modeling (DCM) was implemented on datasets obtained from an externally-triggered finger tapping functional MRI experiment performed by 5 male and female subjects. The objective was to model the effective connectivity between two significantly activated primary motor regions (M1). The left and right hemisphere M1s are found to be effectively and bidirectionally connected to each other. Both connections are modulated by the stimulus-free contextual input. These connectivities are however not gated (influenced) by any of the two M1s, ruling out the possibility of the non-linear behavior of connections between both M1s. A dynamic causal model was finally suggested.

Movement pattern recognition in basketball free-throw shooting.

PubMed

Schmidt, Andrea

2012-04-01

The purpose of the present study was to analyze the movement patterns of free-throw shooters in basketball at different skill levels. There were two points of interest. First, to explore what information can be drawn from the movement pattern and second, to examine the methodological possibilities of pattern analysis. To this end, several qualitative and quantitative methods were employed. The resulting data were converged in a triangulation. Using a special kind of ANN named Dynamically Controlled Networks (DyCoN), a 'complex feature' consisting of several isolated features (angle displacements and velocities of the articulations of the kinematic chain) was calculated. This 'complex feature' was displayed by a trajectory combining several neurons of the network, reflecting the devolution of the twelve angle measures over the time course of each shooting action. In further network analyses individual characteristics were detected, as well as movement phases. Throwing patterns were successfully classified and the stability and variability of the realized pattern were established. The movement patterns found were clearly individually shaped as well as formed by the skill level. The triangulation confirmed the individual movement organizations. Finally, a high stability of the network methods was documented. Copyright © 2012. Published by Elsevier B.V.

Indoor Trajectory Tracking Scheme Based on Delaunay Triangulation and Heuristic Information in Wireless Sensor Networks.

PubMed

Qin, Junping; Sun, Shiwen; Deng, Qingxu; Liu, Limin; Tian, Yonghong

2017-06-02

Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator ( RSSI ) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object's trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions.

Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

PubMed

Liu, Siwei; Molenaar, Peter

2016-01-01

This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

Path integral measure and triangulation independence in discrete gravity

NASA Astrophysics Data System (ADS)

Dittrich, Bianca; Steinhaus, Sebastian

2012-02-01

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

Dynamical Causal Modeling from a Quantum Dynamical Perspective

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

Demiralp, Emre; Demiralp, Metin

Recent research suggests that any set of first order linear vector ODEs can be converted to a set of specific vector ODEs adhering to what we have called ''Quantum Harmonical Form (QHF)''. QHF has been developed using a virtual quantum multi harmonic oscillator system where mass and force constants are considered to be time variant and the Hamiltonian is defined as a conic structure over positions and momenta to conserve the Hermiticity. As described in previous works, the conversion to QHF requires the matrix coefficient of the first set of ODEs to be a normal matrix. In this paper, thismore » limitation is circumvented using a space extension approach expanding the potential applicability of this method. Overall, conversion to QHF allows the investigation of a set of ODEs using mathematical tools available to the investigation of the physical concepts underlying quantum harmonic oscillators. The utility of QHF in the context of dynamical systems and dynamical causal modeling in behavioral and cognitive neuroscience is briefly discussed.« less

Development of high resolution target monitor.

DOT National Transportation Integrated Search

2008-01-01

The proposed High-resolution Target Movement Monitor uses triangulation theory but in a unique way. Unlike the commercially available triangulation systems which use sensing diodes to perceive reflected laser signatures and are limited to very short ...

Causal network in a deafferented non-human primate brain.

PubMed

Balasubramanian, Karthikeyan; Takahashi, Kazutaka; Hatsopoulos, Nicholas G

2015-01-01

De-afferented/efferented neural ensembles can undergo causal changes when interfaced to neuroprosthetic devices. These changes occur via recruitment or isolation of neurons, alterations in functional connectivity within the ensemble and/or changes in the role of neurons, i.e., excitatory/inhibitory. In this work, emergence of a causal network and changes in the dynamics are demonstrated for a deafferented brain region exposed to BMI (brain-machine interface) learning. The BMI was controlling a robot for reach-and-grasp behavior. And, the motor cortical regions used for the BMI were deafferented due to chronic amputation, and ensembles of neurons were decoded for velocity control of the multi-DOF robot. A generalized linear model-framework based Granger causality (GLM-GC) technique was used in estimating the ensemble connectivity. Model selection was based on the AIC (Akaike Information Criterion).

Exploratory Causal Analysis in Bivariate Time Series Data

NASA Astrophysics Data System (ADS)

McCracken, James M.

Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data sets, but little research exists of how these tools compare to each other in practice. This work introduces and defines exploratory causal analysis (ECA) to address this issue along with the concept of data causality in the taxonomy of causal studies introduced in this work. The motivation is to provide a framework for exploring potential causal structures in time series data sets. ECA is used on several synthetic and empirical data sets, and it is found that all of the tested time series causality tools agree with each other (and intuitive notions of causality) for many simple systems but can provide conflicting causal inferences for more complicated systems. It is proposed that such disagreements between different time series causality tools during ECA might provide deeper insight into the data than could be found otherwise.

Common species link global ecosystems to climate change: dynamical evidence in the planktonic fossil record.

PubMed

Hannisdal, Bjarte; Haaga, Kristian Agasøster; Reitan, Trond; Diego, David; Liow, Lee Hsiang

2017-07-12

Common species shape the world around us, and changes in their commonness signify large-scale shifts in ecosystem structure and function. However, our understanding of long-term ecosystem response to environmental forcing in the deep past is centred on species richness, neglecting the disproportional impact of common species. Here, we use common and widespread species of planktonic foraminifera in deep-sea sediments to track changes in observed global occupancy (proportion of sampled sites at which a species is present and observed) through the turbulent climatic history of the last 65 Myr. Our approach is sensitive to relative changes in global abundance of the species set and robust to factors that bias richness estimators. Using three independent methods for detecting causality, we show that the observed global occupancy of planktonic foraminifera has been dynamically coupled to past oceanographic changes captured in deep-ocean temperature reconstructions. The causal inference does not imply a direct mechanism, but is consistent with an indirect, time-delayed causal linkage. Given the strong quantitative evidence that a dynamical coupling exists, we hypothesize that mixotrophy (symbiont hosting) may be an ecological factor linking the global abundance of planktonic foraminifera to long-term climate changes via the relative extent of oligotrophic oceans. © 2017 The Authors.

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.

PubMed

Zou, Cunlu; Ladroue, Christophe; Guo, Shuixia; Feng, Jianfeng

2010-06-21

Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

Seeing in three dimensions: correlation and triangulation of Mars Exploration Rover imagery

NASA Technical Reports Server (NTRS)

Deen, Robert; Lorre, Jean

2005-01-01

This paper describes in detail the middle parts of the ground-based terrain derivation process: correlation, which finds matching points in the stereo pair, and triangulation, which converts those points to XYZ coordinates.

Triangulation Error Analysis for the Barium Ion Cloud Experiment. M.S. Thesis - North Carolina State Univ.

NASA Technical Reports Server (NTRS)

Long, S. A. T.

1973-01-01

The triangulation method developed specifically for the Barium Ion Cloud Project is discussed. Expression for the four displacement errors, the three slope errors, and the curvature error in the triangulation solution due to a probable error in the lines-of-sight from the observation stations to points on the cloud are derived. The triangulation method is then used to determine the effect of the following on these different errors in the solution: the number and location of the stations, the observation duration, east-west cloud drift, the number of input data points, and the addition of extra cameras to one of the stations. The pointing displacement errors, and the pointing slope errors are compared. The displacement errors in the solution due to a probable error in the position of a moving station plus the weighting factors for the data from the moving station are also determined.

The Family System and Depressive Symptoms during the College Years: Triangulation, Parental Differential Treatment, and Sibling Warmth as Predictors.

PubMed

Ponappa, Sujata; Bartle-Haring, Suzanne; Holowacz, Eugene; Ferriby, Megan

2017-01-01

Guided by Bowen theory, we investigated the relationships between parent-child triangulation, parental differential treatment (PDT), sibling warmth, and individual depressive symptoms in a sample of 77 sibling dyads, aged 18-25 years, recruited through undergraduate classes at a U.S. public University. Results of the actor-partner interdependence models suggested that being triangulated into parental conflict was positively related to both siblings' perception of PDT; however, as one sibling felt triangulated, the other perceived reduced levels of PDT. For both siblings, the perception of higher levels of PDT was related to decreased sibling warmth and higher sibling warmth was associated with fewer depressive symptoms. The implications of these findings for research and the treatment of depression in the college-aged population are discussed. © 2016 American Association for Marriage and Family Therapy.

Numerical Schemes for the Hamilton-Jacobi and Level Set Equations on Triangulated Domains

NASA Technical Reports Server (NTRS)

Barth, Timothy J.; Sethian, James A.

1997-01-01

Borrowing from techniques developed for conservation law equations, numerical schemes which discretize the Hamilton-Jacobi (H-J), level set, and Eikonal equations on triangulated domains are presented. The first scheme is a provably monotone discretization for certain forms of the H-J equations. Unfortunately, the basic scheme lacks proper Lipschitz continuity of the numerical Hamiltonian. By employing a virtual edge flipping technique, Lipschitz continuity of the numerical flux is restored on acute triangulations. Next, schemes are introduced and developed based on the weaker concept of positive coefficient approximations for homogeneous Hamiltonians. These schemes possess a discrete maximum principle on arbitrary triangulations and naturally exhibit proper Lipschitz continuity of the numerical Hamiltonian. Finally, a class of Petrov-Galerkin approximations are considered. These schemes are stabilized via a least-squares bilinear form. The Petrov-Galerkin schemes do not possess a discrete maximum principle but generalize to high order accuracy.

Fast algorithms of constrained Delaunay triangulation and skeletonization for band images

NASA Astrophysics Data System (ADS)

Zeng, Wei; Yang, ChengLei; Meng, XiangXu; Yang, YiJun; Yang, XiuKun

2004-09-01

For the boundary polygons of band-images, a fast constrained Delaunay triangulation algorithm is presented and based on it an efficient skeletonization algorithm is designed. In the process of triangulation the characters of uniform grid structure and the band-polygons are utilized to improve the speed of computing the third vertex for one edge within its local ranges when forming a Delaunay triangle. The final skeleton of the band-image is derived after reducing each triangle to local skeleton lines according to its topology. The algorithm with a simple data structure is easy to understand and implement. Moreover, it can deal with multiply connected polygons on the fly. Experiments show that there is a nearly linear dependence between triangulation time and size of band-polygons randomly generated. Correspondingly, the skeletonization algorithm is also an improvement over the previously known results in terms of time. Some practical examples are given in the paper.

Improving School Effectiveness: The Dynamics of Implementation.

ERIC Educational Resources Information Center

Gaynor, Alan K.; Clauset, Karl H., Jr.

Six causal-influence diagrams introduce a model showing the difficulties of implementing school improvement policies in such areas as teacher expectations and student behavior. The first diagram deals with basic dynamics of improvement. Cybernetics of activities to correct student achievement are revealed in a second diagram, which portrays…

Extending Bell's beables to encompass dissipation, decoherence, and the quantum-to-classical transition through quantum trajectories

NASA Astrophysics Data System (ADS)

Lorenzen, F.; de Ponte, M. A.; Moussa, M. H. Y.

2009-09-01

In this paper, employing the Itô stochastic Schrödinger equation, we extend Bell’s beable interpretation of quantum mechanics to encompass dissipation, decoherence, and the quantum-to-classical transition through quantum trajectories. For a particular choice of the source of stochasticity, the one leading to a dissipative Lindblad-type correction to the Hamiltonian dynamics, we find that the diffusive terms in Nelsons stochastic trajectories are naturally incorporated into Bohm’s causal dynamics, yielding a unified Bohm-Nelson theory. In particular, by analyzing the interference between quantum trajectories, we clearly identify the decoherence time, as estimated from the quantum formalism. We also observe the quantum-to-classical transition in the convergence of the infinite ensemble of quantum trajectories to their classical counterparts. Finally, we show that our extended beables circumvent the problems in Bohm’s causal dynamics regarding stationary states in quantum mechanics.

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network.

PubMed

Bui, Ngot; Yen, John; Honavar, Vasant

2016-06-01

Online health communities constitute a useful source of information and social support for patients. American Cancer Society's Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multi-party conversations that often provide a source of social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of an online health community derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a Probabilistic Computation Tree Logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the (i) classification threshold of the sentiment classifier; (ii) and the choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs.

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network

PubMed Central

Bui, Ngot; Yen, John; Honavar, Vasant

2017-01-01

Online health communities constitute a useful source of information and social support for patients. American Cancer Society’s Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multi-party conversations that often provide a source of social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of an online health community derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a Probabilistic Computation Tree Logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the (i) classification threshold of the sentiment classifier; (ii) and the choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs. PMID:29399599

Analyzing brain networks with PCA and conditional Granger causality.

PubMed

Zhou, Zhenyu; Chen, Yonghong; Ding, Mingzhou; Wright, Paul; Lu, Zuhong; Liu, Yijun

2009-07-01

Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Copyright 2009 Wiley-Liss, Inc

The Marriage of Science and Spirit: Dynamic Systems Theory and the Development of Spirituality

ERIC Educational Resources Information Center

Cupit, C. Glenn

2007-01-01

The adherence of traditional developmental theories to a linear paradigm is incompatible with the nature of "spirit". Dynamic Systems Theory (DST), a recent contributor to understanding child development, offers an alternative which avoids these paradigmatic limitations. Concepts of agency, "top-down" causality, emergence and…

Causal Entropies – a measure for determining changes in the temporal organization of neural systems

PubMed Central

Waddell, Jack; Dzakpasu, Rhonda; Booth, Victoria; Riley, Brett; Reasor, Jonathan; Poe, Gina; Zochowski, Michal

2009-01-01

We propose a novel measure to detect temporal ordering in the activity of individual neurons in a local network, which is thought to be a hallmark of activity-dependent synaptic modifications during learning. The measure, called Causal Entropy, is based on the time-adaptive detection of asymmetries in the relative temporal patterning between neuronal pairs. We characterize properties of the measure on both simulated data and experimental multiunit recordings of hippocampal neurons from the awake, behaving rat, and show that the metric can more readily detect those asymmetries than standard cross correlation-based techniques, especially since the temporal sensitivity of causal entropy can detect such changes rapidly and dynamically. PMID:17275095

Does finance affect environmental degradation: evidence from One Belt and One Road Initiative region?

PubMed

Hafeez, Muhammad; Chunhui, Yuan; Strohmaier, David; Ahmed, Manzoor; Jie, Liu

2018-04-01

This paper explores the effects of finance on environmental degradation and investigates environmental Kuznets curve (EKC) of each country among 52 that participate in the One Belt and One Road Initiative (OBORI) using the latest long panel data span (1980-2016). We utilized panel long run econometric models (fully modified ordinary least square and dynamic ordinary least square) to explore the long-run estimates in full panel and country level. Moreover, the Dumitrescu and Hurlin (2012) causality test is applied to examine the short-run causalities among our considered variables. The empirical findings validate the EKC hypothesis; the long-run estimates point out that finance significantly enhances the environmental degradation (negatively in few cases). The short-run heterogeneous causality confirms the bi-directional causality between finance and environmental degradation. The empirical outcomes suggest that policymakers should consider the environmental degradation issue caused by financial development in the One Belt and One Road region.

Bulk viscous cosmology with causal transport theory

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

Piattella, Oliver F.; Fabris, Júlio C.; Zimdahl, Winfried, E-mail: oliver.piattella@gmail.com, E-mail: fabris@pq.cnpq.br, E-mail: winfried.zimdahl@pq.cnpq.br

2011-05-01

We consider cosmological scenarios originating from a single imperfect fluid with bulk viscosity and apply Eckart's and both the full and the truncated Müller-Israel-Stewart's theories as descriptions of the non-equilibrium processes. Our principal objective is to investigate if the dynamical properties of Dark Matter and Dark Energy can be described by a single viscous fluid and how such description changes when a causal theory (Müller-Israel-Stewart's, both in its full and truncated forms) is taken into account instead of Eckart's non-causal one. To this purpose, we find numerical solutions for the gravitational potential and compare its behaviour with the corresponding ΛCDMmore » case. Eckart's and the full causal theory seem to be disfavoured, whereas the truncated theory leads to results similar to those of the ΛCDM model for a bulk viscous speed in the interval 10{sup −11} || cb{sup 2} ∼< 10{sup −8}.« less

The relationship between pollutant emissions, renewable energy, nuclear energy and GDP: empirical evidence from 18 developed and developing countries

NASA Astrophysics Data System (ADS)

Ben Mbarek, Mounir; Saidi, Kais; Amamri, Mounira

2018-07-01

This document investigates the causal relationship between nuclear energy (NE), pollutant emissions (CO2 emissions), gross domestic product (GDP) and renewable energy (RE) using dynamic panel data models for a global panel consisting of 18 countries (developed and developing) covering the 1990-2013 period. Our results indicate that there is a co-integration between variables. The unit root test suggests that all the variables are stationary in first differences. The paper further examines the link using the Granger causality analysis of vector error correction model, which indicates a unidirectional relationship running from GDP per capita to pollutant emissions for the developed and developing countries. However, there is a unidirectional causality from GDP per capita to RE in the short and long run. This finding confirms the conservation hypothesis. Similarly, there is no causality between NE and GDP per capita.

Occultation and Triangulation Camera (OcTriCam) Cubesat

NASA Astrophysics Data System (ADS)

Batchelor, D. A.

2018-02-01

A camera at Earth-Moon L2 would provide a 240,000 km triangulation baseline to augment near-Earth object observations with Earth-based telescopes such as Pan-STARRS, and planetary occultation research to refine ephemerides and probe ring systems.

A tour about existence and uniqueness of dg enhancements and lifts

NASA Astrophysics Data System (ADS)

Canonaco, Alberto; Stellari, Paolo

2017-12-01

This paper surveys the recent advances concerning the relations between triangulated (or derived) categories and their dg enhancements. We explain when some interesting triangulated categories arising in algebraic geometry have a unique dg enhancement. This is the case, for example, for the unbounded derived category of quasi-coherent sheaves on an algebraic stack or for its full triangulated subcategory of perfect complexes. Moreover we give an account of the recent results about the possibility to lift exact functors between the bounded derived categories of coherent sheaves on smooth schemes to dg (quasi-)functors.

Numerical Schemes for the Hamilton-Jacobi and Level Set Equations on Triangulated Domains

NASA Technical Reports Server (NTRS)

Barth, Timothy J.; Sethian, James A.

2006-01-01

Borrowing from techniques developed for conservation law equations, we have developed both monotone and higher order accurate numerical schemes which discretize the Hamilton-Jacobi and level set equations on triangulated domains. The use of unstructured meshes containing triangles (2D) and tetrahedra (3D) easily accommodates mesh adaptation to resolve disparate level set feature scales with a minimal number of solution unknowns. The minisymposium talk will discuss these algorithmic developments and present sample calculations using our adaptive triangulation algorithm applied to various moving interface problems such as etching, deposition, and curvature flow.

Assessment of four midcarpal radiologic determinations.

PubMed

Cho, Mickey S; Battista, Vincent; Dubin, Norman H; Pirela-Cruz, Miguel

2006-03-01

Several radiologic measurement methods have been described for determining static carpal alignment of the wrist. These include the scapholunate, radiolunate, and capitolunate angles. The triangulation method is an alternative radiologic measurement which we believe is easier to use and more reproducible and reliable than the above mentioned methods. The purpose of this study is to assess the intraobserver reproducibility and interobserver reliability of the triangulation method, scapholunate, radiolunate, and capitolunate angles. Twenty orthopaedic residents and staff at varying levels of training made four radiologic measurements including the scapholunate, radiolunate and capitolunate angles as well as the triangulation method on five different lateral, digitized radiographs of the wrist and forearm in neutral radioulnar deviation. Thirty days after the initial measurements, the participants repeated the four radiologic measurements using the same radiographs. The triangulation method had the best intra-and-interobserver agreement of the four methods tested. This agreement was significantly better than the capitolunate and radiolunate angles. The scapholunate angle had the next best intraobserver reproducibility and interobserver reliability. The triangulation method has the best overall observer agreement when compared to the scapholunate, radiolunate, and capitolunate angles in determining static midcarpal alignment. No comment can be made on the validity of the measurements since there is no radiographic gold standard in determining static carpal alignment.

Optical monitoring of scoliosis by 3D medical laser scanner

NASA Astrophysics Data System (ADS)

Rodríguez-Quiñonez, Julio C.; Sergiyenko, Oleg Yu.; Preciado, Luis C. Basaca; Tyrsa, Vera V.; Gurko, Alexander G.; Podrygalo, Mikhail A.; Lopez, Moises Rivas; Balbuena, Daniel Hernandez

2014-03-01

Three dimensional recording of the human body surface or anatomical areas have gained importance in many medical applications. In this paper, our 3D Medical Laser Scanner is presented. It is based on the novel principle of dynamic triangulation. We analyze the method of operation, medical applications, orthopedically diseases as Scoliosis and the most common types of skin to employ the system the most proper way. It is analyzed a group of medical problems related to the application of optical scanning in optimal way. Finally, experiments are conducted to verify the performance of the proposed system and its method uncertainty.

Computer-assisted instruction and diagnosis of radiographic findings.

PubMed

Harper, D; Butler, C; Hodder, R; Allman, R; Woods, J; Riordan, D

1984-04-01

Recent advances in computer technology, including high bit-density storage, digital imaging, and the ability to interface microprocessors with videodisk, create enormous opportunities in the field of medical education. This program, utilizing a personal computer, videodisk, BASIC language, a linked textfile system, and a triangulation approach to the interpretation of radiographs developed by Dr. W. L. Thompson, can enable the user to engage in a user-friendly, dynamic teaching program in radiology, applicable to various levels of expertise. Advantages include a relatively more compact and inexpensive system with rapid access and ease of revision which requires little instruction to the user.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

PubMed

Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan

2017-05-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

Quantitative EEG analysis using error reduction ratio-causality test; validation on simulated and real EEG data.

PubMed

Sarrigiannis, Ptolemaios G; Zhao, Yifan; Wei, Hua-Liang; Billings, Stephen A; Fotheringham, Jayne; Hadjivassiliou, Marios

2014-01-01

To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures. A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data. Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures. We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags. This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*

PubMed Central

Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan

2017-01-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111

Causal discovery in the geosciences-Using synthetic data to learn how to interpret results

NASA Astrophysics Data System (ADS)

Ebert-Uphoff, Imme; Deng, Yi

2017-02-01

Causal discovery algorithms based on probabilistic graphical models have recently emerged in geoscience applications for the identification and visualization of dynamical processes. The key idea is to learn the structure of a graphical model from observed spatio-temporal data, thus finding pathways of interactions in the observed physical system. Studying those pathways allows geoscientists to learn subtle details about the underlying dynamical mechanisms governing our planet. Initial studies using this approach on real-world atmospheric data have shown great potential for scientific discovery. However, in these initial studies no ground truth was available, so that the resulting graphs have been evaluated only by whether a domain expert thinks they seemed physically plausible. The lack of ground truth is a typical problem when using causal discovery in the geosciences. Furthermore, while most of the connections found by this method match domain knowledge, we encountered one type of connection for which no explanation was found. To address both of these issues we developed a simulation framework that generates synthetic data of typical atmospheric processes (advection and diffusion). Applying the causal discovery algorithm to the synthetic data allowed us (1) to develop a better understanding of how these physical processes appear in the resulting connectivity graphs, and thus how to better interpret such connectivity graphs when obtained from real-world data; (2) to solve the mystery of the previously unexplained connections.

Coseismic slip distribution of the 1923 Kanto earthquake, Japan

USGS Publications Warehouse

Pollitz, F.F.; Nyst, M.; Nishimura, T.; Thatcher, W.

2005-01-01

The slip distribution associated with the 1923 M = 7.9 Kanto, Japan, earthquake is reexamined in light of new data and modeling. We utilize a combination of first-order triangulation, second-order triangulation, and leveling data in order to constrain the coseismic deformation. The second-order triangulation data, which have not been utilized in previous studies of 1923 coseismic deformation, are associated with only slightly smaller errors than the first-order triangulation data and expand the available triangulation data set by about a factor of 10. Interpretation of these data in terms of uniform-slip models in a companion study by Nyst et al. shows that a model involving uniform coseismic slip on two distinct rupture planes explains the data very well and matches or exceeds the fit obtained by previous studies, even one which involved distributed slip. Using the geometry of the Nyst et al. two-plane slip model, we perform inversions of the same geodetic data set for distributed slip. Our preferred model of distributed slip on the Philippine Sea plate interface has a moment magnitude of 7.86. We find slip maxima of ???8-9 m beneath Odawara and ???7-8 m beneath the Miura peninsula, with a roughly 2:1 ratio of strike-slip to dip-slip motion, in agreement with a previous study. However, the Miura slip maximum is imaged as a more broadly extended feature in our study, with the high-slip region continuing from the Miura peninsula to the southern Boso peninsula region. The second-order triangulation data provide good evidence for ???3 m right-lateral strike slip on a 35-km-long splay structure occupying the volume between the upper surface of the descending Philippine Sea plate and the southern Boso peninsula. Copyright 2005 by the American Geophysical Union.

Quantum Common Causes and Quantum Causal Models

NASA Astrophysics Data System (ADS)

Allen, John-Mark A.; Barrett, Jonathan; Horsman, Dominic C.; Lee, Ciarán M.; Spekkens, Robert W.

2017-07-01

Reichenbach's principle asserts that if two observed variables are found to be correlated, then there should be a causal explanation of these correlations. Furthermore, if the explanation is in terms of a common cause, then the conditional probability distribution over the variables given the complete common cause should factorize. The principle is generalized by the formalism of causal models, in which the causal relationships among variables constrain the form of their joint probability distribution. In the quantum case, however, the observed correlations in Bell experiments cannot be explained in the manner Reichenbach's principle would seem to demand. Motivated by this, we introduce a quantum counterpart to the principle. We demonstrate that under the assumption that quantum dynamics is fundamentally unitary, if a quantum channel with input A and outputs B and C is compatible with A being a complete common cause of B and C , then it must factorize in a particular way. Finally, we show how to generalize our quantum version of Reichenbach's principle to a formalism for quantum causal models and provide examples of how the formalism works.

Navigating the Neural Space in Search of the Neural Code.

PubMed

Jazayeri, Mehrdad; Afraz, Arash

2017-03-08

The advent of powerful perturbation tools, such as optogenetics, has created new frontiers for probing causal dependencies in neural and behavioral states. These approaches have significantly enhanced the ability to characterize the contribution of different cells and circuits to neural function in health and disease. They have shifted the emphasis of research toward causal interrogations and increased the demand for more precise and powerful tools to control and manipulate neural activity. Here, we clarify the conditions under which measurements and perturbations support causal inferences. We note that the brain functions at multiple scales and that causal dependencies may be best inferred with perturbation tools that interface with the system at the appropriate scale. Finally, we develop a geometric framework to facilitate the interpretation of causal experiments when brain perturbations do or do not respect the intrinsic patterns of brain activity. We describe the challenges and opportunities of applying perturbations in the presence of dynamics, and we close with a general perspective on navigating the activity space of neurons in the search for neural codes. Copyright © 2017 Elsevier Inc. All rights reserved.

A Community Based Systems Diagram of Obesity Causes.

PubMed

Allender, Steven; Owen, Brynle; Kuhlberg, Jill; Lowe, Janette; Nagorcka-Smith, Phoebe; Whelan, Jill; Bell, Colin

2015-01-01

Application of system thinking to the development, implementation and evaluation of childhood obesity prevention efforts represents the cutting edge of community-based prevention. We report on an approach to developing a system oriented community perspective on the causes of obesity. Group model building sessions were conducted in a rural Australian community to address increasing childhood obesity. Stakeholders (n = 12) built a community model that progressed from connection circles to causal loop diagrams using scripts from the system dynamics literature. Participants began this work in identifying change over time in causes and effects of childhood obesity within their community. The initial causal loop diagram was then reviewed and elaborated by 50 community leaders over a full day session. The process created a causal loop diagram representing community perceptions of determinants and causes of obesity. The causal loop diagram can be broken down into four separate domains; social influences; fast food and junk food; participation in sport; and general physical activity. This causal loop diagram can provide the basis for community led planning of a prevention response that engages with multiple levels of existing settings and systems.

Resolving the Quantitative-Qualitative Dilemma: A Critical Realist Approach

ERIC Educational Resources Information Center

Scott, David

2007-01-01

The philosophical issues underpinning the quantitative-qualitative divide in educational research are examined. Three types of argument which support a resolution are considered: pragmatism, false duality and warranty through triangulation. In addition a number of proposed strategies--alignment, sequencing, translation and triangulation--are…

Adjustment technique without explicit formation of normal equations /conjugate gradient method/

NASA Technical Reports Server (NTRS)

Saxena, N. K.

1974-01-01

For a simultaneous adjustment of a large geodetic triangulation system, a semiiterative technique is modified and used successfully. In this semiiterative technique, known as the conjugate gradient (CG) method, original observation equations are used, and thus the explicit formation of normal equations is avoided, 'huge' computer storage space being saved in the case of triangulation systems. This method is suitable even for very poorly conditioned systems where solution is obtained only after more iterations. A detailed study of the CG method for its application to large geodetic triangulation systems was done that also considered constraint equations with observation equations. It was programmed and tested on systems as small as two unknowns and three equations up to those as large as 804 unknowns and 1397 equations. When real data (573 unknowns, 965 equations) from a 1858-km-long triangulation system were used, a solution vector accurate to four decimal places was obtained in 2.96 min after 1171 iterations (i.e., 2.0 times the number of unknowns).

Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy.

PubMed

Papadopoulou, Margarita; Leite, Marco; van Mierlo, Pieter; Vonck, Kristl; Lemieux, Louis; Friston, Karl; Marinazzo, Daniele

2015-02-15

In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space - identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory-inhibitory balance. Copyright © 2014. Published by Elsevier Inc.

Taking Emergence Seriously: The Centrality of Circular Causality for Dynamic Systems Approaches to Development

ERIC Educational Resources Information Center

Witherington, David C.

2011-01-01

The dynamic systems (DS) approach has emerged as an influential and potentially unifying metatheory for developmental science. Its central platform--the argument against design--suggests that structure spontaneously and without prescription emerges through self-organization. In one of the most prominent accounts of DS, Thelen and her colleagues…

Toward a Comprehensive Model of Antisocial Development: A Dynamic Systems Approach

ERIC Educational Resources Information Center

Granic, Isabela; Patterson, Gerald R.

2006-01-01

The purpose of this article is to develop a preliminary comprehensive model of antisocial development based on dynamic systems principles. The model is built on the foundations of behavioral research on coercion theory. First, the authors focus on the principles of multistability, feedback, and nonlinear causality to reconceptualize real-time…

Experimental Investigation of Ultrafast Hydration Structure and Dynamics at Sub-Angstrom Lengthscales

ERIC Educational Resources Information Center

Coridan, Robert Henry

2009-01-01

This thesis outlines how meV-resolution inelastic x-ray scattering and causality-enforcing mathematics can be used to measure the dynamical density-density linear response function for liquid water with Angstrom spatial resolution and 50fs temporal resolution. The results are compared to high-resolution spectroscopic and scattering experiments and…

Sustainable Deforestation Evaluation Model and System Dynamics Analysis

PubMed Central

Feng, Huirong; Lim, C. W.; Chen, Liqun; Zhou, Xinnian; Zhou, Chengjun; Lin, Yi

2014-01-01

The current study used the improved fuzzy analytic hierarchy process to construct a sustainable deforestation development evaluation system and evaluation model, which has refined a diversified system to evaluate the theory of sustainable deforestation development. Leveraging the visual image of the system dynamics causal and power flow diagram, we illustrated here that sustainable forestry development is a complex system that encompasses the interaction and dynamic development of ecology, economy, and society and has reflected the time dynamic effect of sustainable forestry development from the three combined effects. We compared experimental programs to prove the direct and indirect impacts of the ecological, economic, and social effects of the corresponding deforest techniques and fully reflected the importance of developing scientific and rational ecological harvesting and transportation technologies. Experimental and theoretical results illustrated that light cableway skidding is an ecoskidding method that is beneficial for the sustainable development of resources, the environment, the economy, and society and forecasted the broad potential applications of light cableway skidding in timber production technology. Furthermore, we discussed the sustainable development countermeasures of forest ecosystems from the aspects of causality, interaction, and harmony. PMID:25254225

Sustainable deforestation evaluation model and system dynamics analysis.

