SU-E-T-226: Correction of a Standard Model-Based Dose Calculator Using Measurement Data

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

Chen, M; Jiang, S; Lu, W

Purpose: To propose a hybrid method that combines advantages of the model-based and measurement-based method for independent dose calculation. Modeled-based dose calculation, such as collapsed-cone-convolution/superposition (CCCS) or the Monte-Carlo method, models dose deposition in the patient body accurately; however, due to lack of detail knowledge about the linear accelerator (LINAC) head, commissioning for an arbitrary machine is tedious and challenging in case of hardware changes. On the contrary, the measurement-based method characterizes the beam property accurately but lacks the capability of dose disposition modeling in heterogeneous media. Methods: We used a standard CCCS calculator, which is commissioned by published data,more » as the standard model calculator. For a given machine, water phantom measurements were acquired. A set of dose distributions were also calculated using the CCCS for the same setup. The difference between the measurements and the CCCS results were tabulated and used as the commissioning data for a measurement based calculator. Here we used a direct-ray-tracing calculator (ΔDRT). The proposed independent dose calculation consists of the following steps: 1. calculate D-model using CCCS. 2. calculate D-ΔDRT using ΔDRT. 3. combine Results: D=D-model+D-ΔDRT. Results: The hybrid dose calculation was tested on digital phantoms and patient CT data for standard fields and IMRT plan. The results were compared to dose calculated by the treatment planning system (TPS). The agreement of the hybrid and the TPS was within 3%, 3 mm for over 98% of the volume for phantom studies and lung patients. Conclusion: The proposed hybrid method uses the same commissioning data as those for the measurement-based method and can be easily extended to any non-standard LINAC. The results met the accuracy, independence, and simple commissioning criteria for an independent dose calculator.« less

Generative model selection using a scalable and size-independent complex network classifier

NASA Astrophysics Data System (ADS)

Motallebi, Sadegh; Aliakbary, Sadegh; Habibi, Jafar

2013-12-01

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our proposed method, which is named "Generative Model Selection for Complex Networks," outperforms existing methods with respect to accuracy, scalability, and size-independence.

An approximation method for improving dynamic network model fitting.

PubMed

Carnegie, Nicole Bohme; Krivitsky, Pavel N; Hunter, David R; Goodreau, Steven M

There has been a great deal of interest recently in the modeling and simulation of dynamic networks, i.e., networks that change over time. One promising model is the separable temporal exponential-family random graph model (ERGM) of Krivitsky and Handcock, which treats the formation and dissolution of ties in parallel at each time step as independent ERGMs. However, the computational cost of fitting these models can be substantial, particularly for large, sparse networks. Fitting cross-sectional models for observations of a network at a single point in time, while still a non-negligible computational burden, is much easier. This paper examines model fitting when the available data consist of independent measures of cross-sectional network structure and the duration of relationships under the assumption of stationarity. We introduce a simple approximation to the dynamic parameters for sparse networks with relationships of moderate or long duration and show that the approximation method works best in precisely those cases where parameter estimation is most likely to fail-networks with very little change at each time step. We consider a variety of cases: Bernoulli formation and dissolution of ties, independent-tie formation and Bernoulli dissolution, independent-tie formation and dissolution, and dependent-tie formation models.

Crossing statistic: reconstructing the expansion history of the universe

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

Shafieloo, Arman, E-mail: arman@ewha.ac.kr

2012-08-01

We present that by combining Crossing Statistic [1,2] and Smoothing method [3-5] one can reconstruct the expansion history of the universe with a very high precision without considering any prior on the cosmological quantities such as the equation of state of dark energy. We show that the presented method performs very well in reconstruction of the expansion history of the universe independent of the underlying models and it works well even for non-trivial dark energy models with fast or slow changes in the equation of state of dark energy. Accuracy of the reconstructed quantities along with independence of the methodmore » to any prior or assumption gives the proposed method advantages to the other non-parametric methods proposed before in the literature. Applying on the Union 2.1 supernovae combined with WiggleZ BAO data we present the reconstructed results and test the consistency of the two data sets in a model independent manner. Results show that latest available supernovae and BAO data are in good agreement with each other and spatially flat ΛCDM model is in concordance with the current data.« less

Analysis of an optimization-based atomistic-to-continuum coupling method for point defects

DOE PAGES

Olson, Derek; Shapeev, Alexander V.; Bochev, Pavel B.; ...

2015-11-16

Here, we formulate and analyze an optimization-based Atomistic-to-Continuum (AtC) coupling method for problems with point defects. Application of a potential-based atomistic model near the defect core enables accurate simulation of the defect. Away from the core, where site energies become nearly independent of the lattice position, the method switches to a more efficient continuum model. The two models are merged by minimizing the mismatch of their states on an overlap region, subject to the atomistic and continuum force balance equations acting independently in their domains. We prove that the optimization problem is well-posed and establish error estimates.

On an additive partial correlation operator and nonparametric estimation of graphical models.

PubMed

Lee, Kuang-Yao; Li, Bing; Zhao, Hongyu

2016-09-01

We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for its estimation. The additive partial correlation operator completely characterizes additive conditional independence, and has the additional advantage of putting marginal variation on appropriate scales when evaluating interdependence, which leads to more accurate statistical inference. We establish the consistency of the proposed estimator. Through simulation experiments and analysis of the DREAM4 Challenge dataset, we demonstrate that our method performs better than existing methods in cases where the Gaussian or copula Gaussian assumption does not hold, and that a more appropriate scaling for our method further enhances its performance.

On an additive partial correlation operator and nonparametric estimation of graphical models

PubMed Central

Li, Bing; Zhao, Hongyu

2016-01-01

Abstract We introduce an additive partial correlation operator as an extension of partial correlation to the nonlinear setting, and use it to develop a new estimator for nonparametric graphical models. Our graphical models are based on additive conditional independence, a statistical relation that captures the spirit of conditional independence without having to resort to high-dimensional kernels for its estimation. The additive partial correlation operator completely characterizes additive conditional independence, and has the additional advantage of putting marginal variation on appropriate scales when evaluating interdependence, which leads to more accurate statistical inference. We establish the consistency of the proposed estimator. Through simulation experiments and analysis of the DREAM4 Challenge dataset, we demonstrate that our method performs better than existing methods in cases where the Gaussian or copula Gaussian assumption does not hold, and that a more appropriate scaling for our method further enhances its performance. PMID:29422689

Generative model selection using a scalable and size-independent complex network classifier

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

Motallebi, Sadegh, E-mail: motallebi@ce.sharif.edu; Aliakbary, Sadegh, E-mail: aliakbary@ce.sharif.edu; Habibi, Jafar, E-mail: jhabibi@sharif.edu

2013-12-15

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree formore » model selection. Our proposed method, which is named “Generative Model Selection for Complex Networks,” outperforms existing methods with respect to accuracy, scalability, and size-independence.« less

Pairing field methods to improve inference in wildlife surveys while accommodating detection covariance.

PubMed

Clare, John; McKinney, Shawn T; DePue, John E; Loftin, Cynthia S

2017-10-01

It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture-recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters. © 2017 by the Ecological Society of America.

A method for analyzing clustered interval-censored data based on Cox's model.

PubMed

Kor, Chew-Teng; Cheng, Kuang-Fu; Chen, Yi-Hau

2013-02-28

Methods for analyzing interval-censored data are well established. Unfortunately, these methods are inappropriate for the studies with correlated data. In this paper, we focus on developing a method for analyzing clustered interval-censored data. Our method is based on Cox's proportional hazard model with piecewise-constant baseline hazard function. The correlation structure of the data can be modeled by using Clayton's copula or independence model with proper adjustment in the covariance estimation. We establish estimating equations for the regression parameters and baseline hazards (and a parameter in copula) simultaneously. Simulation results confirm that the point estimators follow a multivariate normal distribution, and our proposed variance estimations are reliable. In particular, we found that the approach with independence model worked well even when the true correlation model was derived from Clayton's copula. We applied our method to a family-based cohort study of pandemic H1N1 influenza in Taiwan during 2009-2010. Using the proposed method, we investigate the impact of vaccination and family contacts on the incidence of pH1N1 influenza. Copyright © 2012 John Wiley & Sons, Ltd.

Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

NASA Astrophysics Data System (ADS)

Langlois, A.; Royer, A.; Derksen, C.; Montpetit, B.; Dupont, F.; GoïTa, K.

2012-12-01

Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.

Do College Faculty Embrace Web 2.0 Technology?

ERIC Educational Resources Information Center

Siha, Samia M.; Bell, Reginald Lamar; Roebuck, Deborah

2016-01-01

The authors sought to determine if Rogers's Innovation Decision Process model could analyze Web 2.0 usage within the collegiate environment. The key independent variables studied in relationship to this model were gender, faculty rank, course content delivery method, and age. Chi-square nonparametric tests on the independent variables across…

A method for independent modelling in support of regulatory review of dose assessments.

PubMed

Dverstorp, Björn; Xu, Shulan

2017-11-01

Several countries consider geological disposal facilities as the preferred option for spent nuclear fuel due to their potential to provide isolation from the surface environment on very long timescales. In 2011 the Swedish Nuclear Fuel & Waste Management Co. (SKB) submitted a license application for construction of a spent nuclear fuel repository. The disposal method involves disposing spent fuel in copper canisters with a cast iron insert at about 500 m depth in crystalline basement rock, and each canister is surrounded by a buffer of swelling bentonite clay. SKB's license application is supported by a post-closure safety assessment, SR-Site. SR-Site has been reviewed by the Swedish Radiation Safety Authority (SSM) for five years. The main method for review of SKB's license application is document review, which is carried out by SSM's staff and supported by SSM's external experts. The review has proven a challenging task due to its broad scope, complexity and multidisciplinary nature. SSM and its predecessors have, for several decades, been developing independent models to support regulatory reviews of post-closure safety assessments for geological repositories. For the review of SR-Site, SSM has developed a modelling approach with a structured application of independent modelling activities, including replication modelling, use of alternative conceptual models and bounding calculations, to complement the traditional document review. This paper describes this scheme and its application to biosphere and dose assessment modelling. SSM's independent modelling has provided important insights regarding quality and reasonableness of SKB's rather complex biosphere modelling and has helped quantifying conservatisms and highlighting conceptual uncertainty. Copyright © 2017 Elsevier Ltd. All rights reserved.

Partial least squares density modeling (PLS-DM) - a new class-modeling strategy applied to the authentication of olives in brine by near-infrared spectroscopy.

PubMed

Oliveri, Paolo; López, M Isabel; Casolino, M Chiara; Ruisánchez, Itziar; Callao, M Pilar; Medini, Luca; Lanteri, Silvia

2014-12-03

A new class-modeling method, referred to as partial least squares density modeling (PLS-DM), is presented. The method is based on partial least squares (PLS), using a distance-based sample density measurement as the response variable. Potential function probability density is subsequently calculated on PLS scores and used, jointly with residual Q statistics, to develop efficient class models. The influence of adjustable model parameters on the resulting performances has been critically studied by means of cross-validation and application of the Pareto optimality criterion. The method has been applied to verify the authenticity of olives in brine from cultivar Taggiasca, based on near-infrared (NIR) spectra recorded on homogenized solid samples. Two independent test sets were used for model validation. The final optimal model was characterized by high efficiency and equilibrate balance between sensitivity and specificity values, if compared with those obtained by application of well-established class-modeling methods, such as soft independent modeling of class analogy (SIMCA) and unequal dispersed classes (UNEQ). Copyright © 2014 Elsevier B.V. All rights reserved.

An adaptive signal-processing approach to online adaptive tutoring.

PubMed

Bergeron, Bryan; Cline, Andrew

2011-01-01

Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.

Method-independent, Computationally Frugal Convergence Testing for Sensitivity Analysis Techniques

NASA Astrophysics Data System (ADS)

Mai, J.; Tolson, B.

2017-12-01

The increasing complexity and runtime of environmental models lead to the current situation that the calibration of all model parameters or the estimation of all of their uncertainty is often computationally infeasible. Hence, techniques to determine the sensitivity of model parameters are used to identify most important parameters. All subsequent model calibrations or uncertainty estimation procedures focus then only on these subsets of parameters and are hence less computational demanding. While the examination of the convergence of calibration and uncertainty methods is state-of-the-art, the convergence of the sensitivity methods is usually not checked. If any, bootstrapping of the sensitivity results is used to determine the reliability of the estimated indexes. Bootstrapping, however, might as well become computationally expensive in case of large model outputs and a high number of bootstraps. We, therefore, present a Model Variable Augmentation (MVA) approach to check the convergence of sensitivity indexes without performing any additional model run. This technique is method- and model-independent. It can be applied either during the sensitivity analysis (SA) or afterwards. The latter case enables the checking of already processed sensitivity indexes. To demonstrate the method's independency of the convergence testing method, we applied it to two widely used, global SA methods: the screening method known as Morris method or Elementary Effects (Morris 1991) and the variance-based Sobol' method (Solbol' 1993). The new convergence testing method is first scrutinized using 12 analytical benchmark functions (Cuntz & Mai et al. 2015) where the true indexes of aforementioned three methods are known. This proof of principle shows that the method reliably determines the uncertainty of the SA results when different budgets are used for the SA. The results show that the new frugal method is able to test the convergence and therefore the reliability of SA results in an efficient way. The appealing feature of this new technique is the necessity of no further model evaluation and therefore enables checking of already processed sensitivity results. This is one step towards reliable and transferable, published sensitivity results.

Exploiting jet binning to identify the initial state of high-mass resonances

NASA Astrophysics Data System (ADS)

Ebert, Markus A.; Liebler, Stefan; Moult, Ian; Stewart, Iain W.; Tackmann, Frank J.; Tackmann, Kerstin; Zeune, Lisa

2016-09-01

If a new high-mass resonance is discovered at the Large Hadron Collider, model-independent techniques to identify the production mechanism will be crucial to understand its nature and effective couplings to Standard Model particles. We present a powerful and model-independent method to infer the initial state in the production of any high-mass color-singlet system by using a tight veto on accompanying hadronic jets to divide the data into two mutually exclusive event samples (jet bins). For a resonance of several hundred GeV, the jet binning cut needed to discriminate quark and gluon initial states is in the experimentally accessible range of several tens of GeV. It also yields comparable cross sections for both bins, making this method viable already with the small event samples available shortly after a discovery. Theoretically, the method is made feasible by utilizing an effective field theory setup to compute the jet cut dependence precisely and model independently and to systematically control all sources of theoretical uncertainties in the jet binning, as well as their correlations. We use a 750 GeV scalar resonance as an example to demonstrate the viability of our method.

A Multilevel Testlet Model for Dual Local Dependence

ERIC Educational Resources Information Center

Jiao, Hong; Kamata, Akihito; Wang, Shudong; Jin, Ying

2012-01-01

The applications of item response theory (IRT) models assume local item independence and that examinees are independent of each other. When a representative sample for psychometric analysis is selected using a cluster sampling method in a testlet-based assessment, both local item dependence and local person dependence are likely to be induced.…

An Extension of IRT-Based Equating to the Dichotomous Testlet Response Theory Model

ERIC Educational Resources Information Center

Tao, Wei; Cao, Yi

2016-01-01

Current procedures for equating number-correct scores using traditional item response theory (IRT) methods assume local independence. However, when tests are constructed using testlets, one concern is the violation of the local item independence assumption. The testlet response theory (TRT) model is one way to accommodate local item dependence.…

A case study on modeling and independent practice cycles in teaching beginning science inquiry

NASA Astrophysics Data System (ADS)

Sadeghpour-Kramer, Margaret Ann Plattenberger

With increasing pressure to produce high standardized test scores, school systems will be looking for the surest ways to increase scores. Decision makers uninformed about the value of inquiry science may recommend more direct teaching methods and curricula in the hope that students will more quickly accumulate factual information for high test scores. This researcher and other proponents of inquiry science suggest that the best preparation for any test is the ability to use all available information and problem solving skills to think through to a solution. This study proposes to test the theory that inquiry problem solving skills need to be modeled and practiced in increasingly independent situations to be learned. Students tend to copy what they have been led to believe is correct, and to avoid continued copying, their skills must be applied in new situations requiring independent practice and improvement. This study follows ten sixth grade students, selected for maximum variation, as they participate in a series of five cycles of modeling and practicing inquiry science investigations as part of an ongoing unit on water quality. The cycles were designed to make the students increasingly independent in their use of inquiry. The results showed that all ten students made significant progress from copying teacher modeling in investigation #1 towards independent inquiry, with nine of the ten achieving acceptable to good beginning independent inquiry in investigation #5. Each case was analyzed independently using such case study methodology as pattern matching, case study protocols, and theoretical propositions. Constant comparison and other case study methods were used in a cross-case analysis. Eight cases confirmed a matching set of propositions and the hypothesis, in literal replication, and the other two cases confirmed a set of propositions and the hypothesis through theoretical replication. The study suggests to educators that repeated cycles of modeling and increasingly independent practice serve three purposes; first to develop independent inquiry skills by providing multiple opportunities with intermittent modeling, second to repeat the modeling initially in very similar situations and then encourage transfer to new situations, and third to provide repeated modeling for those students who do not grasp the concepts as quickly as do their classmates.

Survival curve estimation with dependent left truncated data using Cox's model.

PubMed

Mackenzie, Todd

2012-10-19

The Kaplan-Meier and closely related Lynden-Bell estimators are used to provide nonparametric estimation of the distribution of a left-truncated random variable. These estimators assume that the left-truncation variable is independent of the time-to-event. This paper proposes a semiparametric method for estimating the marginal distribution of the time-to-event that does not require independence. It models the conditional distribution of the time-to-event given the truncation variable using Cox's model for left truncated data, and uses inverse probability weighting. We report the results of simulations and illustrate the method using a survival study.

Studying generalised dark matter interactions with extended halo-independent methods

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

Kahlhoefer, Felix; Wild, Sebastian

2016-10-20

The interpretation of dark matter direct detection experiments is complicated by the fact that neither the astrophysical distribution of dark matter nor the properties of its particle physics interactions with nuclei are known in detail. To address both of these issues in a very general way we develop a new framework that combines the full formalism of non-relativistic effective interactions with state-of-the-art halo-independent methods. This approach makes it possible to analyse direct detection experiments for arbitrary dark matter interactions and quantify the goodness-of-fit independent of astrophysical uncertainties. We employ this method in order to demonstrate that the degeneracy between astrophysicalmore » uncertainties and particle physics unknowns is not complete. Certain models can be distinguished in a halo-independent way using a single ton-scale experiment based on liquid xenon, while other models are indistinguishable with a single experiment but can be separated using combined information from several target elements.« less

Method and apparatus for sensor fusion

NASA Technical Reports Server (NTRS)

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

1991-01-01

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

Development of Boundary Condition Independent Reduced Order Thermal Models using Proper Orthogonal Decomposition

NASA Astrophysics Data System (ADS)

Raghupathy, Arun; Ghia, Karman; Ghia, Urmila

2008-11-01

Compact Thermal Models (CTM) to represent IC packages has been traditionally developed using the DELPHI-based (DEvelopment of Libraries of PHysical models for an Integrated design) methodology. The drawbacks of this method are presented, and an alternative method is proposed. A reduced-order model that provides the complete thermal information accurately with less computational resources can be effectively used in system level simulations. Proper Orthogonal Decomposition (POD), a statistical method, can be used to reduce the order of the degree of freedom or variables of the computations for such a problem. POD along with the Galerkin projection allows us to create reduced-order models that reproduce the characteristics of the system with a considerable reduction in computational resources while maintaining a high level of accuracy. The goal of this work is to show that this method can be applied to obtain a boundary condition independent reduced-order thermal model for complex components. The methodology is applied to the 1D transient heat equation.

A Cross-Classified CFA-MTMM Model for Structurally Different and Nonindependent Interchangeable Methods.

PubMed

Koch, Tobias; Schultze, Martin; Jeon, Minjeong; Nussbeck, Fridtjof W; Praetorius, Anna-Katharina; Eid, Michael

2016-01-01

Multirater (multimethod, multisource) studies are increasingly applied in psychology. Eid and colleagues (2008) proposed a multilevel confirmatory factor model for multitrait-multimethod (MTMM) data combining structurally different and multiple independent interchangeable methods (raters). In many studies, however, different interchangeable raters (e.g., peers, subordinates) are asked to rate different targets (students, supervisors), leading to violations of the independence assumption and to cross-classified data structures. In the present work, we extend the ML-CFA-MTMM model by Eid and colleagues (2008) to cross-classified multirater designs. The new C4 model (Cross-Classified CTC[M-1] Combination of Methods) accounts for nonindependent interchangeable raters and enables researchers to explicitly model the interaction between targets and raters as a latent variable. Using a real data application, it is shown how credibility intervals of model parameters and different variance components can be obtained using Bayesian estimation techniques.

Ozone-induced reductions in photosynthesis and transpiration: Parameterizing the Community Land Model (CLM)

NASA Astrophysics Data System (ADS)

Lombardozzi, D.; Bonan, G. B.; Levis, S.; Sparks, J. P.

2010-12-01

Humans are indirectly increasing concentrations of surface ozone (O3) through industrial processes. Ozone is known to have negative impacts on plants, including reductions in crop yields, plant growth, and visible leaf injury. Research also suggests that O3 exposure differentially affects photosynthesis and transpiration because biochemical aspects of photosynthesis are damaged in addition to stomatal conductance, the common link that controls both processes. However, most models incorporate O3 damage as a decrease in photosynthesis, with stomatal conductance responding linearly through the coupling of photosynthesis and conductance calculations. The observed differential effects of O3 on photosynthesis and conductance are not explicitly expressed in most modeling efforts, potentially causing larger decreases in transpiration. We ran five independent simulations of the CLM that compare current methods of incorporating O3 as a decrease in photosynthesis to a new method of separating photosynthesis and transpiration responses to O3 by independently modifying each parameter. We also determine the magnitude of both direct decreases to photosynthesis and transpiration and decreases caused by feedbacks in each parameter. Results show that traditional methods of modeling O3 effects by decreasing photosynthesis cause linear decreases in predicted transpiration that are ~20% larger than observed decreases in transpiration. However, modeled decreases in photosynthesis and transpiration that are incorporated independently of one another predict observed decreases in photosynthesis and improve transpiration predictions by ~13%. Therefore, models best predict carbon and water fluxes when incorporating O3-induced decreases in photosynthesis and transpiration independently.

A Hierarchical Model Predictive Tracking Control for Independent Four-Wheel Driving/Steering Vehicles with Coaxial Steering Mechanism

NASA Astrophysics Data System (ADS)

Itoh, Masato; Hagimori, Yuki; Nonaka, Kenichiro; Sekiguchi, Kazuma

2016-09-01

In this study, we apply a hierarchical model predictive control to omni-directional mobile vehicle, and improve the tracking performance. We deal with an independent four-wheel driving/steering vehicle (IFWDS) equipped with four coaxial steering mechanisms (CSM). The coaxial steering mechanism is a special one composed of two steering joints on the same axis. In our previous study with respect to IFWDS with ideal steering, we proposed a model predictive tracking control. However, this method did not consider constraints of the coaxial steering mechanism which causes delay of steering. We also proposed a model predictive steering control considering constraints of this mechanism. In this study, we propose a hierarchical system combining above two control methods for IFWDS. An upper controller, which deals with vehicle kinematics, runs a model predictive tracking control, and a lower controller, which considers constraints of coaxial steering mechanism, runs a model predictive steering control which tracks the predicted steering angle optimized an upper controller. We verify the superiority of this method by comparing this method with the previous method.

Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies.

PubMed

Meng, Xiang-He; Shen, Hui; Chen, Xiang-Ding; Xiao, Hong-Mei; Deng, Hong-Wen

2018-03-01

Genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with diverse complex phenotypes and diseases, and provided tremendous opportunities for further analyses using summary association statistics. Recently, Pickrell et al. developed a robust method for causal inference using independent putative causal SNPs. However, this method may fail to infer the causal relationship between two phenotypes when only a limited number of independent putative causal SNPs identified. Here, we extended Pickrell's method to make it more applicable for the general situations. We extended the causal inference method by replacing the putative causal SNPs with the lead SNPs (the set of the most significant SNPs in each independent locus) and tested the performance of our extended method using both simulation and empirical data. Simulations suggested that when the same number of genetic variants is used, our extended method had similar distribution of test statistic under the null model as well as comparable power under the causal model compared with the original method by Pickrell et al. But in practice, our extended method would generally be more powerful because the number of independent lead SNPs was often larger than the number of independent putative causal SNPs. And including more SNPs, on the other hand, would not cause more false positives. By applying our extended method to summary statistics from GWAS for blood metabolites and femoral neck bone mineral density (FN-BMD), we successfully identified ten blood metabolites that may causally influence FN-BMD. We extended a causal inference method for inferring putative causal relationship between two phenotypes using summary statistics from GWAS, and identified a number of potential causal metabolites for FN-BMD, which may provide novel insights into the pathophysiological mechanisms underlying osteoporosis.

Robust discovery of genetic associations incorporating gene-environment interaction and independence.

PubMed

Tchetgen Tchetgen, Eric

2011-03-01

This article considers the detection and evaluation of genetic effects incorporating gene-environment interaction and independence. Whereas ordinary logistic regression cannot exploit the assumption of gene-environment independence, the proposed approach makes explicit use of the independence assumption to improve estimation efficiency. This method, which uses both cases and controls, fits a constrained retrospective regression in which the genetic variant plays the role of the response variable, and the disease indicator and the environmental exposure are the independent variables. The regression model constrains the association of the environmental exposure with the genetic variant among the controls to be null, thus explicitly encoding the gene-environment independence assumption, which yields substantial gain in accuracy in the evaluation of genetic effects. The proposed retrospective regression approach has several advantages. It is easy to implement with standard software, and it readily accounts for multiple environmental exposures of a polytomous or of a continuous nature, while easily incorporating extraneous covariates. Unlike the profile likelihood approach of Chatterjee and Carroll (Biometrika. 2005;92:399-418), the proposed method does not require a model for the association of a polytomous or continuous exposure with the disease outcome, and, therefore, it is agnostic to the functional form of such a model and completely robust to its possible misspecification.

Dual-energy x-ray image decomposition by independent component analysis

NASA Astrophysics Data System (ADS)

Jiang, Yifeng; Jiang, Dazong; Zhang, Feng; Zhang, Dengfu; Lin, Gang

2001-09-01

The spatial distributions of bone and soft tissue in human body are separated by independent component analysis (ICA) of dual-energy x-ray images. It is because of the dual energy imaging modelí-s conformity to the ICA model that we can apply this method: (1) the absorption in body is mainly caused by photoelectric absorption and Compton scattering; (2) they take place simultaneously but are mutually independent; and (3) for monochromatic x-ray sources the total attenuation is achieved by linear combination of these two absorption. Compared with the conventional method, the proposed one needs no priori information about the accurate x-ray energy magnitude for imaging, while the results of the separation agree well with the conventional one.

How Many Separable Sources? Model Selection In Independent Components Analysis

PubMed Central

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

ERIC Educational Resources Information Center

Smith, Kent W.; Sasaki, M. S.

1979-01-01

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

Model-independent curvature determination with 21 cm intensity mapping experiments

NASA Astrophysics Data System (ADS)

Witzemann, Amadeus; Bull, Philip; Clarkson, Chris; Santos, Mario G.; Spinelli, Marta; Weltman, Amanda

2018-06-01

Measurements of the spatial curvature of the Universe have improved significantly in recent years, but still tend to require strong assumptions to be made about the equation of state of dark energy (DE) in order to reach sub-percent precision. When these assumptions are relaxed, strong degeneracies arise that make it hard to disentangle DE and curvature, degrading the constraints. We show that forthcoming 21 cm intensity mapping experiments such as Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) are ideally designed to carry out model-independent curvature measurements, as they can measure the clustering signal at high redshift with sufficient precision to break many of the degeneracies. We consider two different model-independent methods, based on `avoiding' the DE-dominated regime and non-parametric modelling of the DE equation of state, respectively. Our forecasts show that HIRAX will be able to improve upon current model-independent constraints by around an order of magnitude, reaching percent-level accuracy even when an arbitrary DE equation of state is assumed. In the same model-independent analysis, the sample variance limit for a similar survey is another order of magnitude better.

Model-independent curvature determination with 21cm intensity mapping experiments

NASA Astrophysics Data System (ADS)

Witzemann, Amadeus; Bull, Philip; Clarkson, Chris; Santos, Mario G.; Spinelli, Marta; Weltman, Amanda

2018-04-01

Measurements of the spatial curvature of the Universe have improved significantly in recent years, but still tend to require strong assumptions to be made about the equation of state of dark energy (DE) in order to reach sub-percent precision. When these assumptions are relaxed, strong degeneracies arise that make it hard to disentangle DE and curvature, degrading the constraints. We show that forthcoming 21cm intensity mapping experiments such as HIRAX are ideally designed to carry out model-independent curvature measurements, as they can measure the clustering signal at high redshift with sufficient precision to break many of the degeneracies. We consider two different model-independent methods, based on `avoiding' the DE-dominated regime and non-parametric modelling of the DE equation of state respectively. Our forecasts show that HIRAX will be able to improve upon current model-independent constraints by around an order of magnitude, reaching percent-level accuracy even when an arbitrary DE equation of state is assumed. In the same model-independent analysis, the sample variance limit for a similar survey is another order of magnitude better.

Use of Video Modeling and Video Prompting Interventions for Teaching Daily Living Skills to Individuals with Autism Spectrum Disorders: A Review

ERIC Educational Resources Information Center

Gardner, Stephanie; Wolfe, Pamela

2013-01-01

Identifying methods to increase the independent functioning of individuals with autism spectrum disorders (ASD) is vital in enhancing their quality of life; teaching students with ASD daily living skills can foster independent functioning. This review examines interventions that implement video modeling and/or prompting to teach individuals with…

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2018-02-01

Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Modeling crime events by d-separation method

NASA Astrophysics Data System (ADS)

Aarthee, R.; Ezhilmaran, D.

2017-11-01

Problematic legal cases have recently called for a scientifically founded method of dealing with the qualitative and quantitative roles of evidence in a case [1].To deal with quantitative, we proposed a d-separation method for modeling the crime events. A d-separation is a graphical criterion for identifying independence in a directed acyclic graph. By developing a d-separation method, we aim to lay the foundations for the development of a software support tool that can deal with the evidential reasoning in legal cases. Such a tool is meant to be used by a judge or juror, in alliance with various experts who can provide information about the details. This will hopefully improve the communication between judges or jurors and experts. The proposed method used to uncover more valid independencies than any other graphical criterion.

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

Incorporating wind availability into land use regression modelling of air quality in mountainous high-density urban environment.

PubMed

Shi, Yuan; Lau, Kevin Ka-Lun; Ng, Edward

2017-08-01

Urban air quality serves as an important function of the quality of urban life. Land use regression (LUR) modelling of air quality is essential for conducting health impacts assessment but more challenging in mountainous high-density urban scenario due to the complexities of the urban environment. In this study, a total of 21 LUR models are developed for seven kinds of air pollutants (gaseous air pollutants CO, NO 2 , NO x , O 3 , SO 2 and particulate air pollutants PM 2.5 , PM 10 ) with reference to three different time periods (summertime, wintertime and annual average of 5-year long-term hourly monitoring data from local air quality monitoring network) in Hong Kong. Under the mountainous high-density urban scenario, we improved the traditional LUR modelling method by incorporating wind availability information into LUR modelling based on surface geomorphometrical analysis. As a result, 269 independent variables were examined to develop the LUR models by using the "ADDRESS" independent variable selection method and stepwise multiple linear regression (MLR). Cross validation has been performed for each resultant model. The results show that wind-related variables are included in most of the resultant models as statistically significant independent variables. Compared with the traditional method, a maximum increase of 20% was achieved in the prediction performance of annual averaged NO 2 concentration level by incorporating wind-related variables into LUR model development. Copyright © 2017 Elsevier Inc. All rights reserved.

Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.

PubMed

Xue, Hongqi; Wu, Shuang; Wu, Yichao; Ramirez Idarraga, Juan C; Wu, Hulin

2018-05-02

Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae. Copyright © 2018 John Wiley & Sons, Ltd.

MUSiC - Model-independent search for deviations from Standard Model predictions in CMS

NASA Astrophysics Data System (ADS)

Pieta, Holger

2010-02-01

We present an approach for a model independent search in CMS. Systematically scanning the data for deviations from the standard model Monte Carlo expectations, such an analysis can help to understand the detector and tune event generators. By minimizing the theoretical bias the analysis is furthermore sensitive to a wide range of models for new physics, including the uncounted number of models not-yet-thought-of. After sorting the events into classes defined by their particle content (leptons, photons, jets and missing transverse energy), a minimally prejudiced scan is performed on a number of distributions. Advanced statistical methods are used to determine the significance of the deviating regions, rigorously taking systematic uncertainties into account. A number of benchmark scenarios, including common models of new physics and possible detector effects, have been used to gauge the power of such a method. )

Data-Driven Model Reduction and Transfer Operator Approximation

NASA Astrophysics Data System (ADS)

Klus, Stefan; Nüske, Feliks; Koltai, Péter; Wu, Hao; Kevrekidis, Ioannis; Schütte, Christof; Noé, Frank

2018-06-01

In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods.

Model-independent test for scale-dependent non-Gaussianities in the cosmic microwave background.

PubMed

Räth, C; Morfill, G E; Rossmanith, G; Banday, A J; Górski, K M

2009-04-03

We present a model-independent method to test for scale-dependent non-Gaussianities in combination with scaling indices as test statistics. Therefore, surrogate data sets are generated, in which the power spectrum of the original data is preserved, while the higher order correlations are partly randomized by applying a scale-dependent shuffling procedure to the Fourier phases. We apply this method to the Wilkinson Microwave Anisotropy Probe data of the cosmic microwave background and find signatures for non-Gaussianities on large scales. Further tests are required to elucidate the origin of the detected anomalies.

Managing Clustered Data Using Hierarchical Linear Modeling

ERIC Educational Resources Information Center

Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.

2012-01-01

Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…

A study on rational function model generation for TerraSAR-X imagery.

PubMed

Eftekhari, Akram; Saadatseresht, Mohammad; Motagh, Mahdi

2013-09-09

The Rational Function Model (RFM) has been widely used as an alternative to rigorous sensor models of high-resolution optical imagery in photogrammetry and remote sensing geometric processing. However, not much work has been done to evaluate the applicability of the RF model for Synthetic Aperture Radar (SAR) image processing. This paper investigates how to generate a Rational Polynomial Coefficient (RPC) for high-resolution TerraSAR-X imagery using an independent approach. The experimental results demonstrate that the RFM obtained using the independent approach fits the Range-Doppler physical sensor model with an accuracy of greater than 10-3 pixel. Because independent RPCs indicate absolute errors in geolocation, two methods can be used to improve the geometric accuracy of the RFM. In the first method, Ground Control Points (GCPs) are used to update SAR sensor orientation parameters, and the RPCs are calculated using the updated parameters. Our experiment demonstrates that by using three control points in the corners of the image, an accuracy of 0.69 pixels in range and 0.88 pixels in the azimuth direction is achieved. For the second method, we tested the use of an affine model for refining RPCs. In this case, by applying four GCPs in the corners of the image, the accuracy reached 0.75 pixels in range and 0.82 pixels in the azimuth direction.

A Study on Rational Function Model Generation for TerraSAR-X Imagery

PubMed Central

Eftekhari, Akram; Saadatseresht, Mohammad; Motagh, Mahdi

2013-01-01

The Rational Function Model (RFM) has been widely used as an alternative to rigorous sensor models of high-resolution optical imagery in photogrammetry and remote sensing geometric processing. However, not much work has been done to evaluate the applicability of the RF model for Synthetic Aperture Radar (SAR) image processing. This paper investigates how to generate a Rational Polynomial Coefficient (RPC) for high-resolution TerraSAR-X imagery using an independent approach. The experimental results demonstrate that the RFM obtained using the independent approach fits the Range-Doppler physical sensor model with an accuracy of greater than 10−3 pixel. Because independent RPCs indicate absolute errors in geolocation, two methods can be used to improve the geometric accuracy of the RFM. In the first method, Ground Control Points (GCPs) are used to update SAR sensor orientation parameters, and the RPCs are calculated using the updated parameters. Our experiment demonstrates that by using three control points in the corners of the image, an accuracy of 0.69 pixels in range and 0.88 pixels in the azimuth direction is achieved. For the second method, we tested the use of an affine model for refining RPCs. In this case, by applying four GCPs in the corners of the image, the accuracy reached 0.75 pixels in range and 0.82 pixels in the azimuth direction. PMID:24021971

Iterative integral parameter identification of a respiratory mechanics model.

PubMed

Schranz, Christoph; Docherty, Paul D; Chiew, Yeong Shiong; Möller, Knut; Chase, J Geoffrey

2012-07-18

Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual's model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS) patients. The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.

Prediction of wastewater quality indicators at the inflow to the wastewater treatment plant using data mining methods

NASA Astrophysics Data System (ADS)

Szeląg, Bartosz; Barbusiński, Krzysztof; Studziński, Jan; Bartkiewicz, Lidia

2017-11-01

In the study, models developed using data mining methods are proposed for predicting wastewater quality indicators: biochemical and chemical oxygen demand, total suspended solids, total nitrogen and total phosphorus at the inflow to wastewater treatment plant (WWTP). The models are based on values measured in previous time steps and daily wastewater inflows. Also, independent prediction systems that can be used in case of monitoring devices malfunction are provided. Models of wastewater quality indicators were developed using MARS (multivariate adaptive regression spline) method, artificial neural networks (ANN) of the multilayer perceptron type combined with the classification model (SOM) and cascade neural networks (CNN). The lowest values of absolute and relative errors were obtained using ANN+SOM, whereas the MARS method produced the highest error values. It was shown that for the analysed WWTP it is possible to obtain continuous prediction of selected wastewater quality indicators using the two developed independent prediction systems. Such models can ensure reliable WWTP work when wastewater quality monitoring systems become inoperable, or are under maintenance.

A global/local analysis method for treating details in structural design

NASA Technical Reports Server (NTRS)

Aminpour, Mohammad A.; Mccleary, Susan L.; Ransom, Jonathan B.

1993-01-01

A method for analyzing global/local behavior of plate and shell structures is described. In this approach, a detailed finite element model of the local region is incorporated within a coarser global finite element model. The local model need not be nodally compatible (i.e., need not have a one-to-one nodal correspondence) with the global model at their common boundary; therefore, the two models may be constructed independently. The nodal incompatibility of the models is accounted for by introducing appropriate constraint conditions into the potential energy in a hybrid variational formulation. The primary advantage of this method is that the need for transition modeling between global and local models is eliminated. Eliminating transition modeling has two benefits. First, modeling efforts are reduced since tedious and complex transitioning need not be performed. Second, errors due to the mesh distortion, often unavoidable in mesh transitioning, are minimized by avoiding distorted elements beyond what is needed to represent the geometry of the component. The method is applied reduced to a plate loaded in tension and transverse bending. The plate has a central hole, and various hole sixes and shapes are studied. The method is also applied to a composite laminated fuselage panel with a crack emanating from a window in the panel. While this method is applied herein to global/local problems, it is also applicable to the coupled analysis of independently modeled components as well as adaptive refinement.

Study of grid independence of finite element method on MHD free convective casson fluid flow with slip effect

NASA Astrophysics Data System (ADS)

Raju, R. Srinivasa; Ramesh, K.

2018-05-01

The purpose of this work is to study the grid independence of finite element method on MHD Casson fluid flow past a vertically inclined plate filled in a porous medium in presence of chemical reaction, heat absorption, an external magnetic field and slip effect has been investigated. For this study of grid independence, a mathematical model is developed and analyzed by using appropriate mathematical technique, called finite element method. Grid study discussed with the help of numerical values of velocity, temperature and concentration profiles in tabular form. avourable comparisons with previously published work on various special cases of the problem are obtained.

Modelling rainfall amounts using mixed-gamma model for Kuantan district

NASA Astrophysics Data System (ADS)

Zakaria, Roslinazairimah; Moslim, Nor Hafizah

2017-05-01

An efficient design of flood mitigation and construction of crop growth models depend upon good understanding of the rainfall process and characteristics. Gamma distribution is usually used to model nonzero rainfall amounts. In this study, the mixed-gamma model is applied to accommodate both zero and nonzero rainfall amounts. The mixed-gamma model presented is for the independent case. The formulae of mean and variance are derived for the sum of two and three independent mixed-gamma variables, respectively. Firstly, the gamma distribution is used to model the nonzero rainfall amounts and the parameters of the distribution (shape and scale) are estimated using the maximum likelihood estimation method. Then, the mixed-gamma model is defined for both zero and nonzero rainfall amounts simultaneously. The formulae of mean and variance for the sum of two and three independent mixed-gamma variables derived are tested using the monthly rainfall amounts from rainfall stations within Kuantan district in Pahang Malaysia. Based on the Kolmogorov-Smirnov goodness of fit test, the results demonstrate that the descriptive statistics of the observed sum of rainfall amounts is not significantly different at 5% significance level from the generated sum of independent mixed-gamma variables. The methodology and formulae demonstrated can be applied to find the sum of more than three independent mixed-gamma variables.

Model specification in oral health-related quality of life research.

PubMed

Kieffer, Jacobien M; Verrips, Erik; Hoogstraten, Johan

2009-10-01

The aim of this study was to analyze conventional wisdom regarding the construction and analysis of oral health-related quality of life (OHRQoL) questionnaires and to outline statistical complications. Most methods used for developing and analyzing questionnaires, such as factor analysis and Cronbach's alpha, presume psychological constructs to be latent, inferring a reflective measurement model with the underlying assumption of local independence. Local independence implies that the latent variable explains why the variables observed are related. Many OHRQoL questionnaires are analyzed as if they were based on a reflective measurement model; local independence is thus assumed. This assumption requires these questionnaires to consist solely of items that reflect, instead of determine, OHRQoL. The tenability of this assumption is the main topic of the present study. It is argued that OHRQoL questionnaires are a mix of both a formative measurement model and a reflective measurement model, thus violating the assumption of local independence. The implications are discussed.

TEMPORAL SIGNATURES OF AIR QUALITY OBSERVATIONS AND MODEL OUTPUTS: DO TIME SERIES DECOMPOSITION METHODS CAPTURE RELEVANT TIME SCALES?

EPA Science Inventory

Time series decomposition methods were applied to meteorological and air quality data and their numerical model estimates. Decomposition techniques express a time series as the sum of a small number of independent modes which hypothetically represent identifiable forcings, thereb...

Inherent limitations of probabilistic models for protein-DNA binding specificity

PubMed Central

Ruan, Shuxiang

2017-01-01

The specificities of transcription factors are most commonly represented with probabilistic models. These models provide a probability for each base occurring at each position within the binding site and the positions are assumed to contribute independently. The model is simple and intuitive and is the basis for many motif discovery algorithms. However, the model also has inherent limitations that prevent it from accurately representing true binding probabilities, especially for the highest affinity sites under conditions of high protein concentration. The limitations are not due to the assumption of independence between positions but rather are caused by the non-linear relationship between binding affinity and binding probability and the fact that independent normalization at each position skews the site probabilities. Generally probabilistic models are reasonably good approximations, but new high-throughput methods allow for biophysical models with increased accuracy that should be used whenever possible. PMID:28686588

The Independent Evolution Method Is Not a Viable Phylogenetic Comparative Method

PubMed Central

2015-01-01

Phylogenetic comparative methods (PCMs) use data on species traits and phylogenetic relationships to shed light on evolutionary questions. Recently, Smaers and Vinicius suggested a new PCM, Independent Evolution (IE), which purportedly employs a novel model of evolution based on Felsenstein’s Adaptive Peak Model. The authors found that IE improves upon previous PCMs by producing more accurate estimates of ancestral states, as well as separate estimates of evolutionary rates for each branch of a phylogenetic tree. Here, we document substantial theoretical and computational issues with IE. When data are simulated under a simple Brownian motion model of evolution, IE produces severely biased estimates of ancestral states and changes along individual branches. We show that these branch-specific changes are essentially ancestor-descendant or “directional” contrasts, and draw parallels between IE and previous PCMs such as “minimum evolution”. Additionally, while comparisons of branch-specific changes between variables have been interpreted as reflecting the relative strength of selection on those traits, we demonstrate through simulations that regressing IE estimated branch-specific changes against one another gives a biased estimate of the scaling relationship between these variables, and provides no advantages or insights beyond established PCMs such as phylogenetically independent contrasts. In light of our findings, we discuss the results of previous papers that employed IE. We conclude that Independent Evolution is not a viable PCM, and should not be used in comparative analyses. PMID:26683838

Modeling Local Item Dependence Due to Common Test Format with a Multidimensional Rasch Model

ERIC Educational Resources Information Center

Baghaei, Purya; Aryadoust, Vahid

2015-01-01

Research shows that test method can exert a significant impact on test takers' performance and thereby contaminate test scores. We argue that common test method can exert the same effect as common stimuli and violate the conditional independence assumption of item response theory models because, in general, subsets of items which have a shared…

Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

NASA Astrophysics Data System (ADS)

E, Jianwei; Bao, Yanling; Ye, Jimin

2017-10-01

As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

Fine tuning breath-hold-based cerebrovascular reactivity analysis models.

PubMed

van Niftrik, Christiaan Hendrik Bas; Piccirelli, Marco; Bozinov, Oliver; Pangalu, Athina; Valavanis, Antonios; Regli, Luca; Fierstra, Jorn

2016-02-01

We elaborate on existing analysis methods for breath-hold (BH)-derived cerebrovascular reactivity (CVR) measurements and describe novel insights and models toward more exact CVR interpretation. Five blood-oxygen-level-dependent (BOLD) fMRI datasets of neurovascular patients with unilateral hemispheric hemodynamic impairment were used to test various BH CVR analysis methods. Temporal lag (phase), percent BOLD signal change (CVR), and explained variance (coherence) maps were calculated using three different sine models and two novel "Optimal Signal" model-free methods based on the unaffected hemisphere and the sagittal sinus fMRI signal time series, respectively. All models showed significant differences in CVR and coherence between the affected-hemodynamic impaired-and unaffected hemisphere. Voxel-wise phase determination significantly increases CVR (0.60 ± 0.18 vs. 0.82 ± 0.27; P < 0.05). Incorporating different durations of breath hold and resting period in one sine model (two-task) did increase coherence in the unaffected hemisphere, as well as eliminating negative phase commonly obtained by one-task frequency models. The novel model-free "optimal signal" methods both explained the BOLD MR data similar to the two task sine model. Our CVR analysis demonstrates an improved CVR and coherence after implementation of voxel-wise phase and frequency adjustment. The novel "optimal signal" methods provide a robust and feasible alternative to the sine models, as both are model-free and independent of compliance. Here, the sagittal sinus model may be advantageous, as it is independent of hemispheric CVR impairment.

Efficient Variable Selection Method for Exposure Variables on Binary Data

NASA Astrophysics Data System (ADS)

Ohno, Manabu; Tarumi, Tomoyuki

In this paper, we propose a new variable selection method for "robust" exposure variables. We define "robust" as property that the same variable can select among original data and perturbed data. There are few studies of effective for the selection method. The problem that selects exposure variables is almost the same as a problem that extracts correlation rules without robustness. [Brin 97] is suggested that correlation rules are possible to extract efficiently using chi-squared statistic of contingency table having monotone property on binary data. But the chi-squared value does not have monotone property, so it's is easy to judge the method to be not independent with an increase in the dimension though the variable set is completely independent, and the method is not usable in variable selection for robust exposure variables. We assume anti-monotone property for independent variables to select robust independent variables and use the apriori algorithm for it. The apriori algorithm is one of the algorithms which find association rules from the market basket data. The algorithm use anti-monotone property on the support which is defined by association rules. But independent property does not completely have anti-monotone property on the AIC of independent probability model, but the tendency to have anti-monotone property is strong. Therefore, selected variables with anti-monotone property on the AIC have robustness. Our method judges whether a certain variable is exposure variable for the independent variable using previous comparison of the AIC. Our numerical experiments show that our method can select robust exposure variables efficiently and precisely.

Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J.M.; Reichle, Rolf H.; Houser, Paul R.; Arsenault, Kristi R.; Verhoest, Niko E.C.; Paulwels, Valentijn R.N.

2009-01-01

An ensemble Kalman filter (EnKF) is used in a suite of synthetic experiments to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of satellite retrievals) into fine-scale (1 km) model simulations. Coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (re-gridding) to the fine-scale model resolution prior to data assimilation. In either case observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated fine-scale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the fine-scale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

Beyond the 'east-west' dichotomy: Global variation in cultural models of selfhood.

PubMed

Vignoles, Vivian L; Owe, Ellinor; Becker, Maja; Smith, Peter B; Easterbrook, Matthew J; Brown, Rupert; González, Roberto; Didier, Nicolas; Carrasco, Diego; Cadena, Maria Paz; Lay, Siugmin; Schwartz, Seth J; Des Rosiers, Sabrina E; Villamar, Juan A; Gavreliuc, Alin; Zinkeng, Martina; Kreuzbauer, Robert; Baguma, Peter; Martin, Mariana; Tatarko, Alexander; Herman, Ginette; de Sauvage, Isabelle; Courtois, Marie; Garðarsdóttir, Ragna B; Harb, Charles; Schweiger Gallo, Inge; Prieto Gil, Paula; Lorente Clemares, Raquel; Campara, Gabriella; Nizharadze, George; Macapagal, Ma Elizabeth J; Jalal, Baland; Bourguignon, David; Zhang, Jianxin; Lv, Shaobo; Chybicka, Aneta; Yuki, Masaki; Zhang, Xiao; Espinosa, Agustín; Valk, Aune; Abuhamdeh, Sami; Amponsah, Benjamin; Özgen, Emre; Güner, E Ülkü; Yamakoğlu, Nil; Chobthamkit, Phatthanakit; Pyszczynski, Tom; Kesebir, Pelin; Vargas Trujillo, Elvia; Balanta, Paola; Cendales Ayala, Boris; Koller, Silvia H; Jaafar, Jas Laile; Gausel, Nicolay; Fischer, Ronald; Milfont, Taciano L; Kusdil, Ersin; Çağlar, Selinay; Aldhafri, Said; Ferreira, M Cristina; Mekonnen, Kassahun Habtamu; Wang, Qian; Fülöp, Márta; Torres, Ana; Camino, Leoncio; Lemos, Flávia Cristina Silveira; Fritsche, Immo; Möller, Bettina; Regalia, Camillo; Manzi, Claudia; Brambilla, Maria; Bond, Michael Harris

2016-08-01

Markus and Kitayama's (1991) theory of independent and interdependent self-construals had a major influence on social, personality, and developmental psychology by highlighting the role of culture in psychological processes. However, research has relied excessively on contrasts between North American and East Asian samples, and commonly used self-report measures of independence and interdependence frequently fail to show predicted cultural differences. We revisited the conceptualization and measurement of independent and interdependent self-construals in 2 large-scale multinational surveys, using improved methods for cross-cultural research. We developed (Study 1: N = 2924 students in 16 nations) and validated across cultures (Study 2: N = 7279 adults from 55 cultural groups in 33 nations) a new 7-dimensional model of self-reported ways of being independent or interdependent. Patterns of global variation support some of Markus and Kitayama's predictions, but a simple contrast between independence and interdependence does not adequately capture the diverse models of selfhood that prevail in different world regions. Cultural groups emphasize different ways of being both independent and interdependent, depending on individualism-collectivism, national socioeconomic development, and religious heritage. Our 7-dimensional model will allow future researchers to test more accurately the implications of cultural models of selfhood for psychological processes in diverse ecocultural contexts. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Material point method of modelling and simulation of reacting flow of oxygen

NASA Astrophysics Data System (ADS)

Mason, Matthew; Chen, Kuan; Hu, Patrick G.

2014-07-01

Aerospace vehicles are continually being designed to sustain flight at higher speeds and higher altitudes than previously attainable. At hypersonic speeds, gases within a flow begin to chemically react and the fluid's physical properties are modified. It is desirable to model these effects within the Material Point Method (MPM). The MPM is a combined Eulerian-Lagrangian particle-based solver that calculates the physical properties of individual particles and uses a background grid for information storage and exchange. This study introduces chemically reacting flow modelling within the MPM numerical algorithm and illustrates a simple application using the AeroElastic Material Point Method (AEMPM) code. The governing equations of reacting flows are introduced and their direct application within an MPM code is discussed. A flow of 100% oxygen is illustrated and the results are compared with independently developed computational non-equilibrium algorithms. Observed trends agree well with results from an independently developed source.

Optimal control of large space structures via generalized inverse matrix

NASA Technical Reports Server (NTRS)

Nguyen, Charles C.; Fang, Xiaowen

1987-01-01

Independent Modal Space Control (IMSC) is a control scheme that decouples the space structure into n independent second-order subsystems according to n controlled modes and controls each mode independently. It is well-known that the IMSC eliminates control and observation spillover caused when the conventional coupled modal control scheme is employed. The independent control of each mode requires that the number of actuators be equal to the number of modelled modes, which is very high for a faithful modeling of large space structures. A control scheme is proposed that allows one to use a reduced number of actuators to control all modeled modes suboptimally. In particular, the method of generalized inverse matrices is employed to implement the actuators such that the eigenvalues of the closed-loop system are as closed as possible to those specified by the optimal IMSC. Computer simulation of the proposed control scheme on a simply supported beam is given.

Systems and methods for modeling and analyzing networks

DOEpatents

Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

2013-10-29

The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

ModelMuse - A Graphical User Interface for MODFLOW-2005 and PHAST

USGS Publications Warehouse

Winston, Richard B.

2009-01-01

ModelMuse is a graphical user interface (GUI) for the U.S. Geological Survey (USGS) models MODFLOW-2005 and PHAST. This software package provides a GUI for creating the flow and transport input file for PHAST and the input files for MODFLOW-2005. In ModelMuse, the spatial data for the model is independent of the grid, and the temporal data is independent of the stress periods. Being able to input these data independently allows the user to redefine the spatial and temporal discretization at will. This report describes the basic concepts required to work with ModelMuse. These basic concepts include the model grid, data sets, formulas, objects, the method used to assign values to data sets, and model features. The ModelMuse main window has a top, front, and side view of the model that can be used for editing the model, and a 3-D view of the model that can be used to display properties of the model. ModelMuse has tools to generate and edit the model grid. It also has a variety of interpolation methods and geographic functions that can be used to help define the spatial variability of the model. ModelMuse can be used to execute both MODFLOW-2005 and PHAST and can also display the results of MODFLOW-2005 models. An example of using ModelMuse with MODFLOW-2005 is included in this report. Several additional examples are described in the help system for ModelMuse, which can be accessed from the Help menu.

Independent data validation of an in vitro method for ...

EPA Pesticide Factsheets

In vitro bioaccessibility assays (IVBA) estimate arsenic (As) relative bioavailability (RBA) in contaminated soils to improve the accuracy of site-specific human exposure assessments and risk calculations. For an IVBA assay to gain acceptance for use in risk assessment, it must be shown to reliably predict in vivo RBA that is determined in an established animal model. Previous studies correlating soil As IVBA with RBA have been limited by the use of few soil types as the source of As. Furthermore, the predictive value of As IVBA assays has not been validated using an independent set of As-contaminated soils. Therefore, the current study was undertaken to develop a robust linear model to predict As RBA in mice using an IVBA assay and to independently validate the predictive capability of this assay using a unique set of As-contaminated soils. Thirty-six As-contaminated soils varying in soil type, As contaminant source, and As concentration were included in this study, with 27 soils used for initial model development and nine soils used for independent model validation. The initial model reliably predicted As RBA values in the independent data set, with a mean As RBA prediction error of 5.3% (range 2.4 to 8.4%). Following validation, all 36 soils were used for final model development, resulting in a linear model with the equation: RBA = 0.59 * IVBA + 9.8 and R2 of 0.78. The in vivo-in vitro correlation and independent data validation presented here provide

Analytical Dynamics and Nonrigid Spacecraft Simulation

NASA Technical Reports Server (NTRS)

Likins, P. W.

1974-01-01

Application to the simulation of idealized spacecraft are considered both for multiple-rigid-body models and for models consisting of combination of rigid bodies and elastic bodies, with the elastic bodies being defined either as continua, as finite-element systems, or as a collection of given modal data. Several specific examples are developed in detail by alternative methods of analytical mechanics, and results are compared to a Newton-Euler formulation. The following methods are developed from d'Alembert's principle in vector form: (1) Lagrange's form of d'Alembert's principle for independent generalized coordinates; (2) Lagrange's form of d'Alembert's principle for simply constrained systems; (3) Kane's quasi-coordinate formulation of D'Alembert's principle; (4) Lagrange's equations for independent generalized coordinates; (5) Lagrange's equations for simply constrained systems; (6) Lagrangian quasi-coordinate equations (or the Boltzmann-Hamel equations); (7) Hamilton's equations for simply constrained systems; and (8) Hamilton's equations for independent generalized coordinates.

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

Evaluation of internal noise methods for Hotelling observers

NASA Astrophysics Data System (ADS)

Zhang, Yani; Pham, Binh T.; Eckstein, Miguel P.

2005-04-01

Including internal noise in computer model observers to degrade model observer performance to human levels is a common method to allow for quantitatively comparisons of human and model performance. In this paper, we studied two different types of methods for injecting internal noise to Hotelling model observers. The first method adds internal noise to the output of the individual channels: a) Independent non-uniform channel noise, b) Independent uniform channel noise. The second method adds internal noise to the decision variable arising from the combination of channel responses: a) internal noise standard deviation proportional to decision variable's standard deviation due to the external noise, b) internal noise standard deviation proportional to decision variable's variance caused by the external noise. We tested the square window Hotelling observer (HO), channelized Hotelling observer (CHO), and Laguerre-Gauss Hotelling observer (LGHO). The studied task was detection of a filling defect of varying size/shape in one of four simulated arterial segment locations with real x-ray angiography backgrounds. Results show that the internal noise method that leads to the best prediction of human performance differs across the studied models observers. The CHO model best predicts human observer performance with the channel internal noise. The HO and LGHO best predict human observer performance with the decision variable internal noise. These results might help explain why previous studies have found different results on the ability of each Hotelling model to predict human performance. Finally, the present results might guide researchers with the choice of method to include internal noise into their Hotelling models.

A two-layer multiple-time-scale turbulence model and grid independence study

NASA Technical Reports Server (NTRS)

Kim, S.-W.; Chen, C.-P.

1989-01-01

A two-layer multiple-time-scale turbulence model is presented. The near-wall model is based on the classical Kolmogorov-Prandtl turbulence hypothesis and the semi-empirical logarithmic law of the wall. In the two-layer model presented, the computational domain of the conservation of mass equation and the mean momentum equation penetrated up to the wall, where no slip boundary condition has been prescribed; and the near wall boundary of the turbulence equations has been located at the fully turbulent region, yet very close to the wall, where the standard wall function method has been applied. Thus, the conservation of mass constraint can be satisfied more rigorously in the two-layer model than in the standard wall function method. In most of the two-layer turbulence models, the number of grid points to be used inside the near-wall layer posed the issue of computational efficiency. The present finite element computational results showed that the grid independent solutions were obtained with as small as two grid points, i.e., one quadratic element, inside the near wall layer. Comparison of the computational results obtained by using the two-layer model and those obtained by using the wall function method is also presented.

Assessing measurement error in surveys using latent class analysis: application to self-reported illicit drug use in data from the Iranian Mental Health Survey.

PubMed

Khalagi, Kazem; Mansournia, Mohammad Ali; Rahimi-Movaghar, Afarin; Nourijelyani, Keramat; Amin-Esmaeili, Masoumeh; Hajebi, Ahmad; Sharif, Vandad; Radgoodarzi, Reza; Hefazi, Mitra; Motevalian, Abbas

2016-01-01

Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.

Two-dimensional model of resonant electron collisions with diatomic molecules and molecular cations

NASA Astrophysics Data System (ADS)

Vana, Martin; Hvizdos, David; Houfek, Karel; Curik, Roman; Greene, Chris H.; Rescigno, Thomas N.; McCurdy, C. William

2016-05-01

A simple model for resonant collisions of electrons with diatomic molecules with one electronic and one nuclear degree of freedom (2D model) which was solved numerically exactly within the time-independent approach was used to probe the local complex potential approximation and nonlocal approximation to nuclear dynamics of these collisions. This model was reformulated in the time-dependent picture and extended to model also electron collisions with molecular cations, especially with H2+.This model enables an assessment of approximate methods, such as the boomerang model or the frame transformation theory. We will present both time-dependent and time-independent results and show how we can use the model to extract deeper insight into the dynamics of the resonant collisions.

Modeling haplotype block variation using Markov chains.

PubMed

Greenspan, G; Geiger, D

2006-04-01

Models of background variation in genomic regions form the basis of linkage disequilibrium mapping methods. In this work we analyze a background model that groups SNPs into haplotype blocks and represents the dependencies between blocks by a Markov chain. We develop an error measure to compare the performance of this model against the common model that assumes that blocks are independent. By examining data from the International Haplotype Mapping project, we show how the Markov model over haplotype blocks is most accurate when representing blocks in strong linkage disequilibrium. This contrasts with the independent model, which is rendered less accurate by linkage disequilibrium. We provide a theoretical explanation for this surprising property of the Markov model and relate its behavior to allele diversity.

Modeling Haplotype Block Variation Using Markov Chains

PubMed Central

Greenspan, G.; Geiger, D.

2006-01-01

Models of background variation in genomic regions form the basis of linkage disequilibrium mapping methods. In this work we analyze a background model that groups SNPs into haplotype blocks and represents the dependencies between blocks by a Markov chain. We develop an error measure to compare the performance of this model against the common model that assumes that blocks are independent. By examining data from the International Haplotype Mapping project, we show how the Markov model over haplotype blocks is most accurate when representing blocks in strong linkage disequilibrium. This contrasts with the independent model, which is rendered less accurate by linkage disequilibrium. We provide a theoretical explanation for this surprising property of the Markov model and relate its behavior to allele diversity. PMID:16361244

Global root zone storage capacity from satellite-based evaporation data

NASA Astrophysics Data System (ADS)

Wang-Erlandsson, Lan; Bastiaanssen, Wim; Gao, Hongkai; Jägermeyr, Jonas; Senay, Gabriel; van Dijk, Albert; Guerschman, Juan; Keys, Patrick; Gordon, Line; Savenije, Hubert

2016-04-01

We present an "earth observation-based" method for estimating root zone storage capacity - a critical, yet uncertain parameter in hydrological and land surface modelling. By assuming that vegetation optimises its root zone storage capacity to bridge critical dry periods, we were able to use state-of-the-art satellite-based evaporation data computed with independent energy balance equations to derive gridded root zone storage capacity at global scale. This approach does not require soil or vegetation information, is model independent, and is in principle scale-independent. In contrast to traditional look-up table approaches, our method captures the variability in root zone storage capacity within land cover type, including in rainforests where direct measurements of root depth otherwise are scarce. Implementing the estimated root zone storage capacity in the global hydrological model STEAM improved evaporation simulation overall, and in particular during the least evaporating months in sub-humid to humid regions with moderate to high seasonality. We find that evergreen forests are able to create a large storage to buffer for extreme droughts (with a return period of up to 60 years), in contrast to short vegetation and crops (which seem to adapt to a drought return period of about 2 years). The presented method to estimate root zone storage capacity eliminates the need for soils and rooting depth information, which could be a game-changer in global land surface modelling.

Long distance measurement-device-independent quantum key distribution with entangled photon sources

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

Xu, Feihu; Qi, Bing; Liao, Zhongfa

2013-08-05

We present a feasible method that can make quantum key distribution (QKD), both ultra-long-distance and immune, to all attacks in the detection system. This method is called measurement-device-independent QKD (MDI-QKD) with entangled photon sources in the middle. By proposing a model and simulating a QKD experiment, we find that MDI-QKD with one entangled photon source can tolerate 77 dB loss (367 km standard fiber) in the asymptotic limit and 60 dB loss (286 km standard fiber) in the finite-key case with state-of-the-art detectors. Our general model can also be applied to other non-QKD experiments involving entanglement and Bell state measurements.

Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease.

PubMed

Abraham, Gad; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

2013-02-01

A central goal of medical genetics is to accurately predict complex disease from genotypes. Here, we present a comprehensive analysis of simulated and real data using lasso and elastic-net penalized support-vector machine models, a mixed-effects linear model, a polygenic score, and unpenalized logistic regression. In simulation, the sparse penalized models achieved lower false-positive rates and higher precision than the other methods for detecting causal SNPs. The common practice of prefiltering SNP lists for subsequent penalized modeling was examined and shown to substantially reduce the ability to recover the causal SNPs. Using genome-wide SNP profiles across eight complex diseases within cross-validation, lasso and elastic-net models achieved substantially better predictive ability in celiac disease, type 1 diabetes, and Crohn's disease, and had equivalent predictive ability in the rest, with the results in celiac disease strongly replicating between independent datasets. We investigated the effect of linkage disequilibrium on the predictive models, showing that the penalized methods leverage this information to their advantage, compared with methods that assume SNP independence. Our findings show that sparse penalized approaches are robust across different disease architectures, producing as good as or better phenotype predictions and variance explained. This has fundamental ramifications for the selection and future development of methods to genetically predict human disease. © 2012 WILEY PERIODICALS, INC.

Kalman and particle filtering methods for full vehicle and tyre identification

NASA Astrophysics Data System (ADS)

Bogdanski, Karol; Best, Matthew C.

2018-05-01

This paper considers identification of all significant vehicle handling dynamics of a test vehicle, including identification of a combined-slip tyre model, using only those sensors currently available on most vehicle controller area network buses. Using an appropriately simple but efficient model structure, all of the independent parameters are found from test vehicle data, with the resulting model accuracy demonstrated on independent validation data. The paper extends previous work on augmented Kalman Filter state estimators to concentrate wholly on parameter identification. It also serves as a review of three alternative filtering methods; identifying forms of the unscented Kalman filter, extended Kalman filter and particle filter are proposed and compared for effectiveness, complexity and computational efficiency. All three filters are suited to applications of system identification and the Kalman Filters can also operate in real-time in on-line model predictive controllers or estimators.

Global root zone storage capacity from satellite-based evaporation

NASA Astrophysics Data System (ADS)

Wang-Erlandsson, Lan; Bastiaanssen, Wim G. M.; Gao, Hongkai; Jägermeyr, Jonas; Senay, Gabriel B.; van Dijk, Albert I. J. M.; Guerschman, Juan P.; Keys, Patrick W.; Gordon, Line J.; Savenije, Hubert H. G.

2016-04-01

This study presents an "Earth observation-based" method for estimating root zone storage capacity - a critical, yet uncertain parameter in hydrological and land surface modelling. By assuming that vegetation optimises its root zone storage capacity to bridge critical dry periods, we were able to use state-of-the-art satellite-based evaporation data computed with independent energy balance equations to derive gridded root zone storage capacity at global scale. This approach does not require soil or vegetation information, is model independent, and is in principle scale independent. In contrast to a traditional look-up table approach, our method captures the variability in root zone storage capacity within land cover types, including in rainforests where direct measurements of root depths otherwise are scarce. Implementing the estimated root zone storage capacity in the global hydrological model STEAM (Simple Terrestrial Evaporation to Atmosphere Model) improved evaporation simulation overall, and in particular during the least evaporating months in sub-humid to humid regions with moderate to high seasonality. Our results suggest that several forest types are able to create a large storage to buffer for severe droughts (with a very long return period), in contrast to, for example, savannahs and woody savannahs (medium length return period), as well as grasslands, shrublands, and croplands (very short return period). The presented method to estimate root zone storage capacity eliminates the need for poor resolution soil and rooting depth data that form a limitation for achieving progress in the global land surface modelling community.

An improved error assessment for the GEM-T1 gravitational model

NASA Technical Reports Server (NTRS)

Lerch, F. J.; Marsh, J. G.; Klosko, S. M.; Pavlis, E. C.; Patel, G. B.; Chinn, D. S.; Wagner, C. A.

1988-01-01

Several tests were designed to determine the correct error variances for the Goddard Earth Model (GEM)-T1 gravitational solution which was derived exclusively from satellite tracking data. The basic method employs both wholly independent and dependent subset data solutions and produces a full field coefficient estimate of the model uncertainties. The GEM-T1 errors were further analyzed using a method based upon eigenvalue-eigenvector analysis which calibrates the entire covariance matrix. Dependent satellite and independent altimetric and surface gravity data sets, as well as independent satellite deep resonance information, confirm essentially the same error assessment. These calibrations (utilizing each of the major data subsets within the solution) yield very stable calibration factors which vary by approximately 10 percent over the range of tests employed. Measurements of gravity anomalies obtained from altimetry were also used directly as observations to show that GEM-T1 is calibrated. The mathematical representation of the covariance error in the presence of unmodeled systematic error effects in the data is analyzed and an optimum weighting technique is developed for these conditions. This technique yields an internal self-calibration of the error model, a process which GEM-T1 is shown to approximate.

A Practical Guide to Calibration of a GSSHA Hydrologic Model Using ERDC Automated Model Calibration Software - Efficient Local Search

DTIC Science & Technology

2012-02-01

use the ERDC software implementation of the secant LM method that accommodates the PEST model independent interface to calibrate a GSSHA...how the method works. We will also demonstrate how our LM/SLM implementation compares with its counterparts as implemented in the popular PEST ...function values and total model calls for local search to converge) associated with Examples 1 and 3 using the PEST LM/SLM implementations

Impact imaging of aircraft composite structure based on a model-independent spatial-wavenumber filter.

PubMed

Qiu, Lei; Liu, Bin; Yuan, Shenfang; Su, Zhongqing

2016-01-01

The spatial-wavenumber filtering technique is an effective approach to distinguish the propagating direction and wave mode of Lamb wave in spatial-wavenumber domain. Therefore, it has been gradually studied for damage evaluation in recent years. But for on-line impact monitoring in practical application, the main problem is how to realize the spatial-wavenumber filtering of impact signal when the wavenumber of high spatial resolution cannot be measured or the accurate wavenumber curve cannot be modeled. In this paper, a new model-independent spatial-wavenumber filter based impact imaging method is proposed. In this method, a 2D cross-shaped array constructed by two linear piezoelectric (PZT) sensor arrays is used to acquire impact signal on-line. The continuous complex Shannon wavelet transform is adopted to extract the frequency narrowband signals from the frequency wideband impact response signals of the PZT sensors. A model-independent spatial-wavenumber filter is designed based on the spatial-wavenumber filtering technique. Based on the designed filter, a wavenumber searching and best match mechanism is proposed to implement the spatial-wavenumber filtering of the frequency narrowband signals without modeling, which can be used to obtain a wavenumber-time image of the impact relative to a linear PZT sensor array. By using the two wavenumber-time images of the 2D cross-shaped array, the impact direction can be estimated without blind angle. The impact distance relative to the 2D cross-shaped array can be calculated by using the difference of time-of-flight between the frequency narrowband signals of two different central frequencies and the corresponding group velocities. The validations performed on a carbon fiber composite laminate plate and an aircraft composite oil tank show a good impact localization accuracy of the model-independent spatial-wavenumber filter based impact imaging method. Copyright © 2015 Elsevier B.V. All rights reserved.

Spatially constrained incoherent motion method improves diffusion-weighted MRI signal decay analysis in the liver and spleen

PubMed Central

Taimouri, Vahid; Afacan, Onur; Perez-Rossello, Jeannette M.; Callahan, Michael J.; Mulkern, Robert V.; Warfield, Simon K.; Freiman, Moti

2015-01-01

Purpose: To evaluate the effect of the spatially constrained incoherent motion (SCIM) method on improving the precision and robustness of fast and slow diffusion parameter estimates from diffusion-weighted MRI in liver and spleen in comparison to the independent voxel-wise intravoxel incoherent motion (IVIM) model. Methods: We collected diffusion-weighted MRI (DW-MRI) data of 29 subjects (5 healthy subjects and 24 patients with Crohn’s disease in the ileum). We evaluated parameters estimates’ robustness against different combinations of b-values (i.e., 4 b-values and 7 b-values) by comparing the variance of the estimates obtained with the SCIM and the independent voxel-wise IVIM model. We also evaluated the improvement in the precision of parameter estimates by comparing the coefficient of variation (CV) of the SCIM parameter estimates to that of the IVIM. Results: The SCIM method was more robust compared to IVIM (up to 70% in liver and spleen) for different combinations of b-values. Also, the CV values of the parameter estimations using the SCIM method were significantly lower compared to repeated acquisition and signal averaging estimated using IVIM, especially for the fast diffusion parameter in liver (CVIV IM = 46.61 ± 11.22, CVSCIM = 16.85 ± 2.160, p < 0.001) and spleen (CVIV IM = 95.15 ± 19.82, CVSCIM = 52.55 ± 1.91, p < 0.001). Conclusions: The SCIM method characterizes fast and slow diffusion more precisely compared to the independent voxel-wise IVIM model fitting in the liver and spleen. PMID:25832079

Optimal model-based sensorless adaptive optics for epifluorescence microscopy.

PubMed

Pozzi, Paolo; Soloviev, Oleg; Wilding, Dean; Vdovin, Gleb; Verhaegen, Michel

2018-01-01

We report on a universal sample-independent sensorless adaptive optics method, based on modal optimization of the second moment of the fluorescence emission from a point-like excitation. Our method employs a sample-independent precalibration, performed only once for the particular system, to establish the direct relation between the image quality and the aberration. The method is potentially applicable to any form of microscopy with epifluorescence detection, including the practically important case of incoherent fluorescence emission from a three dimensional object, through minor hardware modifications. We have applied the technique successfully to a widefield epifluorescence microscope and to a multiaperture confocal microscope.

Separation of crack extension modes in orthotropic delamination models

NASA Technical Reports Server (NTRS)

Beuth, Jack L.

1995-01-01

In the analysis of an interface crack between dissimilar elastic materials, the mode of crack extension is typically not unique, due to oscillatory behavior of near-tip stresses and displacements. This behavior currently limits the applicability of interfacial fracture mechanics as a means to predict composite delamination. The Virtual Crack Closure Technique (VCCT) is a method used to extract mode 1 and mode 2 energy release rates from numerical fracture solutions. The mode of crack extension extracted from an oscillatory solution using the VCCT is not unique due to the dependence of mode on the virtual crack extension length, Delta. In this work, a method is presented for using the VCCT to extract Delta-independent crack extension modes for the case of an interface crack between two in-plane orthotropic materials. The method does not involve altering the analysis to eliminate its oscillatory behavior. Instead, it is argued that physically reasonable, Delta-independent modes of crack extension can be extracted from oscillatory solutions. Knowledge of near-tip fields is used to determine the explicit Delta dependence of energy release rate parameters. Energy release rates are then defined that are separated from the oscillatory dependence on Delta. A modified VCCT using these energy release rate definitions is applied to results from finite element analyses, showing that Delta-independent modes of crack extension result. The modified technique has potential as a consistent method for extracting crack extension modes from numerical solutions. The Delta-independent modes extracted using this technique can also serve as guides for testing the convergence of finite element models. Direct applications of this work include the analysis of planar composite delamination problems, where plies or debonded laminates are modeled as in-plane orthotropic materials.

Verification of Sulfate Attack Penetration Rates for Saltstone Disposal Unit Modeling

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

Flach, G. P.

Recent Special Analysis modeling of Saltstone Disposal Units consider sulfate attack on concrete and utilize degradation rates estimated from Cementitious Barriers Partnership software simulations. This study provides an independent verification of those simulation results using an alternative analysis method and an independent characterization data source. The sulfate penetration depths estimated herein are similar to the best-estimate values in SRNL-STI-2013-00118 Rev. 2 and well below the nominal values subsequently used to define Saltstone Special Analysis base cases.

How to test validity in orthodontic research: a mixed dentition analysis example.

PubMed

Donatelli, Richard E; Lee, Shin-Jae

2015-02-01

The data used to test the validity of a prediction method should be different from the data used to generate the prediction model. In this study, we explored whether an independent data set is mandatory for testing the validity of a new prediction method and how validity can be tested without independent new data. Several validation methods were compared in an example using the data from a mixed dentition analysis with a regression model. The validation errors of real mixed dentition analysis data and simulation data were analyzed for increasingly large data sets. The validation results of both the real and the simulation studies demonstrated that the leave-1-out cross-validation method had the smallest errors. The largest errors occurred in the traditional simple validation method. The differences between the validation methods diminished as the sample size increased. The leave-1-out cross-validation method seems to be an optimal validation method for improving the prediction accuracy in a data set with limited sample sizes. Copyright © 2015 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Multigrid and Krylov Subspace Methods for the Discrete Stokes Equations

NASA Technical Reports Server (NTRS)

Elman, Howard C.

1996-01-01

Discretization of the Stokes equations produces a symmetric indefinite system of linear equations. For stable discretizations, a variety of numerical methods have been proposed that have rates of convergence independent of the mesh size used in the discretization. In this paper, we compare the performance of four such methods: variants of the Uzawa, preconditioned conjugate gradient, preconditioned conjugate residual, and multigrid methods, for solving several two-dimensional model problems. The results indicate that where it is applicable, multigrid with smoothing based on incomplete factorization is more efficient than the other methods, but typically by no more than a factor of two. The conjugate residual method has the advantage of being both independent of iteration parameters and widely applicable.

Procedure for the systematic orientation of digitised cranial models. Design and validation.

PubMed

Bailo, M; Baena, S; Marín, J J; Arredondo, J M; Auría, J M; Sánchez, B; Tardío, E; Falcón, L

2015-12-01

Comparison of bony pieces requires that they are oriented systematically to ensure that homologous regions are compared. Few orientation methods are highly accurate; this is particularly true for methods applied to three-dimensional models obtained by surface scanning, a technique whose special features make it a powerful tool in forensic contexts. The aim of this study was to develop and evaluate a systematic, assisted orientation method for aligning three-dimensional cranial models relative to the Frankfurt Plane, which would be produce accurate orientations independent of operator and anthropological expertise. The study sample comprised four crania of known age and sex. All the crania were scanned and reconstructed using an Eva Artec™ portable 3D surface scanner and subsequently, the position of certain characteristic landmarks were determined by three different operators using the Rhinoceros 3D surface modelling software. Intra-observer analysis showed a tendency for orientation to be more accurate when using the assisted method than when using conventional manual orientation. Inter-observer analysis showed that experienced evaluators achieve results at least as accurate if not more accurate using the assisted method than those obtained using manual orientation; while inexperienced evaluators achieved more accurate orientation using the assisted method. The method tested is a an innovative system capable of providing very precise, systematic and automatised spatial orientations of virtual cranial models relative to standardised anatomical planes independent of the operator and operator experience. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Relation between cooperative molecular motors and active Brownian particles.

PubMed

Touya, Clément; Schwalger, Tilo; Lindner, Benjamin

2011-05-01

Active Brownian particles (ABPs), obeying a nonlinear Langevin equation with speed-dependent drift and noise amplitude, are well-known models used to describe self-propelled motion in biology. In this paper we study a model describing the stochastic dynamics of a group of coupled molecular motors (CMMs). Using two independent numerical methods, one based on the stationary velocity distribution of the motors and the other one on the local increments (also known as the Kramers-Moyal coefficients) of the velocity, we establish a connection between the CMM and the ABP models. The parameters extracted for the ABP via the two methods show good agreement for both symmetric and asymmetric cases and are independent of N, the number of motors, provided that N is not too small. This indicates that one can indeed describe the CMM problem with a simpler ABP model. However, the power spectrum of velocity fluctuations in the CMM model reveals a peak at a finite frequency, a peak which is absent in the velocity spectrum of the ABP model. This implies richer dynamic features of the CMM model which cannot be captured by an ABP model.

Relation between cooperative molecular motors and active Brownian particles

NASA Astrophysics Data System (ADS)

Touya, Clément; Schwalger, Tilo; Lindner, Benjamin

2011-05-01

Active Brownian particles (ABPs), obeying a nonlinear Langevin equation with speed-dependent drift and noise amplitude, are well-known models used to describe self-propelled motion in biology. In this paper we study a model describing the stochastic dynamics of a group of coupled molecular motors (CMMs). Using two independent numerical methods, one based on the stationary velocity distribution of the motors and the other one on the local increments (also known as the Kramers-Moyal coefficients) of the velocity, we establish a connection between the CMM and the ABP models. The parameters extracted for the ABP via the two methods show good agreement for both symmetric and asymmetric cases and are independent of N, the number of motors, provided that N is not too small. This indicates that one can indeed describe the CMM problem with a simpler ABP model. However, the power spectrum of velocity fluctuations in the CMM model reveals a peak at a finite frequency, a peak which is absent in the velocity spectrum of the ABP model. This implies richer dynamic features of the CMM model which cannot be captured by an ABP model.

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

PubMed Central

Rana, Mohit; Prasad, Vinod A.; Guan, Cuntai; Birbaumer, Niels; Sitaram, Ranganatha

2016-01-01

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity. PMID:27467528

Test of the FLRW Metric and Curvature with Strong Lens Time Delays

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

Liao, Kai; Li, Zhengxiang; Wang, Guo-Jian

We present a new model-independent strategy for testing the Friedmann–Lemaître–Robertson–Walker (FLRW) metric and constraining cosmic curvature, based on future time-delay measurements of strongly lensed quasar-elliptical galaxy systems from the Large Synoptic Survey Telescope and supernova observations from the Dark Energy Survey. The test only relies on geometric optics. It is independent of the energy contents of the universe and the validity of the Einstein equation on cosmological scales. The study comprises two levels: testing the FLRW metric through the distance sum rule (DSR) and determining/constraining cosmic curvature. We propose an effective and efficient (redshift) evolution model for performing the formermore » test, which allows us to concretely specify the violation criterion for the FLRW DSR. If the FLRW metric is consistent with the observations, then on the second level the cosmic curvature parameter will be constrained to ∼0.057 or ∼0.041 (1 σ ), depending on the availability of high-redshift supernovae, which is much more stringent than current model-independent techniques. We also show that the bias in the time-delay method might be well controlled, leading to robust results. The proposed method is a new independent tool for both testing the fundamental assumptions of homogeneity and isotropy in cosmology and for determining cosmic curvature. It is complementary to cosmic microwave background plus baryon acoustic oscillation analyses, which normally assume a cosmological model with dark energy domination in the late-time universe.« less

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

PubMed

Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

2011-07-25

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

Model-independent Exoplanet Transit Spectroscopy

NASA Astrophysics Data System (ADS)

Aronson, Erik; Piskunov, Nikolai

2018-05-01

We propose a new data analysis method for obtaining transmission spectra of exoplanet atmospheres and brightness variation across the stellar disk from transit observations. The new method is capable of recovering exoplanet atmosphere absorption spectra and stellar specific intensities without relying on theoretical models of stars and planets. We simultaneously fit both stellar specific intensity and planetary radius directly to transit light curves. This allows stellar models to be removed from the data analysis. Furthermore, we use a data quality weighted filtering technique to achieve an optimal trade-off between spectral resolution and reconstruction fidelity homogenizing the signal-to-noise ratio across the wavelength range. Such an approach is more efficient than conventional data binning onto a low-resolution wavelength grid. We demonstrate that our analysis is capable of reproducing results achieved by using an explicit quadratic limb-darkening equation and that the filtering technique helps eliminate spurious spectral features in regions with strong telluric absorption. The method is applied to the VLT FORS2 observations of the exoplanets GJ 1214 b and WASP-49 b, and our results are in agreement with previous studies. Comparisons between obtained stellar specific intensity and numerical models indicates that the method is capable of accurately reconstructing the specific intensity. The proposed method enables more robust characterization of exoplanetary atmospheres by separating derivation of planetary transmission and stellar specific intensity spectra (that is model-independent) from chemical and physical interpretation.

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Separation of the atmospheric variability into non-Gaussian multidimensional sources by projection pursuit techniques

NASA Astrophysics Data System (ADS)

Pires, Carlos A. L.; Ribeiro, Andreia F. S.

2017-02-01

We develop an expansion of space-distributed time series into statistically independent uncorrelated subspaces (statistical sources) of low-dimension and exhibiting enhanced non-Gaussian probability distributions with geometrically simple chosen shapes (projection pursuit rationale). The method relies upon a generalization of the principal component analysis that is optimal for Gaussian mixed signals and of the independent component analysis (ICA), optimized to split non-Gaussian scalar sources. The proposed method, supported by information theory concepts and methods, is the independent subspace analysis (ISA) that looks for multi-dimensional, intrinsically synergetic subspaces such as dyads (2D) and triads (3D), not separable by ICA. Basically, we optimize rotated variables maximizing certain nonlinear correlations (contrast functions) coming from the non-Gaussianity of the joint distribution. As a by-product, it provides nonlinear variable changes `unfolding' the subspaces into nearly Gaussian scalars of easier post-processing. Moreover, the new variables still work as nonlinear data exploratory indices of the non-Gaussian variability of the analysed climatic and geophysical fields. The method (ISA, followed by nonlinear unfolding) is tested into three datasets. The first one comes from the Lorenz'63 three-dimensional chaotic model, showing a clear separation into a non-Gaussian dyad plus an independent scalar. The second one is a mixture of propagating waves of random correlated phases in which the emergence of triadic wave resonances imprints a statistical signature in terms of a non-Gaussian non-separable triad. Finally the method is applied to the monthly variability of a high-dimensional quasi-geostrophic (QG) atmospheric model, applied to the Northern Hemispheric winter. We find that quite enhanced non-Gaussian dyads of parabolic shape, perform much better than the unrotated variables in which concerns the separation of the four model's centroid regimes (positive and negative phases of the Arctic Oscillation and of the North Atlantic Oscillation). Triads are also likely in the QG model but of weaker expression than dyads due to the imposed shape and dimension. The study emphasizes the existence of nonlinear dyadic and triadic nonlinear teleconnections.

Colorimetric characterization models based on colorimetric characteristics evaluation for active matrix organic light emitting diode panels.

PubMed

Gong, Rui; Xu, Haisong; Tong, Qingfen

2012-10-20

The colorimetric characterization of active matrix organic light emitting diode (AMOLED) panels suffers from their poor channel independence. Based on the colorimetric characteristics evaluation of channel independence and chromaticity constancy, an accurate colorimetric characterization method, namely, the polynomial compensation model (PC model) considering channel interactions was proposed for AMOLED panels. In this model, polynomial expressions are employed to calculate the relationship between the prediction errors of XYZ tristimulus values and the digital inputs to compensate the XYZ prediction errors of the conventional piecewise linear interpolation assuming the variable chromaticity coordinates (PLVC) model. The experimental results indicated that the proposed PC model outperformed other typical characterization models for the two tested AMOLED smart-phone displays and for the professional liquid crystal display monitor as well.

A study of hierarchical clustering of galaxies in an expanding universe

NASA Astrophysics Data System (ADS)

Porter, D. H.

The nonlinear hierarchical clustering of galaxies in an Einstein-deSitter (Omega = 1), initially white noise mass fluctuations (n = 0) model universe is investigated and shown to be in contradiction with previous results. The model is done in terms of an 11,000-body numerical simulation. The independent statics of 0.72 million particles are used to simulte the boundary conditions. A new method for integrating the Newtonian N-body gravity equations, which has controllable accuracy, incorporates a recursive center of mass reduction, and regularizes two body encounters is used to do the simulation. The coordinate system used here is well suited for the investigation of galaxy clustering, incorporating the independent positions and velocities of an arbitrary number of particles into a logarithmic hierarchy of center of mass nodes. The boundary for the simulation is created by using this hierarchy to map the independent statics of 0.72 million particles into just 4,000 particles. This method for simulating the boundary conditions also has controllable accuracy.

Modeling Working Memory Tasks on the Item Level

ERIC Educational Resources Information Center

Luo, Dasen; Chen, Guopeng; Zen, Fanlin; Murray, Bronwyn

2010-01-01

Item responses to Digit Span and Letter-Number Sequencing were analyzed to develop a better-refined model of the two working memory tasks using the finite mixture (FM) modeling method. Models with ordinal latent traits were found to better account for the independent sources of the variability in the tasks than those with continuous traits, and…

A general probabilistic model for group independent component analysis and its estimation methods

PubMed Central

Guo, Ying

2012-01-01

SUMMARY Independent component analysis (ICA) has become an important tool for analyzing data from functional magnetic resonance imaging (fMRI) studies. ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix and the uncertainty in between-subjects variability in fMRI data. We present a general probabilistic ICA (PICA) model that can accommodate varying group structures of multi-subject spatio-temporal processes. An advantage of the proposed model is that it can flexibly model various types of group structures in different underlying neural source signals and under different experimental conditions in fMRI studies. A maximum likelihood method is used for estimating this general group ICA model. We propose two EM algorithms to obtain the ML estimates. The first method is an exact EM algorithm which provides an exact E-step and an explicit noniterative M-step. The second method is an variational approximation EM algorithm which is computationally more efficient than the exact EM. In simulation studies, we first compare the performance of the proposed general group PICA model and the existing probabilistic group ICA approach. We then compare the two proposed EM algorithms and show the variational approximation EM achieves comparable accuracy to the exact EM with significantly less computation time. An fMRI data example is used to illustrate application of the proposed methods. PMID:21517789

Appliance of Independent Component Analysis to System Intrusion Analysis

NASA Astrophysics Data System (ADS)

Ishii, Yoshikazu; Takagi, Tarou; Nakai, Kouji

In order to analyze the output of the intrusion detection system and the firewall, we evaluated the applicability of ICA(independent component analysis). We developed a simulator for evaluation of intrusion analysis method. The simulator consists of the network model of an information system, the service model and the vulnerability model of each server, and the action model performed on client and intruder. We applied the ICA for analyzing the audit trail of simulated information system. We report the evaluation result of the ICA on intrusion analysis. In the simulated case, ICA separated two attacks correctly, and related an attack and the abnormalities of the normal application produced under the influence of the attach.

Student Perceptions of Staged Transfer to Independent Research Skills during a Four-Year Honours Science Undergraduate Program

ERIC Educational Resources Information Center

Symons, Sarah L.; Colgoni, Andrew; Harvey, Chad T.

2017-01-01

We describe interim results of an ongoing longitudinal pedagogical study investigating the efficacy of the Honours Integrated Science Program (iSci). We describe the pedagogical methods we use to prompt research skill development in a model from instructor-dependence to independent original research. We also describe a tool we use to help students…

Using Educational Data Mining Methods to Assess Field-Dependent and Field-Independent Learners' Complex Problem Solving

ERIC Educational Resources Information Center

Angeli, Charoula; Valanides, Nicos

2013-01-01

The present study investigated the problem-solving performance of 101 university students and their interactions with a computer modeling tool in order to solve a complex problem. Based on their performance on the hidden figures test, students were assigned to three groups of field-dependent (FD), field-mixed (FM), and field-independent (FI)…

Multi-spectrometer calibration transfer based on independent component analysis.

PubMed

Liu, Yan; Xu, Hao; Xia, Zhenzhen; Gong, Zhiyong

2018-02-26

Calibration transfer is indispensable for practical applications of near infrared (NIR) spectroscopy due to the need for precise and consistent measurements across different spectrometers. In this work, a method for multi-spectrometer calibration transfer is described based on independent component analysis (ICA). A spectral matrix is first obtained by aligning the spectra measured on different spectrometers. Then, by using independent component analysis, the aligned spectral matrix is decomposed into the mixing matrix and the independent components of different spectrometers. These differing measurements between spectrometers can then be standardized by correcting the coefficients within the independent components. Two NIR datasets of corn and edible oil samples measured with three and four spectrometers, respectively, were used to test the reliability of this method. The results of both datasets reveal that spectra measurements across different spectrometers can be transferred simultaneously and that the partial least squares (PLS) models built with the measurements on one spectrometer can predict that the spectra can be transferred correctly on another.

Using independent component analysis for electrical impedance tomography

NASA Astrophysics Data System (ADS)

Yan, Peimin; Mo, Yulong

2004-05-01

Independent component analysis (ICA) is a way to resolve signals into independent components based on the statistical characteristics of the signals. It is a method for factoring probability densities of measured signals into a set of densities that are as statistically independent as possible under the assumptions of a linear model. Electrical impedance tomography (EIT) is used to detect variations of the electric conductivity of the human body. Because there are variations of the conductivity distributions inside the body, EIT presents multi-channel data. In order to get all information contained in different location of tissue it is necessary to image the individual conductivity distribution. In this paper we consider to apply ICA to EIT on the signal subspace (individual conductivity distribution). Using ICA the signal subspace will then be decomposed into statistically independent components. The individual conductivity distribution can be reconstructed by the sensitivity theorem in this paper. Compute simulations show that the full information contained in the multi-conductivity distribution will be obtained by this method.

Certified randomness in quantum physics.

PubMed

Acín, Antonio; Masanes, Lluis

2016-12-07

The concept of randomness plays an important part in many disciplines. On the one hand, the question of whether random processes exist is fundamental for our understanding of nature. On the other, randomness is a resource for cryptography, algorithms and simulations. Standard methods for generating randomness rely on assumptions about the devices that are often not valid in practice. However, quantum technologies enable new methods for generating certified randomness, based on the violation of Bell inequalities. These methods are referred to as device-independent because they do not rely on any modelling of the devices. Here we review efforts to design device-independent randomness generators and the associated challenges.

The quest for conditional independence in prospectivity modeling: weights-of-evidence, boost weights-of-evidence, and logistic regression

NASA Astrophysics Data System (ADS)

Schaeben, Helmut; Semmler, Georg

2016-09-01

The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.

Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling

NASA Astrophysics Data System (ADS)

Chen, Jie; Li, Chao; Brissette, François P.; Chen, Hua; Wang, Mingna; Essou, Gilles R. C.

2018-05-01

Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed hydrology. However, a thorough validation and a comparison with other methods are recommended before using the JBC method, since it may perform worse than the IBC method for some cases due to bias nonstationarity of climate model outputs.

A simple iterative independent component analysis algorithm for vibration source signal identification of complex structures

NASA Astrophysics Data System (ADS)

Lee, Dong-Sup; Cho, Dae-Seung; Kim, Kookhyun; Jeon, Jae-Jin; Jung, Woo-Jin; Kang, Myeng-Hwan; Kim, Jae-Ho

2015-01-01

Independent Component Analysis (ICA), one of the blind source separation methods, can be applied for extracting unknown source signals only from received signals. This is accomplished by finding statistical independence of signal mixtures and has been successfully applied to myriad fields such as medical science, image processing, and numerous others. Nevertheless, there are inherent problems that have been reported when using this technique: instability and invalid ordering of separated signals, particularly when using a conventional ICA technique in vibratory source signal identification of complex structures. In this study, a simple iterative algorithm of the conventional ICA has been proposed to mitigate these problems. The proposed method to extract more stable source signals having valid order includes an iterative and reordering process of extracted mixing matrix to reconstruct finally converged source signals, referring to the magnitudes of correlation coefficients between the intermediately separated signals and the signals measured on or nearby sources. In order to review the problems of the conventional ICA technique and to validate the proposed method, numerical analyses have been carried out for a virtual response model and a 30 m class submarine model. Moreover, in order to investigate applicability of the proposed method to real problem of complex structure, an experiment has been carried out for a scaled submarine mockup. The results show that the proposed method could resolve the inherent problems of a conventional ICA technique.

Angular analysis of B → J/ψK1: Towards a model independent determination of the photon polarization with B → K1γ

NASA Astrophysics Data System (ADS)

Kou, E.; Le Yaouanc, A.; Tayduganov, A.

2016-12-01

We propose a model independent extraction of the hadronic information needed to determine the photon polarization of the b → sγ process by the method utilizing the B →K1 γ → Kππγ angular distribution. We show that exactly the same hadronic information can be obtained by using the B → J / ψK1 → J / ψKππ channel, which leads to a much higher precision.

Maximum Likelihood Estimation in Meta-Analytic Structural Equation Modeling

ERIC Educational Resources Information Center

Oort, Frans J.; Jak, Suzanne

2016-01-01

Meta-analytic structural equation modeling (MASEM) involves fitting models to a common population correlation matrix that is estimated on the basis of correlation coefficients that are reported by a number of independent studies. MASEM typically consist of two stages. The method that has been found to perform best in terms of statistical…

Hidden asymmetry and forward-backward correlations

NASA Astrophysics Data System (ADS)

Bialas, A.; Zalewski, K.

2010-09-01

A model-independent method of studying the forward-backward correlations in symmetric high-energy processes is developed. The method allows a systematic study of the properties of various particle sources and allows one to uncover asymmetric structures hidden in symmetric hadron-hadron and nucleus-nucleus inelastic reactions.

Evaluation of trends in wheat yield models

NASA Technical Reports Server (NTRS)

Ferguson, M. C.

1982-01-01

Trend terms in models for wheat yield in the U.S. Great Plains for the years 1932 to 1976 are evaluated. The subset of meteorological variables yielding the largest adjusted R(2) is selected using the method of leaps and bounds. Latent root regression is used to eliminate multicollinearities, and generalized ridge regression is used to introduce bias to provide stability in the data matrix. The regression model used provides for two trends in each of two models: a dependent model in which the trend line is piece-wise continuous, and an independent model in which the trend line is discontinuous at the year of the slope change. It was found that the trend lines best describing the wheat yields consisted of combinations of increasing, decreasing, and constant trend: four combinations for the dependent model and seven for the independent model.

Improving consensus contact prediction via server correlation reduction.

PubMed

Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

2009-05-06

Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

[Assessment on the ability of emergency response at the county center for disease control and prevention level in flooding-prone areas].

PubMed

Chen, Wei; Zeng, Guang

2006-02-01

To establish a comprehensive assessment model on the ability of emergency response within the public health system in flooding-prone areas. A hierarchy process theory was used to establish the initial assessing framework. Delphi method was used to screen and choose the ultimate indicators and their weights before an assessment model was set up under the 'synthetic scored method' to assess the ability of the emergency response among twenty county public health units. We then used the 'analysis of variation (ANOVA)' methodology to test the feasibility of distinguishing the ability of emergency response among different county health units and correlation analysis was used to assess the independence of indicators in the assessing model. A comprehensive model was then established including twenty first-class indicators and fifty-six second-class indicators and the degree of ability to emergency response with flooding of public health units was evaluated. There were five public health units having higher, ten having moderate but five with lower levels on emergency response. The assessment model was proved to be a good method in differentiating the ability of public health units, using independent indicators. The assessment model which we established seemed to be practical and reliable.

Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion.

PubMed

Chen, Te; Chen, Long; Xu, Xing; Cai, Yingfeng; Jiang, Haobin; Sun, Xiaoqiang

2018-04-20

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.

Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion

PubMed Central

Chen, Long; Xu, Xing; Cai, Yingfeng; Jiang, Haobin; Sun, Xiaoqiang

2018-01-01

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified. PMID:29677124

Grouping individual independent BOLD effects: a new way to ICA group analysis

NASA Astrophysics Data System (ADS)

Duann, Jeng-Ren; Jung, Tzyy-Ping; Sejnowski, Terrence J.; Makeig, Scott

2009-04-01

A new group analysis method to summarize the task-related BOLD responses based on independent component analysis (ICA) was presented. As opposite to the previously proposed group ICA (gICA) method, which first combined multi-subject fMRI data in either temporal or spatial domain and applied ICA decomposition only once to the combined fMRI data to extract the task-related BOLD effects, the method presented here applied ICA decomposition to the individual subjects' fMRI data to first find the independent BOLD effects specifically for each individual subject. Then, the task-related independent BOLD component was selected among the resulting independent components from the single-subject ICA decomposition and hence grouped across subjects to derive the group inference. In this new ICA group analysis (ICAga) method, one does not need to assume that the task-related BOLD time courses are identical across brain areas and subjects as used in the grand ICA decomposition on the spatially concatenated fMRI data. Neither does one need to assume that after spatial normalization, the voxels at the same coordinates represent exactly the same functional or structural brain anatomies across different subjects. These two assumptions have been problematic given the recent BOLD activation evidences. Further, since the independent BOLD effects were obtained from each individual subject, the ICAga method can better account for the individual differences in the task-related BOLD effects. Unlike the gICA approach whereby the task-related BOLD effects could only be accounted for by a single unified BOLD model across multiple subjects. As a result, the newly proposed method, ICAga, was able to better fit the task-related BOLD effects at individual level and thus allow grouping more appropriate multisubject BOLD effects in the group analysis.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

PubMed

Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

Comparison of Bolton analysis and Little’s irregularity index on laser scanned three-dimensional digital study models with conventional study models

NASA Astrophysics Data System (ADS)

Kurnia, H.; Noerhadi, N. A. I.

2017-08-01

Three-dimensional digital study models were introduced following advances in digital technology. This study was carried out to assess the reliability of digital study models scanned by a laser scanning device newly assembled. The aim of this study was to compare the digital study models and conventional models. Twelve sets of dental impressions were taken from patients with mild-to-moderate crowding. The impressions were taken twice, one with alginate and the other with polyvinylsiloxane. The alginate impressions were made into conventional models, and the polyvinylsiloxane impressions were scanned to produce digital models. The mesiodistal tooth width and Little’s irregularity index (LII) were measured manually with digital calipers on the conventional models and digitally on the digital study models. Bolton analysis was performed on each study models. Each method was carried out twice to check for intra-observer variability. The reproducibility (comparison of the methods) was assessed using independent-sample t-tests. The mesiodistal tooth width between conventional and digital models did not significantly differ (p > 0.05). Independent-sample t-tests did not identify statistically significant differences for Bolton analysis and LII (p = 0.603 for Bolton and p = 0894 for LII). The measurements of the digital study models are as accurate as those of the conventional models.

Determination of the optimal number of components in independent components analysis.

PubMed

Kassouf, Amine; Jouan-Rimbaud Bouveresse, Delphine; Rutledge, Douglas N

2018-03-01

Independent components analysis (ICA) may be considered as one of the most established blind source separation techniques for the treatment of complex data sets in analytical chemistry. Like other similar methods, the determination of the optimal number of latent variables, in this case, independent components (ICs), is a crucial step before any modeling. Therefore, validation methods are required in order to decide about the optimal number of ICs to be used in the computation of the final model. In this paper, three new validation methods are formally presented. The first one, called Random_ICA, is a generalization of the ICA_by_blocks method. Its specificity resides in the random way of splitting the initial data matrix into two blocks, and then repeating this procedure several times, giving a broader perspective for the selection of the optimal number of ICs. The second method, called KMO_ICA_Residuals is based on the computation of the Kaiser-Meyer-Olkin (KMO) index of the transposed residual matrices obtained after progressive extraction of ICs. The third method, called ICA_corr_y, helps to select the optimal number of ICs by computing the correlations between calculated proportions and known physico-chemical information about samples, generally concentrations, or between a source signal known to be present in the mixture and the signals extracted by ICA. These three methods were tested using varied simulated and experimental data sets and compared, when necessary, to ICA_by_blocks. Results were relevant and in line with expected ones, proving the reliability of the three proposed methods. Copyright © 2017 Elsevier B.V. All rights reserved.

Assessing Consumer Responses to PREPs: A Review of Tobacco Industry and Independent Research Methods

PubMed Central

Rees, Vaughan W.; Kreslake, Jennifer M.; Cummings, K. Michael; O'Connor, Richard J.; Hatsukami, Dorothy K.; Parascandola, Mark; Shields, Peter G.; Connolly, Gregory N.

2009-01-01

Objective Internal tobacco industry documents and the mainstream literature are reviewed to identify methods and measures for evaluating tobacco consumer response. The review aims to outline areas in which established methods exist, identify gaps in current methods for assessing CR, and consider how these methods might be applied to evaluate PREPs and new products. Methods Internal industry research reviewed included published papers, manuscript drafts, presentations, protocols, and instruments relating to consumer response measures were identified and analyzed. Peer-reviewed research was identified using PubMed and Scopus. Results Industry research on consumer response focuses on product development and marketing. To develop and refine new products, the tobacco industry has developed notable strategies for assessing consumers' sensory and subjective responses to product design characteristics. Independent research is often conducted to gauge the likelihood of future product adoption by measuring consumers' risk perceptions, responses to product, and product acceptability. Conclusions A model which conceptualizes consumer response as comprising the separate, but interacting domains of product perceptions and response to product is outlined. Industry and independent research supports the dual domain model, and provides a wide range of methods for assessment of the construct components of consumer response. Further research is needed to validate consumer response constructs, determine the relationship between consumer response and tobacco user behavior, and improve reliability of consumer response measures. Scientifically rigorous consumer response assessment methods will provide a needed empirical basis for future regulation of PREPs and new products, to counteract tobacco industry influence on consumers, and enhance the public health. PMID:19959675

Influence maximization in social networks under an independent cascade-based model

NASA Astrophysics Data System (ADS)

Wang, Qiyao; Jin, Yuehui; Lin, Zhen; Cheng, Shiduan; Yang, Tan

2016-02-01

The rapid growth of online social networks is important for viral marketing. Influence maximization refers to the process of finding influential users who make the most of information or product adoption. An independent cascade-based model for influence maximization, called IMIC-OC, was proposed to calculate positive influence. We assumed that influential users spread positive opinions. At the beginning, users held positive or negative opinions as their initial opinions. When more users became involved in the discussions, users balanced their own opinions and those of their neighbors. The number of users who did not change positive opinions was used to determine positive influence. Corresponding influential users who had maximum positive influence were then obtained. Experiments were conducted on three real networks, namely, Facebook, HEP-PH and Epinions, to calculate maximum positive influence based on the IMIC-OC model and two other baseline methods. The proposed model resulted in larger positive influence, thus indicating better performance compared with the baseline methods.

Mechanisms underlying subunit independence in pyramidal neuron dendrites

PubMed Central

Behabadi, Bardia F.; Mel, Bartlett W.

2014-01-01

Pyramidal neuron (PN) dendrites compartmentalize voltage signals and can generate local spikes, which has led to the proposal that their dendrites act as independent computational subunits within a multilayered processing scheme. However, when a PN is strongly activated, back-propagating action potentials (bAPs) sweeping outward from the soma synchronize dendritic membrane potentials many times per second. How PN dendrites maintain the independence of their voltage-dependent computations, despite these repeated voltage resets, remains unknown. Using a detailed compartmental model of a layer 5 PN, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated “cross-talk” from other dendrites over a 100-fold range. Then, through an analysis of conductance, voltage, and current waveforms within the model cell, we identify three biophysical mechanisms that together help make independent dendritic computation possible in a firing neuron, suggesting that a major subtype of neocortical neuron has been optimized for layered, compartmentalized processing under in-vivo–like spiking conditions. PMID:24357611

Modeling Terrorism Risk to the Air Transportation System: An Independent Assessment of TSA’s Risk Management Analysis Tool and Associated Methods

DTIC Science & Technology

2012-01-01

our own work for this discussion. DoD Instruction 5000.61 defines model validation as “the pro - cess of determining the degree to which a model and its... determined that RMAT is highly con - crete code, potentially leading to redundancies in the code itself and making RMAT more difficult to maintain...system con - ceptual models valid, and are the data used to support them adequate? (Chapters Two and Three) 2. Are the sources and methods for populating

Floating Node Method and Virtual Crack Closure Technique for Modeling Matrix Cracking-Delamination Interaction

NASA Technical Reports Server (NTRS)

DeCarvalho, N. V.; Chen, B. Y.; Pinho, S. T.; Baiz, P. M.; Ratcliffe, J. G.; Tay, T. E.