PubMed

Feng, Huirong; Lim, C W; Chen, Liqun; Zhou, Xinnian; Zhou, Chengjun; Lin, Yi

2014-01-01

The current study used the improved fuzzy analytic hierarchy process to construct a sustainable deforestation development evaluation system and evaluation model, which has refined a diversified system to evaluate the theory of sustainable deforestation development. Leveraging the visual image of the system dynamics causal and power flow diagram, we illustrated here that sustainable forestry development is a complex system that encompasses the interaction and dynamic development of ecology, economy, and society and has reflected the time dynamic effect of sustainable forestry development from the three combined effects. We compared experimental programs to prove the direct and indirect impacts of the ecological, economic, and social effects of the corresponding deforest techniques and fully reflected the importance of developing scientific and rational ecological harvesting and transportation technologies. Experimental and theoretical results illustrated that light cableway skidding is an ecoskidding method that is beneficial for the sustainable development of resources, the environment, the economy, and society and forecasted the broad potential applications of light cableway skidding in timber production technology. Furthermore, we discussed the sustainable development countermeasures of forest ecosystems from the aspects of causality, interaction, and harmony.

B-spline Method in Fluid Dynamics

NASA Technical Reports Server (NTRS)

Botella, Olivier; Shariff, Karim; Mansour, Nagi N. (Technical Monitor)

2001-01-01

B-spline functions are bases for piecewise polynomials that possess attractive properties for complex flow simulations : they have compact support, provide a straightforward handling of boundary conditions and grid nonuniformities, and yield numerical schemes with high resolving power, where the order of accuracy is a mere input parameter. This paper reviews the progress made on the development and application of B-spline numerical methods to computational fluid dynamics problems. Basic B-spline approximation properties is investigated, and their relationship with conventional numerical methods is reviewed. Some fundamental developments towards efficient complex geometry spline methods are covered, such as local interpolation methods, fast solution algorithms on cartesian grid, non-conformal block-structured discretization, formulation of spline bases of higher continuity over triangulation, and treatment of pressure oscillations in Navier-Stokes equations. Application of some of these techniques to the computation of viscous incompressible flows is presented.

STS-74/MIR Photogrammetric Appendage Structural Dynamics Experiment Preliminary Data Analysis

NASA Technical Reports Server (NTRS)

Gilbert, Michael G.; Welch, Sharon S.; Pappa, Richard S.; Demeo, Martha E.

1997-01-01

The Photogrammetric Appendage Structural Dynamics Experiment was designed, developed, and flown to demonstrate and prove measurement of the structural vibration response of a Russian Space Station Mir solar array using photogrammetric methods. The experiment flew on the STS-74 Space Shuttle mission to Mir in November 1995 and obtained video imagery of solar array structural response to various excitation events. The video imagery has been digitized and triangulated to obtain response time history data at discrete points on the solar array. This data has been further processed using the Eigensystem Realization Algorithm modal identification technique to determine the natural vibration frequencies, damping, and mode shapes of the solar array. The results demonstrate that photogrammetric measurement of articulating, nonoptically targeted, flexible solar arrays and appendages is a viable, low-cost measurement option for the International Space Station.

The 3-D unstructured mesh generation using local transformations

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

1993-01-01

The topics are presented in viewgraph form and include the following: 3D combinatorial edge swapping; 3D incremental triangulation via local transformations; a new approach to multigrid for unstructured meshes; surface mesh generation using local transforms; volume triangulations; viscous mesh generation; and future directions.

Sex differences in the inference and perception of causal relations within a video game

PubMed Central

Young, Michael E.

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations. PMID:25202293

Sex differences in the inference and perception of causal relations within a video game.

PubMed

Young, Michael E

2014-01-01

The learning of immediate causation within a dynamic environment was examined. Participants encountered seven decision points in which they needed to choose, which of three possible candidates was the cause of explosions in the environment. Each candidate was firing a weapon at random every few seconds, but only one of them produced an immediate effect. Some participants showed little learning, but most demonstrated increases in accuracy across time. On average, men showed higher accuracy and shorter latencies that were not explained by differences in self-reported prior video game experience. This result suggests that prior reports of sex differences in causal choice in the game are not specific to situations involving delayed or probabilistic causal relations.

Formalizing the role of agent-based modeling in causal inference and epidemiology.

PubMed

Marshall, Brandon D L; Galea, Sandro

2015-01-15

Calls for the adoption of complex systems approaches, including agent-based modeling, in the field of epidemiology have largely centered on the potential for such methods to examine complex disease etiologies, which are characterized by feedback behavior, interference, threshold dynamics, and multiple interacting causal effects. However, considerable theoretical and practical issues impede the capacity of agent-based methods to examine and evaluate causal effects and thus illuminate new areas for intervention. We build on this work by describing how agent-based models can be used to simulate counterfactual outcomes in the presence of complexity. We show that these models are of particular utility when the hypothesized causal mechanisms exhibit a high degree of interdependence between multiple causal effects and when interference (i.e., one person's exposure affects the outcome of others) is present and of intrinsic scientific interest. Although not without challenges, agent-based modeling (and complex systems methods broadly) represent a promising novel approach to identify and evaluate complex causal effects, and they are thus well suited to complement other modern epidemiologic methods of etiologic inquiry. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Is human immunodeficiency virus/acquired immunodeficiency syndrome decreasing among Brazilian injection drug users? Recent findings and how to interpret them.

PubMed

Bastos, Francisco I; Bongertz, Vera; Teixeira, Sylvia Lopes; Morgado, Mariza G; Hacker, Mariana A

2005-02-01

We briefly review findings from Brazilian settings where the human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) epidemic among injection drug users (IDUs) seems to be decreasing, highlighting recent findings from Rio de Janeiro and discussing methodological alternatives. Former analyses using serologic testing algorithm for recent HIV seroconversion have shown that HIV incidence has been low in IDUs recruited by two different surveys carried out in Rio, where low injection frequencies and infection rates have been found among new injectors. The proportion of AIDS cases among IDUs in Rio has been fairly modest, compared to São Paulo and especially to the southernmost states. Notwithstanding, the interpretation of findings from serial surveys constitutes a challenge, magnified in the assessment of HIV spread among IDUs due to the dynamic nature of the drug scenes and limitations of sampling strategies targeting hard-to-reach populations. Assessment of epidemic trends may profit from the triangulation of data, but cannot avert biases associated with sampling errors. Efforts should be made to triangulate data from different sources, besides exploring specific studies from different perspectives. In an attempt to further assess the observed trends, we carried out original analyses using data from Brazilian AIDS databank.

Curating viscoelastic properties of icosahedral viruses, virus-based nanomaterials, and protein cages.

PubMed

Kant, Ravi; Rayaprolu, Vamseedhar; McDonald, Kaitlyn; Bothner, Brian

2018-06-01

The beauty, symmetry, and functionality of icosahedral virus capsids has attracted the attention of biologists, physicists, and mathematicians ever since they were first observed. Viruses and protein cages assemble into functional architectures in a range of sizes, shapes, and symmetries. To fulfill their biological roles, these structures must self-assemble, resist stress, and are often dynamic. The increasing use of icosahedral capsids and cages in materials science has driven the need to quantify them in terms of structural properties such as rigidity, stiffness, and viscoelasticity. In this study, we employed Quartz Crystal Microbalance with Dissipation technology (QCM-D) to characterize and compare the mechanical rigidity of different protein cages and viruses. We attempted to unveil the relationships between rigidity, radius, shell thickness, and triangulation number. We show that the rigidity and triangulation numbers are inversely related to each other and the comparison of rigidity and radius also follows the same trend. Our results suggest that subunit orientation, protein-protein interactions, and protein-nucleic acid interactions are important for the resistance to deformation of these complexes, however, the relationships are complex and need to be explored further. The QCM-D based viscoelastic measurements presented here help us elucidate these relationships and show the future prospect of this technique in the field of physical virology and nano-biotechnology.

High-Stakes Collaborative Testing: Why Not?

PubMed

Levine, Ruth E; Borges, Nicole J; Roman, Brenda J B; Carchedi, Lisa R; Townsend, Mark H; Cluver, Jeffrey S; Frank, Julia; Morey, Oma; Haidet, Paul; Thompson, Britta M

2018-01-01

Phenomenon: Studies of high-stakes collaborative testing remain sparse, especially in medical education. We explored high-stakes collaborative testing in medical education, looking specifically at the experiences of students in established and newly formed teams. Third-year psychiatry students at 5 medical schools across 6 sites participated, with 4 participating as established team sites and 2 as comparison team sites. For the collaborative test, we used the National Board of Medical Examiners Psychiatry subject test, administering it via a 2-stage process. Students at all sites were randomly selected to participate in a focus group, with 8-10 students per site (N = 49). We also examined quantitative data for additional triangulation. Students described a range of heightened emotions around the collaborative test yet perceived it as valuable regardless if they were in established or newly formed teams. Students described learning about the subject matter, themselves, others, and interpersonal dynamics during collaborative testing. Triangulation of these results via quantitative data supported these themes. Insights: Despite student concerns, high-stakes collaborative tests may be both valuable and feasible. The data suggest that high-stakes tests (tests of learning or summative evaluation) could also become tests for learning or formative evaluation. The paucity of research into this methodology in medical education suggests more research is needed.

Causal dissipation for the relativistic dynamics of ideal gases

NASA Astrophysics Data System (ADS)

Freistühler, Heinrich; Temple, Blake

2017-05-01

We derive a general class of relativistic dissipation tensors by requiring that, combined with the relativistic Euler equations, they form a second-order system of partial differential equations which is symmetric hyperbolic in a second-order sense when written in the natural Godunov variables that make the Euler equations symmetric hyperbolic in the first-order sense. We show that this class contains a unique element representing a causal formulation of relativistic dissipative fluid dynamics which (i) is equivalent to the classical descriptions by Eckart and Landau to first order in the coefficients of viscosity and heat conduction and (ii) has its signal speeds bounded sharply by the speed of light. Based on these properties, we propose this system as a natural candidate for the relativistic counterpart of the classical Navier-Stokes equations.

Causal dissipation for the relativistic dynamics of ideal gases

PubMed Central

2017-01-01

We derive a general class of relativistic dissipation tensors by requiring that, combined with the relativistic Euler equations, they form a second-order system of partial differential equations which is symmetric hyperbolic in a second-order sense when written in the natural Godunov variables that make the Euler equations symmetric hyperbolic in the first-order sense. We show that this class contains a unique element representing a causal formulation of relativistic dissipative fluid dynamics which (i) is equivalent to the classical descriptions by Eckart and Landau to first order in the coefficients of viscosity and heat conduction and (ii) has its signal speeds bounded sharply by the speed of light. Based on these properties, we propose this system as a natural candidate for the relativistic counterpart of the classical Navier–Stokes equations. PMID:28588397

Causal dissipation for the relativistic dynamics of ideal gases.

PubMed

Freistühler, Heinrich; Temple, Blake

2017-05-01

We derive a general class of relativistic dissipation tensors by requiring that, combined with the relativistic Euler equations, they form a second-order system of partial differential equations which is symmetric hyperbolic in a second-order sense when written in the natural Godunov variables that make the Euler equations symmetric hyperbolic in the first-order sense. We show that this class contains a unique element representing a causal formulation of relativistic dissipative fluid dynamics which (i) is equivalent to the classical descriptions by Eckart and Landau to first order in the coefficients of viscosity and heat conduction and (ii) has its signal speeds bounded sharply by the speed of light. Based on these properties, we propose this system as a natural candidate for the relativistic counterpart of the classical Navier-Stokes equations.

Interstitial and Interlayer Ion Diffusion Geometry Extraction in Graphitic Nanosphere Battery Materials.

PubMed

Gyulassy, Attila; Knoll, Aaron; Lau, Kah Chun; Wang, Bei; Bremer, Peer-Timo; Papka, Michael E; Curtiss, Larry A; Pascucci, Valerio

2016-01-01

Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermally annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.

Interstitial and Interlayer Ion Diffusion Geometry Extraction in Graphitic Nanosphere Battery Materials

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

Gyulassy, Attila; Knoll, Aaron; Lau, Kah Chun

2016-01-01

Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermallymore » annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.« less

Interstitial and interlayer ion diffusion geometry extraction in graphitic nanosphere battery materials

DOE PAGES

Gyulassy, Attila; Knoll, Aaron; Lau, Kah Chun; ...

2016-01-31

Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermallymore » annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Lastly, our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.« less

Laser Altimeter for Flight Simulator

NASA Technical Reports Server (NTRS)

Webster, L. D.

1986-01-01

Height of flight-simulator probe above model of terrain measured by automatic laser triangulation system. Airplane simulated by probe that moves over model of terrain. Altitude of airplane scaled from height of probe above model. Height measured by triangulation of laser beam aimed at intersection of model surface with plumb line of probe.

Communication Intervention in an Organization: Measuring the Results through a Triangulation Approach.

ERIC Educational Resources Information Center

Zamanou, Sonia; Glaser, Susan R.

A study examined organizational culture change to determine the effectiveness of a communication based intervention program to increase productivity and motivation. The cultural change was measured through a triangulation approach combining questionnaires, interview data and direct observation. Pre- and post-intervention data were obtained in the…

Triangulation and Mixed Methods Designs: Data Integration with New Research Technologies

ERIC Educational Resources Information Center

Fielding, Nigel G.

2012-01-01

Data integration is a crucial element in mixed methods analysis and conceptualization. It has three principal purposes: illustration, convergent validation (triangulation), and the development of analytic density or "richness." This article discusses such applications in relation to new technologies for social research, looking at three…

Macroeconomic susceptibility, inflation, and aggregate supply

NASA Astrophysics Data System (ADS)

Hawkins, Raymond J.

2017-03-01

We unify aggregate-supply dynamics as a time-dependent susceptibility-mediated relationship between inflation and aggregate economic output. In addition to representing well various observations of inflation-output dynamics this parsimonious formalism provides a straightforward derivation of popular representations of aggregate-supply dynamics and a natural basis for economic-agent expectations as an element of inflation formation. Our formalism also illuminates questions of causality and time-correlation that challenge central banks for whom aggregate-supply dynamics is a key constraint in their goal of achieving macroeconomic stability.

Causal and causally separable processes

NASA Astrophysics Data System (ADS)

Oreshkov, Ognyan; Giarmatzi, Christina

2016-09-01

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and outcomes for each party, these correlations form a polytope whose facets define causal inequalities. The case of quantum correlations in this paradigm is captured by the process matrix formalism. We investigate the link between causality and the closely related notion of causal separability of quantum processes, which we here define rigorously in analogy with the link between Bell locality and separability of quantum states. We show that causality and causal separability are not equivalent in general by giving an example of a physically admissible tripartite quantum process that is causal but not causally separable. We also show that there are causally separable quantum processes that become non-causal if extended by supplying the parties with entangled ancillas. This motivates the concepts of extensibly causal and extensibly causally separable (ECS) processes, for which the respective property remains invariant under extension. We characterize the class of ECS quantum processes in the tripartite case via simple conditions on the form of the process matrix. We show that the processes realizable by classically controlled quantum circuits are ECS and conjecture that the reverse also holds.

Diffusion in Colocation Contact Networks: The Impact of Nodal Spatiotemporal Dynamics.

PubMed

Thomas, Bryce; Jurdak, Raja; Zhao, Kun; Atkinson, Ian

2016-01-01

Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable diseases or information dissemination. To establish how spatiotemporal dynamics of nodes impact spreading potential in colocation contact networks, we propose "inducement-shuffling" null models which break one or more correlations between times, locations and nodes. By reconfiguring the time and/or location of each node's presence in the network, these models induce alternative sets of colocation events giving rise to contact networks with varying spreading potential. This enables second-order causal reasoning about how correlations in nodes' spatiotemporal preferences not only lead to a given contact network but ultimately influence the network's spreading potential. We find the correlation between nodes and times to be the greatest impediment to spreading, while the correlation between times and locations slightly catalyzes spreading. Under each of the presented null models we measure both the number of contacts and infection prevalence as a function of time, with the surprising finding that the two have no direct causality.

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

Kim, J; Nguyen, D; O’Brien, R

Purpose: Kilovoltage intrafraction monitoring (KIM) scheme has been successfully used to simultaneously monitor 3D tumor motion during radiotherapy. Recently, an iterative closest point (ICP) algorithm was implemented in KIM to also measure rotations about three axes, enabling real-time tracking of tumor motion in six degrees-of-freedom (DoF). This study aims to evaluate the accuracy of the six DoF motion estimates of KIM by comparing it with the corresponding motion (i) measured by the Calypso; and (ii) derived from kV/MV triangulation. Methods: (i) Various motions (static and dynamic) were applied to a CIRS phantom with three embedded electromagnetic transponders (Calypso Medical) usingmore » a 5D motion platform (HexaMotion) and a rotating treatment couch while both KIM and Calypso were used to concurrently track the phantom motion in six DoF. (ii) KIM was also used to retrospectively estimate six DoF motion from continuous sets of kV projections of a prostate, implanted with three gold fiducial markers (2 patients with 80 fractions in total), acquired during the treatment. Corresponding motion was obtained from kV/MV triangulation using a closed form least squares method based on three markers’ positions. Only the frames where all three markers were present were used in the analysis. The mean differences between the corresponding motion estimates were calculated for each DoF. Results: Experimental results showed that the mean of absolute differences in six DoF phantom motion measured by Calypso and KIM were within 1.1° and 0.7 mm. kV/MV triangulation derived six DoF prostate tumor better agreed with KIM estimated motion with the mean (s.d.) difference of up to 0.2° (1.36°) and 0.2 (0.25) mm for rotation and translation, respectively. Conclusion: These results suggest that KIM can provide an accurate six DoF intrafraction tumor during radiotherapy.« less

Attributing causal agents to nationwide maps of forest disturbance

Treesearch

Gretchen G. Moisen; Todd A. Schroeder; Karen Schleeweis; Chris Toney; Warren B. Cohen; Samuel N. Goward

2012-01-01

Currently in its third phase, the North American Forest Dynamics (NAFD) project has launched nationwide processing of historic Landsat data to provide a comprehensive annual, wall-to-wall analysis of U.S. disturbance history over the last 30+ years. Because understanding the cause of disturbance is important to quantifying carbon dynamics, work is underway to attribute...

Predicting depression based on dynamic regional connectivity: a windowed Granger causality analysis of MEG recordings.

PubMed

Lu, Qing; Bi, Kun; Liu, Chu; Luo, Guoping; Tang, Hao; Yao, Zhijian

2013-10-16

Abnormal inter-regional causalities can be mapped for the objective diagnosis of various diseases. These inter-regional connectivities are usually calculated over an entire scan and used to characterize the stationary strength of the connections. However, the connectivity within networks may undergo substantial changes during a scan. In this study, we developed an objective depression recognition approach using the dynamic regional interactions that occur in response to sad facial stimuli. The whole time-period magnetoencephalography (MEG) signals from the visual cortex, amygdala, anterior cingulate cortex (ACC) and inferior frontal gyrus (IFG) were separated into sequential time intervals. The Granger causality mapping method was used to identify the pairwise interaction pattern within each time interval. Feature selection was then undertaken within a minimum redundancy-maximum relevance (mRMR) framework. Typical classifiers were utilized to predict those patients who had depression. The overall performances of these classifiers were similar, and the highest classification accuracy rate was 87.5%. The best discriminative performance was obtained when the number of features was within a robust range. The discriminative network pattern obtained through support vector machine (SVM) analyses displayed abnormal causal connectivities that involved the amygdala during the early and late stages. These early and late connections in the amygdala appear to reveal a negative bias to coarse expression information processing and abnormal negative modulation in patients with depression, which may critically affect depression discrimination. © 2013 Elsevier B.V. All rights reserved.

Robotic tool positioning process using a multi-line off-axis laser triangulation sensor

NASA Astrophysics Data System (ADS)

Pinto, T. C.; Matos, G.

2018-03-01

Proper positioning of a friction stir welding head for pin insertion, driven by a closed chain robot, is important to ensure quality repair of cracks. A multi-line off-axis laser triangulation sensor was designed to be integrated to the robot, allowing relative measurements of the surface to be repaired. This work describes the sensor characteristics, its evaluation and the measurement process for tool positioning to a surface point of interest. The developed process uses a point of interest image and a measured point cloud to define the translation and rotation for tool positioning. Sensor evaluation and tests are described. Keywords: laser triangulation, 3D measurement, tool positioning, robotics.

Identifying causal linkages between environmental variables and African conflicts

NASA Astrophysics Data System (ADS)

Nguy-Robertson, A. L.; Dartevelle, S.

2017-12-01

Environmental variables that contribute to droughts, flooding, and other natural hazards are often identified as factors contributing to conflict; however, few studies attempt to quantify these causal linkages. Recent research has demonstrated that the environment operates within a dynamical system framework and the influence of variables can be identified from convergent cross mapping (CCM) between shadow manifolds. We propose to use CCM to identify causal linkages between environmental variables and incidences of conflict. This study utilizes time series data from Climate Forecast System ver. 2 and MODIS satellite sensors processed using Google Earth Engine to aggregate country and regional trends. These variables are then compared to Armed Conflict Location & Event Data Project observations at similar scales. Results provide relative rankings of variables and their linkage to conflict. Being able to identify which factors contributed more strongly to a conflict can allow policy makers to prepare solutions to mitigate future crises. Knowledge of the primary environmental factors can lead to the identification of other variables to examine in the causal network influencing conflict.

Progress in high-level exploratory vision

NASA Astrophysics Data System (ADS)

Brand, Matthew

1993-08-01

We have been exploring the hypothesis that vision is an explanatory process, in which causal and functional reasoning about potential motion plays an intimate role in mediating the activity of low-level visual processes. In particular, we have explored two of the consequences of this view for the construction of purposeful vision systems: Causal and design knowledge can be used to (1) drive focus of attention, and (2) choose between ambiguous image interpretations. An important result of visual understanding is an explanation of the scene's causal structure: How action is originated, constrained, and prevented, and what will happen in the immediate future. In everyday visual experience, most action takes the form of motion, and most causal analysis takes the form of dynamical analysis. This is even true of static scenes, where much of a scene's interest lies in how possible motions are arrested. This paper describes our progress in developing domain theories and visual processes for the understanding of various kinds of structured scenes, including structures built out of children's constructive toys and simple mechanical devices.

Selective Entrainment of Theta Oscillations in the Dorsal Stream Causally Enhances Auditory Working Memory Performance.

PubMed

Albouy, Philippe; Weiss, Aurélien; Baillet, Sylvain; Zatorre, Robert J

2017-04-05

The implication of the dorsal stream in manipulating auditory information in working memory has been recently established. However, the oscillatory dynamics within this network and its causal relationship with behavior remain undefined. Using simultaneous MEG/EEG, we show that theta oscillations in the dorsal stream predict participants' manipulation abilities during memory retention in a task requiring the comparison of two patterns differing in temporal order. We investigated the causal relationship between brain oscillations and behavior by applying theta-rhythmic TMS combined with EEG over the MEG-identified target (left intraparietal sulcus) during the silent interval between the two stimuli. Rhythmic TMS entrained theta oscillation and boosted participants' accuracy. TMS-induced oscillatory entrainment scaled with behavioral enhancement, and both gains varied with participants' baseline abilities. These effects were not seen for a melody-comparison control task and were not observed for arrhythmic TMS. These data establish theta activity in the dorsal stream as causally related to memory manipulation. VIDEO ABSTRACT. Copyright © 2017 Elsevier Inc. All rights reserved.

Globally conditioned Granger causality in brain–brain and brain–heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study

PubMed Central

Passamonti, Luca; Wald, Lawrence L.; Barbieri, Riccardo

2016-01-01

The causal, directed interactions between brain regions at rest (brain–brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain–heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain–brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain–brain and brain–heart interactions reflecting central modulation of ANS outflow. PMID:27044985

Evaluating Team Project-Work Using Triangulation: Lessons from Communities in Northern Ghana

ERIC Educational Resources Information Center

Clark, Gordon; Jasaw, Godfred Seidu

2014-01-01

This paper uses triangulation to assess key aspects of a team-based, participatory action research programme for undergraduates in rural communities across northern Ghana. The perceptions of the programme and its effects on the students, staff and host communities are compared, showing areas of agreement and disagreement. The successes of the…

The Triangulation Interview Form: A Possible Criterion for Field-Based Objectives.

ERIC Educational Resources Information Center

Licata, Joseph W.; Norman, Reuben L.

1979-01-01

To test the general reliability and validity of the Triangulation Interview Form (TIF), 52 observers viewed a videotape simulation of an interview situation. Agreement among observers for each of 16 TIF questions ranged from 85 to 98 percent. Observers significantly discriminated between eight behaviors judged complete and eight behaviors judged…

The Use of Triangulation Methods in Qualitative Educational Research

ERIC Educational Resources Information Center

Oliver-Hoyo, Maria; Allen, DeeDee

2006-01-01

Triangulation involves the careful reviewing of data collected through different methods in order to achieve a more accurate and valid estimate of qualitative results for a particular construct. This paper describes how we used three qualitative methods of data collection to study attitudes of students toward graphing, hands-on activities, and…

Assessing the Potential Use of Eye-Tracking Triangulation for Evaluating the Usability of an Online Diabetes Exercise System.

PubMed

Schaarup, Clara; Hartvigsen, Gunnar; Larsen, Lars Bo; Tan, Zheng-Hua; Årsand, Eirik; Hejlesen, Ole Kristian

2015-01-01

The Online Diabetes Exercise System was developed to motivate people with Type 2 diabetes to do a 25 minutes low-volume high-intensity interval training program. In a previous multi-method evaluation of the system, several usability issues were identified and corrected. Despite the thorough testing, it was unclear whether all usability problems had been identified using the multi-method evaluation. Our hypothesis was that adding the eye-tracking triangulation to the multi-method evaluation would increase the accuracy and completeness when testing the usability of the system. The study design was an Eye-tracking Triangulation; conventional eye-tracking with predefined tasks followed by The Post-Experience Eye-Tracked Protocol (PEEP). Six Areas of Interests were the basis for the PEEP-session. The eye-tracking triangulation gave objective and subjective results, which are believed to be highly relevant for designing, implementing, evaluating and optimizing systems in the field of health informatics. Future work should include testing the method on a larger and more representative group of users and apply the method on different system types.

Constrained CVT meshes and a comparison of triangular mesh generators

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

Nguyen, Hoa; Burkardt, John; Gunzburger, Max

2009-01-01

Mesh generation in regions in Euclidean space is a central task in computational science, and especially for commonly used numerical methods for the solution of partial differential equations, e.g., finite element and finite volume methods. We focus on the uniform Delaunay triangulation of planar regions and, in particular, on how one selects the positions of the vertices of the triangulation. We discuss a recently developed method, based on the centroidal Voronoi tessellation (CVT) concept, for effecting such triangulations and present two algorithms, including one new one, for CVT-based grid generation. We also compare several methods, including CVT-based methods, for triangulatingmore » planar domains. To this end, we define several quantitative measures of the quality of uniform grids. We then generate triangulations of several planar regions, including some having complexities that are representative of what one may encounter in practice. We subject the resulting grids to visual and quantitative comparisons and conclude that all the methods considered produce high-quality uniform grids and that the CVT-based grids are at least as good as any of the others.« less

Passive range estimation using dual baseline triangulation

NASA Astrophysics Data System (ADS)

Pieper, Ronald J.; Cooper, Alfred W.; Pelegris, G.

1996-03-01

Modern combat systems based on active radar sensing suffer disadvantages against low-flying targets in cluttered backgrounds. Use of passive infrared sensors with these systems, either in cooperation or as an alternative, shows potential for improving target detection and declaration range for targets crossing the horizon. Realization of this potential requires fusion of target position data from dissimilar sensors, or passive sensor measurement of target range. The availability of passive sensors that can supply both range and bearing data on such targets would significantly extend the robustness of an integrated ship self-defense system. This paper considers a new method of range determination with passive sensors based on the principle of triangulation, extending the principle to two orthogonal baselines. The performance of single or double baseline triangulation depends on sensor bearing precision and direction to target. An expression for maximum triangulation range at a required accuracy is derived as a function of polar angle relative to the center of the dual-baseline system. Limitations in the dual- baseline model due to the geometrically assessed horizon are also considered.

The opportune time to invest in residential properties - Engle-Granger cointegration test and Granger causality test approach

NASA Astrophysics Data System (ADS)

Chee-Yin, Yip; Hock-Eam, Lim

2014-12-01

This paper examines using housing supply as proxy to house prices, the causal relationship on house prices among 8 states in Malaysia by applying the Engle-Granger cointegration test and Granger causality test approach. The target states are Perak, Selangor, Penang, Federal Territory of Kuala Lumpur (WPKL or Kuala Lumpur), Kedah, Negeri Sembilan, Sabah and Sarawak. The primary aim of this study is to estimate how long (in months) house prices in Perak lag behind that of Selangor, Penang and WPKL. We classify the 8 states into two categories - developed and developing states. We use Engle-Granger cointegration test and Granger causality test to examine the long run and short run equilibrium relationship among the two categories.. It is found that the causal relationship is bidirectional in Perak and Sabah, Perak and Selangor while it is unidirectional for Perak and Sarawak, Perak and Penang, Perak and WPKL. The speed of deviation adjustment is about 273%, suggesting that the pricing dynamic of Perak has a 32- month or 2 3/4- year lag behind that of WPKL, Selangor and Penang. Such information will be useful to investors, house buyers and speculators.

Impact of military on biofuels consumption and GHG emissions: the evidence from G7 countries.

PubMed

Bildirici, Melike

2018-05-01

It was aimed to test the relation among the greenhouse gases emissions, economic growth, biofuels consumption, and militarization in G7 countries during the 1985-2015 period by Pedroni 1995 and panel Johansen tests and two long-run estimators-dynamic OLS and fully modified OLS. Long-run estimators found that economic growth and militarization have statistically significant positive impact on CO 2 emission of G7 countries. Furthermore, the panel causality tests were applied: Dumitrescu and Hurlin (Econ Model 29(4):1450-1460, 2012) and panel Granger causality. These tests determined the causal relationship between the variables. The results of this paper implied that economic growth and biofuels consumption depend on militarization, and economic growth and militarization are granger causes of the greenhouse gases emissions.

Dynamic test input generation for multiple-fault isolation

NASA Technical Reports Server (NTRS)

Schaefer, Phil

1990-01-01

Recent work is Causal Reasoning has provided practical techniques for multiple fault diagnosis. These techniques provide a hypothesis/measurement diagnosis cycle. Using probabilistic methods, they choose the best measurements to make, then update fault hypotheses in response. For many applications such as computers and spacecraft, few measurement points may be accessible, or values may change quickly as the system under diagnosis operates. In these cases, a hypothesis/measurement cycle is insufficient. A technique is presented for a hypothesis/test-input/measurement diagnosis cycle. In contrast to generating tests a priori for determining device functionality, it dynamically generates tests in response to current knowledge about fault probabilities. It is shown how the mathematics previously used for measurement specification can be applied to the test input generation process. An example from an efficient implementation called Multi-Purpose Causal (MPC) is presented.

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2015-01-01

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

Distinguishing anticipation from causality: anticipatory bias in the estimation of information flow.

PubMed

Hahs, Daniel W; Pethel, Shawn D

2011-09-16

We report that transfer entropy estimates obtained from low-resolution and/or small data sets show net information flow away from a purely anticipatory element whereas transfer entropy calculated using exact distributions show the flow towards it. This means that for real-world data sets anticipatory elements can appear to be strongly driving the network dynamics even when there is no possibility of such an influence. Furthermore, we show that in the low-resolution limit there is no statistic that can distinguish anticipatory elements from causal ones.

Dynamic mechanical properties of hydroxyapatite/polyethylene oxide nanocomposites: characterizing isotropic and post-processing microstructures

NASA Astrophysics Data System (ADS)

Shofner, Meisha; Lee, Ji Hoon

2012-02-01

Compatible component interfaces in polymer nanocomposites can be used to facilitate a dispersed morphology and improved physical properties as has been shown extensively in experimental results concerning amorphous matrix nanocomposites. In this research, a block copolymer compatibilized interface is employed in a semi-crystalline matrix to prevent large scale nanoparticle clustering and enable microstructure construction with post-processing drawing. The specific materials used are hydroxyapatite nanoparticles coated with a polyethylene oxide-b-polymethacrylic acid block copolymer and a polyethylene oxide matrix. Two particle shapes are used: spherical and needle-shaped. Characterization of the dynamic mechanical properties indicated that the two nanoparticle systems provided similar levels of reinforcement to the matrix. For the needle-shaped nanoparticles, the post-processing step increased matrix crystallinity and changed the thermomechanical reinforcement trends. These results will be used to further refine the post-processing parameters to achieve a nanocomposite microstructure with triangulated arrays of nanoparticles.

Temporal Information of Directed Causal Connectivity in Multi-Trial ERP Data using Partial Granger Causality.

PubMed

Youssofzadeh, Vahab; Prasad, Girijesh; Naeem, Muhammad; Wong-Lin, KongFatt

2016-01-01

Partial Granger causality (PGC) has been applied to analyse causal functional neural connectivity after effectively mitigating confounding influences caused by endogenous latent variables and exogenous environmental inputs. However, it is not known how this connectivity obtained from PGC evolves over time. Furthermore, PGC has yet to be tested on realistic nonlinear neural circuit models and multi-trial event-related potentials (ERPs) data. In this work, we first applied a time-domain PGC technique to evaluate simulated neural circuit models, and demonstrated that the PGC measure is more accurate and robust in detecting connectivity patterns as compared to conditional Granger causality and partial directed coherence, especially when the circuit is intrinsically nonlinear. Moreover, the connectivity in PGC settles faster into a stable and correct configuration over time. After method verification, we applied PGC to reveal the causal connections of ERP trials of a mismatch negativity auditory oddball paradigm. The PGC analysis revealed a significant bilateral but asymmetrical localised activity in the temporal lobe close to the auditory cortex, and causal influences in the frontal, parietal and cingulate cortical areas, consistent with previous studies. Interestingly, the time to reach a stable connectivity configuration (~250–300 ms) coincides with the deviation of ensemble ERPs of oddball from standard tones. Finally, using a sliding time window, we showed higher resolution dynamics of causal connectivity within an ERP trial. In summary, time-domain PGC is promising in deciphering directed functional connectivity in nonlinear and ERP trials accurately, and at a sufficiently early stage. This data-driven approach can reduce computational time, and determine the key architecture for neural circuit modeling.

Ohm's law in the fast lane: general relatiivistic charge dynamics

NASA Technical Reports Server (NTRS)

Meier, D.

2004-01-01

Fully relativistic and causal equations for the flow of charge in curved spacetime are derived. It is believed that this is the first set of equations to be published that correctly describes the flow of charge, as well as the evolution of the electromagnetic field, in highly dynamical relativistic environments on timescales much shorter than the collapse time (GM/c3).

Dynamics of acorn production by five species of Southern Appalachian oaks

Treesearch

Cathryn H. Greenberg; Bernard R. Parresol

2002-01-01

The management implications of fluctuations in acorn crop size underscore the need to better understand their patterns, causal factors, and predictability (both within a year and long term). Acorn yield has a demonstrable influence on the population dynamics of many wildlife species, both game (Eiler et al. 1989, Wentworth et al. 1992) and nongame (Hannon et al. 1987,...

Understanding the dynamic effects of returning patients toward emergency department density

NASA Astrophysics Data System (ADS)

Ahmad, Norazura; Zulkepli, Jafri; Ramli, Razamin; Ghani, Noraida Abdul; Teo, Aik Howe

2017-11-01

This paper presents the development of a dynamic hypothesis for the effect of returning patients to the emergency department (ED). A logical tree from the Theory of Constraint known as Current Reality Tree was used to identify the key variables. Then, a hypothetical framework portraying the interrelated variables and its influencing relationships was developed using causal loop diagrams (CLD). The conceptual framework was designed as the basis for the development of a system dynamics model.

Feminist Approaches to Triangulation: Uncovering Subjugated Knowledge and Fostering Social Change in Mixed Methods Research

ERIC Educational Resources Information Center

Hesse-Biber, Sharlene

2012-01-01

This article explores the deployment of triangulation in the service of uncovering subjugated knowledge and promoting social change for women and other oppressed groups. Feminist approaches to mixed methods praxis create a tight link between the research problem and the research design. An analysis of selected case studies of feminist praxis…

A Triangulation Method for Identifying Hydrostratigraphic Locations of Well Screens

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

Whiteside, T. S.

2015-01-31

A method to identify the hydrostratigraphic location of well screens was developed using triangulation with known locations. This method was applied to all of the monitor wells being used to develop the new GSA groundwater model. Results from this method are closely aligned with those from an alternate method which uses a mesh surface.

The Marginalized "Model" Minority: An Empirical Examination of the Racial Triangulation of Asian Americans

ERIC Educational Resources Information Center

Xu, Jun; Lee, Jennifer C.

2013-01-01

In this article, we propose a shift in race research from a one-dimensional hierarchical approach to a multidimensional system of racial stratification. Building upon Claire Kim's (1999) racial triangulation theory, we examine how the American public rates Asians relative to blacks and whites along two dimensions of racial stratification: racial…

Multi-Sensor Triangulation of Multi-Source Spatial Data

NASA Technical Reports Server (NTRS)

Habib, Ayman; Kim, Chang-Jae; Bang, Ki-In

2007-01-01

The introduced methodologies are successful in: a) Ising LIDAR features for photogrammetric geo-refererncing; b) Delivering a geo-referenced imagery of the same quality as point-based geo-referencing procedures; c) Taking advantage of the synergistic characteristics of spatial data acquisition systems. The triangulation output can be used for the generation of 3-D perspective views.

Making the Most of Obesity Research: Developing Research and Policy Objectives through Evidence Triangulation

ERIC Educational Resources Information Center

Oliver, Kathryn; Aicken, Catherine; Arai, Lisa

2013-01-01

Drawing lessons from research can help policy makers make better decisions. If a large and methodologically varied body of research exists, as with childhood obesity, this is challenging. We present new research and policy objectives for child obesity developed by triangulating user involvement data with a mapping study of interventions aimed at…

A Mixed Methods Approach to Understanding School Counseling Program Evaluation: High School Counselors' Methods and Perceptions

ERIC Educational Resources Information Center

Aucoin, Jennifer Mangrum

2013-01-01

The purpose of this mixed methods concurrent triangulation study was to examine the program evaluation practices of high school counselors. A total of 294 high school counselors in Texas were assessed using a mixed methods concurrent triangulation design. A researcher-developed survey, the School Counseling Program Evaluation Questionnaire…

An Array of Qualitative Data Analysis Tools: A Call for Data Analysis Triangulation

ERIC Educational Resources Information Center

Leech, Nancy L.; Onwuegbuzie, Anthony J.

2007-01-01

One of the most important steps in the qualitative research process is analysis of data. The purpose of this article is to provide elements for understanding multiple types of qualitative data analysis techniques available and the importance of utilizing more than one type of analysis, thus utilizing data analysis triangulation, in order to…

Hand-held triangulation laser profilometer with audio output for blind people Profilométre laser à triangulation tenu en main avec sortie sonare pour non-voyants

NASA Astrophysics Data System (ADS)

Farcy, R.; Damaschini, R.

1998-06-01

We describe a device currently under industrial development which will give to the blind a means of three-dimensional space perception. It consists of a 350 g hand-held triangulating laser telemeter including electronic parts and batteries, with auditory feedback either inside the apparatus or close to the ear. The microprocessor unit converts in real time the distance measured by the telemeter into a musical note. Scanning the space with an adequate movement of the hand produces musical lines corresponding to the profiles of the environment. We discuss the optical configuration of the system relative to our first year of clinical experimentation.

Discovery and problem solving: Triangulation as a weak heuristic

NASA Technical Reports Server (NTRS)

Rochowiak, Daniel

1987-01-01

Recently the artificial intelligence community has turned its attention to the process of discovery and found that the history of science is a fertile source for what Darden has called compiled hindsight. Such hindsight generates weak heuristics for discovery that do not guarantee that discoveries will be made but do have proven worth in leading to discoveries. Triangulation is one such heuristic that is grounded in historical hindsight. This heuristic is explored within the general framework of the BACON, GLAUBER, STAHL, DALTON, and SUTTON programs. In triangulation different bases of information are compared in an effort to identify gaps between the bases. Thus, assuming that the bases of information are relevantly related, the gaps that are identified should be good locations for discovery and robust analysis.

Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: a case study in the Rechna Doab watershed, Pakistan.

PubMed

Inam, Azhar; Adamowski, Jan; Halbe, Johannes; Prasher, Shiv

2015-04-01

Over the course of the last twenty years, participatory modeling has increasingly been advocated as an integral component of integrated, adaptive, and collaborative water resources management. However, issues of high cost, time, and expertise are significant hurdles to the widespread adoption of participatory modeling in many developing countries. In this study, a step-wise method to initialize the involvement of key stakeholders in the development of qualitative system dynamics models (i.e. causal loop diagrams) is presented. The proposed approach is designed to overcome the challenges of low expertise, time and financial resources that have hampered previous participatory modeling efforts in developing countries. The methodological framework was applied in a case study of soil salinity management in the Rechna Doab region of Pakistan, with a focus on the application of qualitative modeling through stakeholder-built causal loop diagrams to address soil salinity problems in the basin. Individual causal loop diagrams were developed by key stakeholder groups, following which an overall group causal loop diagram of the entire system was built based on the individual causal loop diagrams to form a holistic qualitative model of the whole system. The case study demonstrates the usefulness of the proposed approach, based on using causal loop diagrams in initiating stakeholder involvement in the participatory model building process. In addition, the results point to social-economic aspects of soil salinity that have not been considered by other modeling studies to date. Copyright © 2015 Elsevier Ltd. All rights reserved.

Challenges to inferring causality from viral information dispersion in dynamic social networks

NASA Astrophysics Data System (ADS)

Ternovski, John

2014-06-01

Understanding the mechanism behind large-scale information dispersion through complex networks has important implications for a variety of industries ranging from cyber-security to public health. With the unprecedented availability of public data from online social networks (OSNs) and the low cost nature of most OSN outreach, randomized controlled experiments, the "gold standard" of causal inference methodologies, have been used with increasing regularity to study viral information dispersion. And while these studies have dramatically furthered our understanding of how information disseminates through social networks by isolating causal mechanisms, there are still major methodological concerns that need to be addressed in future research. This paper delineates why modern OSNs are markedly different from traditional sociological social networks and why these differences present unique challenges to experimentalists and data scientists. The dynamic nature of OSNs is particularly troublesome for researchers implementing experimental designs, so this paper identifies major sources of bias arising from network mutability and suggests strategies to circumvent and adjust for these biases. This paper also discusses the practical considerations of data quality and collection, which may adversely impact the efficiency of the estimator. The major experimental methodologies used in the current literature on virality are assessed at length, and their strengths and limits identified. Other, as-yetunsolved threats to the efficiency and unbiasedness of causal estimators--such as missing data--are also discussed. This paper integrates methodologies and learnings from a variety of fields under an experimental and data science framework in order to systematically consolidate and identify current methodological limitations of randomized controlled experiments conducted in OSNs.

[Causal analysis approaches in epidemiology].

PubMed

Dumas, O; Siroux, V; Le Moual, N; Varraso, R

2014-02-01

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the formulation of causal hypotheses, which will be a basis for all methodological choices. Beyond this step, statistical analysis tools recently developed offer new possibilities to delineate complex relationships, in particular in life course epidemiology. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Transfer Entropy as a Log-Likelihood Ratio

NASA Astrophysics Data System (ADS)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Transfer entropy as a log-likelihood ratio.

PubMed

Barnett, Lionel; Bossomaier, Terry

2012-09-28

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Towards thermodynamics of universal horizons in Einstein-æther theory.

PubMed

Berglund, Per; Bhattacharyya, Jishnu; Mattingly, David

2013-02-15

Holography grew out of black hole thermodynamics, which relies on the causal structure and general covariance of general relativity. In Einstein-æther theory, a generally covariant theory with a dynamical timelike unit vector, every solution breaks local Lorentz invariance, thereby grossly modifying the causal structure of gravity. However, there are still absolute causal boundaries, called "universal horizons," which are not Killing horizons yet obey a first law of black hole mechanics and must have an entropy if they do not violate a generalized second law. We couple a scalar field to the timelike vector and show via the tunneling approach that the universal horizon radiates as a blackbody at a fixed temperature, even if the scalar field equations also violate local Lorentz invariance. This suggests that the class of holographic theories may be much broader than currently assumed.

Indicators of causal agency in physical interactions: the role of the prior context.

PubMed

Mayrhofer, Ralf; Waldmann, Michael R

2014-09-01

The question how agent and patient roles are assigned to causal participants has largely been neglected in the psychological literature on force dynamics. Inspired by the linguistic theory of Dowty (1991), we propose that agency attributions are based on a prototype concept of human intervention. We predicted that the number of criteria a participant in a causal interaction shares with this prototype determines the strength of agency intuitions. We showed in two experiments using versions of Michotte's (1963) launching scenarios that agency intuitions were moderated by manipulations of the context prior to the launching event. Altering features, such as relative movement, sequence of visibility, and self-propelled motion, tended to increase agency attributions to the participant that is normally viewed as patient in the standard scenario. Copyright © 2014 Elsevier B.V. All rights reserved.

L1 Use in EFL Classes with English-Only Policy: Insights from Triangulated Data

ERIC Educational Resources Information Center

Sa'd, Seyyed Hatam Tamimi; Qadermazi, Zohre

2015-01-01

This study examines the role of the use of the L1 in EFL classes from the perspective of EFL learners. The triangulated data were collected using class observations, focus group semi-structured interviews and the learners' written reports of their perceptions and attitudes in a purpose-designed questionnaire. The participants consisted of sixty…

Academic Rigour, Managerial Relevance and Triangulation of Research Methods: A Perspective of Expectations Fulfilment in Postgraduate Education

ERIC Educational Resources Information Center

Hui, Loi Teck; Fatt, Quek Kia

2008-01-01

Developing high-quality human capital and advancing existing knowledge stocks are crucial for the competitive advantage of a nation. The authors argue that offering postgraduate programmes that give great emphasis to academic rigour, managerial relevance and the triangulation of research methods is vital if these ends are to be achieved. They…

Interprofessional collaboration from nurses and physicians – A triangulation of quantitative and qualitative data

PubMed

Schärli, Marianne; Müller, Rita; Martin, Jacqueline S; Spichiger, Elisabeth; Spirig, Rebecca

2017-01-01

Background: Interprofessional collaboration between nurses and physicians is a recurrent challenge in daily clinical practice. To ameliorate the situation, quantitative or qualitative studies are conducted. However, the results of these studies have often been limited by the methods chosen. Aim: To describe the synthesis of interprofessional collaboration from the nursing perspective by triangulating quantitative and qualitative data. Method: Data triangulation was performed as a sub-project of the interprofessional Sinergia DRG Research program. Initially, quantitative and qualitative data were analyzed separately in a mixed methods design. By means of triangulation a „meta-matrix“ resulted in a four-step process. Results: The „meta-matrix“ displays all relevant quantitative and qualitative results as well as their interrelations on one page. Relevance, influencing factors as well as consequences of interprofessional collaboration for patients, relatives and systems become visible. Conclusion: For the first time, the interprofessional collaboration from the nursing perspective at five Swiss hospitals is shown in a „meta-matrix“. The consequences of insufficient collaboration between nurses and physicians are considerable. This is why it’s necessary to invest in interprofessional concepts. In the „meta-matrix“ the factors which influence the interprofessional collaboration positively or negatively are visible.

Research on Visualization of Ground Laser Radar Data Based on Osg

NASA Astrophysics Data System (ADS)

Huang, H.; Hu, C.; Zhang, F.; Xue, H.

2018-04-01

Three-dimensional (3D) laser scanning is a new advanced technology integrating light, machine, electricity, and computer technologies. It can conduct 3D scanning to the whole shape and form of space objects with high precision. With this technology, you can directly collect the point cloud data of a ground object and create the structure of it for rendering. People use excellent 3D rendering engine to optimize and display the 3D model in order to meet the higher requirements of real time realism rendering and the complexity of the scene. OpenSceneGraph (OSG) is an open source 3D graphics engine. Compared with the current mainstream 3D rendering engine, OSG is practical, economical, and easy to expand. Therefore, OSG is widely used in the fields of virtual simulation, virtual reality, science and engineering visualization. In this paper, a dynamic and interactive ground LiDAR data visualization platform is constructed based on the OSG and the cross-platform C++ application development framework Qt. In view of the point cloud data of .txt format and the triangulation network data file of .obj format, the functions of 3D laser point cloud and triangulation network data display are realized. It is proved by experiments that the platform is of strong practical value as it is easy to operate and provides good interaction.

Dynamic causal modelling: a critical review of the biophysical and statistical foundations.

PubMed

Daunizeau, J; David, O; Stephan, K E

2011-09-15

The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003. In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs. Copyright © 2009 Elsevier Inc. All rights reserved.

Advancing understanding of affect labeling with dynamic causal modeling

PubMed Central

Torrisi, Salvatore J.; Lieberman, Matthew D.; Bookheimer, Susan Y.; Altshuler, Lori L.

2013-01-01

Mechanistic understandings of forms of incidental emotion regulation have implications for basic and translational research in the affective sciences. In this study we applied Dynamic Causal Modeling (DCM) for fMRI to a common paradigm of labeling facial affect to elucidate prefrontal to subcortical influences. Four brain regions were used to model affect labeling, including right ventrolateral prefrontal cortex (vlPFC), amygdala and Broca’s area. 64 models were compared, for each of 45 healthy subjects. Family level inference split the model space to a likely driving input and Bayesian Model Selection within the winning family of 32 models revealed a strong pattern of endogenous network connectivity. Modulatory effects of labeling were most prominently observed following Bayesian Model Averaging, with the dampening influence on amygdala originating from Broca’s area but much more strongly from right vlPFC. These results solidify and extend previous correlation and regression-based estimations of negative corticolimbic coupling. PMID:23774393

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time.

PubMed

Galán, S F; Aguado, F; Díez, F J; Mira, J

2002-07-01

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

Histone modification: cause or cog?

PubMed

Henikoff, Steven; Shilatifard, Ali

2011-10-01

Histone modifications are key components of chromatin packaging but whether they constitute a 'code' has been contested. We believe that the central issue is causality: are histone modifications responsible for differences between chromatin states, or are differences in modifications mostly consequences of dynamic processes, such as transcription and nucleosome remodeling? We find that inferences of causality are often based on correlation and that patterns of some key histone modifications are more easily explained as consequences of nucleosome disruption in the presence of histone modifying enzymes. We suggest that the 35-year-old DNA accessibility paradigm provides a mechanistically sound basis for understanding the role of nucleosomes in gene regulation and epigenetic inheritance. Based on this view, histone modifications and variants contribute to diversification of a chromatin landscape shaped by dynamic processes that are driven primarily by transcription and nucleosome remodeling. Copyright © 2011 Elsevier Ltd. All rights reserved.

Construction of Penrose Diagrams for Dynamic Black Holes

NASA Technical Reports Server (NTRS)

Brown, Beth A.; Lindesay, James

2008-01-01

A set of Penrose diagrams is constructed in order to examine the large-scale causal structure of black holes with dynamic horizons. Coordinate dependencies of significant features, such as the event horizon and radial mass scale, are demonstrated on the diagrams. Unlike in static Schwarzschild geometries, the radial mass scale is clearly seen to differ from the horizon. Trajectories for photons near the horizon are briefly discussed.

Design of an impact evaluation using a mixed methods model--an explanatory assessment of the effects of results-based financing mechanisms on maternal healthcare services in Malawi.

PubMed

Brenner, Stephan; Muula, Adamson S; Robyn, Paul Jacob; Bärnighausen, Till; Sarker, Malabika; Mathanga, Don P; Bossert, Thomas; De Allegri, Manuela

2014-04-22

In this article we present a study design to evaluate the causal impact of providing supply-side performance-based financing incentives in combination with a demand-side cash transfer component on equitable access to and quality of maternal and neonatal healthcare services. This intervention is introduced to selected emergency obstetric care facilities and catchment area populations in four districts in Malawi. We here describe and discuss our study protocol with regard to the research aims, the local implementation context, and our rationale for selecting a mixed methods explanatory design with a quasi-experimental quantitative component. The quantitative research component consists of a controlled pre- and post-test design with multiple post-test measurements. This allows us to quantitatively measure 'equitable access to healthcare services' at the community level and 'healthcare quality' at the health facility level. Guided by a theoretical framework of causal relationships, we determined a number of input, process, and output indicators to evaluate both intended and unintended effects of the intervention. Overall causal impact estimates will result from a difference-in-difference analysis comparing selected indicators across intervention and control facilities/catchment populations over time.To further explain heterogeneity of quantitatively observed effects and to understand the experiential dimensions of financial incentives on clients and providers, we designed a qualitative component in line with the overall explanatory mixed methods approach. This component consists of in-depth interviews and focus group discussions with providers, service user, non-users, and policy stakeholders. In this explanatory design comprehensive understanding of expected and unexpected effects of the intervention on both access and quality will emerge through careful triangulation at two levels: across multiple quantitative elements and across quantitative and qualitative elements. Combining a traditional quasi-experimental controlled pre- and post-test design with an explanatory mixed methods model permits an additional assessment of organizational and behavioral changes affecting complex processes. Through this impact evaluation approach, our design will not only create robust evidence measures for the outcome of interest, but also generate insights on how and why the investigated interventions produce certain intended and unintended effects and allows for a more in-depth evaluation approach.

Basic research and data analysis for the National Geodetic Satellite Program

NASA Technical Reports Server (NTRS)

1972-01-01

Investigations of triangulation nets and scaling are reported. Adjustment of the BC-4 worldwide geometric satellite triangulation net is described, along with procedures for correcting type II data and the contents of magnetic tapes containing data from the Pageos network. Computational steps for the further reduction of partially reduced satellite image plate coordinates are outlined. The problem of improving existing triangulation systems by means of satellite super-control points was studied. The SAO-69 geometric solution was scaled with C-band radar data, resulting in SAO and C-band adjustment compatible with one another in the Western Hemisphere. The North America solution NA-6, obtained from GEOS 1 data, was readjusted with new heights as constraints on all 30 optical stations and is referred to as the NA-8 solution.

Quality Tetrahedral Mesh Smoothing via Boundary-Optimized Delaunay Triangulation

PubMed Central

Gao, Zhanheng; Yu, Zeyun; Holst, Michael

2012-01-01

Despite its great success in improving the quality of a tetrahedral mesh, the original optimal Delaunay triangulation (ODT) is designed to move only inner vertices and thus cannot handle input meshes containing “bad” triangles on boundaries. In the current work, we present an integrated approach called boundary-optimized Delaunay triangulation (B-ODT) to smooth (improve) a tetrahedral mesh. In our method, both inner and boundary vertices are repositioned by analytically minimizing the error between a paraboloid function and its piecewise linear interpolation over the neighborhood of each vertex. In addition to the guaranteed volume-preserving property, the proposed algorithm can be readily adapted to preserve sharp features in the original mesh. A number of experiments are included to demonstrate the performance of our method. PMID:23144522

Influence of Resting Venous Blood Volume Fraction on Dynamic Causal Modeling and System Identifiability.

PubMed

Hu, Zhenghui; Ni, Pengyu; Wan, Qun; Zhang, Yan; Shi, Pengcheng; Lin, Qiang

2016-07-08

Changes in BOLD signals are sensitive to the regional blood content associated with the vasculature, which is known as V0 in hemodynamic models. In previous studies involving dynamic causal modeling (DCM) which embodies the hemodynamic model to invert the functional magnetic resonance imaging signals into neuronal activity, V0 was arbitrarily set to a physiolog-ically plausible value to overcome the ill-posedness of the inverse problem. It is interesting to investigate how the V0 value influences DCM. In this study we addressed this issue by using both synthetic and real experiments. The results show that the ability of DCM analysis to reveal information about brain causality depends critically on the assumed V0 value used in the analysis procedure. The choice of V0 value not only directly affects the strength of system connections, but more importantly also affects the inferences about the network architecture. Our analyses speak to a possible refinement of how the hemody-namic process is parameterized (i.e., by making V0 a free parameter); however, the conditional dependencies induced by a more complex model may create more problems than they solve. Obtaining more realistic V0 information in DCM can improve the identifiability of the system and would provide more reliable inferences about the properties of brain connectivity.

Causal dissipation and shock profiles in the relativistic fluid dynamics of pure radiation.

PubMed

Freistühler, Heinrich; Temple, Blake

2014-06-08

CURRENT THEORIES OF DISSIPATION IN THE RELATIVISTIC REGIME SUFFER FROM ONE OF TWO DEFICITS: either their dissipation is not causal or no profiles for strong shock waves exist. This paper proposes a relativistic Navier-Stokes-Fourier-type viscosity and heat conduction tensor such that the resulting second-order system of partial differential equations for the fluid dynamics of pure radiation is symmetric hyperbolic. This system has causal dissipation as well as the property that all shock waves of arbitrary strength have smooth profiles. Entropy production is positive both on gradients near those of solutions to the dissipation-free equations and on gradients of shock profiles. This shows that the new dissipation stress tensor complies to leading order with the principles of thermodynamics. Whether higher order modifications of the ansatz are required to obtain full compatibility with the second law far from the zero-dissipation equilibrium is left to further investigations. The system has exactly three a priori free parameters χ , η , ζ , corresponding physically to heat conductivity, shear viscosity and bulk viscosity. If the bulk viscosity is zero (as is stated in the literature) and the total stress-energy tensor is trace free, the entire viscosity and heat conduction tensor is determined to within a constant factor.

Causal dissipation and shock profiles in the relativistic fluid dynamics of pure radiation

PubMed Central

Freistühler, Heinrich; Temple, Blake

2014-01-01

Current theories of dissipation in the relativistic regime suffer from one of two deficits: either their dissipation is not causal or no profiles for strong shock waves exist. This paper proposes a relativistic Navier–Stokes–Fourier-type viscosity and heat conduction tensor such that the resulting second-order system of partial differential equations for the fluid dynamics of pure radiation is symmetric hyperbolic. This system has causal dissipation as well as the property that all shock waves of arbitrary strength have smooth profiles. Entropy production is positive both on gradients near those of solutions to the dissipation-free equations and on gradients of shock profiles. This shows that the new dissipation stress tensor complies to leading order with the principles of thermodynamics. Whether higher order modifications of the ansatz are required to obtain full compatibility with the second law far from the zero-dissipation equilibrium is left to further investigations. The system has exactly three a priori free parameters χ,η,ζ, corresponding physically to heat conductivity, shear viscosity and bulk viscosity. If the bulk viscosity is zero (as is stated in the literature) and the total stress–energy tensor is trace free, the entire viscosity and heat conduction tensor is determined to within a constant factor. PMID:24910526

Dynamic Uncertain Causality Graph for Knowledge Representation and Probabilistic Reasoning: Directed Cyclic Graph and Joint Probability Distribution.

PubMed

Zhang, Qin

2015-07-01

Probabilistic graphical models (PGMs) such as Bayesian network (BN) have been widely applied in uncertain causality representation and probabilistic reasoning. Dynamic uncertain causality graph (DUCG) is a newly presented model of PGMs, which can be applied to fault diagnosis of large and complex industrial systems, disease diagnosis, and so on. The basic methodology of DUCG has been previously presented, in which only the directed acyclic graph (DAG) was addressed. However, the mathematical meaning of DUCG was not discussed. In this paper, the DUCG with directed cyclic graphs (DCGs) is addressed. In contrast, BN does not allow DCGs, as otherwise the conditional independence will not be satisfied. The inference algorithm for the DUCG with DCGs is presented, which not only extends the capabilities of DUCG from DAGs to DCGs but also enables users to decompose a large and complex DUCG into a set of small, simple sub-DUCGs, so that a large and complex knowledge base can be easily constructed, understood, and maintained. The basic mathematical definition of a complete DUCG with or without DCGs is proved to be a joint probability distribution (JPD) over a set of random variables. The incomplete DUCG as a part of a complete DUCG may represent a part of JPD. Examples are provided to illustrate the methodology.

Stability analysis for the background equations for inflation with dissipation and in a viscous radiation bath

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

Bastero-Gil, Mar; Cerezo, Rafael; Berera, Arjun

2012-11-01

The effects of bulk viscosity are examined for inflationary dynamics in which dissipation and thermalization are present. A complete stability analysis is done for the background inflaton evolution equations, which includes both inflaton dissipation and radiation bulk viscous effects. Three representative approaches of bulk viscous irreversible thermodynamics are analyzed: the Eckart noncausal theory, the linear and causal theory of Israel-Stewart and a more recent nonlinear and causal bulk viscous theory. It is found that the causal theories allow for larger bulk viscosities before encountering an instability in comparison to the noncausal Eckart theory. It is also shown that the causalmore » theories tend to suppress the radiation production due to bulk viscous pressure, because of the presence of relaxation effects implicit in these theories. Bulk viscosity coefficients derived from quantum field theory are applied to warm inflation model building and an analysis is made of the effects to the duration of inflation. The treatment of bulk pressure would also be relevant to the reheating phase after inflation in cold inflation dynamics and during the radiation dominated regime, although very little work in both areas has been done; the methodology developed in this paper could be extended to apply to these other problems.« less

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

NASA Astrophysics Data System (ADS)

Kretschmer, Marlene; Runge, Jakob; Coumou, Dim

2017-08-01

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

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

NASA Astrophysics Data System (ADS)

Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger-)causes vegetation dynamics in most regions globally. More specifically, water availability is the most dominant vegetation driver, being the dominant vegetation driver in 54% of the vegetated surface. Furthermore, our results show that precipitation and soil moisture have prolonged impacts on vegetation in semiarid regions, with up to 10% of additional explained variance on the vegetation dynamics occurring three months later. Finally, hydro-climatic extremes seem to have a remarkable impact on vegetation, since they also explain up to 10% of additional variance of vegetation in certain regions despite their infrequent occurrence. References [1] Papagiannopoulou, C., Miralles, D. G., Verhoest, N. E. C., Dorigo, W. A., and Waegeman, W.: A non-linear Granger causality framework to investigate climate-vegetation dynamics, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-266, in review, 2016.

Large-eddy simulation of propeller noise

NASA Astrophysics Data System (ADS)

Keller, Jacob; Mahesh, Krishnan

2016-11-01

We will discuss our ongoing work towards developing the capability to predict far field sound from the large-eddy simulation of propellers. A porous surface Ffowcs-Williams and Hawkings (FW-H) acoustic analogy, with a dynamic endcapping method (Nitzkorski and Mahesh, 2014) is developed for unstructured grids in a rotating frame of reference. The FW-H surface is generated automatically using Delaunay triangulation and is representative of the underlying volume mesh. The approach is validated for tonal trailing edge sound from a NACA 0012 airfoil. LES of flow around a propeller at design advance ratio is compared to experiment and good agreement is obtained. Results for the emitted far field sound will be discussed. This work is supported by ONR.

Social and Emotional Learning Competencies and Cross-Thematic Curriculum-Related Skills of Greek Students: A Multifactorial and Triangulation Analysis

ERIC Educational Resources Information Center

Tsolou, Olympia; Margaritis, Vasileios

2013-01-01

The cross-thematic curriculum (CTC) for school education has recently been implemented so that the quality of the Greek educational system is improved. This study aimed at assessing social and emotional learning competencies and CTC-related skills of 541 Greek students aged 11-13. Data triangulation was also used for validating these findings,…

Keys to Successful Implementation and Sustainment of Managed Maintenance for Healthcare Facilities

DTIC Science & Technology

2004-03-23

second they involve studying those phenomena in all their complexity (Leedy and Ormrod, 2001). According to Denzin and Lincoln (1994), qualitative...people being studied (Leedy and Ormrod, 2001). Research Design Methodological Triangulation Denzin and Lincoln (1994) suggest because different...the setting. This dual view is refereed to as methodological triangulation ( Denzin and Lincoln , 1994). A research design develops a logical plan for

Neutral Theory and Scale-Free Neural Dynamics

NASA Astrophysics Data System (ADS)

Martinello, Matteo; Hidalgo, Jorge; Maritan, Amos; di Santo, Serena; Plenz, Dietmar; Muñoz, Miguel A.