2013-01-01

A novel approach is proposed for high-fidelity modeling of progressive damage and failure in composite materials that combines the Floating Node Method (FNM) and the Virtual Crack Closure Technique (VCCT) to represent multiple interacting failure mechanisms in a mesh-independent fashion. In this study, the approach is applied to the modeling of delamination migration in cross-ply tape laminates. Delamination, matrix cracking, and migration are all modeled using fracture mechanics based failure and migration criteria. The methodology proposed shows very good qualitative and quantitative agreement with experiments.

Floating Node Method and Virtual Crack Closure Technique for Modeling Matrix Cracking-Delamination Migration

NASA Technical Reports Server (NTRS)

DeCarvalho, Nelson V.; Chen, B. Y.; Pinho, Silvestre T.; Baiz, P. M.; Ratcliffe, James G.; Tay, T. E.

2013-01-01

A novel approach is proposed for high-fidelity modeling of progressive damage and failure in composite materials that combines the Floating Node Method (FNM) and the Virtual Crack Closure Technique (VCCT) to represent multiple interacting failure mechanisms in a mesh-independent fashion. In this study, the approach is applied to the modeling of delamination migration in cross-ply tape laminates. Delamination, matrix cracking, and migration are all modeled using fracture mechanics based failure and migration criteria. The methodology proposed shows very good qualitative and quantitative agreement with experiments.

Gauge-independent decoherence models for solids in external fields

NASA Astrophysics Data System (ADS)

Wismer, Michael S.; Yakovlev, Vladislav S.

2018-04-01

We demonstrate gauge-invariant modeling of an open system of electrons in a periodic potential interacting with an optical field. For this purpose, we adapt the covariant derivative to the case of mixed states and put forward a decoherence model that has simple analytical forms in the length and velocity gauges. We demonstrate our methods by calculating harmonic spectra in the strong-field regime and numerically verifying the equivalence of the deterministic master equation to the stochastic Monte Carlo wave-function method.

Geodesic least squares regression on information manifolds

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

Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be

We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply thismore » to scaling laws in magnetic confinement fusion.« less

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

Comparing Distribution of Harbour Porpoises (Phocoena phocoena) Derived from Satellite Telemetry and Passive Acoustic Monitoring

PubMed Central

Rigét, Frank F.; Kyhn, Line A.; Sveegaard, Signe; Dietz, Rune; Tougaard, Jakob; Carlström, Julia A. K.; Carlén, Ida; Koblitz, Jens C.; Teilmann, Jonas

2016-01-01

Cetacean monitoring is essential in determining the status of a population. Different monitoring methods should reflect the real trends in abundance and patterns in distribution, and results should therefore ideally be independent of the selected method. Here, we compare two independent methods of describing harbour porpoise (Phocoena phocoena) relative distribution pattern in the western Baltic Sea. Satellite locations from 13 tagged harbour porpoises were used to build a Maximum Entropy (MaxEnt) model of suitable habitats. The data set was subsampled to one location every second day, which were sufficient to make reliable models over the summer (Jun-Aug) and autumn (Sep-Nov) seasons. The modelled results were compared to harbour porpoise acoustic activity obtained from 36 static acoustic monitoring stations (C-PODs) covering the same area. The C-POD data was expressed as the percentage of porpoise positive days/hours (the number of days/hours per day with porpoise detections) by season. The MaxEnt model and C-POD data showed a significant linear relationship with a strong decline in porpoise occurrence from west to east. This study shows that two very different methods provide comparable information on relative distribution patterns of harbour porpoises even in a low density area. PMID:27463509

Figure-ground segmentation based on class-independent shape priors

NASA Astrophysics Data System (ADS)

Li, Yang; Liu, Yang; Liu, Guojun; Guo, Maozu

2018-01-01

We propose a method to generate figure-ground segmentation by incorporating shape priors into the graph-cuts algorithm. Given an image, we first obtain a linear representation of an image and then apply directional chamfer matching to generate class-independent, nonparametric shape priors, which provide shape clues for the graph-cuts algorithm. We then enforce shape priors in a graph-cuts energy function to produce object segmentation. In contrast to previous segmentation methods, the proposed method shares shape knowledge for different semantic classes and does not require class-specific model training. Therefore, the approach obtains high-quality segmentation for objects. We experimentally validate that the proposed method outperforms previous approaches using the challenging PASCAL VOC 2010/2012 and Berkeley (BSD300) segmentation datasets.

Direct estimation and correction of bias from temporally variable non-stationary noise in a channelized Hotelling model observer.

PubMed

Fetterly, Kenneth A; Favazza, Christopher P

2016-08-07

Channelized Hotelling model observer (CHO) methods were developed to assess performance of an x-ray angiography system. The analytical methods included correction for known bias error due to finite sampling. Detectability indices ([Formula: see text]) corresponding to disk-shaped objects with diameters in the range 0.5-4 mm were calculated. Application of the CHO for variable detector target dose (DTD) in the range 6-240 nGy frame(-1) resulted in [Formula: see text] estimates which were as much as 2.9×  greater than expected of a quantum limited system. Over-estimation of [Formula: see text] was presumed to be a result of bias error due to temporally variable non-stationary noise. Statistical theory which allows for independent contributions of 'signal' from a test object (o) and temporally variable non-stationary noise (ns) was developed. The theory demonstrates that the biased [Formula: see text] is the sum of the detectability indices associated with the test object [Formula: see text] and non-stationary noise ([Formula: see text]). Given the nature of the imaging system and the experimental methods, [Formula: see text] cannot be directly determined independent of [Formula: see text]. However, methods to estimate [Formula: see text] independent of [Formula: see text] were developed. In accordance with the theory, [Formula: see text] was subtracted from experimental estimates of [Formula: see text], providing an unbiased estimate of [Formula: see text]. Estimates of [Formula: see text] exhibited trends consistent with expectations of an angiography system that is quantum limited for high DTD and compromised by detector electronic readout noise for low DTD conditions. Results suggest that these methods provide [Formula: see text] estimates which are accurate and precise for [Formula: see text]. Further, results demonstrated that the source of bias was detector electronic readout noise. In summary, this work presents theory and methods to test for the presence of bias in Hotelling model observers due to temporally variable non-stationary noise and correct this bias when the temporally variable non-stationary noise is independent and additive with respect to the test object signal.

A numerical model for simulation of bioremediation of hydrocarbons in aquifers

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

Munoz, J.F.; Irarrazaval, M.J.

1998-03-01

A numerical model was developed to describe the bioremediation of hydrocarbons in ground water aquifers considering aerobic degradation. The model solves the independent transport of three solutes (oxygen, hydrocarbons, and microorganisms) in ground water flow using the method of characteristics. Interactions between the three solutes, in which oxygen and hydrocarbons are consumed by microorganisms, are represented by Monod kinetics, solved using a Runge-Kutta method. Model simulations showed good correlation as compared with results of soil column experiments. The model was used to estimate the time needed to remediate the columns, which varied from one to two years.

Lithology-derived structure classification from the joint interpretation of magnetotelluric and seismic models

USGS Publications Warehouse

Bedrosian, P.A.; Maercklin, N.; Weckmann, U.; Bartov, Y.; Ryberg, T.; Ritter, O.

2007-01-01

Magnetotelluric and seismic methods provide complementary information about the resistivity and velocity structure of the subsurface on similar scales and resolutions. No global relation, however, exists between these parameters, and correlations are often valid for only a limited target area. Independently derived inverse models from these methods can be combined using a classification approach to map geologic structure. The method employed is based solely on the statistical correlation of physical properties in a joint parameter space and is independent of theoretical or empirical relations linking electrical and seismic parameters. Regions of high correlation (classes) between resistivity and velocity can in turn be mapped back and re-examined in depth section. The spatial distribution of these classes, and the boundaries between them, provide structural information not evident in the individual models. This method is applied to a 10 km long profile crossing the Dead Sea Transform in Jordan. Several prominent classes are identified with specific lithologies in accordance with local geology. An abrupt change in lithology across the fault, together with vertical uplift of the basement suggest the fault is sub-vertical within the upper crust. ?? 2007 The Authors Journal compilation ?? 2007 RAS.

Rate-independent dissipation in phase-field modelling of displacive transformations

NASA Astrophysics Data System (ADS)

Tůma, K.; Stupkiewicz, S.; Petryk, H.

2018-05-01

In this paper, rate-independent dissipation is introduced into the phase-field framework for modelling of displacive transformations, such as martensitic phase transformation and twinning. The finite-strain phase-field model developed recently by the present authors is here extended beyond the limitations of purely viscous dissipation. The variational formulation, in which the evolution problem is formulated as a constrained minimization problem for a global rate-potential, is enhanced by including a mixed-type dissipation potential that combines viscous and rate-independent contributions. Effective computational treatment of the resulting incremental problem of non-smooth optimization is developed by employing the augmented Lagrangian method. It is demonstrated that a single Lagrange multiplier field suffices to handle the dissipation potential vertex and simultaneously to enforce physical constraints on the order parameter. In this way, the initially non-smooth problem of evolution is converted into a smooth stationarity problem. The model is implemented in a finite-element code and applied to solve two- and three-dimensional boundary value problems representative for shape memory alloys.

Multiple transmitter performance with appropriate amplitude modulation for free-space optical communication.

PubMed

Tellez, Jason A; Schmidt, Jason D

2011-08-20

The propagation of a free-space optical communications signal through atmospheric turbulence experiences random fluctuations in intensity, including signal fades, which negatively impact the performance of the communications link. The gamma-gamma probability density function is commonly used to model the scintillation of a single beam. One proposed method to reduce the occurrence of scintillation-induced fades at the receiver plane involves the use of multiple beams propagating through independent paths, resulting in a sum of independent gamma-gamma random variables. Recently an analytical model for the probability distribution of irradiance from the sum of multiple independent beams was developed. Because truly independent beams are practically impossible to create, we present here a more general but approximate model for the distribution of beams traveling through partially correlated paths. This model compares favorably with wave-optics simulations and highlights the reduced scintillation as the number of transmitted beams is increased. Additionally, a pulse-position modulation scheme is used to reduce the impact of signal fades when they occur. Analytical and simulated results showed significantly improved performance when compared to fixed threshold on/off keying. © 2011 Optical Society of America

New error calibration tests for gravity models using subset solutions and independent data - Applied to GEM-T3

NASA Technical Reports Server (NTRS)

Lerch, F. J.; Nerem, R. S.; Chinn, D. S.; Chan, J. C.; Patel, G. B.; Klosko, S. M.

1993-01-01

A new method has been developed to provide a direct test of the error calibrations of gravity models based on actual satellite observations. The basic approach projects the error estimates of the gravity model parameters onto satellite observations, and the results of these projections are then compared with data residual computed from the orbital fits. To allow specific testing of the gravity error calibrations, subset solutions are computed based on the data set and data weighting of the gravity model. The approach is demonstrated using GEM-T3 to show that the gravity error estimates are well calibrated and that reliable predictions of orbit accuracies can be achieved for independent orbits.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Assessing consumer responses to potential reduced-exposure tobacco products: a review of tobacco industry and independent research methods.

PubMed

Rees, Vaughan W; Kreslake, Jennifer M; Cummings, K Michael; O'Connor, Richard J; Hatsukami, Dorothy K; Parascandola, Mark; Shields, Peter G; Connolly, Gregory N

2009-12-01

Internal tobacco industry documents and the mainstream literature are reviewed to identify methods and measures for evaluating tobacco consumer response. The review aims to outline areas in which established methods exist, identify gaps in current methods for assessing consumer response, and consider how these methods might be applied to evaluate potentially reduced exposure tobacco products and new products. Internal industry research reviewed included published articles, manuscript drafts, presentations, protocols, and instruments relating to consumer response measures were identified and analyzed. Peer-reviewed research was identified using PubMed and Scopus. Industry research on consumer response focuses on product development and marketing. To develop and refine new products, the tobacco industry has developed notable strategies for assessing consumers' sensory and subjective responses to product design characteristics. Independent research is often conducted to gauge the likelihood of future product adoption by measuring consumers' risk perceptions, responses to product, and product acceptability. A model that conceptualizes consumer response as comprising the separate, but interacting, domains of product perceptions and response to product is outlined. Industry and independent research supports the dual domain model and provides a wide range of methods for assessment of the construct components of consumer response. Further research is needed to validate consumer response constructs, determine the relationship between consumer response and tobacco user behavior, and improve reliability of consumer response measures. Scientifically rigorous consumer response assessment methods will provide a needed empirical basis for future regulation of potentially reduced-exposure tobacco products and new products, to counteract tobacco industry influence on consumers, and enhance the public health.

Studying the location of SACs and DACs regions in the environment of hot emission stars

NASA Astrophysics Data System (ADS)

Antoniou, A.; Danezis, E.; Lyratzi, E.; Popović, L. Č.; Dimitrijević, M. S.; Theodossiou, E.

Hot emission stars (Oe and Be stars) present complex spectral line profiles, which are formed by a number of DACs and/or SACs. In order to explain and reproduce theoretically these complex line profiles we use the GR model (Gauss-Rotation model). This model presupposes that the regions, where the spectral lines are created, consist of a number of independent and successive absorbing or emitting density regions of matter. Here we are testing a new approach of the GR model, which supposes that the independent density regions are not successive. We use this new approach in the spectral lines of some Oe and Be stars and we compare the results of this method with the results deriving from the classical GR model that supposes successive regions.

New formulation feed method in tariff model of solar PV in Indonesia

NASA Astrophysics Data System (ADS)

Djamal, Muchlishah Hadi; Setiawan, Eko Adhi; Setiawan, Aiman

2017-03-01

Geographically, Indonesia has 18 latitudes that correlated strongly with the potential of solar radiation for the implementation of solar photovoltaic (PV) technologies. This is becoming the basis assumption to develop a proportional model of Feed In Tariff (FIT), consequently the FIT will be vary, according to the various of latitudes in Indonesia. This paper proposed a new formulation of solar PV FIT based on the potential of solar radiation and some independent variables such as latitude, longitude, Levelized Cost of Electricity (LCOE), and also socio-economic. The Principal Component Regression (PCR) method is used to analyzed the correlation of six independent variables C1-C6 then three models of FIT are presented. Model FIT-2 is chosen because it has a small residual value and has higher financial benefit compared to the other models. This study reveals the value of variable FIT associated with solar energy potential in each region, can reduce the total FIT to be paid by the state around 80 billion rupiahs in 10 years of 1 MW photovoltaic operation at each 34 provinces in Indonesia.

Solid-State Kinetic Investigations of Nonisothermal Reduction of Iron Species Supported on SBA-15

PubMed Central

2017-01-01

Iron oxide catalysts supported on nanostructured silica SBA-15 were synthesized with various iron loadings using two different precursors. Structural characterization of the as-prepared FexOy/SBA-15 samples was performed by nitrogen physisorption, X-ray diffraction, DR-UV-Vis spectroscopy, and Mössbauer spectroscopy. An increasing size of the resulting iron species correlated with an increasing iron loading. Significantly smaller iron species were obtained from (Fe(III), NH4)-citrate precursors compared to Fe(III)-nitrate precursors. Moreover, smaller iron species resulted in a smoother surface of the support material. Temperature-programmed reduction (TPR) of the FexOy/SBA-15 samples with H2 revealed better reducibility of the samples originating from Fe(III)-nitrate precursors. Varying the iron loading led to a change in reduction mechanism. TPR traces were analyzed by model-independent Kissinger method, Ozawa, Flynn, and Wall (OFW) method, and model-dependent Coats-Redfern method. JMAK kinetic analysis afforded a one-dimensional reduction process for the FexOy/SBA-15 samples. The Kissinger method yielded the lowest apparent activation energy for the lowest loaded citrate sample (Ea ≈ 39 kJ/mol). Conversely, the lowest loaded nitrate sample possessed the highest apparent activation energy (Ea ≈ 88 kJ/mol). For samples obtained from Fe(III)-nitrate precursors, Ea decreased with increasing iron loading. Apparent activation energies from model-independent analysis methods agreed well with those from model-dependent methods. Nucleation as rate-determining step in the reduction of the iron oxide species was consistent with the Mampel solid-state reaction model. PMID:29230346

Comparing the cognitive differences resulting from modeling instruction: Using computer microworld and physical object instruction to model real world problems

NASA Astrophysics Data System (ADS)

Oursland, Mark David

This study compared the modeling achievement of students receiving mathematical modeling instruction using the computer microworld, Interactive Physics, and students receiving instruction using physical objects. Modeling instruction included activities where students applied the (a) linear model to a variety of situations, (b) linear model to two-rate situations with a constant rate, (c) quadratic model to familiar geometric figures. Both quantitative and qualitative methods were used to analyze achievement differences between students (a) receiving different methods of modeling instruction, (b) with different levels of beginning modeling ability, or (c) with different levels of computer literacy. Student achievement was analyzed quantitatively through a three-factor analysis of variance where modeling instruction, beginning modeling ability, and computer literacy were used as the three independent factors. The SOLO (Structure of the Observed Learning Outcome) assessment framework was used to design written modeling assessment instruments to measure the students' modeling achievement. The same three independent factors were used to collect and analyze the interviews and observations of student behaviors. Both methods of modeling instruction used the data analysis approach to mathematical modeling. The instructional lessons presented problem situations where students were asked to collect data, analyze the data, write a symbolic mathematical equation, and use equation to solve the problem. The researcher recommends the following practice for modeling instruction based on the conclusions of this study. A variety of activities with a common structure are needed to make explicit the modeling process of applying a standard mathematical model. The modeling process is influenced strongly by prior knowledge of the problem context and previous modeling experiences. The conclusions of this study imply that knowledge of the properties about squares improved the students' ability to model a geometric problem more than instruction in data analysis modeling. The uses of computer microworlds such as Interactive Physics in conjunction with cooperative groups are a viable method of modeling instruction.

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

Unseren, M.A.

The report reviews a method for modeling and controlling two serial link manipulators which mutually lift and transport a rigid body object in a three dimensional workspace. A new vector variable is introduced which parameterizes the internal contact force controlled degrees of freedom. A technique for dynamically distributing the payload between the manipulators is suggested which yields a family of solutions for the contact forces and torques the manipulators impart to the object. A set of rigid body kinematic constraints which restricts the values of the joint velocities of both manipulators is derived. A rigid body dynamical model for themore » closed chain system is first developed in the joint space. The model is obtained by generalizing the previous methods for deriving the model. The joint velocity and acceleration variables in the model are expressed in terms of independent pseudovariables. The pseudospace model is transformed to obtain reduced order equations of motion and a separate set of equations governing the internal components of the contact forces and torques. A theoretic control architecture is suggested which explicitly decouples the two sets of equations comprising the model. The controller enables the designer to develop independent, non-interacting control laws for the position control and internal force control of the system.« less

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

Unseren, M.A.

The paper reviews a method for modeling and controlling two serial link manipulators which mutually lift and transport a rigid body object in a three dimensional workspace. A new vector variable is introduced which parameterizes the internal contact force controlled degrees of freedom. A technique for dynamically distributing the payload between the manipulators is suggested which yields a family of solutions for the contact forces and torques the manipulators impart to the object. A set of rigid body kinematic constraints which restrict the values of the joint velocities of both manipulators is derived. A rigid body dynamical model for themore » closed chain system is first developed in the joint space. The model is obtained by generalizing the previous methods for deriving the model. The joint velocity and acceleration variables in the model are expressed in terms of independent pseudovariables. The pseudospace model is transformed to obtain reduced order equations of motion and a separate set of equations governing the internal components of the contact forces and torques. A theoretic control architecture is suggested which explicitly decouples the two sets of equations comprising the model. The controller enables the designer to develop independent, non-interacting control laws for the position control and internal force control of the system.« less

Microseismic imaging using a source function independent full waveform inversion method

NASA Astrophysics Data System (ADS)

Wang, Hanchen; Alkhalifah, Tariq

2018-07-01

At the heart of microseismic event measurements is the task to estimate the location of the source microseismic events, as well as their ignition times. The accuracy of locating the sources is highly dependent on the velocity model. On the other hand, the conventional microseismic source locating methods require, in many cases, manual picking of traveltime arrivals, which do not only lead to manual effort and human interaction, but also prone to errors. Using full waveform inversion (FWI) to locate and image microseismic events allows for an automatic process (free of picking) that utilizes the full wavefield. However, FWI of microseismic events faces incredible nonlinearity due to the unknown source locations (space) and functions (time). We developed a source function independent FWI of microseismic events to invert for the source image, source function and the velocity model. It is based on convolving reference traces with these observed and modelled to mitigate the effect of an unknown source ignition time. The adjoint-state method is used to derive the gradient for the source image, source function and velocity updates. The extended image for the source wavelet in Z axis is extracted to check the accuracy of the inverted source image and velocity model. Also, angle gathers are calculated to assess the quality of the long wavelength component of the velocity model. By inverting for the source image, source wavelet and the velocity model simultaneously, the proposed method produces good estimates of the source location, ignition time and the background velocity for synthetic examples used here, like those corresponding to the Marmousi model and the SEG/EAGE overthrust model.

Statistical image reconstruction from correlated data with applications to PET

PubMed Central

Alessio, Adam; Sauer, Ken; Kinahan, Paul

2008-01-01

Most statistical reconstruction methods for emission tomography are designed for data modeled as conditionally independent Poisson variates. In reality, due to scanner detectors, electronics and data processing, correlations are introduced into the data resulting in dependent variates. In general, these correlations are ignored because they are difficult to measure and lead to computationally challenging statistical reconstruction algorithms. This work addresses the second concern, seeking to simplify the reconstruction of correlated data and provide a more precise image estimate than the conventional independent methods. In general, correlated variates have a large non-diagonal covariance matrix that is computationally challenging to use as a weighting term in a reconstruction algorithm. This work proposes two methods to simplify the use of a non-diagonal covariance matrix as the weighting term by (a) limiting the number of dimensions in which the correlations are modeled and (b) adopting flexible, yet computationally tractable, models for correlation structure. We apply and test these methods with simple simulated PET data and data processed with the Fourier rebinning algorithm which include the one-dimensional correlations in the axial direction and the two-dimensional correlations in the transaxial directions. The methods are incorporated into a penalized weighted least-squares 2D reconstruction and compared with a conventional maximum a posteriori approach. PMID:17921576

Stochastic differential equation (SDE) model of opening gold share price of bursa saham malaysia

NASA Astrophysics Data System (ADS)

Hussin, F. N.; Rahman, H. A.; Bahar, A.

2017-09-01

Black and Scholes option pricing model is one of the most recognized stochastic differential equation model in mathematical finance. Two parameter estimation methods have been utilized for the Geometric Brownian model (GBM); historical and discrete method. The historical method is a statistical method which uses the property of independence and normality logarithmic return, giving out the simplest parameter estimation. Meanwhile, discrete method considers the function of density of transition from the process of diffusion normal log which has been derived from maximum likelihood method. These two methods are used to find the parameter estimates samples of Malaysians Gold Share Price data such as: Financial Times and Stock Exchange (FTSE) Bursa Malaysia Emas, and Financial Times and Stock Exchange (FTSE) Bursa Malaysia Emas Shariah. Modelling of gold share price is essential since fluctuation of gold affects worldwide economy nowadays, including Malaysia. It is found that discrete method gives the best parameter estimates than historical method due to the smallest Root Mean Square Error (RMSE) value.

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning.

PubMed

Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

2016-06-29

The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images' spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

Comparison between geodetic and oceanographic approaches to estimate mean dynamic topography for vertical datum unification: evaluation at Australian tide gauges

NASA Astrophysics Data System (ADS)

Filmer, M. S.; Hughes, C. W.; Woodworth, P. L.; Featherstone, W. E.; Bingham, R. J.

2018-04-01

The direct method of vertical datum unification requires estimates of the ocean's mean dynamic topography (MDT) at tide gauges, which can be sourced from either geodetic or oceanographic approaches. To assess the suitability of different types of MDT for this purpose, we evaluate 13 physics-based numerical ocean models and six MDTs computed from observed geodetic and/or ocean data at 32 tide gauges around the Australian coast. We focus on the viability of numerical ocean models for vertical datum unification, classifying the 13 ocean models used as either independent (do not contain assimilated geodetic data) or non-independent (do contain assimilated geodetic data). We find that the independent and non-independent ocean models deliver similar results. Maximum differences among ocean models and geodetic MDTs reach >150 mm at several Australian tide gauges and are considered anomalous at the 99% confidence level. These differences appear to be of geodetic origin, but without additional independent information, or formal error estimates for each model, some of these errors remain inseparable. Our results imply that some ocean models have standard deviations of differences with other MDTs (using geodetic and/or ocean observations) at Australian tide gauges, and with levelling between some Australian tide gauges, of ˜ ± 50 mm . This indicates that they should be considered as an alternative to geodetic MDTs for the direct unification of vertical datums. They can also be used as diagnostics for errors in geodetic MDT in coastal zones, but the inseparability problem remains, where the error cannot be discriminated between the geoid model or altimeter-derived mean sea surface.

A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies

PubMed Central

Tang, Li

2014-01-01

Summary An important goal in fMRI studies is to decompose the observed series of brain images to identify and characterize underlying brain functional networks. Independent component analysis (ICA) has been shown to be a powerful computational tool for this purpose. Classic ICA has been successfully applied to single-subject fMRI data. The extension of ICA to group inferences in neuroimaging studies, however, is challenging due to the unavailability of a pre-specified group design matrix. Existing group ICA methods generally concatenate observed fMRI data across subjects on the temporal domain and then decompose multi-subject data in a similar manner to single-subject ICA. The major limitation of existing methods is that they ignore between-subject variability in spatial distributions of brain functional networks in group ICA. In this paper, we propose a new hierarchical probabilistic group ICA method to formally model subject-specific effects in both temporal and spatial domains when decomposing multi-subject fMRI data. The proposed method provides model-based estimation of brain functional networks at both the population and subject level. An important advantage of the hierarchical model is that it provides a formal statistical framework to investigate similarities and differences in brain functional networks across subjects, e.g., subjects with mental disorders or neurodegenerative diseases such as Parkinson’s as compared to normal subjects. We develop an EM algorithm for model estimation where both the E-step and M-step have explicit forms. We compare the performance of the proposed hierarchical model with that of two popular group ICA methods via simulation studies. We illustrate our method with application to an fMRI study of Zen meditation. PMID:24033125

Three-dimensional wave field modeling by a collocated-grid finite-difference method in the anelastic model with surface topography

NASA Astrophysics Data System (ADS)

Wang, N.; Li, J.; Borisov, D.; Gharti, H. N.; Shen, Y.; Zhang, W.; Savage, B. K.

2016-12-01

We incorporate 3D anelastic attenuation into the collocated-grid finite-difference method on curvilinear grids (Zhang et al., 2012), using the rheological model of the generalized Maxwell body (Emmerich and Korn, 1987; Moczo and Kristek, 2005; Käser et al., 2007). We follow a conventional procedure to calculate the anelastic coefficients (Emmerich and Korn, 1987) determined by the Q(ω)-law, with a modification in the choice of frequency band and thus the relaxation frequencies that equidistantly cover the logarithmic frequency range. We show that such an optimization of anelastic coefficients is more accurate when using a fixed number of relaxation mechanisms to fit the frequency independent Q-factors. We use curvilinear grids to represent the surface topography. The velocity-stress form of the 3D isotropic anelastic wave equation is solved with a collocated-grid finite-difference method. Compared with the elastic case, we need to solve additional material-independent anelastic functions (Kristek and Moczo, 2003) for the mechanisms at each relaxation frequency. Based on the stress-strain relation, we calculate the spatial partial derivatives of the anelastic functions indirectly thereby saving computational storage and improving computational efficiency. The complex-frequency-shifted perfectly matched layer (CFS-PML) is used for the absorbing boundary condition based on the auxiliary difference equation (Zhang and Shen, 2010). The traction image method (Zhang and Chen, 2006) is employed for the free-surface boundary condition. We perform several numerical experiments including homogeneous full-space models and layered half-space models, considering both flat and 3D Gaussian-shape hill surfaces. The results match very well with those of the spectral-element method (Komatitisch and Tromp, 2002; Savage et al., 2010), verifying the simulations by our method in the anelastic model with surface topography.

Representing Functions in n Dimensions to Arbitrary Accuracy

NASA Technical Reports Server (NTRS)

Scotti, Stephen J.

2007-01-01

A method of approximating a scalar function of n independent variables (where n is a positive integer) to arbitrary accuracy has been developed. This method is expected to be attractive for use in engineering computations in which it is necessary to link global models with local ones or in which it is necessary to interpolate noiseless tabular data that have been computed from analytic functions or numerical models in n-dimensional spaces of design parameters.

Plausible combinations: An improved method to evaluate the covariate structure of Cormack-Jolly-Seber mark-recapture models

USGS Publications Warehouse

Bromaghin, Jeffrey F.; McDonald, Trent L.; Amstrup, Steven C.

2013-01-01

Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly- Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.

Cognitive and Neural Bases of Skilled Performance.

DTIC Science & Technology

1987-10-04

advantage is that this method is not computationally demanding, and model -specific analyses such as high -precision source localization with realistic...and a two- < " high -threshold model satisfy theoretical and pragmatic independence. Discrimination and bias measures from these two models comparing...recognition memory of patients with dementing diseases, amnesics, and normal controls. We found the two- high -threshold model to be more sensitive Lloyd

Pitfalls in statistical landslide susceptibility modelling

NASA Astrophysics Data System (ADS)

Schröder, Boris; Vorpahl, Peter; Märker, Michael; Elsenbeer, Helmut

2010-05-01

The use of statistical methods is a well-established approach to predict landslide occurrence probabilities and to assess landslide susceptibility. This is achieved by applying statistical methods relating historical landslide inventories to topographic indices as predictor variables. In our contribution, we compare several new and powerful methods developed in machine learning and well-established in landscape ecology and macroecology for predicting the distribution of shallow landslides in tropical mountain rainforests in southern Ecuador (among others: boosted regression trees, multivariate adaptive regression splines, maximum entropy). Although these methods are powerful, we think it is necessary to follow a basic set of guidelines to avoid some pitfalls regarding data sampling, predictor selection, and model quality assessment, especially if a comparison of different models is contemplated. We therefore suggest to apply a novel toolbox to evaluate approaches to the statistical modelling of landslide susceptibility. Additionally, we propose some methods to open the "black box" as an inherent part of machine learning methods in order to achieve further explanatory insights into preparatory factors that control landslides. Sampling of training data should be guided by hypotheses regarding processes that lead to slope failure taking into account their respective spatial scales. This approach leads to the selection of a set of candidate predictor variables considered on adequate spatial scales. This set should be checked for multicollinearity in order to facilitate model response curve interpretation. Model quality assesses how well a model is able to reproduce independent observations of its response variable. This includes criteria to evaluate different aspects of model performance, i.e. model discrimination, model calibration, and model refinement. In order to assess a possible violation of the assumption of independency in the training samples or a possible lack of explanatory information in the chosen set of predictor variables, the model residuals need to be checked for spatial auto¬correlation. Therefore, we calculate spline correlograms. In addition to this, we investigate partial dependency plots and bivariate interactions plots considering possible interactions between predictors to improve model interpretation. Aiming at presenting this toolbox for model quality assessment, we investigate the influence of strategies in the construction of training datasets for statistical models on model quality.

Application of Model-based Systems Engineering Methods to Development of Combat System Architectures

DTIC Science & Technology

2009-04-22

Spt: Chacon , Hoang, Matela, Sarabia Supportability *Carpenter, Banner-Bacin, Chacon , Kinberg M&S Across Acq Manz Independent Kang, Chandler Advisor M...W entland SW / O A *W entland, Sysavath; Spt: Carpenter, Sung, Mend iola M&S/CORE *Pham , Kong, Va ldez, Vasquez; Spt: Chacon , Hoang, Matela...Sarabia Supportability *Carpenter, Banner-Bacin, Chacon , Kinberg M&S Across Acq Manz Independent Kang, Chandler Advisor M Green Capstone Architect

Optimization of reaction parameters of radiation induced grafting of 1-vinylimidazole onto poly(ethylene-co-tetraflouroethene) using response surface method

NASA Astrophysics Data System (ADS)

Nasef, Mohamed Mahmoud; Aly, Amgad Ahmed; Saidi, Hamdani; Ahmad, Arshad

2011-11-01

Radiation induced grafting of 1-vinylimidazole (1-VIm) onto poly(ethylene-co-tetraflouroethene) (ETFE) was investigated. The grafting parameters such as absorbed dose, monomer concentration, grafting time and temperature were optimized using response surface method (RSM). The Box-Behnken module available in the design expert software was used to investigate the effect of reaction conditions (independent parameters) varied in four levels on the degree of grafting ( G%) (response parameter). The model yielded a polynomial equation that relates the linear, quadratic and interaction effects of the independent parameters to the response parameter. The analysis of variance (ANOVA) was used to evaluate the results of the model and detect the significant values for the independent parameters. The optimum parameters to achieve a maximum G% were found to be monomer concentration of 55 vol%, absorbed dose of 100 kGy, time in the range of 14-20 h and a temperature of 61 °C. Fourier transform infrared (FTIR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used to investigate the properties of the obtained films and provide evidence for grafting.

Dodging the cosmic curvature to probe the constancy of the speed of light

NASA Astrophysics Data System (ADS)

Cai, Rong-Gen; Guo, Zong-Kuan; Yang, Tao

2016-08-01

We develop a new model-independent method to probe the constancy of the speed of light c. In our method, the degeneracy between the cosmic curvature and the speed of light can be eliminated, which makes the test more natural and general. Combining the independent observations of Hubble parameter H(z) and luminosity distance dL(z), we use the model-independent smoothing technique, Gaussian processes, to reconstruct them and then detect variation of the speed of light. We find no signal of deviation from the present value of the speed of light c0. Moreover, to demonstrate the improvement in probing the constancy of the speed of light from future experiments, we produce a series of simulated data. The Dark Energy Survey will be able to detect Δc/c0 ~ 1% at ~ 1.5σ confidence level and Δc/c0 ~ 2% at ~ 3σ confidence level. If the errors are reduced to one-tenth of the expected DES ones, it can detect a Δc/c0 ~ 0.1% variation at ~ 2σ confidence level.

Applications of hybrid genetic algorithms in seismic tomography

NASA Astrophysics Data System (ADS)

Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet T.; Papazachos, Constantinos

2011-11-01

Almost all earth sciences inverse problems are nonlinear and involve a large number of unknown parameters, making the application of analytical inversion methods quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem equations, adopting an iterative procedure which typically employs partial derivatives in order to optimize the starting (initial) model by minimizing a misfit (penalty) function. Unfortunately, especially for highly non-linear cases, the final model strongly depends on the initial model, hence it is prone to solution-entrapment in local minima of the misfit function, while the derivative calculation is often computationally inefficient and creates instabilities when numerical approximations are used. An alternative is to employ global techniques which do not rely on partial derivatives, are independent of the misfit form and are computationally robust. Such methods employ pseudo-randomly generated models (sampling an appropriately selected section of the model space) which are assessed in terms of their data-fit. A typical example is the class of methods known as genetic algorithms (GA), which achieves the aforementioned approximation through model representation and manipulations, and has attracted the attention of the earth sciences community during the last decade, with several applications already presented for several geophysical problems. In this paper, we examine the efficiency of the combination of the typical regularized least-squares and genetic methods for a typical seismic tomography problem. The proposed approach combines a local (LOM) and a global (GOM) optimization method, in an attempt to overcome the limitations of each individual approach, such as local minima and slow convergence, respectively. The potential of both optimization methods is tested and compared, both independently and jointly, using the several test models and synthetic refraction travel-time date sets that employ the same experimental geometry, wavelength and geometrical characteristics of the model anomalies. Moreover, real data from a crosswell tomographic project for the subsurface mapping of an ancient wall foundation are used for testing the efficiency of the proposed algorithm. The results show that the combined use of both methods can exploit the benefits of each approach, leading to improved final models and producing realistic velocity models, without significantly increasing the required computation time.

System and method for anomaly detection

DOEpatents

Scherrer, Chad

2010-06-15

A system and method for detecting one or more anomalies in a plurality of observations is provided. In one illustrative embodiment, the observations are real-time network observations collected from a stream of network traffic. The method includes performing a discrete decomposition of the observations, and introducing derived variables to increase storage and query efficiencies. A mathematical model, such as a conditional independence model, is then generated from the formatted data. The formatted data is also used to construct frequency tables which maintain an accurate count of specific variable occurrence as indicated by the model generation process. The formatted data is then applied to the mathematical model to generate scored data. The scored data is then analyzed to detect anomalies.

Deriving the expected utility of a predictive model when the utilities are uncertain.

PubMed

Cooper, Gregory F; Visweswaran, Shyam

2005-01-01

Predictive models are often constructed from clinical databases with the goal of eventually helping make better clinical decisions. Evaluating models using decision theory is therefore natural. When constructing a model using statistical and machine learning methods, however, we are often uncertain about precisely how the model will be used. Thus, decision-independent measures of classification performance, such as the area under an ROC curve, are popular. As a complementary method of evaluation, we investigate techniques for deriving the expected utility of a model under uncertainty about the model's utilities. We demonstrate an example of the application of this approach to the evaluation of two models that diagnose coronary artery disease.

Teaching Experientially with the Madeline Hunter Method: An Application in a Marketing Research Course

ERIC Educational Resources Information Center

Burns, Alvin C.

2006-01-01

Due to concerns about the disparity of learning and the high nonresponse rates encountered by student marketing research teams working with sponsors, the author adopted the Hunter Method to restructure his course. This method requires the use of a model onto which students can map their learning via guided practice as well as independent practice.…

Alternative to the Palatini method: A new variational principle

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

Goenner, Hubert

2010-06-15

A variational principle is suggested within Riemannian geometry, in which an auxiliary metric and the Levi Civita connection are varied independently. The auxiliary metric plays the role of a Lagrange multiplier and introduces nonminimal coupling of matter to the curvature scalar. The field equations are 2nd order PDEs and easier to handle than those following from the so-called Palatini method. Moreover, in contrast to the latter method, no gradients of the matter variables appear. In cosmological modeling, the physics resulting from the alternative variational principle will differ from the modeling using the standard Palatini method.

Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification.

PubMed

Harrington, S; Reeder, T W

2017-02-01

The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have shown that the standard procedure of comparing the fit of the BiSSE model to constant-rate birth-death models often inappropriately favours the BiSSE model when diversification rates vary in a state-independent fashion. The newly developed HiSSE model enables researchers to identify state-dependent diversification rates while accounting for state-independent diversification at the same time. The HiSSE model also allows researchers to test state-dependent models against appropriate state-independent null models that have the same number of parameters as the state-dependent models being tested. We reanalyse two data sets that originally used BiSSE to reconstruct ancestral states within squamate reptiles and reached surprising conclusions regarding the evolution of toepads within Gekkota and viviparity across Squamata. We used this new method to demonstrate that there are many shifts in diversification rates across squamates. We then fit various HiSSE submodels and null models to the state and phylogenetic data and reconstructed states under these models. We found that there is no single, consistent signal for state-dependent diversification associated with toepads in gekkotans or viviparity across all squamates. Our reconstructions show limited support for the recently proposed hypotheses that toepads evolved multiple times independently in Gekkota and that transitions from viviparity to oviparity are common in Squamata. Our results highlight the importance of considering an adequate pool of models and null models when estimating diversification rate parameters and reconstructing ancestral states. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Regression Is a Univariate General Linear Model Subsuming Other Parametric Methods as Special Cases.

ERIC Educational Resources Information Center

Vidal, Sherry

Although the concept of the general linear model (GLM) has existed since the 1960s, other univariate analyses such as the t-test and the analysis of variance models have remained popular. The GLM produces an equation that minimizes the mean differences of independent variables as they are related to a dependent variable. From a computer printout…

Lattice modeling and application of independent component analysis to high power, long bunch beams in the Los Alamos Proton Storage Ring

NASA Astrophysics Data System (ADS)

Kolski, Jeffrey

The linear lattice properties of the Proton Storage Ring (PSR) at the Los Alamos Neutron Science Center (LANSCE) in Los Alamos, NM were measured and applied to determine a better linear accelerator model. We found that the initial model was deficient in predicting the vertical focusing strength. The additional vertical focusing was located through fundamental understanding of experiment and statistically rigorous analysis. An improved model was constructed and compared against the initial model and measurement at operation set points and set points far away from nominal and was shown to indeed be an enhanced model. Independent component analysis (ICA) is a tool for data mining in many fields of science. Traditionally, ICA is applied to turn-by-turn beam position data as a means to measure the lattice functions of the real machine. Due to the diagnostic setup for the PSR, this method is not applicable. A new application method for ICA is derived, ICA applied along the length of the bunch. The ICA modes represent motions within the beam pulse. Several of the dominate ICA modes are experimentally identified.

External evaluation of population pharmacokinetic models of vancomycin in neonates: the transferability of published models to different clinical settings

PubMed Central

Zhao, Wei; Kaguelidou, Florentia; Biran, Valérie; Zhang, Daolun; Allegaert, Karel; Capparelli, Edmund V; Holford, Nick; Kimura, Toshimi; Lo, Yoke-Lin; Peris, José-Esteban; Thomson, Alison; Anker, John N; Fakhoury, May; Jacqz-Aigrain, Evelyne

2013-01-01

Aims Vancomycin is one of the most evaluated antibiotics in neonates using modeling and simulation approaches. However no clear consensus on optimal dosing has been achieved. The objective of the present study was to perform an external evaluation of published models, in order to test their predictive performances in an independent dataset and to identify the possible study-related factors influencing the transferability of pharmacokinetic models to different clinical settings. Method Published neonatal vancomycin pharmacokinetic models were screened from the literature. The predictive performance of six models was evaluated using an independent dataset (112 concentrations from 78 neonates). The evaluation procedures used simulation-based diagnostics [visual predictive check (VPC) and normalized prediction distribution errors (NPDE)]. Results Differences in predictive performances of models for vancomycin pharmacokinetics in neonates were found. The mean of NPDE for six evaluated models were 1.35, −0.22, −0.36, 0.24, 0.66 and 0.48, respectively. These differences were explained, at least partly, by taking into account the method used to measure serum creatinine concentrations. The adult conversion factor of 1.3 (enzymatic to Jaffé) was tested with an improvement in the VPC and NPDE, but it still needs to be evaluated and validated in neonates. Differences were also identified between analytical methods for vancomycin. Conclusion The importance of analytical techniques for serum creatinine concentrations and vancomycin as predictors of vancomycin concentrations in neonates have been confirmed. Dosage individualization of vancomycin in neonates should consider not only patients' characteristics and clinical conditions, but also the methods used to measure serum creatinine and vancomycin. PMID:23148919

Predicting the role of assistive technologies in the lives of people with dementia using objective care recipient factors.

PubMed

Czarnuch, Stephen; Ricciardelli, Rose; Mihailidis, Alex

2016-07-20

The population of people with dementia is not homogeneous. People with dementia exhibit a wide range of needs, each characterized by diverse factors including age, sex, ethnicity, and place of residence. These needs and characterizing factors may influence the applicability, and ultimately the acceptance, of assistive technologies developed to support the independence of people with dementia. Accordingly, predicting the needs of users before developing the technologies may increase the applicability and acceptance of assistive technologies. Current methods of prediction rely on the difficult collection of subjective, potentially invasive information. We propose a method of prediction that uses objective, unobtrusive, easy to collect information to help inform the development of assistive technologies. We develop a set of models that can predict the level of independence of people with dementia during 20 activities of daily living using simple, objective information. Using data collected from a Canadian survey conducted with caregivers of people with dementia, we create an ordered logistic regression model for each of the twenty daily tasks in the Bristol ADL scale. Data collected from 430 Canadian caregivers of people with dementia were analyzed to reveal: most care recipients were mothers or husbands, married, living in private housing with their caregivers, English-speaking, Canadian born, clinically diagnosed with dementia 1 to 6 years prior to the study, and were dependent on their caregiver. Next, we developed models that use 13 factors to predict a person with dementia's ability to complete the 20 Bristol activities of daily living independently. The 13 factors include caregiver relation, age, marital status, place of residence, language, housing type, proximity to caregiver, service use, informal primary caregiver, diagnosis of Alzheimer's disease or dementia, time since diagnosis, and level of dependence on caregiver. The resulting models predicted the aggregate level of independence correctly for 88 of 100 total responses categories, marginally for nine, and incorrectly for three. Objective, easy to collect information can predict caregiver-reported level of task independence for a person with dementia. Knowledge of task independence can then inform the development of assistive technologies for people with dementia, improving their applicability and acceptance.

Quantifying the uncertainty introduced by discretization and time-averaging in two-fluid model predictions

DOE PAGES

Syamlal, Madhava; Celik, Ismail B.; Benyahia, Sofiane

2017-07-12

The two-fluid model (TFM) has become a tool for the design and troubleshooting of industrial fluidized bed reactors. To use TFM for scale up with confidence, the uncertainty in its predictions must be quantified. Here, we study two sources of uncertainty: discretization and time-averaging. First, we show that successive grid refinement may not yield grid-independent transient quantities, including cross-section–averaged quantities. Successive grid refinement would yield grid-independent time-averaged quantities on sufficiently fine grids. A Richardson extrapolation can then be used to estimate the discretization error, and the grid convergence index gives an estimate of the uncertainty. Richardson extrapolation may not workmore » for industrial-scale simulations that use coarse grids. We present an alternative method for coarse grids and assess its ability to estimate the discretization error. Second, we assess two methods (autocorrelation and binning) and find that the autocorrelation method is more reliable for estimating the uncertainty introduced by time-averaging TFM data.« less

Assessing compatibility of direct detection data: halo-independent global likelihood analyses

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

Gelmini, Graciela B.; Huh, Ji-Haeng; Witte, Samuel J.

2016-10-18

We present two different halo-independent methods to assess the compatibility of several direct dark matter detection data sets for a given dark matter model using a global likelihood consisting of at least one extended likelihood and an arbitrary number of Gaussian or Poisson likelihoods. In the first method we find the global best fit halo function (we prove that it is a unique piecewise constant function with a number of down steps smaller than or equal to a maximum number that we compute) and construct a two-sided pointwise confidence band at any desired confidence level, which can then be comparedmore » with those derived from the extended likelihood alone to assess the joint compatibility of the data. In the second method we define a “constrained parameter goodness-of-fit” test statistic, whose p-value we then use to define a “plausibility region” (e.g. where p≥10%). For any halo function not entirely contained within the plausibility region, the level of compatibility of the data is very low (e.g. p<10%). We illustrate these methods by applying them to CDMS-II-Si and SuperCDMS data, assuming dark matter particles with elastic spin-independent isospin-conserving interactions or exothermic spin-independent isospin-violating interactions.« less

Robust independent modal space control of a coupled nano-positioning piezo-stage

NASA Astrophysics Data System (ADS)

Zhu, Wei; Yang, Fufeng; Rui, Xiaoting

2018-06-01

In order to accurately control a coupled 3-DOF nano-positioning piezo-stage, this paper designs a hybrid controller. In this controller, a hysteresis observer based on a Bouc-Wen model is established to compensate the hysteresis nonlinearity of the piezoelectric actuator first. Compared to hysteresis compensations using Preisach model and Prandt-Ishlinskii model, the compensation method using the hysteresis observer is computationally lighter. Then, based on the proposed dynamics model, by constructing the modal filter, a robust H∞ independent modal space controller is designed and utilized to decouple the piezo-stage and deal with the unmodeled dynamics, disturbance, and hysteresis compensation error. The effectiveness of the proposed controller is demonstrated experimentally. The experimental results show that the proposed controller can significantly achieve the high-precision positioning.

EAS fluctuation approach to primary mass composition investigation

NASA Technical Reports Server (NTRS)

Stamenov, J. N.; Janminchev, V. D.

1985-01-01

The analysis of muon and electron fluctuation distribution shapes by statistical method of invers problem solution gives the possibility to obtain the relative contribution values of the five main primary nuclei groups. The method is model-independent for a big class of interaction models and can give good results for observation levels not too far from the development maximum and for the selection of showers with fixed sizes and zenith angles not bigger than 30 deg.

On the Accretion Rates of SW Sextantis Nova-like Variables

NASA Astrophysics Data System (ADS)

Ballouz, Ronald-Louis; Sion, Edward M.

2009-06-01

We present accretion rates for selected samples of nova-like variables having IUE archival spectra and distances uniformly determined using an infrared method by Knigge. A comparison with accretion rates derived independently with a multiparametric optimization modeling approach by Puebla et al. is carried out. The accretion rates of SW Sextantis nova-like systems are compared with the accretion rates of non-SW Sextantis systems in the Puebla et al. sample and in our sample, which was selected in the orbital period range of three to four and a half hours, with all systems having distances using the method of Knigge. Based upon the two independent modeling approaches, we find no significant difference between the accretion rates of SW Sextantis systems and non-SW Sextantis nova-like systems insofar as optically thick disk models are appropriate. We find little evidence to suggest that the SW Sex stars have higher accretion rates than other nova-like cataclysmic variables (CVs) above the period gap within the same range of orbital periods.

Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer

PubMed Central

Weis, Jared A.; Flint, Katelyn M.; Sanchez, Violeta; Yankeelov, Thomas E.; Miga, Michael I.

2015-01-01

Abstract. Cancer progression has been linked to mechanics. Therefore, there has been recent interest in developing noninvasive imaging tools for cancer assessment that are sensitive to changes in tissue mechanical properties. We have developed one such method, modality independent elastography (MIE), that estimates the relative elastic properties of tissue by fitting anatomical image volumes acquired before and after the application of compression to biomechanical models. The aim of this study was to assess the accuracy and reproducibility of the method using phantoms and a murine breast cancer model. Magnetic resonance imaging data were acquired, and the MIE method was used to estimate relative volumetric stiffness. Accuracy was assessed using phantom data by comparing to gold-standard mechanical testing of elasticity ratios. Validation error was <12%. Reproducibility analysis was performed on animal data, and within-subject coefficients of variation ranged from 2 to 13% at the bulk level and 32% at the voxel level. To our knowledge, this is the first study to assess the reproducibility of an elasticity imaging metric in a preclinical cancer model. Our results suggest that the MIE method can reproducibly generate accurate estimates of the relative mechanical stiffness and provide guidance on the degree of change needed in order to declare biological changes rather than experimental error in future therapeutic studies. PMID:26158120

Model-independent plot of dynamic PET data facilitates data interpretation and model selection.

PubMed

Munk, Ole Lajord

2012-02-21

When testing new PET radiotracers or new applications of existing tracers, the blood-tissue exchange and the metabolism need to be examined. However, conventional plots of measured time-activity curves from dynamic PET do not reveal the inherent kinetic information. A novel model-independent volume-influx plot (vi-plot) was developed and validated. The new vi-plot shows the time course of the instantaneous distribution volume and the instantaneous influx rate. The vi-plot visualises physiological information that facilitates model selection and it reveals when a quasi-steady state is reached, which is a prerequisite for the use of the graphical analyses by Logan and Gjedde-Patlak. Both axes of the vi-plot have direct physiological interpretation, and the plot shows kinetic parameter in close agreement with estimates obtained by non-linear kinetic modelling. The vi-plot is equally useful for analyses of PET data based on a plasma input function or a reference region input function. The vi-plot is a model-independent and informative plot for data exploration that facilitates the selection of an appropriate method for data analysis. Copyright © 2011 Elsevier Ltd. All rights reserved.

The desorptivity model of bulk soil-water evaporation

NASA Technical Reports Server (NTRS)

Clapp, R. B.

1983-01-01

Available models of bulk evaporation from a bare-surfaced soil are difficult to apply to field conditions where evaporation is complicated by two main factors: rate-limiting climatic conditions and redistribution of soil moisture following infiltration. Both factors are included in the "desorptivity model', wherein the evaporation rate during the second stage (the soil-limiting stage) of evaporation is related to the desorptivity parameter, A. Analytical approximations for A are presented. The approximations are independent of the surface soil moisture. However, calculations using the approximations indicate that both soil texture and soil moisture content at depth significantly affect A. Because the moisture content at depth decreases in time during redistribution, it follows that the A parameter also changes with time. Consequently, a method to calculate a representative value of A was developed. When applied to field data, the desorptivity model estimated cumulative evaporation well. The model is easy to calculate, but its usefulness is limited because it requires an independent estimate of the time of transition between the first and second stages of evaporation. The model shows that bulk evaporation after the transition to the second stage is largely independent of climatic conditions.

A DTI-based model for TMS using the independent impedance method with frequency-dependent tissue parameters

NASA Astrophysics Data System (ADS)

De Geeter, N.; Crevecoeur, G.; Dupré, L.; Van Hecke, W.; Leemans, A.

2012-04-01

Accurate simulations on detailed realistic head models are necessary to gain a better understanding of the response to transcranial magnetic stimulation (TMS). Hitherto, head models with simplified geometries and constant isotropic material properties are often used, whereas some biological tissues have anisotropic characteristics which vary naturally with frequency. Moreover, most computational methods do not take the tissue permittivity into account. Therefore, we calculate the electromagnetic behaviour due to TMS in a head model with realistic geometry and where realistic dispersive anisotropic tissue properties are incorporated, based on T1-weighted and diffusion-weighted magnetic resonance images. This paper studies the impact of tissue anisotropy, permittivity and frequency dependence, using the anisotropic independent impedance method. The results show that anisotropy yields differences up to 32% and 19% of the maximum induced currents and electric field, respectively. Neglecting the permittivity values leads to a decrease of about 72% and 24% of the maximum currents and field, respectively. Implementing the dispersive effects of biological tissues results in a difference of 6% of the maximum currents. The cerebral voxels show limited sensitivity of the induced electric field to changes in conductivity and permittivity, whereas the field varies approximately linearly with frequency. These findings illustrate the importance of including each of the above parameters in the model and confirm the need for accuracy in the applied patient-specific method, which can be used in computer-assisted TMS.

SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

PubMed

Suresh, V; Parthasarathy, S

2014-01-01

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

A Conceptual Model and Assessment Template for Capacity Evaluation in Adult Guardianship

ERIC Educational Resources Information Center

Moye, Jennifer; Butz, Steven W.; Marson, Daniel C.; Wood, Erica

2007-01-01

Purpose: We develop a conceptual model and associated assessment template that is usable across state jurisdictions for evaluating the independent-living capacity of older adults in guardianship proceedings. Design and Methods: We used an iterative process in which legal provisions for guardianship and prevailing clinical practices for capacity…

Comment: Spurious Correlation and Other Observations on Experimental Design for Engineering Dimensional Analysis

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

Piepel, Gregory F.

2013-08-01

This article discusses the paper "Experimental Design for Engineering Dimensional Analysis" by Albrecht et al. (2013, Technometrics). That paper provides and overview of engineering dimensional analysis (DA) for use in developing DA models. The paper proposes methods for generating model-robust experimental designs to supporting fitting DA models. The specific approach is to develop a design that maximizes the efficiency of a specified empirical model (EM) in the original independent variables, subject to a minimum efficiency for a DA model expressed in terms of dimensionless groups (DGs). This discussion article raises several issues and makes recommendations regarding the proposed approach. Also,more » the concept of spurious correlation is raised and discussed. Spurious correlation results from the response DG being calculated using several independent variables that are also used to calculate predictor DGs in the DA model.« less

Huygens Titan Probe Trajectory Reconstruction Using Traditional Methods and the Program to Optimize Simulated Trajectories II

NASA Technical Reports Server (NTRS)

Striepe, Scott A.; Blanchard, Robert C.; Kirsch, Michael F.; Fowler, Wallace T.

2007-01-01

On January 14, 2005, ESA's Huygens probe separated from NASA's Cassini spacecraft, entered the Titan atmosphere and landed on its surface. As part of NASA Engineering Safety Center Independent Technical Assessment of the Huygens entry, descent, and landing, and an agreement with ESA, NASA provided results of all EDL analyses and associated findings to the Huygens project team prior to probe entry. In return, NASA was provided the flight data from the probe so that trajectory reconstruction could be done and simulation models assessed. Trajectory reconstruction of the Huygens entry probe at Titan was accomplished using two independent approaches: a traditional method and a POST2-based method. Results from both approaches are discussed in this paper.

Improved application of independent component analysis to functional magnetic resonance imaging study via linear projection techniques.

PubMed

Long, Zhiying; Chen, Kewei; Wu, Xia; Reiman, Eric; Peng, Danling; Yao, Li

2009-02-01

Spatial Independent component analysis (sICA) has been widely used to analyze functional magnetic resonance imaging (fMRI) data. The well accepted implicit assumption is the spatially statistical independency of intrinsic sources identified by sICA, making the sICA applications difficult for data in which there exist interdependent sources and confounding factors. This interdependency can arise, for instance, from fMRI studies investigating two tasks in a single session. In this study, we introduced a linear projection approach and considered its utilization as a tool to separate task-related components from two-task fMRI data. The robustness and feasibility of the method are substantiated through simulation on computer data and fMRI real rest data. Both simulated and real two-task fMRI experiments demonstrated that sICA in combination with the projection method succeeded in separating spatially dependent components and had better detection power than pure model-based method when estimating activation induced by each task as well as both tasks.

A simple linear model for estimating ozone AOT40 at forest sites from raw passive sampling data.

PubMed

Ferretti, Marco; Cristofolini, Fabiana; Cristofori, Antonella; Gerosa, Giacomo; Gottardini, Elena

2012-08-01

A rapid, empirical method is described for estimating weekly AOT40 from ozone concentrations measured with passive samplers at forest sites. The method is based on linear regression and was developed after three years of measurements in Trentino (northern Italy). It was tested against an independent set of data from passive sampler sites across Italy. It provides good weekly estimates compared with those measured by conventional monitors (0.85 ≤R(2)≤ 0.970; 97 ≤ RMSE ≤ 302). Estimates obtained using passive sampling at forest sites are comparable to those obtained by another estimation method based on modelling hourly concentrations (R(2) = 0.94; 131 ≤ RMSE ≤ 351). Regression coefficients of passive sampling are similar to those obtained with conventional monitors at forest sites. Testing against an independent dataset generated by passive sampling provided similar results (0.86 ≤R(2)≤ 0.99; 65 ≤ RMSE ≤ 478). Errors tend to accumulate when weekly AOT40 estimates are summed to obtain the total AOT40 over the May-July period, and the median deviation between the two estimation methods based on passive sampling is 11%. The method proposed does not require any assumptions, complex calculation or modelling technique, and can be useful when other estimation methods are not feasible, either in principle or in practice. However, the method is not useful when estimates of hourly concentrations are of interest.

Review of Methods for Buildings Energy Performance Modelling

NASA Astrophysics Data System (ADS)

Krstić, Hrvoje; Teni, Mihaela

2017-10-01

Research presented in this paper gives a brief review of methods used for buildings energy performance modelling. This paper gives also a comprehensive review of the advantages and disadvantages of available methods as well as the input parameters used for modelling buildings energy performance. European Directive EPBD obliges the implementation of energy certification procedure which gives an insight on buildings energy performance via exiting energy certificate databases. Some of the methods for buildings energy performance modelling mentioned in this paper are developed by employing data sets of buildings which have already undergone an energy certification procedure. Such database is used in this paper where the majority of buildings in the database have already gone under some form of partial retrofitting - replacement of windows or installation of thermal insulation but still have poor energy performance. The case study presented in this paper utilizes energy certificates database obtained from residential units in Croatia (over 400 buildings) in order to determine the dependence between buildings energy performance and variables from database by using statistical dependencies tests. Building energy performance in database is presented with building energy efficiency rate (from A+ to G) which is based on specific annual energy needs for heating for referential climatic data [kWh/(m2a)]. Independent variables in database are surfaces and volume of the conditioned part of the building, building shape factor, energy used for heating, CO2 emission, building age and year of reconstruction. Research results presented in this paper give an insight in possibilities of methods used for buildings energy performance modelling. Further on it gives an analysis of dependencies between buildings energy performance as a dependent variable and independent variables from the database. Presented results could be used for development of new building energy performance predictive model.

Simulating Dissolution of Intravitreal Triamcinolone Acetonide Suspensions in an Anatomically Accurate Rabbit Eye Model

PubMed Central

Horner, Marc; Muralikrishnan, R.

2010-01-01

ABSTRACT Purpose A computational fluid dynamics (CFD) study examined the impact of particle size on dissolution rate and residence of intravitreal suspension depots of Triamcinolone Acetonide (TAC). Methods A model for the rabbit eye was constructed using insights from high-resolution NMR imaging studies (Sawada 2002). The current model was compared to other published simulations in its ability to predict clearance of various intravitreally injected materials. Suspension depots were constructed explicitly rendering individual particles in various configurations: 4 or 16 mg drug confined to a 100 μL spherical depot, or 4 mg exploded to fill the entire vitreous. Particle size was reduced systematically in each configuration. The convective diffusion/dissolution process was simulated using a multiphase model. Results Release rate became independent of particle diameter below a certain value. The size-independent limits occurred for particle diameters ranging from 77 to 428 μM depending upon the depot configuration. Residence time predicted for the spherical depots in the size-independent limit was comparable to that observed in vivo. Conclusions Since the size-independent limit was several-fold greater than the particle size of commercially available pharmaceutical TAC suspensions, differences in particle size amongst such products are predicted to be immaterial to their duration or performance. PMID:20467888

Self-efficacy is independently associated with brain volume in older women

PubMed Central

Davis, Jennifer C.; Nagamatsu, Lindsay S.; Hsu, Chun Liang; Beattie, B. Lynn; Liu-Ambrose, Teresa

2015-01-01

Background Aging is highly associated with neurodegeneration and atrophy of the brain. Evidence suggests that personality variables are risk factors for reduced brain volume. We examine whether falls-related self-efficacy is independently associated with brain volume. Method A cross-sectional analysis of whether falls-related self-efficacy is independently associated with brain volumes (total, grey, and white matter). Three multivariate regression models were constructed. Covariates included in the models were age, global cognition, systolic blood pressure, functional comorbidity index, and current physical activity level. MRI scans were acquired from 79 community-dwelling senior women aged 65 to 75 years old. Falls-related self-efficacy was assessed by the Activities Specific Balance Confidence (ABC) Scale. Results After accounting for covariates, falls-related self-efficacy was independently associated with both total brain volume and total grey matter volume. The final model for total brain volume accounted for 17% of the variance, with the ABC score accounting for 8%. For total grey matter volume, the final model accounted for 24% of the variance, with the ABC score accounting for 10%. Conclusion We provide novel evidence that falls-related self-efficacy, a modifiable risk factor for healthy aging, is positively associated with total brain volume and total grey matter volume. Trial Registration ClinicalTrials.gov Identifier: NCT00426881. PMID:22436405

An Information-Correction Method for Testlet-Based Test Analysis: From the Perspectives of Item Response Theory and Generalizability Theory. Research Report. ETS RR-17-27

ERIC Educational Resources Information Center

Li, Feifei

2017-01-01

An information-correction method for testlet-based tests is introduced. This method takes advantage of both generalizability theory (GT) and item response theory (IRT). The measurement error for the examinee proficiency parameter is often underestimated when a unidimensional conditional-independence IRT model is specified for a testlet dataset. By…

Micro-seismic imaging using a source function independent full waveform inversion method

NASA Astrophysics Data System (ADS)

Wang, Hanchen; Alkhalifah, Tariq

2018-03-01

At the heart of micro-seismic event measurements is the task to estimate the location of the source micro-seismic events, as well as their ignition times. The accuracy of locating the sources is highly dependent on the velocity model. On the other hand, the conventional micro-seismic source locating methods require, in many cases manual picking of traveltime arrivals, which do not only lead to manual effort and human interaction, but also prone to errors. Using full waveform inversion (FWI) to locate and image micro-seismic events allows for an automatic process (free of picking) that utilizes the full wavefield. However, full waveform inversion of micro-seismic events faces incredible nonlinearity due to the unknown source locations (space) and functions (time). We developed a source function independent full waveform inversion of micro-seismic events to invert for the source image, source function and the velocity model. It is based on convolving reference traces with these observed and modeled to mitigate the effect of an unknown source ignition time. The adjoint-state method is used to derive the gradient for the source image, source function and velocity updates. The extended image for the source wavelet in Z axis is extracted to check the accuracy of the inverted source image and velocity model. Also, angle gathers is calculated to assess the quality of the long wavelength component of the velocity model. By inverting for the source image, source wavelet and the velocity model simultaneously, the proposed method produces good estimates of the source location, ignition time and the background velocity for synthetic examples used here, like those corresponding to the Marmousi model and the SEG/EAGE overthrust model.

Markov Logic Networks in the Analysis of Genetic Data

PubMed Central

Sakhanenko, Nikita A.

2010-01-01

Abstract Complex, non-additive genetic interactions are common and can be critical in determining phenotypes. Genome-wide association studies (GWAS) and similar statistical studies of linkage data, however, assume additive models of gene interactions in looking for genotype-phenotype associations. These statistical methods view the compound effects of multiple genes on a phenotype as a sum of influences of each gene and often miss a substantial part of the heritable effect. Such methods do not use any biological knowledge about underlying mechanisms. Modeling approaches from the artificial intelligence (AI) field that incorporate deterministic knowledge into models to perform statistical analysis can be applied to include prior knowledge in genetic analysis. We chose to use the most general such approach, Markov Logic Networks (MLNs), for combining deterministic knowledge with statistical analysis. Using simple, logistic regression-type MLNs we can replicate the results of traditional statistical methods, but we also show that we are able to go beyond finding independent markers linked to a phenotype by using joint inference without an independence assumption. The method is applied to genetic data on yeast sporulation, a complex phenotype with gene interactions. In addition to detecting all of the previously identified loci associated with sporulation, our method identifies four loci with smaller effects. Since their effect on sporulation is small, these four loci were not detected with methods that do not account for dependence between markers due to gene interactions. We show how gene interactions can be detected using more complex models, which can be used as a general framework for incorporating systems biology with genetics. PMID:20958249

Towards a constructionist approach to emotions: verification of the three-dimensional model of affect with EEG-independent component analysis.

PubMed

Wyczesany, Miroslaw; Ligeza, Tomasz S

2015-03-01

The locationist model of affect, which assumes separate brain structures devoted to particular discrete emotions, is currently being questioned as it has not received enough convincing experimental support. An alternative, constructionist approach suggests that our emotional states emerge from the interaction between brain functional networks, which are related to more general, continuous affective categories. In the study, we tested whether the three-dimensional model of affect based on valence, arousal, and dominance (VAD) can reflect brain activity in a more coherent way than the traditional locationist approach. Independent components of brain activity were derived from spontaneous EEG recordings and localized using the DIPFIT method. The correspondence between the spectral power of the revealed brain sources and a mood self-report quantified on the VAD space was analysed. Activation of four (out of nine) clusters of independent brain sources could be successfully explained by the specific combination of three VAD dimensions. The results support the constructionist theory of emotions.

The roles of climate, phylogenetic relatedness, introduction effort, and reproductive traits in the establishment of non-native reptiles and amphibians.

PubMed

van Wilgen, Nicola J; Richardson, David M

2012-04-01

We developed a method to predict the potential of non-native reptiles and amphibians (herpetofauna) to establish populations. This method may inform efforts to prevent the introduction of invasive non-native species. We used boosted regression trees to determine whether nine variables influence establishment success of introduced herpetofauna in California and Florida. We used an independent data set to assess model performance. Propagule pressure was the variable most strongly associated with establishment success. Species with short juvenile periods and species with phylogenetically more distant relatives in regional biotas were more likely to establish than species that start breeding later and those that have close relatives. Average climate match (the similarity of climate between native and non-native range) and life form were also important. Frogs and lizards were the taxonomic groups most likely to establish, whereas a much lower proportion of snakes and turtles established. We used results from our best model to compile a spreadsheet-based model for easy use and interpretation. Probability scores obtained from the spreadsheet model were strongly correlated with establishment success as were probabilities predicted for independent data by the boosted regression tree model. However, the error rate for predictions made with independent data was much higher than with cross validation using training data. This difference in predictive power does not preclude use of the model to assess the probability of establishment of herpetofauna because (1) the independent data had no information for two variables (meaning the full predictive capacity of the model could not be realized) and (2) the model structure is consistent with the recent literature on the primary determinants of establishment success for herpetofauna. It may still be difficult to predict the establishment probability of poorly studied taxa, but it is clear that non-native species (especially lizards and frogs) that mature early and come from environments similar to that of the introduction region have the highest probability of establishment. ©2012 Society for Conservation Biology.

Accounting for imperfect detection of groups and individuals when estimating abundance.

PubMed

Clement, Matthew J; Converse, Sarah J; Royle, J Andrew

2017-09-01

If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double-observer models, distance sampling models and combined double-observer and distance sampling models (known as mark-recapture-distance-sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under-counted, but not over-counted. The estimator combines an MRDS model with an N-mixture model to account for imperfect detection of individuals. The new MRDS-Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS-Nmix model to an MRDS model. Abundance estimates generated by the MRDS-Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re-allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.

Accounting for imperfect detection of groups and individuals when estimating abundance

USGS Publications Warehouse

Clement, Matthew J.; Converse, Sarah J.; Royle, J. Andrew

2017-01-01

If animals are independently detected during surveys, many methods exist for estimating animal abundance despite detection probabilities <1. Common estimators include double-observer models, distance sampling models and combined double-observer and distance sampling models (known as mark-recapture-distance-sampling models; MRDS). When animals reside in groups, however, the assumption of independent detection is violated. In this case, the standard approach is to account for imperfect detection of groups, while assuming that individuals within groups are detected perfectly. However, this assumption is often unsupported. We introduce an abundance estimator for grouped animals when detection of groups is imperfect and group size may be under-counted, but not over-counted. The estimator combines an MRDS model with an N-mixture model to account for imperfect detection of individuals. The new MRDS-Nmix model requires the same data as an MRDS model (independent detection histories, an estimate of distance to transect, and an estimate of group size), plus a second estimate of group size provided by the second observer. We extend the model to situations in which detection of individuals within groups declines with distance. We simulated 12 data sets and used Bayesian methods to compare the performance of the new MRDS-Nmix model to an MRDS model. Abundance estimates generated by the MRDS-Nmix model exhibited minimal bias and nominal coverage levels. In contrast, MRDS abundance estimates were biased low and exhibited poor coverage. Many species of conservation interest reside in groups and could benefit from an estimator that better accounts for imperfect detection. Furthermore, the ability to relax the assumption of perfect detection of individuals within detected groups may allow surveyors to re-allocate resources toward detection of new groups instead of extensive surveys of known groups. We believe the proposed estimator is feasible because the only additional field data required are a second estimate of group size.

NASA Langley developments in response calculations needed for failure and life prediction

NASA Technical Reports Server (NTRS)

Housner, Jerrold M.

1993-01-01

NASA Langley developments in response calculations needed for failure and life predictions are discussed. Topics covered include: structural failure analysis in concurrent engineering; accuracy of independent regional modeling demonstrated on classical example; functional interface method accurately joins incompatible finite element models; interface method for insertion of local detail modeling extended to curve pressurized fuselage window panel; interface concept for joining structural regions; motivation for coupled 2D-3D analysis; compression panel with discontinuous stiffener coupled 2D-3D model and axial surface strains at the middle of the hat stiffener; use of adaptive refinement with multiple methods; adaptive mesh refinement; and studies on quantity effect of bow-type initial imperfections on reliability of stiffened panels.

Literature Reviews on Modeling Internal Geometry of Textile Composites and Rate-Independent Continuum Damage

NASA Technical Reports Server (NTRS)

Su-Yuen, Hsu

2011-01-01

Textile composite materials have good potential for constructing composite structures where the effects of three-dimensional stresses are critical or geometric complexity is a manufacturing concern. There is a recent interest in advancing competence within Langley Research Center for modeling the degradation of mechanical properties of textile composites. In an initial effort, two critical areas are identified to pursue: (1) Construction of internal geometry of textile composites, and (2) Rate-independent continuum damage mechanics. This report documents reviews on the two subjects. Various reviewed approaches are categorized, their assumptions, methods, and progress are briefed, and then critiques are presented. Each review ends with recommended research.

Cosmological model-independent test of ΛCDM with two-point diagnostic by the observational Hubble parameter data

NASA Astrophysics Data System (ADS)

Cao, Shu-Lei; Duan, Xiao-Wei; Meng, Xiao-Lei; Zhang, Tong-Jie

2018-04-01

Aiming at exploring the nature of dark energy (DE), we use forty-three observational Hubble parameter data (OHD) in the redshift range 0 < z ≤slant 2.36 to make a cosmological model-independent test of the ΛCDM model with two-point Omh^2(z2;z1) diagnostic. In ΛCDM model, with equation of state (EoS) w=-1, two-point diagnostic relation Omh^2 ≡ Ωmh^2 is tenable, where Ωm is the present matter density parameter, and h is the Hubble parameter divided by 100 {km s^{-1 Mpc^{-1}}}. We utilize two methods: the weighted mean and median statistics to bin the OHD to increase the signal-to-noise ratio of the measurements. The binning methods turn out to be promising and considered to be robust. By applying the two-point diagnostic to the binned data, we find that although the best-fit values of Omh^2 fluctuate as the continuous redshift intervals change, on average, they are continuous with being constant within 1 σ confidence interval. Therefore, we conclude that the ΛCDM model cannot be ruled out.

Model-independent and model-based local lensing properties of CL0024+1654 from multiply imaged galaxies

NASA Astrophysics Data System (ADS)

Wagner, Jenny; Liesenborgs, Jori; Tessore, Nicolas

2018-04-01

Context. Local gravitational lensing properties, such as convergence and shear, determined at the positions of multiply imaged background objects, yield valuable information on the smaller-scale lensing matter distribution in the central part of galaxy clusters. Highly distorted multiple images with resolved brightness features like the ones observed in CL0024 allow us to study these local lensing properties and to tighten the constraints on the properties of dark matter on sub-cluster scale. Aim. We investigate to what precision local magnification ratios, J, ratios of convergences, f, and reduced shears, g = (g1, g2), can be determined independently of a lens model for the five resolved multiple images of the source at zs = 1.675 in CL0024. We also determine if a comparison to the respective results obtained by the parametric modelling tool Lenstool and by the non-parametric modelling tool Grale can detect biases in the models. For these lens models, we analyse the influence of the number and location of the constraints from multiple images on the lens properties at the positions of the five multiple images of the source at zs = 1.675. Methods: Our model-independent approach uses a linear mapping between the five resolved multiple images to determine the magnification ratios, ratios of convergences, and reduced shears at their positions. With constraints from up to six multiple image systems, we generate Lenstool and Grale models using the same image positions, cosmological parameters, and number of generated convergence and shear maps to determine the local values of J, f, and g at the same positions across all methods. Results: All approaches show strong agreement on the local values of J, f, and g. We find that Lenstool obtains the tightest confidence bounds even for convergences around one using constraints from six multiple-image systems, while the best Grale model is generated only using constraints from all multiple images with resolved brightness features and adding limited small-scale mass corrections. Yet, confidence bounds as large as the values themselves can occur for convergences close to one in all approaches. Conclusions: Our results agree with previous findings, support the light-traces-mass assumption, and the merger hypothesis for CL0024. Comparing the different approaches can detect model biases. The model-independent approach determines the local lens properties to a comparable precision in less than one second.

Maximum ikelihood estimation for the double-count method with independent observers

USGS Publications Warehouse

Manly, Bryan F.J.; McDonald, Lyman L.; Garner, Gerald W.

1996-01-01

Data collected under a double-count protocol during line transect surveys were analyzed using new maximum likelihood methods combined with Akaike's information criterion to provide estimates of the abundance of polar bear (Ursus maritimus Phipps) in a pilot study off the coast of Alaska. Visibility biases were corrected by modeling the detection probabilities using logistic regression functions. Independent variables that influenced the detection probabilities included perpendicular distance of bear groups from the flight line and the number of individuals in the groups. A series of models were considered which vary from (1) the simplest, where the probability of detection was the same for both observers and was not affected by either distance from the flight line or group size, to (2) models where probability of detection is different for the two observers and depends on both distance from the transect and group size. Estimation procedures are developed for the case when additional variables may affect detection probabilities. The methods are illustrated using data from the pilot polar bear survey and some recommendations are given for design of a survey over the larger Chukchi Sea between Russia and the United States.

Spatial scan statistics for detection of multiple clusters with arbitrary shapes.

PubMed

Lin, Pei-Sheng; Kung, Yi-Hung; Clayton, Murray

2016-12-01

In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect the concepts of the likelihood ratio (LR) scan statistic and the quasi-likelihood (QL) scan statistic to provide a series of detection procedures sufficiently flexible to apply to clusters of arbitrary shape. First, we use an independent scan model for detection of clusters and then a variogram tool to examine the existence of spatial correlation and regional variation based on residuals of the independent scan model. When the estimate of regional variation is significantly different from zero, a mixed QL estimating equation is developed to estimate coefficients of geographic clusters and covariates. We use the Benjamini-Hochberg procedure (1995) to find a threshold for p-values to address the multiple testing problem. A quasi-deviance criterion is used to regroup the estimated clusters to find geographic clusters with arbitrary shapes. We conduct simulations to compare the performance of the proposed method with other scan statistics. For illustration, the method is applied to enterovirus data from Taiwan. © 2016, The International Biometric Society.

Identification of statistically independent climatic pattern in GRACE and hydrological model data over West-Africa

NASA Astrophysics Data System (ADS)

Kusche, J.; Forootan, E.; Eicker, A.; Hoffmann-Dobrev, H.

2012-04-01

West-African countries have been exposed to changes in rainfall patterns over the last decades, including a significant negative trend. This causes adverse effects on water resources, for instance reduced freshwater availability, and changes in the frequency, duration and magnitude of droughts and floods. Extracting the main patterns of water storage change in West Africa from remote sensing and linking them to climate variability, is therefore an essential step to understand the hydrological aspects of the region. In this study, the higher order statistical method of Independent Component Analysis (ICA) is employed to extract statistically independent water storage patterns from monthly Gravity Recovery And Climate Experiment (GRACE), from the WaterGAP Global Hydrology Model (WGHM) and from Tropical Rainfall Measuring Mission (TRMM) products over West Africa, for the period 2002-2012. Then, to reveal the influences of climatic teleconnections on the individual patterns, these results were correlated to the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) indices. To study the predictability of water storage changes, advanced statistical methods were applied on the main independent Sea Surface Temperature (SST) patterns over the Atlantic and Indian Oceans for the period 2002-2012 and the ICA results. Our results show a water storage decrease over the coastal regions of West Africa (including Sierra Leone, Liberia, Togo and Nigeria), associated with rainfall decrease. The comparison between GRACE estimations and WGHM results indicates some inconsistencies that underline the importance of forcing data for hydrological modeling of West Africa. Keywords: West Africa; GRACE-derived water storage; ICA; ENSO; IOD

Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

PubMed Central

Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

2016-01-01

The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines. PMID:27367703

How device-independent approaches change the meaning of physical theory

NASA Astrophysics Data System (ADS)

Grinbaum, Alexei

2017-05-01

Dirac sought an interpretation of mathematical formalism in terms of physical entities and Einstein insisted that physics should describe ;the real states of the real systems;. While Bell inequalities put into question the reality of states, modern device-independent approaches do away with the idea of entities: physical theory may contain no physical systems. Focusing on the correlations between operationally defined inputs and outputs, device-independent methods promote a view more distant from the conventional one than Einstein's 'principle theories' were from 'constructive theories'. On the examples of indefinite causal orders and almost quantum correlations, we ask a puzzling question: if physical theory is not about systems, then what is it about? Device-independent models suggest that physical theory can be 'about' languages.

Data mining of tree-based models to analyze freeway accident frequency.

PubMed

Chang, Li-Yen; Chen, Wen-Chieh

2005-01-01

Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.

Simple algorithms for remote determination of mineral abundances and particle sizes from reflectance spectra

NASA Technical Reports Server (NTRS)

Johnson, Paul E.; Smith, Milton O.; Adams, John B.

1992-01-01

Algorithms were developed, based on Hapke's (1981) equations, for remote determinations of mineral abundances and particle sizes from reflectance spectra. In this method, spectra are modeled as a function of end-member abundances and illumination/viewing geometry. The method was tested on a laboratory data set. It is emphasized that, although there exist more sophisticated models, the present algorithms are particularly suited for remotely sensed data, where little opportunity exists to independently measure reflectance versus article size and phase function.

Reconstructing latent dynamical noise for better forecasting observables

NASA Astrophysics Data System (ADS)

Hirata, Yoshito

2018-03-01

I propose a method for reconstructing multi-dimensional dynamical noise inspired by the embedding theorem of Muldoon et al. [Dyn. Stab. Syst. 13, 175 (1998)] by regarding multiple predictions as different observables. Then, applying the embedding theorem by Stark et al. [J. Nonlinear Sci. 13, 519 (2003)] for a forced system, I produce time series forecast by supplying the reconstructed past dynamical noise as auxiliary information. I demonstrate the proposed method on toy models driven by auto-regressive models or independent Gaussian noise.

Contact angle adjustment in equation-of-state-based pseudopotential model.

PubMed

Hu, Anjie; Li, Longjian; Uddin, Rizwan; Liu, Dong

2016-05-01

The single component pseudopotential lattice Boltzmann model has been widely applied in multiphase simulation due to its simplicity and stability. In many studies, it has been claimed that this model can be stable for density ratios larger than 1000. However, the application of the model is still limited to small density ratios when the contact angle is considered. The reason is that the original contact angle adjustment method influences the stability of the model. Moreover, simulation results in the present work show that, by applying the original contact angle adjustment method, the density distribution near the wall is artificially changed, and the contact angle is dependent on the surface tension. Hence, it is very inconvenient to apply this method with a fixed contact angle, and the accuracy of the model cannot be guaranteed. To solve these problems, a contact angle adjustment method based on the geometry analysis is proposed and numerically compared with the original method. Simulation results show that, with our contact angle adjustment method, the stability of the model is highly improved when the density ratio is relatively large, and it is independent of the surface tension.

Contact angle adjustment in equation-of-state-based pseudopotential model

NASA Astrophysics Data System (ADS)

Hu, Anjie; Li, Longjian; Uddin, Rizwan; Liu, Dong

2016-05-01

The single component pseudopotential lattice Boltzmann model has been widely applied in multiphase simulation due to its simplicity and stability. In many studies, it has been claimed that this model can be stable for density ratios larger than 1000. However, the application of the model is still limited to small density ratios when the contact angle is considered. The reason is that the original contact angle adjustment method influences the stability of the model. Moreover, simulation results in the present work show that, by applying the original contact angle adjustment method, the density distribution near the wall is artificially changed, and the contact angle is dependent on the surface tension. Hence, it is very inconvenient to apply this method with a fixed contact angle, and the accuracy of the model cannot be guaranteed. To solve these problems, a contact angle adjustment method based on the geometry analysis is proposed and numerically compared with the original method. Simulation results show that, with our contact angle adjustment method, the stability of the model is highly improved when the density ratio is relatively large, and it is independent of the surface tension.

MODEL-INDEPENDENT LIMITS ON THE LINE-OF-SIGHT DEPTH OF CLUSTERS OF GALAXIES USING X-RAY AND SUNYAEV-ZEL'DOVICH DATA

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

Mahdavi, Andisheh; Chang Weihan

2011-07-01

We derive a model-independent expression for the minimum line-of-sight extent of the hot plasma in a cluster of galaxies. The only inputs are the 1-5 keV X-ray surface brightness and the Comptonization from Sunyaev-Zel'dovich (SZ) data. No a priori assumptions regarding equilibrium or geometry are required. The method applies when the X-ray emitting material has temperatures anywhere between 0.3 keV and 20 keV and metallicities between 0 and twice solar-conditions fulfilled by nearly all intracluster plasma. Using this method, joint APEX-SZ and Chandra X-ray Observatory data on the Bullet Cluster yield a lower limit of 400 {+-} 56 kpc onmore » the half-pressure depth of the main component, limiting it to being at least spherical, if not cigar-shaped primarily along the line of sight.« less

Sorbent, Sublimation, and Icing Modeling Methods: Experimental Validation and Application to an Integrated MTSA Subassembly Thermal Model

NASA Technical Reports Server (NTRS)

Bower, Chad; Padilla, Sebastian; Iacomini, Christie; Paul, Heather L.

2010-01-01

This paper details the validation of modeling methods for the three core components of a Metabolic heat regenerated Temperature Swing Adsorption (MTSA) subassembly, developed for use in a Portable Life Support System (PLSS). The first core component in the subassembly is a sorbent bed, used to capture and reject metabolically produced carbon dioxide (CO2). The sorbent bed performance can be augmented with a temperature swing driven by a liquid CO2 (LCO2) sublimation heat exchanger (SHX) for cooling the sorbent bed, and a condensing, icing heat exchanger (CIHX) for warming the sorbent bed. As part of the overall MTSA effort, scaled design validation test articles for each of these three components have been independently tested in laboratory conditions. Previously described modeling methodologies developed for implementation in Thermal Desktop and SINDA/FLUINT are reviewed and updated, their application in test article models outlined, and the results of those model correlations relayed. Assessment of the applicability of each modeling methodology to the challenge of simulating the response of the test articles and their extensibility to a full scale integrated subassembly model is given. The independent verified and validated modeling methods are applied to the development of a MTSA subassembly prototype model and predictions of the subassembly performance are given. These models and modeling methodologies capture simulation of several challenging and novel physical phenomena in the Thermal Desktop and SINDA/FLUINT software suite. Novel methodologies include CO2 adsorption front tracking and associated thermal response in the sorbent bed, heat transfer associated with sublimation of entrained solid CO2 in the SHX, and water mass transfer in the form of ice as low as 210 K in the CIHX.

Validity and power of association testing in family-based sampling designs: evidence for and against the common wisdom.

PubMed

Knight, Stacey; Camp, Nicola J

2011-04-01

Current common wisdom posits that association analyses using family-based designs have inflated type 1 error rates (if relationships are ignored) and independent controls are more powerful than familial controls. We explore these suppositions. We show theoretically that family-based designs can have deflated type-error rates. Through simulation, we examine the validity and power of family designs for several scenarios: cases from randomly or selectively ascertained pedigrees; and familial or independent controls. Family structures considered are as follows: sibships, nuclear families, moderate-sized and extended pedigrees. Three methods were considered with the χ(2) test for trend: variance correction (VC), weighted (weights assigned to account for genetic similarity), and naïve (ignoring relatedness) as well as the Modified Quasi-likelihood Score (MQLS) test. Selectively ascertained pedigrees had similar levels of disease enrichment; random ascertainment had no such restriction. Data for 1,000 cases and 1,000 controls were created under the null and alternate models. The VC and MQLS methods were always valid. The naïve method was anti-conservative if independent controls were used and valid or conservative in designs with familial controls. The weighted association method was generally valid for independent controls, and was conservative for familial controls. With regard to power, independent controls were more powerful for small-to-moderate selectively ascertained pedigrees, but familial and independent controls were equivalent in the extended pedigrees and familial controls were consistently more powerful for all randomly ascertained pedigrees. These results suggest a more complex situation than previously assumed, which has important implications for study design and analysis. © 2011 Wiley-Liss, Inc.

A New Method for Conceptual Modelling of Information Systems

NASA Astrophysics Data System (ADS)

Gustas, Remigijus; Gustiene, Prima

Service architecture is not necessarily bound to the technical aspects of information system development. It can be defined by using conceptual models that are independent of any implementation technology. Unfortunately, the conventional information system analysis and design methods cover just a part of required modelling notations for engineering of service architectures. They do not provide effective support to maintain semantic integrity between business processes and data. Service orientation is a paradigm that can be applied for conceptual modelling of information systems. The concept of service is rather well understood in different domains. It can be applied equally well for conceptualization of organizational and technical information system components. This chapter concentrates on analysis of the differences between service-oriented modelling and object-oriented modelling. Service-oriented method is used for semantic integration of information system static and dynamic aspects.

Path Analysis and Residual Plotting as Methods of Environmental Scanning in Higher Education: An Illustration with Applications and Enrollments.

ERIC Educational Resources Information Center

Morcol, Goktug; McLaughlin, Gerald W.

1990-01-01

The study proposes using path analysis and residual plotting as methods supporting environmental scanning in strategic planning for higher education institutions. Path models of three levels of independent variables are developed. Dependent variables measuring applications and enrollments at Virginia Polytechnic Institute and State University are…

P-code enhanced method for processing encrypted GPS signals without knowledge of the encryption code

NASA Technical Reports Server (NTRS)

Young, Lawrence E. (Inventor); Meehan, Thomas K. (Inventor); Thomas, Jr., Jess Brooks (Inventor)

2000-01-01

In the preferred embodiment, an encrypted GPS signal is down-converted from RF to baseband to generate two quadrature components for each RF signal (L1 and L2). Separately and independently for each RF signal and each quadrature component, the four down-converted signals are counter-rotated with a respective model phase, correlated with a respective model P code, and then successively summed and dumped over presum intervals substantially coincident with chips of the respective encryption code. Without knowledge of the encryption-code signs, the effect of encryption-code sign flips is then substantially reduced by selected combinations of the resulting presums between associated quadrature components for each RF signal, separately and independently for the L1 and L2 signals. The resulting combined presums are then summed and dumped over longer intervals and further processed to extract amplitude, phase and delay for each RF signal. Precision of the resulting phase and delay values is approximately four times better than that obtained from straight cross-correlation of L1 and L2. This improved method provides the following options: separate and independent tracking of the L1-Y and L2-Y channels; separate and independent measurement of amplitude, phase and delay L1-Y channel; and removal of the half-cycle ambiguity in L1-Y and L2-Y carrier phase.

A Feature Fusion Based Forecasting Model for Financial Time Series

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

2014-01-01

Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

Modeling rainfall-runoff process using soft computing techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Shiri, Jalal; Tombul, Mustafa

2013-02-01

Rainfall-runoff process was modeled for a small catchment in Turkey, using 4 years (1987-1991) of measurements of independent variables of rainfall and runoff values. The models used in the study were Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gene Expression Programming (GEP) which are Artificial Intelligence (AI) approaches. The applied models were trained and tested using various combinations of the independent variables. The goodness of fit for the model was evaluated in terms of the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and scatter index (SI). A comparison was also made between these models and traditional Multi Linear Regression (MLR) model. The study provides evidence that GEP (with RMSE=17.82 l/s, MAE=6.61 l/s, CE=0.72 and R2=0.978) is capable of modeling rainfall-runoff process and is a viable alternative to other applied artificial intelligence and MLR time-series methods.

An Independent Asteroseismic Analysis of the Fundamental Parameters and Internal Structure of the Solar-like Oscillator KIC 6225718

NASA Astrophysics Data System (ADS)

Wu, Tao; Li, Yan

2017-09-01

Asteroseismology is a useful tool that is usually used to probe stellar interiors and to determine stellar fundamental parameters, such as stellar mass, radius, and surface gravity. In order to probe stellar interiors, making comparisons between observations and models is usually used with the {χ }2-minimization method. The work of Wu & Li reported that the best parameter determined by the {χ }2-matching process is the acoustic radius for pure p-mode oscillations. In the present work, based on the theoretical calculations of Wu & Li, we will independently analyze the seismic observations of KIC 6225718 to determine its fundamental parameters and to investigate its interior properties. First, in order to test the method, we use it in the Sun to determine its fundamental parameters and to investigate interiors. Second, we independently determine the fundamental parameters of KIC 6225718 without any other non-seismic constraint. Therefore, those determined fundamental parameters are independent of those determined by other methods. They can be regarded as independent references in other analyses. Finally, we analyze the stellar internal structure and find that KIC 6225718 has a convective core with the size of 0.078-0.092 {R}⊙ . Its overshooting parameter {f}{ov} in the core is around 0.010. In addition, its center hydrogen {X}{{c}} is about 0.264-0.355.

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

PubMed

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Fast method for reactor and feature scale coupling in ALD and CVD

DOEpatents

Yanguas-Gil, Angel; Elam, Jeffrey W.

2017-08-08

Transport and surface chemistry of certain deposition techniques is modeled. Methods provide a model of the transport inside nanostructures as a single-particle discrete Markov chain process. This approach decouples the complexity of the surface chemistry from the transport model, thus allowing its application under general surface chemistry conditions, including atomic layer deposition (ALD) and chemical vapor deposition (CVD). Methods provide for determination of determine statistical information of the trajectory of individual molecules, such as the average interaction time or the number of wall collisions for molecules entering the nanostructures as well as to track the relative contributions to thin-film growth of different independent reaction pathways at each point of the feature.

Independent Children's Social Work Practice Pilots: Evaluating Practitioners' Job Control and Burnout

ERIC Educational Resources Information Center

Hussein, Shereen; Manthorpe, Jill; Ridley, Julie; Austerberry, Helen; Farrelly, Nicola; Larkins, Cath; Bilson, Andy; Stanley, Nicky

2014-01-01

Objectives: To investigate whether a new model that delegates some out-of-home care services from the public to the private and not-for-profit sectors in England enhances practitioners' job control and stress levels. Methods: A 3-year longitudinal matched-control evaluation examined changes in Karasek demand-control model and Maslach burnout…

Computing the stability of steady-state solutions of mathematical models of the electrical activity in the heart.

PubMed

Tveito, Aslak; Skavhaug, Ola; Lines, Glenn T; Artebrant, Robert

2011-08-01

Instabilities in the electro-chemical resting state of the heart can generate ectopic waves that in turn can initiate arrhythmias. We derive methods for computing the resting state for mathematical models of the electro-chemical process underpinning a heartbeat, and we estimate the stability of the resting state by invoking the largest real part of the eigenvalues of a linearized model. The implementation of the methods is described and a number of numerical experiments illustrate the feasibility of the methods. In particular, we test the methods for problems where we can compare the solutions with analytical results, and problems where we have solutions computed by independent software. The software is also tested for a fairly realistic 3D model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Blind separation of positive sources by globally convergent gradient search.

PubMed

Oja, Erkki; Plumbley, Mark

2004-09-01

The instantaneous noise-free linear mixing model in independent component analysis is largely a solved problem under the usual assumption of independent nongaussian sources and full column rank mixing matrix. However, with some prior information on the sources, like positivity, new analysis and perhaps simplified solution methods may yet become possible. In this letter, we consider the task of independent component analysis when the independent sources are known to be nonnegative and well grounded, which means that they have a nonzero pdf in the region of zero. It can be shown that in this case, the solution method is basically very simple: an orthogonal rotation of the whitened observation vector into nonnegative outputs will give a positive permutation of the original sources. We propose a cost function whose minimum coincides with nonnegativity and derive the gradient algorithm under the whitening constraint, under which the separating matrix is orthogonal. We further prove that in the Stiefel manifold of orthogonal matrices, the cost function is a Lyapunov function for the matrix gradient flow, implying global convergence. Thus, this algorithm is guaranteed to find the nonnegative well-grounded independent sources. The analysis is complemented by a numerical simulation, which illustrates the algorithm.

Derivation and computation of discrete-delay and continuous-delay SDEs in mathematical biology.

PubMed

Allen, Edward J

2014-06-01

Stochastic versions of several discrete-delay and continuous-delay differential equations, useful in mathematical biology, are derived from basic principles carefully taking into account the demographic, environmental, or physiological randomness in the dynamic processes. In particular, stochastic delay differential equation (SDDE) models are derived and studied for Nicholson's blowflies equation, Hutchinson's equation, an SIS epidemic model with delay, bacteria/phage dynamics, and glucose/insulin levels. Computational methods for approximating the SDDE models are described. Comparisons between computational solutions of the SDDEs and independently formulated Monte Carlo calculations support the accuracy of the derivations and of the computational methods.

Evaluating the statistical performance of less applied algorithms in classification of worldview-3 imagery data in an urbanized landscape

NASA Astrophysics Data System (ADS)

Ranaie, Mehrdad; Soffianian, Alireza; Pourmanafi, Saeid; Mirghaffari, Noorollah; Tarkesh, Mostafa

2018-03-01

In recent decade, analyzing the remotely sensed imagery is considered as one of the most common and widely used procedures in the environmental studies. In this case, supervised image classification techniques play a central role. Hence, taking a high resolution Worldview-3 over a mixed urbanized landscape in Iran, three less applied image classification methods including Bagged CART, Stochastic gradient boosting model and Neural network with feature extraction were tested and compared with two prevalent methods: random forest and support vector machine with linear kernel. To do so, each method was run ten time and three validation techniques was used to estimate the accuracy statistics consist of cross validation, independent validation and validation with total of train data. Moreover, using ANOVA and Tukey test, statistical difference significance between the classification methods was significantly surveyed. In general, the results showed that random forest with marginal difference compared to Bagged CART and stochastic gradient boosting model is the best performing method whilst based on independent validation there was no significant difference between the performances of classification methods. It should be finally noted that neural network with feature extraction and linear support vector machine had better processing speed than other.

Dodging the cosmic curvature to probe the constancy of the speed of light

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

Cai, Rong-Gen; Guo, Zong-Kuan; Yang, Tao, E-mail: cairg@itp.ac.cn, E-mail: guozk@itp.ac.cn, E-mail: yangtao@itp.ac.cn

We develop a new model-independent method to probe the constancy of the speed of light c . In our method, the degeneracy between the cosmic curvature and the speed of light can be eliminated, which makes the test more natural and general. Combining the independent observations of Hubble parameter H ( z ) and luminosity distance d {sub L} ( z ), we use the model-independent smoothing technique, Gaussian processes, to reconstruct them and then detect variation of the speed of light. We find no signal of deviation from the present value of the speed of light c {sub 0}.more » Moreover, to demonstrate the improvement in probing the constancy of the speed of light from future experiments, we produce a series of simulated data. The Dark Energy Survey will be able to detect Δ c / c {sub 0} ∼ 1% at ∼ 1.5σ confidence level and Δ c / c {sub 0} ∼ 2% at ∼ 3σ confidence level. If the errors are reduced to one-tenth of the expected DES ones, it can detect a Δ c / c {sub 0} ∼ 0.1% variation at ∼ 2σ confidence level.« less

[Effect of stock abundance and environmental factors on the recruitment success of small yellow croaker in the East China Sea].

PubMed

Liu, Zun-lei; Yuan, Xing-wei; Yang, Lin-lin; Yan, Li-ping; Zhang, Hui; Cheng, Jia-hua

2015-02-01

Multiple hypotheses are available to explain recruitment rate. Model selection methods can be used to identify the best model that supports a particular hypothesis. However, using a single model for estimating recruitment success is often inadequate for overexploited population because of high model uncertainty. In this study, stock-recruitment data of small yellow croaker in the East China Sea collected from fishery dependent and independent surveys between 1992 and 2012 were used to examine density-dependent effects on recruitment success. Model selection methods based on frequentist (AIC, maximum adjusted R2 and P-values) and Bayesian (Bayesian model averaging, BMA) methods were applied to identify the relationship between recruitment and environment conditions. Interannual variability of the East China Sea environment was indicated by sea surface temperature ( SST) , meridional wind stress (MWS), zonal wind stress (ZWS), sea surface pressure (SPP) and runoff of Changjiang River ( RCR). Mean absolute error, mean squared predictive error and continuous ranked probability score were calculated to evaluate the predictive performance of recruitment success. The results showed that models structures were not consistent based on three kinds of model selection methods, predictive variables of models were spawning abundance and MWS by AIC, spawning abundance by P-values, spawning abundance, MWS and RCR by maximum adjusted R2. The recruitment success decreased linearly with stock abundance (P < 0.01), suggesting overcompensation effect in the recruitment success might be due to cannibalism or food competition. Meridional wind intensity showed marginally significant and positive effects on the recruitment success (P = 0.06), while runoff of Changjiang River showed a marginally negative effect (P = 0.07). Based on mean absolute error and continuous ranked probability score, predictive error associated with models obtained from BMA was the smallest amongst different approaches, while that from models selected based on the P-value of the independent variables was the highest. However, mean squared predictive error from models selected based on the maximum adjusted R2 was highest. We found that BMA method could improve the prediction of recruitment success, derive more accurate prediction interval and quantitatively evaluate model uncertainty.

The effect of binary mixtures of zinc, copper, cadmium, and nickel on the growth of the freshwater diatom Navicula pelliculosa and comparison with mixture toxicity model predictions.

PubMed

Nagai, Takashi; De Schamphelaere, Karel A C

2016-11-01

The authors investigated the effect of binary mixtures of zinc (Zn), copper (Cu), cadmium (Cd), and nickel (Ni) on the growth of a freshwater diatom, Navicula pelliculosa. A 7 × 7 full factorial experimental design (49 combinations in total) was used to test each binary metal mixture. A 3-d fluorescence microplate toxicity assay was used to test each combination. Mixture effects were predicted by concentration addition and independent action models based on a single-metal concentration-response relationship between the relative growth rate and the calculated free metal ion activity. Although the concentration addition model predicted the observed mixture toxicity significantly better than the independent action model for the Zn-Cu mixture, the independent action model predicted the observed mixture toxicity significantly better than the concentration addition model for the Cd-Zn, Cd-Ni, and Cd-Cu mixtures. For the Zn-Ni and Cu-Ni mixtures, it was unclear which of the 2 models was better. Statistical analysis concerning antagonistic/synergistic interactions showed that the concentration addition model is generally conservative (with the Zn-Ni mixture being the sole exception), indicating that the concentration addition model would be useful as a method for a conservative first-tier screening-level risk analysis of metal mixtures. Environ Toxicol Chem 2016;35:2765-2773. © 2016 SETAC. © 2016 SETAC.

The effects of modeling instruction on high school physics academic achievement

NASA Astrophysics Data System (ADS)

Wright, Tiffanie L.

The purpose of this study was to explore whether Modeling Instruction, compared to traditional lecturing, is an effective instructional method to promote academic achievement in selected high school physics classes at a rural middle Tennessee high school. This study used an ex post facto , quasi-experimental research methodology. The independent variables in this study were the instructional methods of teaching. The treatment variable was Modeling Instruction and the control variable was traditional lecture instruction. The Treatment Group consisted of participants in Physical World Concepts who received Modeling Instruction. The Control Group consisted of participants in Physical Science who received traditional lecture instruction. The dependent variable was gains scores on the Force Concepts Inventory (FCI). The participants for this study were 133 students each in both the Treatment and Control Groups (n = 266), who attended a public, high school in rural middle Tennessee. The participants were administered the Force Concepts Inventory (FCI) prior to being taught the mechanics of physics. The FCI data were entered into the computer-based Statistical Package for the Social Science (SPSS). Two independent samples t-tests were conducted to answer the research questions. There was a statistically significant difference between the treatment and control groups concerning the instructional method. Modeling Instructional methods were found to be effective in increasing the academic achievement of students in high school physics. There was no statistically significant difference between FCI gains scores for gender. Gender was found to have no effect on the academic achievement of students in high school physics classes. However, even though there was not a statistically significant difference, female students' gains scores were higher than male students' gains scores when Modeling Instructional methods of teaching were used. Based on these findings, it is recommended that high school science teachers should use Modeling Instructional methods of teaching daily in their classrooms. A recommendation for further research is to expand the Modeling Instructional methods of teaching into different content areas, (i.e., reading and language arts) to explore academic achievement gains.

Multi-modal data fusion using source separation: Two effective models based on ICA and IVA and their properties

PubMed Central

Adali, Tülay; Levin-Schwartz, Yuri; Calhoun, Vince D.

2015-01-01

Fusion of information from multiple sets of data in order to extract a set of features that are most useful and relevant for the given task is inherent to many problems we deal with today. Since, usually, very little is known about the actual interaction among the datasets, it is highly desirable to minimize the underlying assumptions. This has been the main reason for the growing importance of data-driven methods, and in particular of independent component analysis (ICA) as it provides useful decompositions with a simple generative model and using only the assumption of statistical independence. A recent extension of ICA, independent vector analysis (IVA) generalizes ICA to multiple datasets by exploiting the statistical dependence across the datasets, and hence, as we discuss in this paper, provides an attractive solution to fusion of data from multiple datasets along with ICA. In this paper, we focus on two multivariate solutions for multi-modal data fusion that let multiple modalities fully interact for the estimation of underlying features that jointly report on all modalities. One solution is the Joint ICA model that has found wide application in medical imaging, and the second one is the the Transposed IVA model introduced here as a generalization of an approach based on multi-set canonical correlation analysis. In the discussion, we emphasize the role of diversity in the decompositions achieved by these two models, present their properties and implementation details to enable the user make informed decisions on the selection of a model along with its associated parameters. Discussions are supported by simulation results to help highlight the main issues in the implementation of these methods. PMID:26525830

Nuclear constraints on the age of the universe

NASA Technical Reports Server (NTRS)

Schramm, D. N.

1982-01-01

A review is made of how one can use nuclear physics to put rather stringent limits on the age of the universe and thus the cosmic distance scale. The age can be estimated to a fair degree of accuracy. No single measurement of the time since the Big Bang gives a specific, unambiguous age. There are several methods that together fix the age with surprising precision. In particular, there are three totally independent techniques for estimating an age and a fourth technique which involves finding consistency of the other three in the framework of the standard Big Bang cosmological model. The three independent methods are: cosmological dynamics, the age of the oldest stars, and radioactive dating. This paper concentrates on the third of the three methods, and the consistency technique.

Mono-isotope Prediction for Mass Spectra Using Bayes Network.

PubMed

Li, Hui; Liu, Chunmei; Rwebangira, Mugizi Robert; Burge, Legand

2014-12-01

Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized naïve Bayes network as the classifier with the assumption that the selected features are independent to predict mono-isotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to publicMo dataset demonstrates that our naïve Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.

Matching vehicle responses using the model-following control method

DOT National Transportation Integrated Search

1997-02-24

The Variable Dynamic Testbed Vehicle (VDTV) is presently being developed by the National Highway Traffic Safety Administration (NHTSA). It is being designed to have a steer-by-wire" front steering system and an independent rear steering system. Th...

Methods for Evaluating Mammography Imaging Techniques

DTIC Science & Technology

2000-06-01

independent of disease prevalence . When test outcomes are dichotomous, sensitivity and specificity measure test accuracy. Sensitivity is the...phers were not provided with the disease prevalence in the The model we use accounts for within mammographer test set. Mammographers provided one

Integral formulae of the canonical correlation functions for the one dimensional transverse Ising model

NASA Astrophysics Data System (ADS)

Inoue, Makoto

2017-12-01

Some new formulae of the canonical correlation functions for the one dimensional quantum transverse Ising model are found by the ST-transformation method using a Morita's sum rule and its extensions for the two dimensional classical Ising model. As a consequence we obtain a time-independent term of the dynamical correlation functions. Differences of quantum version and classical version of these formulae are also discussed.

An atomic model of brome mosaic virus using direct electron detection and real-space optimization.

PubMed

Wang, Zhao; Hryc, Corey F; Bammes, Benjamin; Afonine, Pavel V; Jakana, Joanita; Chen, Dong-Hua; Liu, Xiangan; Baker, Matthew L; Kao, Cheng; Ludtke, Steven J; Schmid, Michael F; Adams, Paul D; Chiu, Wah

2014-09-04

Advances in electron cryo-microscopy have enabled structure determination of macromolecules at near-atomic resolution. However, structure determination, even using de novo methods, remains susceptible to model bias and overfitting. Here we describe a complete workflow for data acquisition, image processing, all-atom modelling and validation of brome mosaic virus, an RNA virus. Data were collected with a direct electron detector in integrating mode and an exposure beyond the traditional radiation damage limit. The final density map has a resolution of 3.8 Å as assessed by two independent data sets and maps. We used the map to derive an all-atom model with a newly implemented real-space optimization protocol. The validity of the model was verified by its match with the density map and a previous model from X-ray crystallography, as well as the internal consistency of models from independent maps. This study demonstrates a practical approach to obtain a rigorously validated atomic resolution electron cryo-microscopy structure.

Does transport time help explain the high trauma mortality rates in rural areas? New and traditional predictors assessed by new and traditional statistical methods

PubMed Central

Røislien, Jo; Lossius, Hans Morten; Kristiansen, Thomas

2015-01-01

Background Trauma is a leading global cause of death. Trauma mortality rates are higher in rural areas, constituting a challenge for quality and equality in trauma care. The aim of the study was to explore population density and transport time to hospital care as possible predictors of geographical differences in mortality rates, and to what extent choice of statistical method might affect the analytical results and accompanying clinical conclusions. Methods Using data from the Norwegian Cause of Death registry, deaths from external causes 1998–2007 were analysed. Norway consists of 434 municipalities, and municipality population density and travel time to hospital care were entered as predictors of municipality mortality rates in univariate and multiple regression models of increasing model complexity. We fitted linear regression models with continuous and categorised predictors, as well as piecewise linear and generalised additive models (GAMs). Models were compared using Akaike's information criterion (AIC). Results Population density was an independent predictor of trauma mortality rates, while the contribution of transport time to hospital care was highly dependent on choice of statistical model. A multiple GAM or piecewise linear model was superior, and similar, in terms of AIC. However, while transport time was statistically significant in multiple models with piecewise linear or categorised predictors, it was not in GAM or standard linear regression. Conclusions Population density is an independent predictor of trauma mortality rates. The added explanatory value of transport time to hospital care is marginal and model-dependent, highlighting the importance of exploring several statistical models when studying complex associations in observational data. PMID:25972600

Male body dissatisfaction scale (MBDS): proposal for a reduced model.

PubMed

da Silva, Wanderson Roberto; Marôco, João; Ochner, Christopher N; Campos, Juliana Alvares Duarte Bonini

2017-09-01

To evaluate the psychometric properties of the male body dissatisfaction scale (MBDS) in Brazilian and Portuguese university students; to present a reduced model of the scale; to compare two methods of computing global scores for participants' body dissatisfaction; and to estimate the prevalence of participants' body dissatisfaction. A total of 932 male students participated in this study. A confirmatory factor analysis (CFA) was used to assess the scale's psychometric properties. Multi-group analysis was used to test transnational invariance and invariance in independent samples. The body dissatisfaction score was calculated using two methods (mean and matrix of weights in the CFA), which were compared. Finally, individuals were classified according to level of body dissatisfaction, using the best method. The MBDS model did not show adequate fit for the sample and was, therefore, refined. Thirteen items were excluded and two factors were combined. A reduced model of 12 items and 2 factors was proposed and shown to have adequate psychometric properties. There was a significant difference (p < 0.001) between the methods for calculating the score for body dissatisfaction, since the mean overestimated the scores. Among student participants, the prevalence of body dissatisfaction with musculature and general appearance was 11.2 and 5.3%, respectively. The reduced bi-factorial model of the MBDS showed adequate validity, reliability, and transnational invariance and invariance in independent samples for Brazilian and Portuguese students. The new proposal for calculating the global score was able to more accurately show their body dissatisfaction. No level of evidence Basic Science.

Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

PubMed

Zeng, Ping; Zhou, Xiang; Huang, Shuiping

2017-05-11

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2  ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2  ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS

PubMed Central

Ozaki, Vitor A.; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo

2009-01-01

This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited. PMID:19890450

A simplified model for dynamics of cell rolling and cell-surface adhesion

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

Cimrák, Ivan, E-mail: ivan.cimrak@fri.uniza.sk

2015-03-10

We propose a three dimensional model for the adhesion and rolling of biological cells on surfaces. We study cells moving in shear flow above a wall to which they can adhere via specific receptor-ligand bonds based on receptors from selectin as well as integrin family. The computational fluid dynamics are governed by the lattice-Boltzmann method. The movement and the deformation of the cells is described by the immersed boundary method. Both methods are fully coupled by implementing a two-way fluid-structure interaction. The adhesion mechanism is modelled by adhesive bonds including stochastic rules for their creation and rupture. We explore amore » simplified model with dissociation rate independent of the length of the bonds. We demonstrate that this model is able to resemble the mesoscopic properties, such as velocity of rolling cells.« less

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

PubMed Central

Ju, Jin Hyun; Crystal, Ronald G.

2017-01-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL. PMID:28505156

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

PubMed

Ju, Jin Hyun; Shenoy, Sushila A; Crystal, Ronald G; Mezey, Jason G

2017-05-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data

PubMed Central

Guinney, Justin; Wang, Tao; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J Christopher; Neto, Elias Chaibub; Khan, Suleiman A; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M; Shen, Liji; Abdallah, Kald; Norman, Thea; Friend, Stephen; Stolovitzky, Gustavo; Soule, Howard; Sweeney, Christopher J; Ryan, Charles J; Scher, Howard I; Sartor, Oliver; Xie, Yang; Aittokallio, Tero; Zhou, Fang Liz; Costello, James C

2016-01-01

Summary Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest—namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial—ENTHUSE M1—in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39–4·62, p<0·0001; reference model: 2·56, 1·85–3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. Funding Sanofi US Services, Project Data Sphere. PMID:27864015

Separated-pair independent particle model and the generalized Brillouin theorem: ab initio calculations on the dissociation of polyatomic molecules

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

Sundberg, Kenneth Randall

1976-01-01

A method is developed to optimize the separated-pair independent particle (SPIP) wave function; it is a special case of the separated-pair theory obtained by using two-term natural expansions of the geminals. The orbitals are optimized by a theory based on the generalized Brillouin theorem and iterative configuration interaction (CI) calculations in the space of the SPIP function and its single excitations. The geminal expansion coefficients are optimized by serial 2 x 2 CI calculations. Formulas are derived for the matrix elements. An algorithm to implement the method is presented, and the work needed to evaluate the molecular integrals is discussed.

Computational helioseismology in the frequency domain: acoustic waves in axisymmetric solar models with flows

NASA Astrophysics Data System (ADS)

Gizon, Laurent; Barucq, Hélène; Duruflé, Marc; Hanson, Chris S.; Leguèbe, Michael; Birch, Aaron C.; Chabassier, Juliette; Fournier, Damien; Hohage, Thorsten; Papini, Emanuele

2017-04-01

Context. Local helioseismology has so far relied on semi-analytical methods to compute the spatial sensitivity of wave travel times to perturbations in the solar interior. These methods are cumbersome and lack flexibility. Aims: Here we propose a convenient framework for numerically solving the forward problem of time-distance helioseismology in the frequency domain. The fundamental quantity to be computed is the cross-covariance of the seismic wavefield. Methods: We choose sources of wave excitation that enable us to relate the cross-covariance of the oscillations to the Green's function in a straightforward manner. We illustrate the method by considering the 3D acoustic wave equation in an axisymmetric reference solar model, ignoring the effects of gravity on the waves. The symmetry of the background model around the rotation axis implies that the Green's function can be written as a sum of longitudinal Fourier modes, leading to a set of independent 2D problems. We use a high-order finite-element method to solve the 2D wave equation in frequency space. The computation is embarrassingly parallel, with each frequency and each azimuthal order solved independently on a computer cluster. Results: We compute travel-time sensitivity kernels in spherical geometry for flows, sound speed, and density perturbations under the first Born approximation. Convergence tests show that travel times can be computed with a numerical precision better than one millisecond, as required by the most precise travel-time measurements. Conclusions: The method presented here is computationally efficient and will be used to interpret travel-time measurements in order to infer, e.g., the large-scale meridional flow in the solar convection zone. It allows the implementation of (full-waveform) iterative inversions, whereby the axisymmetric background model is updated at each iteration.

SU-F-T-494: A Multi-Institutional Study of Independent Dose Verification Using Golden Beam Data

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

Itano, M; Yamazaki, T; Tachibana, R

Purpose: In general, beam data of individual linac is measured for independent dose verification software program and the verification is performed as a secondary check. In this study, independent dose verification using golden beam data was compared to that using individual linac’s beam data. Methods: Six institutions were participated and three different beam data were prepared. The one was individual measured data (Original Beam Data, OBD) .The others were generated by all measurements from same linac model (Model-GBD) and all linac models (All-GBD). The three different beam data were registered to the independent verification software program for each institute. Subsequently,more » patient’s plans in eight sites (brain, head and neck, lung, esophagus, breast, abdomen, pelvis and bone) were analyzed using the verification program to compare doses calculated using the three different beam data. Results: 1116 plans were collected from six institutes. Compared to using the OBD, the results shows the variation using the Model-GBD based calculation and the All-GBD was 0.0 ± 0.3% and 0.0 ± 0.6%, respectively. The maximum variations were 1.2% and 2.3%, respectively. The plans with the variation over 1% shows the reference points were located away from the central axis with/without physical wedge. Conclusion: The confidence limit (2SD) using the Model-GBD and the All-GBD was within 0.6% and 1.2%, respectively. Thus, the use of golden beam data may be feasible for independent verification. In addition to it, the verification using golden beam data provide quality assurance of planning from the view of audit. This research is partially supported by Japan Agency for Medical Research and Development(AMED)« less

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

NASA Astrophysics Data System (ADS)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

Elastic models: a comparative study applied to retinal images.

PubMed

Karali, E; Lambropoulou, S; Koutsouris, D

2011-01-01

In this work various methods of parametric elastic models are compared, namely the classical snake, the gradient vector field snake (GVF snake) and the topology-adaptive snake (t-snake), as well as the method of self-affine mapping system as an alternative to elastic models. We also give a brief overview of the methods used. The self-affine mapping system is implemented using an adapting scheme and minimum distance as optimization criterion, which is more suitable for weak edges detection. All methods are applied to glaucomatic retinal images with the purpose of segmenting the optical disk. The methods are compared in terms of segmentation accuracy and speed, as these are derived from cross-correlation coefficients between real and algorithm extracted contours and segmentation time, respectively. As a result, the method of self-affine mapping system presents adequate segmentation time and segmentation accuracy, and significant independence from initialization.

Model of Learning Using iLearning on Independent Study Classes at University

ERIC Educational Resources Information Center

Sudaryono; Padeli; Febriyanto, Erick

2017-01-01

Raharja College is one of the universities who apply a learning method that is quite different which does not only rely on the conventional learning system in which Teaching and Learning Activity is done by students and lecturers are required to come face to face directly, but also applying e-learning method learning or better known as iLearning…

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

PubMed

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

PubMed Central

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

Optimization of Paclitaxel Containing pH-Sensitive Liposomes By 3 Factor, 3 Level Box-Behnken Design.

PubMed

Rane, Smita; Prabhakar, Bala

2013-07-01

The aim of this study was to investigate the combined influence of 3 independent variables in the preparation of paclitaxel containing pH-sensitive liposomes. A 3 factor, 3 levels Box-Behnken design was used to derive a second order polynomial equation and construct contour plots to predict responses. The independent variables selected were molar ratio phosphatidylcholine:diolylphosphatidylethanolamine (X1), molar concentration of cholesterylhemisuccinate (X2), and amount of drug (X3). Fifteen batches were prepared by thin film hydration method and evaluated for percent drug entrapment, vesicle size, and pH sensitivity. The transformed values of the independent variables and the percent drug entrapment were subjected to multiple regression to establish full model second order polynomial equation. F was calculated to confirm the omission of insignificant terms from the full model equation to derive a reduced model polynomial equation to predict the dependent variables. Contour plots were constructed to show the effects of X1, X2, and X3 on the percent drug entrapment. A model was validated for accurate prediction of the percent drug entrapment by performing checkpoint analysis. The computer optimization process and contour plots predicted the levels of independent variables X1, X2, and X3 (0.99, -0.06, 0, respectively), for maximized response of percent drug entrapment with constraints on vesicle size and pH sensitivity.

Parameter estimation of history-dependent leaky integrate-and-fire neurons using maximum-likelihood methods

PubMed Central

Dong, Yi; Mihalas, Stefan; Russell, Alexander; Etienne-Cummings, Ralph; Niebur, Ernst

2012-01-01

When a neuronal spike train is observed, what can we say about the properties of the neuron that generated it? A natural way to answer this question is to make an assumption about the type of neuron, select an appropriate model for this type, and then to choose the model parameters as those that are most likely to generate the observed spike train. This is the maximum likelihood method. If the neuron obeys simple integrate and fire dynamics, Paninski, Pillow, and Simoncelli (2004) showed that its negative log-likelihood function is convex and that its unique global minimum can thus be found by gradient descent techniques. The global minimum property requires independence of spike time intervals. Lack of history dependence is, however, an important constraint that is not fulfilled in many biological neurons which are known to generate a rich repertoire of spiking behaviors that are incompatible with history independence. Therefore, we expanded the integrate and fire model by including one additional variable, a variable threshold (Mihalas & Niebur, 2009) allowing for history-dependent firing patterns. This neuronal model produces a large number of spiking behaviors while still being linear. Linearity is important as it maintains the distribution of the random variables and still allows for maximum likelihood methods to be used. In this study we show that, although convexity of the negative log-likelihood is not guaranteed for this model, the minimum of the negative log-likelihood function yields a good estimate for the model parameters, in particular if the noise level is treated as a free parameter. Furthermore, we show that a nonlinear function minimization method (r-algorithm with space dilation) frequently reaches the global minimum. PMID:21851282

Efficient view based 3-D object retrieval using Hidden Markov Model

NASA Astrophysics Data System (ADS)

Jain, Yogendra Kumar; Singh, Roshan Kumar

2013-12-01

Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

Direct method for imaging elemental distribution profiles with long-period x-ray standing waves

NASA Astrophysics Data System (ADS)

Kohli, Vaibhav; Bedzyk, Michael J.; Fenter, Paul

2010-02-01

A model-independent Fourier-inversion method for imaging elemental profiles from multilayer and total-external reflection x-ray standing wave (XSW) data is developed for the purpose of understanding the assembly of atoms, ions, and molecules at well-defined interfaces in complex environments. The direct-method formalism is derived for the case of a long-period XSW generated by low-angle specular reflection in an attenuating overlayer medium. It is validated through comparison with simulated and experimental data to directly obtain an elemental distribution contained within the overlayer. We demonstrate this formalism by extracting the one-dimensional profile of Ti normal to the surface for a TiO2/Si/Mo trilayer deposited on a Si substrate using the TiKα fluorescence yield measured in air and under an aqueous electrolyte. The model-independent results demonstrate reduced coherent fractions for the in situ results associated with an incoherency of the x-ray beam (which are attributed to fluorescence excitation by diffusely or incoherently scattered x-rays). The uniqueness and limitations of the approach are discussed.

The SIETTE Automatic Assessment Environment

ERIC Educational Resources Information Center

Conejo, Ricardo; Guzmán, Eduardo; Trella, Monica

2016-01-01

This article describes the evolution and current state of the domain-independent Siette assessment environment. Siette supports different assessment methods--including classical test theory, item response theory, and computer adaptive testing--and integrates them with multidimensional student models used by intelligent educational systems.…

SPATIALLY AUTOCORRELATED DEMOGRAPHY AND INTERPOND MIGRATION IN THE CALIFORNIA TIGER SALAMANDER (AMBYSTOME CALIFORNIENSE)

EPA Science Inventory

We investigated the metapopulation structure of the California tiger salamander (Ambystoma californiense) using a combination of indirect and direct methods to evaluate two key requirements of modern metapopulation models: 1) that patches support somewhat independent populations ...

Unfolding the Second Riemann sheet with Pade Approximants: hunting resonance poles

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

Masjuan, Pere; Departamento de Fisica Teorica y del Cosmos, Universidad de Granada, Campus de Fuentenueva, E-18071 Granada

2011-05-23

Based on Pade Theory, a new procedure for extracting the pole mass and width of resonances is proposed. The method is systematic and provides a model-independent treatment for the prediction and the errors of the approximation.

The UMO (University of Maine, Orono) Teacher Training Program: A Case Study and a Model.

ERIC Educational Resources Information Center

Miller, James R.; McNally, Harry

This case study presents a model of the University of Maine, Orono, pre-service program for preparing secondary social studies teachers. Focus is on the Foundations Component and the Methods Component, either of which can function independently of the other. Only brief mention is made of either the Exploratory Field Experience Component or the…

A unified framework for group independent component analysis for multi-subject fMRI data

PubMed Central

Guo, Ying; Pagnoni, Giuseppe

2008-01-01

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT (Calhoun et al., 2001) and tensor PICA (Beckmann and Smith, 2005), make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation-Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation. PMID:18650105

Alignment-Independent Comparisons of Human Gastrointestinal Tract Microbial Communities in a Multidimensional 16S rRNA Gene Evolutionary Space▿

PubMed Central

Rudi, Knut; Zimonja, Monika; Kvenshagen, Bente; Rugtveit, Jarle; Midtvedt, Tore; Eggesbø, Merete

2007-01-01

We present a novel approach for comparing 16S rRNA gene clone libraries that is independent of both DNA sequence alignment and definition of bacterial phylogroups. These steps are the major bottlenecks in current microbial comparative analyses. We used direct comparisons of taxon density distributions in an absolute evolutionary coordinate space. The coordinate space was generated by using alignment-independent bilinear multivariate modeling. Statistical analyses for clone library comparisons were based on multivariate analysis of variance, partial least-squares regression, and permutations. Clone libraries from both adult and infant gastrointestinal tract microbial communities were used as biological models. We reanalyzed a library consisting of 11,831 clones covering complete colons from three healthy adults in addition to a smaller 390-clone library from infant feces. We show that it is possible to extract detailed information about microbial community structures using our alignment-independent method. Our density distribution analysis is also very efficient with respect to computer operation time, meeting the future requirements of large-scale screenings to understand the diversity and dynamics of microbial communities. PMID:17337554

Combined proportional and additive residual error models in population pharmacokinetic modelling.

PubMed

Proost, Johannes H

2017-11-15

In pharmacokinetic modelling, a combined proportional and additive residual error model is often preferred over a proportional or additive residual error model. Different approaches have been proposed, but a comparison between approaches is still lacking. The theoretical background of the methods is described. Method VAR assumes that the variance of the residual error is the sum of the statistically independent proportional and additive components; this method can be coded in three ways. Method SD assumes that the standard deviation of the residual error is the sum of the proportional and additive components. Using datasets from literature and simulations based on these datasets, the methods are compared using NONMEM. The different coding of methods VAR yield identical results. Using method SD, the values of the parameters describing residual error are lower than for method VAR, but the values of the structural parameters and their inter-individual variability are hardly affected by the choice of the method. Both methods are valid approaches in combined proportional and additive residual error modelling, and selection may be based on OFV. When the result of an analysis is used for simulation purposes, it is essential that the simulation tool uses the same method as used during analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

An Improved Interferometric Calibration Method Based on Independent Parameter Decomposition

NASA Astrophysics Data System (ADS)

Fan, J.; Zuo, X.; Li, T.; Chen, Q.; Geng, X.

2018-04-01

Interferometric SAR is sensitive to earth surface undulation. The accuracy of interferometric parameters plays a significant role in precise digital elevation model (DEM). The interferometric calibration is to obtain high-precision global DEM by calculating the interferometric parameters using ground control points (GCPs). However, interferometric parameters are always calculated jointly, making them difficult to decompose precisely. In this paper, we propose an interferometric calibration method based on independent parameter decomposition (IPD). Firstly, the parameters related to the interferometric SAR measurement are determined based on the three-dimensional reconstruction model. Secondly, the sensitivity of interferometric parameters is quantitatively analyzed after the geometric parameters are completely decomposed. Finally, each interferometric parameter is calculated based on IPD and interferometric calibration model is established. We take Weinan of Shanxi province as an example and choose 4 TerraDEM-X image pairs to carry out interferometric calibration experiment. The results show that the elevation accuracy of all SAR images is better than 2.54 m after interferometric calibration. Furthermore, the proposed method can obtain the accuracy of DEM products better than 2.43 m in the flat area and 6.97 m in the mountainous area, which can prove the correctness and effectiveness of the proposed IPD based interferometric calibration method. The results provide a technical basis for topographic mapping of 1 : 50000 and even larger scale in the flat area and mountainous area.

A Unified Approach to Modeling Multidisciplinary Interactions

NASA Technical Reports Server (NTRS)

Samareh, Jamshid A.; Bhatia, Kumar G.

2000-01-01

There are a number of existing methods to transfer information among various disciplines. For a multidisciplinary application with n disciplines, the traditional methods may be required to model (n(exp 2) - n) interactions. This paper presents a unified three-dimensional approach that reduces the number of interactions from (n(exp 2) - n) to 2n by using a computer-aided design model. The proposed modeling approach unifies the interactions among various disciplines. The approach is independent of specific discipline implementation, and a number of existing methods can be reformulated in the context of the proposed unified approach. This paper provides an overview of the proposed unified approach and reformulations for two existing methods. The unified approach is specially tailored for application environments where the geometry is created and managed through a computer-aided design system. Results are presented for a blended-wing body and a high-speed civil transport.

Numerical Approximation of Elasticity Tensor Associated With Green-Naghdi Rate.

PubMed

Liu, Haofei; Sun, Wei

2017-08-01

Objective stress rates are often used in commercial finite element (FE) programs. However, deriving a consistent tangent modulus tensor (also known as elasticity tensor or material Jacobian) associated with the objective stress rates is challenging when complex material models are utilized. In this paper, an approximation method for the tangent modulus tensor associated with the Green-Naghdi rate of the Kirchhoff stress is employed to simplify the evaluation process. The effectiveness of the approach is demonstrated through the implementation of two user-defined fiber-reinforced hyperelastic material models. Comparisons between the approximation method and the closed-form analytical method demonstrate that the former can simplify the material Jacobian evaluation with satisfactory accuracy while retaining its computational efficiency. Moreover, since the approximation method is independent of material models, it can facilitate the implementation of complex material models in FE analysis using shell/membrane elements in abaqus.

Rapid and Portable Methods for Identification of Bacterially Influenced Calcite: Application of Laser-Induced Breakdown Spectroscopy and AOTF Reflectance Spectroscopy, Fort Stanton Cave, New Mexico

NASA Astrophysics Data System (ADS)

McMillan, N. J.; Chavez, A.; Chanover, N.; Voelz, D.; Uckert, K.; Tawalbeh, R.; Gariano, J.; Dragulin, I.; Xiao, X.; Hull, R.

2014-12-01

Rapid, in-situ methods for identification of biologic and non-biologic mineral precipitation sites permit mapping of biological hot spots. Two portable spectrometers, Laser-Induced Breakdown Spectroscopy (LIBS) and Acoustic-Optic Tunable Filter Reflectance Spectroscopy (AOTFRS) were used to differentiate between bacterially influenced and inorganically precipitated calcite specimens from Fort Stanton Cave, NM, USA. LIBS collects light emitted from the decay of excited electrons in a laser ablation plasma; the spectrum is a chemical fingerprint of the analyte. AOTFRS collects light reflected from the surface of a specimen and provides structural information about the material (i.e., the presence of O-H bonds). These orthogonal data sets provide a rigorous method to determine the origin of calcite in cave deposits. This study used a set of 48 calcite samples collected from Fort Stanton cave. Samples were examined in SEM for the presence of biologic markers; these data were used to separate the samples into biologic and non-biologic groups. Spectra were modeled using the multivariate technique Partial Least Squares Regression (PLSR). Half of the spectra were used to train a PLSR model, in which biologic samples were assigned to the independent variable "0" and non-biologic samples were assigned the variable "1". Values of the independent variable were calculated for each of the training samples, which were close to 0 for the biologic samples (-0.09 - 0.23) and close to 1 for the non-biologic samples (0.57 - 1.14). A Value of Apparent Distinction (VAD) of 0.55 was used to numerically distinguish between the two groups; any sample with an independent variable value < 0.55 was classified as having a biologic origin; a sample with a value > 0.55 was determined to be non-biologic in origin. After the model was trained, independent variable values for the remaining half of the samples were calculated. Biologic or non-biologic origin was assigned by comparison to the VAD. Using LIBS data alone, the model has a 92% success rate, correctly identifying 23 of 25 samples. Modeling of AOTFRS spectra and the combined LIBS-AOTFRS data set have similar success rates. This study demonstrates that rapid, portable LIBS and AOTFRS instruments can be used to map the spatial distribution of biologic precipitation in caves.

Quantifying (dis)agreement between direct detection experiments in a halo-independent way

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

Feldstein, Brian; Kahlhoefer, Felix, E-mail: brian.feldstein@physics.ox.ac.uk, E-mail: felix.kahlhoefer@physics.ox.ac.uk

We propose an improved method to study recent and near-future dark matter direct detection experiments with small numbers of observed events. Our method determines in a quantitative and halo-independent way whether the experiments point towards a consistent dark matter signal and identifies the best-fit dark matter parameters. To achieve true halo independence, we apply a recently developed method based on finding the velocity distribution that best describes a given set of data. For a quantitative global analysis we construct a likelihood function suitable for small numbers of events, which allows us to determine the best-fit particle physics properties of darkmore » matter considering all experiments simultaneously. Based on this likelihood function we propose a new test statistic that quantifies how well the proposed model fits the data and how large the tension between different direct detection experiments is. We perform Monte Carlo simulations in order to determine the probability distribution function of this test statistic and to calculate the p-value for both the dark matter hypothesis and the background-only hypothesis.« less

Sequence-independent construction of ordered combinatorial libraries with predefined crossover points.

PubMed

Jézéquel, Laetitia; Loeper, Jacqueline; Pompon, Denis

2008-11-01

Combinatorial libraries coding for mosaic enzymes with predefined crossover points constitute useful tools to address and model structure-function relationships and for functional optimization of enzymes based on multivariate statistics. The presented method, called sequence-independent generation of a chimera-ordered library (SIGNAL), allows easy shuffling of any predefined amino acid segment between two or more proteins. This method is particularly well adapted to the exchange of protein structural modules. The procedure could also be well suited to generate ordered combinatorial libraries independent of sequence similarities in a robotized manner. Sequence segments to be recombined are first extracted by PCR from a single-stranded template coding for an enzyme of interest using a biotin-avidin-based method. This technique allows the reduction of parental template contamination in the final library. Specific PCR primers allow amplification of two complementary mosaic DNA fragments, overlapping in the region to be exchanged. Fragments are finally reassembled using a fusion PCR. The process is illustrated via the construction of a set of mosaic CYP2B enzymes using this highly modular approach.

[Development of the soft independent modelling of class analogies model to discrimination Vibrio parahemolyticus by Smartongue].

PubMed

Huang, Jianfeng; Zhao, Guangying; Dou, Wenchao

2011-04-01

To explore a new rapid detection method for detecting of Food pathogens. We used the Smartongue, to determine the composition informations of the liquid culture samples and combined with soft independent modelling of class analogies (SIMCA) to analyze their respective species, then set up a Smartongue -SIMCA model to discriminate the V. parahaemolyticus. The Smartongue has 6 working electrodes and three frequency segments, we can built 18 discrimination models in one detection. After comparing all the 18 discrimination models, the optimal working electrodes and frequency segments were selected out, they were: palladium electrode in 1 Hz frequency segment, tungsten electrode in 100 Hz and silver electrode in 100 Hz. Then 10 species of pathogenic Vibrio were discriminated by the 3 models. The V. damsela, V. metschnikovii, V. alginalyticus, V. cincinnatiensis, V. metschnikovii and V. cholerae O serogroup samples could be discriminated by the SIMCA model of V. parahaemolyticus with palladium electrode 1 Hz frequency segment; V. mimicus and V. vulnincus samples could be discriminated by the SIMCA model of V. parahaemolyticus with tungsten electrode 100 Hz frequency segment; V. carcariae and V. cholerae non-O serogroup samples could be discriminated with the SIMCA model of V. parahaemolyticus in silver electrode 100 Hz frequency segment. The accurate discrimination of ten species of Vibrio samples is 100%. The Smartongue combined with SIMCA can discriminate V. parahaemolyticus with other pathogenic Vibrio effectively. It has a promising future as a new rapid detection method for V. parahaemolyticus.

Comparison of Histograms for Use in Cloud Observation and Modeling

NASA Technical Reports Server (NTRS)

Green, Lisa; Xu, Kuan-Man

2005-01-01

Cloud observation and cloud modeling data can be presented in histograms for each characteristic to be measured. Combining information from single-cloud histograms yields a summary histogram. Summary histograms can be compared to each other to reach conclusions about the behavior of an ensemble of clouds in different places at different times or about the accuracy of a particular cloud model. As in any scientific comparison, it is necessary to decide whether any apparent differences are statistically significant. The usual methods of deciding statistical significance when comparing histograms do not apply in this case because they assume independent data. Thus, a new method is necessary. The proposed method uses the Euclidean distance metric and bootstrapping to calculate the significance level.

Distorted Born iterative T-matrix method for inversion of CSEM data in anisotropic media

NASA Astrophysics Data System (ADS)

Jakobsen, Morten; Tveit, Svenn

2018-05-01

We present a direct iterative solutions to the nonlinear controlled-source electromagnetic (CSEM) inversion problem in the frequency domain, which is based on a volume integral equation formulation of the forward modelling problem in anisotropic conductive media. Our vectorial nonlinear inverse scattering approach effectively replaces an ill-posed nonlinear inverse problem with a series of linear ill-posed inverse problems, for which there already exist efficient (regularized) solution methods. The solution update the dyadic Green's function's from the source to the scattering-volume and from the scattering-volume to the receivers, after each iteration. The T-matrix approach of multiple scattering theory is used for efficient updating of all dyadic Green's functions after each linearized inversion step. This means that we have developed a T-matrix variant of the Distorted Born Iterative (DBI) method, which is often used in the acoustic and electromagnetic (medical) imaging communities as an alternative to contrast-source inversion. The main advantage of using the T-matrix approach in this context, is that it eliminates the need to perform a full forward simulation at each iteration of the DBI method, which is known to be consistent with the Gauss-Newton method. The T-matrix allows for a natural domain decomposition, since in the sense that a large model can be decomposed into an arbitrary number of domains that can be treated independently and in parallel. The T-matrix we use for efficient model updating is also independent of the source-receiver configuration, which could be an advantage when performing fast-repeat modelling and time-lapse inversion. The T-matrix is also compatible with the use of modern renormalization methods that can potentially help us to reduce the sensitivity of the CSEM inversion results on the starting model. To illustrate the performance and potential of our T-matrix variant of the DBI method for CSEM inversion, we performed a numerical experiments based on synthetic CSEM data associated with 2D VTI and 3D orthorombic model inversions. The results of our numerical experiment suggest that the DBIT method for inversion of CSEM data in anisotropic media is both accurate and efficient.

Model Insensitive and Calibration Independent Method for Determination of the Downstream Neutral Hydrogen Density Through Ly-alpha Glow Observations

NASA Technical Reports Server (NTRS)

Gangopadhyay, P.; Judge, D. L.

1996-01-01

Our knowledge of the various heliospheric phenomena (location of the solar wind termination shock, heliopause configuration and very local interstellar medium parameters) is limited by uncertainties in the available heliospheric plasma models and by calibration uncertainties in the observing instruments. There is, thus, a strong motivation to develop model insensitive and calibration independent methods to reduce the uncertainties in the relevant heliospheric parameters. We have developed such a method to constrain the downstream neutral hydrogen density inside the heliospheric tail. In our approach we have taken advantage of the relative insensitivity of the downstream neutral hydrogen density profile to the specific plasma model adopted. We have also used the fact that the presence of an asymmetric neutral hydrogen cavity surrounding the sun, characteristic of all neutral densities models, results in a higher multiple scattering contribution to the observed glow in the downstream region than in the upstream region. This allows us to approximate the actual density profile with one which is spatially uniform for the purpose of calculating the downstream backscattered glow. Using different spatially constant density profiles, radiative transfer calculations are performed, and the radial dependence of the predicted glow is compared with the observed I/R dependence of Pioneer 10 UV data. Such a comparison bounds the large distance heliospheric neutral hydrogen density in the downstream direction to a value between 0.05 and 0.1/cc.

Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models

USGS Publications Warehouse

McKenna, James E.; Carlson, Douglas M.; Payne-Wynne, Molly L.

2013-01-01

Aim: Rare aquatic species are a substantial component of biodiversity, and their conservation is a major objective of many management plans. However, they are difficult to assess, and their optimal habitats are often poorly known. Methods to effectively predict the likely locations of suitable rare aquatic species habitats are needed. We combine two modelling approaches to predict occurrence and general abundance of several rare fish species. Location: Allegheny watershed of western New York State (USA) Methods: Our method used two empirical neural network modelling approaches (species specific and assemblage based) to predict stream-by-stream occurrence and general abundance of rare darters, based on broad-scale habitat conditions. Species-specific models were developed for longhead darter (Percina macrocephala), spotted darter (Etheostoma maculatum) and variegate darter (Etheostoma variatum) in the Allegheny drainage. An additional model predicted the type of rare darter-containing assemblage expected in each stream reach. Predictions from both models were then combined inclusively and exclusively and compared with additional independent data. Results Example rare darter predictions demonstrate the method's effectiveness. Models performed well (R2 ≥ 0.79), identified where suitable darter habitat was most likely to occur, and predictions matched well to those of collection sites. Additional independent data showed that the most conservative (exclusive) model slightly underestimated the distributions of these rare darters or predictions were displaced by one stream reach, suggesting that new darter habitat types were detected in the later collections. Main conclusions Broad-scale habitat variables can be used to effectively identify rare species' habitats. Combining species-specific and assemblage-based models enhances our ability to make use of the sparse data on rare species and to identify habitat units most likely and least likely to support those species. This hybrid approach may assist managers with the prioritization of habitats to be examined or conserved for rare species.

Oceanic eddy detection and lifetime forecast using machine learning methods

NASA Astrophysics Data System (ADS)

Ashkezari, Mohammad D.; Hill, Christopher N.; Follett, Christopher N.; Forget, Gaël.; Follows, Michael J.

2016-12-01

We report a novel altimetry-based machine learning approach for eddy identification and characterization. The machine learning models use daily maps of geostrophic velocity anomalies and are trained according to the phase angle between the zonal and meridional components at each grid point. The trained models are then used to identify the corresponding eddy phase patterns and to predict the lifetime of a detected eddy structure. The performance of the proposed method is examined at two dynamically different regions to demonstrate its robust behavior and region independency.

Classification of Phase Transitions by Microcanonical Inflection-Point Analysis

NASA Astrophysics Data System (ADS)

Qi, Kai; Bachmann, Michael

2018-05-01

By means of the principle of minimal sensitivity we generalize the microcanonical inflection-point analysis method by probing derivatives of the microcanonical entropy for signals of transitions in complex systems. A strategy of systematically identifying and locating independent and dependent phase transitions of any order is proposed. The power of the generalized method is demonstrated in applications to the ferromagnetic Ising model and a coarse-grained model for polymer adsorption onto a substrate. The results shed new light on the intrinsic phase structure of systems with cooperative behavior.

Computational Simulations and the Scientific Method

NASA Technical Reports Server (NTRS)

Kleb, Bil; Wood, Bill

2005-01-01

As scientific simulation software becomes more complicated, the scientific-software implementor's need for component tests from new model developers becomes more crucial. The community's ability to follow the basic premise of the Scientific Method requires independently repeatable experiments, and model innovators are in the best position to create these test fixtures. Scientific software developers also need to quickly judge the value of the new model, i.e., its cost-to-benefit ratio in terms of gains provided by the new model and implementation risks such as cost, time, and quality. This paper asks two questions. The first is whether other scientific software developers would find published component tests useful, and the second is whether model innovators think publishing test fixtures is a feasible approach.

Tree STEM and Canopy Biomass Estimates from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Olofsson, K.; Holmgren, J.

2017-10-01

In this study an automatic method for estimating both the tree stem and the tree canopy biomass is presented. The point cloud tree extraction techniques operate on TLS data and models the biomass using the estimated stem and canopy volume as independent variables. The regression model fit error is of the order of less than 5 kg, which gives a relative model error of about 5 % for the stem estimate and 10-15 % for the spruce and pine canopy biomass estimates. The canopy biomass estimate was improved by separating the models by tree species which indicates that the method is allometry dependent and that the regression models need to be recomputed for different areas with different climate and different vegetation.

Modelling municipal solid waste generation: a review.

PubMed

Beigl, Peter; Lebersorger, Sandra; Salhofer, Stefan

2008-01-01

The objective of this paper is to review previously published models of municipal solid waste generation and to propose an implementation guideline which will provide a compromise between information gain and cost-efficient model development. The 45 modelling approaches identified in a systematic literature review aim at explaining or estimating the present or future waste generation using economic, socio-demographic or management-orientated data. A classification was developed in order to categorise these highly heterogeneous models according to the following criteria--the regional scale, the modelled waste streams, the hypothesised independent variables and the modelling method. A procedural practice guideline was derived from a discussion of the underlying models in order to propose beneficial design options concerning regional sampling (i.e., number and size of observed areas), waste stream definition and investigation, selection of independent variables and model validation procedures. The practical application of the findings was demonstrated with two case studies performed on different regional scales, i.e., on a household and on a city level. The findings of this review are finally summarised in the form of a relevance tree for methodology selection.

Determining if an older adult can make and execute decisions to live safely at home: a capacity assessment and intervention model

PubMed Central

Skelton, Felicia; Kunik, Mark E.; Regev, Tziona; Naik, Aanand D.

2009-01-01

Determining an older adult’s capacity to live safely and independently in the community presents a serious and complicated challenge to the health care system. Evaluating one’s ability to make and execute decisions regarding safe and independent living incorporates clinical assessments, bioethical considerations, and often legal declarations of capacity. Capacity assessments usually result in life changes for patients and their families, including a caregiver managing some everyday tasks, placement outside of the home, and even legal guardianship. The process of determining capacity and recommending intervention is often inefficient and highly variable in most cases. Physicians are rarely trained to conduct capacity assessments and assessment methods are heterogeneous. An interdisciplinary team of clinicians developed the capacity assessment and intervention (CAI) model at a community outpatient geriatrics clinic to address these critical gaps. This report follows one patient through the entire CAI model, describing processes for a typical case. It then examines two additional case reports that highlight common challenges in capacity assessment. The CAI model uses assessment methods common to geriatrics clinical practice and conducts assessments and interventions in a standardized fashion. Reliance on common, validated measures increases generalizability of the model across geriatrics practice settings and patient populations. PMID:19481271

Fractional Brownian motion and long term clinical trial recruitment

PubMed Central

Zhang, Qiang; Lai, Dejian

2015-01-01

Prediction of recruitment in clinical trials has been a challenging task. Many methods have been studied, including models based on Poisson process and its large sample approximation by Brownian motion (BM), however, when the independent incremental structure is violated for BM model, we could use fractional Brownian motion to model and approximate the underlying Poisson processes with random rates. In this paper, fractional Brownian motion (FBM) is considered for such conditions and compared to BM model with illustrated examples from different trials and simulations. PMID:26347306

Fractional Brownian motion and long term clinical trial recruitment.

PubMed

Zhang, Qiang; Lai, Dejian

2011-05-01

Prediction of recruitment in clinical trials has been a challenging task. Many methods have been studied, including models based on Poisson process and its large sample approximation by Brownian motion (BM), however, when the independent incremental structure is violated for BM model, we could use fractional Brownian motion to model and approximate the underlying Poisson processes with random rates. In this paper, fractional Brownian motion (FBM) is considered for such conditions and compared to BM model with illustrated examples from different trials and simulations.

Classification of complex networks based on similarity of topological network features

NASA Astrophysics Data System (ADS)

Attar, Niousha; Aliakbary, Sadegh

2017-09-01

Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.

Population Analysis of Disabled Children by Departments in France

NASA Astrophysics Data System (ADS)

Meidatuzzahra, Diah; Kuswanto, Heri; Pech, Nicolas; Etchegaray, Amélie

2017-06-01

In this study, a statistical analysis is performed by model the variations of the disabled about 0-19 years old population among French departments. The aim is to classify the departments according to their profile determinants (socioeconomic and behavioural profiles). The analysis is focused on two types of methods: principal component analysis (PCA) and multiple correspondences factorial analysis (MCA) to review which one is the best methods for interpretation of the correlation between the determinants of disability (independent variable). The PCA is the best method for interpretation of the correlation between the determinants of disability (independent variable). The PCA reduces 14 determinants of disability to 4 axes, keeps 80% of total information, and classifies them into 7 classes. The MCA reduces the determinants to 3 axes, retains only 30% of information, and classifies them into 4 classes.

Nuclear constraints on the age of the universe

NASA Technical Reports Server (NTRS)

Schramm, D. N.

1983-01-01

A review is made of how one can use nuclear physics to put rather stringent limits on the age of the universe and thus the cosmic distance scale. The age can be estimated to a fair degree of accuracy. No single measurement of the time since the Big Bang gives a specific, unambiguous age. There are several methods that together fix the age with surprising precision. In particular, there are three totally independent techniques for estimating an age and a fourth technique which involves finding consistency of the other three in the framework of the standard Big Bang cosmological model. The three independent methods are: cosmological dynamics, the age of the oldest stars, and radioactive dating. This paper concentrates on the third of the three methods, and the consistency technique. Previously announced in STAR as N83-34868

Pole orientation of 16 Psyche by two independent methods

NASA Technical Reports Server (NTRS)

Tedesco, E. F.; Taylor, R. C.

1985-01-01

Nineteen new lightcurves of 16 Psyche are presented along with a pole orientation derived using two independent methods, namely, photometric astrometry and magnitude-amplitude-shape-aspect. The pole orientations found using these two methods agree to within 4 deg. The results from applying photometric astrometry were prograde rotation, a sidereal period of 0.1748143 days + or - 0.0000003 days, and a pole at longitude 223 deg and latitude +37 deg, with an uncertainty of 10 deg, and, from applying magnitude-amplitude-shape-aspect a pole at 220 + or - 1 deg, +40 + or - 4 deg, and a modeled triaxial ellipsoid shape (a greater than b greater than c) and a/b = 1.33 + or - 0.07. The discrepancy between the high-pole latitude found here and the low latitudes reported by Lupishko et al. (1982) and Zhou and Yang (1982) is discussed.

Validation of New Wind Resource Maps

NASA Astrophysics Data System (ADS)

Elliott, D.; Schwartz, M.

2002-05-01

The National Renewable Energy Laboratory (NREL) recently led a project to validate updated state wind resource maps for the northwestern United States produced by a private U.S. company, TrueWind Solutions (TWS). The independent validation project was a cooperative activity among NREL, TWS, and meteorological consultants. The independent validation concept originated at a May 2001 technical workshop held at NREL to discuss updating the Wind Energy Resource Atlas of the United States. Part of the workshop, which included more than 20 attendees from the wind resource mapping and consulting community, was dedicated to reviewing the latest techniques for wind resource assessment. It became clear that using a numerical modeling approach for wind resource mapping was rapidly gaining ground as a preferred technique and if the trend continues, it will soon become the most widely-used technique around the world. The numerical modeling approach is a relatively fast application compared to older mapping methods and, in theory, should be quite accurate because it directly estimates the magnitude of boundary-layer processes that affect the wind resource of a particular location. Numerical modeling output combined with high resolution terrain data can produce useful wind resource information at a resolution of 1 km or lower. However, because the use of the numerical modeling approach is new (last 35 years) and relatively unproven, meteorological consultants question the accuracy of the approach. It was clear that new state or regional wind maps produced by this method would have to undergo independent validation before the results would be accepted by the wind energy community and developers.

On the joint inversion of geophysical data for models of the coupled core-mantle system

NASA Technical Reports Server (NTRS)

Voorhies, Coerte V.

1991-01-01

Joint inversion of magnetic, earth rotation, geoid, and seismic data for a unified model of the coupled core-mantle system is proposed and shown to be possible. A sample objective function is offered and simplified by targeting results from independent inversions and summary travel time residuals instead of original observations. These data are parameterized in terms of a very simple, closed model of the topographically coupled core-mantle system. Minimization of the simplified objective function leads to a nonlinear inverse problem; an iterative method for solution is presented. Parameterization and method are emphasized; numerical results are not presented.

Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.

Multi-approach assessment of the spatial distribution of the specific yield: application to the Crau plain aquifer, France

NASA Astrophysics Data System (ADS)

Seraphin, Pierre; Gonçalvès, Julio; Vallet-Coulomb, Christine; Champollion, Cédric

2018-06-01

Spatially distributed values of the specific yield, a fundamental parameter for transient groundwater mass balance calculations, were obtained by means of three independent methods for the Crau plain, France. In contrast to its traditional use to assess recharge based on a given specific yield, the water-table fluctuation (WTF) method, applied using major recharging events, gave a first set of reference values. Then, large infiltration processes recorded by monitored boreholes and caused by major precipitation events were interpreted in terms of specific yield by means of a one-dimensional vertical numerical model solving Richards' equations within the unsaturated zone. Finally, two gravity field campaigns, at low and high piezometric levels, were carried out to assess the groundwater mass variation and thus alternative specific yield values. The range obtained by the WTF method for this aquifer made of alluvial detrital material was 2.9- 26%, in line with the scarce data available so far. The average spatial value of specific yield by the WTF method (9.1%) is consistent with the aquifer scale value from the hydro-gravimetric approach. In this investigation, an estimate of the hitherto unknown spatial distribution of the specific yield over the Crau plain was obtained using the most reliable method (the WTF method). A groundwater mass balance calculation over the domain using this distribution yielded similar results to an independent quantification based on a stable isotope-mixing model. This agreement reinforces the relevance of such estimates, which can be used to build a more accurate transient hydrogeological model.

Multi-approach assessment of the spatial distribution of the specific yield: application to the Crau plain aquifer, France

NASA Astrophysics Data System (ADS)

Seraphin, Pierre; Gonçalvès, Julio; Vallet-Coulomb, Christine; Champollion, Cédric

2018-03-01

Spatially distributed values of the specific yield, a fundamental parameter for transient groundwater mass balance calculations, were obtained by means of three independent methods for the Crau plain, France. In contrast to its traditional use to assess recharge based on a given specific yield, the water-table fluctuation (WTF) method, applied using major recharging events, gave a first set of reference values. Then, large infiltration processes recorded by monitored boreholes and caused by major precipitation events were interpreted in terms of specific yield by means of a one-dimensional vertical numerical model solving Richards' equations within the unsaturated zone. Finally, two gravity field campaigns, at low and high piezometric levels, were carried out to assess the groundwater mass variation and thus alternative specific yield values. The range obtained by the WTF method for this aquifer made of alluvial detrital material was 2.9- 26%, in line with the scarce data available so far. The average spatial value of specific yield by the WTF method (9.1%) is consistent with the aquifer scale value from the hydro-gravimetric approach. In this investigation, an estimate of the hitherto unknown spatial distribution of the specific yield over the Crau plain was obtained using the most reliable method (the WTF method). A groundwater mass balance calculation over the domain using this distribution yielded similar results to an independent quantification based on a stable isotope-mixing model. This agreement reinforces the relevance of such estimates, which can be used to build a more accurate transient hydrogeological model.

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer: development of a prognostic model through a crowdsourced challenge with open clinical trial data.

PubMed

Guinney, Justin; Wang, Tao; Laajala, Teemu D; Winner, Kimberly Kanigel; Bare, J Christopher; Neto, Elias Chaibub; Khan, Suleiman A; Peddinti, Gopal; Airola, Antti; Pahikkala, Tapio; Mirtti, Tuomas; Yu, Thomas; Bot, Brian M; Shen, Liji; Abdallah, Kald; Norman, Thea; Friend, Stephen; Stolovitzky, Gustavo; Soule, Howard; Sweeney, Christopher J; Ryan, Charles J; Scher, Howard I; Sartor, Oliver; Xie, Yang; Aittokallio, Tero; Zhou, Fang Liz; Costello, James C

2017-01-01

Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·39-4·62, p<0·0001; reference model: 2·56, 1·85-3·53, p<0·0001). The new model was validated further on the ENTHUSE M1 cohort with similarly high performance (iAUC 0·768). Meta-analysis across all methods confirmed previously identified predictive clinical variables and revealed aspartate aminotransferase as an important, albeit previously under-reported, prognostic biomarker. Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer. Sanofi US Services, Project Data Sphere. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Parental physical activity, safety perceptions and children’s independent mobility

PubMed Central

2013-01-01

Background Parents are likely to be a basic influence on their children's behavior. There is an absence of information about the associations between parents' physical activity and perception of neighborhood environment with children’s independent mobility. The purpose of this study is to examine the contribution of parental physical activity and perception of neighborhood safety to children’s independent mobility. Methods In this cross-sectional study of 354 pupils and their parents, independent mobility, perceptions of neighborhood safety and physical activity were evaluated by questionnaire. Categorical principal components analyses were used to determine the underlying dimensions of both independent mobility and perceptions of neighborhood safety items. Results The strongest predictor of independent mobility was the parental perception of sidewalk and street safety (ß = 0.132). Parent’s physical activity was also a significant predictor. The final model accounted for 13.0% of the variance. Conclusions Parental perception of neighborhood safety and parents’ self reported physical activity might be associated with children’s independent mobility. Further research in this topic is needed to explore this possible association. PMID:23767778

Pauci ex tanto numero: reducing redundancy in multi-model ensembles

NASA Astrophysics Data System (ADS)

Solazzo, E.; Riccio, A.; Kioutsioukis, I.; Galmarini, S.

2013-02-01

We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date no attempts in this direction are documented within the air quality (AQ) community, although the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared biases among models will determine a biased ensemble, making therefore essential the errors of the ensemble members to be independent so that bias can cancel out. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated) we discourage selecting the members of the ensemble simply on the basis of scores, that is, independence and skills need to be considered disjointly.

Comparison of Dissolution Similarity Assessment Methods for Products with Large Variations: f2 Statistics and Model-Independent Multivariate Confidence Region Procedure for Dissolution Profiles of Multiple Oral Products.

PubMed

Yoshida, Hiroyuki; Shibata, Hiroko; Izutsu, Ken-Ichi; Goda, Yukihiro

2017-01-01

The current Japanese Ministry of Health Labour and Welfare (MHLW)'s Guideline for Bioequivalence Studies of Generic Products uses averaged dissolution rates for the assessment of dissolution similarity between test and reference formulations. This study clarifies how the application of model-independent multivariate confidence region procedure (Method B), described in the European Medical Agency and U.S. Food and Drug Administration guidelines, affects similarity outcomes obtained empirically from dissolution profiles with large variations in individual dissolution rates. Sixty-one datasets of dissolution profiles for immediate release, oral generic, and corresponding innovator products that showed large variation in individual dissolution rates in generic products were assessed on their similarity by using the f 2 statistics defined in the MHLW guidelines (MHLW f 2 method) and two different Method B procedures, including a bootstrap method applied with f 2 statistics (BS method) and a multivariate analysis method using the Mahalanobis distance (MV method). The MHLW f 2 and BS methods provided similar dissolution similarities between reference and generic products. Although a small difference in the similarity assessment may be due to the decrease in the lower confidence interval for expected f 2 values derived from the large variation in individual dissolution rates, the MV method provided results different from those obtained through MHLW f 2 and BS methods. Analysis of actual dissolution data for products with large individual variations would provide valuable information towards an enhanced understanding of these methods and their possible incorporation in the MHLW guidelines.

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

PubMed

Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul

2016-01-15

Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models.

PubMed

Ouyang, Yicun; Yin, Hujun

2018-05-01

Many real-world problems require modeling and forecasting of time series, such as weather temperature, electricity demand, stock prices and foreign exchange (FX) rates. Often, the tasks involve predicting over a long-term period, e.g. several weeks or months. Most existing time series models are inheritably for one-step prediction, that is, predicting one time point ahead. Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. The main existing approaches, iterative and independent, either use one-step model recursively or treat the multi-step task as an independent model. They generally perform poorly in practical applications. In this paper, as an extension of the self-organizing mixture autoregressive (AR) model, the varied length mixture (VLM) models are proposed to model and forecast time series over multi-steps. The key idea is to preserve the dependencies between the time points within the prediction horizon. Training data are segmented to various lengths corresponding to various forecasting horizons, and the VLM models are trained in a self-organizing fashion on these segments to capture these dependencies in its component AR models of various predicting horizons. The VLM models form a probabilistic mixture of these varied length models. A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. The effectiveness of the proposed methods and their marked improvements over the existing methods are demonstrated through a number of experiments on synthetic data, real-world FX rates and weather temperatures.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

NASA Astrophysics Data System (ADS)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

NASA Astrophysics Data System (ADS)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

On macromolecular refinement at subatomic resolution with interatomic scatterers

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

Afonine, Pavel V., E-mail: pafonine@lbl.gov; Grosse-Kunstleve, Ralf W.; Adams, Paul D.

2007-11-01

Modelling deformation electron density using interatomic scatters is simpler than multipolar methods, produces comparable results at subatomic resolution and can easily be applied to macromolecules. A study of the accurate electron-density distribution in molecular crystals at subatomic resolution (better than ∼1.0 Å) requires more detailed models than those based on independent spherical atoms. A tool that is conventionally used in small-molecule crystallography is the multipolar model. Even at upper resolution limits of 0.8–1.0 Å, the number of experimental data is insufficient for full multipolar model refinement. As an alternative, a simpler model composed of conventional independent spherical atoms augmented bymore » additional scatterers to model bonding effects has been proposed. Refinement of these mixed models for several benchmark data sets gave results that were comparable in quality with the results of multipolar refinement and superior to those for conventional models. Applications to several data sets of both small molecules and macromolecules are shown. These refinements were performed using the general-purpose macromolecular refinement module phenix.refine of the PHENIX package.« less

Merging for Particle-Mesh Complex Particle Kinetic Modeling of the Multiple Plasma Beams

NASA Technical Reports Server (NTRS)

Lipatov, Alexander S.