2017-10-01

Neural tissues have been consistently observed to be spontaneously active and to generate highly variable (scale-free distributed) outbursts of activity in vivo and in vitro. Understanding whether these heterogeneous patterns of activity stem from the underlying neural dynamics operating at the edge of a phase transition is a fascinating possibility, as criticality has been argued to entail many possible important functional advantages in biological computing systems. Here, we employ a well-accepted model for neural dynamics to elucidate an alternative scenario in which diverse neuronal avalanches, obeying scaling, can coexist simultaneously, even if the network operates in a regime far from the edge of any phase transition. We show that perturbations to the system state unfold dynamically according to a "neutral drift" (i.e., guided only by stochasticity) with respect to the background of endogenous spontaneous activity, and that such a neutral dynamics—akin to neutral theories of population genetics and of biogeography—implies marginal propagation of perturbations and scale-free distributed causal avalanches. We argue that causal information, not easily accessible to experiments, is essential to elucidate the nature and statistics of neural avalanches, and that neutral dynamics is likely to play an important role in the cortex functioning. We discuss the implications of these findings to design new empirical approaches to shed further light on how the brain processes and stores information.

Computational dynamic approaches for temporal omics data with applications to systems medicine.

PubMed

Liang, Yulan; Kelemen, Arpad

2017-01-01

Modeling and predicting biological dynamic systems and simultaneously estimating the kinetic structural and functional parameters are extremely important in systems and computational biology. This is key for understanding the complexity of the human health, drug response, disease susceptibility and pathogenesis for systems medicine. Temporal omics data used to measure the dynamic biological systems are essentials to discover complex biological interactions and clinical mechanism and causations. However, the delineation of the possible associations and causalities of genes, proteins, metabolites, cells and other biological entities from high throughput time course omics data is challenging for which conventional experimental techniques are not suited in the big omics era. In this paper, we present various recently developed dynamic trajectory and causal network approaches for temporal omics data, which are extremely useful for those researchers who want to start working in this challenging research area. Moreover, applications to various biological systems, health conditions and disease status, and examples that summarize the state-of-the art performances depending on different specific mining tasks are presented. We critically discuss the merits, drawbacks and limitations of the approaches, and the associated main challenges for the years ahead. The most recent computing tools and software to analyze specific problem type, associated platform resources, and other potentials for the dynamic trajectory and interaction methods are also presented and discussed in detail.

A coupled human-water system from a systems dynamics perspective

NASA Astrophysics Data System (ADS)

Kuil, Linda; Blöschl, Günter; Carr, Gemma

2013-04-01

Traditionally, models used in hydrological studies have frequently assumed stationarity. Moreover, human-induced water resources management activities are often included as external forcings in water cycle dynamics. However, considering humans' current impact on the water cycle in terms of a growing population, river basins increasingly being managed and a climate considerably changing, it has recently been questioned whether this is still correct. Furthermore, research directed at the evolution of water resources and society has shown that the components constituting the human-water system are changing interdependently. Goal of this study is therefore to approach water cycle dynamics from an integrated perspective in which humans are considered as endogenous forces to the system. The method used to model a coupled, urban human-water system is system dynamics. In system dynamics, particular emphasis is placed on feedback loops resulting in dynamic behavior. Time delays and non-linearity can relatively easily be included, making the method appropriate for studying complex systems that change over time. The approach of this study is as follows. First, a conceptual model is created incorporating the key components of the urban human-water system. Subsequently, only those components are selected that are both relevant and show causal loop behavior. Lastly, the causal narratives are translated into mathematical relationships. The outcome will be a simple model that shows only those characteristics with which we are able to explore the two-way coupling between the societal behavior and the water system we depend on.

The Holst spin foam model via cubulations

NASA Astrophysics Data System (ADS)

Baratin, Aristide; Flori, Cecilia; Thiemann, Thomas

2012-10-01

Spin foam models are an attempt at a covariant or path integral formulation of canonical loop quantum gravity. The construction of such models usually relies on the Plebanski formulation of general relativity as a constrained BF theory and is based on the discretization of the action on a simplicial triangulation, which may be viewed as an ultraviolet regulator. The triangulation dependence can be removed by means of group field theory techniques, which allows one to sum over all triangulations. The main tasks for these models are the correct quantum implementation of the Plebanski constraints, the existence of a semiclassical sector implementing additional ‘Regge-like’ constraints arising from simplicial triangulations and the definition of the physical inner product of loop quantum gravity via group field theory. Here we propose a new approach to tackle these issues stemming directly from the Holst action for general relativity, which is also a proper starting point for canonical loop quantum gravity. The discretization is performed by means of a ‘cubulation’ of the manifold rather than a triangulation. We give a direct interpretation of the resulting spin foam model as a generating functional for the n-point functions on the physical Hilbert space at finite regulator. This paper focuses on ideas and tasks to be performed before the model can be taken seriously. However, our analysis reveals some interesting features of this model: firstly, the structure of its amplitudes differs from the standard spin foam models. Secondly, the tetrad n-point functions admit a ‘Wick-like’ structure. Thirdly, the restriction to simple representations does not automatically occur—unless one makes use of the time gauge, just as in the classical theory.

Timing and Causality in the Generation of Learned Eyelid Responses

PubMed Central

Sánchez-Campusano, Raudel; Gruart, Agnès; Delgado-García, José M.

2011-01-01

The cerebellum-red nucleus-facial motoneuron (Mn) pathway has been reported as being involved in the proper timing of classically conditioned eyelid responses. This special type of associative learning serves as a model of event timing for studying the role of the cerebellum in dynamic motor control. Here, we have re-analyzed the firing activities of cerebellar posterior interpositus (IP) neurons and orbicularis oculi (OO) Mns in alert behaving cats during classical eyeblink conditioning, using a delay paradigm. The aim was to revisit the hypothesis that the IP neurons (IPns) can be considered a neuronal phase-modulating device supporting OO Mns firing with an emergent timing mechanism and an explicit correlation code during learned eyelid movements. Optimized experimental and computational tools allowed us to determine the different causal relationships (temporal order and correlation code) during and between trials. These intra- and inter-trial timing strategies expanding from sub-second range (millisecond timing) to longer-lasting ranges (interval timing) expanded the functional domain of cerebellar timing beyond motor control. Interestingly, the results supported the above-mentioned hypothesis. The causal inferences were influenced by the precise motor and pre-motor spike timing in the cause-effect interval, and, in addition, the timing of the learned responses depended on cerebellar–Mn network causality. Furthermore, the timing of CRs depended upon the probability of simulated causal conditions in the cause-effect interval and not the mere duration of the inter-stimulus interval. In this work, the close relation between timing and causality was verified. It could thus be concluded that the firing activities of IPns may be related more to the proper performance of ongoing CRs (i.e., the proper timing as a consequence of the pertinent causality) than to their generation and/or initiation. PMID:21941469

The transmission of fluctuation among price indices based on Granger causality network

NASA Astrophysics Data System (ADS)

Sun, Qingru; Gao, Xiangyun; Wen, Shaobo; Chen, Zhihua; Hao, Xiaoqing

2018-09-01

In this paper, we provide a method of statistical physics to analyze the fluctuation of transmission by constructing Granger causality network among price indices (PIGCN) from a systematical perspective, using complex network theory combined with Granger causality method. In economic system, there are numerous price indices, of which the relationships are extreme complicated. Thus, time series data of 6 types of price indices of China, including 113 kinds of sub price indices, are selected as example of empirical study. Through the analysis of the structure of PIGCN, we identify important price indices with high transmission range, high intermediation capacity, high cohesion and the fluctuation transmission path of price indices, respectively. Furthermore, dynamic relationships among price indices are revealed. Based on these results, we provide several policy implications for monitoring the diffusion of risk of price fluctuation. Our method can also be used to study the price indices of other countries, which is generally applicable.

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer.

PubMed

Besserve, Michel; Lowe, Scott C; Logothetis, Nikos K; Schölkopf, Bernhard; Panzeri, Stefano

2015-01-01

Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections. Yet, the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood. We investigated the role of gamma band (50-80 Hz) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer. We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex. The relationships between gamma phases at different sites (phase shifts) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer. We observed transient stimulus-related changes in the spatial configuration of phases (compatible with changes in direction of gamma wave propagation) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave. These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark, and possibly causally mediate, the dynamic reconfiguration of functional connections.

Shifts of Gamma Phase across Primary Visual Cortical Sites Reflect Dynamic Stimulus-Modulated Information Transfer

PubMed Central

Besserve, Michel; Lowe, Scott C.; Logothetis, Nikos K.; Schölkopf, Bernhard; Panzeri, Stefano

2015-01-01

Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections. Yet, the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood. We investigated the role of gamma band (50–80 Hz) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer. We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex. The relationships between gamma phases at different sites (phase shifts) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer. We observed transient stimulus-related changes in the spatial configuration of phases (compatible with changes in direction of gamma wave propagation) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave. These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark, and possibly causally mediate, the dynamic reconfiguration of functional connections. PMID:26394205

Derivation of formulas for root-mean-square errors in location, orientation, and shape in triangulation solution of an elongated object in space

NASA Technical Reports Server (NTRS)

Long, S. A. T.

1974-01-01

Formulas are derived for the root-mean-square (rms) displacement, slope, and curvature errors in an azimuth-elevation image trace of an elongated object in space, as functions of the number and spacing of the input data points and the rms elevation error in the individual input data points from a single observation station. Also, formulas are derived for the total rms displacement, slope, and curvature error vectors in the triangulation solution of an elongated object in space due to the rms displacement, slope, and curvature errors, respectively, in the azimuth-elevation image traces from different observation stations. The total rms displacement, slope, and curvature error vectors provide useful measure numbers for determining the relative merits of two or more different triangulation procedures applicable to elongated objects in space.

Illness causal beliefs in Turkish immigrants

PubMed Central

Minas, Harry; Klimidis, Steven; Tuncer, Can

2007-01-01

Background People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Methods Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Results Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Conclusion Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different types of causal beliefs are held in relation to somatic or mental illness, and a variety of apparently logically incompatible beliefs may be concurrently held. Illness causal beliefs are dynamic and are related to demographic, modernizing, and acculturative factors, and to the current presence of illness. Any assumption of uniformity of illness causal beliefs within a community, even one that is relatively culturally homogeneous, is likely to be misleading. A better understanding of the diversity, and determinants, of illness causal beliefs can be of value in improving our understanding of illness experience, the clinical process, and in developing more effective health services and population health strategies. PMID:17645806

Illness causal beliefs in Turkish immigrants.

PubMed

Minas, Harry; Klimidis, Steven; Tuncer, Can

2007-07-24

People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and acculturative influences. Different types of causal beliefs are held in relation to somatic or mental illness, and a variety of apparently logically incompatible beliefs may be concurrently held. Illness causal beliefs are dynamic and are related to demographic, modernizing, and acculturative factors, and to the current presence of illness. Any assumption of uniformity of illness causal beliefs within a community, even one that is relatively culturally homogeneous, is likely to be misleading. A better understanding of the diversity, and determinants, of illness causal beliefs can be of value in improving our understanding of illness experience, the clinical process, and in developing more effective health services and population health strategies.

Triangulation of written assessments from patients, teachers and students: useful for students and teachers?

PubMed

Gran, Sarah Frandsen; Braend, Anja Maria; Lindbaek, Morten

2010-01-01

Many medical students in general practice clerkships experience lack of observation-based feedback. The StudentPEP project combined written feedback from patients, observing teachers and students. This study analyzes the perceived usefulness of triangulated written feedback. A total of 71 general practitioners and 79 medical students at the University of Oslo completed project evaluation forms after a 6-week clerkship. A principal component analysis was performed to find structures within the questionnaire. Regression analysis was performed regarding students' answers to whether StudentPEP was worthwhile. Free-text answers were analyzed qualitatively. Student and teacher responses were mixed within six subscales, with highest agreement on 'Teachers oral and written feedback' and 'Attitude to patient evaluation'. Fifty-four per cent of the students agreed that the triangulation gave concrete feedback on their weaknesses, and 59% valued the teachers' feedback provided. Two statements regarding the teacher's attitudes towards StudentPEP were significantly associated with the student's perception of worthwhileness. Qualitative analysis showed that patient evaluations were encouraging or distrusted. Some students thought that StudentPEP ensured observation and feedback. The patient evaluations increased the students' awareness of the patient perspective. A majority of the students considered the triangulated written feedback beneficial, although time-consuming. The teacher's attitudes strongly influenced how the students perceived the usefulness of StudentPEP.

Shared decision-making in medical encounters regarding breast cancer treatment: the contribution of methodological triangulation.

PubMed

Durif-Bruckert, C; Roux, P; Morelle, M; Mignotte, H; Faure, C; Moumjid-Ferdjaoui, N

2015-07-01

The aim of this study on shared decision-making in the doctor-patient encounter about surgical treatment for early-stage breast cancer, conducted in a regional cancer centre in France, was to further the understanding of patient perceptions on shared decision-making. The study used methodological triangulation to collect data (both quantitative and qualitative) about patient preferences in the context of a clinical consultation in which surgeons followed a shared decision-making protocol. Data were analysed from a multi-disciplinary research perspective (social psychology and health economics). The triangulated data collection methods were questionnaires (n = 132), longitudinal interviews (n = 47) and observations of consultations (n = 26). Methodological triangulation revealed levels of divergence and complementarity between qualitative and quantitative results that suggest new perspectives on the three inter-related notions of decision-making, participation and information. Patients' responses revealed important differences between shared decision-making and participation per se. The authors note that subjecting patients to a normative behavioural model of shared decision-making in an era when paradigms of medical authority are shifting may undermine the patient's quest for what he or she believes is a more important right: a guarantee of the best care available. © 2014 John Wiley & Sons Ltd.

Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data.

PubMed

Sharaev, Maksim G; Zavyalova, Viktoria V; Ushakov, Vadim L; Kartashov, Sergey I; Velichkovsky, Boris M

2016-01-01

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state.

Evaluation of UK Integrated Care Pilots: research protocol

PubMed Central

Ling, Tom; Bardsley, Martin; Adams, John; Lewis, Richard; Roland, Martin

2010-01-01

Background In response to concerns that the needs of the aging population for well-integrated care were increasing, the English National Health Service (NHS) appointed 16 Integrated Care Pilots following a national competition. The pilots have a range of aims including development of new organisational structures to support integration, changes in staff roles, reducing unscheduled emergency hospital admissions, reduced length of hospital stay, increasing patient satisfaction, and reducing cost. This paper describes the evaluation of the initiative which has been commissioned. Study design and data collection methods A mixed methods approach has been adopted including interviews with staff and patients, non-participant observation of meetings, structured written feedback from sites, questionnaires to patients and staff, and analysis of routinely collected hospital utilisation data for patients/service users. The qualitative analysis aims to identify the approaches taken to integration by the sites, the benefits which result, the context in which benefits have resulted, and the mechanisms by which they occur. Methods of analysis The quantitative analysis adopts a ‘difference in differences’ approach comparing health care utilisation before and after the intervention with risk-matched controls. The qualitative data analysis adopts a ‘theory of change’ approach in which we triangulate data from the quantitative analysis with qualitative data in order to describe causal effects (what happens when an independent variable changes) and causal mechanisms (what connects causes to their effects). An economic analysis will identify what incremental resources are required to make integration succeed and how they can be combined efficiently to produce better outcomes for patients. Conclusion This evaluation will produce a portfolio of evidence aimed at strengthening the evidence base for integrated care, and in particular identifying the context in which interventions are likely to be effective. These data will support a series of evaluation judgements aimed at reducing uncertainties about the role of integrated care in improving the efficient and effective delivery of healthcare. PMID:20922068

Evaluation of UK Integrated Care Pilots: research protocol.

PubMed

Ling, Tom; Bardsley, Martin; Adams, John; Lewis, Richard; Roland, Martin

2010-09-27

In response to concerns that the needs of the aging population for well-integrated care were increasing, the English National Health Service (NHS) appointed 16 Integrated Care Pilots following a national competition. The pilots have a range of aims including development of new organisational structures to support integration, changes in staff roles, reducing unscheduled emergency hospital admissions, reduced length of hospital stay, increasing patient satisfaction, and reducing cost. This paper describes the evaluation of the initiative which has been commissioned. A mixed methods approach has been adopted including interviews with staff and patients, non-participant observation of meetings, structured written feedback from sites, questionnaires to patients and staff, and analysis of routinely collected hospital utilisation data for patients/service users. The qualitative analysis aims to identify the approaches taken to integration by the sites, the benefits which result, the context in which benefits have resulted, and the mechanisms by which they occur. The quantitative analysis adopts a 'difference in differences' approach comparing health care utilisation before and after the intervention with risk-matched controls. The qualitative data analysis adopts a 'theory of change' approach in which we triangulate data from the quantitative analysis with qualitative data in order to describe causal effects (what happens when an independent variable changes) and causal mechanisms (what connects causes to their effects). An economic analysis will identify what incremental resources are required to make integration succeed and how they can be combined efficiently to produce better outcomes for patients. This evaluation will produce a portfolio of evidence aimed at strengthening the evidence base for integrated care, and in particular identifying the context in which interventions are likely to be effective. These data will support a series of evaluation judgements aimed at reducing uncertainties about the role of integrated care in improving the efficient and effective delivery of healthcare.

Information transfer and synchronization among the scales of climate variability: clues for understanding anomalies and extreme events?

NASA Astrophysics Data System (ADS)

Palus, Milan

2017-04-01

Deeper understanding of complex dynamics of the Earth atmosphere and climate is inevitable for sustainable development, mitigation and adaptation strategies for global change and for prediction of and resilience against extreme events. Traditional (linear) approaches cannot explain or even detect nonlinear interactions of dynamical processes evolving on multiple spatial and temporal scales. Combination of nonlinear dynamics and information theory explains synchronization as a process of adjustment of information rates [1] and causal relations (à la Granger) as information transfer [2]. Information born in dynamical complexity or information transferred among systems on a way to synchronization might appear as an abstract quantity, however, information transfer is tied to a transfer of mass and energy, as demonstrated in a recent study using directed (causal) climate networks [2]. Recently, an information transfer across scales of atmospheric dynamics has been observed [3]. In particular, a climate oscillation with the period around 7-8 years has been identified as a factor influencing variability of surface air temperature (SAT) on shorter time scales. Its influence on the amplitude of the SAT annual cycle was estimated in the range 0.7-1.4 °C and the effect on the overall variability of the SAT anomalies (SATA) leads to the changes 1.5-1.7 °C in the annual SATA means. The strongest effect of the 7-8 year cycle was observed in the winter SATA means where it reaches 4-5 °C in central European station and reanalysis data [4]. In the dynamics of El Niño-Southern Oscillation, three principal time scales have been identified: the annual cycle (AC), the quasibiennial (QB) mode(s) and the low-frequency (LF) variability. An intricate causal network of information flows among these modes helps to understand the occurrence of extreme El Niño events, characterized by synchronization of the QB modes and AC, and modulation of the QB amplitude by the LF mode. The latter also influences the phase of the AC and QB modes. These examples provide an inspiration for a discussion how novel data analysis methods, based on topics from nonlinear dynamical systems, their synchronization, (Granger) causality and information transfer, in combination with dynamical and statistical models of different complexity, can help in understanding and prediction of climate variability on different scales and in estimating probability of occurrence of extreme climate events. [1] M. Palus, V. Komarek, Z. Hrncir, K. Sterbova, Phys. Rev. E, 63(4), 046211 (2001) http://www.cs.cas.cz/mp/epr/sir1-a.html [2] J. Hlinka, N. Jajcay, D. Hartman, M. Palus, Smooth Information Flow in Temperature Climate Network Reflects Mass Transport, submitted to Chaos. http://www.cs.cas.cz/mp/epr/vetry-a.html [3] M. Palus, Phys. Rev. Lett. 112 078702 (2014) http://www.cs.cas.cz/mp/epr/xf1-a.html [4] N. Jajcay, J. Hlinka, S. Kravtsov, A. A. Tsonis, M. Palus, Geophys. Res. Lett. 43(2), 902-909 (2016) http://www.cs.cas.cz/mp/epr/xfgrl1-a.html

Limitations in electrophysiological model development and validation caused by differences between simulations and experimental protocols.

PubMed

Carro, Jesús; Rodríguez-Matas, José F; Monasterio, Violeta; Pueyo, Esther

2017-10-01

Models of ion channel dynamics are usually built by fitting isolated cell experimental values of individual parameters while neglecting the interaction between them. Another shortcoming regards the estimation of ionic current conductances, which is often based on quantification of Action Potential (AP)-derived markers. Although this procedure reduces the uncertainty in the calculation of conductances, many studies evaluate electrophysiological AP-derived markers from single cell simulations, whereas experimental measurements are obtained from tissue preparations. In this work, we explore the limitations of these approaches to estimate ion channel dynamics and maximum current conductances and how they could be overcome by using multiscale simulations of experimental protocols. Four human ventricular cell models, namely ten Tusscher and Panfilov (2006), Grandi et al. (2010), O'Hara et al. (2011), and Carro et al. (2011), were used. Two problems involving scales from ion channels to tissue were investigated: 1) characterization of L-type calcium voltage-dependent inactivation I Ca,L ; 2) identification of major ionic conductance contributors to steady-state AP markers, including APD 90 , APD 75 , APD 50 , APD 25 , Triangulation and maximal and minimal values of V and dV/dt during the AP (V max , V min , dV/dt max , dV/dt min ). Our results show that: 1) I Ca,L inactivation characteristics differed significantly when calculated from model equations and from simulations reproducing the experimental protocols. 2) Large differences were found in the ionic currents contributors to APD 25 , Triangulation, V max , dV/dt max and dV/dt min between single cells and 1D-tissue. When proposing any new model formulation, or evaluating an existing model, consistency between simulated and experimental data should be verified considering all involved effects and scales. Copyright © 2016 Elsevier Ltd. All rights reserved.

Models of conceptual understanding in human respiration and strategies for instruction

NASA Astrophysics Data System (ADS)

Rea-Ramirez, Mary Anne

Prior research has indicated that students of all ages show little understanding of respiration beyond breathing in and out and the need for air to survive. This occurs even after instruction with alternative conceptions persisting into adulthood. Whether this is due to specific educational strategies or to the level of difficulty in understanding a complex system is an important question. The purpose of this study was to obtain a deeper understanding of middle school students' development of mental models of human respiration. The study was composed of two major parts, one concerned with documenting and analyzing how students learn, and one concerned with measuring the effect of teaching strategies. This was carried out through a pre-test, "learning aloud" case studies in which students engaged in one-on-one tutoring interviews with the researcher, and a post-test. Transcript data from the intervention and post-test indicates that all students in this study were successful in constructing mental models of a complex concept, respiration, and in successfully applying these mental models to transfer problems. Differences in the pretest and posttest means were on the order of two standard deviations in size. While findings were uncovered in the use of a variety of strategies, possibly most interesting are the new views of analogies as an instructional strategy. Some analogies appear to be effective in supporting construction of visual/spatial features. Providing multiple, simple analogies that allow the student to construct new models in small steps, using student generated analogies, and using analogies to determine prior knowledge may also increase the effectiveness of analogies. Evidence suggested that students were able to extend the dynamic properties of certain analogies to the dynamics of the target conception and that this, in turn, allowed students to use the new models to explain causal relationships and give new function to models. This suggests that construction of causal, dynamic mental models is supported by the use of analogies containing dynamic and causal relationships.

Integrated information in discrete dynamical systems: motivation and theoretical framework.

PubMed

Balduzzi, David; Tononi, Giulio

2008-06-13

This paper introduces a time- and state-dependent measure of integrated information, phi, which captures the repertoire of causal states available to a system as a whole. Specifically, phi quantifies how much information is generated (uncertainty is reduced) when a system enters a particular state through causal interactions among its elements, above and beyond the information generated independently by its parts. Such mathematical characterization is motivated by the observation that integrated information captures two key phenomenological properties of consciousness: (i) there is a large repertoire of conscious experiences so that, when one particular experience occurs, it generates a large amount of information by ruling out all the others; and (ii) this information is integrated, in that each experience appears as a whole that cannot be decomposed into independent parts. This paper extends previous work on stationary systems and applies integrated information to discrete networks as a function of their dynamics and causal architecture. An analysis of basic examples indicates the following: (i) phi varies depending on the state entered by a network, being higher if active and inactive elements are balanced and lower if the network is inactive or hyperactive. (ii) phi varies for systems with identical or similar surface dynamics depending on the underlying causal architecture, being low for systems that merely copy or replay activity states. (iii) phi varies as a function of network architecture. High phi values can be obtained by architectures that conjoin functional specialization with functional integration. Strictly modular and homogeneous systems cannot generate high phi because the former lack integration, whereas the latter lack information. Feedforward and lattice architectures are capable of generating high phi but are inefficient. (iv) In Hopfield networks, phi is low for attractor states and neutral states, but increases if the networks are optimized to achieve tension between local and global interactions. These basic examples appear to match well against neurobiological evidence concerning the neural substrates of consciousness. More generally, phi appears to be a useful metric to characterize the capacity of any physical system to integrate information.

Relationships between infant mortality, birth spacing and fertility in Matlab, Bangladesh.

PubMed

van Soest, Arthur; Saha, Unnati Rani

2018-01-01

Although research on the fertility response to childhood mortality is widespread in demographic literature, very few studies focused on the two-way causal relationships between infant mortality and fertility. Understanding the nature of such relationships is important in order to design effective policies to reduce child mortality and improve family planning. In this study, we use dynamic panel data techniques to analyse the causal effects of infant mortality on birth intervals and fertility, as well as the causal effects of birth intervals on mortality in rural Bangladesh, accounting for unobserved heterogeneity and reverse causality. Simulations based upon the estimated model show whether (and to what extent) mortality and fertility can be reduced by breaking the causal links between short birth intervals and infant mortality. We find a replacement effect of infant mortality on total fertility of about 0.54 children for each infant death in the comparison area with standard health services. Eliminating the replacement effect would lengthen birth intervals and reduce the total number of births, resulting in a fall in mortality by 2.45 children per 1000 live births. These effects are much smaller in the treatment area with extensive health services and information on family planning, where infant mortality is smaller, birth intervals are longer, and total fertility is lower. In both areas, we find evidence of boy preference in family planning.

Relationships between infant mortality, birth spacing and fertility in Matlab, Bangladesh

PubMed Central

van Soest, Arthur

2018-01-01

Although research on the fertility response to childhood mortality is widespread in demographic literature, very few studies focused on the two-way causal relationships between infant mortality and fertility. Understanding the nature of such relationships is important in order to design effective policies to reduce child mortality and improve family planning. In this study, we use dynamic panel data techniques to analyse the causal effects of infant mortality on birth intervals and fertility, as well as the causal effects of birth intervals on mortality in rural Bangladesh, accounting for unobserved heterogeneity and reverse causality. Simulations based upon the estimated model show whether (and to what extent) mortality and fertility can be reduced by breaking the causal links between short birth intervals and infant mortality. We find a replacement effect of infant mortality on total fertility of about 0.54 children for each infant death in the comparison area with standard health services. Eliminating the replacement effect would lengthen birth intervals and reduce the total number of births, resulting in a fall in mortality by 2.45 children per 1000 live births. These effects are much smaller in the treatment area with extensive health services and information on family planning, where infant mortality is smaller, birth intervals are longer, and total fertility is lower. In both areas, we find evidence of boy preference in family planning. PMID:29702692

Designing Incentives for Marine Corps Cyber Workforce Retention

DTIC Science & Technology

2014-12-01

transformation, which Burke and Litwin (1992) describe as distinct sets of organizational dynamics that are required for genuine change in...information- security-analysts.htm . Burke, W. Warner, and George H. Litwin . 1992. “A Causal Model of Organizational Performance and Change.” Journal

Resting-state brain networks revealed by granger causal connectivity in frogs.

PubMed

Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

2016-10-15

Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Three-dimensional unstructured grid refinement and optimization using edge-swapping

NASA Technical Reports Server (NTRS)

Gandhi, Amar; Barth, Timothy

1993-01-01

This paper presents a three-dimensional (3-D) 'edge-swapping method based on local transformations. This method extends Lawson's edge-swapping algorithm into 3-D. The 3-D edge-swapping algorithm is employed for the purpose of refining and optimizing unstructured meshes according to arbitrary mesh-quality measures. Several criteria including Delaunay triangulations are examined. Extensions from two to three dimensions of several known properties of Delaunay triangulations are also discussed.

Personal authentication using hand vein triangulation and knuckle shape.

PubMed

Kumar, Ajay; Prathyusha, K Venkata

2009-09-01

This paper presents a new approach to authenticate individuals using triangulation of hand vein images and simultaneous extraction of knuckle shape information. The proposed method is fully automated and employs palm dorsal hand vein images acquired from the low-cost, near infrared, contactless imaging. The knuckle tips are used as key points for the image normalization and extraction of region of interest. The matching scores are generated in two parallel stages: (i) hierarchical matching score from the four topologies of triangulation in the binarized vein structures and (ii) from the geometrical features consisting of knuckle point perimeter distances in the acquired images. The weighted score level combination from these two matching scores are used to authenticate the individuals. The achieved experimental results from the proposed system using contactless palm dorsal-hand vein images are promising (equal error rate of 1.14%) and suggest more user friendly alternative for user identification.

Application of Stereo Vision to the Reconnection Scaling Experiment

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

Klarenbeek, Johnny; Sears, Jason A.; Gao, Kevin W.

The measurement and simulation of the three-dimensional structure of magnetic reconnection in astrophysical and lab plasmas is a challenging problem. At Los Alamos National Laboratory we use the Reconnection Scaling Experiment (RSX) to model 3D magnetohydrodynamic (MHD) relaxation of plasma filled tubes. These magnetic flux tubes are called flux ropes. In RSX, the 3D structure of the flux ropes is explored with insertable probes. Stereo triangulation can be used to compute the 3D position of a probe from point correspondences in images from two calibrated cameras. While common applications of stereo triangulation include 3D scene reconstruction and robotics navigation, wemore » will investigate the novel application of stereo triangulation in plasma physics to aid reconstruction of 3D data for RSX plasmas. Several challenges will be explored and addressed, such as minimizing 3D reconstruction errors in stereo camera systems and dealing with point correspondence problems.« less

Interactive Display of Surfaces Using Subdivision Surfaces and Wavelets

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

Duchaineau, M A; Bertram, M; Porumbescu, S

2001-10-03

Complex surfaces and solids are produced by large-scale modeling and simulation activities in a variety of disciplines. Productive interaction with these simulations requires that these surfaces or solids be viewable at interactive rates--yet many of these surfaced solids can contain hundreds of millions of polygondpolyhedra. Interactive display of these objects requires compression techniques to minimize storage, and fast view-dependent triangulation techniques to drive the graphics hardware. In this paper, we review recent advances in subdivision-surface wavelet compression and optimization that can be used to provide a framework for both compression and triangulation. These techniques can be used to produce suitablemore » approximations of complex surfaces of arbitrary topology, and can be used to determine suitable triangulations for display. The techniques can be used in a variety of applications in computer graphics, computer animation and visualization.« less

U(1) current from the AdS/CFT: diffusion, conductivity and causality

NASA Astrophysics Data System (ADS)

Bu, Yanyan; Lublinsky, Michael; Sharon, Amir

2016-04-01

For a holographically defined finite temperature theory, we derive an off-shell constitutive relation for a global U(1) current driven by a weak external non-dynamical electromagnetic field. The constitutive relation involves an all order gradient expansion resummed into three momenta-dependent transport coefficient functions: diffusion, electric conductivity, and "magnetic" conductivity. These transport functions are first computed analytically in the hydrodynamic limit, up to third order in the derivative expansion, and then numerically for generic values of momenta. We also compute a diffusion memory function, which, as a result of all order gradient resummation, is found to be causal.

Predictive monitoring research: Summary of the PREMON system

NASA Technical Reports Server (NTRS)

Doyle, Richard J.; Sellers, Suzanne M.; Atkinson, David J.

1987-01-01

Traditional approaches to monitoring are proving inadequate in the face of two important issues: the dynamic adjustment of expectations about sensor values when the behavior of the device is too complex to enumerate beforehand, and the selective but effective interpretation of sensor readings when the number of sensors becomes overwhelming. This system addresses these issues by building an explicit model of a device and applying common-sense theories of physics to model causality in the device. The resulting causal simulation of the device supports planning decisions about how to efficiently yet reliably utilize a limited number of sensors to verify correct operation of the device.

Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue

PubMed Central

Kjeldsen, Henrik D.; Kaiser, Marcus; Whittington, Miles A.