2011-01-01

We suggest a merging procedure for the Particle-Mesh Complex Particle Kinetic (PMCPK) method in case of inter-penetrating flow (multiple plasma beams). We examine the standard particle-in-cell (PIC) and the PMCPK methods in the case of particle acceleration by shock surfing for a wide range of the control numerical parameters. The plasma dynamics is described by a hybrid (particle-ion-fluid-electron) model. Note that one may need a mesh if modeling with the computation of an electromagnetic field. Our calculations use specified, time-independent electromagnetic fields for the shock, rather than self-consistently generated fields. While a particle-mesh method is a well-verified approach, the CPK method seems to be a good approach for multiscale modeling that includes multiple regions with various particle/fluid plasma behavior. However, the CPK method is still in need of a verification for studying the basic plasma phenomena: particle heating and acceleration by collisionless shocks, magnetic field reconnection, beam dynamics, etc.

Speaker-independent phoneme recognition with a binaural auditory image model

NASA Astrophysics Data System (ADS)

Francis, Keith Ivan

1997-09-01

This dissertation presents phoneme recognition techniques based on a binaural fusion of outputs of the auditory image model and subsequent azimuth-selective phoneme recognition in a noisy environment. Background information concerning speech variations, phoneme recognition, current binaural fusion techniques and auditory modeling issues is explained. The research is constrained to sources in the frontal azimuthal plane of a simulated listener. A new method based on coincidence detection of neural activity patterns from the auditory image model of Patterson is used for azimuth-selective phoneme recognition. The method is tested in various levels of noise and the results are reported in contrast to binaural fusion methods based on various forms of correlation to demonstrate the potential of coincidence- based binaural phoneme recognition. This method overcomes smearing of fine speech detail typical of correlation based methods. Nevertheless, coincidence is able to measure similarity of left and right inputs and fuse them into useful feature vectors for phoneme recognition in noise.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data

NASA Astrophysics Data System (ADS)

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-01

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Machine learning-based kinetic modeling: a robust and reproducible solution for quantitative analysis of dynamic PET data.

PubMed

Pan, Leyun; Cheng, Caixia; Haberkorn, Uwe; Dimitrakopoulou-Strauss, Antonia

2017-05-07

A variety of compartment models are used for the quantitative analysis of dynamic positron emission tomography (PET) data. Traditionally, these models use an iterative fitting (IF) method to find the least squares between the measured and calculated values over time, which may encounter some problems such as the overfitting of model parameters and a lack of reproducibility, especially when handling noisy data or error data. In this paper, a machine learning (ML) based kinetic modeling method is introduced, which can fully utilize a historical reference database to build a moderate kinetic model directly dealing with noisy data but not trying to smooth the noise in the image. Also, due to the database, the presented method is capable of automatically adjusting the models using a multi-thread grid parameter searching technique. Furthermore, a candidate competition concept is proposed to combine the advantages of the ML and IF modeling methods, which could find a balance between fitting to historical data and to the unseen target curve. The machine learning based method provides a robust and reproducible solution that is user-independent for VOI-based and pixel-wise quantitative analysis of dynamic PET data.

Composing Models of Geographic Physical Processes

NASA Astrophysics Data System (ADS)

Hofer, Barbara; Frank, Andrew U.

Processes are central for geographic information science; yet geographic information systems (GIS) lack capabilities to represent process related information. A prerequisite to including processes in GIS software is a general method to describe geographic processes independently of application disciplines. This paper presents such a method, namely a process description language. The vocabulary of the process description language is derived formally from mathematical models. Physical processes in geography can be described in two equivalent languages: partial differential equations or partial difference equations, where the latter can be shown graphically and used as a method for application specialists to enter their process models. The vocabulary of the process description language comprises components for describing the general behavior of prototypical geographic physical processes. These process components can be composed by basic models of geographic physical processes, which is shown by means of an example.

Hybrid Multiscale Finite Volume method for multiresolution simulations of flow and reactive transport in porous media

NASA Astrophysics Data System (ADS)

Barajas-Solano, D. A.; Tartakovsky, A. M.

2017-12-01

We present a multiresolution method for the numerical simulation of flow and reactive transport in porous, heterogeneous media, based on the hybrid Multiscale Finite Volume (h-MsFV) algorithm. The h-MsFV algorithm allows us to couple high-resolution (fine scale) flow and transport models with lower resolution (coarse) models to locally refine both spatial resolution and transport models. The fine scale problem is decomposed into various "local'' problems solved independently in parallel and coordinated via a "global'' problem. This global problem is then coupled with the coarse model to strictly ensure domain-wide coarse-scale mass conservation. The proposed method provides an alternative to adaptive mesh refinement (AMR), due to its capacity to rapidly refine spatial resolution beyond what's possible with state-of-the-art AMR techniques, and the capability to locally swap transport models. We illustrate our method by applying it to groundwater flow and reactive transport of multiple species.

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS.

USGS Publications Warehouse

Cooley, Richard L.; Vecchia, Aldo V.

1987-01-01

A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that inclusion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.

A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models

NASA Technical Reports Server (NTRS)

Giunta, Anthony A.; Watson, Layne T.

1998-01-01

Two methods of creating approximation models are compared through the calculation of the modeling accuracy on test problems involving one, five, and ten independent variables. Here, the test problems are representative of the modeling challenges typically encountered in realistic engineering optimization problems. The first approximation model is a quadratic polynomial created using the method of least squares. This type of polynomial model has seen considerable use in recent engineering optimization studies due to its computational simplicity and ease of use. However, quadratic polynomial models may be of limited accuracy when the response data to be modeled have multiple local extrema. The second approximation model employs an interpolation scheme known as kriging developed in the fields of spatial statistics and geostatistics. This class of interpolating model has the flexibility to model response data with multiple local extrema. However, this flexibility is obtained at an increase in computational expense and a decrease in ease of use. The intent of this study is to provide an initial exploration of the accuracy and modeling capabilities of these two approximation methods.

MODFLOW-2005 : the U.S. Geological Survey modular ground-water model--the ground-water flow process

USGS Publications Warehouse

Harbaugh, Arlen W.

2005-01-01

This report presents MODFLOW-2005, which is a new version of the finite-difference ground-water model commonly called MODFLOW. Ground-water flow is simulated using a block-centered finite-difference approach. Layers can be simulated as confined or unconfined. Flow associated with external stresses, such as wells, areal recharge, evapotranspiration, drains, and rivers, also can be simulated. The report includes detailed explanations of physical and mathematical concepts on which the model is based, an explanation of how those concepts are incorporated in the modular structure of the computer program, instructions for using the model, and details of the computer code. The modular structure consists of a MAIN Program and a series of highly independent subroutines. The subroutines are grouped into 'packages.' Each package deals with a specific feature of the hydrologic system that is to be simulated, such as flow from rivers or flow into drains, or with a specific method of solving the set of simultaneous equations resulting from the finite-difference method. Several solution methods are incorporated, including the Preconditioned Conjugate-Gradient method. The division of the program into packages permits the user to examine specific hydrologic features of the model independently. This also facilitates development of additional capabilities because new packages can be added to the program without modifying the existing packages. The input and output systems of the computer program also are designed to permit maximum flexibility. The program is designed to allow other capabilities, such as transport and optimization, to be incorporated, but this report is limited to describing the ground-water flow capability. The program is written in Fortran 90 and will run without modification on most computers that have a Fortran 90 compiler.

One input-class and two input-class classifications for differentiating olive oil from other edible vegetable oils by use of the normal-phase liquid chromatography fingerprint of the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; Pérez-Castaño, Estefanía; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-04-15

A new method for differentiation of olive oil (independently of the quality category) from other vegetable oils (canola, safflower, corn, peanut, seeds, grapeseed, palm, linseed, sesame and soybean) has been developed. The analytical procedure for chromatographic fingerprinting of the methyl-transesterified fraction of each vegetable oil, using normal-phase liquid chromatography, is described and the chemometric strategies applied and discussed. Some chemometric methods, such as k-nearest neighbours (kNN), partial least squared-discriminant analysis (PLS-DA), support vector machine classification analysis (SVM-C), and soft independent modelling of class analogies (SIMCA), were applied to build classification models. Performance of the classification was evaluated and ranked using several classification quality metrics. The discriminant analysis, based on the use of one input-class, (plus a dummy class) was applied for the first time in this study. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multitasking 3-D forward modeling using high-order finite difference methods on the Cray X-MP/416

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

Terki-Hassaine, O.; Leiss, E.L.

1988-01-01

The CRAY X-MP/416 was used to multitask 3-D forward modeling by the high-order finite difference method. Flowtrace analysis reveals that the most expensive operation in the unitasked program is a matrix vector multiplication. The in-core and out-of-core versions of a reentrant subroutine can perform any fraction of the matrix vector multiplication independently, a pattern compatible with multitasking. The matrix vector multiplication routine can be distributed over two to four processors. The rest of the program utilizes the microtasking feature that lets the system treat independent iterations of DO-loops as subtasks to be performed by any available processor. The availability ofmore » the Solid-State Storage Device (SSD) meant the I/O wait time was virtually zero. A performance study determined a theoretical speedup, taking into account the multitasking overhead. Multitasking programs utilizing both macrotasking and microtasking features obtained actual speedups that were approximately 80% of the ideal speedup.« less

Models and finite element approximations for interacting nanosized piezoelectric bodies and acoustic medium

NASA Astrophysics Data System (ADS)

Nasedkin, A. V.

2017-01-01

This research presents the new size-dependent models of piezoelectric materials oriented to finite element applications. The proposed models include the facilities of taking into account different mechanisms of damping for mechanical and electric fields. The coupled models also incorporate the equations of the theory of acoustics for viscous fluids. In particular cases, these models permit to use the mode superposition method with full separation of the finite element systems into independent equations for the independent modes for transient and harmonic problems. The main boundary conditions were supplemented with the facilities of taking into account the coupled surface effects, allowing to explore the nanoscale piezoelectric materials in the framework of theories of continuous media with surface stresses and their generalizations. For the considered problems we have implemented the finite element technologies and various numerical algorithms to maintain a symmetrical structure of the finite element quasi-definite matrices (matrix structure for the problems with a saddle point).

Interannual Rainfall Variability in North-East Brazil: Observation and Model Simulation

NASA Astrophysics Data System (ADS)

Harzallah, A.; Rocha de Aragão, J. O.; Sadourny, R.

1996-08-01

The relationship between interannual variability of rainfall in north-east Brazil and tropical sea-surface temperature is studied using observations and model simulations. The simulated precipitation is the average of seven independent realizations performed using the Laboratoire de Météorologie Dynamique atmospheric general model forced by the 1970-1988 observed sea-surface temperature. The model reproduces very well the rainfall anomalies (correlation of 091 between observed and modelled anomalies). The study confirms that precipitation in north-east Brazil is highly correlated to the sea-surface temperature in the tropical Atlantic and Pacific oceans. Using the singular value decomposition method, we find that Nordeste rainfall is modulated by two independent oscillations, both governed by the Atlantic dipole, but one involving only the Pacific, the other one having a period of about 10 years. Correlations between precipitation in north-east Brazil during February-May and the sea-surface temperature 6 months earlier indicate that both modes are essential to estimate the quality of the rainy season.

Modeling thermal infrared (2-14 micrometer) reflectance spectra of frost and snow

NASA Technical Reports Server (NTRS)

Wald, Andrew E.

1994-01-01

Existing theories of radiative transfer in close-packed media assume that each particle scatters independently of its neighbors. For opaque particles, such as are common in the thermal infrared, this assumption is not valid, and these radiative transfer theories will not be accurate. A new method is proposed, called 'diffraction subtraction', which modifies the scattering cross section of close-packed large, opaque spheres to account for the effect of close packing on the diffraction cross section of a scattering particle. This method predicts the thermal infrared reflectance of coarse (greater than 50 micrometers radius), disaggregated granular snow. However, such coarse snow is typically old and metamorphosed, with adjacent grains welded together. The reflectance of such a welded block can be described as partly Fresnel in nature and cannot be predicted using Mie inputs to radiative transfer theory. Owing to the high absorption coefficient of ice in the thermal infrared, a rough surface reflectance model can be used to calculate reflectance from such a block. For very small (less than 50 micrometers), disaggregated particles, it is incorrect in principle to treat diffraction independently of reflection and refraction, and the theory fails. However, for particles larger than 50 micrometers, independent scattering is a valid assumption, and standard radiative transfer theory works.

Using Patient Health Questionnaire-9 item parameters of a common metric resulted in similar depression scores compared to independent item response theory model reestimation.

PubMed

Liegl, Gregor; Wahl, Inka; Berghöfer, Anne; Nolte, Sandra; Pieh, Christoph; Rose, Matthias; Fischer, Felix

2016-03-01

To investigate the validity of a common depression metric in independent samples. We applied a common metrics approach based on item-response theory for measuring depression to four German-speaking samples that completed the Patient Health Questionnaire (PHQ-9). We compared the PHQ item parameters reported for this common metric to reestimated item parameters that derived from fitting a generalized partial credit model solely to the PHQ-9 items. We calibrated the new model on the same scale as the common metric using two approaches (estimation with shifted prior and Stocking-Lord linking). By fitting a mixed-effects model and using Bland-Altman plots, we investigated the agreement between latent depression scores resulting from the different estimation models. We found different item parameters across samples and estimation methods. Although differences in latent depression scores between different estimation methods were statistically significant, these were clinically irrelevant. Our findings provide evidence that it is possible to estimate latent depression scores by using the item parameters from a common metric instead of reestimating and linking a model. The use of common metric parameters is simple, for example, using a Web application (http://www.common-metrics.org) and offers a long-term perspective to improve the comparability of patient-reported outcome measures. Copyright © 2016 Elsevier Inc. All rights reserved.

Human cell structure-driven model construction for predicting protein subcellular location from biological images.

PubMed

Shao, Wei; Liu, Mingxia; Zhang, Daoqiang

2016-01-01

The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting. We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method. The dataset and code can be downloaded from https://github.com/shaoweinuaa/. dqzhang@nuaa.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A scaled-ionic-charge simulation model that reproduces enhanced and suppressed water diffusion in aqueous salt solutions.

PubMed

Kann, Z R; Skinner, J L

2014-09-14

Non-polarizable models for ions and water quantitatively and qualitatively misrepresent the salt concentration dependence of water diffusion in electrolyte solutions. In particular, experiment shows that the water diffusion coefficient increases in the presence of salts of low charge density (e.g., CsI), whereas the results of simulations with non-polarizable models show a decrease of the water diffusion coefficient in all alkali halide solutions. We present a simple charge-scaling method based on the ratio of the solvent dielectric constants from simulation and experiment. Using an ion model that was developed independently of a solvent, i.e., in the crystalline solid, this method improves the water diffusion trends across a range of water models. When used with a good-quality water model, e.g., TIP4P/2005 or E3B, this method recovers the qualitative behaviour of the water diffusion trends. The model and method used were also shown to give good results for other structural and dynamic properties including solution density, radial distribution functions, and ion diffusion coefficients.

A Bayesian model averaging method for improving SMT phrase table

NASA Astrophysics Data System (ADS)

Duan, Nan

2013-03-01

Previous methods on improving translation quality by employing multiple SMT models usually carry out as a second-pass decision procedure on hypotheses from multiple systems using extra features instead of using features in existing models in more depth. In this paper, we propose translation model generalization (TMG), an approach that updates probability feature values for the translation model being used based on the model itself and a set of auxiliary models, aiming to alleviate the over-estimation problem and enhance translation quality in the first-pass decoding phase. We validate our approach for translation models based on auxiliary models built by two different ways. We also introduce novel probability variance features into the log-linear models for further improvements. We conclude our approach can be developed independently and integrated into current SMT pipeline directly. We demonstrate BLEU improvements on the NIST Chinese-to-English MT tasks for single-system decodings.

Predicting protein-binding RNA nucleotides with consideration of binding partners.

PubMed

Tuvshinjargal, Narankhuu; Lee, Wook; Park, Byungkyu; Han, Kyungsook

2015-06-01

In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in most performance measures. To the best of our knowledge, this is the first sequence-based prediction of protein-binding nucleotides in RNA which considers the binding partner of RNA. The new model will provide valuable information for designing biochemical experiments to find putative protein-binding sites in RNA with unknown structure. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Gradient-free MCMC methods for dynamic causal modelling

DOE PAGES

Sengupta, Biswa; Friston, Karl J.; Penny, Will D.

2015-03-14

Here, we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-based samplers are more efficient compared with random walk Metropolis sampler or slice-sampling; yet adaptive MCMC sampling is more promising in terms of compute time. Slice-sampling yields the highest number of independent samples from the target density -- albeit at almost 1000% increase in computational time, in comparisonmore » to the most efficient algorithm (i.e., the adaptive MCMC sampler).« less

“Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram

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

Han, Kyungsik; Lee, Sanghack; Jang, Jin

We present behavioral characteristics of teens and adults in Instagram and prediction of them from their behaviors. Based on two independently created datasets from user profiles and tags, we identify teens and adults, and carry out comparative analyses on their online behaviors. Our study reveals: (1) significant behavioral differences between two age groups; (2) the empirical evidence of classifying teens and adults with up to 82% accuracy, using traditional predictive models, while two baseline methods achieve 68% at best; and (3) the robustness of our models by achieving 76%—81% when tested against an independent dataset obtained without using user profilesmore » or tags.« less

Investigating different approaches to develop informative priors in hierarchical Bayesian safety performance functions.

PubMed

Yu, Rongjie; Abdel-Aty, Mohamed

2013-07-01

The Bayesian inference method has been frequently adopted to develop safety performance functions. One advantage of the Bayesian inference is that prior information for the independent variables can be included in the inference procedures. However, there are few studies that discussed how to formulate informative priors for the independent variables and evaluated the effects of incorporating informative priors in developing safety performance functions. This paper addresses this deficiency by introducing four approaches of developing informative priors for the independent variables based on historical data and expert experience. Merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance information criterion (DIC), R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparison across the models indicated that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies. Furthermore, informative priors for the inverse dispersion parameter have also been introduced and tested. Different types of informative priors' effects on the model estimations and goodness-of-fit have been compared and concluded. Finally, based on the results, recommendations for future research topics and study applications have been made. Copyright © 2013 Elsevier Ltd. All rights reserved.

Model-independent extraction of |V cb| from B ¯ → D * ℓ ν -

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

Grinstein, Benjamín; Kobach, Andrew

We fit the unfolded data ofmore » $$-\\atop{B}$$ 0→D *+ℓ$$-\\atop{v}$$ from the Belle experiment, where ℓ ≡ e,μ, using a method independent of heavy quark symmetry to extrapolate to zero-recoil and extract the value of |V cb|. This results in |V cb| (41.9$$+2.0\\atop{-1.9}$$) × 10 -3, which is robust to changes in the theoretical inputs and very consistent with the value extracted from inclusive semileptonic B decays.« less

Model-independent extraction of |V cb| from B ¯ → D * ℓ ν -

DOE PAGES

Grinstein, Benjamín; Kobach, Andrew

2017-08-10

We fit the unfolded data ofmore » $$-\\atop{B}$$ 0→D *+ℓ$$-\\atop{v}$$ from the Belle experiment, where ℓ ≡ e,μ, using a method independent of heavy quark symmetry to extrapolate to zero-recoil and extract the value of |V cb|. This results in |V cb| (41.9$$+2.0\\atop{-1.9}$$) × 10 -3, which is robust to changes in the theoretical inputs and very consistent with the value extracted from inclusive semileptonic B decays.« less

Culture-independent Profiling of the Fecal Microbiome to Identify Microbial Species Associated with a Diarrheal Outbreak in Immunocompromised Mice.

PubMed

Misic, Ana M; Miedel, Emily L; Brice, Angela K; Cole, Stephen; Zhang, Grace F; Dyer, Cecilia D; Secreto, Anthony; Smith, Abigail L; Danet-Desnoyers, Gwenn; Beiting, Daniel P

2018-06-13

Immunocompromised mice are used frequently in biomedical research, in part because they accommodate the engraftmentandstudy of primary human cells within a mouse model; however, these animals are susceptible to opportunistic infectionsand require special husbandry considerations. In 2015, an outbreak marked by high morbidity but low mortality swept througha colony of immunocompromised mice; this outbreak rapidly affected 75% of the colony and ultimately required completedepopulation of the barrier suite. Conventional microbiologic and molecular diagnostics were unsuccessful in determiningthe cause; therefore, we explored culture-independent methods to broadly profile the microbial community in the feces of affected animals. This approach identified 4 bacterial taxa-Candidatus Arthromitus, Clostridium celatum, Clostridiales bacterium VE202-01, and Bifidobacterium pseudolongum strain PV8-2- that were significantly enriched in the affected mice. Based on these results, specific changes were made to the animal husbandry procedures for immunocompromised mice. This case report highlights the utility of culture-independent methods in laboratory animal diagnostics.

Equivalent water height extracted from GRACE gravity field model with robust independent component analysis

NASA Astrophysics Data System (ADS)

Guo, Jinyun; Mu, Dapeng; Liu, Xin; Yan, Haoming; Dai, Honglei

2014-08-01

The Level-2 monthly GRACE gravity field models issued by Center for Space Research (CSR), GeoForschungs Zentrum (GFZ), and Jet Propulsion Laboratory (JPL) are treated as observations used to extract the equivalent water height (EWH) with the robust independent component analysis (RICA). The smoothing radii of 300, 400, and 500 km are tested, respectively, in the Gaussian smoothing kernel function to reduce the observation Gaussianity. Three independent components are obtained by RICA in the spatial domain; the first component matches the geophysical signal, and the other two match the north-south strip and the other noises. The first mode is used to estimate EWHs of CSR, JPL, and GFZ, and compared with the classical empirical decorrelation method (EDM). The EWH STDs for 12 months in 2010 extracted by RICA and EDM show the obvious fluctuation. The results indicate that the sharp EWH changes in some areas have an important global effect, like in Amazon, Mekong, and Zambezi basins.

Research on the digital education resources of sharing pattern in independent colleges based on cloud computing environment

NASA Astrophysics Data System (ADS)

Xiong, Ting; He, Zhiwen

2017-06-01

Cloud computing was first proposed by Google Company in the United States, which was based on the Internet center, providing a standard and open network sharing service approach. With the rapid development of the higher education in China, the educational resources provided by colleges and universities had greatly gap in the actual needs of teaching resources. therefore, Cloud computing of using the Internet technology to provide shared methods liked the timely rain, which had become an important means of the Digital Education on sharing applications in the current higher education. Based on Cloud computing environment, the paper analyzed the existing problems about the sharing of digital educational resources in Jiangxi Province Independent Colleges. According to the sharing characteristics of mass storage, efficient operation and low input about Cloud computing, the author explored and studied the design of the sharing model about the digital educational resources of higher education in Independent College. Finally, the design of the shared model was put into the practical applications.

Results from a model-independent method of monitoring a geodetic network for patterns of transient deformation

NASA Technical Reports Server (NTRS)

Hurst, Kenneth; Granat, Robert

2005-01-01

We have implmented two multi-station detectors for transient crustal deformation within the Southern California Integrated GPS (SCGIN). One the the primary goals of SCIGN is to detect transient deformation associated with the earthquake cycle in Southern California.

Rectification of depth measurement using pulsed thermography with logarithmic peak second derivative method

NASA Astrophysics Data System (ADS)

Li, Xiaoli; Zeng, Zhi; Shen, Jingling; Zhang, Cunlin; Zhao, Yuejin

2018-03-01

Logarithmic peak second derivative (LPSD) method is the most popular method for depth prediction in pulsed thermography. It is widely accepted that this method is independent of defect size. The theoretical model for LPSD method is based on the one-dimensional solution of heat conduction without considering the effect of defect size. When a decay term considering defect aspect ratio is introduced into the solution to correct the three-dimensional thermal diffusion effect, we found that LPSD method is affected by defect size by analytical model. Furthermore, we constructed the relation between the characteristic time of LPSD method and defect aspect ratio, which was verified with the experimental results of stainless steel and glass fiber reinforced plate (GFRP) samples. We also proposed an improved LPSD method for depth prediction when the effect of defect size was considered, and the rectification results of stainless steel and GFRP samples were presented and discussed.

A method for characterizing after-pulsing and dark noise of PMTs and SiPMs

NASA Astrophysics Data System (ADS)

Butcher, A.; Doria, L.; Monroe, J.; Retière, F.; Smith, B.; Walding, J.

2017-12-01

Photo-multiplier tubes (PMTs) and silicon photo-multipliers (SiPMs) are detectors sensitive to single photons that are widely used for the detection of scintillation and Cerenkov light in subatomic physics and medical imaging. This paper presents a method for characterizing two of the main noise sources that PMTs and SiPMs share: dark noise and correlated noise (after-pulsing). The proposed method allows for a model-independent measurement of the after-pulsing timing distribution and dark noise rate.

On the Direct Assimilation of Along-track Sea Surface Height Observations into a Free-surface Ocean Model Using a Weak Constraints Four Dimensional Variational (4dvar) Method

NASA Astrophysics Data System (ADS)

Ngodock, H.; Carrier, M.; Smith, S. R.; Souopgui, I.; Martin, P.; Jacobs, G. A.

2016-02-01

The representer method is adopted for solving a weak constraints 4dvar problem for the assimilation of ocean observations including along-track SSH, using a free surface ocean model. Direct 4dvar assimilation of SSH observations along the satellite tracks requires that the adjoint model be integrated with Dirac impulses on the right hand side of the adjoint equations for the surface elevation equation. The solution of this adjoint model will inevitably include surface gravity waves, and it constitutes the forcing for the tangent linear model (TLM) according to the representer method. This yields an analysis that is contaminated by gravity waves. A method for avoiding the generation of the surface gravity waves in the analysis is proposed in this study; it consists of removing the adjoint of the free surface from the right hand side (rhs) of the free surface mode in the TLM. The information from the SSH observations will still propagate to all other variables via the adjoint of the balance relationship between the barotropic and baroclinic modes, resulting in the correction to the surface elevation. Two assimilation experiments are carried out in the Gulf of Mexico: one with adjoint forcing included on the rhs of the TLM free surface equation, and the other without. Both analyses are evaluated against the assimilated SSH observations, SSH maps from Aviso and independent surface drifters, showing that the analysis that did not include adjoint forcing in the free surface is more accurate. This study shows that when a weak constraint 4dvar approach is considered for the assimilation of along-track SSH observations using a free surface model, with the aim of correcting the mesoscale circulation, an independent model error should not be assigned to the free surface.

A new model-independent approach for finding the arrival direction of an extensive air shower

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

Hedayati, H. Kh., E-mail: hedayati@kntu.ac.ir

2016-11-01

A new accurate method for reconstructing the arrival direction of an extensive air shower (EAS) is described. Compared to existing methods, it is not subject to minimization of a function and, therefore, is fast and stable. This method also does not need to know detailed curvature or thickness structure of an EAS. It can have angular resolution of about 1 degree for a typical surface array in central regions. Also, it has better angular resolution than other methods in the marginal area of arrays.

Assessing the significance of global and local correlations under spatial autocorrelation: a nonparametric approach.

PubMed

Viladomat, Júlia; Mazumder, Rahul; McInturff, Alex; McCauley, Douglas J; Hastie, Trevor

2014-06-01

We propose a method to test the correlation of two random fields when they are both spatially autocorrelated. In this scenario, the assumption of independence for the pair of observations in the standard test does not hold, and as a result we reject in many cases where there is no effect (the precision of the null distribution is overestimated). Our method recovers the null distribution taking into account the autocorrelation. It uses Monte-Carlo methods, and focuses on permuting, and then smoothing and scaling one of the variables to destroy the correlation with the other, while maintaining at the same time the initial autocorrelation. With this simulation model, any test based on the independence of two (or more) random fields can be constructed. This research was motivated by a project in biodiversity and conservation in the Biology Department at Stanford University. © 2014, The International Biometric Society.

Development and Validation of a qRT-PCR Classifier for Lung Cancer Prognosis

PubMed Central

Chen, Guoan; Kim, Sinae; Taylor, Jeremy MG; Wang, Zhuwen; Lee, Oliver; Ramnath, Nithya; Reddy, Rishindra M; Lin, Jules; Chang, Andrew C; Orringer, Mark B; Beer, David G

2011-01-01

Purpose This prospective study aimed to develop a robust and clinically-applicable method to identify high-risk early stage lung cancer patients and then to validate this method for use in future translational studies. Patients and Methods Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model and random survival forest (RSF) analysis. A final set of 91 genes was selected and tested as a predictor of survival using a qRT-PCR-based assay utilizing an independent cohort of 101 lung adenocarcinomas. Results The RSF model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index (MRI) was significantly related to survival (Cox model p < 0.00001) and separated all patients into low, medium, and high-risk groups (HR = 1.00, 2.82, 4.42). The MRI was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low, medium, and high-risk groups (HR = 1.00, 3.29, 3.77). Conclusions The development and validation of this robust qRT-PCR platform allows prediction of patient survival with early stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival. PMID:21792073

Opinion control in complex networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki

2015-03-01

In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.

Independent component analysis decomposition of hospital emergency department throughput measures

NASA Astrophysics Data System (ADS)

He, Qiang; Chu, Henry

2016-05-01

We present a method adapted from medical sensor data analysis, viz. independent component analysis of electroencephalography data, to health system analysis. Timely and effective care in a hospital emergency department is measured by throughput measures such as median times patients spent before they were admitted as an inpatient, before they were sent home, before they were seen by a healthcare professional. We consider a set of five such measures collected at 3,086 hospitals distributed across the U.S. One model of the performance of an emergency department is that these correlated throughput measures are linear combinations of some underlying sources. The independent component analysis decomposition of the data set can thus be viewed as transforming a set of performance measures collected at a site to a collection of outputs of spatial filters applied to the whole multi-measure data. We compare the independent component sources with the output of the conventional principal component analysis to show that the independent components are more suitable for understanding the data sets through visualizations.

Predicting critical transitions in dynamical systems from time series using nonstationary probability density modeling.

PubMed

Kwasniok, Frank

2013-11-01

A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.

Improving Acoustic Models by Watching Television

NASA Technical Reports Server (NTRS)

Witbrock, Michael J.; Hauptmann, Alexander G.

1998-01-01

Obtaining sufficient labelled training data is a persistent difficulty for speech recognition research. Although well transcribed data is expensive to produce, there is a constant stream of challenging speech data and poor transcription broadcast as closed-captioned television. We describe a reliable unsupervised method for identifying accurately transcribed sections of these broadcasts, and show how these segments can be used to train a recognition system. Starting from acoustic models trained on the Wall Street Journal database, a single iteration of our training method reduced the word error rate on an independent broadcast television news test set from 62.2% to 59.5%.

Molecular simulations of carbohydrates and protein-carbohydrate interactions: motivation, issues and prospects.

PubMed

Fadda, Elisa; Woods, Robert J

2010-08-01

The characterization of the 3D structure of oligosaccharides, their conjugates and analogs is particularly challenging for traditional experimental methods. Molecular simulation methods provide a basis for interpreting sparse experimental data and for independently predicting conformational and dynamic properties of glycans. Here, we summarize and analyze the issues associated with modeling carbohydrates, with a detailed discussion of four of the most recently developed carbohydrate force fields, reviewed in terms of applicability to natural glycans, carbohydrate-protein complexes and the emerging area of glycomimetic drugs. In addition, we discuss prospectives and new applications of carbohydrate modeling in drug discovery.

Statistical variation in progressive scrambling

NASA Astrophysics Data System (ADS)

Clark, Robert D.; Fox, Peter C.

2004-07-01

The two methods most often used to evaluate the robustness and predictivity of partial least squares (PLS) models are cross-validation and response randomization. Both methods may be overly optimistic for data sets that contain redundant observations, however. The kinds of perturbation analysis widely used for evaluating model stability in the context of ordinary least squares regression are only applicable when the descriptors are independent of each other and errors are independent and normally distributed; neither assumption holds for QSAR in general and for PLS in particular. Progressive scrambling is a novel, non-parametric approach to perturbing models in the response space in a way that does not disturb the underlying covariance structure of the data. Here, we introduce adjustments for two of the characteristic values produced by a progressive scrambling analysis - the deprecated predictivity (Q_s^{ast^2}) and standard error of prediction (SDEP s * ) - that correct for the effect of introduced perturbation. We also explore the statistical behavior of the adjusted values (Q_0^{ast^2} and SDEP 0 * ) and the sensitivity to perturbation (d q 2/d r yy ' 2). It is shown that the three statistics are all robust for stable PLS models, in terms of the stochastic component of their determination and of their variation due to sampling effects involved in training set selection.

Kernel Smoothing Methods for Non-Poissonian Seismic Hazard Analysis

NASA Astrophysics Data System (ADS)

Woo, Gordon

2017-04-01

For almost fifty years, the mainstay of probabilistic seismic hazard analysis has been the methodology developed by Cornell, which assumes that earthquake occurrence is a Poisson process, and that the spatial distribution of epicentres can be represented by a set of polygonal source zones, within which seismicity is uniform. Based on Vere-Jones' use of kernel smoothing methods for earthquake forecasting, these methods were adapted in 1994 by the author for application to probabilistic seismic hazard analysis. There is no need for ambiguous boundaries of polygonal source zones, nor for the hypothesis of time independence of earthquake sequences. In Europe, there are many regions where seismotectonic zones are not well delineated, and where there is a dynamic stress interaction between events, so that they cannot be described as independent. From the Amatrice earthquake of 24 August, 2016, the subsequent damaging earthquakes in Central Italy over months were not independent events. Removing foreshocks and aftershocks is not only an ill-defined task, it has a material effect on seismic hazard computation. Because of the spatial dispersion of epicentres, and the clustering of magnitudes for the largest events in a sequence, which might all be around magnitude 6, the specific event causing the highest ground motion can vary from one site location to another. Where significant active faults have been clearly identified geologically, they should be modelled as individual seismic sources. The remaining background seismicity should be modelled as non-Poissonian using statistical kernel smoothing methods. This approach was first applied for seismic hazard analysis at a UK nuclear power plant two decades ago, and should be included within logic-trees for future probabilistic seismic hazard at critical installations within Europe. In this paper, various salient European applications are given.

Adaptive sleep-wake discrimination for wearable devices.

PubMed

Karlen, Walter; Floreano, Dario

2011-04-01

Sleep/wake classification systems that rely on physiological signals suffer from intersubject differences that make accurate classification with a single, subject-independent model difficult. To overcome the limitations of intersubject variability, we suggest a novel online adaptation technique that updates the sleep/wake classifier in real time. The objective of the present study was to evaluate the performance of a newly developed adaptive classification algorithm that was embedded on a wearable sleep/wake classification system called SleePic. The algorithm processed ECG and respiratory effort signals for the classification task and applied behavioral measurements (obtained from accelerometer and press-button data) for the automatic adaptation task. When trained as a subject-independent classifier algorithm, the SleePic device was only able to correctly classify 74.94 ± 6.76% of the human-rated sleep/wake data. By using the suggested automatic adaptation method, the mean classification accuracy could be significantly improved to 92.98 ± 3.19%. A subject-independent classifier based on activity data only showed a comparable accuracy of 90.44 ± 3.57%. We demonstrated that subject-independent models used for online sleep-wake classification can successfully be adapted to previously unseen subjects without the intervention of human experts or off-line calibration.

Permutation inference for the general linear model

PubMed Central

Winkler, Anderson M.; Ridgway, Gerard R.; Webster, Matthew A.; Smith, Stephen M.; Nichols, Thomas E.

2014-01-01

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (glms) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on glm parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm – the “randomise” algorithm – for permutation inference with the glm. PMID:24530839

[Hungarian health resource allocation from the viewpoint of the English methodology].

PubMed

Fadgyas-Freyler, Petra

2018-02-01

This paper describes both the English health resource allocation and the attempt of its Hungarian adaptation. We describe calculations for a Hungarian regression model using the English 'weighted capitation formula'. The model has proven statistically correct. New independent variables and homogenous regional units have to be found for Hungary. The English method can be used with adequate variables. Hungarian patient-level health data can support a much more sophisticated model. Further research activities are needed. Orv Hetil. 2018; 159(5): 183-191.

A fragmentation and reassembly method for ab initio phasing.

PubMed

Shrestha, Rojan; Zhang, Kam Y J

2015-02-01

Ab initio phasing with de novo models has become a viable approach for structural solution from protein crystallographic diffraction data. This approach takes advantage of the known protein sequence information, predicts de novo models and uses them for structure determination by molecular replacement. However, even the current state-of-the-art de novo modelling method has a limit as to the accuracy of the model predicted, which is sometimes insufficient to be used as a template for successful molecular replacement. A fragment-assembly phasing method has been developed that starts from an ensemble of low-accuracy de novo models, disassembles them into fragments, places them independently in the crystallographic unit cell by molecular replacement and then reassembles them into a whole structure that can provide sufficient phase information to enable complete structure determination by automated model building. Tests on ten protein targets showed that the method could solve structures for eight of these targets, although the predicted de novo models cannot be used as templates for successful molecular replacement since the best model for each target is on average more than 4.0 Å away from the native structure. The method has extended the applicability of the ab initio phasing by de novo models approach. The method can be used to solve structures when the best de novo models are still of low accuracy.

Development and validation of a combined phased acoustical radiosity and image source model for predicting sound fields in rooms.

PubMed

Marbjerg, Gerd; Brunskog, Jonas; Jeong, Cheol-Ho; Nilsson, Erling

2015-09-01

A model, combining acoustical radiosity and the image source method, including phase shifts on reflection, has been developed. The model is denoted Phased Acoustical Radiosity and Image Source Method (PARISM), and it has been developed in order to be able to model both specular and diffuse reflections with complex-valued and angle-dependent boundary conditions. This paper mainly describes the combination of the two models and the implementation of the angle-dependent boundary conditions. It furthermore describes how a pressure impulse response is obtained from the energy-based acoustical radiosity by regarding the model as being stochastic. Three methods of implementation are proposed and investigated, and finally, recommendations are made for their use. Validation of the image source method is done by comparison with finite element simulations of a rectangular room with a porous absorber ceiling. Results from the full model are compared with results from other simulation tools and with measurements. The comparisons of the full model are done for real-valued and angle-independent surface properties. The proposed model agrees well with both the measured results and the alternative theories, and furthermore shows a more realistic spatial variation than energy-based methods due to the fact that interference is considered.

Structured Matrix Completion with Applications to Genomic Data Integration.

PubMed

Cai, Tianxi; Cai, T Tony; Zhang, Anru

2016-01-01

Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. We provide theoretical justification for the proposed SMC method and derive lower bound for the estimation errors, which together establish the optimal rate of recovery over certain classes of approximately low-rank matrices. Simulation studies show that the method performs well in finite sample under a variety of configurations. The method is applied to integrate several ovarian cancer genomic studies with different extent of genomic measurements, which enables us to construct more accurate prediction rules for ovarian cancer survival.

Forecast and analysis of the cosmological redshift drift.

PubMed

Lazkoz, Ruth; Leanizbarrutia, Iker; Salzano, Vincenzo

2018-01-01

The cosmological redshift drift could lead to the next step in high-precision cosmic geometric observations, becoming a direct and irrefutable test for cosmic acceleration. In order to test the viability and possible properties of this effect, also called Sandage-Loeb (SL) test, we generate a model-independent mock data set in order to compare its constraining power with that of the future mock data sets of Type Ia Supernovae (SNe) and Baryon Acoustic Oscillations (BAO). The performance of those data sets is analyzed by testing several cosmological models with the Markov chain Monte Carlo (MCMC) method, both independently as well as combining all data sets. Final results show that, in general, SL data sets allow for remarkable constraints on the matter density parameter today [Formula: see text] on every tested model, showing also a great complementarity with SNe and BAO data regarding dark energy parameters.

Constructing a cosmological model-independent Hubble diagram of type Ia supernovae with cosmic chronometers

NASA Astrophysics Data System (ADS)

Li, Zhengxiang; Gonzalez, J. E.; Yu, Hongwei; Zhu, Zong-Hong; Alcaniz, J. S.

2016-02-01

We apply two methods, i.e., the Gaussian processes and the nonparametric smoothing procedure, to reconstruct the Hubble parameter H (z ) as a function of redshift from 15 measurements of the expansion rate obtained from age estimates of passively evolving galaxies. These reconstructions enable us to derive the luminosity distance to a certain redshift z , calibrate the light-curve fitting parameters accounting for the (unknown) intrinsic magnitude of type Ia supernova (SNe Ia), and construct cosmological model-independent Hubble diagrams of SNe Ia. In order to test the compatibility between the reconstructed functions of H (z ), we perform a statistical analysis considering the latest SNe Ia sample, the so-called joint light-curve compilation. We find that, for the Gaussian processes, the reconstructed functions of Hubble parameter versus redshift, and thus the following analysis on SNe Ia calibrations and cosmological implications, are sensitive to prior mean functions. However, for the nonparametric smoothing method, the reconstructed functions are not dependent on initial guess models, and consistently require high values of H0, which are in excellent agreement with recent measurements of this quantity from Cepheids and other local distance indicators.

Simultaneous two-view epipolar geometry estimation and motion segmentation by 4D tensor voting.

PubMed

Tong, Wai-Shun; Tang, Chi-Keung; Medioni, Gérard

2004-09-01

We address the problem of simultaneous two-view epipolar geometry estimation and motion segmentation from nonstatic scenes. Given a set of noisy image pairs containing matches of n objects, we propose an unconventional, efficient, and robust method, 4D tensor voting, for estimating the unknown n epipolar geometries, and segmenting the static and motion matching pairs into n independent motions. By considering the 4D isotropic and orthogonal joint image space, only two tensor voting passes are needed, and a very high noise to signal ratio (up to five) can be tolerated. Epipolar geometries corresponding to multiple, rigid motions are extracted in succession. Only two uncalibrated frames are needed, and no simplifying assumption (such as affine camera model or homographic model between images) other than the pin-hole camera model is made. Our novel approach consists of propagating a local geometric smoothness constraint in the 4D joint image space, followed by global consistency enforcement for extracting the fundamental matrices corresponding to independent motions. We have performed extensive experiments to compare our method with some representative algorithms to show that better performance on nonstatic scenes are achieved. Results on challenging data sets are presented.

The Research of Multiple Attenuation Based on Feedback Iteration and Independent Component Analysis

NASA Astrophysics Data System (ADS)

Xu, X.; Tong, S.; Wang, L.

2017-12-01

How to solve the problem of multiple suppression is a difficult problem in seismic data processing. The traditional technology for multiple attenuation is based on the principle of the minimum output energy of the seismic signal, this criterion is based on the second order statistics, and it can't achieve the multiple attenuation when the primaries and multiples are non-orthogonal. In order to solve the above problems, we combine the feedback iteration method based on the wave equation and the improved independent component analysis (ICA) based on high order statistics to suppress the multiple waves. We first use iterative feedback method to predict the free surface multiples of each order. Then, in order to predict multiples from real multiple in amplitude and phase, we design an expanded pseudo multi-channel matching filtering method to get a more accurate matching multiple result. Finally, we present the improved fast ICA algorithm which is based on the maximum non-Gauss criterion of output signal to the matching multiples and get better separation results of the primaries and the multiples. The advantage of our method is that we don't need any priori information to the prediction of the multiples, and can have a better separation result. The method has been applied to several synthetic data generated by finite-difference model technique and the Sigsbee2B model multiple data, the primaries and multiples are non-orthogonal in these models. The experiments show that after three to four iterations, we can get the perfect multiple results. Using our matching method and Fast ICA adaptive multiple subtraction, we can not only effectively preserve the effective wave energy in seismic records, but also can effectively suppress the free surface multiples, especially the multiples related to the middle and deep areas.

Detection of explosives on the surface of banknotes by Raman hyperspectral imaging and independent component analysis.

PubMed

Almeida, Mariana R; Correa, Deleon N; Zacca, Jorge J; Logrado, Lucio Paulo Lima; Poppi, Ronei J

2015-02-20

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50 μg cm(-2). Copyright © 2014 Elsevier B.V. All rights reserved.

A cosmology-independent calibration of type Ia supernovae data

NASA Astrophysics Data System (ADS)

Hauret, C.; Magain, P.; Biernaux, J.

2018-06-01

Recently, the common methodology used to transform type Ia supernovae (SNe Ia) into genuine standard candles has been suffering criticism. Indeed, it assumes a particular cosmological model (namely the flat ΛCDM) to calibrate the standardisation corrections parameters, i.e. the dependency of the supernova peak absolute magnitude on its colour, post-maximum decline rate and host galaxy mass. As a result, this assumption could make the data compliant to the assumed cosmology and thus nullify all works previously conducted on model comparison. In this work, we verify the viability of these hypotheses by developing a cosmology-independent approach to standardise SNe Ia data from the recent JLA compilation. Our resulting corrections turn out to be very close to the ΛCDM-based corrections. Therefore, even if a ΛCDM-based calibration is questionable from a theoretical point of view, the potential compliance of SNe Ia data does not happen in practice for the JLA compilation. Previous works of model comparison based on these data do not have to be called into question. However, as this cosmology-independent standardisation method has the same degree of complexity than the model-dependent one, it is worth using it in future works, especially if smaller samples are considered, such as the superluminous type Ic supernovae.

Crowd-Sourced Verification of Computational Methods and Data in Systems Toxicology: A Case Study with a Heat-Not-Burn Candidate Modified Risk Tobacco Product.

PubMed

Poussin, Carine; Belcastro, Vincenzo; Martin, Florian; Boué, Stéphanie; Peitsch, Manuel C; Hoeng, Julia

2017-04-17

Systems toxicology intends to quantify the effect of toxic molecules in biological systems and unravel their mechanisms of toxicity. The development of advanced computational methods is required for analyzing and integrating high throughput data generated for this purpose as well as for extrapolating predictive toxicological outcomes and risk estimates. To ensure the performance and reliability of the methods and verify conclusions from systems toxicology data analysis, it is important to conduct unbiased evaluations by independent third parties. As a case study, we report here the results of an independent verification of methods and data in systems toxicology by crowdsourcing. The sbv IMPROVER systems toxicology computational challenge aimed to evaluate computational methods for the development of blood-based gene expression signature classification models with the ability to predict smoking exposure status. Participants created/trained models on blood gene expression data sets including smokers/mice exposed to 3R4F (a reference cigarette) or noncurrent smokers/Sham (mice exposed to air). Participants applied their models on unseen data to predict whether subjects classify closer to smoke-exposed or nonsmoke exposed groups. The data sets also included data from subjects that had been exposed to potential modified risk tobacco products (MRTPs) or that had switched to a MRTP after exposure to conventional cigarette smoke. The scoring of anonymized participants' predictions was done using predefined metrics. The top 3 performers' methods predicted class labels with area under the precision recall scores above 0.9. Furthermore, although various computational approaches were used, the crowd's results confirmed our own data analysis outcomes with regards to the classification of MRTP-related samples. Mice exposed directly to a MRTP were classified closer to the Sham group. After switching to a MRTP, the confidence that subjects belonged to the smoke-exposed group decreased significantly. Smoking exposure gene signatures that contributed to the group separation included a core set of genes highly consistent across teams such as AHRR, LRRN3, SASH1, and P2RY6. In conclusion, crowdsourcing constitutes a pertinent approach, in complement to the classical peer review process, to independently and unbiasedly verify computational methods and data for risk assessment using systems toxicology.

Manufacturing Methods and Technology Engineering for Tape Chip Carrier.

DTIC Science & Technology

1981-08-01

equipment and fixtures were used in the manufacturer of the Sync Counter hybrid microcircuit. o Continuous Tape Plater - Model No. STP, Microplate ...Headquarters 001 Commander ATTN: Ray L. Gilbert Naval Ocean Systems Center 608 Independence Ave., SW ATTN: Dr. W. D. McKee, Jr. Washington, DC 20546 Code

The maximum depth of shower with E sub 0 larger than 10(17) eV on average characteristics of EAS different components

NASA Technical Reports Server (NTRS)

Glushkov, A. V.; Efimov, N. N.; Makarov, I. T.; Pravdin, M. I.; Dedenko, L. G.

1985-01-01

The extensive air shower (EAS) development model independent method of the determination of a maximum depth of shower (X sub m) is considered. X sub m values obtained on various EAS parameters are in a good agreement.

Influences of Tone on Vowel Articulation in Mandarin Chinese

ERIC Educational Resources Information Center

Shaw, Jason A.; Chen, Wei-rong; Proctor, Michael I.; Derrick, Donald

2016-01-01

Purpose: Models of speech production often abstract away from shared physiology in pitch control and lingual articulation, positing independent control of tone and vowel units. We assess the validity of this assumption in Mandarin Chinese by evaluating the stability of lingual articulation for vowels across variation in tone. Method:…

Conceptual Change through Changing the Process of Comparison

ERIC Educational Resources Information Center

Wasmann-Frahm, Astrid

2009-01-01

Classification can serve as a tool for conceptualising ideas about vertebrates. Training enhances classification skills as well as sharpening concepts. The method described in this paper is based on the "hybrid-model" of comparison that proposes two independently working processes: associative and theory-based. The two interact during a…

Fluid mechanical model of the Helmholtz resonator

NASA Technical Reports Server (NTRS)

Hersh, A. S.; Walker, B.

1977-01-01

A semi-empirical fluid mechanical model of the acoustic behavior of Helmholtz resonators is presented which predicts impedance as a function of the amplitude and frequency of the incident sound pressure field and resonator geometry. The model assumes that the particle velocity approaches the orifice in a spherical manner. The incident and cavity sound fields are connected by solving the governing oscillating mass and momentum conservation equations. The model is in agreement with the Rayleigh slug-mass model at low values of incident sound pressure level. At high values, resistance is predicted to be independent of frequency, proportional to the square root of the amplitude of the incident sound pressure field, and virtually independent of resonator geometry. Reactance is predicted to depend in a very complicated way upon resonator geometry, incident sound pressure level, and frequency. Nondimensional parameters are defined that divide resonator impedance into three categories corresponding to low, moderately low, and intense incident sound pressure amplitudes. The two-microphone method was used to measure the impedance of a variety of resonators. The data were used to refine and verify the model.

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

PubMed

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

2016-12-01

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

Modeling heading and path perception from optic flow in the case of independently moving objects

PubMed Central

Raudies, Florian; Neumann, Heiko

2013-01-01

Humans are usually accurate when estimating heading or path from optic flow, even in the presence of independently moving objects (IMOs) in an otherwise rigid scene. To invoke significant biases in perceived heading, IMOs have to be large and obscure the focus of expansion (FOE) in the image plane, which is the point of approach. For the estimation of path during curvilinear self-motion no significant biases were found in the presence of IMOs. What makes humans robust in their estimation of heading or path using optic flow? We derive analytical models of optic flow for linear and curvilinear self-motion using geometric scene models. Heading biases of a linear least squares method, which builds upon these analytical models, are large, larger than those reported for humans. This motivated us to study segmentation cues that are available from optic flow. We derive models of accretion/deletion, expansion/contraction, acceleration/deceleration, local spatial curvature, and local temporal curvature, to be used as cues to segment an IMO from the background. Integrating these segmentation cues into our method of estimating heading or path now explains human psychophysical data and extends, as well as unifies, previous investigations. Our analysis suggests that various cues available from optic flow help to segment IMOs and, thus, make humans' heading and path perception robust in the presence of such IMOs. PMID:23554589

Adaptive Shape Functions and Internal Mesh Adaptation for Modelling Progressive Failure in Adhesively Bonded Joints

NASA Technical Reports Server (NTRS)

Stapleton, Scott; Gries, Thomas; Waas, Anthony M.; Pineda, Evan J.

2014-01-01

Enhanced finite elements are elements with an embedded analytical solution that can capture detailed local fields, enabling more efficient, mesh independent finite element analysis. The shape functions are determined based on the analytical model rather than prescribed. This method was applied to adhesively bonded joints to model joint behavior with one element through the thickness. This study demonstrates two methods of maintaining the fidelity of such elements during adhesive non-linearity and cracking without increasing the mesh needed for an accurate solution. The first method uses adaptive shape functions, where the shape functions are recalculated at each load step based on the softening of the adhesive. The second method is internal mesh adaption, where cracking of the adhesive within an element is captured by further discretizing the element internally to represent the partially cracked geometry. By keeping mesh adaptations within an element, a finer mesh can be used during the analysis without affecting the global finite element model mesh. Examples are shown which highlight when each method is most effective in reducing the number of elements needed to capture adhesive nonlinearity and cracking. These methods are validated against analogous finite element models utilizing cohesive zone elements.

Bilevel Model-Based Discriminative Dictionary Learning for Recognition.

PubMed

Zhou, Pan; Zhang, Chao; Lin, Zhouchen

2017-03-01

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l 0 or l 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

A method to investigate the diffusion properties of nuclear calcium.

PubMed

Queisser, Gillian; Wittum, Gabriel

2011-10-01

Modeling biophysical processes in general requires knowledge about underlying biological parameters. The quality of simulation results is strongly influenced by the accuracy of these parameters, hence the identification of parameter values that the model includes is a major part of simulating biophysical processes. In many cases, secondary data can be gathered by experimental setups, which are exploitable by mathematical inverse modeling techniques. Here we describe a method for parameter identification of diffusion properties of calcium in the nuclei of rat hippocampal neurons. The method is based on a Gauss-Newton method for solving a least-squares minimization problem and was formulated in such a way that it is ideally implementable in the simulation platform uG. Making use of independently published space- and time-dependent calcium imaging data, generated from laser-assisted calcium uncaging experiments, here we could identify the diffusion properties of nuclear calcium and were able to validate a previously published model that describes nuclear calcium dynamics as a diffusion process.

Utterance independent bimodal emotion recognition in spontaneous communication

NASA Astrophysics Data System (ADS)

Tao, Jianhua; Pan, Shifeng; Yang, Minghao; Li, Ya; Mu, Kaihui; Che, Jianfeng

2011-12-01

Emotion expressions sometimes are mixed with the utterance expression in spontaneous face-to-face communication, which makes difficulties for emotion recognition. This article introduces the methods of reducing the utterance influences in visual parameters for the audio-visual-based emotion recognition. The audio and visual channels are first combined under a Multistream Hidden Markov Model (MHMM). Then, the utterance reduction is finished by finding the residual between the real visual parameters and the outputs of the utterance related visual parameters. This article introduces the Fused Hidden Markov Model Inversion method which is trained in the neutral expressed audio-visual corpus to solve the problem. To reduce the computing complexity the inversion model is further simplified to a Gaussian Mixture Model (GMM) mapping. Compared with traditional bimodal emotion recognition methods (e.g., SVM, CART, Boosting), the utterance reduction method can give better results of emotion recognition. The experiments also show the effectiveness of our emotion recognition system when it was used in a live environment.

A component prediction method for flue gas of natural gas combustion based on nonlinear partial least squares method.

PubMed

Cao, Hui; Yan, Xingyu; Li, Yaojiang; Wang, Yanxia; Zhou, Yan; Yang, Sanchun

2014-01-01

Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.

Differential Equations Models to Study Quorum Sensing.

PubMed

Pérez-Velázquez, Judith; Hense, Burkhard A

2018-01-01

Mathematical models to study quorum sensing (QS) have become an important tool to explore all aspects of this type of bacterial communication. A wide spectrum of mathematical tools and methods such as dynamical systems, stochastics, and spatial models can be employed. In this chapter, we focus on giving an overview of models consisting of differential equations (DE), which can be used to describe changing quantities, for example, the dynamics of one or more signaling molecule in time and space, often in conjunction with bacterial growth dynamics. The chapter is divided into two sections: ordinary differential equations (ODE) and partial differential equations (PDE) models of QS. Rates of change are represented mathematically by derivatives, i.e., in terms of DE. ODE models allow describing changes in one independent variable, for example, time. PDE models can be used to follow changes in more than one independent variable, for example, time and space. Both types of models often consist of systems (i.e., more than one equation) of equations, such as equations for bacterial growth and autoinducer concentration dynamics. Almost from the onset, mathematical modeling of QS using differential equations has been an interdisciplinary endeavor and many of the works we revised here will be placed into their biological context.

Online Statistical Modeling (Regression Analysis) for Independent Responses

NASA Astrophysics Data System (ADS)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Classification and Identification of Plant Fibrous Material with Different Species Using near Infrared Technique—A New Way to Approach Determining Biomass Properties Accurately within Different Species

PubMed Central

Jiang, Wei; Zhou, Chengfeng; Han, Guangting; Via, Brian; Swain, Tammy; Fan, Zhaofei; Liu, Shaoyang

2017-01-01

Plant fibrous material is a good resource in textile and other industries. Normally, several kinds of plant fibrous materials used in one process are needed to be identified and characterized in advance. It is easy to identify them when they are in raw condition. However, most of the materials are semi products which are ground, rotted or pre-hydrolyzed. To classify these samples which include different species with high accuracy is a big challenge. In this research, both qualitative and quantitative analysis methods were chosen to classify six different species of samples, including softwood, hardwood, bast, and aquatic plant. Soft Independent Modeling of Class Analogy (SIMCA) and partial least squares (PLS) were used. The algorithm to classify different species of samples using PLS was created independently in this research. Results found that the six species can be successfully classified using SIMCA and PLS methods, and these two methods show similar results. The identification rates of kenaf, ramie and pine are 100%, and the identification rates of lotus, eucalyptus and tallow are higher than 94%. It is also found that spectra loadings can help pick up best wavenumber ranges for constructing the NIR model. Inter material distance can show how close between two species. Scores graph is helpful to choose the principal components numbers during the model construction. PMID:28105037

An Analysis on the Unemployment Rate in the Philippines: A Time Series Data Approach

NASA Astrophysics Data System (ADS)

Urrutia, J. D.; Tampis, R. L.; E Atienza, JB

2017-03-01

This study aims to formulate a mathematical model for forecasting and estimating unemployment rate in the Philippines. Also, factors which can predict the unemployment is to be determined among the considered variables namely Labor Force Rate, Population, Inflation Rate, Gross Domestic Product, and Gross National Income. Granger-causal relationship and integration among the dependent and independent variables are also examined using Pairwise Granger-causality test and Johansen Cointegration Test. The data used were acquired from the Philippine Statistics Authority, National Statistics Office, and Bangko Sentral ng Pilipinas. Following the Box-Jenkins method, the formulated model for forecasting the unemployment rate is SARIMA (6, 1, 5) × (0, 1, 1)4 with a coefficient of determination of 0.79. The actual values are 99 percent identical to the predicted values obtained through the model, and are 72 percent closely relative to the forecasted ones. According to the results of the regression analysis, Labor Force Rate and Population are the significant factors of unemployment rate. Among the independent variables, Population, GDP, and GNI showed to have a granger-causal relationship with unemployment. It is also found that there are at least four cointegrating relations between the dependent and independent variables.

Probing New Long-Range Interactions by Isotope Shift Spectroscopy

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

Berengut, Julian C.; Budker, Dmitry; Delaunay, Cédric

We explore a method to probe new long- and intermediate-range interactions using precision atomic isotope shift spectroscopy. We develop a formalism to interpret linear King plots as bounds on new physics with minimal theory inputs. We focus only on bounding the new physics contributions that can be calculated independently of the standard model nuclear effects. We apply our method to existing Ca + data and project its sensitivity to conjectured new bosons with spin-independent couplings to the electron and the neutron using narrow transitions in other atoms and ions, specifically, Sr and Yb. Future measurements are expected to improve themore » relative precision by 5 orders of magnitude, and they can potentially lead to an unprecedented sensitivity for bosons within the 0.3 to 10 MeV mass range.« less

Probing New Long-Range Interactions by Isotope Shift Spectroscopy.

PubMed

Berengut, Julian C; Budker, Dmitry; Delaunay, Cédric; Flambaum, Victor V; Frugiuele, Claudia; Fuchs, Elina; Grojean, Christophe; Harnik, Roni; Ozeri, Roee; Perez, Gilad; Soreq, Yotam

2018-03-02

We explore a method to probe new long- and intermediate-range interactions using precision atomic isotope shift spectroscopy. We develop a formalism to interpret linear King plots as bounds on new physics with minimal theory inputs. We focus only on bounding the new physics contributions that can be calculated independently of the standard model nuclear effects. We apply our method to existing Ca^{+} data and project its sensitivity to conjectured new bosons with spin-independent couplings to the electron and the neutron using narrow transitions in other atoms and ions, specifically, Sr and Yb. Future measurements are expected to improve the relative precision by 5 orders of magnitude, and they can potentially lead to an unprecedented sensitivity for bosons within the 0.3 to 10 MeV mass range.

Probing New Long-Range Interactions by Isotope Shift Spectroscopy

DOE PAGES

Berengut, Julian C.; Budker, Dmitry; Delaunay, Cédric; ...

2018-02-26

We explore a method to probe new long- and intermediate-range interactions using precision atomic isotope shift spectroscopy. We develop a formalism to interpret linear King plots as bounds on new physics with minimal theory inputs. We focus only on bounding the new physics contributions that can be calculated independently of the standard model nuclear effects. We apply our method to existing Ca + data and project its sensitivity to conjectured new bosons with spin-independent couplings to the electron and the neutron using narrow transitions in other atoms and ions, specifically, Sr and Yb. Future measurements are expected to improve themore » relative precision by 5 orders of magnitude, and they can potentially lead to an unprecedented sensitivity for bosons within the 0.3 to 10 MeV mass range.« less

Score Estimating Equations from Embedded Likelihood Functions under Accelerated Failure Time Model

PubMed Central

NING, JING; QIN, JING; SHEN, YU

2014-01-01

SUMMARY The semiparametric accelerated failure time (AFT) model is one of the most popular models for analyzing time-to-event outcomes. One appealing feature of the AFT model is that the observed failure time data can be transformed to identically independent distributed random variables without covariate effects. We describe a class of estimating equations based on the score functions for the transformed data, which are derived from the full likelihood function under commonly used semiparametric models such as the proportional hazards or proportional odds model. The methods of estimating regression parameters under the AFT model can be applied to traditional right-censored survival data as well as more complex time-to-event data subject to length-biased sampling. We establish the asymptotic properties and evaluate the small sample performance of the proposed estimators. We illustrate the proposed methods through applications in two examples. PMID:25663727

Micromechanics of brain white matter tissue: A fiber-reinforced hyperelastic model using embedded element technique.

PubMed

Yousefsani, Seyed Abdolmajid; Shamloo, Amir; Farahmand, Farzam

2018-04-01

A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results indicated that with a sufficiently large representative volume element (RVE) and fine mesh, the statistically randomized microstructure implemented in the RVE exhibits directional independency in transverse plane, and the model predictions for the overall and local tissue responses, characterized by the normalized strain energy density and Cauchy and von Mises stresses, are independent from the modeling parameters. Comparison of the responses of the probabilistic model with that of a simple uniform RVE revealed that only the first one is capable of representing the localized behavior of the tissue constituents. The validity test of the model predictions for the corona radiata against experimental data from the literature indicated a very close agreement. In comparison with the conventional direct meshing method, the model provided almost the same results after correcting the stiffness redundancy, however, with much less computational cost and facilitated geometrical modeling, meshing, and boundary conditions imposing. It was concluded that the EET can be used effectively for detailed probabilistic micromechanical modeling of the white matter in order to provide more accurate predictions for the axonal responses, which are of great importance when simulating the brain trauma or tumor growth. Copyright © 2018 Elsevier Ltd. All rights reserved.

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

USGS Publications Warehouse

Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

2017-01-01

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm.

PubMed

Cui, Lizhi; Poon, Josiah; Poon, Simon K; Chen, Hao; Gao, Junbin; Kwan, Paul; Fan, Kei; Ling, Zhihao

2014-01-01

The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective.

INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).

Capture-Recapture Estimators in Epidemiology with Applications to Pertussis and Pneumococcal Invasive Disease Surveillance

PubMed Central

Braeye, Toon; Verheagen, Jan; Mignon, Annick; Flipse, Wim; Pierard, Denis; Huygen, Kris; Schirvel, Carole; Hens, Niel

2016-01-01

Introduction Surveillance networks are often not exhaustive nor completely complementary. In such situations, capture-recapture methods can be used for incidence estimation. The choice of estimator and their robustness with respect to the homogeneity and independence assumptions are however not well documented. Methods We investigated the performance of five different capture-recapture estimators in a simulation study. Eight different scenarios were used to detect and combine case-information. The scenarios increasingly violated assumptions of independence of samples and homogeneity of detection probabilities. Belgian datasets on invasive pneumococcal disease (IPD) and pertussis provided motivating examples. Results No estimator was unbiased in all scenarios. Performance of the parametric estimators depended on how much of the dependency and heterogeneity were correctly modelled. Model building was limited by parameter estimability, availability of additional information (e.g. covariates) and the possibilities inherent to the method. In the most complex scenario, methods that allowed for detection probabilities conditional on previous detections estimated the total population size within a 20–30% error-range. Parametric estimators remained stable if individual data sources lost up to 50% of their data. The investigated non-parametric methods were more susceptible to data loss and their performance was linked to the dependence between samples; overestimating in scenarios with little dependence, underestimating in others. Issues with parameter estimability made it impossible to model all suggested relations between samples for the IPD and pertussis datasets. For IPD, the estimates for the Belgian incidence for cases aged 50 years and older ranged from 44 to58/100,000 in 2010. The estimates for pertussis (all ages, Belgium, 2014) ranged from 24.2 to30.8/100,000. Conclusion We encourage the use of capture-recapture methods, but epidemiologists should preferably include datasets for which the underlying dependency structure is not too complex, a priori investigate this structure, compensate for it within the model and interpret the results with the remaining unmodelled heterogeneity in mind. PMID:27529167

Post-Processing of Low Dose Mammography Images

DTIC Science & Technology

2002-05-01

method of restoring images in the presence of blur as well as noise ” (12:276). The deblurring and denoising characteristics make Wiener filtering...independent noise . The signal dependant scatter noise can be modeled as blur in the mammography image. A Wiener filter with deblurring characteristics can...centered on. This method is used to eradicate noise impulses with high 26 pixel values (2:7). For the research at hand, the median filter would

Studies of uncontrolled air traffic patterns, phase 1

NASA Technical Reports Server (NTRS)

Baxa, E. G., Jr.; Scharf, L. L.; Ruedger, W. H.; Modi, J. A.; Wheelock, S. L.; Davis, C. M.

1975-01-01

The general aviation air traffic flow patterns at uncontrolled airports are investigated and analyzed and traffic pattern concepts are developed to minimize the midair collision hazard in uncontrolled airspace. An analytical approach to evaluate midair collision hazard probability as a function of traffic densities is established which is basically independent of path structure. Two methods of generating space-time interrelationships between terminal area aircraft are presented; one is a deterministic model to generate pseudorandom aircraft tracks, the other is a statistical model in preliminary form. Some hazard measures are presented for selected traffic densities. It is concluded that the probability of encountering a hazard should be minimized independently of any other considerations and that the number of encounters involving visible-avoidable aircraft should be maximized at the expense of encounters in other categories.

The AMIDAS Website: An Online Tool for Direct Dark Matter Detection Experiments

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

Shan, Chung-Lin

2010-02-10

Following our long-erm work on development of model-independent data analysis methods for reconstructing the one-dimensional velocity distribution function of halo WIMPs as well as for determining their mass and couplings on nucleons by using data from direct Dark Matter detection experiments directly, we combined the simulation programs to a compact system: AMIDAS (A Model-Independent Data Analysis System). For users' convenience an online system has also been established at the same time. AMIDAS has the ability to do full Monte Carlo simulations, faster theoretical estimations, as well as to analyze (real) data sets recorded in direct detection experiments without modifying themore » source code. In this article, I give an overview of functions of the AMIDAS code based on the use of its website.« less

Gradient-free MCMC methods for dynamic causal modelling.

PubMed

Sengupta, Biswa; Friston, Karl J; Penny, Will D

2015-05-15

In this technical note we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-based samplers are more efficient compared with random walk Metropolis sampler or slice-sampling; yet adaptive MCMC sampling is more promising in terms of compute time. Slice-sampling yields the highest number of independent samples from the target density - albeit at almost 1000% increase in computational time, in comparison to the most efficient algorithm (i.e., the adaptive MCMC sampler). Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants.

PubMed

Sauzet, Odile; Peacock, Janet L

2017-07-20

The analysis of perinatal outcomes often involves datasets with some multiple births. These are datasets mostly formed of independent observations and a limited number of clusters of size two (twins) and maybe of size three or more. This non-independence needs to be accounted for in the statistical analysis. Using simulated data based on a dataset of preterm infants we have previously investigated the performance of several approaches to the analysis of continuous outcomes in the presence of some clusters of size two. Mixed models have been developed for binomial outcomes but very little is known about their reliability when only a limited number of small clusters are present. Using simulated data based on a dataset of preterm infants we investigated the performance of several approaches to the analysis of binomial outcomes in the presence of some clusters of size two. Logistic models, several methods of estimation for the logistic random intercept models and generalised estimating equations were compared. The presence of even a small percentage of twins means that a logistic regression model will underestimate all parameters but a logistic random intercept model fails to estimate the correlation between siblings if the percentage of twins is too small and will provide similar estimates to logistic regression. The method which seems to provide the best balance between estimation of the standard error and the parameter for any percentage of twins is the generalised estimating equations. This study has shown that the number of covariates or the level two variance do not necessarily affect the performance of the various methods used to analyse datasets containing twins but when the percentage of small clusters is too small, mixed models cannot capture the dependence between siblings.

Predicting radiotherapy outcomes using statistical learning techniques

NASA Astrophysics Data System (ADS)

El Naqa, Issam; Bradley, Jeffrey D.; Lindsay, Patricia E.; Hope, Andrew J.; Deasy, Joseph O.

2009-09-01

Radiotherapy outcomes are determined by complex interactions between treatment, anatomical and patient-related variables. A common obstacle to building maximally predictive outcome models for clinical practice is the failure to capture potential complexity of heterogeneous variable interactions and applicability beyond institutional data. We describe a statistical learning methodology that can automatically screen for nonlinear relations among prognostic variables and generalize to unseen data before. In this work, several types of linear and nonlinear kernels to generate interaction terms and approximate the treatment-response function are evaluated. Examples of institutional datasets of esophagitis, pneumonitis and xerostomia endpoints were used. Furthermore, an independent RTOG dataset was used for 'generalizabilty' validation. We formulated the discrimination between risk groups as a supervised learning problem. The distribution of patient groups was initially analyzed using principle components analysis (PCA) to uncover potential nonlinear behavior. The performance of the different methods was evaluated using bivariate correlations and actuarial analysis. Over-fitting was controlled via cross-validation resampling. Our results suggest that a modified support vector machine (SVM) kernel method provided superior performance on leave-one-out testing compared to logistic regression and neural networks in cases where the data exhibited nonlinear behavior on PCA. For instance, in prediction of esophagitis and pneumonitis endpoints, which exhibited nonlinear behavior on PCA, the method provided 21% and 60% improvements, respectively. Furthermore, evaluation on the independent pneumonitis RTOG dataset demonstrated good generalizabilty beyond institutional data in contrast with other models. This indicates that the prediction of treatment response can be improved by utilizing nonlinear kernel methods for discovering important nonlinear interactions among model variables. These models have the capacity to predict on unseen data. Part of this work was first presented at the Seventh International Conference on Machine Learning and Applications, San Diego, CA, USA, 11-13 December 2008.

Applying independent component analysis to detect silent speech in magnetic resonance imaging signals.

PubMed

Abe, Kazuhiro; Takahashi, Toshimitsu; Takikawa, Yoriko; Arai, Hajime; Kitazawa, Shigeru

2011-10-01

Independent component analysis (ICA) can be usefully applied to functional imaging studies to evaluate the spatial extent and temporal profile of task-related brain activity. It requires no a priori assumptions about the anatomical areas that are activated or the temporal profile of the activity. We applied spatial ICA to detect a voluntary but hidden response of silent speech. To validate the method against a standard model-based approach, we used the silent speech of a tongue twister as a 'Yes' response to single questions that were delivered at given times. In the first task, we attempted to estimate one number that was chosen by a participant from 10 possibilities. In the second task, we increased the possibilities to 1000. In both tasks, spatial ICA was as effective as the model-based method for determining the number in the subject's mind (80-90% correct per digit), but spatial ICA outperformed the model-based method in terms of time, especially in the 1000-possibility task. In the model-based method, calculation time increased by 30-fold, to 15 h, because of the necessity of testing 1000 possibilities. In contrast, the calculation time for spatial ICA remained as short as 30 min. In addition, spatial ICA detected an unexpected response that occurred by mistake. This advantage was validated in a third task, with 13 500 possibilities, in which participants had the freedom to choose when to make one of four responses. We conclude that spatial ICA is effective for detecting the onset of silent speech, especially when it occurs unexpectedly. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

HIGH DIMENSIONAL COVARIANCE MATRIX ESTIMATION IN APPROXIMATE FACTOR MODELS.

PubMed

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2011-01-01

The variance covariance matrix plays a central role in the inferential theories of high dimensional factor models in finance and economics. Popular regularization methods of directly exploiting sparsity are not directly applicable to many financial problems. Classical methods of estimating the covariance matrices are based on the strict factor models, assuming independent idiosyncratic components. This assumption, however, is restrictive in practical applications. By assuming sparse error covariance matrix, we allow the presence of the cross-sectional correlation even after taking out common factors, and it enables us to combine the merits of both methods. We estimate the sparse covariance using the adaptive thresholding technique as in Cai and Liu (2011), taking into account the fact that direct observations of the idiosyncratic components are unavailable. The impact of high dimensionality on the covariance matrix estimation based on the factor structure is then studied.

Differentially Methylated Region-Representational Difference Analysis (DMR-RDA): A Powerful Method to Identify DMRs in Uncharacterized Genomes.

PubMed

Sasheva, Pavlina; Grossniklaus, Ueli

2017-01-01

Over the last years, it has become increasingly clear that environmental influences can affect the epigenomic landscape and that some epigenetic variants can have heritable, phenotypic effects. While there are a variety of methods to perform genome-wide analyses of DNA methylation in model organisms, this is still a challenging task for non-model organisms without a reference genome. Differentially methylated region-representational difference analysis (DMR-RDA) is a sensitive and powerful PCR-based technique that isolates DNA fragments that are differentially methylated between two otherwise identical genomes. The technique does not require special equipment and is independent of prior knowledge about the genome. It is even applicable to genomes that have high complexity and a large size, being the method of choice for the analysis of plant non-model systems.

A basket two-part model to analyze medical expenditure on interdependent multiple sectors.

PubMed

Sugawara, Shinya; Wu, Tianyi; Yamanishi, Kenji

2018-05-01

This study proposes a novel statistical methodology to analyze expenditure on multiple medical sectors using consumer data. Conventionally, medical expenditure has been analyzed by two-part models, which separately consider purchase decision and amount of expenditure. We extend the traditional two-part models by adding the step of basket analysis for dimension reduction. This new step enables us to analyze complicated interdependence between multiple sectors without an identification problem. As an empirical application for the proposed method, we analyze data of 13 medical sectors from the Medical Expenditure Panel Survey. In comparison with the results of previous studies that analyzed the multiple sector independently, our method provides more detailed implications of the impacts of individual socioeconomic status on the composition of joint purchases from multiple medical sectors; our method has a better prediction performance.

Wind Tunnel Database Development using Modern Experiment Design and Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2003-01-01

A wind tunnel experiment for characterizing the aerodynamic and propulsion forces and moments acting on a research model airplane is described. The model airplane called the Free-flying Airplane for Sub-scale Experimental Research (FASER), is a modified off-the-shelf radio-controlled model airplane, with 7 ft wingspan, a tractor propeller driven by an electric motor, and aerobatic capability. FASER was tested in the NASA Langley 12-foot Low-Speed Wind Tunnel, using a combination of traditional sweeps and modern experiment design. Power level was included as an independent variable in the wind tunnel test, to allow characterization of power effects on aerodynamic forces and moments. A modeling technique that employs multivariate orthogonal functions was used to develop accurate analytic models for the aerodynamic and propulsion force and moment coefficient dependencies from the wind tunnel data. Efficient methods for generating orthogonal modeling functions, expanding the orthogonal modeling functions in terms of ordinary polynomial functions, and analytical orthogonal blocking were developed and discussed. The resulting models comprise a set of smooth, differentiable functions for the non-dimensional aerodynamic force and moment coefficients in terms of ordinary polynomials in the independent variables, suitable for nonlinear aircraft simulation.

Optimizing placements of ground-based snow sensors for areal snow cover estimation using a machine-learning algorithm and melt-season snow-LiDAR data

NASA Astrophysics Data System (ADS)

Oroza, C.; Zheng, Z.; Glaser, S. D.; Bales, R. C.; Conklin, M. H.

2016-12-01

We present a structured, analytical approach to optimize ground-sensor placements based on time-series remotely sensed (LiDAR) data and machine-learning algorithms. We focused on catchments within the Merced and Tuolumne river basins, covered by the JPL Airborne Snow Observatory LiDAR program. First, we used a Gaussian mixture model to identify representative sensor locations in the space of independent variables for each catchment. Multiple independent variables that govern the distribution of snow depth were used, including elevation, slope, and aspect. Second, we used a Gaussian process to estimate the areal distribution of snow depth from the initial set of measurements. This is a covariance-based model that also estimates the areal distribution of model uncertainty based on the independent variable weights and autocorrelation. The uncertainty raster was used to strategically add sensors to minimize model uncertainty. We assessed the temporal accuracy of the method using LiDAR-derived snow-depth rasters collected in water-year 2014. In each area, optimal sensor placements were determined using the first available snow raster for the year. The accuracy in the remaining LiDAR surveys was compared to 100 configurations of sensors selected at random. We found the accuracy of the model from the proposed placements to be higher and more consistent in each remaining survey than the average random configuration. We found that a relatively small number of sensors can be used to accurately reproduce the spatial patterns of snow depth across the basins, when placed using spatial snow data. Our approach also simplifies sensor placement. At present, field surveys are required to identify representative locations for such networks, a process that is labor intensive and provides limited guarantees on the networks' representation of catchment independent variables.

Certification in Structural Health Monitoring Systems

DTIC Science & Technology

2011-09-01

validation [3,8]. This may be accomplished by computing the sum of squares of pure error ( SSPE ) and its associated squared correlation [3,8]. To compute...these values, a cross- validation sample must be established. In general, if the SSPE is high, the model does not predict well on independent data...plethora of cross- validation methods, some of which are more useful for certain models than others [3,8]. When possible, a disclosure of the SSPE

Compositional Specification of Software Architecture

NASA Technical Reports Server (NTRS)

Penix, John; Lau, Sonie (Technical Monitor)

1998-01-01

This paper describes our experience using parameterized algebraic specifications to model properties of software architectures. The goal is to model the decomposition of requirements independent of the style used to implement the architecture. We begin by providing an overview of the role of architecture specification in software development. We then describe how architecture specifications are build up from component and connector specifications and give an overview of insights gained from a case study used to validate the method.

Lung segmentation from HRCT using united geometric active contours

NASA Astrophysics Data System (ADS)

Liu, Junwei; Li, Chuanfu; Xiong, Jin; Feng, Huanqing

2007-12-01

Accurate lung segmentation from high resolution CT images is a challenging task due to various detail tracheal structures, missing boundary segments and complex lung anatomy. One popular method is based on gray-level threshold, however its results are usually rough. A united geometric active contours model based on level set is proposed for lung segmentation in this paper. Particularly, this method combines local boundary information and region statistical-based model synchronously: 1) Boundary term ensures the integrality of lung tissue.2) Region term makes the level set function evolve with global characteristic and independent on initial settings. A penalizing energy term is introduced into the model, which forces the level set function evolving without re-initialization. The method is found to be much more efficient in lung segmentation than other methods that are only based on boundary or region. Results are shown by 3D lung surface reconstruction, which indicates that the method will play an important role in the design of computer-aided diagnostic (CAD) system.

Body Parts Dependent Joint Regressors for Human Pose Estimation in Still Images.

PubMed

Dantone, Matthias; Gall, Juergen; Leistner, Christian; Van Gool, Luc

2014-11-01

In this work, we address the problem of estimating 2d human pose from still images. Articulated body pose estimation is challenging due to the large variation in body poses and appearances of the different body parts. Recent methods that rely on the pictorial structure framework have shown to be very successful in solving this task. They model the body part appearances using discriminatively trained, independent part templates and the spatial relations of the body parts using a tree model. Within such a framework, we address the problem of obtaining better part templates which are able to handle a very high variation in appearance. To this end, we introduce parts dependent body joint regressors which are random forests that operate over two layers. While the first layer acts as an independent body part classifier, the second layer takes the estimated class distributions of the first one into account and is thereby able to predict joint locations by modeling the interdependence and co-occurrence of the parts. This helps to overcome typical ambiguities of tree structures, such as self-similarities of legs and arms. In addition, we introduce a novel data set termed FashionPose that contains over 7,000 images with a challenging variation of body part appearances due to a large variation of dressing styles. In the experiments, we demonstrate that the proposed parts dependent joint regressors outperform independent classifiers or regressors. The method also performs better or similar to the state-of-the-art in terms of accuracy, while running with a couple of frames per second.

Autoregressive models for estimating phylogenetic and environmental effects: accounting for within-species variations.

PubMed

Cornillon, P A; Pontier, D; Rochet, M J

2000-02-21

Comparative methods are used to investigate the attributes of present species or higher taxa. Difficulties arise from the phylogenetic heritage: taxa are not independent and neglecting phylogenetic inertia can lead to inaccurate results. Within-species variations in life-history traits are also not negligible, but most comparative methods are not designed to take them into account. Taxa are generally described by a single value for each trait. We have developed a new model which permits the incorporation of both the phylogenetic relationships among populations and within-species variations. This is an extension of classical autoregressive models. This family of models was used to study the effect of fishing on six demographic traits measured on 77 populations of teleost fishes. Copyright 2000 Academic Press.

A Hybrid Numerical Analysis Method for Structural Health Monitoring

NASA Technical Reports Server (NTRS)

Forth, Scott C.; Staroselsky, Alexander

2001-01-01

A new hybrid surface-integral-finite-element numerical scheme has been developed to model a three-dimensional crack propagating through a thin, multi-layered coating. The finite element method was used to model the physical state of the coating (far field), and the surface integral method was used to model the fatigue crack growth. The two formulations are coupled through the need to satisfy boundary conditions on the crack surface and the external boundary. The coupling is sufficiently weak that the surface integral mesh of the crack surface and the finite element mesh of the uncracked volume can be set up independently. Thus when modeling crack growth, the finite element mesh can remain fixed for the duration of the simulation as the crack mesh is advanced. This method was implemented to evaluate the feasibility of fabricating a structural health monitoring system for real-time detection of surface cracks propagating in engine components. In this work, the authors formulate the hybrid surface-integral-finite-element method and discuss the mechanical issues of implementing a structural health monitoring system in an aircraft engine environment.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

An approximate block Newton method for coupled iterations of nonlinear solvers: Theory and conjugate heat transfer applications

NASA Astrophysics Data System (ADS)

Yeckel, Andrew; Lun, Lisa; Derby, Jeffrey J.

2009-12-01

A new, approximate block Newton (ABN) method is derived and tested for the coupled solution of nonlinear models, each of which is treated as a modular, black box. Such an approach is motivated by a desire to maintain software flexibility without sacrificing solution efficiency or robustness. Though block Newton methods of similar type have been proposed and studied, we present a unique derivation and use it to sort out some of the more confusing points in the literature. In particular, we show that our ABN method behaves like a Newton iteration preconditioned by an inexact Newton solver derived from subproblem Jacobians. The method is demonstrated on several conjugate heat transfer problems modeled after melt crystal growth processes. These problems are represented by partitioned spatial regions, each modeled by independent heat transfer codes and linked by temperature and flux matching conditions at the boundaries common to the partitions. Whereas a typical block Gauss-Seidel iteration fails about half the time for the model problem, quadratic convergence is achieved by the ABN method under all conditions studied here. Additional performance advantages over existing methods are demonstrated and discussed.

Electronic field emission models beyond the Fowler-Nordheim one

NASA Astrophysics Data System (ADS)

Lepetit, Bruno

2017-12-01

We propose several quantum mechanical models to describe electronic field emission from first principles. These models allow us to correlate quantitatively the electronic emission current with the electrode surface details at the atomic scale. They all rely on electronic potential energy surfaces obtained from three dimensional density functional theory calculations. They differ by the various quantum mechanical methods (exact or perturbative, time dependent or time independent), which are used to describe tunneling through the electronic potential energy barrier. Comparison of these models between them and with the standard Fowler-Nordheim one in the context of one dimensional tunneling allows us to assess the impact on the accuracy of the computed current of the approximations made in each model. Among these methods, the time dependent perturbative one provides a well-balanced trade-off between accuracy and computational cost.

Combining multiple positive training sets to generate confidence scores for protein-protein interactions.

PubMed

Yu, Jingkai; Finley, Russell L

2009-01-01

High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space. We demonstrate a method to score protein interactions by utilizing multiple independent sets of training positives to reduce the potential bias inherent in using a single training set. We used a set of benchmark yeast protein interactions to show that our approach outperforms other scoring methods. Our approach can also score interactions across data types, which makes it more widely applicable than many previously proposed methods. We applied the method to protein interaction data from both Drosophila melanogaster and Homo sapiens. Independent evaluations show that the resulting confidence scores accurately reflect the biological significance of the interactions.

Equilibrium Molecular Thermodynamics from Kirkwood Sampling

PubMed Central

2015-01-01

We present two methods for barrierless equilibrium sampling of molecular systems based on the recently proposed Kirkwood method (J. Chem. Phys.2009, 130, 134102). Kirkwood sampling employs low-order correlations among internal coordinates of a molecule for random (or non-Markovian) sampling of the high dimensional conformational space. This is a geometrical sampling method independent of the potential energy surface. The first method is a variant of biased Monte Carlo, where Kirkwood sampling is used for generating trial Monte Carlo moves. Using this method, equilibrium distributions corresponding to different temperatures and potential energy functions can be generated from a given set of low-order correlations. Since Kirkwood samples are generated independently, this method is ideally suited for massively parallel distributed computing. The second approach is a variant of reservoir replica exchange, where Kirkwood sampling is used to construct a reservoir of conformations, which exchanges conformations with the replicas performing equilibrium sampling corresponding to different thermodynamic states. Coupling with the Kirkwood reservoir enhances sampling by facilitating global jumps in the conformational space. The efficiency of both methods depends on the overlap of the Kirkwood distribution with the target equilibrium distribution. We present proof-of-concept results for a model nine-atom linear molecule and alanine dipeptide. PMID:25915525

Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks

PubMed Central

Kaltenbacher, Barbara; Hasenauer, Jan

2017-01-01

Mechanistic mathematical modeling of biochemical reaction networks using ordinary differential equation (ODE) models has improved our understanding of small- and medium-scale biological processes. While the same should in principle hold for large- and genome-scale processes, the computational methods for the analysis of ODE models which describe hundreds or thousands of biochemical species and reactions are missing so far. While individual simulations are feasible, the inference of the model parameters from experimental data is computationally too intensive. In this manuscript, we evaluate adjoint sensitivity analysis for parameter estimation in large scale biochemical reaction networks. We present the approach for time-discrete measurement and compare it to state-of-the-art methods used in systems and computational biology. Our comparison reveals a significantly improved computational efficiency and a superior scalability of adjoint sensitivity analysis. The computational complexity is effectively independent of the number of parameters, enabling the analysis of large- and genome-scale models. Our study of a comprehensive kinetic model of ErbB signaling shows that parameter estimation using adjoint sensitivity analysis requires a fraction of the computation time of established methods. The proposed method will facilitate mechanistic modeling of genome-scale cellular processes, as required in the age of omics. PMID:28114351

A prediction model of drug-induced ototoxicity developed by an optimal support vector machine (SVM) method.

PubMed

Zhou, Shu; Li, Guo-Bo; Huang, Lu-Yi; Xie, Huan-Zhang; Zhao, Ying-Lan; Chen, Yu-Zong; Li, Lin-Li; Yang, Sheng-Yong

2014-08-01

Drug-induced ototoxicity, as a toxic side effect, is an important issue needed to be considered in drug discovery. Nevertheless, current experimental methods used to evaluate drug-induced ototoxicity are often time-consuming and expensive, indicating that they are not suitable for a large-scale evaluation of drug-induced ototoxicity in the early stage of drug discovery. We thus, in this investigation, established an effective computational prediction model of drug-induced ototoxicity using an optimal support vector machine (SVM) method, GA-CG-SVM. Three GA-CG-SVM models were developed based on three training sets containing agents bearing different risk levels of drug-induced ototoxicity. For comparison, models based on naïve Bayesian (NB) and recursive partitioning (RP) methods were also used on the same training sets. Among all the prediction models, the GA-CG-SVM model II showed the best performance, which offered prediction accuracies of 85.33% and 83.05% for two independent test sets, respectively. Overall, the good performance of the GA-CG-SVM model II indicates that it could be used for the prediction of drug-induced ototoxicity in the early stage of drug discovery. Copyright © 2014 Elsevier Ltd. All rights reserved.

An alternative method for centrifugal compressor loading factor modelling

NASA Astrophysics Data System (ADS)

Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

2017-08-01

The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

Independent Component Analysis of Textures

NASA Technical Reports Server (NTRS)

Manduchi, Roberto; Portilla, Javier

2000-01-01

A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on Independent Components Analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product over the marginals most closely approximates the joint probability density function of the filter outputs. The algorithm is implemented using a steerable filter space. Experiments involving both texture classification and synthesis show that compared to Principal Components Analysis, ICA provides superior performance for modeling of natural and synthetic textures.

A Platform-Independent Plugin for Navigating Online Radiology Cases.

PubMed

Balkman, Jason D; Awan, Omer A

2016-06-01

Software methods that enable navigation of radiology cases on various digital platforms differ between handheld devices and desktop computers. This has resulted in poor compatibility of online radiology teaching files across mobile smartphones, tablets, and desktop computers. A standardized, platform-independent, or "agnostic" approach for presenting online radiology content was produced in this work by leveraging modern hypertext markup language (HTML) and JavaScript web software technology. We describe the design and evaluation of this software, demonstrate its use across multiple viewing platforms, and make it publicly available as a model for future development efforts.

Improving estimates of genetic maps: a meta-analysis-based approach.

PubMed

Stewart, William C L

2007-07-01

Inaccurate genetic (or linkage) maps can reduce the power to detect linkage, increase type I error, and distort haplotype and relationship inference. To improve the accuracy of existing maps, I propose a meta-analysis-based method that combines independent map estimates into a single estimate of the linkage map. The method uses the variance of each independent map estimate to combine them efficiently, whether the map estimates use the same set of markers or not. As compared with a joint analysis of the pooled genotype data, the proposed method is attractive for three reasons: (1) it has comparable efficiency to the maximum likelihood map estimate when the pooled data are homogeneous; (2) relative to existing map estimation methods, it can have increased efficiency when the pooled data are heterogeneous; and (3) it avoids the practical difficulties of pooling human subjects data. On the basis of simulated data modeled after two real data sets, the proposed method can reduce the sampling variation of linkage maps commonly used in whole-genome linkage scans. Furthermore, when the independent map estimates are also maximum likelihood estimates, the proposed method performs as well as or better than when they are estimated by the program CRIMAP. Since variance estimates of maps may not always be available, I demonstrate the feasibility of three different variance estimators. Overall, the method should prove useful to investigators who need map positions for markers not contained in publicly available maps, and to those who wish to minimize the negative effects of inaccurate maps. Copyright 2007 Wiley-Liss, Inc.

Predictors of Virological Response in 3,235 Chronic HCV Egyptian Patients Treated with Peginterferon Alpha-2a Compared with Peginterferon Alpha-2b Using Statistical Methods and Data Mining Techniques.

PubMed

El Raziky, Maissa; Fathalah, Waleed Fouad; Zakaria, Zeinab; Eldeen, Hadeel Gamal; Abul-Fotouh, Amr; Salama, Ahmed; Awad, Abubakr; Esmat, Gamal; Mabrouk, Mahasen

2016-05-01

Despite the appearance of new oral antiviral drugs, pegylated interferon (PEG-IFN)/RBV may remain the standard of care therapy for some time, and several viral and host factors are reported to be correlated with therapeutic effects. This study aimed to reveal the independent variables associated with failure of sustained virological response (SVR) to PEG-IFN alpha-2a versus PEG-IFN alpha-2b in treatment of naive chronic hepatitis C virus (HCV) Egyptian patients using both statistical methods and data mining techniques. This retrospective cohort study included 3,235 chronic hepatitis C patients enrolled in a large Egyptian medical center: 1,728 patients had been treated with PEG-IFN alpha-2a plus ribavirin (RBV) and 1,507 patients with PEG-IFN alpha-2b plus RBV between 2007 and 2011. Both multivariate analysis and Reduced Error Pruning Tree (REPTree)-based model were used to reveal the independent variables associated with treatment response. In both treatment types, alpha-fetoprotein (AFP) >10 ng/mL and HCV viremia >600 × 10(3) IU/mL were the independent baseline variables associated with failure of SVR, while male gender, decreased hemoglobin, and thyroid-stimulating hormone were the independent variables associated with good response (P < 0.05). Using REPTree-based model showed that low AFP was the factor of initial split (best predictor) of response for either PEG-IFN alpha-2a or PEG-IFN alpha-2b (cutoff value 8.53, 4.89 ng/mL, AUROC = 0.68 and 0.61, P = 0.05). Serum AFP >10 ng/mL and viral load >600 × 10(3) IU/mL are variables associated with failure of response in both treatment types. REPTree-based model could be used to assess predictors of response.

Lead Slowing-Down Spectrometry Time Spectral Analysis for Spent Fuel Assay: FY11 Status Report

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

Kulisek, Jonathan A.; Anderson, Kevin K.; Bowyer, Sonya M.

2011-09-30

Developing a method for the accurate, direct, and independent assay of the fissile isotopes in bulk materials (such as used fuel) from next-generation domestic nuclear fuel cycles is a goal of the Office of Nuclear Energy, Fuel Cycle R&D, Material Protection and Control Technology (MPACT) Campaign. To meet this goal, MPACT supports a multi-institutional collaboration, of which PNNL is a part, to study the feasibility of Lead Slowing Down Spectroscopy (LSDS). This technique is an active nondestructive assay method that has the potential to provide independent, direct measurement of Pu and U isotopic masses in used fuel with an uncertaintymore » considerably lower than the approximately 10% typical of today's confirmatory assay methods. This document is a progress report for FY2011 PNNL analysis and algorithm development. Progress made by PNNL in FY2011 continues to indicate the promise of LSDS analysis and algorithms applied to used fuel. PNNL developed an empirical model based on calibration of the LSDS to responses generated from well-characterized used fuel. The empirical model, which accounts for self-shielding effects using empirical basis vectors calculated from the singular value decomposition (SVD) of a matrix containing the true self-shielding functions of the used fuel assembly models. The potential for the direct and independent assay of the sum of the masses of 239Pu and 241Pu to within approximately 3% over a wide used fuel parameter space was demonstrated. Also, in FY2011, PNNL continued to develop an analytical model. Such efforts included the addition of six more non-fissile absorbers in the analytical shielding function and the non-uniformity of the neutron flux across the LSDS assay chamber. A hybrid analytical-empirical approach was developed to determine the mass of total Pu (sum of the masses of 239Pu, 240Pu, and 241Pu), which is an important quantity in safeguards. Results using this hybrid method were of approximately the same accuracy as the pure empirical approach. In addition, total Pu with much better accuracy with the hybrid approach than the pure analytical approach. In FY2012, PNNL will continue efforts to optimize its empirical model and minimize its reliance on calibration data. In addition, PNNL will continue to develop an analytical model, considering effects such as neutron-scattering in the fuel and cladding, as well as neutrons streaming through gaps between fuel pins in the fuel assembly.« less

The discounting model selector: Statistical software for delay discounting applications.

PubMed

Gilroy, Shawn P; Franck, Christopher T; Hantula, Donald A

2017-05-01

Original, open-source computer software was developed and validated against established delay discounting methods in the literature. The software executed approximate Bayesian model selection methods from user-supplied temporal discounting data and computed the effective delay 50 (ED50) from the best performing model. Software was custom-designed to enable behavior analysts to conveniently apply recent statistical methods to temporal discounting data with the aid of a graphical user interface (GUI). The results of independent validation of the approximate Bayesian model selection methods indicated that the program provided results identical to that of the original source paper and its methods. Monte Carlo simulation (n = 50,000) confirmed that true model was selected most often in each setting. Simulation code and data for this study were posted to an online repository for use by other researchers. The model selection approach was applied to three existing delay discounting data sets from the literature in addition to the data from the source paper. Comparisons of model selected ED50 were consistent with traditional indices of discounting. Conceptual issues related to the development and use of computer software by behavior analysts and the opportunities afforded by free and open-sourced software are discussed and a review of possible expansions of this software are provided. © 2017 Society for the Experimental Analysis of Behavior.

Geoengineering as a design problem

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

Kravitz, Ben; MacMartin, Douglas G.; Wang, Hailong

2016-01-01

Understanding the climate impacts of solar geoengineering is essential for evaluating its benefits and risks. Most previous simulations have prescribed a particular strategy and evaluated its modeled effects. Here we turn this approach around by first choosing example climate objectives and then designing a strategy to meet those objectives in climate models. There are four essential criteria for designing a strategy: (i) an explicit specification of the objectives, (ii) defining what climate forcing agents to modify so the objectives are met, (iii) a method for managing uncertainties, and (iv) independent verification of the strategy in an evaluation model. We demonstrate this design perspective throughmore » two multi-objective examples. First, changes in Arctic temperature and the position of tropical precipitation due to CO 2 increases are offset by adjusting high-latitude insolation in each hemisphere independently. Second, three different latitude-dependent patterns of insolation are modified to offset CO 2-induced changes in global mean temperature, interhemispheric temperature asymmetry, and the Equator-to-pole temperature gradient. In both examples, the "design" and "evaluation" models are state-of-the-art fully coupled atmosphere–ocean general circulation models.« less

Predictive models for radial sap flux variation in coniferous, diffuse-porous and ring-porous temperate trees.

PubMed

Berdanier, Aaron B; Miniat, Chelcy F; Clark, James S

2016-08-01

Accurately scaling sap flux observations to tree or stand levels requires accounting for variation in sap flux between wood types and by depth into the tree. However, existing models for radial variation in axial sap flux are rarely used because they are difficult to implement, there is uncertainty about their predictive ability and calibration measurements are often unavailable. Here we compare different models with a diverse sap flux data set to test the hypotheses that radial profiles differ by wood type and tree size. We show that radial variation in sap flux is dependent on wood type but independent of tree size for a range of temperate trees. The best-fitting model predicted out-of-sample sap flux observations and independent estimates of sapwood area with small errors, suggesting robustness in the new settings. We develop a method for predicting whole-tree water use with this model and include computer code for simple implementation in other studies. Published by Oxford University Press 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS) with Quantum Chemical Descriptors

PubMed Central

Jhin, Changho; Hwang, Keum Taek

2014-01-01

Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm

PubMed Central

2014-01-01

Background The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. Methods In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. Results Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. Conclusions Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective. PMID:25474487

[METHODS OF MATHEMATICAL MODELING IN MORPHOLOGICAL DIAGNOSTICS OF CHORNOBYL FACTOR INFLUENCE ON PROSTATE GLAND OF COAL MINERS-- THE CHERNOBYL DISASTER FIGHTERS].

PubMed

Danylov, Iu V; Motkov, K V; Shevchenko, T I

2014-01-01

The morphometric estimation of parenchyma and stroma condition included the determination of 25 parameters in a prostate gland at 27 persons. The mathematical model of morphogenesis of prostate gland was created by Bayes' method. The method of differential diagnosis of a prostate gland tissues' changes conditioned by the influence of the Chernobyl factor and/or unfavorable terms of the work in underground coal mines have been worked out. Its practical use provides exactness and reliability of the diagnosis (not less than 95%), independence from the level of the qualification and personal experience of the doctor, allows us to unify, optimize and individualize the diagnostic algorithms, answer the requirements of evidential medicine.

[Methods of mathematical modeling in morphological diagnostics of Chernobyl factor influence on the testes of coal miners of Donbas--the Chernobyl disaster fighters].

PubMed

Danylov, Iu V; Motkov, K V; Shevchenko, T I

2014-01-01

The morphometric estimation of parenchyma and stroma condition included the determination of 29 parameters in testicles at 27 persons. The mathematical model of morphogenesis of testicles was created by Bayes' method. The method of differential diagnosis of testicles tissues' changes conditioned by the influence of the Chernobyl factor and/or unfavorable terms of the work in underground coal mines have been worked out. Its practical use provides exactness and reliability of the diagnosis (not less than 95%), independence from the level of the qualification and personal experience of the doctor, allows us to unify, optimize and individualize the diagnostic algorithms, answer the requirements of evidential medicine.

SEPARABLE FACTOR ANALYSIS WITH APPLICATIONS TO MORTALITY DATA

PubMed Central

Fosdick, Bailey K.; Hoff, Peter D.

2014-01-01

Human mortality data sets can be expressed as multiway data arrays, the dimensions of which correspond to categories by which mortality rates are reported, such as age, sex, country and year. Regression models for such data typically assume an independent error distribution or an error model that allows for dependence along at most one or two dimensions of the data array. However, failing to account for other dependencies can lead to inefficient estimates of regression parameters, inaccurate standard errors and poor predictions. An alternative to assuming independent errors is to allow for dependence along each dimension of the array using a separable covariance model. However, the number of parameters in this model increases rapidly with the dimensions of the array and, for many arrays, maximum likelihood estimates of the covariance parameters do not exist. In this paper, we propose a submodel of the separable covariance model that estimates the covariance matrix for each dimension as having factor analytic structure. This model can be viewed as an extension of factor analysis to array-valued data, as it uses a factor model to estimate the covariance along each dimension of the array. We discuss properties of this model as they relate to ordinary factor analysis, describe maximum likelihood and Bayesian estimation methods, and provide a likelihood ratio testing procedure for selecting the factor model ranks. We apply this methodology to the analysis of data from the Human Mortality Database, and show in a cross-validation experiment how it outperforms simpler methods. Additionally, we use this model to impute mortality rates for countries that have no mortality data for several years. Unlike other approaches, our methodology is able to estimate similarities between the mortality rates of countries, time periods and sexes, and use this information to assist with the imputations. PMID:25489353

Modeling sustainability in renewable energy supply chain systems

NASA Astrophysics Data System (ADS)

Xie, Fei

This dissertation aims at modeling sustainability of renewable fuel supply chain systems against emerging challenges. In particular, the dissertation focuses on the biofuel supply chain system design, and manages to develop advanced modeling framework and corresponding solution methods in tackling challenges in sustaining biofuel supply chain systems. These challenges include: (1) to integrate "environmental thinking" into the long-term biofuel supply chain planning; (2) to adopt multimodal transportation to mitigate seasonality in biofuel supply chain operations; (3) to provide strategies in hedging against uncertainty from conversion technology; and (4) to develop methodologies in long-term sequential planning of the biofuel supply chain under uncertainties. All models are mixed integer programs, which also involves multi-objective programming method and two-stage/multistage stochastic programming methods. In particular for the long-term sequential planning under uncertainties, to reduce the computational challenges due to the exponential expansion of the scenario tree, I also developed efficient ND-Max method which is more efficient than CPLEX and Nested Decomposition method. Through result analysis of four independent studies, it is found that the proposed modeling frameworks can effectively improve the economic performance, enhance environmental benefits and reduce risks due to systems uncertainties for the biofuel supply chain systems.

Online Calibration Methods for the DINA Model with Independent Attributes in CD-CAT

ERIC Educational Resources Information Center

Chen, Ping; Xin, Tao; Wang, Chun; Chang, Hua-Hua

2012-01-01

Item replenishing is essential for item bank maintenance in cognitive diagnostic computerized adaptive testing (CD-CAT). In regular CAT, online calibration is commonly used to calibrate the new items continuously. However, until now no reference has publicly become available about online calibration for CD-CAT. Thus, this study investigates the…

Big Think: A Model for Critical Inquiry in Economics Courses

ERIC Educational Resources Information Center

McGoldrick, KimMarie; Garnett, Robert

2013-01-01

Economic educators often profess the goal of teaching our students to "think like economists." Since Siegfried and colleagues (1991) coined this phrase, its meaning has been interpreted as a focus on analytical concepts and methods of economics as opposed to the broader goal of preparing students for independent, critical thought in the…

Clinical Problem Analysis (CPA): A Systematic Approach To Teaching Complex Medical Problem Solving.

ERIC Educational Resources Information Center

Custers, Eugene J. F. M.; Robbe, Peter F. De Vries; Stuyt, Paul M. J.

2000-01-01

Discusses clinical problem analysis (CPA) in medical education, an approach to solving complex clinical problems. Outlines the five step CPA model and examines the value of CPA's content-independent (methodical) approach. Argues that teaching students to use CPA will enable them to avoid common diagnostic reasoning errors and pitfalls. Compares…

Advances in Time and Frequency Transfer From Dual-Frequency GPS Pseudorange and Carrier-Phase Observations

DTIC Science & Technology

2008-12-01

collocated independent time transfer techniques such as Two-Way Satellite Time and Frequency Transfer ( TWSTFT ) [10,11]. The issue of pseudorange errors...transfer methods, e.g. TWSTFT . There is a side benefit that far exceeds just meeting the objective we have set. The new model explicitly reveals, on

Naïve Bayes classification in R.

PubMed

Zhang, Zhongheng

2016-06-01

Naïve Bayes classification is a kind of simple probabilistic classification methods based on Bayes' theorem with the assumption of independence between features. The model is trained on training dataset to make predictions by predict() function. This article introduces two functions naiveBayes() and train() for the performance of Naïve Bayes classification.

Efficient simulation and likelihood methods for non-neutral multi-allele models.

PubMed

Joyce, Paul; Genz, Alan; Buzbas, Erkan Ozge

2012-06-01

Throughout the 1980s, Simon Tavaré made numerous significant contributions to population genetics theory. As genetic data, in particular DNA sequence, became more readily available, a need to connect population-genetic models to data became the central issue. The seminal work of Griffiths and Tavaré (1994a , 1994b , 1994c) was among the first to develop a likelihood method to estimate the population-genetic parameters using full DNA sequences. Now, we are in the genomics era where methods need to scale-up to handle massive data sets, and Tavaré has led the way to new approaches. However, performing statistical inference under non-neutral models has proved elusive. In tribute to Simon Tavaré, we present an article in spirit of his work that provides a computationally tractable method for simulating and analyzing data under a class of non-neutral population-genetic models. Computational methods for approximating likelihood functions and generating samples under a class of allele-frequency based non-neutral parent-independent mutation models were proposed by Donnelly, Nordborg, and Joyce (DNJ) (Donnelly et al., 2001). DNJ (2001) simulated samples of allele frequencies from non-neutral models using neutral models as auxiliary distribution in a rejection algorithm. However, patterns of allele frequencies produced by neutral models are dissimilar to patterns of allele frequencies produced by non-neutral models, making the rejection method inefficient. For example, in some cases the methods in DNJ (2001) require 10(9) rejections before a sample from the non-neutral model is accepted. Our method simulates samples directly from the distribution of non-neutral models, making simulation methods a practical tool to study the behavior of the likelihood and to perform inference on the strength of selection.