2015-01-01

Background Brain function is dependent upon the concerted, dynamical interactions between a great many neurons distributed over many cortical subregions. Current methods of quantifying such interactions are limited by consideration only of single direct or indirect measures of a subsample of all neuronal population activity. New method Here we present a new derivation of the electromagnetic analogy to near-field acoustic holography allowing high-resolution, vectored estimates of interactions between sources of electromagnetic activity that significantly improves this situation. In vitro voltage potential recordings were used to estimate pseudo-electromagnetic energy flow vector fields, current and energy source densities and energy dissipation in reconstruction planes at depth into the neural tissue parallel to the recording plane of the microelectrode array. Results The properties of the reconstructed near-field estimate allowed both the utilization of super-resolution techniques to increase the imaging resolution beyond that of the microelectrode array, and facilitated a novel approach to estimating causal relationships between activity in neocortical subregions. Comparison with existing methods The holographic nature of the reconstruction method allowed significantly better estimation of the fine spatiotemporal detail of neuronal population activity, compared with interpolation alone, beyond the spatial resolution of the electrode arrays used. Pseudo-energy flow vector mapping was possible with high temporal precision, allowing a near-realtime estimate of causal interaction dynamics. Conclusions Basic near-field electromagnetic holography provides a powerful means to increase spatial resolution from electrode array data with careful choice of spatial filters and distance to reconstruction plane. More detailed approaches may provide the ability to volumetrically reconstruct activity patterns on neuronal tissue, but the ability to extract vectored data with the method presented already permits the study of dynamic causal interactions without bias from any prior assumptions on anatomical connectivity. PMID:26026581

Near-field electromagnetic holography for high-resolution analysis of network interactions in neuronal tissue.

PubMed

Kjeldsen, Henrik D; Kaiser, Marcus; Whittington, Miles A

2015-09-30

Brain function is dependent upon the concerted, dynamical interactions between a great many neurons distributed over many cortical subregions. Current methods of quantifying such interactions are limited by consideration only of single direct or indirect measures of a subsample of all neuronal population activity. Here we present a new derivation of the electromagnetic analogy to near-field acoustic holography allowing high-resolution, vectored estimates of interactions between sources of electromagnetic activity that significantly improves this situation. In vitro voltage potential recordings were used to estimate pseudo-electromagnetic energy flow vector fields, current and energy source densities and energy dissipation in reconstruction planes at depth into the neural tissue parallel to the recording plane of the microelectrode array. The properties of the reconstructed near-field estimate allowed both the utilization of super-resolution techniques to increase the imaging resolution beyond that of the microelectrode array, and facilitated a novel approach to estimating causal relationships between activity in neocortical subregions. The holographic nature of the reconstruction method allowed significantly better estimation of the fine spatiotemporal detail of neuronal population activity, compared with interpolation alone, beyond the spatial resolution of the electrode arrays used. Pseudo-energy flow vector mapping was possible with high temporal precision, allowing a near-realtime estimate of causal interaction dynamics. Basic near-field electromagnetic holography provides a powerful means to increase spatial resolution from electrode array data with careful choice of spatial filters and distance to reconstruction plane. More detailed approaches may provide the ability to volumetrically reconstruct activity patterns on neuronal tissue, but the ability to extract vectored data with the method presented already permits the study of dynamic causal interactions without bias from any prior assumptions on anatomical connectivity. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals

PubMed Central

Stetter, Olav; Battaglia, Demian; Soriano, Jordi; Geisel, Theo

2012-01-01

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local. PMID:22927808

A Free Energy Principle for Biological Systems

PubMed Central

Karl, Friston

2012-01-01

This paper describes a free energy principle that tries to explain the ability of biological systems to resist a natural tendency to disorder. It appeals to circular causality of the sort found in synergetic formulations of self-organization (e.g., the slaving principle) and models of coupled dynamical systems, using nonlinear Fokker Planck equations. Here, circular causality is induced by separating the states of a random dynamical system into external and internal states, where external states are subject to random fluctuations and internal states are not. This reduces the problem to finding some (deterministic) dynamics of the internal states that ensure the system visits a limited number of external states; in other words, the measure of its (random) attracting set, or the Shannon entropy of the external states is small. We motivate a solution using a principle of least action based on variational free energy (from statistical physics) and establish the conditions under which it is formally equivalent to the information bottleneck method. This approach has proved useful in understanding the functional architecture of the brain. The generality of variational free energy minimisation and corresponding information theoretic formulations may speak to interesting applications beyond the neurosciences; e.g., in molecular or evolutionary biology. PMID:23204829

The dynamic relationships between economic status and health measures among working-age adults in the United States.

PubMed

Meraya, Abdulkarim M; Dwibedi, Nilanjana; Tan, Xi; Innes, Kim; Mitra, Sophie; Sambamoorthi, Usha

2018-04-18

We examine the dynamic relationships between economic status and health measures using data from 8 waves of the Panel Study of Income Dynamics from 1999 to 2013. Health measures are self-rated health (SRH) and functional limitations; economic status measures are labor income (earnings), family income, and net wealth. We use 3 different types of models: (a) ordinary least squares regression, (b) first-difference, and (c) system-generalized method of moment (GMM). Using ordinary least squares regression and first difference models, we find that higher levels of economic status are associated with better SRH and functional status among both men and women, although declines in income and wealth are associated with a decline in health for men only. Using system-GMM estimators, we find evidence of a causal link from labor income to SRH and functional status for both genders. Among men only, system-GMM results indicate that there is a causal link from net wealth to SRH and functional status. Results overall highlight the need for integrated economic and health policies, and for policies that mitigate the potential adverse health effects of short-term changes in economic status. Copyright © 2018 John Wiley & Sons, Ltd.

Influence of Resting Venous Blood Volume Fraction on Dynamic Causal Modeling and System Identifiability

PubMed Central

Hu, Zhenghui; Ni, Pengyu; Wan, Qun; Zhang, Yan; Shi, Pengcheng; Lin, Qiang

2016-01-01

Changes in BOLD signals are sensitive to the regional blood content associated with the vasculature, which is known as V0 in hemodynamic models. In previous studies involving dynamic causal modeling (DCM) which embodies the hemodynamic model to invert the functional magnetic resonance imaging signals into neuronal activity, V0 was arbitrarily set to a physiolog-ically plausible value to overcome the ill-posedness of the inverse problem. It is interesting to investigate how the V0 value influences DCM. In this study we addressed this issue by using both synthetic and real experiments. The results show that the ability of DCM analysis to reveal information about brain causality depends critically on the assumed V0 value used in the analysis procedure. The choice of V0 value not only directly affects the strength of system connections, but more importantly also affects the inferences about the network architecture. Our analyses speak to a possible refinement of how the hemody-namic process is parameterized (i.e., by making V0 a free parameter); however, the conditional dependencies induced by a more complex model may create more problems than they solve. Obtaining more realistic V0 information in DCM can improve the identifiability of the system and would provide more reliable inferences about the properties of brain connectivity. PMID:27389074

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature

DOE PAGES

Tsonis, Anastasios A.; Deyle, Ethan R.; May, Robert M.; ...

2015-03-02

As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR). Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research and elsewhere, demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we findmore » no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. Furthermore, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.« less

Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature

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

Tsonis, Anastasios A.; Deyle, Ethan R.; May, Robert M.

As early as 1959, it was hypothesized that an indirect link between solar activity and climate could be mediated by mechanisms controlling the flux of galactic cosmic rays (CR). Although the connection between CR and climate remains controversial, a significant body of laboratory evidence has emerged at the European Organization for Nuclear Research and elsewhere, demonstrating the theoretical mechanism of this link. In this article, we present an analysis based on convergent cross mapping, which uses observational time series data to directly examine the causal link between CR and year-to-year changes in global temperature. Despite a gross correlation, we findmore » no measurable evidence of a causal effect linking CR to the overall 20th-century warming trend. Furthermore, on short interannual timescales, we find a significant, although modest, causal effect between CR and short-term, year-to-year variability in global temperature that is consistent with the presence of nonlinearities internal to the system. Thus, although CR do not contribute measurably to the 20th-century global warming trend, they do appear as a nontraditional forcing in the climate system on short interannual timescales.« less

A system dynamics evaluation model: implementation of health information exchange for public health reporting

PubMed Central

Merrill, Jacqueline A; Deegan, Michael; Wilson, Rosalind V; Kaushal, Rainu; Fredericks, Kimberly

2013-01-01

Objective To evaluate the complex dynamics involved in implementing electronic health information exchange (HIE) for public health reporting at a state health department, and to identify policy implications to inform similar implementations. Materials and methods Qualitative data were collected over 8 months from seven experts at New York State Department of Health who implemented web services and protocols for querying, receipt, and validation of electronic data supplied by regional health information organizations. Extensive project documentation was also collected. During group meetings experts described the implementation process and created reference modes and causal diagrams that the evaluation team used to build a preliminary model. System dynamics modeling techniques were applied iteratively to build causal loop diagrams representing the implementation. The diagrams were validated iteratively by individual experts followed by group review online, and through confirmatory review of documents and artifacts. Results Three casual loop diagrams captured well-recognized system dynamics: Sliding Goals, Project Rework, and Maturity of Resources. The findings were associated with specific policies that address funding, leadership, ensuring expertise, planning for rework, communication, and timeline management. Discussion This evaluation illustrates the value of a qualitative approach to system dynamics modeling. As a tool for strategic thinking on complicated and intense processes, qualitative models can be produced with fewer resources than a full simulation, yet still provide insights that are timely and relevant. Conclusions System dynamics techniques clarified endogenous and exogenous factors at play in a highly complex technology implementation, which may inform other states engaged in implementing HIE supported by federal Health Information Technology for Economic and Clinical Health (HITECH) legislation. PMID:23292910

A system dynamics evaluation model: implementation of health information exchange for public health reporting.

PubMed

Merrill, Jacqueline A; Deegan, Michael; Wilson, Rosalind V; Kaushal, Rainu; Fredericks, Kimberly

2013-06-01

To evaluate the complex dynamics involved in implementing electronic health information exchange (HIE) for public health reporting at a state health department, and to identify policy implications to inform similar implementations. Qualitative data were collected over 8 months from seven experts at New York State Department of Health who implemented web services and protocols for querying, receipt, and validation of electronic data supplied by regional health information organizations. Extensive project documentation was also collected. During group meetings experts described the implementation process and created reference modes and causal diagrams that the evaluation team used to build a preliminary model. System dynamics modeling techniques were applied iteratively to build causal loop diagrams representing the implementation. The diagrams were validated iteratively by individual experts followed by group review online, and through confirmatory review of documents and artifacts. Three casual loop diagrams captured well-recognized system dynamics: Sliding Goals, Project Rework, and Maturity of Resources. The findings were associated with specific policies that address funding, leadership, ensuring expertise, planning for rework, communication, and timeline management. This evaluation illustrates the value of a qualitative approach to system dynamics modeling. As a tool for strategic thinking on complicated and intense processes, qualitative models can be produced with fewer resources than a full simulation, yet still provide insights that are timely and relevant. System dynamics techniques clarified endogenous and exogenous factors at play in a highly complex technology implementation, which may inform other states engaged in implementing HIE supported by federal Health Information Technology for Economic and Clinical Health (HITECH) legislation.

Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools.

PubMed

Siettos, Constantinos; Starke, Jens

2016-09-01

The extreme complexity of the brain naturally requires mathematical modeling approaches on a large variety of scales; the spectrum ranges from single neuron dynamics over the behavior of groups of neurons to neuronal network activity. Thus, the connection between the microscopic scale (single neuron activity) to macroscopic behavior (emergent behavior of the collective dynamics) and vice versa is a key to understand the brain in its complexity. In this work, we attempt a review of a wide range of approaches, ranging from the modeling of single neuron dynamics to machine learning. The models include biophysical as well as data-driven phenomenological models. The discussed models include Hodgkin-Huxley, FitzHugh-Nagumo, coupled oscillators (Kuramoto oscillators, Rössler oscillators, and the Hindmarsh-Rose neuron), Integrate and Fire, networks of neurons, and neural field equations. In addition to the mathematical models, important mathematical methods in multiscale modeling and reconstruction of the causal connectivity are sketched. The methods include linear and nonlinear tools from statistics, data analysis, and time series analysis up to differential equations, dynamical systems, and bifurcation theory, including Granger causal connectivity analysis, phase synchronization connectivity analysis, principal component analysis (PCA), independent component analysis (ICA), and manifold learning algorithms such as ISOMAP, and diffusion maps and equation-free techniques. WIREs Syst Biol Med 2016, 8:438-458. doi: 10.1002/wsbm.1348 For further resources related to this article, please visit the WIREs website. © 2016 Wiley Periodicals, Inc.

Synchronization and Causality Across Time-scales: Complex Dynamics and Extremes in El Niño/Southern Oscillation

NASA Astrophysics Data System (ADS)

Jajcay, N.; Kravtsov, S.; Tsonis, A.; Palus, M.

2017-12-01

A better understanding of dynamics in complex systems, such as the Earth's climate is one of the key challenges for contemporary science and society. A large amount of experimental data requires new mathematical and computational approaches. Natural complex systems vary on many temporal and spatial scales, often exhibiting recurring patterns and quasi-oscillatory phenomena. The statistical inference of causal interactions and synchronization between dynamical phenomena evolving on different temporal scales is of vital importance for better understanding of underlying mechanisms and a key for modeling and prediction of such systems. This study introduces and applies information theory diagnostics to phase and amplitude time series of different wavelet components of the observed data that characterizes El Niño. A suite of significant interactions between processes operating on different time scales was detected, and intermittent synchronization among different time scales has been associated with the extreme El Niño events. The mechanisms of these nonlinear interactions were further studied in conceptual low-order and state-of-the-art dynamical, as well as statistical climate models. Observed and simulated interactions exhibit substantial discrepancies, whose understanding may be the key to an improved prediction. Moreover, the statistical framework which we apply here is suitable for direct usage of inferring cross-scale interactions in nonlinear time series from complex systems such as the terrestrial magnetosphere, solar-terrestrial interactions, seismic activity or even human brain dynamics.

Mutation-induced protein interaction kinetics changes affect apoptotic network dynamic properties and facilitate oncogenesis

PubMed Central

Zhao, Linjie; Sun, Tanlin; Pei, Jianfeng; Ouyang, Qi

2015-01-01

It has been a consensus in cancer research that cancer is a disease caused primarily by genomic alterations, especially somatic mutations. However, the mechanism of mutation-induced oncogenesis is not fully understood. Here, we used the mitochondrial apoptotic pathway as a case study and performed a systematic analysis of integrating pathway dynamics with protein interaction kinetics to quantitatively investigate the causal molecular mechanism of mutation-induced oncogenesis. A mathematical model of the regulatory network was constructed to establish the functional role of dynamic bifurcation in the apoptotic process. The oncogenic mutation enrichment of each of the protein functional domains involved was found strongly correlated with the parameter sensitivity of the bifurcation point. We further dissected the causal mechanism underlying this correlation by evaluating the mutational influence on protein interaction kinetics using molecular dynamics simulation. We analyzed 29 matched mutant–wild-type and 16 matched SNP—wild-type protein systems. We found that the binding kinetics changes reflected by the changes of free energy changes induced by protein interaction mutations, which induce variations in the sensitive parameters of the bifurcation point, were a major cause of apoptosis pathway dysfunction, and mutations involved in sensitive interaction domains show high oncogenic potential. Our analysis provided a molecular basis for connecting protein mutations, protein interaction kinetics, network dynamics properties, and physiological function of a regulatory network. These insights provide a framework for coupling mutation genotype to tumorigenesis phenotype and help elucidate the logic of cancer initiation. PMID:26170328

Population dynamics of the felted beech scale and associated Neonectria species, causal agents of beech bark disease

Treesearch

Jeffrey Garnas; David Houston; Matthew Ayres; Celia Evans

2009-01-01

Biotic threats to tree growth, survival, or reproduction often arise from interactions among a suite of species, primarily insects and fungi, that function together to varying degrees to defeat host defenses, secure resources, and infect...

Bianchi I cosmology in the presence of a causally regularized viscous fluid

NASA Astrophysics Data System (ADS)

Montani, Giovanni; Venanzi, Marta

2017-07-01

We analyze the dynamics of a Bianchi I cosmology in the presence of a viscous fluid, causally regularized according to the Lichnerowicz approach. We show how the effect induced by shear viscosity is still able to produce a matter creation phenomenon, meaning that also in the regularized theory we address, the Universe is emerging from a singularity with a vanishing energy density value. We discuss the structure of the singularity in the isotropic limit, when bulk viscosity is the only retained contribution. We see that, as far as viscosity is not a dominant effect, the dynamics of the isotropic Universe possesses the usual non-viscous power-law behaviour but in correspondence to an effective equation of state, depending on the bulk viscosity coefficient. Finally, we show that, in the limit of a strong non-thermodynamical equilibrium of the Universe mimicked by a dominant contribution of the effective viscous pressure, a power-law inflation behaviour of the Universe appears, the cosmological horizons are removed and a significant amount of entropy is produced.

Bidirectional communication between amygdala and fusiform gyrus during facial recognition.

PubMed

Herrington, John D; Taylor, James M; Grupe, Daniel W; Curby, Kim M; Schultz, Robert T

2011-06-15

Decades of research have documented the specialization of fusiform gyrus (FG) for facial information processes. Recent theories indicate that FG activity is shaped by input from amygdala, but effective connectivity from amygdala to FG remains undocumented. In this fMRI study, 39 participants completed a face recognition task. 11 participants underwent the same experiment approximately four months later. Robust face-selective activation of FG, amygdala, and lateral occipital cortex were observed. Dynamic causal modeling and Bayesian Model Selection (BMS) were used to test the intrinsic connections between these structures, and their modulation by face perception. BMS results strongly favored a dynamic causal model with bidirectional, face-modulated amygdala-FG connections. However, the right hemisphere connections diminished at time 2, with the face modulation parameter no longer surviving Bonferroni correction. These findings suggest that amygdala strongly influences FG function during face perception, and that this influence is shaped by experience and stimulus salience. Copyright © 2011 Elsevier Inc. All rights reserved.

Bayesian model reduction and empirical Bayes for group (DCM) studies

PubMed Central

Friston, Karl J.; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E.; van Wijk, Bernadette C.M.; Ziegler, Gabriel; Zeidman, Peter

2016-01-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level – e.g., dynamic causal models – and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. PMID:26569570

Effective connectivities of cortical regions for top-down face processing: A Dynamic Causal Modeling study

PubMed Central

Li, Jun; Liu, Jiangang; Liang, Jimin; Zhang, Hongchuan; Zhao, Jizheng; Rieth, Cory A.; Huber, David E.; Li, Wu; Shi, Guangming; Ai, Lin; Tian, Jie; Lee, Kang

2013-01-01

To study top-down face processing, the present study used an experimental paradigm in which participants detected non-existent faces in pure noise images. Conventional BOLD signal analysis identified three regions involved in this illusory face detection. These regions included the left orbitofrontal cortex (OFC) in addition to the right fusiform face area (FFA) and right occipital face area (OFA), both of which were previously known to be involved in both top-down and bottom-up processing of faces. We used Dynamic Causal Modeling (DCM) and Bayesian model selection to further analyze the data, revealing both intrinsic and modulatory effective connectivities among these three cortical regions. Specifically, our results support the claim that the orbitofrontal cortex plays a crucial role in the top-down processing of faces by regulating the activities of the occipital face area, and the occipital face area in turn detects the illusory face features in the visual stimuli and then provides this information to the fusiform face area for further analysis. PMID:20423709

Comparing Families of Dynamic Causal Models

PubMed Central

Penny, Will D.; Stephan, Klaas E.; Daunizeau, Jean; Rosa, Maria J.; Friston, Karl J.; Schofield, Thomas M.; Leff, Alex P.

2010-01-01

Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of that model. This “best model” approach is very useful but can become brittle if there are a large number of models to compare, and if different subjects use different models. To overcome this shortcoming we propose the combination of two further approaches: (i) family level inference and (ii) Bayesian model averaging within families. Family level inference removes uncertainty about aspects of model structure other than the characteristic of interest. For example: What are the inputs to the system? Is processing serial or parallel? Is it linear or nonlinear? Is it mediated by a single, crucial connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data. PMID:20300649

A case-study in the clinical epidemiology of psoriatic arthritis: multistate models and causal arguments

PubMed Central

O'Keeffe, Aidan G; Tom, Brian D M; Farewell, Vernon T

2011-01-01

In psoriatic arthritis, permanent joint damage characterizes disease progression and represents a major debilitating aspect of the disease. Understanding the process of joint damage will assist in the treatment and disease management of patients. Multistate models provide a means to examine patterns of disease, such as symmetric joint damage. Additionally, the link between damage and the dynamic course of disease activity (represented by joint swelling and stress pain) at both the individual joint level and otherwise can be represented within a correlated multistate model framework. Correlation is reflected through the use of random effects for progressive models and robust variance estimation for non-progressive models. Such analyses, undertaken with data from a large psoriatic arthritis cohort, are discussed and the extent to which they permit causal reasoning is considered. For this, emphasis is given to the use of the Bradford Hill criteria for causation in observational studies and the concept of local (in)dependence to capture the dynamic nature of the relationships. PMID:22163372

Energy prediction using spatiotemporal pattern networks

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

Jiang, Zhanhong; Liu, Chao; Akintayo, Adedotun

This paper presents a novel data-driven technique based on the spatiotemporal pattern network (STPN) for energy/power prediction for complex dynamical systems. Built on symbolic dynamical filtering, the STPN framework is used to capture not only the individual system characteristics but also the pair-wise causal dependencies among different sub-systems. To quantify causal dependencies, a mutual information based metric is presented and an energy prediction approach is subsequently proposed based on the STPN framework. To validate the proposed scheme, two case studies are presented, one involving wind turbine power prediction (supply side energy) using the Western Wind Integration data set generated bymore » the National Renewable Energy Laboratory (NREL) for identifying spatiotemporal characteristics, and the other, residential electric energy disaggregation (demand side energy) using the Building America 2010 data set from NREL for exploring temporal features. In the energy disaggregation context, convex programming techniques beyond the STPN framework are developed and applied to achieve improved disaggregation performance.« less

Profiling neuronal ion channelopathies with non-invasive brain imaging and dynamic causal models: Case studies of single gene mutations

PubMed Central

Gilbert, Jessica R.; Symmonds, Mkael; Hanna, Michael G.; Dolan, Raymond J.; Friston, Karl J.; Moran, Rosalyn J.

2016-01-01

Clinical assessments of brain function rely upon visual inspection of electroencephalographic waveform abnormalities in tandem with functional magnetic resonance imaging. However, no current technology proffers in vivo assessments of activity at synapses, receptors and ion-channels, the basis of neuronal communication. Using dynamic causal modeling we compared electrophysiological responses from two patients with distinct monogenic ion channelopathies and a large cohort of healthy controls to demonstrate the feasibility of assaying synaptic-level channel communication non-invasively. Synaptic channel abnormality was identified in both patients (100% sensitivity) with assay specificity above 89%, furnishing estimates of neurotransmitter and voltage-gated ion throughput of sodium, calcium, chloride and potassium. This performance indicates a potential novel application as an adjunct for clinical assessments in neurological and psychiatric settings. More broadly, these findings indicate that biophysical models of synaptic channels can be estimated non-invasively, having important implications for advancing human neuroimaging to the level of non-invasive ion channel assays. PMID:26342528

Dynamics of the CRRES barium releases in the magnetosphere

NASA Technical Reports Server (NTRS)

Fuselier, S. A.; Mende, S. B.; Geller, S. P.; Miller, M.; Hoffman, R. A.; Wygant, J. R.; Pongratz, M.; Meredith, N. P.; Anderson, R. R.

1994-01-01

The Combined Release and Radiation Effects Satellite (CRRES) G-2, G-3, and G-4 ionized and neutral barium cloud positions are triangulated from ground-based optical data. From the time history of the ionized cloud motion perpendicular to the magnetic field, the late time coupling of the ionized cloud with the collisionless ambient plasma in the magnetosphere is investigated for each of the releases. The coupling of the ionized clouds with the ambient medium is quantitatively consistent with predictions from theory in that the coupling time increases with increasing distance from the Earth. Quantitative comparison with simple theory for the couping time also yields reasonable agreement. Other effects not predicted by the theory are discussed in the context of the observations.

RIACS

NASA Technical Reports Server (NTRS)

Oliger, Joseph

1997-01-01

Topics considered include: high-performance computing; cognitive and perceptual prostheses (computational aids designed to leverage human abilities); autonomous systems. Also included: development of a 3D unstructured grid code based on a finite volume formulation and applied to the Navier-stokes equations; Cartesian grid methods for complex geometry; multigrid methods for solving elliptic problems on unstructured grids; algebraic non-overlapping domain decomposition methods for compressible fluid flow problems on unstructured meshes; numerical methods for the compressible navier-stokes equations with application to aerodynamic flows; research in aerodynamic shape optimization; S-HARP: a parallel dynamic spectral partitioner; numerical schemes for the Hamilton-Jacobi and level set equations on triangulated domains; application of high-order shock capturing schemes to direct simulation of turbulence; multicast technology; network testbeds; supercomputer consolidation project.

Automatic anatomical structures location based on dynamic shape measurement

NASA Astrophysics Data System (ADS)

Witkowski, Marcin; Rapp, Walter; Sitnik, Robert; Kujawinska, Malgorzata; Vander Sloten, Jos; Haex, Bart; Bogaert, Nico; Heitmann, Kjell

2005-09-01

New image processing methods and active photonics apparatus have made possible the development of relatively inexpensive optical systems for complex shape and object measurements. We present dynamic 360° scanning method for analysis of human lower body biomechanics, with an emphasis on the analysis of the knee joint. The anatomical structure (of high medical interest) that is possible to scan and analyze, is patella. Tracking of patella position and orientation under dynamic conditions may lead to detect pathological patella movements and help in knee joint disease diagnosis. The processed data is obtained from a dynamic laser triangulation surface measurement system, able to capture slow to normal movements with a scan frequency between 15 and 30 Hz. These frequency rates are enough to capture controlled movements used e.g. for medical examination purposes. The purpose of the work presented is to develop surface analysis methods that may be used as support of diagnosis of motoric abilities of lower limbs. The paper presents algorithms used to process acquired lower limbs surface data in order to find the position and orientation of patella. The algorithms implemented include input data preparation, curvature description methods, knee region discrimination and patella assumed position/orientation calculation. Additionally, a method of 4D (3D + time) medical data visualization is proposed. Also some exemplary results are presented.

Diagnosing and Reconstructing Real-World Hydroclimatic Dynamics from Time Sequenced Data: The Case of Saltwater Intrusion into Coastal Wetlands in Everglades National Park

NASA Astrophysics Data System (ADS)

Huffaker, R.; Munoz-Carpena, R.

2016-12-01

There are increasing calls to audit decision-support models used for environmental policy to ensure that they correspond with the reality facing policy makers. Modelers can establish correspondence by providing empirical evidence of real-world dynamic behavior that their models skillfully simulate. We present a pre-modeling diagnostic framework—based on nonlinear dynamic analysis—for detecting and reconstructing real-world environmental dynamics from observed time-sequenced data. Phenomenological (data-driven) modeling—based on machine learning regression techniques—extracts a set of ordinary differential equations governing empirically-diagnosed system dynamics from a single time series, or from multiple time series on causally-interacting variables. We apply the framework to investigate saltwater intrusion into coastal wetlands in Everglades National Park, Florida, USA. We test the following hypotheses posed in the literature linking regional hydrologic variables with global climatic teleconnections: (1) Sea level in Florida Bay drives well level and well salinity in the coastal Everglades; (2) Atlantic Multidecadal Oscillation (AMO) drives sea level, well level and well salinity; and (3) AMO and (El Niño Southern Oscillation) ENSO bi-causally interact. The thinking is that salt water intrusion links ocean-surface salinity with salinity of inland water sources, and sea level with inland water; that AMO and ENSO share a teleconnective relationship (perhaps through the atmosphere); and that AMO and ENSO both influence inland precipitation and thus well levels. Our results support these hypotheses, and we successfully construct a parsimonious phenomenological model that reproduces diagnosed nonlinear dynamics and system interactions. We propose that reconstructed data dynamics be used, along with other expert information, as a rigorous benchmark to guide specification and testing of hydrologic decision support models corresponding with real-world behavior.

Angles-Only Navigation: Position and Velocity Solution from Absolute Triangulation

DTIC Science & Technology

2011-01-01

geocentric position vectors. Using two vectors derived from each such observation (see next section), a solution for a portion of the boat’s track was...t)x0 describes the curvature of the path in the direction x 0, which, for a geocentric coordinate system and /(t) < 0, will be toward the center of...finite distances, with geocentric coordinates known to a meter or better (readily available on the Internet) a straightfor- ward triangulation method

General Theory and Algorithms for the Non-Casual Inversion, Slewing and Control of Space-Based Articulated Structures

DTIC Science & Technology

1993-10-01

Structures: Simultaneous Trajectory Tracking and Vibration Reduction ... 10 3 . Buckling Control of a Flexible Beam Using Piezoelectric Actuators...bounded solution for the inverse dynamic torque has to be non-causal. Bayo, et. al. [ 3 ], extended the inverse dynamics to planar, multiple-link systems...presented by &ayo and Moulin [4] for the single link system, with provisions for 3 extension to multiple link systems. An equivalent time domain approach for

Arrows in Comprehending and Producing Mechanical Diagrams

ERIC Educational Resources Information Center

Heiser, Julie; Tversky, Barbara

2006-01-01

Mechanical systems have structural organizations--parts, and their relations--and functional organizations--temporal, dynamic, and causal processes--which can be explained using text or diagrams. Two experiments illustrate the role of arrows in diagrams of mechanical systems. In Experiment 1, people described diagrams with or without arrows,…

From Dualism to Unity in Quantum Physics

NASA Astrophysics Data System (ADS)

Landé, Alfred

2016-02-01

Preface; Introduction; 1. Causality, chance, continuity; 2. States, observables, probabilities; 3. The metric law of probabilities; 4. Quantum dynamics; 5. Quantum fact and fiction; Retrospect. From dualism to unity, from positivism to realism; Appendix 1. Survey of elementary postulates; Appendix 2. Two problems of uniqueness; References; Index.

Location accuracy evaluation of lightning location systems using natural lightning flashes recorded by a network of high-speed cameras

NASA Astrophysics Data System (ADS)

Alves, J.; Saraiva, A. C. V.; Campos, L. Z. D. S.; Pinto, O., Jr.; Antunes, L.

2014-12-01

This work presents a method for the evaluation of location accuracy of all Lightning Location System (LLS) in operation in southeastern Brazil, using natural cloud-to-ground (CG) lightning flashes. This can be done through a multiple high-speed cameras network (RAMMER network) installed in the Paraiba Valley region - SP - Brazil. The RAMMER network (Automated Multi-camera Network for Monitoring and Study of Lightning) is composed by four high-speed cameras operating at 2,500 frames per second. Three stationary black-and-white (B&W) cameras were situated in the cities of São José dos Campos and Caçapava. A fourth color camera was mobile (installed in a car), but operated in a fixed location during the observation period, within the city of São José dos Campos. The average distance among cameras was 13 kilometers. Each RAMMER sensor position was determined so that the network can observe the same lightning flash from different angles and all recorded videos were GPS (Global Position System) time stamped, allowing comparisons of events between cameras and the LLS. The RAMMER sensor is basically composed by a computer, a Phantom high-speed camera version 9.1 and a GPS unit. The lightning cases analyzed in the present work were observed by at least two cameras, their position was visually triangulated and the results compared with BrasilDAT network, during the summer seasons of 2011/2012 and 2012/2013. The visual triangulation method is presented in details. The calibration procedure showed an accuracy of 9 meters between the accurate GPS position of the object triangulated and the result from the visual triangulation method. Lightning return stroke positions, estimated with the visual triangulation method, were compared with LLS locations. Differences between solutions were not greater than 1.8 km.

Qualitative to quantitative: linked trajectory of method triangulation in a study on HIV/AIDS in Goa, India.

PubMed

Bailey, Ajay; Hutter, Inge

2008-10-01

With 3.1 million people estimated to be living with HIV/AIDS in India and 39.5 million people globally, the epidemic has posed academics the challenge of identifying behaviours and their underlying beliefs in the effort to reduce the risk of HIV transmission. The Health Belief Model (HBM) is frequently used to identify risk behaviours and adherence behaviour in the field of HIV/AIDS. Risk behaviour studies that apply HBM have been largely quantitative and use of qualitative methodology is rare. The marriage of qualitative and quantitative methods has never been easy. The challenge is in triangulating the methods. Method triangulation has been largely used to combine insights from the qualitative and quantitative methods but not to link both the methods. In this paper we suggest a linked trajectory of method triangulation (LTMT). The linked trajectory aims to first gather individual level information through in-depth interviews and then to present the information as vignettes in focus group discussions. We thus validate information obtained from in-depth interviews and gather emic concepts that arise from the interaction. We thus capture both the interpretation and the interaction angles of the qualitative method. Further, using the qualitative information gained, a survey is designed. In doing so, the survey questions are grounded and contextualized. We employed this linked trajectory of method triangulation in a study on the risk assessment of HIV/AIDS among migrant and mobile men. Fieldwork was carried out in Goa, India. Data come from two waves of studies, first an explorative qualitative study (2003), second a larger study (2004-2005), including in-depth interviews (25), focus group discussions (21) and a survey (n=1259). By employing the qualitative to quantitative LTMT we can not only contextualize the existing concepts of the HBM, but also validate new concepts and identify new risk groups.

Investigation of shock waves in the relativistic Riemann problem: A comparison of viscous fluid dynamics to kinetic theory

NASA Astrophysics Data System (ADS)

Bouras, I.; Molnár, E.; Niemi, H.; Xu, Z.; El, A.; Fochler, O.; Greiner, C.; Rischke, D. H.

2010-08-01

We solve the relativistic Riemann problem in viscous matter using the relativistic Boltzmann equation and the relativistic causal dissipative fluid-dynamical approach of Israel and Stewart. Comparisons between these two approaches clarify and point out the regime of validity of second-order fluid dynamics in relativistic shock phenomena. The transition from ideal to viscous shocks is demonstrated by varying the shear viscosity to entropy density ratio η/s. We also find that a good agreement between these two approaches requires a Knudsen number Kn<1/2.

Investigation of shock waves in the relativistic Riemann problem: A comparison of viscous fluid dynamics to kinetic theory

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

Bouras, I.; El, A.; Fochler, O.

2010-08-15

We solve the relativistic Riemann problem in viscous matter using the relativistic Boltzmann equation and the relativistic causal dissipative fluid-dynamical approach of Israel and Stewart. Comparisons between these two approaches clarify and point out the regime of validity of second-order fluid dynamics in relativistic shock phenomena. The transition from ideal to viscous shocks is demonstrated by varying the shear viscosity to entropy density ratio {eta}/s. We also find that a good agreement between these two approaches requires a Knudsen number Kn<1/2.

Entanglement, holography and causal diamonds

NASA Astrophysics Data System (ADS)

de Boer, Jan; Haehl, Felix M.; Heller, Michal P.; Myers, Robert C.

2016-08-01

We argue that the degrees of freedom in a d-dimensional CFT can be reorganized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2 d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglemententropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

Lending to Parents and Insuring Children: Is There a Role for Microcredit in Complementing Health Insurance in Rural China?

PubMed

You, Jing

2016-05-01

This paper assesses the causal impact on child health of borrowing formal microcredit for Chinese rural households by exploiting a panel dataset (2000 and 2004) in a poor northwest province. Endogenous borrowing is controlled for in a dynamic regression-discontinuity design creating a quasi-experimental environment for causal inferences. There is causal relationship running from formal microcredit to improved child health in the short term, while past borrowing behaviour has no protracted impact on subsequent child health outcomes. Moreover, formal microcredit appears to be a complement to health insurance in improving child health through two mechanisms-it enhances affordability for out-of-pocket health care expenditure and helps buffer consumption against adverse health shocks and financial risk incurred by current health insurance arrangements. Government efforts in expanding health insurance for rural households would be more likely to achieve its optimal goals of improving child health outcomes if combined with sufficient access to formal microcredit. Copyright © 2015 John Wiley & Sons, Ltd.

[The dual process model of addiction. Towards an integrated model?].

PubMed

Vandermeeren, R; Hebbrecht, M

2012-01-01

Neurobiology and cognitive psychology have provided us with a dual process model of addiction. According to this model, behavior is considered to be the dynamic result of a combination of automatic and controlling processes. In cases of addiction the balance between these two processes is severely disturbed. Automated processes will continue to produce impulses that ensure the continuance of addictive behavior. Weak, reflective or controlling processes are both the reason for and the result of the inability to forgo addiction. To identify features that are common to current neurocognitive insights into addiction and psychodynamic views on addiction. The picture that emerges from research is not clear. There is some evidence that attentional bias has a causal effect on addiction. There is no evidence that automatic associations have a causal effect, but there is some evidence that automatic action-tendencies do have a causal effect. Current neurocognitive views on the dual process model of addiction can be integrated with an evidence-based approach to addiction and with psychodynamic views on addiction.

Quantum Interactive Dualism: An Alternative to Materialism

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

Stapp, Henry P

2005-06-01

Materialism rest implicitly upon the general conception of nature promoted by Galileo and Newton during the seventeenth century. It features the causal closure of the physical: The course of physically described events for all time is fixed by laws that refer exclusively to the physically describeable features of nature, and initial conditions on these feature. No reference to subjective thoughts or feeling of human beings enter. That simple conception of nature was found during the first quarter of the twentieth century to be apparently incompatible with the empirical facts. The founders of quantum theory created a new fundamental physical theory,more » quantum theory, which introduced crucially into the causal structure certain conscious choices made by human agents about how they will act. These conscious human choices are ''free'' in the sense that they are not fixed by the known laws. But they can influence the course of physically described events. Thus the principle of the causal closure of the physical fails. Applications in psycho-neuro-dynamics are described.« less

Causal Inference and Explaining Away in a Spiking Network

PubMed Central

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Causal Inference and Explaining Away in a Spiking Network.

PubMed

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-12-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

A note on statistical analysis of shape through triangulation of landmarks

PubMed Central

Rao, C. Radhakrishna

2000-01-01

In an earlier paper, the author jointly with S. Suryawanshi proposed statistical analysis of shape through triangulation of landmarks on objects. It was observed that the angles of the triangles are invariant to scaling, location, and rotation of objects. No distinction was made between an object and its reflection. The present paper provides the methodology of shape discrimination when reflection is also taken into account and makes suggestions for modifications to be made when some of the landmarks are collinear. PMID:10737780

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

Wu, Qishi; Berry, M. L..; Grieme, M.

We propose a localization-based radiation source detection (RSD) algorithm using the Ratio of Squared Distance (ROSD) method. Compared with the triangulation-based method, the advantages of this ROSD method are multi-fold: i) source location estimates based on four detectors improve their accuracy, ii) ROSD provides closed-form source location estimates and thus eliminates the imaginary-roots issue, and iii) ROSD produces a unique source location estimate as opposed to two real roots (if any) in triangulation, and obviates the need to identify real phantom roots during clustering.

Understanding factors that influence the integration of acute malnutrition interventions into the national health system in Niger.

PubMed

Deconinck, Hedwig; Hallarou, Mahaman Elh; Pesonen, Anais; Gérard, Jean Christophe; Criel, Bart; Donnen, Philippe; Macq, Jean

2016-12-01

Since 2007 to address a high burden, integration of acute malnutrition has been promoted in Niger. This paper studies factors that influenced the integration process of acute malnutrition into the Niger national health system.We used qualitative methods of observation, key informant interviews and focus group discussions at national level, two districts and nine communities selected through convenience sampling, as well as document review. A framework approach constructed around the problem, intervention, adoption system, health system characteristics and broad context guided the analysis. Data were recorded on paper, transcribed in a descriptive record, coded by themes deduced by building on the framework and triangulated for comprehensiveness.Key facilitating factors identified were knowledge and recognition of the problem helped by accurate information; effectiveness of decentralized continuity of care; compatibility with goals, support and involvement of health actors; and leadership for aligning policies and partnerships and mobilizing resources within a favourable political context driven by multisectoral development goals. Key hindering factors identified were not fully understanding severity, causes and consequences of the problem; limited utilization and trust in health interventions; high workload, and health worker turnover and attrition; and high dependence on financial and technical support based on short-term emergency funding within a context of high demographic pressure.The study uncovered influencing factors of integrating acute malnutrition into the national health system and their complex dynamics and relationships. It elicited the need for goal-oriented strategies and alignment of health actors to achieve sustainability, and systems thinking to understand pathways that foster integration. We recommend that context-specific learning of integrating acute malnutrition may expand to include causal modelling and scenario testing to inform strategy designs. The method may also be applied to monitor progress of integrating nutrition by the multisectoral nutrition plan to guide change. © The Author 2016. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Quantum Dynamics of Multi Harmonic Oscillators Described by Time Variant Conic Hamiltonian and their Use in Contemporary Sciences

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

Demiralp, Metin

This work focuses on the dynamics of a system of quantum multi harmonic oscillators whose Hamiltonian is conic in positions and momenta with time variant coefficients. While it is simple, this system is useful for modeling the dynamics of a number of systems in contemporary sciences where the equations governing spatial or temporal changes are described by sets of ODEs. The dynamical causal models used readily in neuroscience can be indirectly described by these systems. In this work, we want to show that it is possible to describe these systems using quantum wave function type entities and expectations if themore » dynamic of the system is related to a set of ODEs.« less

Directed dynamical influence is more detectable with noise

PubMed Central

Jiang, Jun-Jie; Huang, Zi-Gang; Huang, Liang; Liu, Huan; Lai, Ying-Cheng

2016-01-01

Successful identification of directed dynamical influence in complex systems is relevant to significant problems of current interest. Traditional methods based on Granger causality and transfer entropy have issues such as difficulty with nonlinearity and large data requirement. Recently a framework based on nonlinear dynamical analysis was proposed to overcome these difficulties. We find, surprisingly, that noise can counterintuitively enhance the detectability of directed dynamical influence. In fact, intentionally injecting a proper amount of asymmetric noise into the available time series has the unexpected benefit of dramatically increasing confidence in ascertaining the directed dynamical influence in the underlying system. This result is established based on both real data and model time series from nonlinear ecosystems. We develop a physical understanding of the beneficial role of noise in enhancing detection of directed dynamical influence. PMID:27066763

Directed dynamical influence is more detectable with noise.

PubMed

Jiang, Jun-Jie; Huang, Zi-Gang; Huang, Liang; Liu, Huan; Lai, Ying-Cheng

2016-04-12

Successful identification of directed dynamical influence in complex systems is relevant to significant problems of current interest. Traditional methods based on Granger causality and transfer entropy have issues such as difficulty with nonlinearity and large data requirement. Recently a framework based on nonlinear dynamical analysis was proposed to overcome these difficulties. We find, surprisingly, that noise can counterintuitively enhance the detectability of directed dynamical influence. In fact, intentionally injecting a proper amount of asymmetric noise into the available time series has the unexpected benefit of dramatically increasing confidence in ascertaining the directed dynamical influence in the underlying system. This result is established based on both real data and model time series from nonlinear ecosystems. We develop a physical understanding of the beneficial role of noise in enhancing detection of directed dynamical influence.

Numerical simulation of distributed snow processes in complex terrain utilizing triangulated irregular networks (TINs)

NASA Astrophysics Data System (ADS)

Rinehart, A. J.; Vivoni, E. R.

2005-12-01

Snow processes play a significant role in the hydrologic cycle of mountainous and high-latitude catchments in the western United States. Snowmelt runoff contributes to a large percentage of stream runoff while snow covered regions remain highly localized to small portions of the catchment area. The appropriate representation of snow dynamics at a given range of spatial and temporal scales is critical for adequately predicting runoff responses in snowmelt-dominated watersheds. In particular, the accurate depiction of snow cover patterns is important as a range of topographic, land-use and geographic parameters create zones of preferential snow accumulation or ablation that significantly affect the timing of a region's snow melt and the persistence of a snow pack. In this study, we present the development and testing of a distributed snow model designed for simulations over complex terrain. The snow model is developed within the context of the TIN-based Real-time Integrated Basin Simulator (tRIBS), a fully-distributed watershed model capable of continuous simulations of coupled hydrological processes, including unsaturated-saturated zone dynamics, land-atmosphere interactions and runoff generation via multiple mechanisms. The use of triangulated irregular networks as a domain discretization allows tRIBS to accurately represent topography with a reduced number of computational nodes, as compared to traditional grid-based models. This representation is developed using a Delauney optimization criterion that causes areas of topographic homogeneity to be represented at larger spatial scales than the original grid, while more heterogeneous areas are represented at higher resolutions. We utilize the TIN-based terrain representation to simulate microscale (10-m to 100-m) snow pack dynamics over a catchment. The model includes processes such as the snow pack energy balance, wind and bulk redistribution, and snow interception by vegetation. For this study, we present tests from a distributed one-layer energy balance model as applied to a northern New Mexico hillslope in a ponderosa pine forest using both synthetic and real meteorological forcing. We also provide tests of the model's capability to represent spatial patterns within a small watershed in the Jemez Mountain region. Finally, we discuss the interaction of the tested snow process module with existing components in the watershed model and additional applications and capabilities under development.

Causal simulation and sensor planning in predictive monitoring

NASA Technical Reports Server (NTRS)

Doyle, Richard J.

1989-01-01

Two issues are addressed which arise in the task of detecting anomalous behavior in complex systems with numerous sensor channels: how to adjust alarm thresholds dynamically, within the changing operating context of the system, and how to utilize sensors selectively, so that nominal operation can be verified reliably without processing a prohibitive amount of sensor data. The approach involves simulation of a causal model of the system, which provides information on expected sensor values, and on dependencies between predicted events, useful in assessing the relative importance of events so that sensor resources can be allocated effectively. The potential applicability of this work to the execution monitoring of robot task plans is briefly discussed.

Spatio-temporal dynamics of Fusarium head blight and Trichothecene toxin types in Canada

USDA-ARS?s Scientific Manuscript database

In many parts of the world Fusarium graminearum is the primary causal agent of Fusarium head blight (FHB), a disease of cereal crops that adversely affects crop yield, food safety, and animal health. We previously demonstrated population structure associated with differences in trichothecene toxin t...

Consumer Online Search and New-Product Marketing

ERIC Educational Resources Information Center

Kim, Ho

2013-01-01

This dissertation contains three essays that study the implications of online search activity for new-product marketing. Using the U.S. motion picture industry as a test case, the first essay examines the dynamic causal relationship between traditional media, consumers' media generation activity, media consumption activity, and market demand…

Choosing Among Causal Agents in a Dynamic Environment

DTIC Science & Technology

2009-07-30

Participants in a video game environment were required to make a series of decisions in which they must identify which of three targets was causing a...was higher but not when prior video game experience was controlled for. In contrast, women observed their targets for much longer before making a

Gender-Linked Perceptions and Causal Attributions of Female/Male Competencies.

ERIC Educational Resources Information Center

Major, Harriet; Plake, Barbara S.

Undergraduate students (N=518) rated graduate application materials for males or females applying to traditionally perceived masculine or feminine fields. Independent variables were rater's pro/anti feminism, sex of subject, sex of referent, sex of field, and sex of attributes. Dependent variables were academic competence, personal dynamics,…

A "Bottom-Up" Approach to Food Web Construction

ERIC Educational Resources Information Center

Demetriou, Dorita; Korfiatis, Konstantinos; Constantinou, Constantinos

2009-01-01

The ability to comprehend trophic (nutritional) relationships and food web dynamics is an essential part of environmental literacy. However, students face severe difficulties in grasping the variety of causal patterns in food webs. We propose a curriculum for comprehending trophic relations in elementary school. The curriculum allows students to…

Non-recursive augmented Lagrangian algorithms for the forward and inverse dynamics of constrained flexible multibodies

NASA Technical Reports Server (NTRS)

Bayo, Eduardo; Ledesma, Ragnar

1993-01-01

A technique is presented for solving the inverse dynamics of flexible planar multibody systems. This technique yields the non-causal joint efforts (inverse dynamics) as well as the internal states (inverse kinematics) that produce a prescribed nominal trajectory of the end effector. A non-recursive global Lagrangian approach is used in formulating the equations for motion as well as in solving the inverse dynamics equations. Contrary to the recursive method previously presented, the proposed method solves the inverse problem in a systematic and direct manner for both open-chain as well as closed-chain configurations. Numerical simulation shows that the proposed procedure provides an excellent tracking of the desired end effector trajectory.

Depth measurements of drilled holes in bone by laser triangulation for the field of oral implantology

NASA Astrophysics Data System (ADS)

Quest, D.; Gayer, C.; Hering, P.

2012-01-01

Laser osteotomy is one possible method of preparing beds for dental implants in the human jaw. A major problem in using this contactless treatment modality is the lack of haptic feedback to control the depth while drilling the implant bed. A contactless measurement system called laser triangulation is presented as a new procedure to overcome this problem. Together with a tomographic picture the actual position of the laser ablation in the bone can be calculated. Furthermore, the laser response is sufficiently fast as to pose little risk to surrounding sensitive areas such as nerves and blood vessels. In the jaw two different bone structures exist, namely the cancellous bone and the compact bone. Samples of both bone structures were examined with test drillings performed either by laser osteotomy or by a conventional rotating drilling tool. The depth of these holes was measured using laser triangulation. The results and the setup are reported in this study.

Spatiotemporal neural network dynamics for the processing of dynamic facial expressions.

PubMed

Sato, Wataru; Kochiyama, Takanori; Uono, Shota

2015-07-24

The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150-200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300-350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual-motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions.

Spatiotemporal neural network dynamics for the processing of dynamic facial expressions

PubMed Central

Sato, Wataru; Kochiyama, Takanori; Uono, Shota

2015-01-01

The dynamic facial expressions of emotion automatically elicit multifaceted psychological activities; however, the temporal profiles and dynamic interaction patterns of brain activities remain unknown. We investigated these issues using magnetoencephalography. Participants passively observed dynamic facial expressions of fear and happiness, or dynamic mosaics. Source-reconstruction analyses utilizing functional magnetic-resonance imaging data revealed higher activation in broad regions of the bilateral occipital and temporal cortices in response to dynamic facial expressions than in response to dynamic mosaics at 150–200 ms and some later time points. The right inferior frontal gyrus exhibited higher activity for dynamic faces versus mosaics at 300–350 ms. Dynamic causal-modeling analyses revealed that dynamic faces activated the dual visual routes and visual–motor route. Superior influences of feedforward and feedback connections were identified before and after 200 ms, respectively. These results indicate that hierarchical, bidirectional neural network dynamics within a few hundred milliseconds implement the processing of dynamic facial expressions. PMID:26206708

Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

PubMed

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M

Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data.

Using systems science to understand the determinants of inequities in healthy eating

PubMed Central

Pescud, Melanie; Malbon, Eleanor; Lee, Amanda; Carter, Robert; Greenfield, Joanne; Cobcroft, Megan; Potter, Jane; Rychetnik, Lucie; Meertens, Beth

2017-01-01

Introduction Systems thinking has emerged in recent years as a promising approach to understanding and acting on the prevention and amelioration of non-communicable disease. However, the evidence on inequities in non-communicable diseases and their risks factors, particularly diet, has not been examined from a systems perspective. We report on an approach to developing a system oriented policy actor perspective on the multiple causes of inequities in healthy eating. Methods Collaborative conceptual modelling workshops were held in 2015 with an expert group of representatives from government, non-government health organisations and academia in Australia. The expert group built a systems model using a system dynamics theoretical perspective. The model developed from individual mind maps to pair blended maps, before being finalised as a causal loop diagram. Results The work of the expert stakeholders generated a comprehensive causal loop diagram of the determinants of inequity in healthy eating (the HE2 Diagram). This complex dynamic system has seven sub-systems: (1) food supply and environment; (2) transport; (3) housing and the built environment; (4) employment; (5) social protection; (6) health literacy; and (7) food preferences. Discussion The HE2 causal loop diagram illustrates the complexity of determinants of inequities in healthy eating. This approach, both the process of construction and the final visualisation, can provide the basis for planning the prevention and amelioration of inequities in healthy eating that engages with multiple levels of causes and existing policies and programs. PMID:29190662

Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis.

PubMed

Rossi, Sergio; Morin, Hubert; Deslauriers, Annie

2012-03-01

Although habitually considered as a whole, xylogenesis is a complex process of division and maturation of a pool of cells where the relationship between the phenological phases generating such a growth pattern remains essentially unknown. This study investigated the causal relationships in cambium phenology of black spruce [Picea mariana (Mill.) BSP] monitored for 8 years on four sites of the boreal forest of Quebec, Canada. The dependency links connecting the timing of xylem cell differentiation and cell production were defined and the resulting causal model was analysed with d-sep tests and generalized mixed models with repeated measurements, and tested with Fisher's C statistics to determine whether and how causality propagates through the measured variables. The higher correlations were observed between the dates of emergence of the first developing cells and between the ending of the differentiation phases, while the number of cells was significantly correlated with all phenological phases. The model with eight dependency links was statistically valid for explaining the causes and correlations between the dynamics of cambium phenology. Causal modelling suggested that the phenological phases involved in xylogenesis are closely interconnected by complex relationships of cause and effect, with the onset of cell differentiation being the main factor directly or indirectly triggering all successive phases of xylem maturation.

Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis

PubMed Central

Rossi, Sergio; Morin, Hubert; Deslauriers, Annie

2012-01-01

Although habitually considered as a whole, xylogenesis is a complex process of division and maturation of a pool of cells where the relationship between the phenological phases generating such a growth pattern remains essentially unknown. This study investigated the causal relationships in cambium phenology of black spruce [Picea mariana (Mill.) BSP] monitored for 8 years on four sites of the boreal forest of Quebec, Canada. The dependency links connecting the timing of xylem cell differentiation and cell production were defined and the resulting causal model was analysed with d-sep tests and generalized mixed models with repeated measurements, and tested with Fisher’s C statistics to determine whether and how causality propagates through the measured variables. The higher correlations were observed between the dates of emergence of the first developing cells and between the ending of the differentiation phases, while the number of cells was significantly correlated with all phenological phases. The model with eight dependency links was statistically valid for explaining the causes and correlations between the dynamics of cambium phenology. Causal modelling suggested that the phenological phases involved in xylogenesis are closely interconnected by complex relationships of cause and effect, with the onset of cell differentiation being the main factor directly or indirectly triggering all successive phases of xylem maturation. PMID:22174441

Non-Separability and Synchronicity: Pauli, Jung and a New Historical, Philosophical Perspective on Quantum Physics

NASA Astrophysics Data System (ADS)

Giannetto, E. A.; Pozzi, F.

We would like to discuss the historical emergence of quantum physics and quantum non-separability, by analysing Pauli's point of view in relation to Jung's ideas. Recent inquiries on EPR shows that quantum non-separability indicates an a-causal connection of the "quantum reality" for space-like intervals ("simultaneity region ") of world (measurement) events: this non-causal connection is the physical counterpart of what Jung called "synchronicity " with an assessment given also by Pauli. This does not imply any violation of mechanical causality by any introduction of action-at-a-distance. From a physical point of view a-causal connections can be interpreted as implying a particular quantum topology of space-time, which leads to a non-mechanistic conception of nature and which could be related to a holistic quantum dynamical reality of the world like Bohm's "holomovement" or "light". This kind of non-mechanistic conception of nature as well as the idea of non-separability of the world and of synchronicity, as stated by Jung itself, was developed by Leibnitz: from this point of view, we can look at quantum physics (as well as for relativity it was shown) as related to a new emergence of concepts belonging to the Leibnitzian (anti-Newtonian) tradition.

A Program to Improve the Triangulated Surface Mesh Quality Along Aircraft Component Intersections

NASA Technical Reports Server (NTRS)

Cliff, Susan E.

2005-01-01

A computer program has been developed for improving the quality of unstructured triangulated surface meshes in the vicinity of component intersections. The method relies solely on point removal and edge swapping for improving the triangulations. It can be applied to any lifting surface component such as a wing, canard or horizontal tail component intersected with a fuselage, or it can be applied to a pylon that is intersected with a wing, fuselage or nacelle. The lifting surfaces or pylon are assumed to be aligned in the axial direction with closed trailing edges. The method currently maintains salient edges only at leading and trailing edges of the wing or pylon component. This method should work well for any shape of fuselage that is free of salient edges at the intersection. The method has been successfully demonstrated on a total of 125 different test cases that include both blunt and sharp wing leading edges. The code is targeted for use in the automated environment of numerical optimization where geometric perturbations to individual components can be critical to the aerodynamic performance of a vehicle. Histograms of triangle aspect ratios are reported to assess the quality of the triangles attached to the intersection curves before and after application of the program. Large improvements to the quality of the triangulations were obtained for the 125 test cases; the quality was sufficient for use with an automated tetrahedral mesh generation program that is used as part of an aerodynamic shape optimization method.

Triangulated Proxy Reporting: a technique for improving how communication partners come to know people with severe cognitive impairment.

PubMed

Lyons, Gordon; De Bortoli, Tania; Arthur-Kelly, Michael

2017-09-01

This paper explains and demonstrates the pilot application of Triangulated Proxy Reporting (TPR); a practical technique for enhancing communication around people who have severe cognitive impairment (SCI). An introduction explains SCI and how this impacts on communication; and consequently on quality of care and quality of life. This is followed by an explanation of TPR and its origins in triangulation research techniques. An illustrative vignette explicates its utility and value in a group home for a resident with profound multiple disabilities. The Discussion and Conclusion sections propose the wider application of TPR for different cohorts of people with SCIs, their communication partners and service providers. TPR presents as a practical technique for enhancing communication interactions with people who have SCI. The paper demonstrates the potential of the technique for improving engagement amongst those with profound multiple disabilities, severe acquired brain injury and advanced dementia and their partners in and across different care settings. Implications for Rehabilitation Triangulated Proxy Reporting (TPR) shows potential to improve communications between people with severe cognitive impairments and their communication partners. TPR can lead to improved quality of care and quality of life for people with profound multiple disabilities, very advanced dementia and severe acquired brain injury, who otherwise are very difficult to support. TPR is a relatively simple and inexpensive technique that service providers can incorporate into practice to improving communications between clients with severe cognitive impairments, their carers and other support professionals.

Stochastic and information-thermodynamic structures of population dynamics in a fluctuating environment

NASA Astrophysics Data System (ADS)

Kobayashi, Tetsuya J.; Sughiyama, Yuki

2017-07-01

Adaptation in a fluctuating environment is a process of fueling environmental information to gain fitness. Living systems have gradually developed strategies for adaptation from random and passive diversification of the phenotype to more proactive decision making, in which environmental information is sensed and exploited more actively and effectively. Understanding the fundamental relation between fitness and information is therefore crucial to clarify the limits and universal properties of adaptation. In this work, we elucidate the underlying stochastic and information-thermodynamic structure in this process, by deriving causal fluctuation relations (FRs) of fitness and information. Combined with a duality between phenotypic and environmental dynamics, the FRs reveal the limit of fitness gain, the relation of time reversibility with the achievability of the limit, and the possibility and condition for gaining excess fitness due to environmental fluctuation. The loss of fitness due to causal constraints and the limited capacity of real organisms is shown to be the difference between time-forward and time-backward path probabilities of phenotypic and environmental dynamics. Furthermore, the FRs generalize the concept of the evolutionary stable state (ESS) for fluctuating environment by giving the probability that the optimal strategy on average can be invaded by a suboptimal one owing to rare environmental fluctuation. These results clarify the information-thermodynamic structures in adaptation and evolution.

Analysis of Factors Influencing PM2.5 in Beijing: A Microcosmic and Dynamic Perspective for Sustainable Development

NASA Astrophysics Data System (ADS)

Wang, Yani; Wang, Jun; Tao, Guiping

2017-12-01

Haze pollution has become a hot issue concerned with the process of modernization and one serious problem requiring urgent solution, especially in Beijing. PM2.5 is the main reason causing haze and its harm. Although there has been research centering on factors affecting PM2.5, little attention has been devoted to the microcosmic and dynamic effects on it. Vector auto-regression (VAR) mode is applied in this study to explore the interaction between PM2.5, PM10, SO2, CO and NO2. Results of Granger causality tests tell that there exists causal relationship between PM10, SO2, CO, NO2 and PM2.5. Impulse response functions (IRFs) show that the response of PM2.5 to a shock in CO is positive and large in the short period, while the reaction of PM2.5 to a shock in SO2 increases over time. Meanwhile, variance decomposition indicate that PM2.5 is more closely related to CO in the short term while SO2’ influence accounts for a higher proportion in the long run. The findings provide a novel perspective to analyze the factors influencing PM2.5 dynamically and contribute to a better understanding of haze and its relationship with sustainable development.

An innovations approach to decoupling of multibody dynamics and control

NASA Technical Reports Server (NTRS)

Rodriguez, G.

1989-01-01

The problem of hinged multibody dynamics is solved using an extension of the innovations approach of linear filtering and prediction theory to the problem of mechanical system modeling and control. This approach has been used quite effectively to diagonalize the equations for filtering and prediction for linear state space systems. It has similar advantages in the study of dynamics and control of multibody systems. The innovations approach advanced here consists of expressing the equations of motion in terms of two closely related processes: (1) the innovations process e, a sequence of moments, obtained from the applied moments T by means of a spatially recursive Kalman filter that goes from the tip of the manipulator to its base; (2) a residual process, a sequence of velocities, obtained from the joint-angle velocities by means of an outward smoothing operations. The innovations e and the applied moments T are related by means of the relationships e = (I - L)T and T = (I + K)e. The operation (I - L) is a causal lower triangular matrix which is generated by a spatially recursive Kalman filter and the corresponding discrete-step Riccati equation. Hence, the innovations and the applied moments can be obtained from each other by means of a causal operation which is itself casually invertible.

Localization and dynamics of Wolbachia infection in Asian citrus psyllid Diaphorina citri, the insect vector of the causal pathogens of Huanglongbing.

PubMed

Ren, Su-Li; Li, Yi-Han; Ou, Da; Guo, Yan-Jun; Qureshi, Jawwad A; Stansly, Philip A; Qiu, Bao-Li

2018-03-23

Wolbachia is a group of intracellular bacteria that infect a wide range of arthropods including the Asian citrus psyllid (ACP), Diaphorina citri Kuwayama. This insect is the vector of Candidatus Liberibacter asiaticus (CLas), the causal pathogen of Huanglongbing or citrus greening disease. Here, we investigated the localization pattern and infection dynamics of Wolbachia in different developmental stages of ACP. Results revealed that all developmental stages of ACP including egg, 1st-5th instar nymphs, and adults of both gender were infected with Wolbachia. FISH visualization of an ACP egg showed that Wolbachia moved from the egg stalk of newly laid eggs to a randomly distributed pattern throughout the egg prior to hatching. The infection rate varied between nymphal instars. The titers of Wolbachia in fourth and fifth instar nymphs were significantly higher than those in the first and second instar nymphs. Wolbachia were scattered in all nymphal stages, but with highest intensity in the U-shaped bacteriome located in the abdomen of the nymph. Wolbachia was confined to two symmetrical organizations in the abdomen of newly emerged female and male adults. The potential mechanisms of Wolbachia infection dynamics are discussed. © 2018 The Authors. MicrobiologyOpen published by John Wiley & Sons Ltd.

How music alters a kiss: superior temporal gyrus controls fusiform-amygdalar effective connectivity.