Optical frequency locked loop for long-term stabilization of broad-line DFB laser frequency difference

NASA Astrophysics Data System (ADS)

Lipka, Michał; Parniak, Michał; Wasilewski, Wojciech

2017-09-01

We present an experimental realization of the optical frequency locked loop applied to long-term frequency difference stabilization of broad-line DFB lasers along with a new independent method to characterize relative phase fluctuations of two lasers. The presented design is based on a fast photodiode matched with an integrated phase-frequency detector chip. The locking setup is digitally tunable in real time, insensitive to environmental perturbations and compatible with commercially available laser current control modules. We present a simple model and a quick method to optimize the loop for a given hardware relying exclusively on simple measurements in time domain. Step response of the system as well as phase characteristics closely agree with the theoretical model. Finally, frequency stabilization for offsets within 4-15 GHz working range achieving <0.1 Hz long-term stability of the beat note frequency for 500 s averaging time period is demonstrated. For these measurements we employ an I/Q mixer that allows us to precisely and independently measure the full phase trace of the beat note signal.

Analysis test of understanding of vectors with the three-parameter logistic model of item response theory and item response curves technique

NASA Astrophysics Data System (ADS)

Rakkapao, Suttida; Prasitpong, Singha; Arayathanitkul, Kwan

2016-12-01

This study investigated the multiple-choice test of understanding of vectors (TUV), by applying item response theory (IRT). The difficulty, discriminatory, and guessing parameters of the TUV items were fit with the three-parameter logistic model of IRT, using the parscale program. The TUV ability is an ability parameter, here estimated assuming unidimensionality and local independence. Moreover, all distractors of the TUV were analyzed from item response curves (IRC) that represent simplified IRT. Data were gathered on 2392 science and engineering freshmen, from three universities in Thailand. The results revealed IRT analysis to be useful in assessing the test since its item parameters are independent of the ability parameters. The IRT framework reveals item-level information, and indicates appropriate ability ranges for the test. Moreover, the IRC analysis can be used to assess the effectiveness of the test's distractors. Both IRT and IRC approaches reveal test characteristics beyond those revealed by the classical analysis methods of tests. Test developers can apply these methods to diagnose and evaluate the features of items at various ability levels of test takers.

[Research on the measurement range of particle size with total light scattering method in vis-IR region].

PubMed

Sun, Xiao-gang; Tang, Hong; Dai, Jing-min

2008-12-01

The problem of determining the particle size range in the visible-infrared region was studied using the independent model algorithm in the total scattering technique. By the analysis and comparison of the accuracy of the inversion results for different R-R distributions, the measurement range of particle size was determined. Meanwhile, the corrected extinction coefficient was used instead of the original extinction coefficient, which could determine the measurement range of particle size with higher accuracy. Simulation experiments illustrate that the particle size distribution can be retrieved very well in the range from 0. 05 to 18 microm at relative refractive index m=1.235 in the visible-infrared spectral region, and the measurement range of particle size will vary with the varied wavelength range and relative refractive index. It is feasible to use the constrained least squares inversion method in the independent model to overcome the influence of the measurement error, and the inverse results are all still satisfactory when 1% stochastic noise is added to the value of the light extinction.

Simple, efficient allocation of modelling runs on heterogeneous clusters with MPI

USGS Publications Warehouse

Donato, David I.

2017-01-01

In scientific modelling and computation, the choice of an appropriate method for allocating tasks for parallel processing depends on the computational setting and on the nature of the computation. The allocation of independent but similar computational tasks, such as modelling runs or Monte Carlo trials, among the nodes of a heterogeneous computational cluster is a special case that has not been specifically evaluated previously. A simulation study shows that a method of on-demand (that is, worker-initiated) pulling from a bag of tasks in this case leads to reliably short makespans for computational jobs despite heterogeneity both within and between cluster nodes. A simple reference implementation in the C programming language with the Message Passing Interface (MPI) is provided.

Development Parenting Model to Increase the Independence of Children

ERIC Educational Resources Information Center

Sunarty, Kustiah; Dirawan, Gufran Darma

2015-01-01

This study examines parenting and the child's independence model. The research problem is whether there is a relationship between parenting and the child's independence. The purpose of research is to determine: firstly, the type of parenting in an effort to increase the independence of the child; and the relationship between parenting models and…

Performance and separation occurrence of binary probit regression estimator using maximum likelihood method and Firths approach under different sample size

NASA Astrophysics Data System (ADS)

Lusiana, Evellin Dewi

2017-12-01

The parameters of binary probit regression model are commonly estimated by using Maximum Likelihood Estimation (MLE) method. However, MLE method has limitation if the binary data contains separation. Separation is the condition where there are one or several independent variables that exactly grouped the categories in binary response. It will result the estimators of MLE method become non-convergent, so that they cannot be used in modeling. One of the effort to resolve the separation is using Firths approach instead. This research has two aims. First, to identify the chance of separation occurrence in binary probit regression model between MLE method and Firths approach. Second, to compare the performance of binary probit regression model estimator that obtained by MLE method and Firths approach using RMSE criteria. Those are performed using simulation method and under different sample size. The results showed that the chance of separation occurrence in MLE method for small sample size is higher than Firths approach. On the other hand, for larger sample size, the probability decreased and relatively identic between MLE method and Firths approach. Meanwhile, Firths estimators have smaller RMSE than MLEs especially for smaller sample sizes. But for larger sample sizes, the RMSEs are not much different. It means that Firths estimators outperformed MLE estimator.

An improved chronology for the Lateglacial palaeoenvironmental record of Lake Haemelsee, Germany: challenges for independent site comparisons

NASA Astrophysics Data System (ADS)

Lane, Christine; Brauer, Achim; Ramsey Christopher, Bronk; Engels, Stefan; Haliuc, Aritina; Hoek, Wim; Hubay, Katalin; Jones, Gwydion; Sachse, Dirk; Staff, Richard; Turner, Falko; Wagner-Cremer, Frederike

2016-04-01

Exploring temporal and spatial variability of environmental response to climatic changes requires the comparison of widespread palaeoenvironmental sequences on their own, independently-derived, age models. High precision age-models can be constructed using statistical methods to combine absolute and relative age estimates measured using a range of techniques. Such an approach may help to highlight otherwise unrecognised uncertainties, where a single dating method has been applied in isolation. Radiocarbon dating, tephrochronology and varve counting have been combined within a Bayesian depositional model to build a chronology for a sediment sequence from Lake Haemelsee (Northern Germany) that continuously covers the entire Lateglacial and early Holocene. Each of the dating techniques used brought its own challenges. Radiocarbon dates provide the only absolute ages measured directly in the record, however a low macrofossil content led to small sample sizes and a limited number of low precision dates. A floating varved interval provided restricted but very precise relative dating for sediments covering the Allerød to Younger Dryas transition. Well-spaced, visible and crypto- tephra layers, including the widespread Laacher See , Vedde Ash, Askja-S and Saksunarvatn tephra layers, allow absolute ages for the tephra layers established in other locations to be imported into the Haemelsee sequence. These layers also provide multiple tie-lines that allow the Haemelsee sequences to be directly compared at particular moments in time, and within particular intervals, to other important Lateglacial archives. However, selecting the "best" published tephra ages to use in the Haemelsee age model is not simple and risks biasing comparison of the palaeoenvironmental record to fit one or another comparative archive. Here we investigate the use of multiple age models for the Haemelsee record, in order to retain an independent approach to investigating the environmental transitions of the Lateglacial to Early Holocene.

Lattice Boltzmann formulation for conjugate heat transfer in heterogeneous media.

PubMed

Karani, Hamid; Huber, Christian

2015-02-01

In this paper, we propose an approach for studying conjugate heat transfer using the lattice Boltzmann method (LBM). The approach is based on reformulating the lattice Boltzmann equation for solving the conservative form of the energy equation. This leads to the appearance of a source term, which introduces the jump conditions at the interface between two phases or components with different thermal properties. The proposed source term formulation conserves conductive and advective heat flux simultaneously, which makes it suitable for modeling conjugate heat transfer in general multiphase or multicomponent systems. The simple implementation of the source term approach avoids any correction of distribution functions neighboring the interface and provides an algorithm that is independent from the topology of the interface. Moreover, our approach is independent of the choice of lattice discretization and can be easily applied to different advection-diffusion LBM solvers. The model is tested against several benchmark problems including steady-state convection-diffusion within two fluid layers with parallel and normal interfaces with respect to the flow direction, unsteady conduction in a three-layer stratified domain, and steady conduction in a two-layer annulus. The LBM results are in excellent agreement with analytical solution. Error analysis shows that our model is first-order accurate in space, but an extension to a second-order scheme is straightforward. We apply our LBM model to heat transfer in a two-component heterogeneous medium with a random microstructure. This example highlights that the method we propose is independent of the topology of interfaces between the different phases and, as such, is ideally suited for complex natural heterogeneous media. We further validate the present LBM formulation with a study of natural convection in a porous enclosure. The results confirm the reliability of the model in simulating complex coupled fluid and thermal dynamics in complex geometries.

Automatic segmentation for brain MR images via a convex optimized segmentation and bias field correction coupled model.

PubMed

Chen, Yunjie; Zhao, Bo; Zhang, Jianwei; Zheng, Yuhui

2014-09-01

Accurate segmentation of magnetic resonance (MR) images remains challenging mainly due to the intensity inhomogeneity, which is also commonly known as bias field. Recently active contour models with geometric information constraint have been applied, however, most of them deal with the bias field by using a necessary pre-processing step before segmentation of MR data. This paper presents a novel automatic variational method, which can segment brain MR images meanwhile correcting the bias field when segmenting images with high intensity inhomogeneities. We first define a function for clustering the image pixels in a smaller neighborhood. The cluster centers in this objective function have a multiplicative factor that estimates the bias within the neighborhood. In order to reduce the effect of the noise, the local intensity variations are described by the Gaussian distributions with different means and variances. Then, the objective functions are integrated over the entire domain. In order to obtain the global optimal and make the results independent of the initialization of the algorithm, we reconstructed the energy function to be convex and calculated it by using the Split Bregman theory. A salient advantage of our method is that its result is independent of initialization, which allows robust and fully automated application. Our method is able to estimate the bias of quite general profiles, even in 7T MR images. Moreover, our model can also distinguish regions with similar intensity distribution with different variances. The proposed method has been rigorously validated with images acquired on variety of imaging modalities with promising results. Copyright © 2014 Elsevier Inc. 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

On the stress calculation within phase-field approaches: a model for finite deformations

NASA Astrophysics Data System (ADS)

Schneider, Daniel; Schwab, Felix; Schoof, Ephraim; Reiter, Andreas; Herrmann, Christoph; Selzer, Michael; Böhlke, Thomas; Nestler, Britta

2017-08-01

Numerical simulations based on phase-field methods are indispensable in order to investigate interesting and important phenomena in the evolution of microstructures. Microscopic phase transitions are highly affected by mechanical driving forces and therefore the accurate calculation of the stresses in the transition region is essential. We present a method for stress calculations within the phase-field framework, which satisfies the mechanical jump conditions corresponding to sharp interfaces, although the sharp interface is represented as a volumetric region using the phase-field approach. This model is formulated for finite deformations, is independent of constitutive laws, and allows using any type of phase inherent inelastic strains.

Using a Quasipotential Transformation for Modeling Diffusion Media inPolymer-Electrolyte Fuel Cells

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

Weber, Adam Z.; Newman, John

2008-08-29

In this paper, a quasipotential approach along with conformal mapping is used to model the diffusion media of a polymer-electrolyte fuel cell. This method provides a series solution that is grid independent and only requires integration along a single boundary to solve the problem. The approach accounts for nonisothermal phenomena, two-phase flow, correct placement of the electronic potential boundary condition, and multilayer media. The method is applied to a cathode diffusion medium to explore the interplay between water and thermal management and performance, the impact of the rib-to-channel ratio, and the existence of diffusion under the rib and flooding phenomena.

Reasoning about Function Objects

NASA Astrophysics Data System (ADS)

Nordio, Martin; Calcagno, Cristiano; Meyer, Bertrand; Müller, Peter; Tschannen, Julian

Modern object-oriented languages support higher-order implementations through function objects such as delegates in C#, agents in Eiffel, or closures in Scala. Function objects bring a new level of abstraction to the object-oriented programming model, and require a comparable extension to specification and verification techniques. We introduce a verification methodology that extends function objects with auxiliary side-effect free (pure) methods to model logical artifacts: preconditions, postconditions and modifies clauses. These pure methods can be used to specify client code abstractly, that is, independently from specific instantiations of the function objects. To demonstrate the feasibility of our approach, we have implemented an automatic prover, which verifies several non-trivial examples.

Molecular simulations of carbohydrates and protein-carbohydrate interactions: motivation, issues and prospects

PubMed Central

Fadda, Elisa; Woods, Robert J.

2014-01-01

The characterization of the 3D structure of oligosaccharides, their conjugates and analogs is particularly challenging for traditional experimental methods. Molecular simulation methods provide a basis for interpreting sparse experimental data and for independently predicting conformational and dynamic properties of glycans. Here, we summarize and analyze the issues associated with modeling carbohydrates, with a detailed discussion of four of the most recently developed carbohydrate force fields, reviewed in terms of applicability to natural glycans, carbohydrate–protein complexes and the emerging area of glycomimetic drugs. In addition, we discuss prospectives and new applications of carbohydrate modeling in drug discovery. PMID:20594934

Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.

PubMed

Ulrich, Ethan J; Sunderland, John J; Smith, Brian J; Mohiuddin, Imran; Parkhurst, Jessica; Plichta, Kristin A; Buatti, John M; Beichel, Reinhard R

2018-01-01

Quality control plays an increasingly important role in quantitative PET imaging and is typically performed using phantoms. The purpose of this work was to develop and validate a fully automated analysis method for two common PET/CT quality assurance phantoms: the NEMA NU-2 IQ and SNMMI/CTN oncology phantom. The algorithm was designed to only utilize the PET scan to enable the analysis of phantoms with thin-walled inserts. We introduce a model-based method for automated analysis of phantoms with spherical inserts. Models are first constructed for each type of phantom to be analyzed. A robust insert detection algorithm uses the model to locate all inserts inside the phantom. First, candidates for inserts are detected using a scale-space detection approach. Second, candidates are given an initial label using a score-based optimization algorithm. Third, a robust model fitting step aligns the phantom model to the initial labeling and fixes incorrect labels. Finally, the detected insert locations are refined and measurements are taken for each insert and several background regions. In addition, an approach for automated selection of NEMA and CTN phantom models is presented. The method was evaluated on a diverse set of 15 NEMA and 20 CTN phantom PET/CT scans. NEMA phantoms were filled with radioactive tracer solution at 9.7:1 activity ratio over background, and CTN phantoms were filled with 4:1 and 2:1 activity ratio over background. For quantitative evaluation, an independent reference standard was generated by two experts using PET/CT scans of the phantoms. In addition, the automated approach was compared against manual analysis, which represents the current clinical standard approach, of the PET phantom scans by four experts. The automated analysis method successfully detected and measured all inserts in all test phantom scans. It is a deterministic algorithm (zero variability), and the insert detection RMS error (i.e., bias) was 0.97, 1.12, and 1.48 mm for phantom activity ratios 9.7:1, 4:1, and 2:1, respectively. For all phantoms and at all contrast ratios, the average RMS error was found to be significantly lower for the proposed automated method compared to the manual analysis of the phantom scans. The uptake measurements produced by the automated method showed high correlation with the independent reference standard (R 2 ≥ 0.9987). In addition, the average computing time for the automated method was 30.6 s and was found to be significantly lower (P ≪ 0.001) compared to manual analysis (mean: 247.8 s). The proposed automated approach was found to have less error when measured against the independent reference than the manual approach. It can be easily adapted to other phantoms with spherical inserts. In addition, it eliminates inter- and intraoperator variability in PET phantom analysis and is significantly more time efficient, and therefore, represents a promising approach to facilitate and simplify PET standardization and harmonization efforts. © 2017 American Association of Physicists in Medicine.

TU-H-CAMPUS-JeP1-02: Fully Automatic Verification of Automatically Contoured Normal Tissues in the Head and Neck

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

McCarroll, R; UT Health Science Center, Graduate School of Biomedical Sciences, Houston, TX; Beadle, B

Purpose: To investigate and validate the use of an independent deformable-based contouring algorithm for automatic verification of auto-contoured structures in the head and neck towards fully automated treatment planning. Methods: Two independent automatic contouring algorithms [(1) Eclipse’s Smart Segmentation followed by pixel-wise majority voting, (2) an in-house multi-atlas based method] were used to create contours of 6 normal structures of 10 head-and-neck patients. After rating by a radiation oncologist, the higher performing algorithm was selected as the primary contouring method, the other used for automatic verification of the primary. To determine the ability of the verification algorithm to detect incorrectmore » contours, contours from the primary method were shifted from 0.5 to 2cm. Using a logit model the structure-specific minimum detectable shift was identified. The models were then applied to a set of twenty different patients and the sensitivity and specificity of the models verified. Results: Per physician rating, the multi-atlas method (4.8/5 point scale, with 3 rated as generally acceptable for planning purposes) was selected as primary and the Eclipse-based method (3.5/5) for verification. Mean distance to agreement and true positive rate were selected as covariates in an optimized logit model. These models, when applied to a group of twenty different patients, indicated that shifts could be detected at 0.5cm (brain), 0.75cm (mandible, cord), 1cm (brainstem, cochlea), or 1.25cm (parotid), with sensitivity and specificity greater than 0.95. If sensitivity and specificity constraints are reduced to 0.9, detectable shifts of mandible and brainstem were reduced by 0.25cm. These shifts represent additional safety margins which might be considered if auto-contours are used for automatic treatment planning without physician review. Conclusion: Automatically contoured structures can be automatically verified. This fully automated process could be used to flag auto-contours for special review or used with safety margins in a fully automatic treatment planning system.« less

Parameter screening: the use of a dummy parameter to identify non-influential parameters in a global sensitivity analysis

NASA Astrophysics Data System (ADS)

Khorashadi Zadeh, Farkhondeh; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy

2017-04-01

Parameter estimation is a major concern in hydrological modeling, which may limit the use of complex simulators with a large number of parameters. To support the selection of parameters to include in or exclude from the calibration process, Global Sensitivity Analysis (GSA) is widely applied in modeling practices. Based on the results of GSA, the influential and the non-influential parameters are identified (i.e. parameters screening). Nevertheless, the choice of the screening threshold below which parameters are considered non-influential is a critical issue, which has recently received more attention in GSA literature. In theory, the sensitivity index of a non-influential parameter has a value of zero. However, since numerical approximations, rather than analytical solutions, are utilized in GSA methods to calculate the sensitivity indices, small but non-zero indices may be obtained for the indices of non-influential parameters. In order to assess the threshold that identifies non-influential parameters in GSA methods, we propose to calculate the sensitivity index of a "dummy parameter". This dummy parameter has no influence on the model output, but will have a non-zero sensitivity index, representing the error due to the numerical approximation. Hence, the parameters whose indices are above the sensitivity index of the dummy parameter can be classified as influential, whereas the parameters whose indices are below this index are within the range of the numerical error and should be considered as non-influential. To demonstrated the effectiveness of the proposed "dummy parameter approach", 26 parameters of a Soil and Water Assessment Tool (SWAT) model are selected to be analyzed and screened, using the variance-based Sobol' and moment-independent PAWN methods. The sensitivity index of the dummy parameter is calculated from sampled data, without changing the model equations. Moreover, the calculation does not even require additional model evaluations for the Sobol' method. A formal statistical test validates these parameter screening results. Based on the dummy parameter screening, 11 model parameters are identified as influential. Therefore, it can be denoted that the "dummy parameter approach" can facilitate the parameter screening process and provide guidance for GSA users to define a screening-threshold, with only limited additional resources. Key words: Parameter screening, Global sensitivity analysis, Dummy parameter, Variance-based method, Moment-independent method

Statistical Calibration and Validation of a Homogeneous Ventilated Wall-Interference Correction Method for the National Transonic Facility

NASA Technical Reports Server (NTRS)

Walker, Eric L.

2005-01-01

Wind tunnel experiments will continue to be a primary source of validation data for many types of mathematical and computational models in the aerospace industry. The increased emphasis on accuracy of data acquired from these facilities requires understanding of the uncertainty of not only the measurement data but also any correction applied to the data. One of the largest and most critical corrections made to these data is due to wall interference. In an effort to understand the accuracy and suitability of these corrections, a statistical validation process for wall interference correction methods has been developed. This process is based on the use of independent cases which, after correction, are expected to produce the same result. Comparison of these independent cases with respect to the uncertainty in the correction process establishes a domain of applicability based on the capability of the method to provide reasonable corrections with respect to customer accuracy requirements. The statistical validation method was applied to the version of the Transonic Wall Interference Correction System (TWICS) recently implemented in the National Transonic Facility at NASA Langley Research Center. The TWICS code generates corrections for solid and slotted wall interference in the model pitch plane based on boundary pressure measurements. Before validation could be performed on this method, it was necessary to calibrate the ventilated wall boundary condition parameters. Discrimination comparisons are used to determine the most representative of three linear boundary condition models which have historically been used to represent longitudinally slotted test section walls. Of the three linear boundary condition models implemented for ventilated walls, the general slotted wall model was the most representative of the data. The TWICS code using the calibrated general slotted wall model was found to be valid to within the process uncertainty for test section Mach numbers less than or equal to 0.60. The scatter among the mean corrected results of the bodies of revolution validation cases was within one count of drag on a typical transport aircraft configuration for Mach numbers at or below 0.80 and two counts of drag for Mach numbers at or below 0.90.

Benefits of object-oriented models and ModeliChart: modern tools and methods for the interdisciplinary research on smart biomedical technology.

PubMed

Gesenhues, Jonas; Hein, Marc; Ketelhut, Maike; Habigt, Moriz; Rüschen, Daniel; Mechelinck, Mare; Albin, Thivaharan; Leonhardt, Steffen; Schmitz-Rode, Thomas; Rossaint, Rolf; Autschbach, Rüdiger; Abel, Dirk

2017-04-01

Computational models of biophysical systems generally constitute an essential component in the realization of smart biomedical technological applications. Typically, the development process of such models is characterized by a great extent of collaboration between different interdisciplinary parties. Furthermore, due to the fact that many underlying mechanisms and the necessary degree of abstraction of biophysical system models are unknown beforehand, the steps of the development process of the application are iteratively repeated when the model is refined. This paper presents some methods and tools to facilitate the development process. First, the principle of object-oriented (OO) modeling is presented and the advantages over classical signal-oriented modeling are emphasized. Second, our self-developed simulation tool ModeliChart is presented. ModeliChart was designed specifically for clinical users and allows independently performing in silico studies in real time including intuitive interaction with the model. Furthermore, ModeliChart is capable of interacting with hardware such as sensors and actuators. Finally, it is presented how optimal control methods in combination with OO models can be used to realize clinically motivated control applications. All methods presented are illustrated on an exemplary clinically oriented use case of the artificial perfusion of the systemic circulation.

Semantic concept-enriched dependence model for medical information retrieval.

PubMed

Choi, Sungbin; Choi, Jinwook; Yoo, Sooyoung; Kim, Heechun; Lee, Youngho

2014-02-01

In medical information retrieval research, semantic resources have been mostly used by expanding the original query terms or estimating the concept importance weight. However, implicit term-dependency information contained in semantic concept terms has been overlooked or at least underused in most previous studies. In this study, we incorporate a semantic concept-based term-dependence feature into a formal retrieval model to improve its ranking performance. Standardized medical concept terms used by medical professionals were assumed to have implicit dependency within the same concept. We hypothesized that, by elaborately revising the ranking algorithms to favor documents that preserve those implicit dependencies, the ranking performance could be improved. The implicit dependence features are harvested from the original query using MetaMap. These semantic concept-based dependence features were incorporated into a semantic concept-enriched dependence model (SCDM). We designed four different variants of the model, with each variant having distinct characteristics in the feature formulation method. We performed leave-one-out cross validations on both a clinical document corpus (TREC Medical records track) and a medical literature corpus (OHSUMED), which are representative test collections in medical information retrieval research. Our semantic concept-enriched dependence model consistently outperformed other state-of-the-art retrieval methods. Analysis shows that the performance gain has occurred independently of the concept's explicit importance in the query. By capturing implicit knowledge with regard to the query term relationships and incorporating them into a ranking model, we could build a more robust and effective retrieval model, independent of the concept importance. Copyright © 2013 Elsevier Inc. All rights reserved.

Personalized Medicine Enrichment Design for DHA Supplementation Clinical Trial.

PubMed

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

2017-03-01

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

An assessment of the trophic structure of the Bay of Biscay continental shelf food web: Comparing estimates derived from an ecosystem model and isotopic data

NASA Astrophysics Data System (ADS)

Lassalle, G.; Chouvelon, T.; Bustamante, P.; Niquil, N.

2014-01-01

Comparing outputs of ecosystem models with estimates derived from experimental and observational approaches is important in creating valuable feedback for model construction, analyses and validation. Stable isotopes and mass-balanced trophic models are well-known and widely used as approximations to describe the structure of food webs, but their consistency has not been properly established as attempts to compare these methods remain scarce. Model construction is a data-consuming step, meaning independent sets for validation are rare. Trophic linkages in the French continental shelf of the Bay of Biscay food webs were recently investigated using both methodologies. Trophic levels for mono-specific compartments representing small pelagic fish and marine mammals and multi-species functional groups corresponding to demersal fish and cephalopods, derived from modelling, were compared with trophic levels calculated from independent carbon and nitrogen isotope ratios. Estimates of the trophic niche width of those species, or groups of species, were compared between these two approaches as well. A significant and close-to-one positive (rSpearman2 = 0.72 , n = 16, p < 0.0001) correlation was found between trophic levels estimated by Ecopath modelling and those derived from isotopic signatures. Differences between estimates were particularly low for mono-specific compartments. No clear relationship existed between indices of trophic niche width derived from both methods. Given the wide recognition of trophic levels as a useful concept in ecosystem-based fisheries management, propositions were made to further combine these two approaches.

Species limits in the Morelet's Alligator lizard (Anguidae: Gerrhonotinae).

PubMed

Solano-Zavaleta, Israel; Nieto-Montes de Oca, Adrián

2018-03-01

The widely distributed, Central American anguid lizard Mesaspis moreletii is currently recognized as a polytypic species with five subspecies (M. m. fulvus, M. m. moreletii, M. m. rafaeli, M. m. salvadorensis, and M. m. temporalis). We reevaluated the species limits within Mesaspis moreletii using DNA sequences of one mitochondrial and three nuclear genes. The multi-locus data set included samples of all of the subspecies of M. moreletii, the other species of Mesaspis in Central America (M. cuchumatanus and M. monticola), and some populations assignable to M. moreletii but of uncertain subspecific identity from Honduras and Nicaragua. We first used a tree-based method for delimiting species based on mtDNA data to identify potential evolutionary independent lineages, and then analized the multilocus dataset with two species delimitation methods that use the multispecies coalescent model to evaluate different competing species delimitation models: the Bayes factors species delimitation method (BFD) implemented in ∗ BEAST, and the Bayesian Phylogenetics and Phylogeography (BP&P) method. Our results suggest that M. m. moreletii, M. m. rafaeli, M. m. salvadorensis, and M. m. temporalis represent distinct evolutionary independent lineages, and that the populations of uncertain status from Honduras and Nicaragua may represent additional undescribed species. Our results also suggest that M. m. fulvus is a synonym of M. m. moreletii. The biogeography of the Central American lineages of Mesaspis is discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

Improved detection of DNA-binding proteins via compression technology on PSSM information.

PubMed

Wang, Yubo; Ding, Yijie; Guo, Fei; Wei, Leyi; Tang, Jijun

2017-01-01

Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix-Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix-Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at https://doi.org/10.6084/m9.figshare.5104084.

Optimization in modeling the ribs-bounded contour from computer tomography scan

NASA Astrophysics Data System (ADS)

Bilinskas, M. J.; Dzemyda, G.

2016-10-01

In this paper a method for analyzing transversal plane images from computer tomography scans is presented. A mathematical model that describes the ribs-bounded contour was created and the problem of approximation is solved by finding out the optimal parameters of the model in the least-squares sense. Such model would be useful in registration of images independently on the patient position on the bed and on the radio-contrast agent injection. We consider the slices, where ribs are visible, because many important internal organs are located here: liver, heart, stomach, pancreas, lung, etc.

A weather-driven model of malaria transmission

PubMed Central

Hoshen, Moshe B; Morse, Andrew P

2004-01-01

Background Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. Methods This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. Results Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. Conclusion A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts. PMID:15350206

The possible modifications of the HISSE model for pure LANDSAT agricultural data

NASA Technical Reports Server (NTRS)

Peters, C.

1981-01-01

A method for relaxing the assumption of class conditional independence of LANDSAT spectral measurements within the same patch (field) is discussed. Theoretical arguments are given which show that any significant refinement of the model beyond this proposal will not allow the reduction, essential to HISSE, of the pure data to patch summary statistics. A slight alteration of the new model is shown to be a reasonable approximation to the model which describes pure data elements from the same patch as jointly Gaussian with a covariance function which exhibits exponential decay with respect to spatial separation.

Three statistical models for estimating length of stay.

PubMed Central

Selvin, S

1977-01-01

The probability density functions implied by three methods of collecting data on the length of stay in an institution are derived. The expected values associated with these density functions are used to calculate unbiased estimates of the expected length of stay. Two of the methods require an assumption about the form of the underlying distribution of length of stay; the third method does not. The three methods are illustrated with hypothetical data exhibiting the Poisson distribution, and the third (distribution-independent) method is used to estimate the length of stay in a skilled nursing facility and in an intermediate care facility for patients enrolled in California's MediCal program. PMID:914532

Three statistical models for estimating length of stay.

PubMed

Selvin, S

1977-01-01

The probability density functions implied by three methods of collecting data on the length of stay in an institution are derived. The expected values associated with these density functions are used to calculate unbiased estimates of the expected length of stay. Two of the methods require an assumption about the form of the underlying distribution of length of stay; the third method does not. The three methods are illustrated with hypothetical data exhibiting the Poisson distribution, and the third (distribution-independent) method is used to estimate the length of stay in a skilled nursing facility and in an intermediate care facility for patients enrolled in California's MediCal program.

Modeling Quasi-Static and Fatigue-Driven Delamination Migration

NASA Technical Reports Server (NTRS)

De Carvalho, N. V.; Ratcliffe, J. G.; Chen, B. Y.; Pinho, S. T.; Baiz, P. M.; Tay, T. E.

2014-01-01

An approach was proposed and assessed for the high-fidelity modeling of progressive damage and failure in composite materials. It combines the Floating Node Method (FNM) and the Virtual Crack Closure Technique (VCCT) to represent multiple interacting failure mechanisms in a mesh-independent fashion. Delamination, matrix cracking, and migration were captured failure and migration criteria based on fracture mechanics. Quasi-static and fatigue loading were modeled within the same overall framework. The methodology proposed was illustrated by simulating the delamination migration test, showing good agreement with the available experimental data.

A nonstandard finite difference scheme for a basic model of cellular immune response to viral infection

NASA Astrophysics Data System (ADS)

Korpusik, Adam

2017-02-01

We present a nonstandard finite difference scheme for a basic model of cellular immune response to viral infection. The main advantage of this approach is that it preserves the essential qualitative features of the original continuous model (non-negativity and boundedness of the solution, equilibria and their stability conditions), while being easy to implement. All of the qualitative features are preserved independently of the chosen step-size. Numerical simulations of our approach and comparison with other conventional simulation methods are presented.

Connected word recognition using a cascaded neuro-computational model

NASA Astrophysics Data System (ADS)

Hoya, Tetsuya; van Leeuwen, Cees

2016-10-01

We propose a novel framework for processing a continuous speech stream that contains a varying number of words, as well as non-speech periods. Speech samples are segmented into word-tokens and non-speech periods. An augmented version of an earlier-proposed, cascaded neuro-computational model is used for recognising individual words within the stream. Simulation studies using both a multi-speaker-dependent and speaker-independent digit string database show that the proposed method yields a recognition performance comparable to that obtained by a benchmark approach using hidden Markov models with embedded training.

Climate Model Ensemble Methodology: Rationale and Challenges

NASA Astrophysics Data System (ADS)

Vezer, M. A.; Myrvold, W.

2012-12-01

A tractable model of the Earth's atmosphere, or, indeed, any large, complex system, is inevitably unrealistic in a variety of ways. This will have an effect on the model's output. Nonetheless, we want to be able to rely on certain features of the model's output in studies aiming to detect, attribute, and project climate change. For this, we need assurance that these features reflect the target system, and are not artifacts of the unrealistic assumptions that go into the model. One technique for overcoming these limitations is to study ensembles of models which employ different simplifying assumptions and different methods of modelling. One then either takes as reliable certain outputs on which models in the ensemble agree, or takes the average of these outputs as the best estimate. Since the Intergovernmental Panel on Climate Change's Fourth Assessment Report (IPCC AR4) modellers have aimed to improve ensemble analysis by developing techniques to account for dependencies among models, and to ascribe unequal weights to models according to their performance. The goal of this paper is to present as clearly and cogently as possible the rationale for climate model ensemble methodology, the motivation of modellers to account for model dependencies, and their efforts to ascribe unequal weights to models. The method of our analysis is as follows. We will consider a simpler, well-understood case of taking the mean of a number of measurements of some quantity. Contrary to what is sometimes said, it is not a requirement of this practice that the errors of the component measurements be independent; one must, however, compensate for any lack of independence. We will also extend the usual accounts to include cases of unknown systematic error. We draw parallels between this simpler illustration and the more complex example of climate model ensembles, detailing how ensembles can provide more useful information than any of their constituent models. This account emphasizes the epistemic importance of considering degrees of model dependence, and the practice of ascribing unequal weights to models of unequal skill.

Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Features

PubMed Central

Tamayo, Pablo; Cho, Yoon-Jae; Tsherniak, Aviad; Greulich, Heidi; Ambrogio, Lauren; Schouten-van Meeteren, Netteke; Zhou, Tianni; Buxton, Allen; Kool, Marcel; Meyerson, Matthew; Pomeroy, Scott L.; Mesirov, Jill P.

2011-01-01

Purpose Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis. Patients and Methods A Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression–derived features representing disease subtype–independent (model B) and disease subtype–dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78). Results On an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse. Conclusion The proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification. PMID:21357789

SPOTting model parameters using a ready-made Python package

NASA Astrophysics Data System (ADS)

Houska, Tobias; Kraft, Philipp; Breuer, Lutz

2015-04-01

The selection and parameterization of reliable process descriptions in ecological modelling is driven by several uncertainties. The procedure is highly dependent on various criteria, like the used algorithm, the likelihood function selected and the definition of the prior parameter distributions. A wide variety of tools have been developed in the past decades to optimize parameters. Some of the tools are closed source. Due to this, the choice for a specific parameter estimation method is sometimes more dependent on its availability than the performance. A toolbox with a large set of methods can support users in deciding about the most suitable method. Further, it enables to test and compare different methods. We developed the SPOT (Statistical Parameter Optimization Tool), an open source python package containing a comprehensive set of modules, to analyze and optimize parameters of (environmental) models. SPOT comes along with a selected set of algorithms for parameter optimization and uncertainty analyses (Monte Carlo, MC; Latin Hypercube Sampling, LHS; Maximum Likelihood, MLE; Markov Chain Monte Carlo, MCMC; Scuffled Complex Evolution, SCE-UA; Differential Evolution Markov Chain, DE-MCZ), together with several likelihood functions (Bias, (log-) Nash-Sutcliff model efficiency, Correlation Coefficient, Coefficient of Determination, Covariance, (Decomposed-, Relative-, Root-) Mean Squared Error, Mean Absolute Error, Agreement Index) and prior distributions (Binomial, Chi-Square, Dirichlet, Exponential, Laplace, (log-, multivariate-) Normal, Pareto, Poisson, Cauchy, Uniform, Weibull) to sample from. The model-independent structure makes it suitable to analyze a wide range of applications. We apply all algorithms of the SPOT package in three different case studies. Firstly, we investigate the response of the Rosenbrock function, where the MLE algorithm shows its strengths. Secondly, we study the Griewank function, which has a challenging response surface for optimization methods. Here we see simple algorithms like the MCMC struggling to find the global optimum of the function, while algorithms like SCE-UA and DE-MCZ show their strengths. Thirdly, we apply an uncertainty analysis of a one-dimensional physically based hydrological model build with the Catchment Modelling Framework (CMF). The model is driven by meteorological and groundwater data from a Free Air Carbon Enrichment (FACE) experiment in Linden (Hesse, Germany). Simulation results are evaluated with measured soil moisture data. We search for optimal parameter sets of the van Genuchten-Mualem function and find different equally optimal solutions with some of the algorithms. The case studies reveal that the implemented SPOT methods work sufficiently well. They further show the benefit of having one tool at hand that includes a number of parameter search methods, likelihood functions and a priori parameter distributions within one platform independent package.

Comparison of DGT with traditional extraction methods for assessing arsenic bioavailability to Brassica chinensis in different soils.

PubMed

Dai, Yunchao; Nasir, Mubasher; Zhang, Yulin; Gao, Jiakai; Lv, Yamin; Lv, Jialong

2018-01-01

Several predictive models and methods have been used for heavy metals bioavailability, but there is no universally accepted approach in evaluating the bioavailability of arsenic (As) in soil. The technique of diffusive gradients in thin-films (DGT) is a promising tool, but there is a considerable debate with respect to its suitability. The DGT method was compared with other traditional chemical extractions techniques (soil solution, NaHCO 3 , NH 4 Cl, HCl, and total As method) for estimating As bioavailability in soil based on a greenhouse experiment using Brassica chinensis grown in various soils from 15 provinces in China. In addition, we assessed whether these methods are independent of soil properties. The correlations between plant and soil As concentration measured with traditional extraction techniques were pH and iron oxide (Fe ox ) dependent, indicating that these methods are influenced by soil properties. In contrast, DGT measurements were independent of soil properties and also showed a better correlation coefficient than other traditional techniques. Thus, DGT technique is superior to traditional techniques and should be preferable for evaluating As bioavailability in different type of soils. Copyright © 2017 Elsevier Ltd. All rights reserved.

Three New Genetic Loci (R1210C in CFH, Variants in COL8A1 and RAD51B) Are Independently Related to Progression to Advanced Macular Degeneration

PubMed Central

Seddon, Johanna M.; Reynolds, Robyn; Yu, Yi; Rosner, Bernard

2014-01-01

Objectives To assess the independent impact of new genetic variants on conversion to advanced stages of AMD, controlling for established risk factors, and to determine the contribution of genes in predictive models. Methods In this prospective longitudinal study of 2765 individuals, 777 subjects progressed to neovascular disease (NV) or geographic atrophy (GA) in either eye over 12 years. Recently reported genetic loci were assessed for their independent effects on incident advanced AMD after controlling for 6 established loci in 5 genes, and demographic, behavioral, and macular characteristics. New variants which remained significantly related to progression were then added to a final multivariate model to assess their independent effects. The contribution of genes to risk models was assessed using reclassification tables by determining risk within cross-classified quintiles for alternative models. Results Three new genetic variants were significantly related to progression: rare variant R1210C in CFH (hazard ratio (HR) 2.5, 95% confidence interval [CI] 1.2–5.3, P = 0.01), and common variants in genes COL8A1 (HR 2.0, 95% CI 1.1–3.5, P = 0.02) and RAD51B (HR 0.8, 95% CI 0.60–0.97, P = 0.03). The area under the curve statistic (AUC) was significantly higher for the 9 gene model (.884) vs the 0 gene model (.873), P = .01. AUC’s for the 9 vs 6 gene models were not significantly different, but reclassification analyses indicated significant added information for more genes, with adjusted odds ratios (OR) for progression within 5 years per one quintile increase in risk score of 2.7, P<0.001 for the 9 vs 6 loci model, and OR 3.5, P<0.001 for the 9 vs. 0 gene model. Similar results were seen for NV and GA. Conclusions Rare variant CFH R1210C and common variants in COL8A1 and RAD51B plus six genes in previous models contribute additional predictive information for advanced AMD beyond macular and behavioral phenotypes. PMID:24498017

Using imputed genotype data in the joint score tests for genetic association and gene-environment interactions in case-control studies.

PubMed

Song, Minsun; Wheeler, William; Caporaso, Neil E; Landi, Maria Teresa; Chatterjee, Nilanjan

2018-03-01

Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population. As increasingly large-scale GWAS are being performed through consortia effort where it is preferable to share only summary-level information across studies, we also describe simple mechanisms for implementing score tests based on standard meta-analysis of "one-step" maximum-likelihood estimates across studies. Applications of the methods in simulation studies and a dataset from GWAS of lung cancer illustrate ability of the proposed methods to maintain type-I error rates for the underlying testing procedures. For analysis of imputed SNPs, similar to typed SNPs, the retrospective methods can lead to considerable efficiency gain for modeling of gene-environment interactions under the assumption of gene-environment independence. Methods are made available for public use through CGEN R software package. © 2017 WILEY PERIODICALS, INC.

Physical, Cognitive, and Psychosocial Variables from the Disablement Process Model Predict Patterns of Independence and the Transition into Disability for the Oldest-Old

ERIC Educational Resources Information Center

Fauth, Elizabeth Braungart; Zarit, Steven H.; Malmberg, Bo; Johansson, Boo

2007-01-01

Purpose: This study used the Disablement Process Model to predict whether a sample of the oldest-old maintained their disability or disability-free status over a 2- and 4-year follow-up, or whether they transitioned into a state of disability during this time. Design and Methods: We followed a sample of 149 Swedish adults who were 86 years of age…

Manufacturing Methods and Technology for Digital Fault Isolation of Hybrid Microelectronic Assemblies.

DTIC Science & Technology

1982-03-01

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A contribution toward rational modeling of the pressure-strain-rate correlation

NASA Technical Reports Server (NTRS)

Lee, Moon Joo

1990-01-01

A novel method of obtaining an analytical expression of the 'linear part' of the pressure-strain-rate tensor in terms of the anisotropy tensor of the Reynolds stresses has been developed, where the coefficients of the seven independent tensor terms are functions of the invariants of the Reynolds-stress anisotropy. The coefficients are evaluated up to fourth order in the anisotropy of the Reynolds stresses to provide guidance for development of a turbulence model.

Multifractal texture estimation for detection and segmentation of brain tumors.

PubMed

Islam, Atiq; Reza, Syed M S; Iftekharuddin, Khan M

2013-11-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available.

Multifractal Texture Estimation for Detection and Segmentation of Brain Tumors

PubMed Central

Islam, Atiq; Reza, Syed M. S.

2016-01-01

A stochastic model for characterizing tumor texture in brain magnetic resonance (MR) images is proposed. The efficacy of the model is demonstrated in patient-independent brain tumor texture feature extraction and tumor segmentation in magnetic resonance images (MRIs). Due to complex appearance in MRI, brain tumor texture is formulated using a multiresolution-fractal model known as multifractional Brownian motion (mBm). Detailed mathematical derivation for mBm model and corresponding novel algorithm to extract spatially varying multifractal features are proposed. A multifractal feature-based brain tumor segmentation method is developed next. To evaluate efficacy, tumor segmentation performance using proposed multifractal feature is compared with that using Gabor-like multiscale texton feature. Furthermore, novel patient-independent tumor segmentation scheme is proposed by extending the well-known AdaBoost algorithm. The modification of AdaBoost algorithm involves assigning weights to component classifiers based on their ability to classify difficult samples and confidence in such classification. Experimental results for 14 patients with over 300 MRIs show the efficacy of the proposed technique in automatic segmentation of tumors in brain MRIs. Finally, comparison with other state-of-the art brain tumor segmentation works with publicly available low-grade glioma BRATS2012 dataset show that our segmentation results are more consistent and on the average outperforms these methods for the patients where ground truth is made available. PMID:23807424

Applied Prevalence Ratio estimation with different Regression models: An example from a cross-national study on substance use research.

PubMed

Espelt, Albert; Marí-Dell'Olmo, Marc; Penelo, Eva; Bosque-Prous, Marina

2016-06-14

To examine the differences between Prevalence Ratio (PR) and Odds Ratio (OR) in a cross-sectional study and to provide tools to calculate PR using two statistical packages widely used in substance use research (STATA and R). We used cross-sectional data from 41,263 participants of 16 European countries participating in the Survey on Health, Ageing and Retirement in Europe (SHARE). The dependent variable, hazardous drinking, was calculated using the Alcohol Use Disorders Identification Test - Consumption (AUDIT-C). The main independent variable was gender. Other variables used were: age, educational level and country of residence. PR of hazardous drinking in men with relation to women was estimated using Mantel-Haenszel method, log-binomial regression models and poisson regression models with robust variance. These estimations were compared to the OR calculated using logistic regression models. Prevalence of hazardous drinkers varied among countries. Generally, men have higher prevalence of hazardous drinking than women [PR=1.43 (1.38-1.47)]. Estimated PR was identical independently of the method and the statistical package used. However, OR overestimated PR, depending on the prevalence of hazardous drinking in the country. In cross-sectional studies, where comparisons between countries with differences in the prevalence of the disease or condition are made, it is advisable to use PR instead of OR.

Automated measurement of uptake in cerebellum, liver, and aortic arch in full-body FDG PET/CT scans.

PubMed

Bauer, Christian; Sun, Shanhui; Sun, Wenqing; Otis, Justin; Wallace, Audrey; Smith, Brian J; Sunderland, John J; Graham, Michael M; Sonka, Milan; Buatti, John M; Beichel, Reinhard R

2012-06-01

The purpose of this work was to develop and validate fully automated methods for uptake measurement of cerebellum, liver, and aortic arch in full-body PET/CT scans. Such measurements are of interest in the context of uptake normalization for quantitative assessment of metabolic activity and/or automated image quality control. Cerebellum, liver, and aortic arch regions were segmented with different automated approaches. Cerebella were segmented in PET volumes by means of a robust active shape model (ASM) based method. For liver segmentation, a largest possible hyperellipsoid was fitted to the liver in PET scans. The aortic arch was first segmented in CT images of a PET/CT scan by a tubular structure analysis approach, and the segmented result was then mapped to the corresponding PET scan. For each of the segmented structures, the average standardized uptake value (SUV) was calculated. To generate an independent reference standard for method validation, expert image analysts were asked to segment several cross sections of each of the three structures in 134 F-18 fluorodeoxyglucose (FDG) PET/CT scans. For each case, the true average SUV was estimated by utilizing statistical models and served as the independent reference standard. For automated aorta and liver SUV measurements, no statistically significant scale or shift differences were observed between automated results and the independent standard. In the case of the cerebellum, the scale and shift were not significantly different, if measured in the same cross sections that were utilized for generating the reference. In contrast, automated results were scaled 5% lower on average although not shifted, if FDG uptake was calculated from the whole segmented cerebellum volume. The estimated reduction in total SUV measurement error ranged between 54.7% and 99.2%, and the reduction was found to be statistically significant for cerebellum and aortic arch. With the proposed methods, the authors have demonstrated that automated SUV uptake measurements in cerebellum, liver, and aortic arch agree with expert-defined independent standards. The proposed methods were found to be accurate and showed less intra- and interobserver variability, compared to manual analysis. The approach provides an alternative to manual uptake quantification, which is time-consuming. Such an approach will be important for application of quantitative PET imaging to large scale clinical trials. © 2012 American Association of Physicists in Medicine.

Cure models for estimating hospital-based breast cancer survival.

PubMed

Rama, Ranganathan; Swaminathan, Rajaraman; Venkatesan, Perumal

2010-01-01

Research on cancer survival is enriched by development and application of innovative analytical approaches in relation to standard methods. The aim of the present paper is to document the utility of a mixture model to estimate the cure fraction and compare it with other approaches. The data were for 1,107 patients with locally advanced breast cancer, who completed the neo-adjuvant treatment protocol during 1990-99 at the Cancer Institute (WIA), Chennai, India. Tumour stage, post-operative pathological node (PN) and tumour residue (TR) status were studied. Event free survival probability was estimated using the Kaplan-Meier method. Cure models under proportional and non-proportional hazard assumptions following log normal distribution for survival time were used to estimate both the cure fraction and the survival function for the uncured. Event free survival at 5 and 10 years were 64.2% and 52.6% respectively and cure fraction was 47.5% for all cases together. Follow up ranged between 0-15 years and survival probabilities showed minimal changes after 7 years of follow up. TR and PN emerged as independent prognostic factors using Cox and proportional hazard (PH) cure models. Proportionality condition was violated when tumour stage was considered and it was statistically significant only under PH and not under non PH cure models. However, TR and PN continued to be independent prognostic factors after adjusting for tumour stage using the non PH cure model. A consistent ordering of cure fractions with respect to factors of PN and TR was forthcoming across tumour stages using PH and non PH cure models, but perceptible differences in survival were observed between the two. If PH conditions are violated, analysis using a non PH model is advocated and mixture cure models are useful in estimating the cure fraction and constructing survival curves for non-cures.

Building Better Planet Populations for EXOSIMS

NASA Astrophysics Data System (ADS)

Garrett, Daniel; Savransky, Dmitry

2018-01-01

The Exoplanet Open-Source Imaging Mission Simulator (EXOSIMS) software package simulates ensembles of space-based direct imaging surveys to provide a variety of science and engineering yield distributions for proposed mission designs. These mission simulations rely heavily on assumed distributions of planetary population parameters including semi-major axis, planetary radius, eccentricity, albedo, and orbital orientation to provide heuristics for target selection and to simulate planetary systems for detection and characterization. The distributions are encoded in PlanetPopulation modules within EXOSIMS which are selected by the user in the input JSON script when a simulation is run. The earliest written PlanetPopulation modules available in EXOSIMS are based on planet population models where the planetary parameters are considered to be independent from one another. While independent parameters allow for quick computation of heuristics and sampling for simulated planetary systems, results from planet-finding surveys have shown that many parameters (e.g., semi-major axis/orbital period and planetary radius) are not independent. We present new PlanetPopulation modules for EXOSIMS which are built on models based on planet-finding survey results where semi-major axis and planetary radius are not independent and provide methods for sampling their joint distribution. These new modules enhance the ability of EXOSIMS to simulate realistic planetary systems and give more realistic science yield distributions.

Conceptual modeling of coincident failures in multiversion software

NASA Technical Reports Server (NTRS)

Littlewood, Bev; Miller, Douglas R.

1989-01-01

Recent work by Eckhardt and Lee (1985) shows that independently developed program versions fail dependently (specifically, simultaneous failure of several is greater than would be the case under true independence). The present authors show there is a precise duality between input choice and program choice in this model and consider a generalization in which different versions can be developed using diverse methodologies. The use of diverse methodologies is shown to decrease the probability of the simultaneous failure of several versions. Indeed, it is theoretically possible to obtain versions which exhibit better than independent failure behavior. The authors try to formalize the notion of methodological diversity by considering the sequence of decision outcomes that constitute a methodology. They show that diversity of decision implies likely diversity of behavior for the different verions developed under such forced diversity. For certain one-out-of-n systems the authors obtain an optimal method for allocating diversity between versions. For two-out-of-three systems there seem to be no simple optimality results which do not depend on constraints which cannot be verified in practice.

Modeling influenza-like illnesses through composite compartmental models

NASA Astrophysics Data System (ADS)

Levy, Nir; , Michael, Iv; Yom-Tov, Elad

2018-03-01

Epidemiological models for the spread of pathogens in a population are usually only able to describe a single pathogen. This makes their application unrealistic in cases where multiple pathogens with similar symptoms are spreading concurrently within the same population. Here we describe a method which makes possible the application of multiple single-strain models under minimal conditions. As such, our method provides a bridge between theoretical models of epidemiology and data-driven approaches for modeling of influenza and other similar viruses. Our model extends the Susceptible-Infected-Recovered model to higher dimensions, allowing the modeling of a population infected by multiple viruses. We further provide a method, based on an overcomplete dictionary of feasible realizations of SIR solutions, to blindly partition the time series representing the number of infected people in a population into individual components, each representing the effect of a single pathogen. We demonstrate the applicability of our proposed method on five years of seasonal influenza-like illness (ILI) rates, estimated from Twitter data. We demonstrate that our method describes, on average, 44% of the variance in the ILI time series. The individual infectious components derived from our model are matched to known viral profiles in the populations, which we demonstrate matches that of independently collected epidemiological data. We further show that the basic reproductive numbers (R 0) of the matched components are in range known for these pathogens. Our results suggest that the proposed method can be applied to other pathogens and geographies, providing a simple method for estimating the parameters of epidemics in a population.

Regression Analysis and Calibration Recommendations for the Characterization of Balance Temperature Effects

NASA Technical Reports Server (NTRS)

Ulbrich, N.; Volden, T.

2018-01-01

Analysis and use of temperature-dependent wind tunnel strain-gage balance calibration data are discussed in the paper. First, three different methods are presented and compared that may be used to process temperature-dependent strain-gage balance data. The first method uses an extended set of independent variables in order to process the data and predict balance loads. The second method applies an extended load iteration equation during the analysis of balance calibration data. The third method uses temperature-dependent sensitivities for the data analysis. Physical interpretations of the most important temperature-dependent regression model terms are provided that relate temperature compensation imperfections and the temperature-dependent nature of the gage factor to sets of regression model terms. Finally, balance calibration recommendations are listed so that temperature-dependent calibration data can be obtained and successfully processed using the reviewed analysis methods.

Max-margin multiattribute learning with low-rank constraint.

PubMed

Zhang, Qiang; Chen, Lin; Li, Baoxin

2014-07-01

Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.