PubMed

Pehrs, Corinna; Deserno, Lorenz; Bakels, Jan-Hendrik; Schlochtermeier, Lorna H; Kappelhoff, Hermann; Jacobs, Arthur M; Fritz, Thomas Hans; Koelsch, Stefan; Kuchinke, Lars

2014-11-01

While watching movies, the brain integrates the visual information and the musical soundtrack into a coherent percept. Multisensory integration can lead to emotion elicitation on which soundtrack valences may have a modulatory impact. Here, dynamic kissing scenes from romantic comedies were presented to 22 participants (13 females) during functional magnetic resonance imaging scanning. The kissing scenes were either accompanied by happy music, sad music or no music. Evidence from cross-modal studies motivated a predefined three-region network for multisensory integration of emotion, consisting of fusiform gyrus (FG), amygdala (AMY) and anterior superior temporal gyrus (aSTG). The interactions in this network were investigated using dynamic causal models of effective connectivity. This revealed bilinear modulations by happy and sad music with suppression effects on the connectivity from FG and AMY to aSTG. Non-linear dynamic causal modeling showed a suppressive gating effect of aSTG on fusiform-amygdalar connectivity. In conclusion, fusiform to amygdala coupling strength is modulated via feedback through aSTG as region for multisensory integration of emotional material. This mechanism was emotion-specific and more pronounced for sad music. Therefore, soundtrack valences may modulate emotion elicitation in movies by differentially changing preprocessed visual information to the amygdala. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

How music alters a kiss: superior temporal gyrus controls fusiform–amygdalar effective connectivity

PubMed Central

Deserno, Lorenz; Bakels, Jan-Hendrik; Schlochtermeier, Lorna H.; Kappelhoff, Hermann; Jacobs, Arthur M.; Fritz, Thomas Hans; Koelsch, Stefan; Kuchinke, Lars

2014-01-01

While watching movies, the brain integrates the visual information and the musical soundtrack into a coherent percept. Multisensory integration can lead to emotion elicitation on which soundtrack valences may have a modulatory impact. Here, dynamic kissing scenes from romantic comedies were presented to 22 participants (13 females) during functional magnetic resonance imaging scanning. The kissing scenes were either accompanied by happy music, sad music or no music. Evidence from cross-modal studies motivated a predefined three-region network for multisensory integration of emotion, consisting of fusiform gyrus (FG), amygdala (AMY) and anterior superior temporal gyrus (aSTG). The interactions in this network were investigated using dynamic causal models of effective connectivity. This revealed bilinear modulations by happy and sad music with suppression effects on the connectivity from FG and AMY to aSTG. Non-linear dynamic causal modeling showed a suppressive gating effect of aSTG on fusiform–amygdalar connectivity. In conclusion, fusiform to amygdala coupling strength is modulated via feedback through aSTG as region for multisensory integration of emotional material. This mechanism was emotion-specific and more pronounced for sad music. Therefore, soundtrack valences may modulate emotion elicitation in movies by differentially changing preprocessed visual information to the amygdala. PMID:24298171

Diagnosis and Reconfiguration using Bayesian Networks: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Knox, W. Bradley; Mengshoel, Ole

2009-01-01

Automated diagnosis and reconfiguration are important computational techniques that aim to minimize human intervention in autonomous systems. In this paper, we develop novel techniques and models in the context of diagnosis and reconfiguration reasoning using causal Bayesian networks (BNs). We take as starting point a successful diagnostic approach, using a static BN developed for a real-world electrical power system. We discuss in this paper the extension of this diagnostic approach along two dimensions, namely: (i) from a static BN to a dynamic BN; and (ii) from a diagnostic task to a reconfiguration task. More specifically, we discuss the auto-generation of a dynamic Bayesian network from a static Bayesian network. In addition, we discuss subtle, but important, differences between Bayesian networks when used for diagnosis versus reconfiguration. We discuss a novel reconfiguration agent, which models a system causally, including effects of actions through time, using a dynamic Bayesian network. Though the techniques we discuss are general, we demonstrate them in the context of electrical power systems (EPSs) for aircraft and spacecraft. EPSs are vital subsystems on-board aircraft and spacecraft, and many incidents and accidents of these vehicles have been attributed to EPS failures. We discuss a case study that provides initial but promising results for our approach in the setting of electrical power systems.

The application of GPS precise point positioning technology in aerial triangulation

NASA Astrophysics Data System (ADS)

Yuan, Xiuxiao; Fu, Jianhong; Sun, Hongxing; Toth, Charles

In traditional GPS-supported aerotriangulation, differential GPS (DGPS) positioning technology is used to determine the 3-dimensional coordinates of the perspective centers at exposure time with an accuracy of centimeter to decimeter level. This method can significantly reduce the number of ground control points (GCPs). However, the establishment of GPS reference stations for DGPS positioning is not only labor-intensive and costly, but also increases the implementation difficulty of aerial photography. This paper proposes aerial triangulation supported with GPS precise point positioning (PPP) as a way to avoid the use of the GPS reference stations and simplify the work of aerial photography. Firstly, we present the algorithm for GPS PPP in aerial triangulation applications. Secondly, the error law of the coordinate of perspective centers determined using GPS PPP is analyzed. Thirdly, based on GPS PPP and aerial triangulation software self-developed by the authors, four sets of actual aerial images taken from surveying and mapping projects, different in both terrain and photographic scale, are given as experimental models. The four sets of actual data were taken over a flat region at a scale of 1:2500, a mountainous region at a scale of 1:3000, a high mountainous region at a scale of 1:32000 and an upland region at a scale of 1:60000 respectively. In these experiments, the GPS PPP results were compared with results obtained through DGPS positioning and traditional bundle block adjustment. In this way, the empirical positioning accuracy of GPS PPP in aerial triangulation can be estimated. Finally, the results of bundle block adjustment with airborne GPS controls from GPS PPP are analyzed in detail. The empirical results show that GPS PPP applied in aerial triangulation has a systematic error of half-meter level and a stochastic error within a few decimeters. However, if a suitable adjustment solution is adopted, the systematic error can be eliminated in GPS-supported bundle block adjustment. When four full GCPs are emplaced in the corners of the adjustment block, then the systematic error is compensated using a set of independent unknown parameters for each strip, the final result of the bundle block adjustment with airborne GPS controls from PPP is the same as that of bundle block adjustment with airborne GPS controls from DGPS. Although the accuracy of the former is a little lower than that of traditional bundle block adjustment with dense GCPs, it can still satisfy the accuracy requirement of photogrammetric point determination for topographic mapping at many scales.

A fast high-precision six-degree-of-freedom relative position sensor

NASA Astrophysics Data System (ADS)

Hughes, Gary B.; Macasaet, Van P.; Griswold, Janelle; Sison, Claudia A.; Lubin, Philip; Meinhold, Peter; Suen, Jonathan; Brashears, Travis; Zhang, Qicheng; Madajian, Jonathan

2016-03-01

Lasers are commonly used in high-precision measurement and profiling systems. Some laser measurement systems are based on interferometry principles, and others are based on active triangulation, depending on requirements of the application. This paper describes an active triangulation laser measurement system for a specific application wherein the relative position of two fixed, rigid mechanical components is to be measured dynamically with high precision in six degrees of freedom (DOF). Potential applications include optical systems with feedback to control for mechanical vibration, such as target acquisition devices with multiple focal planes. The method uses an array of several laser emitters mounted on one component. The lasers are directed at a reflective surface on the second component. The reflective surface consists of a piecewise-planar pattern such as a pyramid, or more generally a curved reflective surface such as a hyperbolic paraboloid. The reflected spots are sensed at 2-dimensional photodiode arrays on the emitter component. Changes in the relative position of the emitter component and reflective surface will shift the location of the reflected spots within photodiode arrays. Relative motion in any degree of freedom produces independent shifts in the reflected spot locations, allowing full six-DOF relative position determination between the two component positions. Response time of the sensor is limited by the read-out rate of the photodiode arrays. Algorithms are given for position determination with limits on uncertainty and sensitivity, based on laser and spot-sensor characteristics, and assuming regular surfaces. Additional uncertainty analysis is achievable for surface irregularities based on calibration data.

Report on New Mission Concept Study: Stereo X-Ray Corona Imager Mission

NASA Technical Reports Server (NTRS)

Liewer, Paulett C.; Davis, John M.; DeJong, E. M.; Gary, G. Allen; Klimchuk, James A.; Reinert, R. P.

1998-01-01

Studies of the three-dimensional structure and dynamics of the solar corona have been severely limited by the constraint of single viewpoint observations. The Stereo X-Ray Coronal Imager (SXCI) mission will send a single instrument, an X-ray telescope, into deep space expressly to record stereoscopic images of the solar corona. The SXCI spacecraft will be inserted into a approximately 1 AU heliocentric orbit leading Earth by approximately 25 deg at the end of nine months. The SXCI X-ray telescope forms one element of a stereo pair, the second element being an identical X-ray telescope in Earth orbit placed there as part of the NOAA GOES program. X-ray emission is a powerful diagnostic of the corona and its magnetic fields, and three dimensional information on the coronal magnetic structure would be obtained by combining the data from the two X-ray telescopes. This information can be used to address the major solar physics questions of (1) what causes explosive coronal events such as coronal mass ejections (CMEs), eruptive flares and prominence eruptions and (2) what causes the transient heating of coronal loops. Stereoscopic views of the optically thin corona will resolve some ambiguities inherent in single line-of-sight observations. Triangulation gives 3D solar coordinates of features which can be seen in the simultaneous images from both telescopes. As part of this study, tools were developed for determining the 3D geometry of coronal features using triangulation. Advanced technologies for visualization and analysis of stereo images were tested. Results of mission and spacecraft studies are also reported.

Dynamics of Mid-Palaeocene North Atlantic rifting linked with European intra-plate deformations.

PubMed

Nielsen, Søren B; Stephenson, Randell; Thomsen, Erik

2007-12-13

The process of continental break-up provides a large-scale experiment that can be used to test causal relations between plate tectonics and the dynamics of the Earth's deep mantle. Detailed diagnostic information on the timing and dynamics of such events, which are not resolved by plate kinematic reconstructions, can be obtained from the response of the interior of adjacent continental plates to stress changes generated by plate boundary processes. Here we demonstrate a causal relationship between North Atlantic continental rifting at approximately 62 Myr ago and an abrupt change of the intra-plate deformation style in the adjacent European continent. The rifting involved a left-lateral displacement between the North American-Greenland plate and Eurasia, which initiated the observed pause in the relative convergence of Europe and Africa. The associated stress change in the European continent was significant and explains the sudden termination of a approximately 20-Myr-long contractional intra-plate deformation within Europe, during the late Cretaceous period to the earliest Palaeocene epoch, which was replaced by low-amplitude intra-plate stress-relaxation features. The pre-rupture tectonic stress was large enough to have been responsible for precipitating continental break-up, so there is no need to invoke a thermal mantle plume as a driving mechanism. The model explains the simultaneous timing of several diverse geological events, and shows how the intra-continental stratigraphic record can reveal the timing and dynamics of stress changes, which cannot be resolved by reconstructions based only on plate kinematics.

Methods of information geometry in computational system biology (consistency between chemical and biological evolution).

PubMed

Astakhov, Vadim

2009-01-01

Interest in simulation of large-scale metabolic networks, species development, and genesis of various diseases requires new simulation techniques to accommodate the high complexity of realistic biological networks. Information geometry and topological formalisms are proposed to analyze information processes. We analyze the complexity of large-scale biological networks as well as transition of the system functionality due to modification in the system architecture, system environment, and system components. The dynamic core model is developed. The term dynamic core is used to define a set of causally related network functions. Delocalization of dynamic core model provides a mathematical formalism to analyze migration of specific functions in biosystems which undergo structure transition induced by the environment. The term delocalization is used to describe these processes of migration. We constructed a holographic model with self-poetic dynamic cores which preserves functional properties under those transitions. Topological constraints such as Ricci flow and Pfaff dimension were found for statistical manifolds which represent biological networks. These constraints can provide insight on processes of degeneration and recovery which take place in large-scale networks. We would like to suggest that therapies which are able to effectively implement estimated constraints, will successfully adjust biological systems and recover altered functionality. Also, we mathematically formulate the hypothesis that there is a direct consistency between biological and chemical evolution. Any set of causal relations within a biological network has its dual reimplementation in the chemistry of the system environment.

Commutative semigroups of real and complex matrices. [with use of the jordan form

NASA Technical Reports Server (NTRS)

Brown, D. R.

1974-01-01

The computation of divergence is studied. Covariance matrices to be analyzed admit a common diagonalization, or even triangulation. Sufficient conditions are given for such phenomena to take place, the arguments cover both real and complex matrices, and are not restricted to Hermotian or other special forms. Specifically, it is shown to be sufficient that the matrices in question commute in order to admit a common triangulation. Several results hold in the case that the matrices in question form a closed and bounded set, rather than only in the finite case.

Error in telemetry studies: Effects of animal movement on triangulation

USGS Publications Warehouse

Schmutz, Joel A.; White, Gary C.

1990-01-01

We used Monte Carlo simulations to investigate the effects of animal movement on error of estimated animal locations derived from radio-telemetry triangulation of sequentially obtained bearings. Simulated movements of 0-534 m resulted in up to 10-fold increases in average location error but <10% decreases in location precision when observer-to-animal distances were <1,000 m. Location error and precision were minimally affected by censorship of poor locations with Chi-square goodness-of-fit tests. Location error caused by animal movement can only be eliminated by taking simultaneous bearings.

"We want the world and we want it now": Materialism, time perspectives and problem spending tendency of Chinese.

PubMed

Ku, Lisbeth; Wu, Anise M S; Lao, Angie K P; Lam, Kerwin I N

2016-10-06

Chinese consumers' spending has been expanding rapidly in the past decade, and along with it household and credit card debt. The present research collected evidence to triangulate the contention that materialism is positively related with Chinese's problem spending tendency (PST), and that present-time-perspective (PTP) and future-time perspectives (FTP) interact systematically with materialism to affect PST. A survey of the general population in Macao, China (Study 1; N = 239) confirmed that materialism was positively correlated with PST. An interaction between materialism and PTP intensified the relationship, whereas an interaction with FTP weakened the relationship. Another survey with a sample of university students (Study 2; N = 223) again found positive relationships among PST, materialism, and PTP, as measured by temporal discount rate. But further exploration showed that PST was only related with temporal discounting among high materialists, but not among low materialists. Study 3 experimentally examined the causal effects of materialism and FTP on PST. When being primed of an orientation towards materialism (n = 33), the participants' planned consumption doubled that of the control group (n = 31). A FTP prime interacted with materialism prime and put a "damper" on participants' planned spending (n = 29), compared to their counterparts who were not primed of such a time perspective. © 2016 International Union of Psychological Science.

Breaking the conflict of tionghoa-java in surakarta at reformation period 1998

NASA Astrophysics Data System (ADS)

Riyadi; Hermawan, ES; Aji, RNB; Trilaksana, A.; Mastuti, S.

2018-01-01

The issues raised in this paper are potential conflicts and efforts to create harmony of the socio-cultural environment of ethnic Chinese-Javanese. This research is to know the historical background of the process, and the development of ethnic Chinese descent in Surakarta City and how far the potential conflict and causal factor of conflict between ethnic Chinese and ethnic indigenous of Java so that known factors become obstacle of social integration process of ethnic of Chinese and indigenous Java in Surakarta. Approach of this research is descriptive qualitative. Data collection techniques were initially used in the questionnaire distribution model, followed by: in-depth interviews and (2) involved observation, document content analysis and FGD. To obtain degree of high validity, done by technique triangulation, recheck and peer debriefing. This research using interactive technique analysis. The result of the research can be concluded that the conflict arising from the existence of domestic economic and political pressure has forced Chinese people to migrate to Southeast Asia, including Indonesia and then there are several conflicts in many areas in Indonesia. The conflict between ethnic Chinese and Javanese in Surakarta occurred in 1980 and 1998. The conflict resolution can be done by optimizing social, cultural, and economic factors. This factor is used as a social adhesive to the integration between ethnic Chinese and Javanese in Surakarta.

Anchoring in 4- to 6-Year-Old Children Relates to Predictors of Reading

ERIC Educational Resources Information Center

Banai, Karen; Yifat, Rachel

2012-01-01

Previous studies suggest that anchoring, a short-term dynamic and implicit process that allows individuals to benefit from contextual information embedded in stimulus sequences, might be causally related to reading acquisition. Here we report findings from two experiments in which two previously untested predictions derived from this anchoring…

Motivational Dynamics in the Development of Career Attitudes among Adolescents

ERIC Educational Resources Information Center

Janeiro, Isabel Nunes

2010-01-01

Super (1990) proposed that the psychological determinants of career development attitudes are time perspective, self-esteem, and causal attributions. The present study analyzed the effects of these determinants on the career development attitudes of 320 students from grade 9 and 300 students from grade 12. The analysis of the data using structural…

The Contagion of Stress across Multiple Roles.

ERIC Educational Resources Information Center

Bolger, Niall; And Others

1989-01-01

Examined causal dynamics of stress contagion across work and home domains in married couples. Results revealed that husbands were more likely than wives to bring home stresses into workplace. Stress contagion from work to home was evident for both husbands and wives. Contagion of work stress into home appeared to set into motion process of dyadic…

The Signal Importance of Noise

ERIC Educational Resources Information Center

Macy, Michael; Tsvetkova, Milena

2015-01-01

Noise is widely regarded as a residual category--the unexplained variance in a linear model or the random disturbance of a predictable pattern. Accordingly, formal models often impose the simplifying assumption that the world is noise-free and social dynamics are deterministic. Where noise is assigned causal importance, it is often assumed to be a…

Reading-Related Causal Attributions for Success and Failure: Dynamic Links with Reading Skill

ERIC Educational Resources Information Center

Frijters, Jan C.; Tsujimoto, Kimberley C.; Boada, Richard; Gottwald, Stephanie; Hill, Dina; Jacobson, Lisa A.; Lovett, Maureen W.; Mahone, E. Mark; Willcutt, Erik G.; Wolf, Maryanne; Bosson-Heenan, Joan; Gruen, Jeffrey R.

2018-01-01

The present study investigated the relation among reading skills and attributions, naming speed, and phonological awareness across a wide range of reading skill. Participants were 1,105 school-age children and youths from two understudied populations: African Americans and Hispanic Americans. Individual assessments of children ranging in age from…

The Model Analyst’s Toolkit: Scientific Model Development, Analysis, and Validation

DTIC Science & Technology

2014-08-20

many different synthetic series can be generated at once. If the series already exists in the dataset, it is updated to reflect the new values. The...Testing for causality: a personal viewpoint. Journal of Economic Dynamics and Control, 2, 329-352. Manning, C., Raghavan, R., and Schutze , H. (2008

Computer-Based Assessment of Complex Problem Solving: Concept, Implementation, and Application

ERIC Educational Resources Information Center

Greiff, Samuel; Wustenberg, Sascha; Holt, Daniel V.; Goldhammer, Frank; Funke, Joachim

2013-01-01

Complex Problem Solving (CPS) skills are essential to successfully deal with environments that change dynamically and involve a large number of interconnected and partially unknown causal influences. The increasing importance of such skills in the 21st century requires appropriate assessment and intervention methods, which in turn rely on adequate…

The Nature of Evolution

ERIC Educational Resources Information Center

Alles, David L.

2005-01-01

The nature of evolution, the historical change in the universe, and the change that is caused by the workings of the dynamic processes at the smallest and largest scales are studied. It is viewed that the cumulative change in the historical systems is caused by evolution, which is a type of causal relationship and evolutionary processes could be…

"A Complicated Tangle of Circumstances"

ERIC Educational Resources Information Center

Miller, Carole; Saxton, Juliana

2009-01-01

The post-modern curriculum, drawing on chaos and complexity theory, recognises the realities of a world in flux and posits that the teacher and the class are always teetering "in the midst" of chaos, "not linked by chains of causality but [by] layers of meaning, recursive dynamics, non-linear effects and chance" (Osberg 2008,…

Modeling Family Dynamics in Children with Fragile X Syndrome

ERIC Educational Resources Information Center

Hall, Scott S.; Burns, David D.; Reiss, Allan L.

2007-01-01

Few studies have examined the impact of children with genetic disorders and their unaffected siblings on family functioning. In this study, the reciprocal causal links between problem behaviors and maternal distress were investigated in 150 families containing a child with fragile X syndrome (FXS) and an unaffected sibling. Both children's…

Dynamical signatures of bound states in waveguide QED

NASA Astrophysics Data System (ADS)

Sánchez-Burillo, E.; Zueco, D.; Martín-Moreno, L.; García-Ripoll, J. J.

2017-08-01

We study the spontaneous decay of an impurity coupled to a linear array of bosonic cavities forming a single-band photonic waveguide. The average frequency of the emitted photon is different from the frequency for single-photon resonant scattering, which perfectly matches the bare frequency of the excited state of the impurity. We study how the energy of the excited state of the impurity influences the spatial profile of the emitted photon. The farther the energy is from the middle of the photonic band, the farther the wave packet is from the causal limit. In particular, if the energy lies in the middle of the band, the wave packet is localized around the causal limit. Besides, the occupation of the excited state of the impurity presents a rich dynamics: it shows an exponential decay up to intermediate times, this is followed by a power-law tail in the long-time regime, and it finally reaches an oscillatory stationary regime. Finally, we show that this phenomenology is robust under the presence of losses, both in the impurity and in the cavities.

Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer's Disease.

PubMed

Penny, Will; Iglesias-Fuster, Jorge; Quiroz, Yakeel T; Lopera, Francisco Javier; Bobes, Maria A

2018-03-16

Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer's disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores.

Bayesian model reduction and empirical Bayes for group (DCM) studies.

PubMed

Friston, Karl J; Litvak, Vladimir; Oswal, Ashwini; Razi, Adeel; Stephan, Klaas E; van Wijk, Bernadette C M; Ziegler, Gabriel; Zeidman, Peter

2016-03-01

This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI.

PubMed

Solo, Victor

2016-05-01

The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability.

State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI

PubMed Central

Solo, Victor

2017-01-01

The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability. PMID:26942749

The dynamic interaction between combustible renewables and waste consumption and international tourism: the case of Tunisia.

PubMed

Ben Jebli, Mehdi; Ben Youssef, Slim; Apergis, Nicholas

2015-08-01

This paper employs the autoregressive distributed lag (ARDL) bounds methodological approach to investigate the relationship between economic growth, combustible renewables and waste consumption, carbon dioxide (CO2) emissions, and international tourism for the case of Tunisia spanning the period 1990-2010. The results from the Fisher statistic of both the Wald test and the Johansen test confirm the presence of a long-run relationship among the variables under investigation. The stability of estimated parameters has been tested, while Granger causality tests recommend a short-run unidirectional causality running from economic growth and combustible renewables and waste consumption to CO2 emissions, a bidirectional causality between economic growth and combustible renewables and waste consumption and unidirectional causality running from economic growth and combustible renewables and waste consumption to international tourism. In the long-run, the error correction terms confirm the presence of bidirectional causality relationships between economic growth, CO2 emissions, combustible renewables and waste consumption, and international tourism. Our long-run estimates show that combustible renewables and waste consumption increases international tourism, and both renewables and waste consumption and international tourism increase CO2 emissions and output. We recommend that (i) Tunisia should use more combustible renewables and waste energy as this eliminates wastes from touristic zones and increases the number of tourist arrivals, leading to economic growth, and (ii) a fraction of this economic growth generated by the increase in combustible renewables and waste consumption should be invested in clean renewable energy production (i.e., solar, wind, geothermal) and energy efficiency projects.

Nonlinear Dynamic Characteristics of Oil-in-Water Emulsions

NASA Astrophysics Data System (ADS)

Yin, Zhaoqi; Han, Yunfeng; Ren, Yingyu; Yang, Qiuyi; Jin, Ningde

2016-08-01

In this article, the nonlinear dynamic characteristics of oil-in-water emulsions under the addition of surfactant were experimentally investigated. Firstly, based on the vertical upward oil-water two-phase flow experiment in 20 mm inner diameter (ID) testing pipe, dynamic response signals of oil-in-water emulsions were recorded using vertical multiple electrode array (VMEA) sensor. Afterwards, the recurrence plot (RP) algorithm and multi-scale weighted complexity entropy causality plane (MS-WCECP) were employed to analyse the nonlinear characteristics of the signals. The results show that the certainty is decreasing and the randomness is increasing with the increment of surfactant concentration. This article provides a novel method for revealing the nonlinear dynamic characteristics, complexity, and randomness of oil-in-water emulsions with experimental measurement signals.

A time domain inverse dynamic method for the end point tracking control of a flexible manipulator

NASA Technical Reports Server (NTRS)

Kwon, Dong-Soo; Book, Wayne J.

1991-01-01

The inverse dynamic equation of a flexible manipulator was solved in the time domain. By dividing the inverse system equation into the causal part and the anticausal part, we calculated the torque and the trajectories of all state variables for a given end point trajectory. The interpretation of this method in the frequency domain was explained in detail using the two-sided Laplace transform and the convolution integral. The open loop control of the inverse dynamic method shows an excellent result in simulation. For real applications, a practical control strategy is proposed by adding a feedback tracking control loop to the inverse dynamic feedforward control, and its good experimental performance is presented.

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

Duchaineau, M.; Wolinsky, M.; Sigeti, D.E.

Terrain visualization is a difficult problem for applications requiring accurate images of large datasets at high frame rates, such as flight simulation and ground-based aircraft testing using synthetic sensor stimulation. On current graphics hardware, the problem is to maintain dynamic, view-dependent triangle meshes and texture maps that produce good images at the required frame rate. We present an algorithm for constructing triangle meshes that optimizes flexible view-dependent error metrics, produces guaranteed error bounds, achieves specified triangle counts directly, and uses frame-to-frame coherence to operate at high frame rates for thousands of triangles per frame. Our method, dubbed Real-time Optimally Adaptingmore » Meshes (ROAM), uses two priority queues to drive split and merge operations that maintain continuous triangulations built from pre-processed bintree triangles. We introduce two additional performance optimizations: incremental triangle stripping and priority-computation deferral lists. ROAM execution time is proportionate to the number of triangle changes per frame, which is typically a few percent of the output mesh size, hence ROAM performance is insensitive to the resolution and extent of the input terrain. Dynamic terrain and simple vertex morphing are supported.« less

Stereo-tomography in triangulated models

NASA Astrophysics Data System (ADS)

Yang, Kai; Shao, Wei-Dong; Xing, Feng-yuan; Xiong, Kai

2018-04-01

Stereo-tomography is a distinctive tomographic method. It is capable of estimating the scatterer position, the local dip of scatterer and the background velocity simultaneously. Building a geologically consistent velocity model is always appealing for applied and earthquake seismologists. Differing from the previous work to incorporate various regularization techniques into the cost function of stereo-tomography, we think extending stereo-tomography to the triangulated model will be the most straightforward way to achieve this goal. In this paper, we provided all the Fréchet derivatives of stereo-tomographic data components with respect to model components for slowness-squared triangulated model (or sloth model) in 2D Cartesian coordinate based on the ray perturbation theory for interfaces. A sloth model representation means a sparser model representation when compared with conventional B-spline model representation. A sparser model representation leads to a smaller scale of stereo-tomographic (Fréchet) matrix, a higher-accuracy solution when solving linear equations, a faster convergence rate and a lower requirement for quantity of data space. Moreover, a quantitative representation of interface strengthens the relationships among different model components, which makes the cross regularizations among these model components, such as node coordinates, scatterer coordinates and scattering angles, etc., more straightforward and easier to be implemented. The sensitivity analysis, the model resolution matrix analysis and a series of synthetic data examples demonstrate the correctness of the Fréchet derivatives, the applicability of the regularization terms and the robustness of the stereo-tomography in triangulated model. It provides a solid theoretical foundation for the real applications in the future.

The on-orbit calibration of geometric parameters of the Tian-Hui 1 (TH-1) satellite

NASA Astrophysics Data System (ADS)

Wang, Jianrong; Wang, Renxiang; Hu, Xin; Su, Zhongbo

2017-02-01

The on-orbit calibration of geometric parameters is a key step in improving the location accuracy of satellite images without using Ground Control Points (GCPs). Most methods of on-orbit calibration are based on the self-calibration using additional parameters. When using additional parameters, different number of additional parameters may lead to different results. The triangulation bundle adjustment is another way to calibrate the geometric parameters of camera, which can describe the changes in each geometric parameter. When triangulation bundle adjustment method is applied to calibrate geometric parameters, a prerequisite is that the strip model can avoid systematic deformation caused by the rate of attitude changes. Concerning the stereo camera, the influence of the intersection angle should be considered during calibration. The Equivalent Frame Photo (EFP) bundle adjustment based on the Line-Matrix CCD (LMCCD) image can solve the systematic distortion of the strip model, and obtain high accuracy location without using GCPs. In this paper, the triangulation bundle adjustment is used to calibrate the geometric parameters of TH-1 satellite cameras based on LMCCD image. During the bundle adjustment, the three-line array cameras are reconstructed by adopting the principle of inverse triangulation. Finally, the geometric accuracy is validated before and after on-orbit calibration using 5 testing fields. After on-orbit calibration, the 3D geometric accuracy is improved to 11.8 m from 170 m. The results show that the location accuracy of TH-1 without using GCPs is significantly improved using the on-orbit calibration of the geometric parameters.

Measuring teamwork in primary care: Triangulation of qualitative and quantitative data.

PubMed

Brown, Judith Belle; Ryan, Bridget L; Thorpe, Cathy; Markle, Emma K R; Hutchison, Brian; Glazier, Richard H

2015-09-01

This article describes the triangulation of qualitative dimensions, reflecting high functioning teams, with the results of standardized teamwork measures. The study used a mixed methods design using qualitative and quantitative approaches to assess teamwork in 19 Family Health Teams in Ontario, Canada. This article describes dimensions from the qualitative phase using grounded theory to explore the issues and challenges to teamwork. Two quantitative measures were used in the study, the Team Climate Inventory (TCI) and the Providing Effective Resources and Knowledge (PERK) scale. For the triangulation analysis, the mean scores of these measures were compared with the qualitatively derived ratings for the dimensions. The final sample for the qualitative component was 107 participants. The qualitative analysis identified 9 dimensions related to high team functioning such as common philosophy, scope of practice, conflict resolution, change management, leadership, and team evolution. From these dimensions, teams were categorized numerically as high, moderate, or low functioning. Three hundred seventeen team members completed the survey measures. Mean site scores for the TCI and PERK were 3.87 and 3.88, respectively (of 5). The TCI was associated will all dimensions except for team location, space allocation, and executive director leadership. The PERK was associated with all dimensions except team location. Data triangulation provided qualitative and quantitative evidence of what constitutes teamwork. Leadership was pivotal in forging a common philosophy and encouraging team collaboration. Teams used conflict resolution strategies and adapted to the changes they encountered. These dimensions advanced the team's evolution toward a high functioning team. (c) 2015 APA, all rights reserved).

Quality assurance for the clinical implementation of kilovoltage intrafraction monitoring for prostate cancer VMAT

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

Ng, J. A.; Booth, J. T.; O’Brien, R. T.

2014-11-01

Purpose: Kilovoltage intrafraction monitoring (KIM) is a real-time 3D tumor monitoring system for cancer radiotherapy. KIM uses the commonly available gantry-mounted x-ray imager as input, making this method potentially more widely available than dedicated real-time 3D tumor monitoring systems. KIM is being piloted in a clinical trial for prostate cancer patients treated with VMAT (NCT01742403). The purpose of this work was to develop clinical process and quality assurance (QA) practices for the clinical implementation of KIM. Methods: Informed by and adapting existing guideline documents from other real-time monitoring systems, KIM-specific QA practices were developed. The following five KIM-specific QA testsmore » were included: (1) static localization accuracy, (2) dynamic localization accuracy, (3) treatment interruption accuracy, (4) latency measurement, and (5) clinical conditions accuracy. Tests (1)–(4) were performed using KIM to measure static and representative patient-derived prostate motion trajectories using a 3D programmable motion stage supporting an anthropomorphic phantom with implanted gold markers to represent the clinical treatment scenario. The threshold for system tolerable latency is <1 s. The tolerances for all other tests are that both the mean and standard deviation of the difference between the programmed trajectory and the measured data are <1 mm. The (5) clinical conditions accuracy test compared the KIM measured positions with those measured by kV/megavoltage (MV) triangulation from five treatment fractions acquired in a previous pilot study. Results: For the (1) static localization, (2) dynamic localization, and (3) treatment interruption accuracy tests, the mean and standard deviation of the difference are <1.0 mm. (4) The measured latency is 350 ms. (5) For the tests with previously acquired patient data, the mean and standard deviation of the difference between KIM and kV/MV triangulation are <1.0 mm. Conclusions: Clinical process and QA practices for the safe clinical implementation of KIM, a novel real-time monitoring system using commonly available equipment, have been developed and implemented for prostate cancer VMAT.« less

Quantized Detector Networks

NASA Astrophysics Data System (ADS)

Jaroszkiewicz, George

2017-12-01

Preface; Acronyms; 1. Introduction; 2. Questions and answers; 3. Classical bits; 4. Quantum bits; 5. Classical and quantum registers; 6. Classical register mechanics; 7. Quantum register dynamics; 8. Partial observations; 9. Mixed states and POVMs; 10. Double-slit experiments; 11. Modules; 12. Computerization and computer algebra; 13. Interferometers; 14. Quantum eraser experiments; 15. Particle decays; 16. Non-locality; 17. Bell inequalities; 18. Change and persistence; 19. Temporal correlations; 20. The Franson experiment; 21. Self-intervening networks; 22. Separability and entanglement; 23. Causal sets; 24. Oscillators; 25. Dynamical theory of observation; 26. Conclusions; Appendix; Index.

The predictive power of singular value decomposition entropy for stock market dynamics

NASA Astrophysics Data System (ADS)

Caraiani, Petre

2014-01-01

We use a correlation-based approach to analyze financial data from the US stock market, both daily and monthly observations from the Dow Jones. We compute the entropy based on the singular value decomposition of the correlation matrix for the components of the Dow Jones Industrial Index. Based on a moving window, we derive time varying measures of entropy for both daily and monthly data. We find that the entropy has a predictive ability with respect to stock market dynamics as indicated by the Granger causality tests.

Brain network dynamics characterization in epileptic seizures. Joint directed graph and pairwise synchronization measures

NASA Astrophysics Data System (ADS)

Rodrigues, A. C.; Machado, B. S.; Florence, G.; Hamad, A. P.; Sakamoto, A. C.; Fujita, A.; Baccalá, L. A.; Amaro, E.; Sameshima, K.

2014-12-01

Here we propose and evaluate a new approach to analyse multichannel mesial temporal lobe epilepsy EEG data from eight patients through complex network and synchronization theories. The method employs a Granger causality test to infer the directed connectivity graphs and a wavelet transform based phase synchronization measure whose characteristics allow studying dynamical transitions during epileptic seizures. We present a new combined graph measure that quantifies the level of network hub formation, called network hub out-degree, which closely reflects the level of synchronization observed during the ictus.

The Detection of Transport Land-Use Data Using Crowdsourcing Taxi Trajectory

NASA Astrophysics Data System (ADS)

Ai, T.; Yang, W.

2016-06-01

This study tries to explore the question of transport land-use change detection by large volume of vehicle trajectory data, presenting a method based on Deluanay triangulation. The whole method includes three steps. The first one is to pre-process the vehicle trajectory data including the point anomaly removing and the conversion of trajectory point to track line. Secondly, construct Deluanay triangulation within the vehicle trajectory line to detect neighborhood relation. Considering the case that some of the trajectory segments are too long, we use a interpolation measure to add more points for the improved triangulation. Thirdly, extract the transport road by cutting short triangle edge and organizing the polygon topology. We have conducted the experiment of transport land-use change discovery using the data of taxi track in Beijing City. We extract not only the transport land-use area but also the semantic information such as the transformation speed, the traffic jam distribution, the main vehicle movement direction and others. Compared with the existed transport network data, such as OpenStreet Map, our method is proved to be quick and accurate.

Triangulation-based 3D surveying borescope

NASA Astrophysics Data System (ADS)

Pulwer, S.; Steglich, P.; Villringer, C.; Bauer, J.; Burger, M.; Franz, M.; Grieshober, K.; Wirth, F.; Blondeau, J.; Rautenberg, J.; Mouti, S.; Schrader, S.

2016-04-01

In this work, a measurement concept based on triangulation was developed for borescopic 3D-surveying of surface defects. The integration of such measurement system into a borescope environment requires excellent space utilization. The triangulation angle, the projected pattern, the numerical apertures of the optical system, and the viewing angle were calculated using partial coherence imaging and geometric optical raytracing methods. Additionally, optical aberrations and defocus were considered by the integration of Zernike polynomial coefficients. The measurement system is able to measure objects with a size of 50 μm in all dimensions with an accuracy of +/- 5 μm. To manage the issue of a low depth of field while using an optical high resolution system, a wavelength dependent aperture was integrated. Thereby, we are able to control depth of field and resolution of the optical system and can use the borescope in measurement mode with high resolution and low depth of field or in inspection mode with low resolution and higher depth of field. First measurements of a demonstrator system are in good agreement with our simulations.

Investigating the effect of external trauma through a dynamic system modeling approach for clustering causality in diabetic foot ulcer development.

PubMed

Salimi, Parisa; Hamedi, Mohsen; Jamshidi, Nima; Vismeh, Milad

2017-04-01

Diabetes and its associated complications are realized as one of the most challenging medical conditions threatening more than 29 million people only in the USA. The forecasts suggest a suffering of more than half a billion worldwide by 2030. Amid all diabetic complications, diabetic foot ulcer (DFU) has attracted much scientific investigations to lead to a better management of this disease. In this paper, a system thinking methodology is adopted to investigate the dynamic nature of the ulceration. The causal loop diagram as a tool is utilized to illustrate the well-researched relations and interrelations between causes of the DFU. The result of clustering causality evaluation suggests a vicious loop that relates external trauma to callus. Consequently a hypothesis is presented which localizes development of foot ulceration considering distribution of normal and shear stress. It specifies that normal and tangential forces, as the main representatives of external trauma, play the most important role in foot ulceration. The evaluation of this hypothesis suggests the significance of the information related to both normal and shear stress for managing DFU. The results also discusses how these two react on different locations on foot such as metatarsal head, heel and hallux. The findings of this study can facilitate tackling the complexity of DFU problem and looking for constructive mitigation measures. Moreover they lead to developing a more promising methodology for managing DFU including better prognosis, designing prosthesis and insoles for DFU and patient caring recommendations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

NASA Astrophysics Data System (ADS)

Liu, Chao; Gong, Yongqiang; Laflamme, Simon; Phares, Brent; Sarkar, Soumik

2017-01-01

The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density.

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis.

PubMed

Wang, Yuan; Wang, Yao; Lui, Yvonne W

2018-05-18

Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure. In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks. Copyright © 2018 Elsevier Inc. All rights reserved.

Modeling and Characterization of Electrical Resistivity of Carbon Composite Laminates

NASA Astrophysics Data System (ADS)

Yasuda, Hiromi

Origami has recently received significant interest from the scientific and engineering communities as a method for designing building blocks of engineered structures to enhance their mechanical properties. However, the primary focus has been placed on their kinematic applications by leveraging the compactness and auxeticity of planar origami platforms. In this thesis, we study two different types of volumetric origami structures, Tachi-Miura Polyhedron (TMP) and Triangulated Cylindrical Origami (TCO), hierarchically from a single unit cell level to an assembly of multi-origami cells. We strategically assemble these origami cells into mechanical metamaterials and demonstrate their unique static/dynamic mechanical responses. In particular, these origami structures exhibit tailorable stiffness and strain softening/hardening behaviors, which leads to rich wave dynamics in origami-based architectures such as tunable frequency bands and new types of nonlinear wave propagations. One of the novel waveforms investigated in this thesis is the rarefaction solitary wave arising from strain-softening nature of origami unit cell. This unique wave dynamic mechanism is analyzed in numerical, analytical, and experimental approaches. By leveraging their tailorable folding mechanisms, the origami-based mechanical metamaterials can be used for designing new types of engineering devices and structures, not only for deployable space and disaster relief applications, but also for vibration filtering, impact mitigation, and energy harvesting.

Ecosystem Research Experience with Two Indigenous Communities of Colombia: The Ecohealth Calendar as a Participatory and Innovative Methodological Tool.

PubMed

SantoDomingo, Andrés Felipe; Castro-Díaz, Laura; González-Uribe, Catalina

2016-12-01

Eco-bio-social factors may increase or decrease a community's susceptibility to vector-borne disease transmission. Traditional studies have contributed information about the association between eco-bio-social factors and health outcomes, but few have provided this information in an integrative way characterizing annual dynamics among indigenous communities. Transdisciplinary research was conducted with the Bari of Karikachaboquira and the Wayúu of Marbacella and El Horno, using qualitative and participatory methods, including seasonal graphics, semi-structured interviews, geo-referencing routes, and participatory observation. The information was triangulated and discussed with local actors in order to validate and complement the results. An ecohealth calendar was obtained for each community, linking the socioecological dynamics to specific diseases, especially malaria. Local dynamics can change, depending on environmental conditions, and these determine the presence or absence of diseases. For both communities, the rainy season is the period with the greatest proliferation of mosquitoes (including Anopheles spp.), during which malaria cases occur. The ecohealth calendar integrates eco-bio-social information from local communities, through participatory and potentially empowering processes, into a comprehensive layout. This can break down the conceptual, demographic, and cultural barriers in the context of community-based interventions and research to action based on an ecosystem framework.

Development of a system dynamics model for financially sustainable management of municipal watermain networks.

PubMed

Rehan, R; Knight, M A; Unger, A J A; Haas, C T

2013-12-15

This paper develops causal loop diagrams and a system dynamics model for financially sustainable management of urban water distribution networks. The developed causal loop diagrams are a novel contribution in that it illustrates the unique characteristics and feedback loops for financially self-sustaining water distribution networks. The system dynamics model is a mathematical realization of the developed interactions among system variables over time and is comprised of three sectors namely watermains network, consumer, and finance. This is the first known development of a water distribution network system dynamics model. The watermains network sector accounts for the unique characteristics of watermain pipes such as service life, deterioration progression, pipe breaks, and water leakage. The finance sector allows for cash reserving by the utility in addition to the pay-as-you-go and borrowing strategies. The consumer sector includes controls to model water fee growth as a function of service performance and a household's financial burden due to water fees. A series of policy levers are provided that allow the impact of various financing strategies to be evaluated in terms of financial sustainability and household affordability. The model also allows for examination of the impact of different management strategies on the water fee in terms of consistency and stability over time. The paper concludes with a discussion on how the developed system dynamics water model can be used by water utilities to achieve a variety of utility short and long-term objectives and to establish realistic and defensible water utility policies. It also discusses how the model can be used by regulatory bodies, government agencies, the financial industry, and researchers. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Behavioral and Neural Correlates of Executive Function: Interplay between Inhibition and Updating Processes.

PubMed

Kim, Na Young; Wittenberg, Ellen; Nam, Chang S

2017-01-01

This study investigated the interaction between two executive function processes, inhibition and updating, through analyses of behavioral, neurophysiological, and effective connectivity metrics. Although, many studies have focused on behavioral effects of executive function processes individually, few studies have examined the dynamic causal interactions between these two functions. A total of twenty participants from a local university performed a dual task combing flanker and n-back experimental paradigms, and completed the Operation Span Task designed to measure working memory capacity. We found that both behavioral (accuracy and reaction time) and neurophysiological (P300 amplitude and alpha band power) metrics on the inhibition task (i.e., flanker task) were influenced by the updating load (n-back level) and modulated by working memory capacity. Using independent component analysis, source localization (DIPFIT), and Granger Causality analysis of the EEG time-series data, the present study demonstrated that manipulation of cognitive demand in a dual executive function task influenced the causal neural network. We compared connectivity across three updating loads (n-back levels) and found that experimental manipulation of working memory load enhanced causal connectivity of a large-scale neurocognitive network. This network contains the prefrontal and parietal cortices, which are associated with inhibition and updating executive function processes. This study has potential applications in human performance modeling and assessment of mental workload, such as the design of training materials and interfaces for those performing complex multitasking under stress.

Dynamic relationship between CO2 emissions, energy consumption and economic growth in three North African countries

NASA Astrophysics Data System (ADS)

Kais, Saidi; Ben Mbarek, Mounir

2017-10-01

This paper investigated the causal relationship between energy consumption (EC), carbon dioxide (CO2) emissions and economic growth for three selected North African countries. It uses a panel co-integration analysis to determine this econometric relationship using data during 1980-2012. Recently developed tests for panel unit root and co-integration tests are applied. In order to test the Granger causality, a panel Vector Error Correction Model is used. The conservation hypothesis is found; the short run panel results show that there is a unidirectional relationship from economic growth to EC. In addition, there is a unidirectional causality running from economic growth to CO2 emissions. A unidirectional relationship from EC to CO2 emissions is detected. Findings shown that there is a big interdependence between EC and economic growth in the long run, which indicates the level of economic activity and EC mutually influence each other in that a high level of economic growth leads to a high level of EC and vice versa. Similarly, a unidirectional causal relationship from EC to CO2 emissions is detected. This study opens up new insights for policy-makers to design comprehensive economic, energy and environmental policy to keep the economic green and a sustainable environment, implying that these three variables could play an important role in the adjustment process as the system changes from the long run equilibrium.

On the Inference of Functional Circadian Networks Using Granger Causality

PubMed Central

Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

2015-01-01

Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

Effective Brain Connectivity in Children with Reading Difficulties during Phonological Processing

ERIC Educational Resources Information Center

Cao, Fan; Bitan, Tali; Booth, James R.

2008-01-01

Using Dynamic Causal Modeling (DCM) and functional magnetic resonance imaging (fMRI), we examined effective connectivity between three left hemisphere brain regions (inferior frontal gyrus, inferior parietal lobule, fusiform gyrus) and bilateral medial frontal gyrus in 12 children with reading difficulties (M age = 12.4, range: 8.11-14.10) and 12…

Discussion: Narrating and Theorizing Activity in Educational Settings

ERIC Educational Resources Information Center

Wells, Gordon

2004-01-01

All the articles in this collection share at least three common features. First, they all give a narrative account of a change that took place within an activity system; in this, they follow Vygotsky's (1978) injunction to investigate the history of the system in order to understand its "causal dynamic basis" (p. 62). Second, in so doing, they…

How to Be a Good Parent: Have a Good Child.

ERIC Educational Resources Information Center

Mohar, Carol J.

It is a common article of belief that each child is unique. Action based on this belief, though, is rare. Researchers have largely neglected the question of the causes of children's individual uniqueness. But, when difficulties and serious problems arise in the course of child rearing, causality is located in the dynamics of family functioning.…

Physical consistency in modeling interplanetary magnetohydrodynamic fluctuations

NASA Technical Reports Server (NTRS)

Zhou, Y.; Matthaeus, W. H.; Roberts, D. A.; Goldstein, M. L.

1990-01-01

The validity of the Velli, Grappin and Mangeney (1989) model is evaluated. It is argued that the model is incorrect because it mixes different dynamical models, assumes weak nonlinearities, makes predictions that vary with observations, and violates causality. It is proposed that self-similar behavior in the coronal source region of the magnetohydrodynamic fluctuations cause the Kolmogorov-like spectra.

The Relationship between Early Literacy Assessment and First-Grade Reading Achievement for Native American Students

ERIC Educational Resources Information Center

Coats-Kitsopoulos, Gloria Jean

2011-01-01

The purpose of this study was to determine the relationship between the Dynamic Indicators of Basic Early Literacy Skills (DIBELS), the Reading Recovery Observation Survey (RROS) early reading sub-tests, and the reading achievement of Native American first-graders as measured by the Stanford 10. A causal-comparative correlation research design…

Applying survival analysis to a large-scale forest inventory for assessment of tree mortality in Minnesota

Treesearch

C.W. Woodall; P.L. Grambsch; W. Thomas

2005-01-01

Tree mortality has traditionally been assessed in forest inventories through summaries of mortality by location, species, and causal agents. Although these methods have historically constituted the majority of tree mortality summarizations, they have had limited use in assessing mortality trends and dynamics. This study proposed a novel method of applying survival...

Study of the Career Intern Program. Final Technical Report--Task C: Program Dynamics: Structure, Function, and Interrelationships.

ERIC Educational Resources Information Center

Fetterman, David M.

A study identified causal linkages and basic interrelationships among components of the Career Intern Program (CIP) and observed outcomes. (The CIP is an alternative high school designed to enable disadvantaged and alienated dropouts or potential dropouts to earn regular high school diplomas, to prepare them for meaningful employment or…

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

Rebay, S.

This work is devoted to the description of an efficient unstructured mesh generation method entirely based on the Delaunay triangulation. The distinctive characteristic of the proposed method is that point positions and connections are computed simultaneously. This result is achieved by taking advantage of the sequential way in which the Bowyer-Watson algorithm computes the Delaunay triangulation. Two methods are proposed which have great geometrical flexibility, in that they allow us to treat domains of arbitrary shape and topology and to generate arbitrarily nonuniform meshes. The methods are computationally efficient and are applicable both in two and three dimensions. 11 refs.,more » 20 figs., 1 tab.« less

Superlative TINs

NASA Technical Reports Server (NTRS)

Chamberlin, R.

2002-01-01

TIN is short for 'triangulated irregular network,' which is a piecewise planar model of a surface. If properly constructed, a TIN can be more than 30 times as efficient as a regular triangulation. In our project (a ground combat simulation to support U.S. Army training exercises), the TIN is used to represent the Earth's surface and is used primarily to determine whether line of sight is blocked by terrain. High efficiency requires accurate identification of ridgelines with as few triangles as possible. The work currently in progress is the implementation of a TINning process that we hope will produce superlative TINs. This presentation describes that process.

A linear photodiode array employed in a short range laser triangulation obstacle avoidance sensor. M.S. Thesis; [Martian roving vehicle sensor

NASA Technical Reports Server (NTRS)

Odenthal, J. P.

1980-01-01

An opto-electronic receiver incorporating a multi-element linear photodiode array as a component of a laser-triangulation rangefinder was developed as an obstacle avoidance sensor for a Martian roving vehicle. The detector can resolve the angle of laser return in 1.5 deg increments within a field of view of 30 deg and a range of five meters. A second receiver with a 1024 elements over 60 deg and a 3 meter range is also documented. Design criteria, circuit operation, schematics, experimental results and calibration procedures are discussed.

First Instances of Generalized Expo-Rational Finite Elements on Triangulations

NASA Astrophysics Data System (ADS)

Dechevsky, Lubomir T.; Zanaty, Peter; Laksa˚, Arne; Bang, Børre

2011-12-01

In this communication we consider a construction of simplicial finite elements on triangulated two-dimensional polygonal domains. This construction is, in some sense, dual to the construction of generalized expo-rational B-splines (GERBS). The main result is in the obtaining of new polynomial simplicial patches of the first several lowest possible total polynomial degrees which exhibit Hermite interpolatory properties. The derivation of these results is based on the theory of piecewise polynomial GERBS called Euler Beta-function B-splines. We also provide 3-dimensional visualization of the graphs of the new polynomial simplicial patches and their control polygons.

Laser triangulation method for measuring the size of parking claw

NASA Astrophysics Data System (ADS)

Liu, Bo; Zhang, Ming; Pang, Ying

2017-10-01

With the development of science and technology and the maturity of measurement technology, the 3D profile measurement technology has been developed rapidly. Three dimensional measurement technology is widely used in mold manufacturing, industrial inspection, automatic processing and manufacturing, etc. There are many kinds of situations in scientific research and industrial production. It is necessary to transform the original mechanical parts into the 3D data model on the computer quickly and accurately. At present, many methods have been developed to measure the contour size, laser triangulation is one of the most widely used methods.

Markerless positional verification using template matching and triangulation of kV images acquired during irradiation for lung tumors treated in breath-hold

NASA Astrophysics Data System (ADS)

Hazelaar, Colien; Dahele, Max; Mostafavi, Hassan; van der Weide, Lineke; Slotman, Ben; Verbakel, Wilko

2018-06-01

Lung tumors treated in breath-hold are subject to inter- and intra-breath-hold variations, which makes tumor position monitoring during each breath-hold important. A markerless technique is desirable, but limited tumor visibility on kV images makes this challenging. We evaluated if template matching  +  triangulation of kV projection images acquired during breath-hold stereotactic treatments could determine 3D tumor position. Band-pass filtering and/or digital tomosynthesis (DTS) were used as image pre-filtering/enhancement techniques. On-board kV images continuously acquired during volumetric modulated arc irradiation of (i) a 3D-printed anthropomorphic thorax phantom with three lung tumors (n  =  6 stationary datasets, n  =  2 gradually moving), and (ii) four patients (13 datasets) were analyzed. 2D reference templates (filtered DRRs) were created from planning CT data. Normalized cross-correlation was used for 2D matching between templates and pre-filtered/enhanced kV images. For 3D verification, each registration was triangulated with multiple previous registrations. Generally applicable image processing/algorithm settings for lung tumors in breath-hold were identified. For the stationary phantom, the interquartile range of the 3D position vector was on average 0.25 mm for 12° DTS  +  band-pass filtering (average detected positions in 2D  =  99.7%, 3D  =  96.1%, and 3D excluding first 12° due to triangulation angle  =  99.9%) compared to 0.81 mm for band-pass filtering only (55.8/52.9/55.0%). For the moving phantom, RMS errors for the lateral/longitudinal/vertical direction after 12° DTS  +  band-pass filtering were 1.5/0.4/1.1 mm and 2.2/0.3/3.2 mm. For the clinical data, 2D position was determined for at least 93% of each dataset and 3D position excluding first 12° for at least 82% of each dataset using 12° DTS  +  band-pass filtering. Template matching  +  triangulation using DTS  +  band-pass filtered images could accurately determine the position of stationary lung tumors. However, triangulation was less accurate/reliable for targets with continuous, gradual displacement in the lateral and vertical directions. This technique is therefore currently most suited to detect/monitor offsets occurring between initial setup and the start of treatment, inter-breath-hold variations, and tumors with predominantly longitudinal motion.

Vector Autoregressive (VAR) Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example

PubMed Central

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.

2016-01-01

Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Discussion Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes tend to occur before changes in HR and SpO2. These findings suggest that contextual assessment of RR changes as the earliest sign of CRI is warranted. Use of VAR modeling may be helpful in other nursing research applications based on time series data. PMID:27977564

Dynamic causal modelling of brain-behaviour relationships.

PubMed

Rigoux, L; Daunizeau, J

2015-08-15

In this work, we expose a mathematical treatment of brain-behaviour relationships, which we coin behavioural Dynamic Causal Modelling or bDCM. This approach aims at decomposing the brain's transformation of stimuli into behavioural outcomes, in terms of the relative contribution of brain regions and their connections. In brief, bDCM places the brain at the interplay between stimulus and behaviour: behavioural outcomes arise from coordinated activity in (hidden) neural networks, whose dynamics are driven by experimental inputs. Estimating neural parameters that control network connectivity and plasticity effectively performs a neurobiologically-constrained approximation to the brain's input-outcome transform. In other words, neuroimaging data essentially serves to enforce the realism of bDCM's decomposition of input-output relationships. In addition, post-hoc artificial lesions analyses allow us to predict induced behavioural deficits and quantify the importance of network features for funnelling input-output relationships. This is important, because this enables one to bridge the gap with neuropsychological studies of brain-damaged patients. We demonstrate the face validity of the approach using Monte-Carlo simulations, and its predictive validity using empirical fMRI/behavioural data from an inhibitory control task. Lastly, we discuss promising applications of this work, including the assessment of functional degeneracy (in the healthy brain) and the prediction of functional recovery after lesions (in neurological patients). Copyright © 2015 Elsevier Inc. All rights reserved.

Exploring the Epileptic Brain Network Using Time-Variant Effective Connectivity and Graph Theory.

PubMed

Storti, Silvia Francesca; Galazzo, Ilaria Boscolo; Khan, Sehresh; Manganotti, Paolo; Menegaz, Gloria

2017-09-01

The application of time-varying measures of causality between source time series can be very informative to elucidate the direction of communication among the regions of an epileptic brain. The aim of the study was to identify the dynamic patterns of epileptic networks in focal epilepsy by applying multivariate adaptive directed transfer function (ADTF) analysis and graph theory to high-density electroencephalographic recordings. The cortical network was modeled after source reconstruction and topology modulations were detected during interictal spikes. First a distributed linear inverse solution, constrained to the individual grey matter, was applied to the averaged spikes and the mean source activity over 112 regions, as identified by the Harvard-Oxford Atlas, was calculated. Then, the ADTF, a dynamic measure of causality, was used to quantify the connectivity strength between pairs of regions acting as nodes in the graph, and the measure of node centrality was derived. The proposed analysis was effective in detecting the focal regions as well as in characterizing the dynamics of the spike propagation, providing evidence of the fact that the node centrality is a reliable feature for the identification of the epileptogenic zones. Validation was performed by multimodal analysis as well as from surgical outcomes. In conclusion, the time-variant connectivity analysis applied to the epileptic patients can distinguish the generator of the abnormal activity from the propagation spread and identify the connectivity pattern over time.

The effect of relationship status on health with dynamic health and persistent relationships.

PubMed

Kohn, Jennifer L; Averett, Susan L

2014-07-01

The dynamic evolution of health and persistent relationship status pose econometric challenges to disentangling the causal effect of relationships on health from the selection effect of health on relationship choice. Using a new econometric strategy we find that marriage is not universally better for health. Rather, cohabitation benefits the health of men and women over 45, being never married is no worse for health, and only divorce marginally harms the health of younger men. We find strong evidence that unobservable health-related factors can confound estimates. Our method can be applied to other research questions with dynamic dependent and multivariate endogenous variables. Copyright © 2014 Elsevier B.V. All rights reserved.

Detection of frequency-mode-shift during thermoacoustic combustion oscillations in a staged aircraft engine model combustor

NASA Astrophysics Data System (ADS)

Kobayashi, Hiroaki; Gotoda, Hiroshi; Tachibana, Shigeru; Yoshida, Seiji

2017-12-01

We conduct an experimental study using time series analysis based on symbolic dynamics to detect a precursor of frequency-mode-shift during thermoacoustic combustion oscillations in a staged aircraft engine model combustor. With increasing amount of the main fuel, a significant shift in the dominant frequency-mode occurs in noisy periodic dynamics, leading to a notable increase in oscillation amplitudes. The sustainment of noisy periodic dynamics during thermoacoustic combustion oscillations is clearly shown by the multiscale complexity-entropy causality plane in terms of statistical complexity. A modified version of the permutation entropy allows us to detect a precursor of the frequency-mode-shift before the amplification of pressure fluctuations.

Dark Energy from Discrete Spacetime

PubMed Central

Trout, Aaron D.

2013-01-01

Dark energy accounts for most of the matter-energy content of our universe, yet current theories of its origin rely on radical physical assumptions such as the holographic principle or controversial anthropic arguments. We give a better motivated explanation for dark energy, claiming that it arises from a small negative scalar-curvature present even in empty spacetime. The vacuum has this curvature because spacetime is fundamentally discrete and there are more ways for a discrete geometry to have negative curvature than positive. We explicitly compute this effect using a variant of the well known dynamical-triangulations (DT) model for quantum gravity. Our model predicts a time-varying non-zero cosmological constant with a current value, in natural units, in agreement with observation. This calculation is made possible by a novel characterization of the possible DT action values combined with numerical evidence concerning their degeneracies. PMID:24312502

Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimer's disease.

PubMed

Iturria-Medina, Yasser; Carbonell, Félix M; Sotero, Roberto C; Chouinard-Decorte, Francois; Evans, Alan C

2017-05-15

Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population-based level for studying late onset Alzheimer's disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid-β (Aβ) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most-likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or Aβ) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population-based multifactorial causal network, we show the crucial advantage of using combinatorial over single-target treatments, explain why one-target Aβ based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional strategies. Copyright © 2017 Elsevier Inc. All rights reserved.

Modelling possible causality in the associations between unemployment, cannabis use, and alcohol misuse.

PubMed

Boden, Joseph M; Lee, Jungeun Olivia; Horwood, L John; Grest, Carolina Villamil; McLeod, Geraldine F H

2017-02-01

There has been considerable interest in the extent to which substance use and unemployment may be related, particularly the causal pathways that may be involved in these associations. It has been argued that these associations may reflect social causation, in which unemployment influences substance use, or that they may reflect social selection, in which substance use increases the risk of becoming and remaining unemployed. The present study sought to test these competing explanations. Data from the Christchurch Health and Development Study, featuring a longitudinal birth cohort, were used to model the associations between unemployment and both cannabis and alcohol. Data on patterns of unemployment, involvement with cannabis, and symptoms of alcohol use disorder were examined from ages 18-35 years. The associations between unemployment and both cannabis dependence and alcohol use disorder (AUD) were modelled using conditional fixed-effects regression models, augmented by time-dynamic covariate factors. The analyses showed evidence of possible reciprocal causal processes in the association between unemployment and cannabis dependence, in which unemployment of at least three months' duration significantly (p < 0.0001) increased the risk of cannabis dependence, and cannabis dependence significantly (p < 0.0001) increased the risk of being unemployed. Similar evidence was found for the associations between unemployment and AUD, although these associations were smaller in magnitude. The present findings support both social causation and social selection arguments, by indicating that unemployment plays a causal role in substance misuse, and that it is also likely that a reverse causal process whereby substance misuse increases the risk of unemployment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models

PubMed Central

Smith, Jason F.; Chen, Kewei; Pillai, Ajay S.; Horwitz, Barry

2013-01-01

The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here, we explicitly define “effective connectivity” using a common set of observation and state equations that are appropriate for three connectivity methods: dynamic causal modeling (DCM), multivariate autoregressive modeling (MAR), and switching linear dynamic systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. PMID:23717258

Circuit to Construct Mapping: A Mathematical Tool for Assisting the Diagnosis and Treatment in Major Depressive Disorder

PubMed Central

Bielczyk, Natalia Z.; Buitelaar, Jan K.; Glennon, Jeffrey C.; Tiesinga, Paul H. E.

2015-01-01

Major depressive disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH research domain criteria initiative, in which a concept of “constructs” as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning – cognition, genetics, brain anatomy, and neurophysiology. In this review, we propose a new approach which we called circuit to construct mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD. The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime toward personalized medicine, assisting the diagnosis and treatment of MDD. PMID:25767450

Dynamic cerebral autoregulation during repeated squat-stand maneuvers

PubMed Central

Claassen, Jurgen A. H. R.; Levine, Benjamin D.; Zhang, Rong

2009-01-01

Transfer function analysis of spontaneous oscillations in blood pressure (BP) and cerebral blood flow (CBF) can quantify the dynamic relationship between BP and CBF. However, such oscillation amplitudes are often small and of questionable clinical significance, vary substantially, and cannot be controlled. At the very low frequencies (<0.07 Hz), coherence between BP and CBF often is low (<0.50) and their causal relationship is debated. Eight healthy subjects performed repeated squat-stand maneuvers to induce large oscillations in BP at frequencies of 0.025 and 0.05 Hz (very low frequency) and 0.1 Hz (low frequency), respectively. BP (Finapres), CBF velocity (CBFV; transcranial Doppler), and end-tidal CO2 (capnography) were monitored. Spectral analysis was used to quantify oscillations in BP and CBFV and to estimate transfer function phase, gain, and coherence. Compared with spontaneous oscillations, induced oscillations had higher coherence [mean 0.8 (SD 0.11); >0.5 in all subjects at all frequencies] and lower variability in phase estimates. However, gain estimates remained unchanged. Under both conditions, the “high-pass filter” characteristics of dynamic autoregulation were observed. In conclusion, using repeated squat-stand maneuvers, we were able to study dynamic cerebral autoregulation in the low frequencies under conditions of hemodynamically strong and causally related oscillations in BP and CBFV. This not only enhances the confidence of transfer function analysis as indicated by high coherence and improved phase estimation but also strengthens the clinical relevance of this method as induced oscillations in BP and CBFV mimic those associated with postural changes in daily life. PMID:18974368

Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding

PubMed Central

2018-01-01

Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows. PMID:29547669

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data.

PubMed

Kroenke, Candyce H; Chubak, Jessica; Johnson, Lisa; Castillo, Adrienne; Weltzien, Erin; Caan, Bette J

2016-03-01

The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification. We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women's Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms-one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV-using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this "triangulation." Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Enhancing Breast Cancer Recurrence Algorithms Through Selective Use of Medical Record Data

PubMed Central

Chubak, Jessica; Johnson, Lisa; Castillo, Adrienne; Weltzien, Erin; Caan, Bette J.

2016-01-01

Abstract Background: The utility of data-based algorithms in research has been questioned because of errors in identification of cancer recurrences. We adapted previously published breast cancer recurrence algorithms, selectively using medical record (MR) data to improve classification. Methods: We evaluated second breast cancer event (SBCE) and recurrence-specific algorithms previously published by Chubak and colleagues in 1535 women from the Life After Cancer Epidemiology (LACE) and 225 women from the Women’s Health Initiative cohorts and compared classification statistics to published values. We also sought to improve classification with minimal MR examination. We selected pairs of algorithms—one with high sensitivity/high positive predictive value (PPV) and another with high specificity/high PPV—using MR information to resolve discrepancies between algorithms, properly classifying events based on review; we called this “triangulation.” Finally, in LACE, we compared associations between breast cancer survival risk factors and recurrence using MR data, single Chubak algorithms, and triangulation. Results: The SBCE algorithms performed well in identifying SBCE and recurrences. Recurrence-specific algorithms performed more poorly than published except for the high-specificity/high-PPV algorithm, which performed well. The triangulation method (sensitivity = 81.3%, specificity = 99.7%, PPV = 98.1%, NPV = 96.5%) improved recurrence classification over two single algorithms (sensitivity = 57.1%, specificity = 95.5%, PPV = 71.3%, NPV = 91.9%; and sensitivity = 74.6%, specificity = 97.3%, PPV = 84.7%, NPV = 95.1%), with 10.6% MR review. Triangulation performed well in survival risk factor analyses vs analyses using MR-identified recurrences. Conclusions: Use of multiple recurrence algorithms in administrative data, in combination with selective examination of MR data, may improve recurrence data quality and reduce research costs. PMID:26582243